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1c33e3bca8133ca52ee4a9c179c8779b0c8dd328 | 2,087 | py | Python | examples/ExGroup.py | janjusti/dcma | 4403b176f33967d446275ee35422ec31236ce7a6 | [
"MIT"
] | null | null | null | examples/ExGroup.py | janjusti/dcma | 4403b176f33967d446275ee35422ec31236ce7a6 | [
"MIT"
] | null | null | null | examples/ExGroup.py | janjusti/dcma | 4403b176f33967d446275ee35422ec31236ce7a6 | [
"MIT"
] | null | null | null | from Bio import Entrez, AlignIO, SeqIO
from Bio.Align import AlignInfo
from Bio.SeqRecord import SeqRecord
from Bio.Align.Applications import MuscleCommandline
import dcma.core as solver
import time
def generate_fasta_from_ids(id_list, fasta_output):
Entrez.email = 'example@example.com'
f = open(fasta_output, 'w+')
for curr_id in id_list:
curr_req = Entrez.efetch(db='nucleotide', id=curr_id, rettype='fasta')
curr_seq = curr_req.read()
f.write(curr_seq)
print(curr_id, 'successfully fetched.')
time.sleep(1)
f.close()
print(fasta_output, 'sucessfully created.')
def align_via_muscle(input_file, output_file):
comm_muscle = MuscleCommandline(
input=input_file, out=output_file
)
comm_muscle()
print('Alignment', input_file, '>', output_file, 'done.')
def get_consensus_seq(fasta_file, q_id):
align = AlignIO.read(fasta_file, 'fasta')
seq = SeqRecord(
AlignInfo.SummaryInfo(align).gap_consensus(),
id=q_id,
description=''
)
print("'" + q_id + "' consensus sequence generated (original: " + fasta_file + ')')
return seq
def write_fasta(fasta_file, seq_list):
with open(fasta_file, 'w') as handle:
SeqIO.write(seq_list, handle, 'fasta')
print(fasta_file, 'successfully written.')
def main_group():
groupA_ids = ['KC662553.1', 'KC662552.1']
groupB_ids = ['KM058604.1', 'KM058603.1']
generate_fasta_from_ids(groupA_ids, 'groupA-unaligned.fasta')
generate_fasta_from_ids(groupB_ids, 'groupB-unaligned.fasta')
align_via_muscle('groupA-unaligned.fasta', 'groupA.fasta')
align_via_muscle('groupB-unaligned.fasta', 'groupB.fasta')
seqA = get_consensus_seq('groupA.fasta', 'groupA_any_sentence')
seqB = get_consensus_seq('groupB.fasta', 'groupB_any_sentence')
write_fasta('groupAB-cons.fasta', [seqA, seqB])
align_via_muscle('groupAB-cons.fasta', 'groups-target.fasta')
results = solver.run('groups-target.fasta', searchable_keyphrase='any sentence')
solver.export(results, 'csv', 'groups') | 35.982759 | 87 | 0.698131 | from Bio import Entrez, AlignIO, SeqIO
from Bio.Align import AlignInfo
from Bio.SeqRecord import SeqRecord
from Bio.Align.Applications import MuscleCommandline
import dcma.core as solver
import time
def generate_fasta_from_ids(id_list, fasta_output):
Entrez.email = 'example@example.com'
f = open(fasta_output, 'w+')
for curr_id in id_list:
curr_req = Entrez.efetch(db='nucleotide', id=curr_id, rettype='fasta')
curr_seq = curr_req.read()
f.write(curr_seq)
print(curr_id, 'successfully fetched.')
time.sleep(1)
f.close()
print(fasta_output, 'sucessfully created.')
def align_via_muscle(input_file, output_file):
comm_muscle = MuscleCommandline(
input=input_file, out=output_file
)
comm_muscle()
print('Alignment', input_file, '>', output_file, 'done.')
def get_consensus_seq(fasta_file, q_id):
align = AlignIO.read(fasta_file, 'fasta')
seq = SeqRecord(
AlignInfo.SummaryInfo(align).gap_consensus(),
id=q_id,
description=''
)
print("'" + q_id + "' consensus sequence generated (original: " + fasta_file + ')')
return seq
def write_fasta(fasta_file, seq_list):
with open(fasta_file, 'w') as handle:
SeqIO.write(seq_list, handle, 'fasta')
print(fasta_file, 'successfully written.')
def main_group():
groupA_ids = ['KC662553.1', 'KC662552.1']
groupB_ids = ['KM058604.1', 'KM058603.1']
generate_fasta_from_ids(groupA_ids, 'groupA-unaligned.fasta')
generate_fasta_from_ids(groupB_ids, 'groupB-unaligned.fasta')
align_via_muscle('groupA-unaligned.fasta', 'groupA.fasta')
align_via_muscle('groupB-unaligned.fasta', 'groupB.fasta')
seqA = get_consensus_seq('groupA.fasta', 'groupA_any_sentence')
seqB = get_consensus_seq('groupB.fasta', 'groupB_any_sentence')
write_fasta('groupAB-cons.fasta', [seqA, seqB])
align_via_muscle('groupAB-cons.fasta', 'groups-target.fasta')
results = solver.run('groups-target.fasta', searchable_keyphrase='any sentence')
solver.export(results, 'csv', 'groups') | true | true |
1c33e502e953fb501cea4f12595b4840aca73557 | 1,156 | py | Python | .modules/.recon-ng/modules/recon/hosts-domains/migrate_hosts.py | termux-one/EasY_HaCk | 0a8d09ca4b126b027b6842e02fa0c29d8250e090 | [
"Apache-2.0"
] | 1,103 | 2018-04-20T14:08:11.000Z | 2022-03-29T06:22:43.000Z | .modules/.recon-ng/modules/recon/hosts-domains/migrate_hosts.py | sshourya948/EasY_HaCk | 0a8d09ca4b126b027b6842e02fa0c29d8250e090 | [
"Apache-2.0"
] | 29 | 2019-04-03T14:52:38.000Z | 2022-03-24T12:33:05.000Z | .modules/.recon-ng/modules/recon/hosts-domains/migrate_hosts.py | sshourya948/EasY_HaCk | 0a8d09ca4b126b027b6842e02fa0c29d8250e090 | [
"Apache-2.0"
] | 161 | 2018-04-20T15:57:12.000Z | 2022-03-15T19:16:16.000Z | from recon.core.module import BaseModule
import os
import re
class Module(BaseModule):
meta = {
'name': 'Hosts to Domains Data Migrator',
'author': 'Tim Tomes (@LaNMaSteR53)',
'description': 'Adds a new domain for all the hostnames stored in the \'hosts\' table.',
'comments': (
'This modules considers that everything after the first element could contain other hosts besides the current. Therefore, hosts > 2 domains deep will create domains > 2 elements in length.',
),
'query': 'SELECT DISTINCT host FROM hosts WHERE host IS NOT NULL',
}
def module_run(self, hosts):
# ip address regex
regex = '[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}'
# only migrate hosts that aren't ip addresses
hosts = [x for x in hosts if not re.match(regex, x[0])]
with open(os.path.join(self.data_path, 'suffixes.txt')) as f:
suffixes = [line.strip().lower() for line in f if len(line)>0 and line[0] is not '#']
domains = self.hosts_to_domains(hosts, suffixes)
for domain in domains:
self.add_domains(domain=domain)
| 42.814815 | 202 | 0.622837 | from recon.core.module import BaseModule
import os
import re
class Module(BaseModule):
meta = {
'name': 'Hosts to Domains Data Migrator',
'author': 'Tim Tomes (@LaNMaSteR53)',
'description': 'Adds a new domain for all the hostnames stored in the \'hosts\' table.',
'comments': (
'This modules considers that everything after the first element could contain other hosts besides the current. Therefore, hosts > 2 domains deep will create domains > 2 elements in length.',
),
'query': 'SELECT DISTINCT host FROM hosts WHERE host IS NOT NULL',
}
def module_run(self, hosts):
regex = '[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}'
hosts = [x for x in hosts if not re.match(regex, x[0])]
with open(os.path.join(self.data_path, 'suffixes.txt')) as f:
suffixes = [line.strip().lower() for line in f if len(line)>0 and line[0] is not '
domains = self.hosts_to_domains(hosts, suffixes)
for domain in domains:
self.add_domains(domain=domain)
| true | true |
1c33e56e472ec7115c80b8127bbef8760db518c3 | 5,466 | py | Python | codes/singintex.py | Hadrien-Montanelli/singintpy | 1706afe42d0cc6e0f3c53759d489f7209e50ef29 | [
"MIT"
] | null | null | null | codes/singintex.py | Hadrien-Montanelli/singintpy | 1706afe42d0cc6e0f3c53759d489f7209e50ef29 | [
"MIT"
] | null | null | null | codes/singintex.py | Hadrien-Montanelli/singintpy | 1706afe42d0cc6e0f3c53759d489f7209e50ef29 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 28 19:24:38 2021
Copyright 2021 by Hadrien Montanelli.
"""
def singintex(u0, v0, dz0):
"""
# Outputs the exact value (computed in Mathematica) of the singular and
# near-singular integrals used in the numerical experiments of [1, Sec. 4].
# These values are computed in the singintex.nb file.
# Inputs
# ------
# u0, v0, dz0 : float
# Position of the singularity is x0 = F(u0, v0) + dz0*z.
# Output
# ------
# Iex : float
# The exact value of the integral.
# References
# ----------
# [1] H. Montanelli, M. Aussal and H. Haddar, Computing weakly singular and
# near-singular integrals in high-order boundary elements, submitted.
# """
# Point near the center:
if (u0 == .2) and (v0 == .4) and (dz0 == 0): # singularity
Iex = 3.240017458404107
if (u0 == .2) and (v0 == .4) and (dz0 == 1e-4): # near-singularity
Iex = 3.239493851850319
if (u0 == .2) and (v0 == .4) and (dz0 == 1e-3): # near-singularity
Iex = 3.234785969247374
if (u0 == .2) and (v0 == .4) and (dz0 == 1e-2): # near-singularity
Iex = 3.188154928666069
# Point near the a1-a2 vertex:
if (u0 == .5) and (v0 == 1e-1) and (dz0 == 0): # singularity
Iex = 3.018547440468339
if (u0 == .5) and (v0 == 1e-1) and (dz0 == 1e-4): # near-singularity
Iex = 3.018116377195088
if (u0 == .5) and (v0 == 1e-1) and (dz0 == 1e-3): # near-singularity
Iex = 3.014240460722516
if (u0 == .5) and (v0 == 1e-2) and (dz0 == 0): # singularity
Iex = 2.44181568875291
if (u0 == .5) and (v0 == 1e-2) and (dz0 == 1e-4): # near-singularity
Iex = 2.441683569912414
if (u0 == .5) and (v0 == 1e-3) and (dz0 == 0): # singularity
Iex = 2.310786384193376
if (u0 == .5) and (v0 == 1e-3) and (dz0 == 1e-4): # near-singularity
Iex = 2.310927133672147
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 0): # singularity
Iex = 2.290532510026764
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 1e-4): # near-singularity
Iex = 2.290950009889399
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 1e-3): # near-singularity
Iex = 2.294299358958351
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 1e-2): # near-singularity
Iex = 2.309420005131348
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 1e0): # near-singularity
Iex = 0.9161025842305255
if (u0 == .5) and (v0 == 1e-5) and (dz0 == 0): # singularity
Iex = 2.287793848213499
if (u0 == .5) and (v0 == 1e-5) and (dz0 == 1e-4): # near-singularity
Iex = 2.288438219470312
if (u0 == .5) and (v0 == 1e-6) and (dz0 == 0): # singularity
Iex = 2.287448672711318
if (u0 == .5) and (v0 == 1e-6) and (dz0 == 1e-4): # near-singularity
Iex = 2.288172343092934
if (u0 == .5) and (v0 == 1e-7) and (dz0 == 0): # singularity
Iex = 2.287407024410108
if (u0 == .5) and (v0 == 1e-7) and (dz0 == 1e-4): # near-singularity
Iex = 2.288145597976325
if (u0 == .5) and (v0 == 1e-10) and (dz0 == 0): # singularity
Iex = 2.287401524368497
if (u0 == .5) and (v0 == 1e-10) and (dz0 == 1e-4): # near-singularity
Iex = 2.288142627543715
if (u0 == .5) and (v0 == 0) and (dz0 == 0): # singularity
Iex = 2.287401516483698
if (u0 == .5) and (v0 == 0) and (dz0 == 1e-4): # near-singularity
Iex = 2.288142624570126
# Point near the a2-a3 vertex:
if (u0 == .5) and (v0 == 0.5-1e-1) and (dz0 == 0): # singularity
Iex = 2.910980479568196
if (u0 == .5) and (v0 == 0.5-1e-2) and (dz0 == 0): # singularity
Iex = 2.328357836622938
if (u0 == .5) and (v0 == 0.5-1e-3) and (dz0 == 0): # singularity
Iex = 2.207283607389093
if (u0 == .5) and (v0 == 0.5-1e-4) and (dz0 == 0): # singularity
Iex = 2.18893530823417
if (u0 == .5) and (v0 == 0.5-1e-5) and (dz0 == 0): # singularity
Iex = 2.186474998717380
if (u0 == .5) and (v0 == 0.5-1e-6) and (dz0 == 0): # singularity
Iex = 2.186166390068990
if (u0 == .5) and (v0 == 0.5-1e-7) and (dz0 == 0): # singularity
Iex = 2.186129270984043
if (u0 == .5) and (v0 == 0.5-1e-10) and (dz0 == 0): # singularity
Iex = 2.186124380996982
if (u0 == .5) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.186124374013931
# Point near the a3-a1 vertex:
if (u0 == 1e-1) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 3.001704553910470
if (u0 == 1e-2) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.455261667489161
if (u0 == 1e-3) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.333635120399480
if (u0 == 1e-4) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.314977231319161
if (u0 == 1e-5) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.312463818083347
if (u0 == 1e-6) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.312147733122362
if (u0 == 1e-7) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.31210965045024
if (u0 == 1e-10) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.312104626952236
if (u0 == 0) and (v0 == 0.5) and (dz0 == 0): # singularity
Iex = 2.312104619763527
return Iex | 43.728 | 80 | 0.526528 |
def singintex(u0, v0, dz0):
if (u0 == .2) and (v0 == .4) and (dz0 == 0):
Iex = 3.240017458404107
if (u0 == .2) and (v0 == .4) and (dz0 == 1e-4):
Iex = 3.239493851850319
if (u0 == .2) and (v0 == .4) and (dz0 == 1e-3):
Iex = 3.234785969247374
if (u0 == .2) and (v0 == .4) and (dz0 == 1e-2):
Iex = 3.188154928666069
if (u0 == .5) and (v0 == 1e-1) and (dz0 == 0):
Iex = 3.018547440468339
if (u0 == .5) and (v0 == 1e-1) and (dz0 == 1e-4):
Iex = 3.018116377195088
if (u0 == .5) and (v0 == 1e-1) and (dz0 == 1e-3):
Iex = 3.014240460722516
if (u0 == .5) and (v0 == 1e-2) and (dz0 == 0):
Iex = 2.44181568875291
if (u0 == .5) and (v0 == 1e-2) and (dz0 == 1e-4):
Iex = 2.441683569912414
if (u0 == .5) and (v0 == 1e-3) and (dz0 == 0):
Iex = 2.310786384193376
if (u0 == .5) and (v0 == 1e-3) and (dz0 == 1e-4):
Iex = 2.310927133672147
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 0):
Iex = 2.290532510026764
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 1e-4):
Iex = 2.290950009889399
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 1e-3):
Iex = 2.294299358958351
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 1e-2):
Iex = 2.309420005131348
if (u0 == .5) and (v0 == 1e-4) and (dz0 == 1e0):
Iex = 0.9161025842305255
if (u0 == .5) and (v0 == 1e-5) and (dz0 == 0):
Iex = 2.287793848213499
if (u0 == .5) and (v0 == 1e-5) and (dz0 == 1e-4):
Iex = 2.288438219470312
if (u0 == .5) and (v0 == 1e-6) and (dz0 == 0):
Iex = 2.287448672711318
if (u0 == .5) and (v0 == 1e-6) and (dz0 == 1e-4):
Iex = 2.288172343092934
if (u0 == .5) and (v0 == 1e-7) and (dz0 == 0):
Iex = 2.287407024410108
if (u0 == .5) and (v0 == 1e-7) and (dz0 == 1e-4):
Iex = 2.288145597976325
if (u0 == .5) and (v0 == 1e-10) and (dz0 == 0):
Iex = 2.287401524368497
if (u0 == .5) and (v0 == 1e-10) and (dz0 == 1e-4):
Iex = 2.288142627543715
if (u0 == .5) and (v0 == 0) and (dz0 == 0):
Iex = 2.287401516483698
if (u0 == .5) and (v0 == 0) and (dz0 == 1e-4):
Iex = 2.288142624570126
if (u0 == .5) and (v0 == 0.5-1e-1) and (dz0 == 0):
Iex = 2.910980479568196
if (u0 == .5) and (v0 == 0.5-1e-2) and (dz0 == 0):
Iex = 2.328357836622938
if (u0 == .5) and (v0 == 0.5-1e-3) and (dz0 == 0):
Iex = 2.207283607389093
if (u0 == .5) and (v0 == 0.5-1e-4) and (dz0 == 0):
Iex = 2.18893530823417
if (u0 == .5) and (v0 == 0.5-1e-5) and (dz0 == 0):
Iex = 2.186474998717380
if (u0 == .5) and (v0 == 0.5-1e-6) and (dz0 == 0):
Iex = 2.186166390068990
if (u0 == .5) and (v0 == 0.5-1e-7) and (dz0 == 0):
Iex = 2.186129270984043
if (u0 == .5) and (v0 == 0.5-1e-10) and (dz0 == 0):
Iex = 2.186124380996982
if (u0 == .5) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.186124374013931
if (u0 == 1e-1) and (v0 == 0.5) and (dz0 == 0):
Iex = 3.001704553910470
if (u0 == 1e-2) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.455261667489161
if (u0 == 1e-3) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.333635120399480
if (u0 == 1e-4) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.314977231319161
if (u0 == 1e-5) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.312463818083347
if (u0 == 1e-6) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.312147733122362
if (u0 == 1e-7) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.31210965045024
if (u0 == 1e-10) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.312104626952236
if (u0 == 0) and (v0 == 0.5) and (dz0 == 0):
Iex = 2.312104619763527
return Iex | true | true |
1c33e63b7914a838eed8ad9be6312285010e66b4 | 3,013 | py | Python | src/dsgrn_net_gen/makejobs.py | breecummins/dsgrn_net_gen | bcd77b71ad3e311d2906f0af986559d2c54ffe6d | [
"MIT"
] | 1 | 2020-03-31T18:44:06.000Z | 2020-03-31T18:44:06.000Z | src/dsgrn_net_gen/makejobs.py | breecummins/dsgrn_net_gen | bcd77b71ad3e311d2906f0af986559d2c54ffe6d | [
"MIT"
] | null | null | null | src/dsgrn_net_gen/makejobs.py | breecummins/dsgrn_net_gen | bcd77b71ad3e311d2906f0af986559d2c54ffe6d | [
"MIT"
] | null | null | null | import dsgrn_net_gen.networksearch as networksearch
import dsgrn_net_gen.fileparsers as fileparsers
import subprocess, os, json, shutil, ast, sys, time
class Job():
def __init__(self,paramfile):
self.paramfile = paramfile
self.params = json.load(open(paramfile))
# use datetime as unique identifier to avoid overwriting
if "datetime" not in self.params:
datetime = subprocess.check_output(['date +%Y_%m_%d_%H_%M_%S'],shell=True).decode(sys.stdout.encoding).strip()
self.params["datetime"] = datetime
else:
datetime = self.params["datetime"]
self.params["random_seed"] = time.time() if "random_seed" not in self.params else self.params["random_seed"]
resultsdir = "" if "resultsdir" not in self.params else self.params["resultsdir"]
resultsdir =os.path.join(os.path.expanduser(resultsdir), "dsgrn_net_gen_results"+datetime)
self.perturbationsdir = os.path.join(resultsdir,"networks"+datetime)
os.makedirs(self.perturbationsdir)
self.inputfilesdir = os.path.join(resultsdir,"inputs"+datetime)
os.makedirs(self.inputfilesdir)
# save parameter file to computations folder
newpfile = os.path.basename(paramfile).split(".")[0]+"_copy.json"
json.dump(self.params,open(newpfile,"w"))
shutil.move(newpfile,self.inputfilesdir)
shutil.copy(self.params["networkfile"], self.inputfilesdir)
#TODO: Record versions/git number of DSGRN and dsgrn_net_gen
def _parsefile(self,eorn,parsefunc):
f = eorn+"file"
l = eorn+"list"
if f in self.params and self.params[f].strip():
try:
self.params[l] = parsefunc(self.params[f])
shutil.copy(self.params[f], self.inputfilesdir)
except:
raise ValueError("Invalid " + eorn + " file.")
else:
self.params[l] = None
def run(self):
# read network file
networks = open(self.params["networkfile"]).read()
try:
if networks[0] == "[":
networks = ast.literal_eval(networks)
if not networks:
networks = [""]
else:
while networks[-1] == '\n':
networks = networks[:-1]
networks = [networks]
except IndexError:
networks = [""]
sys.stdout.flush()
self._parsefile('edge',fileparsers.parseEdgeFile)
self._parsefile('node',fileparsers.parseNodeFile)
print("\nNetwork search beginning.\n")
perturbed_networks = []
for network_spec in networks:
perturbed_networks.extend(networksearch.perturbNetwork(self.params,network_spec))
networks=list(set(perturbed_networks))
with open(os.path.join(self.perturbationsdir,"networks.txt"),"w") as f:
f.write(str(networks))
print("\nNetwork search complete.\n")
sys.stdout.flush()
| 41.847222 | 122 | 0.615002 | import dsgrn_net_gen.networksearch as networksearch
import dsgrn_net_gen.fileparsers as fileparsers
import subprocess, os, json, shutil, ast, sys, time
class Job():
def __init__(self,paramfile):
self.paramfile = paramfile
self.params = json.load(open(paramfile))
if "datetime" not in self.params:
datetime = subprocess.check_output(['date +%Y_%m_%d_%H_%M_%S'],shell=True).decode(sys.stdout.encoding).strip()
self.params["datetime"] = datetime
else:
datetime = self.params["datetime"]
self.params["random_seed"] = time.time() if "random_seed" not in self.params else self.params["random_seed"]
resultsdir = "" if "resultsdir" not in self.params else self.params["resultsdir"]
resultsdir =os.path.join(os.path.expanduser(resultsdir), "dsgrn_net_gen_results"+datetime)
self.perturbationsdir = os.path.join(resultsdir,"networks"+datetime)
os.makedirs(self.perturbationsdir)
self.inputfilesdir = os.path.join(resultsdir,"inputs"+datetime)
os.makedirs(self.inputfilesdir)
newpfile = os.path.basename(paramfile).split(".")[0]+"_copy.json"
json.dump(self.params,open(newpfile,"w"))
shutil.move(newpfile,self.inputfilesdir)
shutil.copy(self.params["networkfile"], self.inputfilesdir)
def _parsefile(self,eorn,parsefunc):
f = eorn+"file"
l = eorn+"list"
if f in self.params and self.params[f].strip():
try:
self.params[l] = parsefunc(self.params[f])
shutil.copy(self.params[f], self.inputfilesdir)
except:
raise ValueError("Invalid " + eorn + " file.")
else:
self.params[l] = None
def run(self):
networks = open(self.params["networkfile"]).read()
try:
if networks[0] == "[":
networks = ast.literal_eval(networks)
if not networks:
networks = [""]
else:
while networks[-1] == '\n':
networks = networks[:-1]
networks = [networks]
except IndexError:
networks = [""]
sys.stdout.flush()
self._parsefile('edge',fileparsers.parseEdgeFile)
self._parsefile('node',fileparsers.parseNodeFile)
print("\nNetwork search beginning.\n")
perturbed_networks = []
for network_spec in networks:
perturbed_networks.extend(networksearch.perturbNetwork(self.params,network_spec))
networks=list(set(perturbed_networks))
with open(os.path.join(self.perturbationsdir,"networks.txt"),"w") as f:
f.write(str(networks))
print("\nNetwork search complete.\n")
sys.stdout.flush()
| true | true |
1c33e67d7a8d75efca51a7f3cd2d594c96a6efdf | 1,279 | py | Python | quad_garl/garl.py | kanishkaganguly/QuadGARL | 2e995861cab98d9623dd36155cb472dcb4e21cd0 | [
"MIT"
] | 1 | 2019-02-08T06:17:34.000Z | 2019-02-08T06:17:34.000Z | quad_garl/garl.py | kanishkaganguly/QuadGARL | 2e995861cab98d9623dd36155cb472dcb4e21cd0 | [
"MIT"
] | null | null | null | quad_garl/garl.py | kanishkaganguly/QuadGARL | 2e995861cab98d9623dd36155cb472dcb4e21cd0 | [
"MIT"
] | null | null | null | from typing import List
from quad_garl import quad_utils
from quad_garl.garl_utils import GARLUtils
from quad_garl.genetic_algorithm import GeneticAlgorithm
from quad_garl.quad_simulator import Quadcopter
from quad_garl.reinforcement_learning import ReinforcementLearning
def main():
# Variables
chromosome_size = 10 # type:int
generations = 10 # type:int
population_size = 20 # type:int
selection_percent = 0.3 # type:float
target_pose = [2, 2, 2, 0, 0, 3.14] # type: List # x,y,z,r,p,y
# Logging
log = quad_utils.quad_logger
# Set up RL, GA, Quad framework
log(log_data="Initialize RL and GA frameworks")
quad_sim = Quadcopter(log, target_pose)
ga = GeneticAlgorithm(log, chromosome_size, generations, population_size, selection_percent)
rl = ReinforcementLearning(log)
utils = GARLUtils(genetic_algorithm=ga, reinforcement_learning=rl)
log(None)
i = 0
while i < 10:
log("Iteration {}".format(i))
# Create population
ga.initialize_population()
# Evaluate current population
pop = ga.population
for each_population in pop:
print(each_population)
pass
i += 1
log(None)
if __name__ == '__main__':
main()
| 26.645833 | 96 | 0.677873 | from typing import List
from quad_garl import quad_utils
from quad_garl.garl_utils import GARLUtils
from quad_garl.genetic_algorithm import GeneticAlgorithm
from quad_garl.quad_simulator import Quadcopter
from quad_garl.reinforcement_learning import ReinforcementLearning
def main():
chromosome_size = 10
generations = 10
population_size = 20
selection_percent = 0.3
target_pose = [2, 2, 2, 0, 0, 3.14] g = quad_utils.quad_logger
log(log_data="Initialize RL and GA frameworks")
quad_sim = Quadcopter(log, target_pose)
ga = GeneticAlgorithm(log, chromosome_size, generations, population_size, selection_percent)
rl = ReinforcementLearning(log)
utils = GARLUtils(genetic_algorithm=ga, reinforcement_learning=rl)
log(None)
i = 0
while i < 10:
log("Iteration {}".format(i))
ga.initialize_population()
pop = ga.population
for each_population in pop:
print(each_population)
pass
i += 1
log(None)
if __name__ == '__main__':
main()
| true | true |
1c33e6e12dcd7bb838f2c7f2077f8cc747f84551 | 13,234 | py | Python | tests/plugins/db2_test.py | flyingbarron/detect-secrets | 5f9887179794ce037d97c1b343623eb5937ce800 | [
"Apache-2.0"
] | null | null | null | tests/plugins/db2_test.py | flyingbarron/detect-secrets | 5f9887179794ce037d97c1b343623eb5937ce800 | [
"Apache-2.0"
] | null | null | null | tests/plugins/db2_test.py | flyingbarron/detect-secrets | 5f9887179794ce037d97c1b343623eb5937ce800 | [
"Apache-2.0"
] | null | null | null | from __future__ import absolute_import
import textwrap
import pytest
from mock import MagicMock
from mock import patch
from detect_secrets.core.constants import VerifiedResult
from detect_secrets.core.potential_secret import PotentialSecret
from detect_secrets.plugins.db2 import Db2Detector
from detect_secrets.plugins.db2 import find_other_factor
from detect_secrets.plugins.db2 import get_hostname_port_database_from_url
DB2_USER = 'fake_user'
DB2_PASSWORD = 'fake_password'
DB2_PORT = '1234'
DB2_HOSTNAME = 'fake.host.name'
DB2_DATABASE = 'fake_database'
DB2_CONN_STRING = 'database={DB2_DATABASE};hostname={DB2_HOSTNAME};port={DB2_PORT};' + \
'protocol=tcpip;uid={DB2_USER};pwd={DB2_PASSWORD};ConnectTimeout=5'
DB2_CONN_STRING = DB2_CONN_STRING.format(
DB2_DATABASE=DB2_DATABASE,
DB2_HOSTNAME=DB2_HOSTNAME,
DB2_PORT=DB2_PORT,
DB2_USER=DB2_USER,
DB2_PASSWORD=DB2_PASSWORD,
)
class TestGheDetector(object):
@pytest.mark.parametrize(
'token, payload, should_flag',
[
(
'secret',
'database=test;hostname=host.test.com;'
'port=1;protocol=tcpip;uid=testid;pwd=secret', True,
),
(
'secret',
'database=test;hostname=host.test.com;'
'port=1;protocol=tcpip;uid=testid;pwd=secret;', True,
),
(
'secret',
'database=test;hostname=host.test.com;'
'port=1;protocol=tcpip;pwd=secret;uid=testid;', True,
),
(
'secret',
'database=test,hostname=host.test.com,'
'port=1,protocol=tcpip,pwd=secret,uid=testid', True,
),
(
'secret',
'database=test,hostname=host.test.com,'
'port=1,protocol=tcpip,uid=testid,pwd=secret', True,
),
(
'secret',
'user=testid,\npassword=secret,\ndatabase=test,\n'
'hostname=host.test.com,\nport=1', True,
),
(
'secret',
'user=testid\npassword=secret\ndatabase=test\n'
'hostname=host.test.com\nport=1', True,
),
(
'secret',
'jdbc:db2://hostname.test.com:1/test:user=testid;password=secret;', True,
),
(
'secret',
'jdbc:db2://hostname.test.com:1/test user=testid password=secret', True,
),
('$omespeci@!ch@r$', 'dbpwd=$omespeci@!ch@r$', True),
('astring', 'db2_password = "astring"', True),
('Iusedb2!', '"password": "Iusedb2!"', True),
('ilikespaces', 'password = "ilikespaces"', True),
(':anothersyntax!', 'pwd::anothersyntax!', True),
('@#!%#', 'DB2_PASSWORD = "@#!%#"', True),
('pass', 'dashdb-password = "pass"', True),
('pass', 'dashdb-password = pass\r', True),
('', 'dashdb_host = notapassword', False),
('', 'someotherpassword = "doesnt start right"', False),
],
)
def test_analyze_line(self, token, payload, should_flag):
logic = Db2Detector()
output = logic.analyze_line(payload, 1, 'mock_filename')
assert len(output) == int(should_flag)
if len(output) > 0:
assert list(output.keys())[0].secret == token
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_invalid_connect_returns_none(self, mock_db2_connect):
mock_db2_connect.return_value = None
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
database={},
host={},
port={}'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.VERIFIED_FALSE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_invalid_connect_throws_exception(self, mock_db2_connect):
mock_db2_connect.side_effect = Exception('oops')
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
database={},
host={},
port={}'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.UNVERIFIED
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_valid_secret(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
database={},
host={},
port={}'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_valid_secret_in_single_quotes(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user='{}',
password='{}',
database='{}',
host='{}',
port='{}'
'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_valid_secret_in_double_quotes(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user="{}",
password="{}",
database="{}",
host="{}",
port="{}"
'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_from_url(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
url=jdbc:db2://{}:{}/{},
'''.format(DB2_USER, DB2_PASSWORD, DB2_HOSTNAME, DB2_PORT, DB2_DATABASE),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_db2_url_key(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''jdbc:db2://{}:{}/{}:user={};password={};
'''.format(DB2_HOSTNAME, DB2_PORT, DB2_DATABASE, DB2_USER, DB2_PASSWORD),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_times_out(self, mock_db2_connect):
mock_db2_connect.side_effect = Exception('Timeout')
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
database={},
host={},
port={}'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.UNVERIFIED
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
def test_verify_no_other_factors(self):
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'password={}'.format(DB2_PASSWORD),
potential_secret,
) == VerifiedResult.UNVERIFIED
@pytest.mark.parametrize(
'content, factor_keyword_regex, factor_regex, expected_output',
(
(
textwrap.dedent("""
user = {}
""")[1:-1].format(
DB2_USER,
),
Db2Detector().username_keyword_regex,
Db2Detector().username_regex,
[DB2_USER],
),
(
textwrap.dedent("""
port = {}
""")[1:-1].format(
DB2_PORT,
),
Db2Detector().port_keyword_regex,
Db2Detector().port_regex,
[DB2_PORT],
),
(
textwrap.dedent("""
database = {}
""")[1:-1].format(
DB2_DATABASE,
),
Db2Detector().database_keyword_regex,
Db2Detector().database_regex,
[DB2_DATABASE],
),
(
textwrap.dedent("""
host = {}
""")[1:-1].format(
DB2_HOSTNAME,
),
Db2Detector().hostname_keyword_regex,
Db2Detector().hostname_regex,
[DB2_HOSTNAME],
),
),
)
def test_find_other_factor(content, factor_keyword_regex, factor_regex, expected_output):
assert find_other_factor(content, factor_keyword_regex, factor_regex) == expected_output
@pytest.mark.parametrize(
'content, hostname_regex, port_regex, database_regex, expected_output',
(
(
textwrap.dedent("""
jdbc:db2://{}:{}/{}
""")[1:-1].format(
DB2_HOSTNAME,
DB2_PORT,
DB2_DATABASE,
),
Db2Detector().hostname_regex,
Db2Detector().port_regex,
Db2Detector().database_regex,
[(DB2_HOSTNAME, DB2_PORT, DB2_DATABASE)],
),
(
textwrap.dedent("""
jdbc:db2://{}:{}/
""")[1:-1].format(
DB2_HOSTNAME,
DB2_PORT,
),
Db2Detector().hostname_regex,
Db2Detector().port_regex,
Db2Detector().database_regex,
[],
),
(
textwrap.dedent("""
nonsense
"""),
Db2Detector().hostname_regex,
Db2Detector().port_regex,
Db2Detector().database_regex,
[],
),
),
)
def test_get_hostname_port_database_from_url(
content, hostname_regex, port_regex, database_regex, expected_output,
):
assert get_hostname_port_database_from_url(
content, hostname_regex, port_regex, database_regex,
) == expected_output
| 37.070028 | 95 | 0.584328 | from __future__ import absolute_import
import textwrap
import pytest
from mock import MagicMock
from mock import patch
from detect_secrets.core.constants import VerifiedResult
from detect_secrets.core.potential_secret import PotentialSecret
from detect_secrets.plugins.db2 import Db2Detector
from detect_secrets.plugins.db2 import find_other_factor
from detect_secrets.plugins.db2 import get_hostname_port_database_from_url
DB2_USER = 'fake_user'
DB2_PASSWORD = 'fake_password'
DB2_PORT = '1234'
DB2_HOSTNAME = 'fake.host.name'
DB2_DATABASE = 'fake_database'
DB2_CONN_STRING = 'database={DB2_DATABASE};hostname={DB2_HOSTNAME};port={DB2_PORT};' + \
'protocol=tcpip;uid={DB2_USER};pwd={DB2_PASSWORD};ConnectTimeout=5'
DB2_CONN_STRING = DB2_CONN_STRING.format(
DB2_DATABASE=DB2_DATABASE,
DB2_HOSTNAME=DB2_HOSTNAME,
DB2_PORT=DB2_PORT,
DB2_USER=DB2_USER,
DB2_PASSWORD=DB2_PASSWORD,
)
class TestGheDetector(object):
@pytest.mark.parametrize(
'token, payload, should_flag',
[
(
'secret',
'database=test;hostname=host.test.com;'
'port=1;protocol=tcpip;uid=testid;pwd=secret', True,
),
(
'secret',
'database=test;hostname=host.test.com;'
'port=1;protocol=tcpip;uid=testid;pwd=secret;', True,
),
(
'secret',
'database=test;hostname=host.test.com;'
'port=1;protocol=tcpip;pwd=secret;uid=testid;', True,
),
(
'secret',
'database=test,hostname=host.test.com,'
'port=1,protocol=tcpip,pwd=secret,uid=testid', True,
),
(
'secret',
'database=test,hostname=host.test.com,'
'port=1,protocol=tcpip,uid=testid,pwd=secret', True,
),
(
'secret',
'user=testid,\npassword=secret,\ndatabase=test,\n'
'hostname=host.test.com,\nport=1', True,
),
(
'secret',
'user=testid\npassword=secret\ndatabase=test\n'
'hostname=host.test.com\nport=1', True,
),
(
'secret',
'jdbc:db2://hostname.test.com:1/test:user=testid;password=secret;', True,
),
(
'secret',
'jdbc:db2://hostname.test.com:1/test user=testid password=secret', True,
),
('$omespeci@!ch@r$', 'dbpwd=$omespeci@!ch@r$', True),
('astring', 'db2_password = "astring"', True),
('Iusedb2!', '"password": "Iusedb2!"', True),
('ilikespaces', 'password = "ilikespaces"', True),
(':anothersyntax!', 'pwd::anothersyntax!', True),
('@#!%#', 'DB2_PASSWORD = "@#!%#"', True),
('pass', 'dashdb-password = "pass"', True),
('pass', 'dashdb-password = pass\r', True),
('', 'dashdb_host = notapassword', False),
('', 'someotherpassword = "doesnt start right"', False),
],
)
def test_analyze_line(self, token, payload, should_flag):
logic = Db2Detector()
output = logic.analyze_line(payload, 1, 'mock_filename')
assert len(output) == int(should_flag)
if len(output) > 0:
assert list(output.keys())[0].secret == token
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_invalid_connect_returns_none(self, mock_db2_connect):
mock_db2_connect.return_value = None
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
database={},
host={},
port={}'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.VERIFIED_FALSE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_invalid_connect_throws_exception(self, mock_db2_connect):
mock_db2_connect.side_effect = Exception('oops')
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
database={},
host={},
port={}'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.UNVERIFIED
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_valid_secret(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
database={},
host={},
port={}'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_valid_secret_in_single_quotes(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user='{}',
password='{}',
database='{}',
host='{}',
port='{}'
'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_valid_secret_in_double_quotes(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user="{}",
password="{}",
database="{}",
host="{}",
port="{}"
'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_from_url(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
url=jdbc:db2://{}:{}/{},
'''.format(DB2_USER, DB2_PASSWORD, DB2_HOSTNAME, DB2_PORT, DB2_DATABASE),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_db2_url_key(self, mock_db2_connect):
mock_db2_connect.return_value = MagicMock()
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''jdbc:db2://{}:{}/{}:user={};password={};
'''.format(DB2_HOSTNAME, DB2_PORT, DB2_DATABASE, DB2_USER, DB2_PASSWORD),
potential_secret,
) == VerifiedResult.VERIFIED_TRUE
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
assert potential_secret.other_factors['database'] == DB2_DATABASE
assert potential_secret.other_factors['hostname'] == DB2_HOSTNAME
assert potential_secret.other_factors['port'] == DB2_PORT
assert potential_secret.other_factors['username'] == DB2_USER
@patch('detect_secrets.plugins.db2.ibm_db.connect')
def test_verify_times_out(self, mock_db2_connect):
mock_db2_connect.side_effect = Exception('Timeout')
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'''user={},
password={},
database={},
host={},
port={}'''.format(DB2_USER, DB2_PASSWORD, DB2_DATABASE, DB2_HOSTNAME, DB2_PORT),
potential_secret,
) == VerifiedResult.UNVERIFIED
mock_db2_connect.assert_called_with(DB2_CONN_STRING, '', '')
def test_verify_no_other_factors(self):
potential_secret = PotentialSecret('test db2', 'test filename', DB2_PASSWORD)
assert Db2Detector().verify(
DB2_PASSWORD,
'password={}'.format(DB2_PASSWORD),
potential_secret,
) == VerifiedResult.UNVERIFIED
@pytest.mark.parametrize(
'content, factor_keyword_regex, factor_regex, expected_output',
(
(
textwrap.dedent("""
user = {}
""")[1:-1].format(
DB2_USER,
),
Db2Detector().username_keyword_regex,
Db2Detector().username_regex,
[DB2_USER],
),
(
textwrap.dedent("""
port = {}
""")[1:-1].format(
DB2_PORT,
),
Db2Detector().port_keyword_regex,
Db2Detector().port_regex,
[DB2_PORT],
),
(
textwrap.dedent("""
database = {}
""")[1:-1].format(
DB2_DATABASE,
),
Db2Detector().database_keyword_regex,
Db2Detector().database_regex,
[DB2_DATABASE],
),
(
textwrap.dedent("""
host = {}
""")[1:-1].format(
DB2_HOSTNAME,
),
Db2Detector().hostname_keyword_regex,
Db2Detector().hostname_regex,
[DB2_HOSTNAME],
),
),
)
def test_find_other_factor(content, factor_keyword_regex, factor_regex, expected_output):
assert find_other_factor(content, factor_keyword_regex, factor_regex) == expected_output
@pytest.mark.parametrize(
'content, hostname_regex, port_regex, database_regex, expected_output',
(
(
textwrap.dedent("""
jdbc:db2://{}:{}/{}
""")[1:-1].format(
DB2_HOSTNAME,
DB2_PORT,
DB2_DATABASE,
),
Db2Detector().hostname_regex,
Db2Detector().port_regex,
Db2Detector().database_regex,
[(DB2_HOSTNAME, DB2_PORT, DB2_DATABASE)],
),
(
textwrap.dedent("""
jdbc:db2://{}:{}/
""")[1:-1].format(
DB2_HOSTNAME,
DB2_PORT,
),
Db2Detector().hostname_regex,
Db2Detector().port_regex,
Db2Detector().database_regex,
[],
),
(
textwrap.dedent("""
nonsense
"""),
Db2Detector().hostname_regex,
Db2Detector().port_regex,
Db2Detector().database_regex,
[],
),
),
)
def test_get_hostname_port_database_from_url(
content, hostname_regex, port_regex, database_regex, expected_output,
):
assert get_hostname_port_database_from_url(
content, hostname_regex, port_regex, database_regex,
) == expected_output
| true | true |
1c33e6f6a138884a527d0ffde240901b01d5617b | 6,981 | py | Python | tests/app/api_v0/test_revisions.py | xwu64/server | d358db21db4a8faf33a3681fc499aeea07e9784b | [
"BSD-3-Clause"
] | null | null | null | tests/app/api_v0/test_revisions.py | xwu64/server | d358db21db4a8faf33a3681fc499aeea07e9784b | [
"BSD-3-Clause"
] | null | null | null | tests/app/api_v0/test_revisions.py | xwu64/server | d358db21db4a8faf33a3681fc499aeea07e9784b | [
"BSD-3-Clause"
] | null | null | null | # TODO: split E2E test to unit test
import pytest
from starlette.testclient import TestClient
from tests.fixtures.mock_service import MockUserService
__all__ = ["test_person_revisions_basic",
"test_person_revisions_offset",
"test_person_revisions_offset_limit",
"test_character_revisions_basic",
"test_character_revisions_offset",
"test_character_revisions_page_limit",
"test_subject_revisions_basic",
"test_subject_revisions_offset",
"test_subject_revisions_page_limit",
"test_episode_revisions_basic",
"test_episode_revisions_offset",
"test_episode_revisions_page_limit"]
person_revisions_api_prefix = "/v0/revisions/persons"
@pytest.mark.env("e2e", "database")
def test_person_revisions_basic(
client: TestClient,
mock_user_service: MockUserService,
):
response = client.get(person_revisions_api_prefix, params={"person_id": 9})
assert response.status_code == 200
assert response.headers["content-type"] == "application/json"
res = response.json()
assert res["total"]
assert res["data"]
assert res["offset"] == 0
assert "limit" in res
for item in res["data"]:
assert "nickname" in item["creator"]
@pytest.mark.env("e2e", "database")
def test_person_revisions_offset(
client: TestClient,
mock_user_service: MockUserService,
):
offset = 1
common_params = {"person_id": 9}
response1 = client.get(
person_revisions_api_prefix, params={"offset": 1, **common_params}
)
assert response1.status_code == 200
assert response1.headers["content-type"] == "application/json"
res = response1.json()
assert (
res["data"][0]["id"]
== client.get(person_revisions_api_prefix, params=common_params).json()["data"][
1
]["id"]
)
assert res["offset"] == offset
@pytest.mark.env("e2e", "database")
def test_person_revisions_offset_limit(
client: TestClient,
mock_user_service: MockUserService,
):
offset = 30000
response = client.get(
person_revisions_api_prefix, params={"offset": offset, "person_id": 9}
)
assert response.status_code == 422, response.text
character_revisions_api_prefix = "/v0/revisions/characters"
@pytest.mark.env("e2e", "database")
def test_character_revisions_basic(
client: TestClient,
mock_user_service: MockUserService,
):
response = client.get(character_revisions_api_prefix, params={"character_id": 1})
assert response.status_code == 200, response.json()
assert response.headers["content-type"] == "application/json"
res = response.json()
assert res["total"]
assert res["offset"] == 0
assert "limit" in res
assert res["data"]
@pytest.mark.env("e2e", "database")
def test_character_revisions_offset(
client: TestClient,
mock_user_service: MockUserService,
):
offset = 1
common_params = {"character_id": 1}
response1 = client.get(
character_revisions_api_prefix, params={"offset": offset, **common_params}
)
assert response1.status_code == 200
assert response1.headers["content-type"] == "application/json"
res = response1.json()
assert (
res["data"][0]["id"]
== client.get(character_revisions_api_prefix, params=common_params).json()[
"data"
][1]["id"]
)
assert res["offset"] == offset
@pytest.mark.env("e2e", "database")
def test_character_revisions_page_limit(
client: TestClient,
mock_user_service: MockUserService,
):
offset = 30000
response = client.get(
character_revisions_api_prefix, params={"character_id": 1, "offset": offset}
)
assert response.status_code == 422, response.text
subject_revisions_api_prefix = "/v0/revisions/subjects"
@pytest.mark.env("e2e", "database")
def test_subject_revisions_basic(
client: TestClient, mock_user_service: MockUserService
):
response = client.get(subject_revisions_api_prefix, params={"subject_id": 26})
assert response.status_code == 200
assert response.headers["content-type"] == "application/json"
res = response.json()
assert "total" in res
assert "limit" in res
assert res["offset"] == 0
if res["total"] <= res["limit"]:
assert res["total"] == len(res["data"])
else:
assert res["limit"] == len(res["data"])
for item in res["data"]:
if item["creator"]:
assert "nickname" in item["creator"]
@pytest.mark.env("e2e", "database")
def test_subject_revisions_offset(client: TestClient):
offset = 1
common_params = {"subject_id": 1}
response1 = client.get(
subject_revisions_api_prefix, params={"offset": offset, **common_params}
)
assert response1.status_code == 200
assert response1.headers["content-type"] == "application/json"
res = response1.json()
assert (
res["data"][0]["id"]
== client.get(subject_revisions_api_prefix, params=common_params).json()[
"data"
][1]["id"]
)
assert res["offset"] == offset
@pytest.mark.env("e2e", "database")
def test_subject_revisions_page_limit(
client: TestClient,
):
offset = 30000
response = client.get(
subject_revisions_api_prefix, params={"subject_id": 1, "offset": offset}
)
assert response.status_code == 422, response.text
episode_revisions_api_prefix = "/v0/revisions/episodes"
@pytest.mark.env("e2e", "database")
def test_episode_revisions_basic(
client: TestClient,
):
response = client.get(episode_revisions_api_prefix, params={"episode_id": 522})
assert response.status_code == 200
assert response.headers["content-type"] == "application/json"
res = response.json()
assert "total" in res
assert "limit" in res
assert res["offset"] == 0
if res["total"] <= res["limit"]:
assert res["total"] == len(res["data"])
else:
assert res["limit"] == len(res["data"])
for item in res["data"]:
assert "nickname" in item["creator"]
@pytest.mark.env("e2e", "database")
def test_episode_revisions_offset(
client: TestClient,
):
offset = 1
common_params = {"episode_id": 1045}
response1 = client.get(
episode_revisions_api_prefix, params={"offset": offset, **common_params}
)
assert response1.status_code == 200
assert response1.headers["content-type"] == "application/json"
res = response1.json()
assert (
res["data"][0]["id"]
== client.get(episode_revisions_api_prefix, params=common_params).json()[
"data"
][1]["id"]
)
assert res["offset"] == offset
@pytest.mark.env("e2e", "database")
def test_episode_revisions_page_limit(
client: TestClient,
):
offset = 30000
response = client.get(
episode_revisions_api_prefix, params={"episode_id": 522, "offset": offset}
)
assert response.status_code == 422, response.text
| 29.0875 | 88 | 0.664661 |
import pytest
from starlette.testclient import TestClient
from tests.fixtures.mock_service import MockUserService
__all__ = ["test_person_revisions_basic",
"test_person_revisions_offset",
"test_person_revisions_offset_limit",
"test_character_revisions_basic",
"test_character_revisions_offset",
"test_character_revisions_page_limit",
"test_subject_revisions_basic",
"test_subject_revisions_offset",
"test_subject_revisions_page_limit",
"test_episode_revisions_basic",
"test_episode_revisions_offset",
"test_episode_revisions_page_limit"]
person_revisions_api_prefix = "/v0/revisions/persons"
@pytest.mark.env("e2e", "database")
def test_person_revisions_basic(
client: TestClient,
mock_user_service: MockUserService,
):
response = client.get(person_revisions_api_prefix, params={"person_id": 9})
assert response.status_code == 200
assert response.headers["content-type"] == "application/json"
res = response.json()
assert res["total"]
assert res["data"]
assert res["offset"] == 0
assert "limit" in res
for item in res["data"]:
assert "nickname" in item["creator"]
@pytest.mark.env("e2e", "database")
def test_person_revisions_offset(
client: TestClient,
mock_user_service: MockUserService,
):
offset = 1
common_params = {"person_id": 9}
response1 = client.get(
person_revisions_api_prefix, params={"offset": 1, **common_params}
)
assert response1.status_code == 200
assert response1.headers["content-type"] == "application/json"
res = response1.json()
assert (
res["data"][0]["id"]
== client.get(person_revisions_api_prefix, params=common_params).json()["data"][
1
]["id"]
)
assert res["offset"] == offset
@pytest.mark.env("e2e", "database")
def test_person_revisions_offset_limit(
client: TestClient,
mock_user_service: MockUserService,
):
offset = 30000
response = client.get(
person_revisions_api_prefix, params={"offset": offset, "person_id": 9}
)
assert response.status_code == 422, response.text
character_revisions_api_prefix = "/v0/revisions/characters"
@pytest.mark.env("e2e", "database")
def test_character_revisions_basic(
client: TestClient,
mock_user_service: MockUserService,
):
response = client.get(character_revisions_api_prefix, params={"character_id": 1})
assert response.status_code == 200, response.json()
assert response.headers["content-type"] == "application/json"
res = response.json()
assert res["total"]
assert res["offset"] == 0
assert "limit" in res
assert res["data"]
@pytest.mark.env("e2e", "database")
def test_character_revisions_offset(
client: TestClient,
mock_user_service: MockUserService,
):
offset = 1
common_params = {"character_id": 1}
response1 = client.get(
character_revisions_api_prefix, params={"offset": offset, **common_params}
)
assert response1.status_code == 200
assert response1.headers["content-type"] == "application/json"
res = response1.json()
assert (
res["data"][0]["id"]
== client.get(character_revisions_api_prefix, params=common_params).json()[
"data"
][1]["id"]
)
assert res["offset"] == offset
@pytest.mark.env("e2e", "database")
def test_character_revisions_page_limit(
client: TestClient,
mock_user_service: MockUserService,
):
offset = 30000
response = client.get(
character_revisions_api_prefix, params={"character_id": 1, "offset": offset}
)
assert response.status_code == 422, response.text
subject_revisions_api_prefix = "/v0/revisions/subjects"
@pytest.mark.env("e2e", "database")
def test_subject_revisions_basic(
client: TestClient, mock_user_service: MockUserService
):
response = client.get(subject_revisions_api_prefix, params={"subject_id": 26})
assert response.status_code == 200
assert response.headers["content-type"] == "application/json"
res = response.json()
assert "total" in res
assert "limit" in res
assert res["offset"] == 0
if res["total"] <= res["limit"]:
assert res["total"] == len(res["data"])
else:
assert res["limit"] == len(res["data"])
for item in res["data"]:
if item["creator"]:
assert "nickname" in item["creator"]
@pytest.mark.env("e2e", "database")
def test_subject_revisions_offset(client: TestClient):
offset = 1
common_params = {"subject_id": 1}
response1 = client.get(
subject_revisions_api_prefix, params={"offset": offset, **common_params}
)
assert response1.status_code == 200
assert response1.headers["content-type"] == "application/json"
res = response1.json()
assert (
res["data"][0]["id"]
== client.get(subject_revisions_api_prefix, params=common_params).json()[
"data"
][1]["id"]
)
assert res["offset"] == offset
@pytest.mark.env("e2e", "database")
def test_subject_revisions_page_limit(
client: TestClient,
):
offset = 30000
response = client.get(
subject_revisions_api_prefix, params={"subject_id": 1, "offset": offset}
)
assert response.status_code == 422, response.text
episode_revisions_api_prefix = "/v0/revisions/episodes"
@pytest.mark.env("e2e", "database")
def test_episode_revisions_basic(
client: TestClient,
):
response = client.get(episode_revisions_api_prefix, params={"episode_id": 522})
assert response.status_code == 200
assert response.headers["content-type"] == "application/json"
res = response.json()
assert "total" in res
assert "limit" in res
assert res["offset"] == 0
if res["total"] <= res["limit"]:
assert res["total"] == len(res["data"])
else:
assert res["limit"] == len(res["data"])
for item in res["data"]:
assert "nickname" in item["creator"]
@pytest.mark.env("e2e", "database")
def test_episode_revisions_offset(
client: TestClient,
):
offset = 1
common_params = {"episode_id": 1045}
response1 = client.get(
episode_revisions_api_prefix, params={"offset": offset, **common_params}
)
assert response1.status_code == 200
assert response1.headers["content-type"] == "application/json"
res = response1.json()
assert (
res["data"][0]["id"]
== client.get(episode_revisions_api_prefix, params=common_params).json()[
"data"
][1]["id"]
)
assert res["offset"] == offset
@pytest.mark.env("e2e", "database")
def test_episode_revisions_page_limit(
client: TestClient,
):
offset = 30000
response = client.get(
episode_revisions_api_prefix, params={"episode_id": 522, "offset": offset}
)
assert response.status_code == 422, response.text
| true | true |
1c33e80294a94d85a9edbb95747f6f5c2e32304a | 1,387 | py | Python | python/ecs/fargate-load-balanced-service/app.py | samtwil/aws-cdk-examples | a0ca373be2bd4e82f888f903fdb7c57d36d5537f | [
"Apache-2.0"
] | null | null | null | python/ecs/fargate-load-balanced-service/app.py | samtwil/aws-cdk-examples | a0ca373be2bd4e82f888f903fdb7c57d36d5537f | [
"Apache-2.0"
] | null | null | null | python/ecs/fargate-load-balanced-service/app.py | samtwil/aws-cdk-examples | a0ca373be2bd4e82f888f903fdb7c57d36d5537f | [
"Apache-2.0"
] | null | null | null | from aws_cdk import (
aws_autoscaling as autoscaling,
aws_ec2 as ec2,
aws_ecs as ecs,
aws_ecs_patterns as ecs_patterns,
App, CfnOutput, Stack
)
from constructs import Construct
class BonjourFargate(Stack):
def __init__(self, scope: Construct, id: str, **kwargs) -> None:
super().__init__(scope, id, **kwargs)
# Create VPC and Fargate Cluster
# NOTE: Limit AZs to avoid reaching resource quotas
vpc = ec2.Vpc(
self, "MyVpc",
max_azs=2
)
cluster = ecs.Cluster(
self, 'Ec2Cluster',
vpc=vpc
)
fargate_service = ecs_patterns.NetworkLoadBalancedFargateService(
self, "FargateService",
cluster=cluster,
task_image_options=ecs_patterns.NetworkLoadBalancedTaskImageOptions(
image=ecs.ContainerImage.from_registry("amazon/amazon-ecs-sample")
)
)
fargate_service.service.connections.security_groups[0].add_ingress_rule(
peer = ec2.Peer.ipv4(vpc.vpc_cidr_block),
connection = ec2.Port.tcp(80),
description="Allow http inbound from VPC"
)
CfnOutput(
self, "LoadBalancerDNS",
value=fargate_service.load_balancer.load_balancer_dns_name
)
app = App()
BonjourFargate(app, "Bonjour")
app.synth()
| 27.74 | 82 | 0.617159 | from aws_cdk import (
aws_autoscaling as autoscaling,
aws_ec2 as ec2,
aws_ecs as ecs,
aws_ecs_patterns as ecs_patterns,
App, CfnOutput, Stack
)
from constructs import Construct
class BonjourFargate(Stack):
def __init__(self, scope: Construct, id: str, **kwargs) -> None:
super().__init__(scope, id, **kwargs)
vpc = ec2.Vpc(
self, "MyVpc",
max_azs=2
)
cluster = ecs.Cluster(
self, 'Ec2Cluster',
vpc=vpc
)
fargate_service = ecs_patterns.NetworkLoadBalancedFargateService(
self, "FargateService",
cluster=cluster,
task_image_options=ecs_patterns.NetworkLoadBalancedTaskImageOptions(
image=ecs.ContainerImage.from_registry("amazon/amazon-ecs-sample")
)
)
fargate_service.service.connections.security_groups[0].add_ingress_rule(
peer = ec2.Peer.ipv4(vpc.vpc_cidr_block),
connection = ec2.Port.tcp(80),
description="Allow http inbound from VPC"
)
CfnOutput(
self, "LoadBalancerDNS",
value=fargate_service.load_balancer.load_balancer_dns_name
)
app = App()
BonjourFargate(app, "Bonjour")
app.synth()
| true | true |
1c33eb22ed93e91c820b6bf40b6fff5c184acf47 | 1,225 | py | Python | tests/core/extraction/test_mapping_analyzer.py | ymoch/preacher | ae68170d14c72791884e91b20054bd13a79b52d0 | [
"MIT"
] | 3 | 2019-08-01T03:14:49.000Z | 2020-01-31T08:55:22.000Z | tests/core/extraction/test_mapping_analyzer.py | ymoch/preacher | ae68170d14c72791884e91b20054bd13a79b52d0 | [
"MIT"
] | 353 | 2019-04-14T14:53:28.000Z | 2022-03-11T03:26:08.000Z | tests/core/extraction/test_mapping_analyzer.py | ymoch/preacher | ae68170d14c72791884e91b20054bd13a79b52d0 | [
"MIT"
] | 1 | 2020-08-01T06:23:08.000Z | 2020-08-01T06:23:08.000Z | from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Mapping
from unittest.mock import sentinel
from lxml.etree import _Element as Element
from pytest import raises
from preacher.core.extraction.analysis import MappingAnalyzer
from preacher.core.extraction.error import ExtractionError
@dataclass(frozen=True)
class Context:
value: object
def test_for_text():
current = datetime(2019, 1, 2, 3, 4, 5, 678, tzinfo=timezone.utc)
analyzer = MappingAnalyzer({"value": [current, 1, "A"]})
def _extract(value: str) -> object:
assert value == '{"value":["2019-01-02T03:04:05.000678+00:00",1,"A"]}'
return sentinel.extracted
assert analyzer.for_text(_extract) is sentinel.extracted
def test_for_mapping():
analyzer = MappingAnalyzer({"value": 1})
def _extract(value: Mapping) -> object:
assert value == {"value": 1}
return sentinel.extracted
assert analyzer.for_mapping(_extract) is sentinel.extracted
def test_for_etree():
analyzer = MappingAnalyzer({"value": 1})
def _extract(_: Element) -> object:
return sentinel.extracted
with raises(ExtractionError):
analyzer.for_etree(_extract)
| 26.06383 | 78 | 0.713469 | from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Mapping
from unittest.mock import sentinel
from lxml.etree import _Element as Element
from pytest import raises
from preacher.core.extraction.analysis import MappingAnalyzer
from preacher.core.extraction.error import ExtractionError
@dataclass(frozen=True)
class Context:
value: object
def test_for_text():
current = datetime(2019, 1, 2, 3, 4, 5, 678, tzinfo=timezone.utc)
analyzer = MappingAnalyzer({"value": [current, 1, "A"]})
def _extract(value: str) -> object:
assert value == '{"value":["2019-01-02T03:04:05.000678+00:00",1,"A"]}'
return sentinel.extracted
assert analyzer.for_text(_extract) is sentinel.extracted
def test_for_mapping():
analyzer = MappingAnalyzer({"value": 1})
def _extract(value: Mapping) -> object:
assert value == {"value": 1}
return sentinel.extracted
assert analyzer.for_mapping(_extract) is sentinel.extracted
def test_for_etree():
analyzer = MappingAnalyzer({"value": 1})
def _extract(_: Element) -> object:
return sentinel.extracted
with raises(ExtractionError):
analyzer.for_etree(_extract)
| true | true |
1c33ebd38ae22627863e394b94ce67736dfeaaf2 | 927 | py | Python | users/migrations/0011_auto_20210412_1908.py | msking18/minor | 17cffab95b5dc1705a131a1ef66ff7f47837de64 | [
"MIT"
] | 3 | 2021-03-22T10:39:18.000Z | 2021-04-30T10:29:37.000Z | users/migrations/0011_auto_20210412_1908.py | msking18/minor | 17cffab95b5dc1705a131a1ef66ff7f47837de64 | [
"MIT"
] | 1 | 2021-04-16T06:54:10.000Z | 2021-04-16T06:54:10.000Z | users/migrations/0011_auto_20210412_1908.py | msking18/minor | 17cffab95b5dc1705a131a1ef66ff7f47837de64 | [
"MIT"
] | 3 | 2021-03-11T10:02:37.000Z | 2021-04-23T07:34:10.000Z | # Generated by Django 3.1.6 on 2021-04-12 13:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('users', '0010_remove_profile_date_of_birth'),
]
operations = [
migrations.AddField(
model_name='profile',
name='branch',
field=models.CharField(default='CSE', max_length=15),
),
migrations.AddField(
model_name='profile',
name='programme',
field=models.CharField(default='B.Tech', max_length=15),
),
migrations.AlterField(
model_name='profile',
name='city',
field=models.CharField(default='Jaipur', max_length=100),
),
migrations.AlterField(
model_name='profile',
name='state',
field=models.CharField(default='Rajasthan', max_length=100),
),
]
| 27.264706 | 72 | 0.567422 |
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('users', '0010_remove_profile_date_of_birth'),
]
operations = [
migrations.AddField(
model_name='profile',
name='branch',
field=models.CharField(default='CSE', max_length=15),
),
migrations.AddField(
model_name='profile',
name='programme',
field=models.CharField(default='B.Tech', max_length=15),
),
migrations.AlterField(
model_name='profile',
name='city',
field=models.CharField(default='Jaipur', max_length=100),
),
migrations.AlterField(
model_name='profile',
name='state',
field=models.CharField(default='Rajasthan', max_length=100),
),
]
| true | true |
1c33ee1592f2be0a3d6959054ddbc20841c3550a | 2,584 | py | Python | base.py | sosterbind/hexaRanger | a0b888061af4858c78962ed8c0154531f00e8455 | [
"MIT"
] | null | null | null | base.py | sosterbind/hexaRanger | a0b888061af4858c78962ed8c0154531f00e8455 | [
"MIT"
] | null | null | null | base.py | sosterbind/hexaRanger | a0b888061af4858c78962ed8c0154531f00e8455 | [
"MIT"
] | null | null | null | from typing import (
Optional,
List,
Tuple,
Dict,
)
from abc import (
ABC,
abstractmethod,
)
class StoreClient(ABC):
__slots__ = ()
@classmethod
@abstractmethod
def query_range_count(
cls,
start_key: Optional[str] = None,
stop_key: Optional[str] = None,
start_inclusive: bool = True,
stop_inclusive: bool = True,
) -> int:
raise NotImplementedError
@classmethod
@abstractmethod
def query_range(
cls,
start_key: Optional[str] = None,
stop_key: Optional[str] = None,
start_inclusive: bool = True,
stop_inclusive: bool = True,
limit: Optional[int] = None,
offset: Optional[int] = None,
) -> list:
raise NotImplementedError
@classmethod
@abstractmethod
def query_range_raw(
cls,
start_key: Optional[str] = None,
stop_key: Optional[str] = None,
start_inclusive: bool = True,
stop_inclusive: bool = True,
limit: Optional[int] = None,
offset: Optional[int] = None,
) -> list:
raise NotImplementedError
@classmethod
@abstractmethod
def add_keys(cls, keys: List[str]):
raise NotImplementedError
@classmethod
@abstractmethod
def remove_keys(cls, keys: List[str]):
raise NotImplementedError
@classmethod
@abstractmethod
def add_and_remove_keys(
cls, keys_to_add: List[str], keys_to_remove: List[str]
):
raise NotImplementedError
class HexaStore(ABC):
@classmethod
@abstractmethod
def add_item(cls, *args, **kwargs):
raise NotImplementedError
@classmethod
@abstractmethod
def remove_item(cls, *args, **kwargs):
raise NotImplementedError
@classmethod
@abstractmethod
def lookup_items(cls, *args, **kwargs):
raise NotImplementedError
@classmethod
@abstractmethod
def count_items(cls, *args, **kwargs):
raise NotImplementedError
@classmethod
@abstractmethod
def update_item(cls, lookup: Dict[str, str], patch: Dict[str, str]):
raise NotImplementedError
@classmethod
@abstractmethod
def to_hexastore_key_set(cls, *args, **kwargs) -> List[str]:
raise NotImplementedError
@classmethod
@abstractmethod
def hexastore_key_to_tuple(cls, key: str) -> Tuple[str, str, str]:
raise NotImplementedError
@classmethod
@abstractmethod
def get_composite_key(cls, *args, **kwargs) -> str:
raise NotImplementedError | 23.279279 | 72 | 0.629257 | from typing import (
Optional,
List,
Tuple,
Dict,
)
from abc import (
ABC,
abstractmethod,
)
class StoreClient(ABC):
__slots__ = ()
@classmethod
@abstractmethod
def query_range_count(
cls,
start_key: Optional[str] = None,
stop_key: Optional[str] = None,
start_inclusive: bool = True,
stop_inclusive: bool = True,
) -> int:
raise NotImplementedError
@classmethod
@abstractmethod
def query_range(
cls,
start_key: Optional[str] = None,
stop_key: Optional[str] = None,
start_inclusive: bool = True,
stop_inclusive: bool = True,
limit: Optional[int] = None,
offset: Optional[int] = None,
) -> list:
raise NotImplementedError
@classmethod
@abstractmethod
def query_range_raw(
cls,
start_key: Optional[str] = None,
stop_key: Optional[str] = None,
start_inclusive: bool = True,
stop_inclusive: bool = True,
limit: Optional[int] = None,
offset: Optional[int] = None,
) -> list:
raise NotImplementedError
@classmethod
@abstractmethod
def add_keys(cls, keys: List[str]):
raise NotImplementedError
@classmethod
@abstractmethod
def remove_keys(cls, keys: List[str]):
raise NotImplementedError
@classmethod
@abstractmethod
def add_and_remove_keys(
cls, keys_to_add: List[str], keys_to_remove: List[str]
):
raise NotImplementedError
class HexaStore(ABC):
@classmethod
@abstractmethod
def add_item(cls, *args, **kwargs):
raise NotImplementedError
@classmethod
@abstractmethod
def remove_item(cls, *args, **kwargs):
raise NotImplementedError
@classmethod
@abstractmethod
def lookup_items(cls, *args, **kwargs):
raise NotImplementedError
@classmethod
@abstractmethod
def count_items(cls, *args, **kwargs):
raise NotImplementedError
@classmethod
@abstractmethod
def update_item(cls, lookup: Dict[str, str], patch: Dict[str, str]):
raise NotImplementedError
@classmethod
@abstractmethod
def to_hexastore_key_set(cls, *args, **kwargs) -> List[str]:
raise NotImplementedError
@classmethod
@abstractmethod
def hexastore_key_to_tuple(cls, key: str) -> Tuple[str, str, str]:
raise NotImplementedError
@classmethod
@abstractmethod
def get_composite_key(cls, *args, **kwargs) -> str:
raise NotImplementedError | true | true |
1c33ef9ee86a9ecba33247c529a8b2b9daa28e69 | 532 | py | Python | Mundo1/des33.py | julimoraislima/Python-CursoEmVideo | d21b0485d2f5767039d819cf743255dfd0f27b18 | [
"MIT"
] | 2 | 2021-01-05T12:31:00.000Z | 2021-03-20T00:31:18.000Z | Mundo1/des33.py | julimoraislima/Python-CursoEmVideo | d21b0485d2f5767039d819cf743255dfd0f27b18 | [
"MIT"
] | null | null | null | Mundo1/des33.py | julimoraislima/Python-CursoEmVideo | d21b0485d2f5767039d819cf743255dfd0f27b18 | [
"MIT"
] | 1 | 2020-12-28T22:56:10.000Z | 2020-12-28T22:56:10.000Z | #desafio 33: Maior e Menor. Programa lê 3 valores, e retorna o maior e o menor valor.
#1-maneira simplificada usando uma lista[].
primeiro = int(input('Digite o primeiro valor inteiro: '))
segundo = int(input('Digite o segundo valor inteiro: '))
terceiro = int(input('Digite o terceiro valor inteiro: '))
numeros = [primeiro, segundo, terceiro]
print('-+-'*20)
print(f'O \33[31mmaior\33[m valor digitado foi \33[31m{max(numeros)}\33[m')
print(f'O \33[32mmenor\33[m valor digitado foi \33[32m{min(numeros)}\33[m')
print('-+-'*20)
| 38 | 85 | 0.708647 |
primeiro = int(input('Digite o primeiro valor inteiro: '))
segundo = int(input('Digite o segundo valor inteiro: '))
terceiro = int(input('Digite o terceiro valor inteiro: '))
numeros = [primeiro, segundo, terceiro]
print('-+-'*20)
print(f'O \33[31mmaior\33[m valor digitado foi \33[31m{max(numeros)}\33[m')
print(f'O \33[32mmenor\33[m valor digitado foi \33[32m{min(numeros)}\33[m')
print('-+-'*20)
| true | true |
1c33f01fc1c0b21826cb0fe8d7917484a3137ed5 | 560 | py | Python | scripts/mv_rednet.py | albert-yue/objectnav | 95ce9bc2c1d953887275e8d9809a506aeb5682fb | [
"MIT",
"Unlicense"
] | 15 | 2021-04-12T04:36:14.000Z | 2022-03-20T04:16:36.000Z | scripts/mv_rednet.py | albert-yue/objectnav | 95ce9bc2c1d953887275e8d9809a506aeb5682fb | [
"MIT",
"Unlicense"
] | 4 | 2021-07-12T18:14:08.000Z | 2021-11-11T13:44:34.000Z | scripts/mv_rednet.py | albert-yue/objectnav | 95ce9bc2c1d953887275e8d9809a506aeb5682fb | [
"MIT",
"Unlicense"
] | 10 | 2021-06-23T23:14:16.000Z | 2022-03-20T07:47:32.000Z | #%%
# Move files around for rednet
from pathlib import Path
from shutil import move
detailed_paths = Path('/srv/flash1/jye72/share/objectnav_detailed')
eval_paths = Path('/srv/flash1/jye72/share/objectnav_eval')
KEY = 'gt_False.pth'
NEW_KEY = 'gt_False_21.pth'
for var_path in eval_paths.glob("*"):
# for var_path in detailed_paths.glob("*"):
for ckpt in var_path.glob("*"):
for val in ckpt.glob("*"):
val = str(val)
if KEY in val:
new_path = val[:-len(KEY)] + NEW_KEY
move(val, new_path)
| 28 | 67 | 0.633929 |
from pathlib import Path
from shutil import move
detailed_paths = Path('/srv/flash1/jye72/share/objectnav_detailed')
eval_paths = Path('/srv/flash1/jye72/share/objectnav_eval')
KEY = 'gt_False.pth'
NEW_KEY = 'gt_False_21.pth'
for var_path in eval_paths.glob("*"):
for ckpt in var_path.glob("*"):
for val in ckpt.glob("*"):
val = str(val)
if KEY in val:
new_path = val[:-len(KEY)] + NEW_KEY
move(val, new_path)
| true | true |
1c33f09cfd5449e71f3c16d72f5d6bf8a34449c9 | 3,500 | py | Python | client.py | murilopereirame/ChatUDP | 979a8ed5927bb0a431314cad2e36505bbbb256c2 | [
"MIT"
] | null | null | null | client.py | murilopereirame/ChatUDP | 979a8ed5927bb0a431314cad2e36505bbbb256c2 | [
"MIT"
] | null | null | null | client.py | murilopereirame/ChatUDP | 979a8ed5927bb0a431314cad2e36505bbbb256c2 | [
"MIT"
] | null | null | null | import socket
import threading
import random
import json
import sys
from RSA import RSA
class Client:
SERVER_UDP_IP_ADDRESS = "127.0.0.1"
SERVER_UDP_PORT_NO = 6789
user = ""
room = "geral"
clientSock = None
def __init__(self, ip):
self.SERVER_UDP_IP_ADDRESS = ip
self.room = 'lobby'
def autenticate(self):
usr = input('Insira seu nickname: ')
if(usr == ''):
usr = 'Visitante'+str(random.randint(1000, 2000))
self.user = usr
print("Autenticado como " + self.user)
def sendMessage(self, message):
messagePackage = {'user': self.user, 'room': self.room, 'connecting': False,
'message': self.RSA.encryptString(message, self.serverPK)}
self.clientSock.sendto(json.dumps(messagePackage).encode(
'utf-8'), (self.SERVER_UDP_IP_ADDRESS, self.SERVER_UDP_PORT_NO))
def changeRoom(self, room):
self.room = room
def connectToServer(self):
self.clientSock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
messagePackage = {'user': self.user, 'room': self.room,
'connecting': True, 'message': '', 'key': self.RSA.getPublicKey()}
self.clientSock.sendto(json.dumps(messagePackage).encode(
'utf-8'), (self.SERVER_UDP_IP_ADDRESS, self.SERVER_UDP_PORT_NO))
def listenMessages(self):
while True:
data, addr = self.clientSock.recvfrom(1024)
incoming = json.loads(data.decode('utf-8'))
if('keys' in incoming):
self.serverPK = incoming['keys']
continue
msg = self.RSA.decryptString(
incoming['message'], self.RSA.getPrivateKey())
if(incoming['user'] == self.SERVER_UDP_IP_ADDRESS+str(self.SERVER_UDP_PORT_NO)):
if(msg[0:5].strip() == 'nick'):
newUser = msg[5:]
self.user = newUser
print(
'[SERVER] -> Nome de usuario em uso! Seu novo nome e ' + newUser)
elif(msg[0:5].strip() == 'room'):
newRoom = msg[5:]
self.room = newRoom
print('[SERVER] -> Sala alterada para ' + newRoom)
else:
sys.stdout.write('\r'+'['+incoming['user']+'] -> '+msg)
sys.stdout.write('\n['+self.user+']: ')
def chat(self):
while True:
data = input("[" + self.user + "]: ")
if data == 'croom':
sys.stdout.write("\033[F")
newRoom = input("Digite a nova sala: ")
self.room = newRoom
self.sendMessage('croom ' + newRoom)
continue
elif data == '':
continue
elif data == 'disconnect':
self.sendMessage(data)
print('Desconectado do servidor')
break
sys.stdout.write("\033[F")
print('['+self.user+'] -> ' + data)
self.sendMessage(data)
def initClient(self):
self.RSA = RSA()
self.autenticate()
self.connectToServer()
threading.Thread(target=self.listenMessages).start()
threading.Thread(target=self.chat).start()
if len(sys.argv) == 1:
print('Para iniciar -> client.py server-ip')
elif len(sys.argv) == 2:
client = Client(sys.argv[1])
client.initClient()
| 34.653465 | 92 | 0.532286 | import socket
import threading
import random
import json
import sys
from RSA import RSA
class Client:
SERVER_UDP_IP_ADDRESS = "127.0.0.1"
SERVER_UDP_PORT_NO = 6789
user = ""
room = "geral"
clientSock = None
def __init__(self, ip):
self.SERVER_UDP_IP_ADDRESS = ip
self.room = 'lobby'
def autenticate(self):
usr = input('Insira seu nickname: ')
if(usr == ''):
usr = 'Visitante'+str(random.randint(1000, 2000))
self.user = usr
print("Autenticado como " + self.user)
def sendMessage(self, message):
messagePackage = {'user': self.user, 'room': self.room, 'connecting': False,
'message': self.RSA.encryptString(message, self.serverPK)}
self.clientSock.sendto(json.dumps(messagePackage).encode(
'utf-8'), (self.SERVER_UDP_IP_ADDRESS, self.SERVER_UDP_PORT_NO))
def changeRoom(self, room):
self.room = room
def connectToServer(self):
self.clientSock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
messagePackage = {'user': self.user, 'room': self.room,
'connecting': True, 'message': '', 'key': self.RSA.getPublicKey()}
self.clientSock.sendto(json.dumps(messagePackage).encode(
'utf-8'), (self.SERVER_UDP_IP_ADDRESS, self.SERVER_UDP_PORT_NO))
def listenMessages(self):
while True:
data, addr = self.clientSock.recvfrom(1024)
incoming = json.loads(data.decode('utf-8'))
if('keys' in incoming):
self.serverPK = incoming['keys']
continue
msg = self.RSA.decryptString(
incoming['message'], self.RSA.getPrivateKey())
if(incoming['user'] == self.SERVER_UDP_IP_ADDRESS+str(self.SERVER_UDP_PORT_NO)):
if(msg[0:5].strip() == 'nick'):
newUser = msg[5:]
self.user = newUser
print(
'[SERVER] -> Nome de usuario em uso! Seu novo nome e ' + newUser)
elif(msg[0:5].strip() == 'room'):
newRoom = msg[5:]
self.room = newRoom
print('[SERVER] -> Sala alterada para ' + newRoom)
else:
sys.stdout.write('\r'+'['+incoming['user']+'] -> '+msg)
sys.stdout.write('\n['+self.user+']: ')
def chat(self):
while True:
data = input("[" + self.user + "]: ")
if data == 'croom':
sys.stdout.write("\033[F")
newRoom = input("Digite a nova sala: ")
self.room = newRoom
self.sendMessage('croom ' + newRoom)
continue
elif data == '':
continue
elif data == 'disconnect':
self.sendMessage(data)
print('Desconectado do servidor')
break
sys.stdout.write("\033[F")
print('['+self.user+'] -> ' + data)
self.sendMessage(data)
def initClient(self):
self.RSA = RSA()
self.autenticate()
self.connectToServer()
threading.Thread(target=self.listenMessages).start()
threading.Thread(target=self.chat).start()
if len(sys.argv) == 1:
print('Para iniciar -> client.py server-ip')
elif len(sys.argv) == 2:
client = Client(sys.argv[1])
client.initClient()
| true | true |
1c33f0e69444ade9a5b966e522f9e8149c28c794 | 1,608 | py | Python | fastreid/layers/rfconv/function.py | SZLSP/reid2020NAIC | d0eaee768e0be606417a27ce5ea2b3071b5a9bc2 | [
"Apache-2.0"
] | 2 | 2021-05-12T13:36:46.000Z | 2021-08-15T10:35:08.000Z | fastreid/layers/rfconv/function.py | SZLSP/reid2020NAIC | d0eaee768e0be606417a27ce5ea2b3071b5a9bc2 | [
"Apache-2.0"
] | 1 | 2021-12-28T12:49:49.000Z | 2021-12-28T12:49:49.000Z | fastreid/layers/rfconv/function.py | SZLSP/reid2020NAIC | d0eaee768e0be606417a27ce5ea2b3071b5a9bc2 | [
"Apache-2.0"
] | null | null | null | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""Rectify function"""
from torch.autograd import Function
from . import lib
__all__ = ['rectify']
class _rectify(Function):
@staticmethod
def forward(ctx, y, x, kernel_size, stride, padding, dilation, average):
ctx.save_for_backward(x)
# assuming kernel_size is 3
kernel_size = [k + 2 * (d - 1) for k, d in zip(kernel_size, dilation)]
ctx.kernel_size = kernel_size
ctx.stride = stride
ctx.padding = padding
ctx.dilation = dilation
ctx.average = average
if x.is_cuda:
lib.gpu.conv_rectify(y, x, kernel_size, stride, padding, dilation, average)
else:
lib.cpu.conv_rectify(y, x, kernel_size, stride, padding, dilation, average)
ctx.mark_dirty(y)
return y
@staticmethod
def backward(ctx, grad_y):
x, = ctx.saved_variables
if x.is_cuda:
lib.gpu.conv_rectify(grad_y, x, ctx.kernel_size, ctx.stride,
ctx.padding, ctx.dilation, ctx.average)
else:
lib.cpu.conv_rectify(grad_y, x, ctx.kernel_size, ctx.stride,
ctx.padding, ctx.dilation, ctx.average)
ctx.mark_dirty(grad_y)
return grad_y, None, None, None, None, None, None
rectify = _rectify.apply
| 32.816327 | 87 | 0.55597 | (d - 1) for k, d in zip(kernel_size, dilation)]
ctx.kernel_size = kernel_size
ctx.stride = stride
ctx.padding = padding
ctx.dilation = dilation
ctx.average = average
if x.is_cuda:
lib.gpu.conv_rectify(y, x, kernel_size, stride, padding, dilation, average)
else:
lib.cpu.conv_rectify(y, x, kernel_size, stride, padding, dilation, average)
ctx.mark_dirty(y)
return y
@staticmethod
def backward(ctx, grad_y):
x, = ctx.saved_variables
if x.is_cuda:
lib.gpu.conv_rectify(grad_y, x, ctx.kernel_size, ctx.stride,
ctx.padding, ctx.dilation, ctx.average)
else:
lib.cpu.conv_rectify(grad_y, x, ctx.kernel_size, ctx.stride,
ctx.padding, ctx.dilation, ctx.average)
ctx.mark_dirty(grad_y)
return grad_y, None, None, None, None, None, None
rectify = _rectify.apply
| true | true |
1c33f10cfc5099f1a9b12d2e015bf0dafde36b97 | 9,559 | py | Python | tensorflow/python/ops/control_flow_grad.py | KosingZhu/tensorflow | 7ac2521a4e609ddef0f0ea3ffc2e76102da934d7 | [
"Apache-2.0"
] | 9 | 2021-11-06T11:09:48.000Z | 2021-12-12T04:52:29.000Z | tensorflow/python/ops/control_flow_grad.py | KosingZhu/tensorflow | 7ac2521a4e609ddef0f0ea3ffc2e76102da934d7 | [
"Apache-2.0"
] | 2 | 2021-10-06T23:12:04.000Z | 2021-10-06T23:12:04.000Z | tensorflow/python/ops/control_flow_grad.py | KosingZhu/tensorflow | 7ac2521a4e609ddef0f0ea3ffc2e76102da934d7 | [
"Apache-2.0"
] | 1 | 2021-11-11T04:43:09.000Z | 2021-11-11T04:43:09.000Z | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Gradients for operators defined in control_flow_ops.py."""
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import indexed_slices
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import control_flow_util
from tensorflow.python.ops import math_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import,undefined-variable,redefined-builtin
from tensorflow.python.ops.control_flow_ops import *
# pylint: enable=wildcard-import
def _SwitchGrad(op, *grad):
"""Gradients for a Switch op is calculated using a Merge op.
If the switch is a loop switch, it will be visited twice. We create
the merge on the first visit, and update the other input of the merge
on the second visit. A next_iteration is also added on second visit.
"""
graph = ops.get_default_graph()
# pylint: disable=protected-access
op_ctxt = op._get_control_flow_context()
grad_ctxt = graph._get_control_flow_context()
# pylint: enable=protected-access
if isinstance(op_ctxt, WhileContext):
merge_grad = grad_ctxt.grad_state.switch_map.get(op)
if merge_grad is not None:
# This is the second time this Switch is visited. It comes from
# the non-exit branch of the Switch, so update the second input
# to the Merge.
# TODO(yuanbyu): Perform shape inference with this new input.
if grad[1] is not None:
# pylint: disable=protected-access
control_flow_ops._AddNextAndBackEdge(merge_grad, grad[1],
enforce_shape_invariant=False)
# pylint: enable=protected-access
return None, None
elif grad[0] is not None:
# This is the first time this Switch is visited. It comes from
# the Exit branch, which is grad[0]. grad[1] is empty at this point.
# Use grad[0] for both inputs to merge for now, but update the second
# input of merge when we see this Switch the second time.
merge_grad = merge([grad[0], grad[0]], name="b_switch")[0]
grad_ctxt.grad_state.switch_map[op] = merge_grad
return merge_grad, None
else:
# This is the first time this Switch is visited. It comes from the
# Identity branch. Such a Switch has `None` gradient for the Exit branch,
# meaning the output is not differentiable.
return None, None
elif isinstance(op_ctxt, CondContext):
zero_grad = grad[1 - op_ctxt.branch]
# At this point, we have created zero_grad guarded by the right switch.
# Unfortunately, we may still get None here for not trainable data types.
if zero_grad is None:
# For resource variables we get None always on the other branch, so bypass
# this.
if op.inputs[0].dtype == dtypes.resource:
return merge(
[grad[op_ctxt.branch]] * 2, name="cond_resource_grad")[0], None
return None, None
return merge(grad, name="cond_grad")[0], None
else:
false_grad = switch(grad[0], op.inputs[1])[0]
true_grad = switch(grad[1], op.inputs[1])[1]
return merge([false_grad, true_grad])[0], None
ops.RegisterGradient("Switch")(_SwitchGrad)
ops.RegisterGradient("RefSwitch")(_SwitchGrad)
@ops.RegisterGradient("Merge")
def _MergeGrad(op, grad, _):
"""Gradients for a Merge op are calculated using a Switch op."""
input_op = op.inputs[0].op
graph = ops.get_default_graph()
# pylint: disable=protected-access
op_ctxt = control_flow_util.GetOutputContext(input_op)
grad_ctxt = graph._get_control_flow_context()
# pylint: enable=protected-access
if isinstance(op_ctxt, WhileContext):
# pylint: disable=protected-access
return control_flow_ops._SwitchRefOrTensor(grad, grad_ctxt.pivot)
# pylint: enable=protected-access
elif isinstance(op_ctxt, CondContext):
pred = op_ctxt.pred
if grad_ctxt and grad_ctxt.grad_state:
# This Merge node is part of a cond within a loop.
# The backprop needs to have the value of this predicate for every
# iteration. So we must have its values accumulated in the forward, and
# use the accumulated values as the predicate for this backprop switch.
grad_state = grad_ctxt.grad_state
real_pred = grad_state.history_map.get(pred.name)
if real_pred is None:
# Remember the value of pred for every iteration.
grad_ctxt = grad_state.grad_context
grad_ctxt.Exit()
history_pred = grad_state.AddForwardAccumulator(pred)
grad_ctxt.Enter()
# Add the stack pop op. If pred.op is in a (outer) CondContext,
# the stack pop will be guarded with a switch.
real_pred = grad_state.AddBackpropAccumulatedValue(history_pred, pred)
grad_state.history_map[pred.name] = real_pred
pred = real_pred
# pylint: disable=protected-access
return control_flow_ops._SwitchRefOrTensor(grad, pred, name="cond_grad")
# pylint: enable=protected-access
else:
num_inputs = len(op.inputs)
cond = [math_ops.equal(op.outputs[1], i) for i in xrange(num_inputs)]
# pylint: disable=protected-access
return [control_flow_ops._SwitchRefOrTensor(grad, cond[i])[1]
for i in xrange(num_inputs)]
# pylint: enable=protected-access
@ops.RegisterGradient("RefMerge")
def _RefMergeGrad(op, grad, _):
return _MergeGrad(op, grad, _)
@ops.RegisterGradient("Exit")
def _ExitGrad(op, grad):
"""Gradients for an exit op are calculated using an Enter op."""
graph = ops.get_default_graph()
# pylint: disable=protected-access
op_ctxt = op._get_control_flow_context()
grad_ctxt = graph._get_control_flow_context()
# pylint: enable=protected-access
if not grad_ctxt.back_prop:
# The flag `back_prop` is set by users to suppress gradient
# computation for this loop. If the attribute `back_prop` is false,
# no gradient computation.
return None
if op_ctxt.grad_state:
raise TypeError("Second-order gradient for while loops not supported.")
if isinstance(grad, ops.Tensor):
grad_ctxt.AddName(grad.name)
else:
if not isinstance(
grad, (indexed_slices.IndexedSlices, sparse_tensor.SparseTensor)):
raise TypeError(f"Type {type(grad)} not supported, must be either"
"`indexed_slices.IndexedSlices` or `SparseTensor`.")
grad_ctxt.AddName(grad.values.name)
grad_ctxt.AddName(grad.indices.name)
dense_shape = grad.dense_shape
if dense_shape is not None:
grad_ctxt.AddName(dense_shape.name)
grad_ctxt.Enter()
# pylint: disable=protected-access
result = control_flow_ops._Enter(
grad, grad_ctxt.name, is_constant=False,
parallel_iterations=grad_ctxt.parallel_iterations,
name="b_exit")
# pylint: enable=protected-access
grad_ctxt.loop_enters.append(result)
grad_ctxt.Exit()
return result
ops.RegisterGradient("RefExit")(_ExitGrad)
@ops.RegisterGradient("NextIteration")
def _NextIterationGrad(_, grad):
"""A forward next_iteration is translated into a backprop identity.
Note that the backprop next_iteration is added in switch grad.
"""
return grad
@ops.RegisterGradient("RefNextIteration")
def _RefNextIterationGrad(_, grad):
return _NextIterationGrad(_, grad)
@ops.RegisterGradient("Enter")
def _EnterGrad(op, grad):
"""Gradients for an Enter are calculated using an Exit op.
For loop variables, grad is the gradient so just add an exit.
For loop invariants, we need to add an accumulator loop.
"""
graph = ops.get_default_graph()
# pylint: disable=protected-access
grad_ctxt = graph._get_control_flow_context()
# pylint: enable=protected-access
if grad_ctxt is None:
return grad
if not grad_ctxt.back_prop:
# Skip gradient computation, if the attribute `back_prop` is false.
return grad
if grad_ctxt.grad_state is None:
# Pass the gradient through if we are not in a gradient while context.
return grad
if op.get_attr("is_constant"):
# Add a gradient accumulator for each loop invariant.
if isinstance(grad, ops.Tensor):
result = grad_ctxt.AddBackpropAccumulator(op, grad)
elif isinstance(grad, indexed_slices.IndexedSlices):
result = grad_ctxt.AddBackpropIndexedSlicesAccumulator(op, grad)
else:
# TODO(yuanbyu, lukasr): Add support for SparseTensor.
raise TypeError(f"Type {type(grad)} not supported,"
"must be Tensor or Indexed Slices")
else:
result = exit(grad)
grad_ctxt.loop_exits.append(result)
grad_ctxt.ExitResult([result])
return result
@ops.RegisterGradient("RefEnter")
def _RefEnterGrad(op, grad):
return _EnterGrad(op, grad)
@ops.RegisterGradient("LoopCond")
def _LoopCondGrad(_):
"""Stop backprop for the predicate of a while loop."""
return None
| 38.857724 | 80 | 0.715661 |
from six.moves import xrange
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import indexed_slices
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import control_flow_util
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.control_flow_ops import *
def _SwitchGrad(op, *grad):
graph = ops.get_default_graph()
op_ctxt = op._get_control_flow_context()
grad_ctxt = graph._get_control_flow_context()
if isinstance(op_ctxt, WhileContext):
merge_grad = grad_ctxt.grad_state.switch_map.get(op)
if merge_grad is not None:
if grad[1] is not None:
control_flow_ops._AddNextAndBackEdge(merge_grad, grad[1],
enforce_shape_invariant=False)
return None, None
elif grad[0] is not None:
merge_grad = merge([grad[0], grad[0]], name="b_switch")[0]
grad_ctxt.grad_state.switch_map[op] = merge_grad
return merge_grad, None
else:
return None, None
elif isinstance(op_ctxt, CondContext):
zero_grad = grad[1 - op_ctxt.branch]
if zero_grad is None:
if op.inputs[0].dtype == dtypes.resource:
return merge(
[grad[op_ctxt.branch]] * 2, name="cond_resource_grad")[0], None
return None, None
return merge(grad, name="cond_grad")[0], None
else:
false_grad = switch(grad[0], op.inputs[1])[0]
true_grad = switch(grad[1], op.inputs[1])[1]
return merge([false_grad, true_grad])[0], None
ops.RegisterGradient("Switch")(_SwitchGrad)
ops.RegisterGradient("RefSwitch")(_SwitchGrad)
@ops.RegisterGradient("Merge")
def _MergeGrad(op, grad, _):
input_op = op.inputs[0].op
graph = ops.get_default_graph()
op_ctxt = control_flow_util.GetOutputContext(input_op)
grad_ctxt = graph._get_control_flow_context()
if isinstance(op_ctxt, WhileContext):
return control_flow_ops._SwitchRefOrTensor(grad, grad_ctxt.pivot)
elif isinstance(op_ctxt, CondContext):
pred = op_ctxt.pred
if grad_ctxt and grad_ctxt.grad_state:
grad_state = grad_ctxt.grad_state
real_pred = grad_state.history_map.get(pred.name)
if real_pred is None:
grad_ctxt = grad_state.grad_context
grad_ctxt.Exit()
history_pred = grad_state.AddForwardAccumulator(pred)
grad_ctxt.Enter()
real_pred = grad_state.AddBackpropAccumulatedValue(history_pred, pred)
grad_state.history_map[pred.name] = real_pred
pred = real_pred
return control_flow_ops._SwitchRefOrTensor(grad, pred, name="cond_grad")
else:
num_inputs = len(op.inputs)
cond = [math_ops.equal(op.outputs[1], i) for i in xrange(num_inputs)]
return [control_flow_ops._SwitchRefOrTensor(grad, cond[i])[1]
for i in xrange(num_inputs)]
@ops.RegisterGradient("RefMerge")
def _RefMergeGrad(op, grad, _):
return _MergeGrad(op, grad, _)
@ops.RegisterGradient("Exit")
def _ExitGrad(op, grad):
graph = ops.get_default_graph()
op_ctxt = op._get_control_flow_context()
grad_ctxt = graph._get_control_flow_context()
if not grad_ctxt.back_prop:
return None
if op_ctxt.grad_state:
raise TypeError("Second-order gradient for while loops not supported.")
if isinstance(grad, ops.Tensor):
grad_ctxt.AddName(grad.name)
else:
if not isinstance(
grad, (indexed_slices.IndexedSlices, sparse_tensor.SparseTensor)):
raise TypeError(f"Type {type(grad)} not supported, must be either"
"`indexed_slices.IndexedSlices` or `SparseTensor`.")
grad_ctxt.AddName(grad.values.name)
grad_ctxt.AddName(grad.indices.name)
dense_shape = grad.dense_shape
if dense_shape is not None:
grad_ctxt.AddName(dense_shape.name)
grad_ctxt.Enter()
result = control_flow_ops._Enter(
grad, grad_ctxt.name, is_constant=False,
parallel_iterations=grad_ctxt.parallel_iterations,
name="b_exit")
grad_ctxt.loop_enters.append(result)
grad_ctxt.Exit()
return result
ops.RegisterGradient("RefExit")(_ExitGrad)
@ops.RegisterGradient("NextIteration")
def _NextIterationGrad(_, grad):
return grad
@ops.RegisterGradient("RefNextIteration")
def _RefNextIterationGrad(_, grad):
return _NextIterationGrad(_, grad)
@ops.RegisterGradient("Enter")
def _EnterGrad(op, grad):
graph = ops.get_default_graph()
grad_ctxt = graph._get_control_flow_context()
if grad_ctxt is None:
return grad
if not grad_ctxt.back_prop:
return grad
if grad_ctxt.grad_state is None:
return grad
if op.get_attr("is_constant"):
if isinstance(grad, ops.Tensor):
result = grad_ctxt.AddBackpropAccumulator(op, grad)
elif isinstance(grad, indexed_slices.IndexedSlices):
result = grad_ctxt.AddBackpropIndexedSlicesAccumulator(op, grad)
else:
raise TypeError(f"Type {type(grad)} not supported,"
"must be Tensor or Indexed Slices")
else:
result = exit(grad)
grad_ctxt.loop_exits.append(result)
grad_ctxt.ExitResult([result])
return result
@ops.RegisterGradient("RefEnter")
def _RefEnterGrad(op, grad):
return _EnterGrad(op, grad)
@ops.RegisterGradient("LoopCond")
def _LoopCondGrad(_):
return None
| true | true |
1c33f139de8a7e5d3fbf8f0aabab26f6e70cf9c7 | 65 | py | Python | tests/__init__.py | cthoyt/apicuron-client | f4988dd12437042492e678ac42a4685b147548d2 | [
"MIT"
] | null | null | null | tests/__init__.py | cthoyt/apicuron-client | f4988dd12437042492e678ac42a4685b147548d2 | [
"MIT"
] | null | null | null | tests/__init__.py | cthoyt/apicuron-client | f4988dd12437042492e678ac42a4685b147548d2 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""Tests for :mod:`apicuron_client`."""
| 16.25 | 39 | 0.553846 | true | true | |
1c33f25ddcc7a95fb0d01c4542249e11bbb23454 | 326 | py | Python | Backend/Trackerapp/migrations/0012_remove_customuser_is_verified.py | OscarMugendi/Project-Tracker | f805e706332bb387d9e0f1ed537e91d1360bf2b1 | [
"MIT"
] | null | null | null | Backend/Trackerapp/migrations/0012_remove_customuser_is_verified.py | OscarMugendi/Project-Tracker | f805e706332bb387d9e0f1ed537e91d1360bf2b1 | [
"MIT"
] | null | null | null | Backend/Trackerapp/migrations/0012_remove_customuser_is_verified.py | OscarMugendi/Project-Tracker | f805e706332bb387d9e0f1ed537e91d1360bf2b1 | [
"MIT"
] | null | null | null | from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('Trackerapp', '0011_merge_0008_auto_20211017_2325_0010_auto_20211019_1133'),
]
operations = [
migrations.RemoveField(
model_name='customuser',
name='is_verified',
),
]
| 20.375 | 85 | 0.634969 | from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('Trackerapp', '0011_merge_0008_auto_20211017_2325_0010_auto_20211019_1133'),
]
operations = [
migrations.RemoveField(
model_name='customuser',
name='is_verified',
),
]
| true | true |
1c33f2c02c78023d8b92c3824c4be9f5fc7bcb0d | 681 | py | Python | _scripts/docker_compose_run_bash.py | gerold-penz/got-your-back | 614559e411e22b25512932833d429cf831b51c4f | [
"ECL-2.0",
"Apache-2.0"
] | 1 | 2020-05-08T08:12:49.000Z | 2020-05-08T08:12:49.000Z | _scripts/docker_compose_run_bash.py | gerold-penz/got-your-back | 614559e411e22b25512932833d429cf831b51c4f | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | _scripts/docker_compose_run_bash.py | gerold-penz/got-your-back | 614559e411e22b25512932833d429cf831b51c4f | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
# coding: utf-8
import os
import sys
import subprocess
THISDIR = os.path.dirname(os.path.realpath(__file__))
DOCKERDIR = os.path.abspath(os.path.join(THISDIR, "..", "docker"))
def main():
print("Docker-Compose RUN\n")
try:
returncode = subprocess.call(
["docker-compose", "run", "--rm", "got-your-back", "/bin/bash"],
cwd=DOCKERDIR,
env=os.environ,
shell=sys.platform.startswith("win")
)
if returncode != 0:
input("Press ENTER to continue...")
except KeyboardInterrupt:
pass
print("Fertig!")
print()
if __name__ == "__main__":
main()
| 20.636364 | 76 | 0.578561 |
import os
import sys
import subprocess
THISDIR = os.path.dirname(os.path.realpath(__file__))
DOCKERDIR = os.path.abspath(os.path.join(THISDIR, "..", "docker"))
def main():
print("Docker-Compose RUN\n")
try:
returncode = subprocess.call(
["docker-compose", "run", "--rm", "got-your-back", "/bin/bash"],
cwd=DOCKERDIR,
env=os.environ,
shell=sys.platform.startswith("win")
)
if returncode != 0:
input("Press ENTER to continue...")
except KeyboardInterrupt:
pass
print("Fertig!")
print()
if __name__ == "__main__":
main()
| true | true |
1c33f38502390e02bec734428384c40a1477ab00 | 19,941 | py | Python | source/_UIAHandler.py | asaranprasad/nvda | e9609694acbfb06398eb6552067a0dcd532d67af | [
"bzip2-1.0.6"
] | 1 | 2018-11-16T10:15:59.000Z | 2018-11-16T10:15:59.000Z | source/_UIAHandler.py | asaranprasad/nvda | e9609694acbfb06398eb6552067a0dcd532d67af | [
"bzip2-1.0.6"
] | null | null | null | source/_UIAHandler.py | asaranprasad/nvda | e9609694acbfb06398eb6552067a0dcd532d67af | [
"bzip2-1.0.6"
] | null | null | null | #_UIAHandler.py
#A part of NonVisual Desktop Access (NVDA)
#Copyright (C) 2011-2018 NV Access Limited, Joseph Lee, Babbage B.V.
#This file is covered by the GNU General Public License.
#See the file COPYING for more details.
from ctypes import *
from ctypes.wintypes import *
import comtypes.client
from comtypes.automation import VT_EMPTY
from comtypes import *
import weakref
import threading
import time
import config
import api
import appModuleHandler
import queueHandler
import controlTypes
import NVDAHelper
import winKernel
import winUser
import eventHandler
from logHandler import log
import UIAUtils
from comtypes.gen.UIAutomationClient import *
#Some newer UIA constants that could be missing
ItemIndex_Property_GUID=GUID("{92A053DA-2969-4021-BF27-514CFC2E4A69}")
ItemCount_Property_GUID=GUID("{ABBF5C45-5CCC-47b7-BB4E-87CB87BBD162}")
HorizontalTextAlignment_Left=0
HorizontalTextAlignment_Centered=1
HorizontalTextAlignment_Right=2
HorizontalTextAlignment_Justified=3
# The name of the WDAG (Windows Defender Application Guard) process
WDAG_PROCESS_NAME=u'hvsirdpclient'
goodUIAWindowClassNames=[
# A WDAG (Windows Defender Application Guard) Window is always native UIA, even if it doesn't report as such.
'RAIL_WINDOW',
]
badUIAWindowClassNames=[
"SysTreeView32",
"WuDuiListView",
"ComboBox",
"msctls_progress32",
"Edit",
"CommonPlacesWrapperWndClass",
"SysMonthCal32",
"SUPERGRID", #Outlook 2010 message list
"RichEdit",
"RichEdit20",
"RICHEDIT50W",
"SysListView32",
"EXCEL7",
"Button",
# #7497: Windows 10 Fall Creators Update has an incomplete UIA implementation for console windows, therefore for now we should ignore it.
# It does not implement caret/selection, and probably has no new text events.
"ConsoleWindowClass",
]
# #8405: used to detect UIA dialogs prior to Windows 10 RS5.
UIADialogClassNames=[
"#32770",
"NUIDialog",
"Credential Dialog Xaml Host", # UAC dialog in Anniversary Update and later
"Shell_Dialog",
"Shell_Flyout",
"Shell_SystemDialog", # Various dialogs in Windows 10 Settings app
]
NVDAUnitsToUIAUnits={
"character":TextUnit_Character,
"word":TextUnit_Word,
"line":TextUnit_Line,
"paragraph":TextUnit_Paragraph,
"readingChunk":TextUnit_Line,
}
UIAControlTypesToNVDARoles={
UIA_ButtonControlTypeId:controlTypes.ROLE_BUTTON,
UIA_CalendarControlTypeId:controlTypes.ROLE_CALENDAR,
UIA_CheckBoxControlTypeId:controlTypes.ROLE_CHECKBOX,
UIA_ComboBoxControlTypeId:controlTypes.ROLE_COMBOBOX,
UIA_EditControlTypeId:controlTypes.ROLE_EDITABLETEXT,
UIA_HyperlinkControlTypeId:controlTypes.ROLE_LINK,
UIA_ImageControlTypeId:controlTypes.ROLE_GRAPHIC,
UIA_ListItemControlTypeId:controlTypes.ROLE_LISTITEM,
UIA_ListControlTypeId:controlTypes.ROLE_LIST,
UIA_MenuControlTypeId:controlTypes.ROLE_POPUPMENU,
UIA_MenuBarControlTypeId:controlTypes.ROLE_MENUBAR,
UIA_MenuItemControlTypeId:controlTypes.ROLE_MENUITEM,
UIA_ProgressBarControlTypeId:controlTypes.ROLE_PROGRESSBAR,
UIA_RadioButtonControlTypeId:controlTypes.ROLE_RADIOBUTTON,
UIA_ScrollBarControlTypeId:controlTypes.ROLE_SCROLLBAR,
UIA_SliderControlTypeId:controlTypes.ROLE_SLIDER,
UIA_SpinnerControlTypeId:controlTypes.ROLE_SPINBUTTON,
UIA_StatusBarControlTypeId:controlTypes.ROLE_STATUSBAR,
UIA_TabControlTypeId:controlTypes.ROLE_TABCONTROL,
UIA_TabItemControlTypeId:controlTypes.ROLE_TAB,
UIA_TextControlTypeId:controlTypes.ROLE_STATICTEXT,
UIA_ToolBarControlTypeId:controlTypes.ROLE_TOOLBAR,
UIA_ToolTipControlTypeId:controlTypes.ROLE_TOOLTIP,
UIA_TreeControlTypeId:controlTypes.ROLE_TREEVIEW,
UIA_TreeItemControlTypeId:controlTypes.ROLE_TREEVIEWITEM,
UIA_CustomControlTypeId:controlTypes.ROLE_UNKNOWN,
UIA_GroupControlTypeId:controlTypes.ROLE_GROUPING,
UIA_ThumbControlTypeId:controlTypes.ROLE_THUMB,
UIA_DataGridControlTypeId:controlTypes.ROLE_DATAGRID,
UIA_DataItemControlTypeId:controlTypes.ROLE_DATAITEM,
UIA_DocumentControlTypeId:controlTypes.ROLE_DOCUMENT,
UIA_SplitButtonControlTypeId:controlTypes.ROLE_SPLITBUTTON,
UIA_WindowControlTypeId:controlTypes.ROLE_WINDOW,
UIA_PaneControlTypeId:controlTypes.ROLE_PANE,
UIA_HeaderControlTypeId:controlTypes.ROLE_HEADER,
UIA_HeaderItemControlTypeId:controlTypes.ROLE_HEADERITEM,
UIA_TableControlTypeId:controlTypes.ROLE_TABLE,
UIA_TitleBarControlTypeId:controlTypes.ROLE_TITLEBAR,
UIA_SeparatorControlTypeId:controlTypes.ROLE_SEPARATOR,
}
UIAPropertyIdsToNVDAEventNames={
UIA_NamePropertyId:"nameChange",
UIA_HelpTextPropertyId:"descriptionChange",
UIA_ExpandCollapseExpandCollapseStatePropertyId:"stateChange",
UIA_ToggleToggleStatePropertyId:"stateChange",
UIA_IsEnabledPropertyId:"stateChange",
UIA_ValueValuePropertyId:"valueChange",
UIA_RangeValueValuePropertyId:"valueChange",
UIA_ControllerForPropertyId:"UIA_controllerFor",
}
UIAEventIdsToNVDAEventNames={
UIA_LiveRegionChangedEventId:"liveRegionChange",
#UIA_Text_TextChangedEventId:"textChanged",
UIA_SelectionItem_ElementSelectedEventId:"UIA_elementSelected",
UIA_MenuOpenedEventId:"gainFocus",
UIA_SelectionItem_ElementAddedToSelectionEventId:"stateChange",
UIA_SelectionItem_ElementRemovedFromSelectionEventId:"stateChange",
#UIA_MenuModeEndEventId:"menuModeEnd",
#UIA_Text_TextSelectionChangedEventId:"caret",
UIA_ToolTipOpenedEventId:"UIA_toolTipOpened",
#UIA_AsyncContentLoadedEventId:"documentLoadComplete",
#UIA_ToolTipClosedEventId:"hide",
UIA_Window_WindowOpenedEventId:"UIA_window_windowOpen",
UIA_SystemAlertEventId:"UIA_systemAlert",
}
class UIAHandler(COMObject):
_com_interfaces_=[IUIAutomationEventHandler,IUIAutomationFocusChangedEventHandler,IUIAutomationPropertyChangedEventHandler,IUIAutomationNotificationEventHandler]
def __init__(self):
super(UIAHandler,self).__init__()
self.MTAThreadInitEvent=threading.Event()
self.MTAThreadStopEvent=threading.Event()
self.MTAThreadInitException=None
self.MTAThread=threading.Thread(target=self.MTAThreadFunc)
self.MTAThread.daemon=True
self.MTAThread.start()
self.MTAThreadInitEvent.wait(2)
if self.MTAThreadInitException:
raise self.MTAThreadInitException
def terminate(self):
MTAThreadHandle=HANDLE(windll.kernel32.OpenThread(winKernel.SYNCHRONIZE,False,self.MTAThread.ident))
self.MTAThreadStopEvent.set()
#Wait for the MTA thread to die (while still message pumping)
if windll.user32.MsgWaitForMultipleObjects(1,byref(MTAThreadHandle),False,200,0)!=0:
log.debugWarning("Timeout or error while waiting for UIAHandler MTA thread")
windll.kernel32.CloseHandle(MTAThreadHandle)
del self.MTAThread
def MTAThreadFunc(self):
try:
oledll.ole32.CoInitializeEx(None,comtypes.COINIT_MULTITHREADED)
isUIA8=False
try:
self.clientObject=CoCreateInstance(CUIAutomation8._reg_clsid_,interface=IUIAutomation,clsctx=CLSCTX_INPROC_SERVER)
isUIA8=True
except (COMError,WindowsError,NameError):
self.clientObject=CoCreateInstance(CUIAutomation._reg_clsid_,interface=IUIAutomation,clsctx=CLSCTX_INPROC_SERVER)
# #7345: Instruct UIA to never map MSAA winEvents to UIA propertyChange events.
# These events are not needed by NVDA, and they can cause the UI Automation client library to become unresponsive if an application firing winEvents has a slow message pump.
pfm=self.clientObject.proxyFactoryMapping
for index in xrange(pfm.count):
e=pfm.getEntry(index)
for propertyID in UIAPropertyIdsToNVDAEventNames.keys():
# Check if this proxy has mapped any winEvents to the UIA propertyChange event for this property ID
try:
oldWinEvents=e.getWinEventsForAutomationEvent(UIA_AutomationPropertyChangedEventId,propertyID)
except IndexError:
# comtypes does not seem to correctly handle a returned empty SAFEARRAY, raising IndexError
oldWinEvents=None
if oldWinEvents:
# As winEvents were mapped, replace them with an empty list
e.setWinEventsForAutomationEvent(UIA_AutomationPropertyChangedEventId,propertyID,[])
# Changes to an enty are not automatically picked up.
# Therefore remove the entry and re-insert it.
pfm.removeEntry(index)
pfm.insertEntry(index,e)
if isUIA8:
# #8009: use appropriate interface based on highest supported interface.
# #8338: made easier by traversing interfaces supported on Windows 8 and later in reverse.
for interface in reversed(CUIAutomation8._com_interfaces_):
try:
self.clientObject=self.clientObject.QueryInterface(interface)
break
except COMError:
pass
# Windows 10 RS5 provides new performance features for UI Automation including event coalescing and connection recovery.
# Enable all of these where available.
if isinstance(self.clientObject,IUIAutomation6):
self.clientObject.CoalesceEvents=CoalesceEventsOptions_Enabled
self.clientObject.ConnectionRecoveryBehavior=ConnectionRecoveryBehaviorOptions_Enabled
log.info("UIAutomation: %s"%self.clientObject.__class__.__mro__[1].__name__)
self.windowTreeWalker=self.clientObject.createTreeWalker(self.clientObject.CreateNotCondition(self.clientObject.CreatePropertyCondition(UIA_NativeWindowHandlePropertyId,0)))
self.windowCacheRequest=self.clientObject.CreateCacheRequest()
self.windowCacheRequest.AddProperty(UIA_NativeWindowHandlePropertyId)
self.UIAWindowHandleCache={}
self.baseTreeWalker=self.clientObject.RawViewWalker
self.baseCacheRequest=self.windowCacheRequest.Clone()
import UIAHandler
self.ItemIndex_PropertyId=NVDAHelper.localLib.registerUIAProperty(byref(ItemIndex_Property_GUID),u"ItemIndex",1)
self.ItemCount_PropertyId=NVDAHelper.localLib.registerUIAProperty(byref(ItemCount_Property_GUID),u"ItemCount",1)
for propertyId in (UIA_FrameworkIdPropertyId,UIA_AutomationIdPropertyId,UIA_ClassNamePropertyId,UIA_ControlTypePropertyId,UIA_ProviderDescriptionPropertyId,UIA_ProcessIdPropertyId,UIA_IsTextPatternAvailablePropertyId,UIA_IsContentElementPropertyId,UIA_IsControlElementPropertyId):
self.baseCacheRequest.addProperty(propertyId)
self.baseCacheRequest.addPattern(UIA_TextPatternId)
self.rootElement=self.clientObject.getRootElementBuildCache(self.baseCacheRequest)
self.reservedNotSupportedValue=self.clientObject.ReservedNotSupportedValue
self.ReservedMixedAttributeValue=self.clientObject.ReservedMixedAttributeValue
self.clientObject.AddFocusChangedEventHandler(self.baseCacheRequest,self)
self.clientObject.AddPropertyChangedEventHandler(self.rootElement,TreeScope_Subtree,self.baseCacheRequest,self,UIAPropertyIdsToNVDAEventNames.keys())
for x in UIAEventIdsToNVDAEventNames.iterkeys():
self.clientObject.addAutomationEventHandler(x,self.rootElement,TreeScope_Subtree,self.baseCacheRequest,self)
# #7984: add support for notification event (IUIAutomation5, part of Windows 10 build 16299 and later).
if isinstance(self.clientObject, IUIAutomation5):
self.clientObject.AddNotificationEventHandler(self.rootElement,TreeScope_Subtree,self.baseCacheRequest,self)
except Exception as e:
self.MTAThreadInitException=e
finally:
self.MTAThreadInitEvent.set()
self.MTAThreadStopEvent.wait()
self.clientObject.RemoveAllEventHandlers()
def IUIAutomationEventHandler_HandleAutomationEvent(self,sender,eventID):
if not self.MTAThreadInitEvent.isSet():
# UIAHandler hasn't finished initialising yet, so just ignore this event.
return
if eventID==UIA_MenuOpenedEventId and eventHandler.isPendingEvents("gainFocus"):
# We don't need the menuOpened event if focus has been fired,
# as focus should be more correct.
return
NVDAEventName=UIAEventIdsToNVDAEventNames.get(eventID,None)
if not NVDAEventName:
return
if not self.isNativeUIAElement(sender):
return
window=self.getNearestWindowHandle(sender)
if window and not eventHandler.shouldAcceptEvent(NVDAEventName,windowHandle=window):
return
import NVDAObjects.UIA
obj=NVDAObjects.UIA.UIA(UIAElement=sender)
if (
not obj
or (NVDAEventName=="gainFocus" and not obj.shouldAllowUIAFocusEvent)
or (NVDAEventName=="liveRegionChange" and not obj._shouldAllowUIALiveRegionChangeEvent)
):
return
focus=api.getFocusObject()
if obj==focus:
obj=focus
eventHandler.queueEvent(NVDAEventName,obj)
def IUIAutomationFocusChangedEventHandler_HandleFocusChangedEvent(self,sender):
if not self.MTAThreadInitEvent.isSet():
# UIAHandler hasn't finished initialising yet, so just ignore this event.
return
if not self.isNativeUIAElement(sender):
return
import NVDAObjects.UIA
if isinstance(eventHandler.lastQueuedFocusObject,NVDAObjects.UIA.UIA):
lastFocus=eventHandler.lastQueuedFocusObject.UIAElement
# Ignore duplicate focus events.
# It seems that it is possible for compareElements to return True, even though the objects are different.
# Therefore, don't ignore the event if the last focus object has lost its hasKeyboardFocus state.
if self.clientObject.compareElements(sender,lastFocus) and lastFocus.currentHasKeyboardFocus:
return
window=self.getNearestWindowHandle(sender)
if window and not eventHandler.shouldAcceptEvent("gainFocus",windowHandle=window):
return
obj=NVDAObjects.UIA.UIA(UIAElement=sender)
if not obj or not obj.shouldAllowUIAFocusEvent:
return
eventHandler.queueEvent("gainFocus",obj)
def IUIAutomationPropertyChangedEventHandler_HandlePropertyChangedEvent(self,sender,propertyId,newValue):
# #3867: For now manually force this VARIANT type to empty to get around a nasty double free in comtypes/ctypes.
# We also don't use the value in this callback.
newValue.vt=VT_EMPTY
if not self.MTAThreadInitEvent.isSet():
# UIAHandler hasn't finished initialising yet, so just ignore this event.
return
NVDAEventName=UIAPropertyIdsToNVDAEventNames.get(propertyId,None)
if not NVDAEventName:
return
if not self.isNativeUIAElement(sender):
return
window=self.getNearestWindowHandle(sender)
if window and not eventHandler.shouldAcceptEvent(NVDAEventName,windowHandle=window):
return
import NVDAObjects.UIA
obj=NVDAObjects.UIA.UIA(UIAElement=sender)
if not obj:
return
focus=api.getFocusObject()
if obj==focus:
obj=focus
eventHandler.queueEvent(NVDAEventName,obj)
def IUIAutomationNotificationEventHandler_HandleNotificationEvent(self,sender,NotificationKind,NotificationProcessing,displayString,activityId):
if not self.MTAThreadInitEvent.isSet():
# UIAHandler hasn't finished initialising yet, so just ignore this event.
return
import NVDAObjects.UIA
obj=NVDAObjects.UIA.UIA(UIAElement=sender)
if not obj:
# Sometimes notification events can be fired on a UIAElement that has no windowHandle and does not connect through parents back to the desktop.
# There is nothing we can do with these.
return
eventHandler.queueEvent("UIA_notification",obj, notificationKind=NotificationKind, notificationProcessing=NotificationProcessing, displayString=displayString, activityId=activityId)
def _isUIAWindowHelper(self,hwnd):
# UIA in NVDA's process freezes in Windows 7 and below
processID=winUser.getWindowThreadProcessID(hwnd)[0]
if windll.kernel32.GetCurrentProcessId()==processID:
return False
import NVDAObjects.window
windowClass=NVDAObjects.window.Window.normalizeWindowClassName(winUser.getClassName(hwnd))
# For certain window classes, we always want to use UIA.
if windowClass in goodUIAWindowClassNames:
return True
# allow the appModule for the window to also choose if this window is good
# An appModule should be able to override bad UIA class names as prescribed by core
appModule=appModuleHandler.getAppModuleFromProcessID(processID)
if appModule and appModule.isGoodUIAWindow(hwnd):
return True
# There are certain window classes that just had bad UIA implementations
if windowClass in badUIAWindowClassNames:
return False
if windowClass=="NetUIHWND":
parentHwnd=winUser.getAncestor(hwnd,winUser.GA_ROOT)
# #2816: Outlook 2010 auto complete does not fire enough UIA events, IAccessible is better.
# #4056: Combo boxes in Office 2010 Options dialogs don't expose a name via UIA, but do via MSAA.
if winUser.getClassName(parentHwnd) in {"Net UI Tool Window","NUIDialog"}:
return False
# allow the appModule for the window to also choose if this window is bad
if appModule and appModule.isBadUIAWindow(hwnd):
return False
# Ask the window if it supports UIA natively
res=windll.UIAutomationCore.UiaHasServerSideProvider(hwnd)
if res:
# the window does support UIA natively, but
# Microsoft Word should not use UIA unless we can't inject or the user explicitly chose to use UIA with Microsoft word
if windowClass=="_WwG" and not (config.conf['UIA']['useInMSWordWhenAvailable'] or not appModule.helperLocalBindingHandle):
return False
return bool(res)
def isUIAWindow(self,hwnd):
now=time.time()
v=self.UIAWindowHandleCache.get(hwnd,None)
if not v or (now-v[1])>0.5:
v=self._isUIAWindowHelper(hwnd),now
self.UIAWindowHandleCache[hwnd]=v
return v[0]
def getNearestWindowHandle(self,UIAElement):
if hasattr(UIAElement,"_nearestWindowHandle"):
# Called previously. Use cached result.
return UIAElement._nearestWindowHandle
try:
processID=UIAElement.cachedProcessID
except COMError:
return None
appModule=appModuleHandler.getAppModuleFromProcessID(processID)
# WDAG (Windows Defender application Guard) UIA elements should be treated as being from a remote machine, and therefore their window handles are completely invalid on this machine.
# Therefore, jump all the way up to the root of the WDAG process and use that window handle as it is local to this machine.
if appModule.appName==WDAG_PROCESS_NAME:
condition=UIAUtils.createUIAMultiPropertyCondition({UIA_ClassNamePropertyId:[u'ApplicationFrameWindow',u'CabinetWClass']})
walker=self.clientObject.createTreeWalker(condition)
else:
# Not WDAG, just walk up to the nearest valid windowHandle
walker=self.windowTreeWalker
try:
new=walker.NormalizeElementBuildCache(UIAElement,self.windowCacheRequest)
except COMError:
return None
try:
window=new.cachedNativeWindowHandle
except COMError:
window=None
# Cache for future use to improve performance.
UIAElement._nearestWindowHandle=window
return window
def isNativeUIAElement(self,UIAElement):
#Due to issues dealing with UIA elements coming from the same process, we do not class these UIA elements as usable.
#It seems to be safe enough to retreave the cached processID, but using tree walkers or fetching other properties causes a freeze.
try:
processID=UIAElement.cachedProcessId
except COMError:
return False
if processID==windll.kernel32.GetCurrentProcessId():
return False
# Whether this is a native element depends on whether its window natively supports UIA.
windowHandle=self.getNearestWindowHandle(UIAElement)
if windowHandle:
if self.isUIAWindow(windowHandle):
return True
if winUser.getClassName(windowHandle)=="DirectUIHWND" and "IEFRAME.dll" in UIAElement.cachedProviderDescription and UIAElement.currentClassName in ("DownloadBox", "accessiblebutton", "DUIToolbarButton", "PushButton"):
# This is the IE 9 downloads list.
# #3354: UiaHasServerSideProvider returns false for the IE 9 downloads list window,
# so we'd normally use MSAA for this control.
# However, its MSAA implementation is broken (fires invalid events) if UIA is initialised,
# whereas its UIA implementation works correctly.
# Therefore, we must use UIA here.
return True
return False
| 46.159722 | 284 | 0.7967 |
from ctypes import *
from ctypes.wintypes import *
import comtypes.client
from comtypes.automation import VT_EMPTY
from comtypes import *
import weakref
import threading
import time
import config
import api
import appModuleHandler
import queueHandler
import controlTypes
import NVDAHelper
import winKernel
import winUser
import eventHandler
from logHandler import log
import UIAUtils
from comtypes.gen.UIAutomationClient import *
ItemIndex_Property_GUID=GUID("{92A053DA-2969-4021-BF27-514CFC2E4A69}")
ItemCount_Property_GUID=GUID("{ABBF5C45-5CCC-47b7-BB4E-87CB87BBD162}")
HorizontalTextAlignment_Left=0
HorizontalTextAlignment_Centered=1
HorizontalTextAlignment_Right=2
HorizontalTextAlignment_Justified=3
WDAG_PROCESS_NAME=u'hvsirdpclient'
goodUIAWindowClassNames=[
'RAIL_WINDOW',
]
badUIAWindowClassNames=[
"SysTreeView32",
"WuDuiListView",
"ComboBox",
"msctls_progress32",
"Edit",
"CommonPlacesWrapperWndClass",
"SysMonthCal32",
"SUPERGRID", #Outlook 2010 message list
"RichEdit",
"RichEdit20",
"RICHEDIT50W",
"SysListView32",
"EXCEL7",
"Button",
# #7497: Windows 10 Fall Creators Update has an incomplete UIA implementation for console windows, therefore for now we should ignore it.
# It does not implement caret/selection, and probably has no new text events.
"ConsoleWindowClass",
]
# #8405: used to detect UIA dialogs prior to Windows 10 RS5.
UIADialogClassNames=[
"#32770",
"NUIDialog",
"Credential Dialog Xaml Host", # UAC dialog in Anniversary Update and later
"Shell_Dialog",
"Shell_Flyout",
"Shell_SystemDialog", # Various dialogs in Windows 10 Settings app
]
NVDAUnitsToUIAUnits={
"character":TextUnit_Character,
"word":TextUnit_Word,
"line":TextUnit_Line,
"paragraph":TextUnit_Paragraph,
"readingChunk":TextUnit_Line,
}
UIAControlTypesToNVDARoles={
UIA_ButtonControlTypeId:controlTypes.ROLE_BUTTON,
UIA_CalendarControlTypeId:controlTypes.ROLE_CALENDAR,
UIA_CheckBoxControlTypeId:controlTypes.ROLE_CHECKBOX,
UIA_ComboBoxControlTypeId:controlTypes.ROLE_COMBOBOX,
UIA_EditControlTypeId:controlTypes.ROLE_EDITABLETEXT,
UIA_HyperlinkControlTypeId:controlTypes.ROLE_LINK,
UIA_ImageControlTypeId:controlTypes.ROLE_GRAPHIC,
UIA_ListItemControlTypeId:controlTypes.ROLE_LISTITEM,
UIA_ListControlTypeId:controlTypes.ROLE_LIST,
UIA_MenuControlTypeId:controlTypes.ROLE_POPUPMENU,
UIA_MenuBarControlTypeId:controlTypes.ROLE_MENUBAR,
UIA_MenuItemControlTypeId:controlTypes.ROLE_MENUITEM,
UIA_ProgressBarControlTypeId:controlTypes.ROLE_PROGRESSBAR,
UIA_RadioButtonControlTypeId:controlTypes.ROLE_RADIOBUTTON,
UIA_ScrollBarControlTypeId:controlTypes.ROLE_SCROLLBAR,
UIA_SliderControlTypeId:controlTypes.ROLE_SLIDER,
UIA_SpinnerControlTypeId:controlTypes.ROLE_SPINBUTTON,
UIA_StatusBarControlTypeId:controlTypes.ROLE_STATUSBAR,
UIA_TabControlTypeId:controlTypes.ROLE_TABCONTROL,
UIA_TabItemControlTypeId:controlTypes.ROLE_TAB,
UIA_TextControlTypeId:controlTypes.ROLE_STATICTEXT,
UIA_ToolBarControlTypeId:controlTypes.ROLE_TOOLBAR,
UIA_ToolTipControlTypeId:controlTypes.ROLE_TOOLTIP,
UIA_TreeControlTypeId:controlTypes.ROLE_TREEVIEW,
UIA_TreeItemControlTypeId:controlTypes.ROLE_TREEVIEWITEM,
UIA_CustomControlTypeId:controlTypes.ROLE_UNKNOWN,
UIA_GroupControlTypeId:controlTypes.ROLE_GROUPING,
UIA_ThumbControlTypeId:controlTypes.ROLE_THUMB,
UIA_DataGridControlTypeId:controlTypes.ROLE_DATAGRID,
UIA_DataItemControlTypeId:controlTypes.ROLE_DATAITEM,
UIA_DocumentControlTypeId:controlTypes.ROLE_DOCUMENT,
UIA_SplitButtonControlTypeId:controlTypes.ROLE_SPLITBUTTON,
UIA_WindowControlTypeId:controlTypes.ROLE_WINDOW,
UIA_PaneControlTypeId:controlTypes.ROLE_PANE,
UIA_HeaderControlTypeId:controlTypes.ROLE_HEADER,
UIA_HeaderItemControlTypeId:controlTypes.ROLE_HEADERITEM,
UIA_TableControlTypeId:controlTypes.ROLE_TABLE,
UIA_TitleBarControlTypeId:controlTypes.ROLE_TITLEBAR,
UIA_SeparatorControlTypeId:controlTypes.ROLE_SEPARATOR,
}
UIAPropertyIdsToNVDAEventNames={
UIA_NamePropertyId:"nameChange",
UIA_HelpTextPropertyId:"descriptionChange",
UIA_ExpandCollapseExpandCollapseStatePropertyId:"stateChange",
UIA_ToggleToggleStatePropertyId:"stateChange",
UIA_IsEnabledPropertyId:"stateChange",
UIA_ValueValuePropertyId:"valueChange",
UIA_RangeValueValuePropertyId:"valueChange",
UIA_ControllerForPropertyId:"UIA_controllerFor",
}
UIAEventIdsToNVDAEventNames={
UIA_LiveRegionChangedEventId:"liveRegionChange",
#UIA_Text_TextChangedEventId:"textChanged",
UIA_SelectionItem_ElementSelectedEventId:"UIA_elementSelected",
UIA_MenuOpenedEventId:"gainFocus",
UIA_SelectionItem_ElementAddedToSelectionEventId:"stateChange",
UIA_SelectionItem_ElementRemovedFromSelectionEventId:"stateChange",
#UIA_MenuModeEndEventId:"menuModeEnd",
#UIA_Text_TextSelectionChangedEventId:"caret",
UIA_ToolTipOpenedEventId:"UIA_toolTipOpened",
#UIA_AsyncContentLoadedEventId:"documentLoadComplete",
#UIA_ToolTipClosedEventId:"hide",
UIA_Window_WindowOpenedEventId:"UIA_window_windowOpen",
UIA_SystemAlertEventId:"UIA_systemAlert",
}
class UIAHandler(COMObject):
_com_interfaces_=[IUIAutomationEventHandler,IUIAutomationFocusChangedEventHandler,IUIAutomationPropertyChangedEventHandler,IUIAutomationNotificationEventHandler]
def __init__(self):
super(UIAHandler,self).__init__()
self.MTAThreadInitEvent=threading.Event()
self.MTAThreadStopEvent=threading.Event()
self.MTAThreadInitException=None
self.MTAThread=threading.Thread(target=self.MTAThreadFunc)
self.MTAThread.daemon=True
self.MTAThread.start()
self.MTAThreadInitEvent.wait(2)
if self.MTAThreadInitException:
raise self.MTAThreadInitException
def terminate(self):
MTAThreadHandle=HANDLE(windll.kernel32.OpenThread(winKernel.SYNCHRONIZE,False,self.MTAThread.ident))
self.MTAThreadStopEvent.set()
#Wait for the MTA thread to die (while still message pumping)
if windll.user32.MsgWaitForMultipleObjects(1,byref(MTAThreadHandle),False,200,0)!=0:
log.debugWarning("Timeout or error while waiting for UIAHandler MTA thread")
windll.kernel32.CloseHandle(MTAThreadHandle)
del self.MTAThread
def MTAThreadFunc(self):
try:
oledll.ole32.CoInitializeEx(None,comtypes.COINIT_MULTITHREADED)
isUIA8=False
try:
self.clientObject=CoCreateInstance(CUIAutomation8._reg_clsid_,interface=IUIAutomation,clsctx=CLSCTX_INPROC_SERVER)
isUIA8=True
except (COMError,WindowsError,NameError):
self.clientObject=CoCreateInstance(CUIAutomation._reg_clsid_,interface=IUIAutomation,clsctx=CLSCTX_INPROC_SERVER)
# #7345: Instruct UIA to never map MSAA winEvents to UIA propertyChange events.
# These events are not needed by NVDA, and they can cause the UI Automation client library to become unresponsive if an application firing winEvents has a slow message pump.
pfm=self.clientObject.proxyFactoryMapping
for index in xrange(pfm.count):
e=pfm.getEntry(index)
for propertyID in UIAPropertyIdsToNVDAEventNames.keys():
# Check if this proxy has mapped any winEvents to the UIA propertyChange event for this property ID
try:
oldWinEvents=e.getWinEventsForAutomationEvent(UIA_AutomationPropertyChangedEventId,propertyID)
except IndexError:
# comtypes does not seem to correctly handle a returned empty SAFEARRAY, raising IndexError
oldWinEvents=None
if oldWinEvents:
# As winEvents were mapped, replace them with an empty list
e.setWinEventsForAutomationEvent(UIA_AutomationPropertyChangedEventId,propertyID,[])
# Changes to an enty are not automatically picked up.
# Therefore remove the entry and re-insert it.
pfm.removeEntry(index)
pfm.insertEntry(index,e)
if isUIA8:
# #8009: use appropriate interface based on highest supported interface.
# #8338: made easier by traversing interfaces supported on Windows 8 and later in reverse.
for interface in reversed(CUIAutomation8._com_interfaces_):
try:
self.clientObject=self.clientObject.QueryInterface(interface)
break
except COMError:
pass
# Windows 10 RS5 provides new performance features for UI Automation including event coalescing and connection recovery.
# Enable all of these where available.
if isinstance(self.clientObject,IUIAutomation6):
self.clientObject.CoalesceEvents=CoalesceEventsOptions_Enabled
self.clientObject.ConnectionRecoveryBehavior=ConnectionRecoveryBehaviorOptions_Enabled
log.info("UIAutomation: %s"%self.clientObject.__class__.__mro__[1].__name__)
self.windowTreeWalker=self.clientObject.createTreeWalker(self.clientObject.CreateNotCondition(self.clientObject.CreatePropertyCondition(UIA_NativeWindowHandlePropertyId,0)))
self.windowCacheRequest=self.clientObject.CreateCacheRequest()
self.windowCacheRequest.AddProperty(UIA_NativeWindowHandlePropertyId)
self.UIAWindowHandleCache={}
self.baseTreeWalker=self.clientObject.RawViewWalker
self.baseCacheRequest=self.windowCacheRequest.Clone()
import UIAHandler
self.ItemIndex_PropertyId=NVDAHelper.localLib.registerUIAProperty(byref(ItemIndex_Property_GUID),u"ItemIndex",1)
self.ItemCount_PropertyId=NVDAHelper.localLib.registerUIAProperty(byref(ItemCount_Property_GUID),u"ItemCount",1)
for propertyId in (UIA_FrameworkIdPropertyId,UIA_AutomationIdPropertyId,UIA_ClassNamePropertyId,UIA_ControlTypePropertyId,UIA_ProviderDescriptionPropertyId,UIA_ProcessIdPropertyId,UIA_IsTextPatternAvailablePropertyId,UIA_IsContentElementPropertyId,UIA_IsControlElementPropertyId):
self.baseCacheRequest.addProperty(propertyId)
self.baseCacheRequest.addPattern(UIA_TextPatternId)
self.rootElement=self.clientObject.getRootElementBuildCache(self.baseCacheRequest)
self.reservedNotSupportedValue=self.clientObject.ReservedNotSupportedValue
self.ReservedMixedAttributeValue=self.clientObject.ReservedMixedAttributeValue
self.clientObject.AddFocusChangedEventHandler(self.baseCacheRequest,self)
self.clientObject.AddPropertyChangedEventHandler(self.rootElement,TreeScope_Subtree,self.baseCacheRequest,self,UIAPropertyIdsToNVDAEventNames.keys())
for x in UIAEventIdsToNVDAEventNames.iterkeys():
self.clientObject.addAutomationEventHandler(x,self.rootElement,TreeScope_Subtree,self.baseCacheRequest,self)
# #7984: add support for notification event (IUIAutomation5, part of Windows 10 build 16299 and later).
if isinstance(self.clientObject, IUIAutomation5):
self.clientObject.AddNotificationEventHandler(self.rootElement,TreeScope_Subtree,self.baseCacheRequest,self)
except Exception as e:
self.MTAThreadInitException=e
finally:
self.MTAThreadInitEvent.set()
self.MTAThreadStopEvent.wait()
self.clientObject.RemoveAllEventHandlers()
def IUIAutomationEventHandler_HandleAutomationEvent(self,sender,eventID):
if not self.MTAThreadInitEvent.isSet():
# UIAHandler hasn't finished initialising yet, so just ignore this event.
return
if eventID==UIA_MenuOpenedEventId and eventHandler.isPendingEvents("gainFocus"):
# as focus should be more correct.
return
NVDAEventName=UIAEventIdsToNVDAEventNames.get(eventID,None)
if not NVDAEventName:
return
if not self.isNativeUIAElement(sender):
return
window=self.getNearestWindowHandle(sender)
if window and not eventHandler.shouldAcceptEvent(NVDAEventName,windowHandle=window):
return
import NVDAObjects.UIA
obj=NVDAObjects.UIA.UIA(UIAElement=sender)
if (
not obj
or (NVDAEventName=="gainFocus" and not obj.shouldAllowUIAFocusEvent)
or (NVDAEventName=="liveRegionChange" and not obj._shouldAllowUIALiveRegionChangeEvent)
):
return
focus=api.getFocusObject()
if obj==focus:
obj=focus
eventHandler.queueEvent(NVDAEventName,obj)
def IUIAutomationFocusChangedEventHandler_HandleFocusChangedEvent(self,sender):
if not self.MTAThreadInitEvent.isSet():
# UIAHandler hasn't finished initialising yet, so just ignore this event.
return
if not self.isNativeUIAElement(sender):
return
import NVDAObjects.UIA
if isinstance(eventHandler.lastQueuedFocusObject,NVDAObjects.UIA.UIA):
lastFocus=eventHandler.lastQueuedFocusObject.UIAElement
if self.clientObject.compareElements(sender,lastFocus) and lastFocus.currentHasKeyboardFocus:
return
window=self.getNearestWindowHandle(sender)
if window and not eventHandler.shouldAcceptEvent("gainFocus",windowHandle=window):
return
obj=NVDAObjects.UIA.UIA(UIAElement=sender)
if not obj or not obj.shouldAllowUIAFocusEvent:
return
eventHandler.queueEvent("gainFocus",obj)
def IUIAutomationPropertyChangedEventHandler_HandlePropertyChangedEvent(self,sender,propertyId,newValue):
# #3867: For now manually force this VARIANT type to empty to get around a nasty double free in comtypes/ctypes.
# We also don't use the value in this callback.
newValue.vt=VT_EMPTY
if not self.MTAThreadInitEvent.isSet():
return
NVDAEventName=UIAPropertyIdsToNVDAEventNames.get(propertyId,None)
if not NVDAEventName:
return
if not self.isNativeUIAElement(sender):
return
window=self.getNearestWindowHandle(sender)
if window and not eventHandler.shouldAcceptEvent(NVDAEventName,windowHandle=window):
return
import NVDAObjects.UIA
obj=NVDAObjects.UIA.UIA(UIAElement=sender)
if not obj:
return
focus=api.getFocusObject()
if obj==focus:
obj=focus
eventHandler.queueEvent(NVDAEventName,obj)
def IUIAutomationNotificationEventHandler_HandleNotificationEvent(self,sender,NotificationKind,NotificationProcessing,displayString,activityId):
if not self.MTAThreadInitEvent.isSet():
# UIAHandler hasn't finished initialising yet, so just ignore this event.
return
import NVDAObjects.UIA
obj=NVDAObjects.UIA.UIA(UIAElement=sender)
if not obj:
return
eventHandler.queueEvent("UIA_notification",obj, notificationKind=NotificationKind, notificationProcessing=NotificationProcessing, displayString=displayString, activityId=activityId)
def _isUIAWindowHelper(self,hwnd):
processID=winUser.getWindowThreadProcessID(hwnd)[0]
if windll.kernel32.GetCurrentProcessId()==processID:
return False
import NVDAObjects.window
windowClass=NVDAObjects.window.Window.normalizeWindowClassName(winUser.getClassName(hwnd))
# For certain window classes, we always want to use UIA.
if windowClass in goodUIAWindowClassNames:
return True
# allow the appModule for the window to also choose if this window is good
# An appModule should be able to override bad UIA class names as prescribed by core
appModule=appModuleHandler.getAppModuleFromProcessID(processID)
if appModule and appModule.isGoodUIAWindow(hwnd):
return True
# There are certain window classes that just had bad UIA implementations
if windowClass in badUIAWindowClassNames:
return False
if windowClass=="NetUIHWND":
parentHwnd=winUser.getAncestor(hwnd,winUser.GA_ROOT)
# #2816: Outlook 2010 auto complete does not fire enough UIA events, IAccessible is better.
# #4056: Combo boxes in Office 2010 Options dialogs don't expose a name via UIA, but do via MSAA.
if winUser.getClassName(parentHwnd) in {"Net UI Tool Window","NUIDialog"}:
return False
if appModule and appModule.isBadUIAWindow(hwnd):
return False
res=windll.UIAutomationCore.UiaHasServerSideProvider(hwnd)
if res:
if windowClass=="_WwG" and not (config.conf['UIA']['useInMSWordWhenAvailable'] or not appModule.helperLocalBindingHandle):
return False
return bool(res)
def isUIAWindow(self,hwnd):
now=time.time()
v=self.UIAWindowHandleCache.get(hwnd,None)
if not v or (now-v[1])>0.5:
v=self._isUIAWindowHelper(hwnd),now
self.UIAWindowHandleCache[hwnd]=v
return v[0]
def getNearestWindowHandle(self,UIAElement):
if hasattr(UIAElement,"_nearestWindowHandle"):
# Called previously. Use cached result.
return UIAElement._nearestWindowHandle
try:
processID=UIAElement.cachedProcessID
except COMError:
return None
appModule=appModuleHandler.getAppModuleFromProcessID(processID)
# WDAG (Windows Defender application Guard) UIA elements should be treated as being from a remote machine, and therefore their window handles are completely invalid on this machine.
# Therefore, jump all the way up to the root of the WDAG process and use that window handle as it is local to this machine.
if appModule.appName==WDAG_PROCESS_NAME:
condition=UIAUtils.createUIAMultiPropertyCondition({UIA_ClassNamePropertyId:[u'ApplicationFrameWindow',u'CabinetWClass']})
walker=self.clientObject.createTreeWalker(condition)
else:
# Not WDAG, just walk up to the nearest valid windowHandle
walker=self.windowTreeWalker
try:
new=walker.NormalizeElementBuildCache(UIAElement,self.windowCacheRequest)
except COMError:
return None
try:
window=new.cachedNativeWindowHandle
except COMError:
window=None
# Cache for future use to improve performance.
UIAElement._nearestWindowHandle=window
return window
def isNativeUIAElement(self,UIAElement):
#Due to issues dealing with UIA elements coming from the same process, we do not class these UIA elements as usable.
#It seems to be safe enough to retreave the cached processID, but using tree walkers or fetching other properties causes a freeze.
try:
processID=UIAElement.cachedProcessId
except COMError:
return False
if processID==windll.kernel32.GetCurrentProcessId():
return False
# Whether this is a native element depends on whether its window natively supports UIA.
windowHandle=self.getNearestWindowHandle(UIAElement)
if windowHandle:
if self.isUIAWindow(windowHandle):
return True
if winUser.getClassName(windowHandle)=="DirectUIHWND" and "IEFRAME.dll" in UIAElement.cachedProviderDescription and UIAElement.currentClassName in ("DownloadBox", "accessiblebutton", "DUIToolbarButton", "PushButton"):
# This is the IE 9 downloads list.
# #3354: UiaHasServerSideProvider returns false for the IE 9 downloads list window,
# so we'd normally use MSAA for this control.
return True
return False
| true | true |
1c33f4adf603c76e12800fda43481ab5d8f5e142 | 26,319 | py | Python | ier_model/run_et.py | diegoolano/biomedical_interpretable_entity_representations | 3c35f02ee8dd7ee0f2a23b0014e4b112beab6461 | [
"MIT"
] | 2 | 2021-09-24T08:54:33.000Z | 2021-11-15T05:15:52.000Z | ier_model/run_et.py | diegoolano/biomedical_interpretable_entity_representations | 3c35f02ee8dd7ee0f2a23b0014e4b112beab6461 | [
"MIT"
] | null | null | null | ier_model/run_et.py | diegoolano/biomedical_interpretable_entity_representations | 3c35f02ee8dd7ee0f2a23b0014e4b112beab6461 | [
"MIT"
] | 2 | 2021-07-05T20:19:01.000Z | 2021-08-01T01:01:41.000Z | #!/usr/bin/env python3
import argparse
import gc
import json
import numpy as np
import pickle
import random
import time
import torch
import torch.nn as nn
from tqdm import tqdm
from transformers import AdamW, get_linear_schedule_with_warmup
import transformer_constant
import transformer_data_utils
from transformer_data_utils import to_torch
from models import TransformerModel
"""
Args
"""
parser = argparse.ArgumentParser()
parser.add_argument("-model_id", help="Identifier for model")
parser.add_argument('-device', type=int, default=0, help='CUDA device')
parser.add_argument("-n_gpu", help="Number of GPUs.", type=int, default=1)
parser.add_argument("-mode", help="Whether to train or test", default="train", choices=["train", "val", "test"])
parser.add_argument("-local_rank", type=int, default=-1, help="For distributed training: local_rank")
# Data
parser.add_argument("-train_data", help="Train data",
default="train/wiki_et_zeroshot_60k_ex_random/train_*.json")
parser.add_argument("-dev_data", help="Dev data",
default="validation/dev_wiki_et_zeroshot_60k_ex_random_999.json")
parser.add_argument("-eval_data", help="Test data", default="")
parser.add_argument("-goal", help="category vocab size.", default="60k", choices=["medwiki","60k", "ufet"])
parser.add_argument("-seed", help="Pytorch random Seed", default=113)
parser.add_argument("-context_window_size", help="Left and right context size.", default=100)
# learning
parser.add_argument("-num_epoch", help="The number of epoch", default=5000, type=int)
parser.add_argument("-per_gpu_train_batch_size", help="The batch size per GPU", default=8, type=int)
parser.add_argument("-per_gpu_eval_batch_size", help="The batch size per GPU", default=8, type=int)
parser.add_argument("-learning_rate_enc", help="BERT: start learning rate", default=2e-5, type=float)
parser.add_argument("-learning_rate_cls", help="BERT: start learning rate", default=1e-3, type=float)
parser.add_argument("-adam_epsilon_enc", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("-adam_epsilon_cls", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("-hidden_dropout_prob", help="Dropout rate", default=.1, type=float)
parser.add_argument("-warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument(
"-gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
# Model
parser.add_argument(
"-model_type",
default="bert-base-uncased",
choices=[
"bert-base-uncased",
"bert-large-uncased",
"bert-large-uncased-whole-word-masking",
"roberta-base",
"roberta-large",
"allenai/biomed_roberta_base",
"monologg/biobert_v1.1_pubmed",
"allenai/scibert_scivocab_uncased",
"microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"
]
)
parser.add_argument("-threshold", help="threshold", default=0.5, type=float)
parser.add_argument("-avg_pooling", help="Averaging all hidden states instead of using [CLS].", action='store_true')
# Save / log related
parser.add_argument("-save_period", help="How often to save", default=1000, type=int)
parser.add_argument("-eval_period", help="How often to run dev", default=500, type=int)
parser.add_argument("-log_period", help="How often to save", default=1000, type=int)
parser.add_argument("-eval_after", help="How often to run dev", default=10, type=int)
parser.add_argument("-load", help="Load existing model.", action='store_true')
parser.add_argument("-reload_model_name", help="")
parser.add_argument("-reload_model_name_desc", help="")
# Extra param So we can run different data for same goal
parser.add_argument("-env", help="data sub for medwiki", default="", choices=["yasu", "0720_3k_full","0720_3k_full_orig", "0720_3k_drugs","0720_600k_full","0720_600k_full_orig","0720_600k_drugs"])
parser.add_argument("-examples_limit", help="How many examples to do eval on in def _val", default=1000, type=int)
"""
Utils
"""
SIGMOID = nn.Sigmoid()
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def get_data_gen(dataname, mode, args, tokenizer):
data_path = transformer_constant.get(args.env, 'FILE_ROOT') + dataname
print("load data path", data_path, "with args.env", args.env)
dataset = transformer_data_utils.DatasetLoader(data_path, args, tokenizer)
if mode == 'train':
data_gen = dataset.get_batch(args.train_batch_size, args.max_position_embeddings, args.num_epoch, eval_data=False)
else: # test mode
data_gen = dataset.get_batch(args.eval_batch_size, args.max_position_embeddings, 1, eval_data=True)
return data_gen
def get_all_datasets(args, tokenizer):
train_gen_list = []
if args.mode in ['train']:
if 'wiki_desc' in args.model_id:
print("load wiki_desc",)
train_gen_list.append(get_data_gen(transformer_constant.get(args.env,'WIKI_TRAIN_DATA'), 'train', args, tokenizer))
else:
train_gen_list.append(get_data_gen(transformer_constant.get(args.env,'TRAIN_DATA'), 'train', args, tokenizer))
#train_gen_list.append(get_data_gen(args.train_data, 'train', args, tokenizer))
return train_gen_list
def get_datasets(data_lists, args, tokenizer):
data_gen_list = []
for dataname, mode in data_lists:
data_gen_list.append(get_data_gen(dataname, mode, args, tokenizer))
return data_gen_list
def evaluate_data(batch_num, dev_fname, model, args, device):
print("in evaluate data, batchnum", batch_num, dev_fname)
#print(args)
model.eval()
dev_gen = get_data_gen(dev_fname, 'test', args, model.transformer_tokenizer)
gold_pred = []
eval_loss = 0.
total_ex_count = 0
#IMPORTANT since this takes so long sub sample for now 500
for batch in tqdm(dev_gen):
if total_ex_count > 500:
break
total_ex_count += len(batch['targets'])
try:
inputs, targets = to_torch(batch, device)
loss, output_logits = model(inputs, targets)
except Exception as e:
print("in Eval to torch error so continue: ",e )
continue
output_index = get_output_index(output_logits, threshold=args.threshold)
gold_pred += get_gold_pred_str(output_index, batch['targets'].data.cpu().clone(), args.goal, args.env)
eval_loss += loss.clone().item()
print("Gold Pred", len(gold_pred),gold_pred[0:4])
eval_str = get_eval_string(gold_pred)
_, _, _, _, _, macro_f1 = macro(gold_pred)
eval_loss_str = 'Eval loss: {0:.7f} at step {1:d}'.format(eval_loss, batch_num)
print('==> EVAL: seen ' + repr(total_ex_count) + ' examples.')
print(eval_loss_str)
print(gold_pred[:3])
print('==> ' + eval_str)
model.train()
dev_gen = None
return eval_loss, macro_f1
def f1(p, r):
if r == 0.:
return 0.
return 2 * p * r / float(p + r)
def macro(true_and_prediction):
num_examples = len(true_and_prediction)
p = 0.
r = 0.
pred_example_count = 0.
pred_label_count = 0.
gold_label_count = 0.
for true_labels, predicted_labels in true_and_prediction:
if predicted_labels:
pred_example_count += 1
pred_label_count += len(predicted_labels)
per_p = len(set(predicted_labels).intersection(set(true_labels))) / float(len(predicted_labels))
p += per_p
if len(true_labels):
gold_label_count += 1
per_r = len(set(predicted_labels).intersection(set(true_labels))) / float(len(true_labels))
r += per_r
if pred_example_count > 0:
precision = p / pred_example_count
if gold_label_count > 0:
recall = r / gold_label_count
if pred_example_count == 0:
print("In Macro: Pred Example Count == 0")
avg_elem_per_pred = 0
else:
avg_elem_per_pred = pred_label_count / pred_example_count
return num_examples, pred_example_count, avg_elem_per_pred, precision, recall, f1(precision, recall)
def micro(true_and_prediction):
num_examples = len(true_and_prediction)
num_predicted_labels = 0.
num_true_labels = 0.
num_correct_labels = 0.
pred_example_count = 0.
for true_labels, predicted_labels in true_and_prediction:
if predicted_labels:
pred_example_count += 1
num_predicted_labels += len(predicted_labels)
num_true_labels += len(true_labels)
num_correct_labels += len(set(predicted_labels).intersection(set(true_labels)))
if pred_example_count == 0:
return num_examples, 0, 0, 0, 0, 0
precision = num_correct_labels / num_predicted_labels
recall = num_correct_labels / num_true_labels
avg_elem_per_pred = num_predicted_labels / pred_example_count
return num_examples, pred_example_count, avg_elem_per_pred, precision, recall, f1(precision, recall)
def load_model(reload_model_name, save_dir, model_id, model,
optimizer_enc=None, optimizer_cls=None, scheduler_enc=None, scheduler_cls=None):
if reload_model_name:
model_file_name = '{0:s}/{1:s}.pt'.format(save_dir, reload_model_name)
else:
model_file_name = '{0:s}/{1:s}.pt'.format(save_dir, model_id)
print("Loading ", model_file_name)
checkpoint = torch.load(model_file_name)
model.load_state_dict(checkpoint['state_dict'])
if optimizer_enc and optimizer_cls: # Continue training
#if optimizer_enc and optimizer_cls and scheduler_enc and scheduler_cls: # Continue training
optimizer_enc.load_state_dict(checkpoint['optimizer_enc'])
optimizer_cls.load_state_dict(checkpoint['optimizer_cls'])
else: # Test
total_params = 0
# Log params
for k in checkpoint['state_dict']:
elem = checkpoint['state_dict'][k]
param_s = 1
for size_dim in elem.size():
param_s = size_dim * param_s
#print(k, elem.size())
total_params += param_s
param_str = ('Number of total parameters..{0:d}'.format(total_params))
print(param_str)
print('Loading model from ... {0:s}'.format(model_file_name))
def get_output_index(outputs, threshold=0.5):
"""
Given outputs from the decoder, generate prediction index.
:param outputs:
:return:
"""
pred_idx = []
outputs = SIGMOID(outputs).data.cpu().clone()
for single_dist in outputs:
single_dist = single_dist.numpy()
arg_max_ind = np.argmax(single_dist)
pred_id = [arg_max_ind]
pred_id.extend(
[i for i in range(len(single_dist)) if single_dist[i] > threshold and i != arg_max_ind])
pred_idx.append(pred_id)
return pred_idx
def get_gold_pred_str(pred_idx, gold, goal, env):
"""
Given predicted ids and gold ids, generate a list of (gold, pred) pairs of length batch_size.
"""
if goal == '60k':
id2word_dict = transformer_constant.ID2ANS_DICT_60K
elif goal == 'ufet':
id2word_dict = transformer_constant.ID2ANS_DICT_UFET
elif goal == 'medwiki':
id2word_dict = transformer_constant.ID2ANS_MEDWIKI_DICT[env]
else:
print('ERROR: Invalid input...' + goal)
raise
gold_strs = []
for gold_i in gold:
gold_strs.append([id2word_dict[i] for i in range(len(gold_i)) if gold_i[i] == 1])
pred_strs = []
for pred_idx1 in pred_idx:
pred_strs.append([(id2word_dict[ind]) for ind in pred_idx1])
else:
return list(zip(gold_strs, pred_strs))
def get_eval_string(true_prediction):
"""
Given a list of (gold, prediction)s, generate output string.
"""
count, pred_count, avg_pred_count, p, r, f1 = micro(true_prediction)
_, _, _, ma_p, ma_r, ma_f1 = macro(true_prediction)
output_str = "Eval: {0} {1} {2:.3f} P:{3:.3f} R:{4:.3f} F1:{5:.3f} Ma_P:{6:.3f} Ma_R:{7:.3f} Ma_F1:{8:.3f}".format(
count, pred_count, avg_pred_count, p, r, f1, ma_p, ma_r, ma_f1)
accuracy = sum([set(y) == set(yp) for y, yp in true_prediction]) * 1.0 / len(true_prediction)
output_str += '\t Dev accuracy: {0:.1f}%'.format(accuracy * 100)
return output_str
"""
Training
"""
def _train(args, model, device):
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
args.eval_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
print('==> Loading data generator... ')
train_gen_list = get_all_datasets(args, model.transformer_tokenizer)
print('done. {} data gen(s)'.format(len(train_gen_list)))
print('Model Type: {}'.format(args.model_type))
total_loss = 0.
batch_num = 0
best_macro_f1 = 0.
start_time = time.time()
init_time = time.time()
print('Total {} named params.'.format(len([n for n, p in model.named_parameters()])))
no_decay = ["bias", "LayerNorm.weight"]
classifier_param_name = ["classifier.linear.weight"]
encoder_parameters = [
{
"params": [p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay) and n not in classifier_param_name],
"weight_decay": 0.0 #args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay) and n not in classifier_param_name],
"weight_decay": 0.0
},
]
classifier_parameters = [
{
"params": [p for n, p in model.named_parameters() if n in classifier_param_name],
"weight_decay": 0.0
},
]
print(
'Encoder {}, Classifier {}'.format(
sum([len(p['params']) for p in encoder_parameters]),
sum([len(p['params']) for p in classifier_parameters])
)
)
optimizer_enc = AdamW(encoder_parameters, lr=args.learning_rate_enc, eps=args.adam_epsilon_enc)
optimizer_cls = AdamW(classifier_parameters, lr=args.learning_rate_cls, eps=args.adam_epsilon_cls)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.load:
load_model(args.reload_model_name, transformer_constant.get(args.env,'EXP_ROOT'), args.model_id, model, optimizer_enc, optimizer_cls)
optimizer_enc.zero_grad()
optimizer_cls.zero_grad()
set_seed(args)
global_train_sum, global_train_n = 0, 0
global_overlap_train_sum, global_overlap_train_n = 0, 0
while True:
batch_num += 1 # single batch composed of all train signal passed by.
for data_gen in train_gen_list:
try:
batch = next(data_gen)
inputs, targets = to_torch(batch, device)
except StopIteration:
print('Done!')
torch.save(
{
'state_dict': model.state_dict(),
'optimizer_cls': optimizer_cls.state_dict(),
'optimizer_enc': optimizer_enc.state_dict(),
'args': args
},
'{0:s}/{1:s}.pt'.format(transformer_constant.get(args.env,'EXP_ROOT'), args.model_id)
)
return
except Exception as e:
print("To torch error so continue: ",e )
print("Batch num",batch_num)
print(batch)
print(inputs)
print(targets)
continue
model.train()
if args.model_type != "distilbert":
inputs["token_type_ids"] = (
batch["token_type_ids"] if args.model_type in ["bert", "xlnet", "albert"] else None
) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
try:
loss, output_logits = model(inputs, targets)
except Exception as e:
print("Error computing loss on batch_num: ", batch_num, e)
#print("Inputs: ", inputs) <-- even printing this out gives an error
#print("Targets: ", targets)
# skip batch and try to figure out what happned
continue
inputs, targets = None, None
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
total_loss += loss.item()
if batch_num % args.gradient_accumulation_steps == 0:
optimizer_enc.step()
optimizer_cls.step()
optimizer_enc.zero_grad()
optimizer_cls.zero_grad()
if batch_num % args.log_period == 0 and batch_num > 0:
gc.collect()
cur_loss = float(1.0 * loss.clone().item())
elapsed = time.time() - start_time
train_loss_str = ('|loss {0:3f} | at {1:d}step | @ {2:.2f} ms/batch'.format(cur_loss, batch_num,
elapsed * 1000 / args.log_period))
start_time = time.time()
print(train_loss_str)
if batch_num % args.eval_period == 0 and batch_num > 0:
output_index = get_output_index(output_logits, threshold=args.threshold)
batch_targets_clone = batch['targets'].data.cpu().clone()
gold_pred_train = get_gold_pred_str(output_index, batch_targets_clone, args.goal, args.env)
#print("OUTPUT INDEX:",output_index[:4])
#print("TARGETS:", batch_targets_clone[:4].shape, type(batch_targets_clone), batch_targets_clone[:4])
#print(torch.nonzero(batch_targets_clone[:4]))
print("1st ten preds (true cats, pred cats)", [(i,len(v[0]), len(v[1]), len(v[0])==len(v[1]), v[0], v[1]) for i,v in enumerate(gold_pred_train[:10])])
accuracy = sum([set(y) == set(yp) for y, yp in gold_pred_train]) * 1.0 / len(gold_pred_train)
print('==> Train accuracy: {0:.1f}%'.format(accuracy * 100))
overlap_accuracy = sum([len(set(y).intersection(set(yp)))/len(yp) for y, yp in gold_pred_train]) * 1.0 / len(gold_pred_train)
print('==> Train overlap accuracy: {0:.1f}%'.format(overlap_accuracy * 100))
global_train_sum += sum([set(y) == set(yp) for y, yp in gold_pred_train]) * 1.0
global_train_n += len(gold_pred_train)
global_acc = global_train_sum / global_train_n
print('==> Global Train accuracy: {0:.1f}%'.format(global_acc * 100))
global_overlap_train_sum += sum([len(set(y).intersection(set(yp)))/len(yp) for y, yp in gold_pred_train]) * 1.0
global_overlap_train_n += len(gold_pred_train)
global_overlap_acc = global_overlap_train_sum / global_overlap_train_n
print('==> Global Train overlap accuracy: {0:.1f}%'.format(global_overlap_acc * 100))
if batch_num % args.eval_period == 0 and batch_num > args.eval_after:
# Evaluate Loss on the Turk Dev dataset.
print('---- eval at step {0:d} ---'.format(batch_num))
if 'wiki_desc' in args.model_id:
_, macro_f1 = evaluate_data(batch_num, transformer_constant.get(args.env,'WIKI_DEV_DATA'), model, args, device)
else:
_, macro_f1 = evaluate_data(batch_num, transformer_constant.get(args.env,'DEV_DATA'), model, args, device)
if best_macro_f1 < macro_f1:
best_macro_f1 = macro_f1
save_fname = '{0:s}/{1:s}_best.pt'.format(transformer_constant.get(args.env,'EXP_ROOT'), args.model_id)
torch.save(
{
'state_dict': model.state_dict(),
'optimizer_cls': optimizer_cls.state_dict(),
'optimizer_enc': optimizer_enc.state_dict(),
'args': args
},
save_fname
)
print(
'Total {0:.2f} minutes have passed, saving at {1:s} '.format((time.time() - init_time) / 60, save_fname))
#if batch_num % args.save_period == 0 and batch_num > 30000:
if batch_num % args.save_period == 0 and batch_num >= args.eval_after:
save_fname = '{0:s}/{1:s}_{2:d}.pt'.format(transformer_constant.get(args.env,'EXP_ROOT'), args.model_id, batch_num)
torch.save(
{
'state_dict': model.state_dict(),
'optimizer_cls': optimizer_cls.state_dict(),
'optimizer_enc': optimizer_enc.state_dict(),
'args': args
},
save_fname
)
print(
'Total {0:.2f} minutes have passed, saving at {1:s} '.format((time.time() - init_time) / 60, save_fname))
"""
VAL
"""
def _val(args, model, device):
examples_limit = args.examples_limit
start_time = time.time()
assert args.load
dev_fname = transformer_constant.get(args.env,'DEV_DATA')
args.eval_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
model.eval()
load_model(args.reload_model_name, transformer_constant.get(args.env,'EXP_ROOT'), args.model_id, model)
print("in evaluate data, ", dev_fname, args.reload_model_name)
dev_gen = get_data_gen(dev_fname, 'test', args, model.transformer_tokenizer)
gold_pred = []
eval_loss = 0.
total_ex_count = 0
#IMPORTANT since this takes so long sub sample for now 500
for batch in tqdm(dev_gen):
if total_ex_count > examples_limit:
break
total_ex_count += len(batch['targets'])
try:
inputs, targets = to_torch(batch, device)
loss, output_logits = model(inputs, targets)
except Exception as e:
print("in Eval to torch error so continue: ",e )
continue
output_index = get_output_index(output_logits, threshold=args.threshold)
gold_pred += get_gold_pred_str(output_index, batch['targets'].data.cpu().clone(), args.goal, args.env)
eval_loss += loss.clone().item()
print("Gold Pred", len(gold_pred),gold_pred[0:4])
eval_str = get_eval_string(gold_pred)
_, _, _, _, _, macro_f1 = macro(gold_pred)
elapsed = start_time - time.time()
eval_loss_str = 'Eval loss: {0:.7f} at step {1} time elapsed {2}'.format(eval_loss, args.reload_model_name, elapsed)
print('==> EVAL: seen ' + repr(total_ex_count) + ' examples.')
print(eval_loss_str)
print(gold_pred[:3])
print('==> ' + eval_str)
"""
Test
"""
def _test(args, model, device):
start_time = time.time()
assert args.load
test_fname = transformer_constant.get(args.env,'EVAL_DATA') #this takes way too long on test, and really we want to see which does best on DEV.. so use _val
#test_fname = transformer_constant.get(args.env,'DEV_DATA')
args.eval_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
data_gens = get_datasets([(test_fname, 'test')], args, model.transformer_tokenizer)
model.eval()
load_model(args.reload_model_name, transformer_constant.get(args.env,'EXP_ROOT'), args.model_id, model)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
print("==> use", torch.cuda.device_count(), "GPUs.")
cur_time = time.time()
for name, dataset in [(test_fname, data_gens[0])]:
print('Processing... ' + name + " with len ",type(dataset), " Elapsed Time: ", cur_time - start_time)
total_gold_pred = []
total_annot_ids = []
total_probs = []
total_ys = []
for batch_num, batch in tqdm(enumerate(dataset)):
if batch_num % 1 == 0:
print(batch_num)
if not isinstance(batch, dict):
print('==> batch: ', batch)
inputs, targets = to_torch(batch, device)
annot_ids = batch.pop('ex_ids')
if args.n_gpu > 1:
output_logits = model(inputs, targets)
else:
_, output_logits = model(inputs)
output_index = get_output_index(output_logits, threshold=args.threshold)
output_prob = model.sigmoid_fn(output_logits).data.cpu().clone().numpy()
"""
print("Inputs: ",inputs)
print("Targets: ", targets)
print("Batch: ",batch)
print("Annot_ids: ",annot_ids)
print("output_index: ",output_index)
print("output_prob: ",output_prob)
"""
#y = inputs['targets'].data.cpu().clone().numpy() #orig
y = batch['targets'].data.cpu().clone().numpy() #maybe fix? maybe should be just targets
gold_pred = get_gold_pred_str(output_index, y, args.goal, args.env)
print("Gold Pred", len(gold_pred),gold_pred[0:3])
eval_str = get_eval_string(gold_pred)
_, _, _, _, _, macro_f1 = macro(gold_pred)
print('==> ' + eval_str)
total_probs.extend(output_prob)
total_ys.extend(y)
total_gold_pred.extend(gold_pred)
total_annot_ids.extend(annot_ids)
cur_time2 = time.time()
print("DONE SAVING PICKLE. Elapsed Time", cur_time2 - cur_time)
pickle.dump({'gold_id_array': total_ys, 'pred_dist': total_probs},
open(transformer_constant.get(args.env,'FILE_ROOT') + '/outputs/{0:s}.pkl'.format(args.model_id), "wb"))
print("LENS",len(total_annot_ids), len(total_gold_pred))
with open(transformer_constant.get(args.env, 'FILE_ROOT') + '/outputs/{0:s}.json'.format(args.model_id), 'w') as f_out:
output_dict = {}
counter = 0
for a_id, (gold, pred) in zip(total_annot_ids, total_gold_pred):
output_dict[a_id] = {"gold": gold, "pred": pred}
counter += 1
json.dump(output_dict, f_out)
#eval_str = get_eval_string(total_gold_pred)
eval_str = 'none'
print("DONE")
print(eval_str)
def main():
args = parser.parse_args()
# Lower text for BERT uncased models
args.do_lower = True if 'uncased' in args.model_type else False
# Setup CUDA, GPU & distributed training
assert torch.cuda.is_available()
if args.local_rank == -1:
device = torch.device("cuda")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
set_seed(args)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
ind = args.goal if args.env == "" else args.env
model = TransformerModel(args, transformer_constant.ANSWER_NUM_DICT[ind])
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
#print(model)
args.max_position_embeddings = model.transformer_config.max_position_embeddings
print("MAX POSITION EMBEDDINGS: ",args.max_position_embeddings )
print('-' * 80)
for k, v in vars(args).items():
print(k, ':', v)
print('-' * 80)
if args.mode == 'train':
print('==> mode: train')
_train(args, model, device)
elif args.mode == 'val':
# helper function 1005
print('==> mode: val')
_val(args, model, device)
elif args.mode == 'test':
print('==> mode: test')
_test(args, model, device)
else:
raise ValueError("invalid value for 'mode': {}".format(args.mode))
if __name__ == '__main__':
main()
| 38.365889 | 196 | 0.682587 |
import argparse
import gc
import json
import numpy as np
import pickle
import random
import time
import torch
import torch.nn as nn
from tqdm import tqdm
from transformers import AdamW, get_linear_schedule_with_warmup
import transformer_constant
import transformer_data_utils
from transformer_data_utils import to_torch
from models import TransformerModel
parser = argparse.ArgumentParser()
parser.add_argument("-model_id", help="Identifier for model")
parser.add_argument('-device', type=int, default=0, help='CUDA device')
parser.add_argument("-n_gpu", help="Number of GPUs.", type=int, default=1)
parser.add_argument("-mode", help="Whether to train or test", default="train", choices=["train", "val", "test"])
parser.add_argument("-local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument("-train_data", help="Train data",
default="train/wiki_et_zeroshot_60k_ex_random/train_*.json")
parser.add_argument("-dev_data", help="Dev data",
default="validation/dev_wiki_et_zeroshot_60k_ex_random_999.json")
parser.add_argument("-eval_data", help="Test data", default="")
parser.add_argument("-goal", help="category vocab size.", default="60k", choices=["medwiki","60k", "ufet"])
parser.add_argument("-seed", help="Pytorch random Seed", default=113)
parser.add_argument("-context_window_size", help="Left and right context size.", default=100)
parser.add_argument("-num_epoch", help="The number of epoch", default=5000, type=int)
parser.add_argument("-per_gpu_train_batch_size", help="The batch size per GPU", default=8, type=int)
parser.add_argument("-per_gpu_eval_batch_size", help="The batch size per GPU", default=8, type=int)
parser.add_argument("-learning_rate_enc", help="BERT: start learning rate", default=2e-5, type=float)
parser.add_argument("-learning_rate_cls", help="BERT: start learning rate", default=1e-3, type=float)
parser.add_argument("-adam_epsilon_enc", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("-adam_epsilon_cls", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("-hidden_dropout_prob", help="Dropout rate", default=.1, type=float)
parser.add_argument("-warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument(
"-gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"-model_type",
default="bert-base-uncased",
choices=[
"bert-base-uncased",
"bert-large-uncased",
"bert-large-uncased-whole-word-masking",
"roberta-base",
"roberta-large",
"allenai/biomed_roberta_base",
"monologg/biobert_v1.1_pubmed",
"allenai/scibert_scivocab_uncased",
"microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"
]
)
parser.add_argument("-threshold", help="threshold", default=0.5, type=float)
parser.add_argument("-avg_pooling", help="Averaging all hidden states instead of using [CLS].", action='store_true')
parser.add_argument("-save_period", help="How often to save", default=1000, type=int)
parser.add_argument("-eval_period", help="How often to run dev", default=500, type=int)
parser.add_argument("-log_period", help="How often to save", default=1000, type=int)
parser.add_argument("-eval_after", help="How often to run dev", default=10, type=int)
parser.add_argument("-load", help="Load existing model.", action='store_true')
parser.add_argument("-reload_model_name", help="")
parser.add_argument("-reload_model_name_desc", help="")
parser.add_argument("-env", help="data sub for medwiki", default="", choices=["yasu", "0720_3k_full","0720_3k_full_orig", "0720_3k_drugs","0720_600k_full","0720_600k_full_orig","0720_600k_drugs"])
parser.add_argument("-examples_limit", help="How many examples to do eval on in def _val", default=1000, type=int)
SIGMOID = nn.Sigmoid()
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def get_data_gen(dataname, mode, args, tokenizer):
data_path = transformer_constant.get(args.env, 'FILE_ROOT') + dataname
print("load data path", data_path, "with args.env", args.env)
dataset = transformer_data_utils.DatasetLoader(data_path, args, tokenizer)
if mode == 'train':
data_gen = dataset.get_batch(args.train_batch_size, args.max_position_embeddings, args.num_epoch, eval_data=False)
else:
data_gen = dataset.get_batch(args.eval_batch_size, args.max_position_embeddings, 1, eval_data=True)
return data_gen
def get_all_datasets(args, tokenizer):
train_gen_list = []
if args.mode in ['train']:
if 'wiki_desc' in args.model_id:
print("load wiki_desc",)
train_gen_list.append(get_data_gen(transformer_constant.get(args.env,'WIKI_TRAIN_DATA'), 'train', args, tokenizer))
else:
train_gen_list.append(get_data_gen(transformer_constant.get(args.env,'TRAIN_DATA'), 'train', args, tokenizer))
return train_gen_list
def get_datasets(data_lists, args, tokenizer):
data_gen_list = []
for dataname, mode in data_lists:
data_gen_list.append(get_data_gen(dataname, mode, args, tokenizer))
return data_gen_list
def evaluate_data(batch_num, dev_fname, model, args, device):
print("in evaluate data, batchnum", batch_num, dev_fname)
model.eval()
dev_gen = get_data_gen(dev_fname, 'test', args, model.transformer_tokenizer)
gold_pred = []
eval_loss = 0.
total_ex_count = 0
for batch in tqdm(dev_gen):
if total_ex_count > 500:
break
total_ex_count += len(batch['targets'])
try:
inputs, targets = to_torch(batch, device)
loss, output_logits = model(inputs, targets)
except Exception as e:
print("in Eval to torch error so continue: ",e )
continue
output_index = get_output_index(output_logits, threshold=args.threshold)
gold_pred += get_gold_pred_str(output_index, batch['targets'].data.cpu().clone(), args.goal, args.env)
eval_loss += loss.clone().item()
print("Gold Pred", len(gold_pred),gold_pred[0:4])
eval_str = get_eval_string(gold_pred)
_, _, _, _, _, macro_f1 = macro(gold_pred)
eval_loss_str = 'Eval loss: {0:.7f} at step {1:d}'.format(eval_loss, batch_num)
print('==> EVAL: seen ' + repr(total_ex_count) + ' examples.')
print(eval_loss_str)
print(gold_pred[:3])
print('==> ' + eval_str)
model.train()
dev_gen = None
return eval_loss, macro_f1
def f1(p, r):
if r == 0.:
return 0.
return 2 * p * r / float(p + r)
def macro(true_and_prediction):
num_examples = len(true_and_prediction)
p = 0.
r = 0.
pred_example_count = 0.
pred_label_count = 0.
gold_label_count = 0.
for true_labels, predicted_labels in true_and_prediction:
if predicted_labels:
pred_example_count += 1
pred_label_count += len(predicted_labels)
per_p = len(set(predicted_labels).intersection(set(true_labels))) / float(len(predicted_labels))
p += per_p
if len(true_labels):
gold_label_count += 1
per_r = len(set(predicted_labels).intersection(set(true_labels))) / float(len(true_labels))
r += per_r
if pred_example_count > 0:
precision = p / pred_example_count
if gold_label_count > 0:
recall = r / gold_label_count
if pred_example_count == 0:
print("In Macro: Pred Example Count == 0")
avg_elem_per_pred = 0
else:
avg_elem_per_pred = pred_label_count / pred_example_count
return num_examples, pred_example_count, avg_elem_per_pred, precision, recall, f1(precision, recall)
def micro(true_and_prediction):
num_examples = len(true_and_prediction)
num_predicted_labels = 0.
num_true_labels = 0.
num_correct_labels = 0.
pred_example_count = 0.
for true_labels, predicted_labels in true_and_prediction:
if predicted_labels:
pred_example_count += 1
num_predicted_labels += len(predicted_labels)
num_true_labels += len(true_labels)
num_correct_labels += len(set(predicted_labels).intersection(set(true_labels)))
if pred_example_count == 0:
return num_examples, 0, 0, 0, 0, 0
precision = num_correct_labels / num_predicted_labels
recall = num_correct_labels / num_true_labels
avg_elem_per_pred = num_predicted_labels / pred_example_count
return num_examples, pred_example_count, avg_elem_per_pred, precision, recall, f1(precision, recall)
def load_model(reload_model_name, save_dir, model_id, model,
optimizer_enc=None, optimizer_cls=None, scheduler_enc=None, scheduler_cls=None):
if reload_model_name:
model_file_name = '{0:s}/{1:s}.pt'.format(save_dir, reload_model_name)
else:
model_file_name = '{0:s}/{1:s}.pt'.format(save_dir, model_id)
print("Loading ", model_file_name)
checkpoint = torch.load(model_file_name)
model.load_state_dict(checkpoint['state_dict'])
if optimizer_enc and optimizer_cls:
load_state_dict(checkpoint['optimizer_enc'])
optimizer_cls.load_state_dict(checkpoint['optimizer_cls'])
else:
total_params = 0
for k in checkpoint['state_dict']:
elem = checkpoint['state_dict'][k]
param_s = 1
for size_dim in elem.size():
param_s = size_dim * param_s
total_params += param_s
param_str = ('Number of total parameters..{0:d}'.format(total_params))
print(param_str)
print('Loading model from ... {0:s}'.format(model_file_name))
def get_output_index(outputs, threshold=0.5):
pred_idx = []
outputs = SIGMOID(outputs).data.cpu().clone()
for single_dist in outputs:
single_dist = single_dist.numpy()
arg_max_ind = np.argmax(single_dist)
pred_id = [arg_max_ind]
pred_id.extend(
[i for i in range(len(single_dist)) if single_dist[i] > threshold and i != arg_max_ind])
pred_idx.append(pred_id)
return pred_idx
def get_gold_pred_str(pred_idx, gold, goal, env):
if goal == '60k':
id2word_dict = transformer_constant.ID2ANS_DICT_60K
elif goal == 'ufet':
id2word_dict = transformer_constant.ID2ANS_DICT_UFET
elif goal == 'medwiki':
id2word_dict = transformer_constant.ID2ANS_MEDWIKI_DICT[env]
else:
print('ERROR: Invalid input...' + goal)
raise
gold_strs = []
for gold_i in gold:
gold_strs.append([id2word_dict[i] for i in range(len(gold_i)) if gold_i[i] == 1])
pred_strs = []
for pred_idx1 in pred_idx:
pred_strs.append([(id2word_dict[ind]) for ind in pred_idx1])
else:
return list(zip(gold_strs, pred_strs))
def get_eval_string(true_prediction):
count, pred_count, avg_pred_count, p, r, f1 = micro(true_prediction)
_, _, _, ma_p, ma_r, ma_f1 = macro(true_prediction)
output_str = "Eval: {0} {1} {2:.3f} P:{3:.3f} R:{4:.3f} F1:{5:.3f} Ma_P:{6:.3f} Ma_R:{7:.3f} Ma_F1:{8:.3f}".format(
count, pred_count, avg_pred_count, p, r, f1, ma_p, ma_r, ma_f1)
accuracy = sum([set(y) == set(yp) for y, yp in true_prediction]) * 1.0 / len(true_prediction)
output_str += '\t Dev accuracy: {0:.1f}%'.format(accuracy * 100)
return output_str
def _train(args, model, device):
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
args.eval_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
print('==> Loading data generator... ')
train_gen_list = get_all_datasets(args, model.transformer_tokenizer)
print('done. {} data gen(s)'.format(len(train_gen_list)))
print('Model Type: {}'.format(args.model_type))
total_loss = 0.
batch_num = 0
best_macro_f1 = 0.
start_time = time.time()
init_time = time.time()
print('Total {} named params.'.format(len([n for n, p in model.named_parameters()])))
no_decay = ["bias", "LayerNorm.weight"]
classifier_param_name = ["classifier.linear.weight"]
encoder_parameters = [
{
"params": [p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay) and n not in classifier_param_name],
"weight_decay": 0.0
},
{
"params": [p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay) and n not in classifier_param_name],
"weight_decay": 0.0
},
]
classifier_parameters = [
{
"params": [p for n, p in model.named_parameters() if n in classifier_param_name],
"weight_decay": 0.0
},
]
print(
'Encoder {}, Classifier {}'.format(
sum([len(p['params']) for p in encoder_parameters]),
sum([len(p['params']) for p in classifier_parameters])
)
)
optimizer_enc = AdamW(encoder_parameters, lr=args.learning_rate_enc, eps=args.adam_epsilon_enc)
optimizer_cls = AdamW(classifier_parameters, lr=args.learning_rate_cls, eps=args.adam_epsilon_cls)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.load:
load_model(args.reload_model_name, transformer_constant.get(args.env,'EXP_ROOT'), args.model_id, model, optimizer_enc, optimizer_cls)
optimizer_enc.zero_grad()
optimizer_cls.zero_grad()
set_seed(args)
global_train_sum, global_train_n = 0, 0
global_overlap_train_sum, global_overlap_train_n = 0, 0
while True:
batch_num += 1
for data_gen in train_gen_list:
try:
batch = next(data_gen)
inputs, targets = to_torch(batch, device)
except StopIteration:
print('Done!')
torch.save(
{
'state_dict': model.state_dict(),
'optimizer_cls': optimizer_cls.state_dict(),
'optimizer_enc': optimizer_enc.state_dict(),
'args': args
},
'{0:s}/{1:s}.pt'.format(transformer_constant.get(args.env,'EXP_ROOT'), args.model_id)
)
return
except Exception as e:
print("To torch error so continue: ",e )
print("Batch num",batch_num)
print(batch)
print(inputs)
print(targets)
continue
model.train()
if args.model_type != "distilbert":
inputs["token_type_ids"] = (
batch["token_type_ids"] if args.model_type in ["bert", "xlnet", "albert"] else None
)
try:
loss, output_logits = model(inputs, targets)
except Exception as e:
print("Error computing loss on batch_num: ", batch_num, e)
#print("Inputs: ", inputs) <-- even printing this out gives an error
#print("Targets: ", targets)
# skip batch and try to figure out what happned
continue
inputs, targets = None, None
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
total_loss += loss.item()
if batch_num % args.gradient_accumulation_steps == 0:
optimizer_enc.step()
optimizer_cls.step()
optimizer_enc.zero_grad()
optimizer_cls.zero_grad()
if batch_num % args.log_period == 0 and batch_num > 0:
gc.collect()
cur_loss = float(1.0 * loss.clone().item())
elapsed = time.time() - start_time
train_loss_str = ('|loss {0:3f} | at {1:d}step | @ {2:.2f} ms/batch'.format(cur_loss, batch_num,
elapsed * 1000 / args.log_period))
start_time = time.time()
print(train_loss_str)
if batch_num % args.eval_period == 0 and batch_num > 0:
output_index = get_output_index(output_logits, threshold=args.threshold)
batch_targets_clone = batch['targets'].data.cpu().clone()
gold_pred_train = get_gold_pred_str(output_index, batch_targets_clone, args.goal, args.env)
#print("OUTPUT INDEX:",output_index[:4])
#print("TARGETS:", batch_targets_clone[:4].shape, type(batch_targets_clone), batch_targets_clone[:4])
#print(torch.nonzero(batch_targets_clone[:4]))
print("1st ten preds (true cats, pred cats)", [(i,len(v[0]), len(v[1]), len(v[0])==len(v[1]), v[0], v[1]) for i,v in enumerate(gold_pred_train[:10])])
accuracy = sum([set(y) == set(yp) for y, yp in gold_pred_train]) * 1.0 / len(gold_pred_train)
print('==> Train accuracy: {0:.1f}%'.format(accuracy * 100))
overlap_accuracy = sum([len(set(y).intersection(set(yp)))/len(yp) for y, yp in gold_pred_train]) * 1.0 / len(gold_pred_train)
print('==> Train overlap accuracy: {0:.1f}%'.format(overlap_accuracy * 100))
global_train_sum += sum([set(y) == set(yp) for y, yp in gold_pred_train]) * 1.0
global_train_n += len(gold_pred_train)
global_acc = global_train_sum / global_train_n
print('==> Global Train accuracy: {0:.1f}%'.format(global_acc * 100))
global_overlap_train_sum += sum([len(set(y).intersection(set(yp)))/len(yp) for y, yp in gold_pred_train]) * 1.0
global_overlap_train_n += len(gold_pred_train)
global_overlap_acc = global_overlap_train_sum / global_overlap_train_n
print('==> Global Train overlap accuracy: {0:.1f}%'.format(global_overlap_acc * 100))
if batch_num % args.eval_period == 0 and batch_num > args.eval_after:
# Evaluate Loss on the Turk Dev dataset.
print('---- eval at step {0:d} ---'.format(batch_num))
if 'wiki_desc' in args.model_id:
_, macro_f1 = evaluate_data(batch_num, transformer_constant.get(args.env,'WIKI_DEV_DATA'), model, args, device)
else:
_, macro_f1 = evaluate_data(batch_num, transformer_constant.get(args.env,'DEV_DATA'), model, args, device)
if best_macro_f1 < macro_f1:
best_macro_f1 = macro_f1
save_fname = '{0:s}/{1:s}_best.pt'.format(transformer_constant.get(args.env,'EXP_ROOT'), args.model_id)
torch.save(
{
'state_dict': model.state_dict(),
'optimizer_cls': optimizer_cls.state_dict(),
'optimizer_enc': optimizer_enc.state_dict(),
'args': args
},
save_fname
)
print(
'Total {0:.2f} minutes have passed, saving at {1:s} '.format((time.time() - init_time) / 60, save_fname))
#if batch_num % args.save_period == 0 and batch_num > 30000:
if batch_num % args.save_period == 0 and batch_num >= args.eval_after:
save_fname = '{0:s}/{1:s}_{2:d}.pt'.format(transformer_constant.get(args.env,'EXP_ROOT'), args.model_id, batch_num)
torch.save(
{
'state_dict': model.state_dict(),
'optimizer_cls': optimizer_cls.state_dict(),
'optimizer_enc': optimizer_enc.state_dict(),
'args': args
},
save_fname
)
print(
'Total {0:.2f} minutes have passed, saving at {1:s} '.format((time.time() - init_time) / 60, save_fname))
def _val(args, model, device):
examples_limit = args.examples_limit
start_time = time.time()
assert args.load
dev_fname = transformer_constant.get(args.env,'DEV_DATA')
args.eval_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
model.eval()
load_model(args.reload_model_name, transformer_constant.get(args.env,'EXP_ROOT'), args.model_id, model)
print("in evaluate data, ", dev_fname, args.reload_model_name)
dev_gen = get_data_gen(dev_fname, 'test', args, model.transformer_tokenizer)
gold_pred = []
eval_loss = 0.
total_ex_count = 0
#IMPORTANT since this takes so long sub sample for now 500
for batch in tqdm(dev_gen):
if total_ex_count > examples_limit:
break
total_ex_count += len(batch['targets'])
try:
inputs, targets = to_torch(batch, device)
loss, output_logits = model(inputs, targets)
except Exception as e:
print("in Eval to torch error so continue: ",e )
continue
output_index = get_output_index(output_logits, threshold=args.threshold)
gold_pred += get_gold_pred_str(output_index, batch['targets'].data.cpu().clone(), args.goal, args.env)
eval_loss += loss.clone().item()
print("Gold Pred", len(gold_pred),gold_pred[0:4])
eval_str = get_eval_string(gold_pred)
_, _, _, _, _, macro_f1 = macro(gold_pred)
elapsed = start_time - time.time()
eval_loss_str = 'Eval loss: {0:.7f} at step {1} time elapsed {2}'.format(eval_loss, args.reload_model_name, elapsed)
print('==> EVAL: seen ' + repr(total_ex_count) + ' examples.')
print(eval_loss_str)
print(gold_pred[:3])
print('==> ' + eval_str)
def _test(args, model, device):
start_time = time.time()
assert args.load
test_fname = transformer_constant.get(args.env,'EVAL_DATA') #this takes way too long on test, and really we want to see which does best on DEV.. so use _val
#test_fname = transformer_constant.get(args.env,'DEV_DATA')
args.eval_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
data_gens = get_datasets([(test_fname, 'test')], args, model.transformer_tokenizer)
model.eval()
load_model(args.reload_model_name, transformer_constant.get(args.env,'EXP_ROOT'), args.model_id, model)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
print("==> use", torch.cuda.device_count(), "GPUs.")
cur_time = time.time()
for name, dataset in [(test_fname, data_gens[0])]:
print('Processing... ' + name + " with len ",type(dataset), " Elapsed Time: ", cur_time - start_time)
total_gold_pred = []
total_annot_ids = []
total_probs = []
total_ys = []
for batch_num, batch in tqdm(enumerate(dataset)):
if batch_num % 1 == 0:
print(batch_num)
if not isinstance(batch, dict):
print('==> batch: ', batch)
inputs, targets = to_torch(batch, device)
annot_ids = batch.pop('ex_ids')
if args.n_gpu > 1:
output_logits = model(inputs, targets)
else:
_, output_logits = model(inputs)
output_index = get_output_index(output_logits, threshold=args.threshold)
output_prob = model.sigmoid_fn(output_logits).data.cpu().clone().numpy()
#y = inputs['targets'].data.cpu().clone().numpy() #orig
y = batch['targets'].data.cpu().clone().numpy() #maybe fix? maybe should be just targets
gold_pred = get_gold_pred_str(output_index, y, args.goal, args.env)
print("Gold Pred", len(gold_pred),gold_pred[0:3])
eval_str = get_eval_string(gold_pred)
_, _, _, _, _, macro_f1 = macro(gold_pred)
print('==> ' + eval_str)
total_probs.extend(output_prob)
total_ys.extend(y)
total_gold_pred.extend(gold_pred)
total_annot_ids.extend(annot_ids)
cur_time2 = time.time()
print("DONE SAVING PICKLE. Elapsed Time", cur_time2 - cur_time)
pickle.dump({'gold_id_array': total_ys, 'pred_dist': total_probs},
open(transformer_constant.get(args.env,'FILE_ROOT') + '/outputs/{0:s}.pkl'.format(args.model_id), "wb"))
print("LENS",len(total_annot_ids), len(total_gold_pred))
with open(transformer_constant.get(args.env, 'FILE_ROOT') + '/outputs/{0:s}.json'.format(args.model_id), 'w') as f_out:
output_dict = {}
counter = 0
for a_id, (gold, pred) in zip(total_annot_ids, total_gold_pred):
output_dict[a_id] = {"gold": gold, "pred": pred}
counter += 1
json.dump(output_dict, f_out)
#eval_str = get_eval_string(total_gold_pred)
eval_str = 'none'
print("DONE")
print(eval_str)
def main():
args = parser.parse_args()
# Lower text for BERT uncased models
args.do_lower = True if 'uncased' in args.model_type else False
# Setup CUDA, GPU & distributed training
assert torch.cuda.is_available()
if args.local_rank == -1:
device = torch.device("cuda")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
set_seed(args)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
ind = args.goal if args.env == "" else args.env
model = TransformerModel(args, transformer_constant.ANSWER_NUM_DICT[ind])
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
#print(model)
args.max_position_embeddings = model.transformer_config.max_position_embeddings
print("MAX POSITION EMBEDDINGS: ",args.max_position_embeddings )
print('-' * 80)
for k, v in vars(args).items():
print(k, ':', v)
print('-' * 80)
if args.mode == 'train':
print('==> mode: train')
_train(args, model, device)
elif args.mode == 'val':
# helper function 1005
print('==> mode: val')
_val(args, model, device)
elif args.mode == 'test':
print('==> mode: test')
_test(args, model, device)
else:
raise ValueError("invalid value for 'mode': {}".format(args.mode))
if __name__ == '__main__':
main()
| true | true |
1c33f502aebb10efa0fd15dc2ab7e98b4c9b9f82 | 8,050 | py | Python | support/closure-library/closure/bin/build/closurebuilder.py | joe-greenawalt/skulpt | 1db078e2f6d453403287233254b012bf31960ef4 | [
"MIT"
] | 2 | 2021-01-10T16:19:38.000Z | 2021-06-14T22:09:59.000Z | support/closure-library/closure/bin/build/closurebuilder.py | csev/skulpt | 9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f | [
"MIT"
] | null | null | null | support/closure-library/closure/bin/build/closurebuilder.py | csev/skulpt | 9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f | [
"MIT"
] | 1 | 2015-06-28T18:58:22.000Z | 2015-06-28T18:58:22.000Z | #!/usr/bin/env python
#
# Copyright 2009 The Closure Library Authors. All Rights Reserved.
"""Utility for Closure Library dependency calculation.
ClosureBuilder scans source files to build dependency info. From the
dependencies, the script can produce a deps.js file, a manifest in dependency
order, a concatenated script, or compiled output from the Closure Compiler.
Paths to files can be expressed as individual arguments to the tool (intended
for use with find and xargs). As a convenience, --root can be used to specify
all JS files below a directory.
usage: %prog [options] [file1.js file2.js ...]
"""
import logging
import optparse
import os
import sys
import depstree
import jscompiler
import source
import treescan
def _GetOptionsParser():
"""Get the options parser."""
parser = optparse.OptionParser(__doc__)
parser.add_option('-i',
'--input',
dest='inputs',
action='append',
default=[],
help='One or more input files to calculate dependencies '
'for. The namespaces in this file will be combined with '
'those given with the -n flag to form the set of '
'namespaces to find dependencies for.')
parser.add_option('-n',
'--namespace',
dest='namespaces',
action='append',
default=[],
help='One or more namespaces to calculate dependencies '
'for. These namespaces will be combined with those given '
'with the -i flag to form the set of namespaces to find '
'dependencies for. A Closure namespace is a '
'dot-delimited path expression declared with a call to '
'goog.provide() (e.g. "goog.array" or "foo.bar").')
parser.add_option('--root',
dest='roots',
action='append',
help='The paths that should be traversed to build the '
'dependencies.')
parser.add_option('-o',
'--output_mode',
dest='output_mode',
type='choice',
action='store',
choices=['list', 'script', 'compiled'],
default='list',
help='The type of output to generate from this script. '
'Options are "list" for a list of filenames, "script" '
'for a single script containing the contents of all the '
'files, or "compiled" to produce compiled output with '
'the Closure Compiler. Default is "list".')
parser.add_option('-c',
'--compiler_jar',
dest='compiler_jar',
action='store',
help='The location of the Closure compiler .jar file.')
parser.add_option('-f',
'--compiler_flags',
dest='compiler_flags',
default=[],
action='append',
help='Additional flags to pass to the Closure compiler.')
parser.add_option('--output_file',
dest='output_file',
action='store',
help=('If specified, write output to this path instead of '
'writing to standard output.'))
return parser
def _GetInputByPath(path, sources):
"""Get the source identified by a path.
Args:
path: str, A path to a file that identifies a source.
sources: An iterable collection of source objects.
Returns:
The source from sources identified by path, if found. Converts to
absolute paths for comparison.
"""
for js_source in sources:
# Convert both to absolute paths for comparison.
if os.path.abspath(path) == os.path.abspath(js_source.GetPath()):
return js_source
def _GetClosureBaseFile(sources):
"""Given a set of sources, returns the one base.js file.
Note that if zero or two or more base.js files are found, an error message
will be written and the program will be exited.
Args:
sources: An iterable of _PathSource objects.
Returns:
The _PathSource representing the base Closure file.
"""
filtered_base_files = filter(_IsClosureBaseFile, sources)
if not filtered_base_files:
logging.error('No Closure base.js file found.')
sys.exit(1)
if len(filtered_base_files) > 1:
logging.error('More than one Closure base.js files found at these paths:')
for base_file in filtered_base_files:
logging.error(base_file.GetPath())
sys.exit(1)
return filtered_base_files[0]
def _IsClosureBaseFile(js_source):
"""Returns true if the given _PathSource is the Closure base.js source."""
if os.path.basename(js_source.GetPath()) == 'base.js':
# Sanity check that this is the Closure base file. Check that this
# is where goog is defined.
for line in js_source.GetSource().splitlines():
if line.startswith('var goog = goog || {};'):
return True
return False
class _PathSource(source.Source):
"""Source file subclass that remembers its file path."""
def __init__(self, path):
"""Initialize a source.
Args:
path: str, Path to a JavaScript file. The source string will be read
from this file.
"""
super(_PathSource, self).__init__(source.GetFileContents(path))
self._path = path
def GetPath(self):
"""Returns the path."""
return self._path
def main():
logging.basicConfig(format=(sys.argv[0] + ': %(message)s'),
level=logging.INFO)
options, args = _GetOptionsParser().parse_args()
# Make our output pipe.
if options.output_file:
out = open(options.output_file, 'w')
else:
out = sys.stdout
sources = set()
logging.info('Scanning paths...')
for path in options.roots:
for js_path in treescan.ScanTreeForJsFiles(path):
sources.add(_PathSource(js_path))
# Add scripts specified on the command line.
for path in args:
sources.add(source.Source(_PathSource(path)))
logging.info('%s sources scanned.', len(sources))
# Though deps output doesn't need to query the tree, we still build it
# to validate dependencies.
logging.info('Building dependency tree..')
tree = depstree.DepsTree(sources)
input_namespaces = set()
inputs = options.inputs or []
for input_path in inputs:
js_input = _GetInputByPath(input_path, sources)
if not js_input:
logging.error('No source matched input %s', input_path)
sys.exit(1)
input_namespaces.update(js_input.provides)
input_namespaces.update(options.namespaces)
if not input_namespaces:
logging.error('No namespaces found. At least one namespace must be '
'specified with the --namespace or --input flags.')
sys.exit(2)
# The Closure Library base file must go first.
base = _GetClosureBaseFile(sources)
deps = [base] + tree.GetDependencies(input_namespaces)
output_mode = options.output_mode
if output_mode == 'list':
out.writelines([js_source.GetPath() + '\n' for js_source in deps])
elif output_mode == 'script':
out.writelines([js_source.GetSource() for js_source in deps])
elif output_mode == 'compiled':
# Make sure a .jar is specified.
if not options.compiler_jar:
logging.error('--compiler_jar flag must be specified if --output is '
'"compiled"')
sys.exit(2)
compiled_source = jscompiler.Compile(
options.compiler_jar,
[js_source.GetPath() for js_source in deps],
options.compiler_flags)
if compiled_source is None:
logging.error('JavaScript compilation failed.')
sys.exit(1)
else:
logging.info('JavaScript compilation succeeded.')
out.write(compiled_source)
else:
logging.error('Invalid value for --output flag.')
sys.exit(2)
if __name__ == '__main__':
main()
| 32.723577 | 79 | 0.623602 |
import logging
import optparse
import os
import sys
import depstree
import jscompiler
import source
import treescan
def _GetOptionsParser():
parser = optparse.OptionParser(__doc__)
parser.add_option('-i',
'--input',
dest='inputs',
action='append',
default=[],
help='One or more input files to calculate dependencies '
'for. The namespaces in this file will be combined with '
'those given with the -n flag to form the set of '
'namespaces to find dependencies for.')
parser.add_option('-n',
'--namespace',
dest='namespaces',
action='append',
default=[],
help='One or more namespaces to calculate dependencies '
'for. These namespaces will be combined with those given '
'with the -i flag to form the set of namespaces to find '
'dependencies for. A Closure namespace is a '
'dot-delimited path expression declared with a call to '
'goog.provide() (e.g. "goog.array" or "foo.bar").')
parser.add_option('--root',
dest='roots',
action='append',
help='The paths that should be traversed to build the '
'dependencies.')
parser.add_option('-o',
'--output_mode',
dest='output_mode',
type='choice',
action='store',
choices=['list', 'script', 'compiled'],
default='list',
help='The type of output to generate from this script. '
'Options are "list" for a list of filenames, "script" '
'for a single script containing the contents of all the '
'files, or "compiled" to produce compiled output with '
'the Closure Compiler. Default is "list".')
parser.add_option('-c',
'--compiler_jar',
dest='compiler_jar',
action='store',
help='The location of the Closure compiler .jar file.')
parser.add_option('-f',
'--compiler_flags',
dest='compiler_flags',
default=[],
action='append',
help='Additional flags to pass to the Closure compiler.')
parser.add_option('--output_file',
dest='output_file',
action='store',
help=('If specified, write output to this path instead of '
'writing to standard output.'))
return parser
def _GetInputByPath(path, sources):
for js_source in sources:
if os.path.abspath(path) == os.path.abspath(js_source.GetPath()):
return js_source
def _GetClosureBaseFile(sources):
filtered_base_files = filter(_IsClosureBaseFile, sources)
if not filtered_base_files:
logging.error('No Closure base.js file found.')
sys.exit(1)
if len(filtered_base_files) > 1:
logging.error('More than one Closure base.js files found at these paths:')
for base_file in filtered_base_files:
logging.error(base_file.GetPath())
sys.exit(1)
return filtered_base_files[0]
def _IsClosureBaseFile(js_source):
if os.path.basename(js_source.GetPath()) == 'base.js':
for line in js_source.GetSource().splitlines():
if line.startswith('var goog = goog || {};'):
return True
return False
class _PathSource(source.Source):
def __init__(self, path):
super(_PathSource, self).__init__(source.GetFileContents(path))
self._path = path
def GetPath(self):
return self._path
def main():
logging.basicConfig(format=(sys.argv[0] + ': %(message)s'),
level=logging.INFO)
options, args = _GetOptionsParser().parse_args()
if options.output_file:
out = open(options.output_file, 'w')
else:
out = sys.stdout
sources = set()
logging.info('Scanning paths...')
for path in options.roots:
for js_path in treescan.ScanTreeForJsFiles(path):
sources.add(_PathSource(js_path))
for path in args:
sources.add(source.Source(_PathSource(path)))
logging.info('%s sources scanned.', len(sources))
# to validate dependencies.
logging.info('Building dependency tree..')
tree = depstree.DepsTree(sources)
input_namespaces = set()
inputs = options.inputs or []
for input_path in inputs:
js_input = _GetInputByPath(input_path, sources)
if not js_input:
logging.error('No source matched input %s', input_path)
sys.exit(1)
input_namespaces.update(js_input.provides)
input_namespaces.update(options.namespaces)
if not input_namespaces:
logging.error('No namespaces found. At least one namespace must be '
'specified with the --namespace or --input flags.')
sys.exit(2)
# The Closure Library base file must go first.
base = _GetClosureBaseFile(sources)
deps = [base] + tree.GetDependencies(input_namespaces)
output_mode = options.output_mode
if output_mode == 'list':
out.writelines([js_source.GetPath() + '\n' for js_source in deps])
elif output_mode == 'script':
out.writelines([js_source.GetSource() for js_source in deps])
elif output_mode == 'compiled':
# Make sure a .jar is specified.
if not options.compiler_jar:
logging.error('--compiler_jar flag must be specified if --output is '
'"compiled"')
sys.exit(2)
compiled_source = jscompiler.Compile(
options.compiler_jar,
[js_source.GetPath() for js_source in deps],
options.compiler_flags)
if compiled_source is None:
logging.error('JavaScript compilation failed.')
sys.exit(1)
else:
logging.info('JavaScript compilation succeeded.')
out.write(compiled_source)
else:
logging.error('Invalid value for --output flag.')
sys.exit(2)
if __name__ == '__main__':
main()
| true | true |
1c33f61ef94624e19a7f0a90cef13310a305cb70 | 9,181 | py | Python | src/nsvqa/nn/interpreter/batch_base_interpreter.py | drewhayward/DFOL-VQA | 8c7d403bac560588ab3ac45774a3e4f71fbe9c90 | [
"MIT"
] | 23 | 2020-08-17T16:18:33.000Z | 2022-03-09T11:47:37.000Z | src/nsvqa/nn/interpreter/batch_base_interpreter.py | drewhayward/DFOL-VQA | 8c7d403bac560588ab3ac45774a3e4f71fbe9c90 | [
"MIT"
] | 1 | 2021-06-11T15:51:24.000Z | 2021-06-11T15:51:24.000Z | src/nsvqa/nn/interpreter/batch_base_interpreter.py | drewhayward/DFOL-VQA | 8c7d403bac560588ab3ac45774a3e4f71fbe9c90 | [
"MIT"
] | 7 | 2020-11-09T07:25:27.000Z | 2022-01-13T04:25:09.000Z | # Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE.md file
# in the project root for full license information.
import torch
import torch.nn as nn
import os
from operator import itemgetter
from nsvqa.nn.interpreter import util
from nsvqa.nn.interpreter.batch_base_types import BatchWorld, BatchVariableSet, BatchAttentionState
from nsvqa.nn.interpreter.data_parallel import gather_results
class BatchInterpreterBase(nn.Module):
def __init__(self, name, oracle, featurizer=None, attention_transfer_state_dim=0, apply_modulation_everywhere=True, cached=False, visual_rule_learner=None, calibrator=None): #, attention_transfer_modulator=None):
super(BatchInterpreterBase, self).__init__()
self._featurizer = featurizer
self._oracle = oracle
self._name = name
self._global_step = nn.Parameter(torch.tensor([0], dtype=torch.float), requires_grad=False)
# self._atm = attention_transfer_modulator
self._has_modulator = False
self._attention_transfer_state_dim = attention_transfer_state_dim
self._apply_modulation_everywhere = apply_modulation_everywhere
self._cached = cached
self._visual_rule_learner = visual_rule_learner
self._calibrator = calibrator
def _execute(self, op_id, world, operator_batch, input_tuple, is_terminal, is_training):
pass
def _transform_attention(self, op_id, is_forward, world, operator_batch, input_tuple, is_terminal, is_training):
pass
def parameter_count(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
def save(self, export_path_base):
torch.save(self.state_dict(), os.path.join(export_path_base, self._name))
def load(self, import_path_base):
self.load_state_dict(torch.load(os.path.join(import_path_base, self._name)), strict=False)
def build_scene(self, device, object_features, batch_index, meta_data):
if self._featurizer is not None:
features = self._featurizer.featurize_scene(device, object_features, batch_index, meta_data)
attribute_features = features['attribute_features']
relation_features = features['relation_features']
object_num = features['object_num']
if self._cached:
attribute_features, relation_features['features'] = self._oracle.compute_all_log_likelihood_2(attribute_features, relation_features['features'])
if self._calibrator is not None:
attribute_features[:, self._oracle._ontology._attribute_index], relation_features = self._calibrator(attribute_features[:, self._oracle._ontology._attribute_index], relation_features)
if self._visual_rule_learner is not None:
relation_features['object_num'] = object_num
attribute_features[:, self._oracle._ontology._attribute_index], relation_features = self._visual_rule_learner(attribute_features[:, self._oracle._ontology._attribute_index], relation_features)
else:
object_num = object_features.size()[0]
attribute_features = object_features.view(object_num, -1)
arg1 = attribute_features.repeat(1, object_num).view(object_num**2, -1)
arg2 = attribute_features.repeat(self._object_num, 1)
relation_features = torch.cat([arg1, arg2], dim=1)
return BatchWorld(device, object_num, attribute_features, relation_features, batch_index, meta_data, \
attention_transfer_state_dim=self._attention_transfer_state_dim).to(object_features.dtype)
def forward(self, program_batch_list, is_training, return_trace=False, modulator_switch=True):
# Initialize the trace
all_traces = []
all_results = []
device = program_batch_list[0].device
# Main loop
for program_batch in program_batch_list:
# Set the objects features
world = self.build_scene(program_batch.device, program_batch._object_features, program_batch._object_batch_index, program_batch._meta_data)
# print('---------------------------------------------')
# Modulator loops
if self._has_modulator and modulator_switch:
if not self._apply_modulation_everywhere:
for i in range(len(program_batch._op_batch_list) - 1):
program_batch._op_batch_list._op_id += 'n'
# Forward loop
trace = []
for i, op_batch in enumerate(program_batch._op_batch_list):
if len(program_batch._dependencies[i]) > 1:
input_tuple = tuple(itemgetter(*program_batch._dependencies[i])(trace))
elif len(program_batch._dependencies[i]) == 1:
input_tuple = (trace[program_batch._dependencies[i][0]],)
else:
input_tuple = (None,)
x, terminate = self._transform_attention(op_batch._op_id, True, world, op_batch, input_tuple, i == len(program_batch._op_batch_list) - 1, is_training)
# Gate the unaffected questions
if i < len(program_batch._op_batch_list) - 1 and input_tuple[0] is not None and op_batch._mask is not None:
x = x.gate(input_tuple[0], op_batch._mask)
trace.append(x)
if terminate:
break
# Backward loop
reversed_dependencies = util.reverse_dependencies(program_batch._dependencies)
first_attention_state = (BatchAttentionState(trace[-1]._name, device, trace[-1]._state, set_zeros=True).to(world.dtype), ) if not isinstance(trace[-1], (tuple, list)) else \
tuple([BatchAttentionState(att._name, device, att._state, set_zeros=True).to(world.dtype) for att in trace[-1]])
trace = [None for _ in range(len(program_batch._op_batch_list))]
for i, op_batch in reversed(list(enumerate(program_batch._op_batch_list))):
if len(reversed_dependencies[i]) == 1:
temp = trace[reversed_dependencies[i][0]]
if isinstance(temp, (tuple, list)):
input_tuple = (temp[1],) if i == len(program_batch._op_batch_list) - 2 else (temp[0],)
else:
input_tuple = (temp,)
else:
input_tuple = first_attention_state
x, terminate = self._transform_attention(op_batch._op_id, False, world, op_batch, input_tuple, i == 0, is_training)
# Gate the unaffected questions
# print(op_batch._op_name)
if len(program_batch._dependencies[i]) > 0 and op_batch._mask is not None and isinstance(x, BatchAttentionState) and i != len(program_batch._op_batch_list) - 1:
x = x.gate(input_tuple[0], op_batch._mask)
trace[i] = x
if terminate:
break
# if self._atm is not None:
# attention_transfer = self._atm(program_batch)
# Execution loop
trace = []
for i, op_batch in enumerate(program_batch._op_batch_list):
# print(op_batch._op_name)
if len(program_batch._dependencies[i]) > 1:
input_tuple = tuple(itemgetter(*program_batch._dependencies[i])(trace))
elif len(program_batch._dependencies[i]) == 1:
input_tuple = (trace[program_batch._dependencies[i][0]],)
else:
input_tuple = ()
x, terminate = self._execute(op_batch._op_id, world, op_batch, input_tuple, i == len(program_batch._op_batch_list) - 1, is_training)
# # Apply the transfer function if available
# if self._atm is not None and isinstance(x, BatchVariableSet):
# alpha = attention_transfer[i, :, 0].unsqueeze(1)
# beta = attention_transfer[i, :, 1].unsqueeze(1)
# temp = alpha * x._log_attention
# x._log_attention = temp - util.safe_log((beta * util.log_not(x._log_attention)).exp() + temp.exp())
# Gate the unaffected questions
if isinstance(x, BatchVariableSet) and len(input_tuple) > 0 and op_batch._mask is not None:
x = x.gate(input_tuple[0], op_batch._mask)
trace.append(x)
if terminate:
break
result = trace[-1] if len(trace) > 0 else None
all_results.append(result)
all_traces.append(trace)
result = gather_results(all_results, device, util.is_cuda(device))
if return_trace:
return result, all_traces
return result
| 49.896739 | 216 | 0.615401 |
import torch
import torch.nn as nn
import os
from operator import itemgetter
from nsvqa.nn.interpreter import util
from nsvqa.nn.interpreter.batch_base_types import BatchWorld, BatchVariableSet, BatchAttentionState
from nsvqa.nn.interpreter.data_parallel import gather_results
class BatchInterpreterBase(nn.Module):
def __init__(self, name, oracle, featurizer=None, attention_transfer_state_dim=0, apply_modulation_everywhere=True, cached=False, visual_rule_learner=None, calibrator=None):
super(BatchInterpreterBase, self).__init__()
self._featurizer = featurizer
self._oracle = oracle
self._name = name
self._global_step = nn.Parameter(torch.tensor([0], dtype=torch.float), requires_grad=False)
self._has_modulator = False
self._attention_transfer_state_dim = attention_transfer_state_dim
self._apply_modulation_everywhere = apply_modulation_everywhere
self._cached = cached
self._visual_rule_learner = visual_rule_learner
self._calibrator = calibrator
def _execute(self, op_id, world, operator_batch, input_tuple, is_terminal, is_training):
pass
def _transform_attention(self, op_id, is_forward, world, operator_batch, input_tuple, is_terminal, is_training):
pass
def parameter_count(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
def save(self, export_path_base):
torch.save(self.state_dict(), os.path.join(export_path_base, self._name))
def load(self, import_path_base):
self.load_state_dict(torch.load(os.path.join(import_path_base, self._name)), strict=False)
def build_scene(self, device, object_features, batch_index, meta_data):
if self._featurizer is not None:
features = self._featurizer.featurize_scene(device, object_features, batch_index, meta_data)
attribute_features = features['attribute_features']
relation_features = features['relation_features']
object_num = features['object_num']
if self._cached:
attribute_features, relation_features['features'] = self._oracle.compute_all_log_likelihood_2(attribute_features, relation_features['features'])
if self._calibrator is not None:
attribute_features[:, self._oracle._ontology._attribute_index], relation_features = self._calibrator(attribute_features[:, self._oracle._ontology._attribute_index], relation_features)
if self._visual_rule_learner is not None:
relation_features['object_num'] = object_num
attribute_features[:, self._oracle._ontology._attribute_index], relation_features = self._visual_rule_learner(attribute_features[:, self._oracle._ontology._attribute_index], relation_features)
else:
object_num = object_features.size()[0]
attribute_features = object_features.view(object_num, -1)
arg1 = attribute_features.repeat(1, object_num).view(object_num**2, -1)
arg2 = attribute_features.repeat(self._object_num, 1)
relation_features = torch.cat([arg1, arg2], dim=1)
return BatchWorld(device, object_num, attribute_features, relation_features, batch_index, meta_data, \
attention_transfer_state_dim=self._attention_transfer_state_dim).to(object_features.dtype)
def forward(self, program_batch_list, is_training, return_trace=False, modulator_switch=True):
all_traces = []
all_results = []
device = program_batch_list[0].device
for program_batch in program_batch_list:
world = self.build_scene(program_batch.device, program_batch._object_features, program_batch._object_batch_index, program_batch._meta_data)
if self._has_modulator and modulator_switch:
if not self._apply_modulation_everywhere:
for i in range(len(program_batch._op_batch_list) - 1):
program_batch._op_batch_list._op_id += 'n'
trace = []
for i, op_batch in enumerate(program_batch._op_batch_list):
if len(program_batch._dependencies[i]) > 1:
input_tuple = tuple(itemgetter(*program_batch._dependencies[i])(trace))
elif len(program_batch._dependencies[i]) == 1:
input_tuple = (trace[program_batch._dependencies[i][0]],)
else:
input_tuple = (None,)
x, terminate = self._transform_attention(op_batch._op_id, True, world, op_batch, input_tuple, i == len(program_batch._op_batch_list) - 1, is_training)
if i < len(program_batch._op_batch_list) - 1 and input_tuple[0] is not None and op_batch._mask is not None:
x = x.gate(input_tuple[0], op_batch._mask)
trace.append(x)
if terminate:
break
reversed_dependencies = util.reverse_dependencies(program_batch._dependencies)
first_attention_state = (BatchAttentionState(trace[-1]._name, device, trace[-1]._state, set_zeros=True).to(world.dtype), ) if not isinstance(trace[-1], (tuple, list)) else \
tuple([BatchAttentionState(att._name, device, att._state, set_zeros=True).to(world.dtype) for att in trace[-1]])
trace = [None for _ in range(len(program_batch._op_batch_list))]
for i, op_batch in reversed(list(enumerate(program_batch._op_batch_list))):
if len(reversed_dependencies[i]) == 1:
temp = trace[reversed_dependencies[i][0]]
if isinstance(temp, (tuple, list)):
input_tuple = (temp[1],) if i == len(program_batch._op_batch_list) - 2 else (temp[0],)
else:
input_tuple = (temp,)
else:
input_tuple = first_attention_state
x, terminate = self._transform_attention(op_batch._op_id, False, world, op_batch, input_tuple, i == 0, is_training)
if len(program_batch._dependencies[i]) > 0 and op_batch._mask is not None and isinstance(x, BatchAttentionState) and i != len(program_batch._op_batch_list) - 1:
x = x.gate(input_tuple[0], op_batch._mask)
trace[i] = x
if terminate:
break
trace = []
for i, op_batch in enumerate(program_batch._op_batch_list):
if len(program_batch._dependencies[i]) > 1:
input_tuple = tuple(itemgetter(*program_batch._dependencies[i])(trace))
elif len(program_batch._dependencies[i]) == 1:
input_tuple = (trace[program_batch._dependencies[i][0]],)
else:
input_tuple = ()
x, terminate = self._execute(op_batch._op_id, world, op_batch, input_tuple, i == len(program_batch._op_batch_list) - 1, is_training)
if isinstance(x, BatchVariableSet) and len(input_tuple) > 0 and op_batch._mask is not None:
x = x.gate(input_tuple[0], op_batch._mask)
trace.append(x)
if terminate:
break
result = trace[-1] if len(trace) > 0 else None
all_results.append(result)
all_traces.append(trace)
result = gather_results(all_results, device, util.is_cuda(device))
if return_trace:
return result, all_traces
return result
| true | true |
1c33f62a8d3491e291306562ed3c4d021d62575a | 36,400 | py | Python | tests/test_transforms.py | weecology/albumentations | cc8fbb6e2fcc4f6a4c87a29b6b0784391b0e2db4 | [
"MIT"
] | 1 | 2021-05-22T09:19:31.000Z | 2021-05-22T09:19:31.000Z | tests/test_transforms.py | weecology/albumentations | cc8fbb6e2fcc4f6a4c87a29b6b0784391b0e2db4 | [
"MIT"
] | null | null | null | tests/test_transforms.py | weecology/albumentations | cc8fbb6e2fcc4f6a4c87a29b6b0784391b0e2db4 | [
"MIT"
] | null | null | null | from functools import partial
import cv2
import numpy as np
import pytest
import random
import albumentations as A
import albumentations.augmentations.functional as F
import albumentations.augmentations.geometric.functional as FGeometric
from torchvision.transforms import ColorJitter
from PIL import Image
def set_seed(seed=0):
random.seed(seed)
np.random.seed(seed)
def test_transpose_both_image_and_mask():
image = np.ones((8, 6, 3))
mask = np.ones((8, 6))
augmentation = A.Transpose(p=1)
augmented = augmentation(image=image, mask=mask)
assert augmented["image"].shape == (6, 8, 3)
assert augmented["mask"].shape == (6, 8)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_safe_rotate_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.SafeRotate(limit=(45, 45), interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = FGeometric.safe_rotate(image, 45, interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101)
expected_mask = FGeometric.safe_rotate(
mask, 45, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_rotate_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.Rotate(limit=(45, 45), interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = FGeometric.rotate(image, 45, interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101)
expected_mask = FGeometric.rotate(mask, 45, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_shift_scale_rotate_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.ShiftScaleRotate(
shift_limit=(0.2, 0.2), scale_limit=(1.1, 1.1), rotate_limit=(45, 45), interpolation=interpolation, p=1
)
data = aug(image=image, mask=mask)
expected_image = FGeometric.shift_scale_rotate(
image, angle=45, scale=2.1, dx=0.2, dy=0.2, interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101
)
expected_mask = FGeometric.shift_scale_rotate(
mask, angle=45, scale=2.1, dx=0.2, dy=0.2, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_optical_distortion_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.OpticalDistortion(distort_limit=(0.05, 0.05), shift_limit=(0, 0), interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = F.optical_distortion(
image, k=0.05, dx=0, dy=0, interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101
)
expected_mask = F.optical_distortion(
mask, k=0.05, dx=0, dy=0, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_grid_distortion_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.GridDistortion(num_steps=1, distort_limit=(0.3, 0.3), interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = F.grid_distortion(
image, num_steps=1, xsteps=[1.3], ysteps=[1.3], interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101
)
expected_mask = F.grid_distortion(
mask,
num_steps=1,
xsteps=[1.3],
ysteps=[1.3],
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_REFLECT_101,
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("size", [17, 21, 33])
def test_grid_distortion_steps(size):
image = np.random.rand(size, size, 3)
aug = A.GridDistortion(num_steps=size - 2, p=1)
data = aug(image=image)
assert np.array_equal(data["image"].shape, (size, size, 3))
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_elastic_transform_interpolation(monkeypatch, interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
monkeypatch.setattr(
"albumentations.augmentations.geometric.ElasticTransform.get_params", lambda *_: {"random_state": 1111}
)
aug = A.ElasticTransform(alpha=1, sigma=50, alpha_affine=50, interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = FGeometric.elastic_transform(
image,
alpha=1,
sigma=50,
alpha_affine=50,
interpolation=interpolation,
border_mode=cv2.BORDER_REFLECT_101,
random_state=np.random.RandomState(1111),
)
expected_mask = FGeometric.elastic_transform(
mask,
alpha=1,
sigma=50,
alpha_affine=50,
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_REFLECT_101,
random_state=np.random.RandomState(1111),
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize(
["augmentation_cls", "params"],
[
[A.ElasticTransform, {}],
[A.GridDistortion, {}],
[A.ShiftScaleRotate, {"rotate_limit": 45}],
[A.RandomScale, {"scale_limit": 0.5}],
[A.RandomSizedCrop, {"min_max_height": (80, 90), "height": 100, "width": 100}],
[A.LongestMaxSize, {"max_size": 50}],
[A.Rotate, {}],
[A.SafeRotate, {}],
[A.OpticalDistortion, {}],
[A.IAAAffine, {"scale": 1.5}],
[A.IAAPiecewiseAffine, {"scale": 1.5}],
[A.IAAPerspective, {}],
[A.GlassBlur, {}],
[A.Perspective, {}],
[A.Affine, {}],
[A.PiecewiseAffine, {}],
],
)
def test_binary_mask_interpolation(augmentation_cls, params):
"""Checks whether transformations based on DualTransform does not introduce a mask interpolation artifacts"""
aug = augmentation_cls(p=1, **params)
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
data = aug(image=image, mask=mask)
assert np.array_equal(np.unique(data["mask"]), np.array([0, 1]))
@pytest.mark.parametrize(
["augmentation_cls", "params"],
[
[A.ElasticTransform, {}],
[A.GridDistortion, {}],
[A.ShiftScaleRotate, {"rotate_limit": 45}],
[A.RandomScale, {"scale_limit": 0.5}],
[A.RandomSizedCrop, {"min_max_height": (80, 90), "height": 100, "width": 100}],
[A.LongestMaxSize, {"max_size": 50}],
[A.Rotate, {}],
[A.SafeRotate, {}],
[A.Resize, {"height": 80, "width": 90}],
[A.Resize, {"height": 120, "width": 130}],
[A.OpticalDistortion, {}],
[A.GlassBlur, {}],
[A.Perspective, {}],
[A.Affine, {}],
[A.PiecewiseAffine, {}],
],
)
def test_semantic_mask_interpolation(augmentation_cls, params):
"""Checks whether transformations based on DualTransform does not introduce a mask interpolation artifacts.
Note: IAAAffine, IAAPiecewiseAffine, IAAPerspective does not properly operate if mask has values other than {0;1}
"""
aug = augmentation_cls(p=1, **params)
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=4, size=(100, 100), dtype=np.uint8) * 64
data = aug(image=image, mask=mask)
assert np.array_equal(np.unique(data["mask"]), np.array([0, 64, 128, 192]))
def __test_multiprocessing_support_proc(args):
x, transform = args
return transform(image=x)
@pytest.mark.parametrize(
["augmentation_cls", "params"],
[
[A.ElasticTransform, {}],
[A.GridDistortion, {}],
[A.ShiftScaleRotate, {"rotate_limit": 45}],
[A.RandomScale, {"scale_limit": 0.5}],
[A.RandomSizedCrop, {"min_max_height": (80, 90), "height": 100, "width": 100}],
[A.LongestMaxSize, {"max_size": 50}],
[A.Rotate, {}],
[A.SafeRotate, {}],
[A.OpticalDistortion, {}],
[A.IAAAffine, {"scale": 1.5}],
[A.IAAPiecewiseAffine, {"scale": 1.5}],
[A.IAAPerspective, {}],
[A.Sharpen, {}],
[A.FancyPCA, {}],
[A.GlassBlur, {}],
[A.Perspective, {}],
[A.Affine, {}],
[A.PiecewiseAffine, {}],
],
)
def test_multiprocessing_support(augmentation_cls, params, multiprocessing_context):
"""Checks whether we can use augmentations in multiprocessing environments"""
aug = augmentation_cls(p=1, **params)
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
pool = multiprocessing_context.Pool(8)
pool.map(__test_multiprocessing_support_proc, map(lambda x: (x, aug), [image] * 100))
pool.close()
pool.join()
def test_force_apply():
"""
Unit test for https://github.com/albumentations-team/albumentations/issues/189
"""
aug = A.Compose(
[
A.OneOrOther(
A.Compose(
[
A.RandomSizedCrop(min_max_height=(256, 1025), height=512, width=512, p=1),
A.OneOf(
[
A.RandomSizedCrop(min_max_height=(256, 512), height=384, width=384, p=0.5),
A.RandomSizedCrop(min_max_height=(256, 512), height=512, width=512, p=0.5),
]
),
]
),
A.Compose(
[
A.RandomSizedCrop(min_max_height=(256, 1025), height=256, width=256, p=1),
A.OneOf([A.HueSaturationValue(p=0.5), A.RGBShift(p=0.7)], p=1),
]
),
),
A.HorizontalFlip(p=1),
A.RandomBrightnessContrast(p=0.5),
]
)
res = aug(image=np.zeros((1248, 1248, 3), dtype=np.uint8))
assert res["image"].shape[0] in (256, 384, 512)
assert res["image"].shape[1] in (256, 384, 512)
@pytest.mark.parametrize(
["augmentation_cls", "params"],
[
[A.ChannelShuffle, {}],
[A.GaussNoise, {}],
[A.Cutout, {}],
[A.CoarseDropout, {}],
[A.ImageCompression, {}],
[A.HueSaturationValue, {}],
[A.RGBShift, {}],
[A.RandomBrightnessContrast, {}],
[A.Blur, {}],
[A.MotionBlur, {}],
[A.MedianBlur, {}],
[A.CLAHE, {}],
[A.InvertImg, {}],
[A.RandomGamma, {}],
[A.ToGray, {}],
[A.VerticalFlip, {}],
[A.HorizontalFlip, {}],
[A.Flip, {}],
[A.Transpose, {}],
[A.RandomRotate90, {}],
[A.Rotate, {}],
[A.SafeRotate, {}],
[A.OpticalDistortion, {}],
[A.GridDistortion, {}],
[A.ElasticTransform, {}],
[A.Normalize, {}],
[A.ToFloat, {}],
[A.FromFloat, {}],
[A.ChannelDropout, {}],
[A.Solarize, {}],
[A.Posterize, {}],
[A.Equalize, {}],
[A.MultiplicativeNoise, {}],
[A.FancyPCA, {}],
[A.GlassBlur, {}],
[A.GridDropout, {}],
[A.ColorJitter, {}],
[A.Perspective, {}],
[A.Sharpen, {"alpha": [0.2, 0.2], "lightness": [0.5, 0.5]}],
],
)
def test_additional_targets_for_image_only(augmentation_cls, params):
aug = A.Compose([augmentation_cls(always_apply=True, **params)], additional_targets={"image2": "image"})
for _i in range(10):
image1 = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
image2 = image1.copy()
res = aug(image=image1, image2=image2)
aug1 = res["image"]
aug2 = res["image2"]
assert np.array_equal(aug1, aug2)
def test_lambda_transform():
def negate_image(image, **kwargs):
return -image
def one_hot_mask(mask, num_channels, **kwargs):
new_mask = np.eye(num_channels, dtype=np.uint8)[mask]
return new_mask
def vflip_bbox(bbox, **kwargs):
return F.bbox_vflip(bbox, **kwargs)
def vflip_keypoint(keypoint, **kwargs):
return F.keypoint_vflip(keypoint, **kwargs)
aug = A.Lambda(
image=negate_image, mask=partial(one_hot_mask, num_channels=16), bbox=vflip_bbox, keypoint=vflip_keypoint, p=1
)
output = aug(
image=np.ones((10, 10, 3), dtype=np.float32),
mask=np.tile(np.arange(0, 10), (10, 1)),
bboxes=[(10, 15, 25, 35)],
keypoints=[(20, 30, 40, 50)],
)
assert (output["image"] < 0).all()
assert output["mask"].shape[2] == 16 # num_channels
assert output["bboxes"] == [F.bbox_vflip((10, 15, 25, 35), 10, 10)]
assert output["keypoints"] == [F.keypoint_vflip((20, 30, 40, 50), 10, 10)]
def test_channel_droput():
img = np.ones((10, 10, 3), dtype=np.float32)
aug = A.ChannelDropout(channel_drop_range=(1, 1), always_apply=True) # Drop one channel
transformed = aug(image=img)["image"]
assert sum(transformed[:, :, c].max() for c in range(img.shape[2])) == 2
aug = A.ChannelDropout(channel_drop_range=(2, 2), always_apply=True) # Drop two channels
transformed = aug(image=img)["image"]
assert sum(transformed[:, :, c].max() for c in range(img.shape[2])) == 1
def test_equalize():
aug = A.Equalize(p=1)
img = np.random.randint(0, 256, 256 * 256 * 3, np.uint8).reshape((256, 256, 3))
a = aug(image=img)["image"]
b = F.equalize(img)
assert np.all(a == b)
mask = np.random.randint(0, 2, 256 * 256, np.uint8).reshape((256, 256))
aug = A.Equalize(mask=mask, p=1)
a = aug(image=img)["image"]
b = F.equalize(img, mask=mask)
assert np.all(a == b)
def mask_func(image, test): # skipcq: PYL-W0613
return mask
aug = A.Equalize(mask=mask_func, mask_params=["test"], p=1)
assert np.all(aug(image=img, test=mask)["image"] == F.equalize(img, mask=mask))
def test_crop_non_empty_mask():
def _test_crop(mask, crop, aug, n=1):
for _ in range(n):
augmented = aug(image=mask, mask=mask)
np.testing.assert_array_equal(augmented["image"], crop)
np.testing.assert_array_equal(augmented["mask"], crop)
# test general case
mask_1 = np.zeros([10, 10])
mask_1[0, 0] = 1
crop_1 = np.array([[1]])
aug_1 = A.CropNonEmptyMaskIfExists(1, 1)
# test empty mask
mask_2 = np.zeros([10, 10])
crop_2 = np.array([[0]])
aug_2 = A.CropNonEmptyMaskIfExists(1, 1)
# test ignore values
mask_3 = np.ones([2, 2])
mask_3[0, 0] = 2
crop_3 = np.array([[2]])
aug_3 = A.CropNonEmptyMaskIfExists(1, 1, ignore_values=[1])
# test ignore channels
mask_4 = np.zeros([2, 2, 2])
mask_4[0, 0, 0] = 1
mask_4[1, 1, 1] = 2
crop_4 = np.array([[[1, 0]]])
aug_4 = A.CropNonEmptyMaskIfExists(1, 1, ignore_channels=[1])
# test full size crop
mask_5 = np.random.random([10, 10, 3])
crop_5 = mask_5
aug_5 = A.CropNonEmptyMaskIfExists(10, 10)
mask_6 = np.zeros([10, 10, 3])
mask_6[0, 0, 0] = 0
crop_6 = mask_6
aug_6 = A.CropNonEmptyMaskIfExists(10, 10, ignore_values=[1])
_test_crop(mask_1, crop_1, aug_1, n=1)
_test_crop(mask_2, crop_2, aug_2, n=1)
_test_crop(mask_3, crop_3, aug_3, n=5)
_test_crop(mask_4, crop_4, aug_4, n=5)
_test_crop(mask_5, crop_5, aug_5, n=1)
_test_crop(mask_6, crop_6, aug_6, n=10)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_downscale(interpolation):
img_float = np.random.rand(100, 100, 3)
img_uint = (img_float * 255).astype("uint8")
aug = A.Downscale(scale_min=0.5, scale_max=0.5, interpolation=interpolation, always_apply=True)
for img in (img_float, img_uint):
transformed = aug(image=img)["image"]
func_applied = F.downscale(img, scale=0.5, interpolation=interpolation)
np.testing.assert_almost_equal(transformed, func_applied)
def test_crop_keypoints():
image = np.random.randint(0, 256, (100, 100), np.uint8)
keypoints = [(50, 50, 0, 0)]
aug = A.Crop(0, 0, 80, 80, p=1)
result = aug(image=image, keypoints=keypoints)
assert result["keypoints"] == keypoints
aug = A.Crop(50, 50, 100, 100, p=1)
result = aug(image=image, keypoints=keypoints)
assert result["keypoints"] == [(0, 0, 0, 0)]
def test_longest_max_size_keypoints():
img = np.random.randint(0, 256, [50, 10], np.uint8)
keypoints = [(9, 5, 0, 0)]
aug = A.LongestMaxSize(max_size=100, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(18, 10, 0, 0)]
aug = A.LongestMaxSize(max_size=5, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(0.9, 0.5, 0, 0)]
aug = A.LongestMaxSize(max_size=50, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(9, 5, 0, 0)]
def test_smallest_max_size_keypoints():
img = np.random.randint(0, 256, [50, 10], np.uint8)
keypoints = [(9, 5, 0, 0)]
aug = A.SmallestMaxSize(max_size=100, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(90, 50, 0, 0)]
aug = A.SmallestMaxSize(max_size=5, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(4.5, 2.5, 0, 0)]
aug = A.SmallestMaxSize(max_size=10, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(9, 5, 0, 0)]
def test_resize_keypoints():
img = np.random.randint(0, 256, [50, 10], np.uint8)
keypoints = [(9, 5, 0, 0)]
aug = A.Resize(height=100, width=5, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(4.5, 10, 0, 0)]
aug = A.Resize(height=50, width=10, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(9, 5, 0, 0)]
@pytest.mark.parametrize(
"image",
[
np.random.randint(0, 256, [256, 320], np.uint8),
np.random.random([256, 320]).astype(np.float32),
np.random.randint(0, 256, [256, 320, 1], np.uint8),
np.random.random([256, 320, 1]).astype(np.float32),
],
)
def test_multiplicative_noise_grayscale(image):
m = 0.5
aug = A.MultiplicativeNoise(m, p=1)
result = aug(image=image)["image"]
image = F.clip(image * m, image.dtype, F.MAX_VALUES_BY_DTYPE[image.dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(elementwise=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == image.shape
result = aug.apply(image, mul)
dtype = image.dtype
image = image.astype(np.float32) * mul
image = F.clip(image, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
@pytest.mark.parametrize(
"image", [np.random.randint(0, 256, [256, 320, 3], np.uint8), np.random.random([256, 320, 3]).astype(np.float32)]
)
def test_multiplicative_noise_rgb(image):
dtype = image.dtype
m = 0.5
aug = A.MultiplicativeNoise(m, p=1)
result = aug(image=image)["image"]
image = F.clip(image * m, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(elementwise=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == image.shape[:2] + (1,)
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(per_channel=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == (3,)
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(elementwise=True, per_channel=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == image.shape
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, image.dtype, F.MAX_VALUES_BY_DTYPE[image.dtype])
assert np.allclose(image, result)
def test_mask_dropout():
# In this case we have mask with all ones, so MaskDropout wipe entire mask and image
img = np.random.randint(0, 256, [50, 10], np.uint8)
mask = np.ones([50, 10], dtype=np.long)
aug = A.MaskDropout(p=1)
result = aug(image=img, mask=mask)
assert np.all(result["image"] == 0)
assert np.all(result["mask"] == 0)
# In this case we have mask with zeros , so MaskDropout will make no changes
img = np.random.randint(0, 256, [50, 10], np.uint8)
mask = np.zeros([50, 10], dtype=np.long)
aug = A.MaskDropout(p=1)
result = aug(image=img, mask=mask)
assert np.all(result["image"] == img)
assert np.all(result["mask"] == 0)
@pytest.mark.parametrize(
"image", [np.random.randint(0, 256, [256, 320, 3], np.uint8), np.random.random([256, 320, 3]).astype(np.float32)]
)
def test_grid_dropout_mask(image):
mask = np.ones([256, 320], dtype=np.uint8)
aug = A.GridDropout(p=1, mask_fill_value=0)
result = aug(image=image, mask=mask)
# with mask on ones and fill_value = 0 the sum of pixels is smaller
assert result["image"].sum() < image.sum()
assert result["image"].shape == image.shape
assert result["mask"].sum() < mask.sum()
assert result["mask"].shape == mask.shape
# with mask of zeros and fill_value = 0 mask should not change
mask = np.zeros([256, 320], dtype=np.uint8)
aug = A.GridDropout(p=1, mask_fill_value=0)
result = aug(image=image, mask=mask)
assert result["image"].sum() < image.sum()
assert np.all(result["mask"] == 0)
# with mask mask_fill_value=100, mask sum is larger
mask = np.random.randint(0, 10, [256, 320], np.uint8)
aug = A.GridDropout(p=1, mask_fill_value=100)
result = aug(image=image, mask=mask)
assert result["image"].sum() < image.sum()
assert result["mask"].sum() > mask.sum()
# with mask mask_fill_value=None, mask is not changed
mask = np.ones([256, 320], dtype=np.uint8)
aug = A.GridDropout(p=1, mask_fill_value=None)
result = aug(image=image, mask=mask)
assert result["image"].sum() < image.sum()
assert result["mask"].sum() == mask.sum()
@pytest.mark.parametrize(
["ratio", "holes_number_x", "holes_number_y", "unit_size_min", "unit_size_max", "shift_x", "shift_y"],
[
(0.00001, 10, 10, 100, 100, 50, 50),
(0.9, 100, None, 200, None, 0, 0),
(0.4556, 10, 20, None, 200, 0, 0),
(0.00004, None, None, 2, 100, None, None),
],
)
def test_grid_dropout_params(ratio, holes_number_x, holes_number_y, unit_size_min, unit_size_max, shift_x, shift_y):
img = np.random.randint(0, 256, [256, 320], np.uint8)
aug = A.GridDropout(
ratio=ratio,
unit_size_min=unit_size_min,
unit_size_max=unit_size_max,
holes_number_x=holes_number_x,
holes_number_y=holes_number_y,
shift_x=shift_x,
shift_y=shift_y,
random_offset=False,
fill_value=0,
p=1,
)
result = aug(image=img)["image"]
# with fill_value = 0 the sum of pixels is smaller
assert result.sum() < img.sum()
assert result.shape == img.shape
params = aug.get_params_dependent_on_targets({"image": img})
holes = params["holes"]
assert len(holes[0]) == 4
# check grid offsets
if shift_x:
assert holes[0][0] == shift_x
else:
assert holes[0][0] == 0
if shift_y:
assert holes[0][1] == shift_y
else:
assert holes[0][1] == 0
# for grid set with limits
if unit_size_min and unit_size_max:
assert max(1, unit_size_min * ratio) <= (holes[0][2] - holes[0][0]) <= min(max(1, unit_size_max * ratio), 256)
elif holes_number_x and holes_number_y:
assert (holes[0][2] - holes[0][0]) == max(1, int(ratio * 320 // holes_number_x))
assert (holes[0][3] - holes[0][1]) == max(1, int(ratio * 256 // holes_number_y))
def test_gauss_noise_incorrect_var_limit_type():
with pytest.raises(TypeError) as exc_info:
A.GaussNoise(var_limit={"low": 70, "high": 90})
message = "Expected var_limit type to be one of (int, float, tuple, list), got <class 'dict'>"
assert str(exc_info.value) == message
@pytest.mark.parametrize(
["blur_limit", "sigma", "result_blur", "result_sigma"],
[
[[0, 0], [1, 1], 0, 1],
[[1, 1], [0, 0], 1, 0],
[[1, 1], [1, 1], 1, 1],
[[0, 0], [0, 0], 3, 0],
[[0, 3], [0, 0], 3, 0],
[[0, 3], [0.1, 0.1], 3, 0.1],
],
)
def test_gaus_blur_limits(blur_limit, sigma, result_blur, result_sigma):
img = np.zeros([100, 100, 3], dtype=np.uint8)
aug = A.Compose([A.GaussianBlur(blur_limit=blur_limit, sigma_limit=sigma, p=1)])
res = aug(image=img)["image"]
assert np.allclose(res, F.gaussian_blur(img, result_blur, result_sigma))
@pytest.mark.parametrize(
["brightness", "contrast", "saturation", "hue"],
[
[1, 1, 1, 0],
[0.123, 1, 1, 0],
[1.321, 1, 1, 0],
[1, 0.234, 1, 0],
[1, 1.432, 1, 0],
[1, 1, 0.345, 0],
[1, 1, 1.543, 0],
],
)
def test_color_jitter(brightness, contrast, saturation, hue):
np.random.seed(0)
img = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
pil_image = Image.fromarray(img)
transform = A.Compose(
[
A.ColorJitter(
brightness=[brightness, brightness],
contrast=[contrast, contrast],
saturation=[saturation, saturation],
hue=[hue, hue],
p=1,
)
]
)
pil_transform = ColorJitter(
brightness=[brightness, brightness],
contrast=[contrast, contrast],
saturation=[saturation, saturation],
hue=[hue, hue],
)
res1 = transform(image=img)["image"]
res2 = np.array(pil_transform(pil_image))
_max = np.abs(res1.astype(np.int16) - res2.astype(np.int16)).max()
assert _max <= 2, "Max: {}".format(_max)
@pytest.mark.parametrize(
["brightness", "contrast", "saturation", "hue"],
[
[1, 1, 1, 0],
[0.123, 1, 1, 0],
[1.321, 1, 1, 0],
[1, 0.234, 1, 0],
[1, 1.432, 1, 0],
[1, 1, 0.345, 0],
[1, 1, 1.543, 0],
[1, 1, 1, 0.456],
[1, 1, 1, -0.432],
],
)
def test_color_jitter_float_uint8_equal(brightness, contrast, saturation, hue):
img = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
transform = A.Compose(
[
A.ColorJitter(
brightness=[brightness, brightness],
contrast=[contrast, contrast],
saturation=[saturation, saturation],
hue=[hue, hue],
p=1,
)
]
)
res1 = transform(image=img)["image"]
res2 = (transform(image=img.astype(np.float32) / 255.0)["image"] * 255).astype(np.uint8)
_max = np.abs(res1.astype(np.int16) - res2.astype(np.int16)).max()
if hue != 0:
assert _max <= 10, "Max: {}".format(_max)
else:
assert _max <= 2, "Max: {}".format(_max)
@pytest.mark.parametrize(["hue", "sat", "val"], [[13, 17, 23], [14, 18, 24], [131, 143, 151], [132, 144, 152]])
def test_hue_saturation_value_float_uint8_equal(hue, sat, val):
img = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
for i in range(2):
sign = 1 if i == 0 else -1
for i in range(4):
if i == 0:
_hue = hue * sign
_sat = 0
_val = 0
elif i == 1:
_hue = 0
_sat = sat * sign
_val = 0
elif i == 2:
_hue = 0
_sat = 0
_val = val * sign
else:
_hue = hue * sign
_sat = sat * sign
_val = val * sign
t1 = A.Compose(
[
A.HueSaturationValue(
hue_shift_limit=[_hue, _hue], sat_shift_limit=[_sat, _sat], val_shift_limit=[_val, _val], p=1
)
]
)
t2 = A.Compose(
[
A.HueSaturationValue(
hue_shift_limit=[_hue / 180 * 360, _hue / 180 * 360],
sat_shift_limit=[_sat / 255, _sat / 255],
val_shift_limit=[_val / 255, _val / 255],
p=1,
)
]
)
res1 = t1(image=img)["image"]
res2 = (t2(image=img.astype(np.float32) / 255.0)["image"] * 255).astype(np.uint8)
_max = np.abs(res1.astype(np.int) - res2).max()
assert _max <= 10, "Max value: {}".format(_max)
def test_shift_scale_separate_shift_x_shift_y(image, mask):
aug = A.ShiftScaleRotate(shift_limit=(0.3, 0.3), shift_limit_y=(0.4, 0.4), scale_limit=0, rotate_limit=0, p=1)
data = aug(image=image, mask=mask)
expected_image = FGeometric.shift_scale_rotate(
image, angle=0, scale=1, dx=0.3, dy=0.4, interpolation=cv2.INTER_LINEAR, border_mode=cv2.BORDER_REFLECT_101
)
expected_mask = FGeometric.shift_scale_rotate(
mask, angle=0, scale=1, dx=0.3, dy=0.4, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize(["val_uint8"], [[0], [1], [128], [255]])
def test_glass_blur_float_uint8_diff_less_than_two(val_uint8):
x_uint8 = np.zeros((5, 5)).astype(np.uint8)
x_uint8[2, 2] = val_uint8
x_float32 = np.zeros((5, 5)).astype(np.float32)
x_float32[2, 2] = val_uint8 / 255.0
glassblur = A.GlassBlur(always_apply=True, max_delta=1)
np.random.seed(0)
blur_uint8 = glassblur(image=x_uint8)["image"]
np.random.seed(0)
blur_float32 = glassblur(image=x_float32)["image"]
# Before comparison, rescale the blur_float32 to [0, 255]
diff = np.abs(blur_uint8 - blur_float32 * 255)
# The difference between the results of float32 and uint8 will be at most 2.
assert np.all(diff <= 2.0)
@pytest.mark.parametrize(
["img_dtype", "px", "percent", "pad_mode", "pad_cval", "keep_size"],
[
[np.uint8, 10, None, cv2.BORDER_CONSTANT, 0, True],
[np.uint8, -10, None, cv2.BORDER_CONSTANT, 0, True],
[np.uint8, 10, None, cv2.BORDER_CONSTANT, 0, False],
[np.uint8, -10, None, cv2.BORDER_CONSTANT, 0, False],
[np.uint8, None, 0.1, cv2.BORDER_CONSTANT, 0, True],
[np.uint8, None, -0.1, cv2.BORDER_CONSTANT, 0, True],
[np.uint8, None, 0.1, cv2.BORDER_CONSTANT, 0, False],
[np.uint8, None, -0.1, cv2.BORDER_CONSTANT, 0, False],
[np.float32, None, 0.1, cv2.BORDER_CONSTANT, 0, False],
[np.float32, None, -0.1, cv2.BORDER_CONSTANT, 0, False],
[np.uint8, None, 0.1, cv2.BORDER_WRAP, 0, False],
[np.uint8, None, 0.1, cv2.BORDER_REPLICATE, 0, False],
[np.uint8, None, 0.1, cv2.BORDER_REFLECT101, 0, False],
],
)
def test_compare_crop_and_pad(img_dtype, px, percent, pad_mode, pad_cval, keep_size):
h, w, c = 100, 100, 3
mode_mapping = {
cv2.BORDER_CONSTANT: "constant",
cv2.BORDER_REPLICATE: "edge",
cv2.BORDER_REFLECT101: "reflect",
cv2.BORDER_WRAP: "wrap",
}
pad_mode_iaa = mode_mapping[pad_mode]
bbox_params = A.BboxParams(format="pascal_voc")
keypoint_params = A.KeypointParams(format="xy", remove_invisible=False)
keypoints = np.random.randint(0, min(h, w), [10, 2])
bboxes = []
for i in range(10):
x1, y1 = np.random.randint(0, min(h, w) - 2, 2)
x2 = np.random.randint(x1 + 1, w - 1)
y2 = np.random.randint(y1 + 1, h - 1)
bboxes.append([x1, y1, x2, y2, 0])
transform_albu = A.Compose(
[
A.CropAndPad(
px=px,
percent=percent,
pad_mode=pad_mode,
pad_cval=pad_cval,
keep_size=keep_size,
p=1,
interpolation=cv2.INTER_AREA
if (px is not None and px < 0) or (percent is not None and percent < 0)
else cv2.INTER_LINEAR,
)
],
bbox_params=bbox_params,
keypoint_params=keypoint_params,
)
transform_iaa = A.Compose(
[A.IAACropAndPad(px=px, percent=percent, pad_mode=pad_mode_iaa, pad_cval=pad_cval, keep_size=keep_size, p=1)],
bbox_params=bbox_params,
keypoint_params=keypoint_params,
)
if img_dtype == np.uint8:
img = np.random.randint(0, 256, (h, w, c), dtype=np.uint8)
else:
img = np.random.random((h, w, c)).astype(img_dtype)
res_albu = transform_albu(image=img, keypoints=keypoints, bboxes=bboxes)
res_iaa = transform_iaa(image=img, keypoints=keypoints, bboxes=bboxes)
for key, item in res_albu.items():
if key == "bboxes":
bboxes = np.array(res_iaa[key])
h = bboxes[:, 3] - bboxes[:, 1]
w = bboxes[:, 2] - bboxes[:, 0]
res_iaa[key] = bboxes[(h > 0) & (w > 0)]
assert np.allclose(item, res_iaa[key]), f"{key} are not equal"
def test_perspective_keep_size():
h, w = 100, 100
img = np.zeros([h, w, 3], dtype=np.uint8)
h, w = img.shape[:2]
bboxes = []
for _ in range(10):
x1 = np.random.randint(0, w - 1)
y1 = np.random.randint(0, h - 1)
x2 = np.random.randint(x1 + 1, w)
y2 = np.random.randint(y1 + 1, h)
bboxes.append([x1, y1, x2, y2])
keypoints = [(np.random.randint(0, w), np.random.randint(0, h), np.random.random()) for _ in range(10)]
transform_1 = A.Compose(
[A.Perspective(keep_size=True, p=1)],
keypoint_params=A.KeypointParams("xys"),
bbox_params=A.BboxParams("pascal_voc", label_fields=["labels"]),
)
transform_2 = A.Compose(
[A.Perspective(keep_size=False, p=1), A.Resize(h, w)],
keypoint_params=A.KeypointParams("xys"),
bbox_params=A.BboxParams("pascal_voc", label_fields=["labels"]),
)
set_seed()
res_1 = transform_1(image=img, bboxes=bboxes, keypoints=keypoints, labels=[0] * len(bboxes))
set_seed()
res_2 = transform_2(image=img, bboxes=bboxes, keypoints=keypoints, labels=[0] * len(bboxes))
assert np.allclose(res_1["bboxes"], res_2["bboxes"])
assert np.allclose(res_1["keypoints"], res_2["keypoints"])
| 36.291127 | 119 | 0.607088 | from functools import partial
import cv2
import numpy as np
import pytest
import random
import albumentations as A
import albumentations.augmentations.functional as F
import albumentations.augmentations.geometric.functional as FGeometric
from torchvision.transforms import ColorJitter
from PIL import Image
def set_seed(seed=0):
random.seed(seed)
np.random.seed(seed)
def test_transpose_both_image_and_mask():
image = np.ones((8, 6, 3))
mask = np.ones((8, 6))
augmentation = A.Transpose(p=1)
augmented = augmentation(image=image, mask=mask)
assert augmented["image"].shape == (6, 8, 3)
assert augmented["mask"].shape == (6, 8)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_safe_rotate_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.SafeRotate(limit=(45, 45), interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = FGeometric.safe_rotate(image, 45, interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101)
expected_mask = FGeometric.safe_rotate(
mask, 45, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_rotate_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.Rotate(limit=(45, 45), interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = FGeometric.rotate(image, 45, interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101)
expected_mask = FGeometric.rotate(mask, 45, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_shift_scale_rotate_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.ShiftScaleRotate(
shift_limit=(0.2, 0.2), scale_limit=(1.1, 1.1), rotate_limit=(45, 45), interpolation=interpolation, p=1
)
data = aug(image=image, mask=mask)
expected_image = FGeometric.shift_scale_rotate(
image, angle=45, scale=2.1, dx=0.2, dy=0.2, interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101
)
expected_mask = FGeometric.shift_scale_rotate(
mask, angle=45, scale=2.1, dx=0.2, dy=0.2, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_optical_distortion_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.OpticalDistortion(distort_limit=(0.05, 0.05), shift_limit=(0, 0), interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = F.optical_distortion(
image, k=0.05, dx=0, dy=0, interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101
)
expected_mask = F.optical_distortion(
mask, k=0.05, dx=0, dy=0, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_grid_distortion_interpolation(interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.GridDistortion(num_steps=1, distort_limit=(0.3, 0.3), interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = F.grid_distortion(
image, num_steps=1, xsteps=[1.3], ysteps=[1.3], interpolation=interpolation, border_mode=cv2.BORDER_REFLECT_101
)
expected_mask = F.grid_distortion(
mask,
num_steps=1,
xsteps=[1.3],
ysteps=[1.3],
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_REFLECT_101,
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize("size", [17, 21, 33])
def test_grid_distortion_steps(size):
image = np.random.rand(size, size, 3)
aug = A.GridDistortion(num_steps=size - 2, p=1)
data = aug(image=image)
assert np.array_equal(data["image"].shape, (size, size, 3))
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_elastic_transform_interpolation(monkeypatch, interpolation):
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
monkeypatch.setattr(
"albumentations.augmentations.geometric.ElasticTransform.get_params", lambda *_: {"random_state": 1111}
)
aug = A.ElasticTransform(alpha=1, sigma=50, alpha_affine=50, interpolation=interpolation, p=1)
data = aug(image=image, mask=mask)
expected_image = FGeometric.elastic_transform(
image,
alpha=1,
sigma=50,
alpha_affine=50,
interpolation=interpolation,
border_mode=cv2.BORDER_REFLECT_101,
random_state=np.random.RandomState(1111),
)
expected_mask = FGeometric.elastic_transform(
mask,
alpha=1,
sigma=50,
alpha_affine=50,
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_REFLECT_101,
random_state=np.random.RandomState(1111),
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize(
["augmentation_cls", "params"],
[
[A.ElasticTransform, {}],
[A.GridDistortion, {}],
[A.ShiftScaleRotate, {"rotate_limit": 45}],
[A.RandomScale, {"scale_limit": 0.5}],
[A.RandomSizedCrop, {"min_max_height": (80, 90), "height": 100, "width": 100}],
[A.LongestMaxSize, {"max_size": 50}],
[A.Rotate, {}],
[A.SafeRotate, {}],
[A.OpticalDistortion, {}],
[A.IAAAffine, {"scale": 1.5}],
[A.IAAPiecewiseAffine, {"scale": 1.5}],
[A.IAAPerspective, {}],
[A.GlassBlur, {}],
[A.Perspective, {}],
[A.Affine, {}],
[A.PiecewiseAffine, {}],
],
)
def test_binary_mask_interpolation(augmentation_cls, params):
aug = augmentation_cls(p=1, **params)
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
data = aug(image=image, mask=mask)
assert np.array_equal(np.unique(data["mask"]), np.array([0, 1]))
@pytest.mark.parametrize(
["augmentation_cls", "params"],
[
[A.ElasticTransform, {}],
[A.GridDistortion, {}],
[A.ShiftScaleRotate, {"rotate_limit": 45}],
[A.RandomScale, {"scale_limit": 0.5}],
[A.RandomSizedCrop, {"min_max_height": (80, 90), "height": 100, "width": 100}],
[A.LongestMaxSize, {"max_size": 50}],
[A.Rotate, {}],
[A.SafeRotate, {}],
[A.Resize, {"height": 80, "width": 90}],
[A.Resize, {"height": 120, "width": 130}],
[A.OpticalDistortion, {}],
[A.GlassBlur, {}],
[A.Perspective, {}],
[A.Affine, {}],
[A.PiecewiseAffine, {}],
],
)
def test_semantic_mask_interpolation(augmentation_cls, params):
aug = augmentation_cls(p=1, **params)
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
mask = np.random.randint(low=0, high=4, size=(100, 100), dtype=np.uint8) * 64
data = aug(image=image, mask=mask)
assert np.array_equal(np.unique(data["mask"]), np.array([0, 64, 128, 192]))
def __test_multiprocessing_support_proc(args):
x, transform = args
return transform(image=x)
@pytest.mark.parametrize(
["augmentation_cls", "params"],
[
[A.ElasticTransform, {}],
[A.GridDistortion, {}],
[A.ShiftScaleRotate, {"rotate_limit": 45}],
[A.RandomScale, {"scale_limit": 0.5}],
[A.RandomSizedCrop, {"min_max_height": (80, 90), "height": 100, "width": 100}],
[A.LongestMaxSize, {"max_size": 50}],
[A.Rotate, {}],
[A.SafeRotate, {}],
[A.OpticalDistortion, {}],
[A.IAAAffine, {"scale": 1.5}],
[A.IAAPiecewiseAffine, {"scale": 1.5}],
[A.IAAPerspective, {}],
[A.Sharpen, {}],
[A.FancyPCA, {}],
[A.GlassBlur, {}],
[A.Perspective, {}],
[A.Affine, {}],
[A.PiecewiseAffine, {}],
],
)
def test_multiprocessing_support(augmentation_cls, params, multiprocessing_context):
aug = augmentation_cls(p=1, **params)
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
pool = multiprocessing_context.Pool(8)
pool.map(__test_multiprocessing_support_proc, map(lambda x: (x, aug), [image] * 100))
pool.close()
pool.join()
def test_force_apply():
aug = A.Compose(
[
A.OneOrOther(
A.Compose(
[
A.RandomSizedCrop(min_max_height=(256, 1025), height=512, width=512, p=1),
A.OneOf(
[
A.RandomSizedCrop(min_max_height=(256, 512), height=384, width=384, p=0.5),
A.RandomSizedCrop(min_max_height=(256, 512), height=512, width=512, p=0.5),
]
),
]
),
A.Compose(
[
A.RandomSizedCrop(min_max_height=(256, 1025), height=256, width=256, p=1),
A.OneOf([A.HueSaturationValue(p=0.5), A.RGBShift(p=0.7)], p=1),
]
),
),
A.HorizontalFlip(p=1),
A.RandomBrightnessContrast(p=0.5),
]
)
res = aug(image=np.zeros((1248, 1248, 3), dtype=np.uint8))
assert res["image"].shape[0] in (256, 384, 512)
assert res["image"].shape[1] in (256, 384, 512)
@pytest.mark.parametrize(
["augmentation_cls", "params"],
[
[A.ChannelShuffle, {}],
[A.GaussNoise, {}],
[A.Cutout, {}],
[A.CoarseDropout, {}],
[A.ImageCompression, {}],
[A.HueSaturationValue, {}],
[A.RGBShift, {}],
[A.RandomBrightnessContrast, {}],
[A.Blur, {}],
[A.MotionBlur, {}],
[A.MedianBlur, {}],
[A.CLAHE, {}],
[A.InvertImg, {}],
[A.RandomGamma, {}],
[A.ToGray, {}],
[A.VerticalFlip, {}],
[A.HorizontalFlip, {}],
[A.Flip, {}],
[A.Transpose, {}],
[A.RandomRotate90, {}],
[A.Rotate, {}],
[A.SafeRotate, {}],
[A.OpticalDistortion, {}],
[A.GridDistortion, {}],
[A.ElasticTransform, {}],
[A.Normalize, {}],
[A.ToFloat, {}],
[A.FromFloat, {}],
[A.ChannelDropout, {}],
[A.Solarize, {}],
[A.Posterize, {}],
[A.Equalize, {}],
[A.MultiplicativeNoise, {}],
[A.FancyPCA, {}],
[A.GlassBlur, {}],
[A.GridDropout, {}],
[A.ColorJitter, {}],
[A.Perspective, {}],
[A.Sharpen, {"alpha": [0.2, 0.2], "lightness": [0.5, 0.5]}],
],
)
def test_additional_targets_for_image_only(augmentation_cls, params):
aug = A.Compose([augmentation_cls(always_apply=True, **params)], additional_targets={"image2": "image"})
for _i in range(10):
image1 = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
image2 = image1.copy()
res = aug(image=image1, image2=image2)
aug1 = res["image"]
aug2 = res["image2"]
assert np.array_equal(aug1, aug2)
def test_lambda_transform():
def negate_image(image, **kwargs):
return -image
def one_hot_mask(mask, num_channels, **kwargs):
new_mask = np.eye(num_channels, dtype=np.uint8)[mask]
return new_mask
def vflip_bbox(bbox, **kwargs):
return F.bbox_vflip(bbox, **kwargs)
def vflip_keypoint(keypoint, **kwargs):
return F.keypoint_vflip(keypoint, **kwargs)
aug = A.Lambda(
image=negate_image, mask=partial(one_hot_mask, num_channels=16), bbox=vflip_bbox, keypoint=vflip_keypoint, p=1
)
output = aug(
image=np.ones((10, 10, 3), dtype=np.float32),
mask=np.tile(np.arange(0, 10), (10, 1)),
bboxes=[(10, 15, 25, 35)],
keypoints=[(20, 30, 40, 50)],
)
assert (output["image"] < 0).all()
assert output["mask"].shape[2] == 16
assert output["bboxes"] == [F.bbox_vflip((10, 15, 25, 35), 10, 10)]
assert output["keypoints"] == [F.keypoint_vflip((20, 30, 40, 50), 10, 10)]
def test_channel_droput():
img = np.ones((10, 10, 3), dtype=np.float32)
aug = A.ChannelDropout(channel_drop_range=(1, 1), always_apply=True)
transformed = aug(image=img)["image"]
assert sum(transformed[:, :, c].max() for c in range(img.shape[2])) == 2
aug = A.ChannelDropout(channel_drop_range=(2, 2), always_apply=True)
transformed = aug(image=img)["image"]
assert sum(transformed[:, :, c].max() for c in range(img.shape[2])) == 1
def test_equalize():
aug = A.Equalize(p=1)
img = np.random.randint(0, 256, 256 * 256 * 3, np.uint8).reshape((256, 256, 3))
a = aug(image=img)["image"]
b = F.equalize(img)
assert np.all(a == b)
mask = np.random.randint(0, 2, 256 * 256, np.uint8).reshape((256, 256))
aug = A.Equalize(mask=mask, p=1)
a = aug(image=img)["image"]
b = F.equalize(img, mask=mask)
assert np.all(a == b)
def mask_func(image, test):
return mask
aug = A.Equalize(mask=mask_func, mask_params=["test"], p=1)
assert np.all(aug(image=img, test=mask)["image"] == F.equalize(img, mask=mask))
def test_crop_non_empty_mask():
def _test_crop(mask, crop, aug, n=1):
for _ in range(n):
augmented = aug(image=mask, mask=mask)
np.testing.assert_array_equal(augmented["image"], crop)
np.testing.assert_array_equal(augmented["mask"], crop)
mask_1 = np.zeros([10, 10])
mask_1[0, 0] = 1
crop_1 = np.array([[1]])
aug_1 = A.CropNonEmptyMaskIfExists(1, 1)
mask_2 = np.zeros([10, 10])
crop_2 = np.array([[0]])
aug_2 = A.CropNonEmptyMaskIfExists(1, 1)
mask_3 = np.ones([2, 2])
mask_3[0, 0] = 2
crop_3 = np.array([[2]])
aug_3 = A.CropNonEmptyMaskIfExists(1, 1, ignore_values=[1])
mask_4 = np.zeros([2, 2, 2])
mask_4[0, 0, 0] = 1
mask_4[1, 1, 1] = 2
crop_4 = np.array([[[1, 0]]])
aug_4 = A.CropNonEmptyMaskIfExists(1, 1, ignore_channels=[1])
mask_5 = np.random.random([10, 10, 3])
crop_5 = mask_5
aug_5 = A.CropNonEmptyMaskIfExists(10, 10)
mask_6 = np.zeros([10, 10, 3])
mask_6[0, 0, 0] = 0
crop_6 = mask_6
aug_6 = A.CropNonEmptyMaskIfExists(10, 10, ignore_values=[1])
_test_crop(mask_1, crop_1, aug_1, n=1)
_test_crop(mask_2, crop_2, aug_2, n=1)
_test_crop(mask_3, crop_3, aug_3, n=5)
_test_crop(mask_4, crop_4, aug_4, n=5)
_test_crop(mask_5, crop_5, aug_5, n=1)
_test_crop(mask_6, crop_6, aug_6, n=10)
@pytest.mark.parametrize("interpolation", [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC])
def test_downscale(interpolation):
img_float = np.random.rand(100, 100, 3)
img_uint = (img_float * 255).astype("uint8")
aug = A.Downscale(scale_min=0.5, scale_max=0.5, interpolation=interpolation, always_apply=True)
for img in (img_float, img_uint):
transformed = aug(image=img)["image"]
func_applied = F.downscale(img, scale=0.5, interpolation=interpolation)
np.testing.assert_almost_equal(transformed, func_applied)
def test_crop_keypoints():
image = np.random.randint(0, 256, (100, 100), np.uint8)
keypoints = [(50, 50, 0, 0)]
aug = A.Crop(0, 0, 80, 80, p=1)
result = aug(image=image, keypoints=keypoints)
assert result["keypoints"] == keypoints
aug = A.Crop(50, 50, 100, 100, p=1)
result = aug(image=image, keypoints=keypoints)
assert result["keypoints"] == [(0, 0, 0, 0)]
def test_longest_max_size_keypoints():
img = np.random.randint(0, 256, [50, 10], np.uint8)
keypoints = [(9, 5, 0, 0)]
aug = A.LongestMaxSize(max_size=100, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(18, 10, 0, 0)]
aug = A.LongestMaxSize(max_size=5, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(0.9, 0.5, 0, 0)]
aug = A.LongestMaxSize(max_size=50, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(9, 5, 0, 0)]
def test_smallest_max_size_keypoints():
img = np.random.randint(0, 256, [50, 10], np.uint8)
keypoints = [(9, 5, 0, 0)]
aug = A.SmallestMaxSize(max_size=100, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(90, 50, 0, 0)]
aug = A.SmallestMaxSize(max_size=5, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(4.5, 2.5, 0, 0)]
aug = A.SmallestMaxSize(max_size=10, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(9, 5, 0, 0)]
def test_resize_keypoints():
img = np.random.randint(0, 256, [50, 10], np.uint8)
keypoints = [(9, 5, 0, 0)]
aug = A.Resize(height=100, width=5, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(4.5, 10, 0, 0)]
aug = A.Resize(height=50, width=10, p=1)
result = aug(image=img, keypoints=keypoints)
assert result["keypoints"] == [(9, 5, 0, 0)]
@pytest.mark.parametrize(
"image",
[
np.random.randint(0, 256, [256, 320], np.uint8),
np.random.random([256, 320]).astype(np.float32),
np.random.randint(0, 256, [256, 320, 1], np.uint8),
np.random.random([256, 320, 1]).astype(np.float32),
],
)
def test_multiplicative_noise_grayscale(image):
m = 0.5
aug = A.MultiplicativeNoise(m, p=1)
result = aug(image=image)["image"]
image = F.clip(image * m, image.dtype, F.MAX_VALUES_BY_DTYPE[image.dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(elementwise=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == image.shape
result = aug.apply(image, mul)
dtype = image.dtype
image = image.astype(np.float32) * mul
image = F.clip(image, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
@pytest.mark.parametrize(
"image", [np.random.randint(0, 256, [256, 320, 3], np.uint8), np.random.random([256, 320, 3]).astype(np.float32)]
)
def test_multiplicative_noise_rgb(image):
dtype = image.dtype
m = 0.5
aug = A.MultiplicativeNoise(m, p=1)
result = aug(image=image)["image"]
image = F.clip(image * m, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(elementwise=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == image.shape[:2] + (1,)
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(per_channel=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == (3,)
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(elementwise=True, per_channel=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == image.shape
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, image.dtype, F.MAX_VALUES_BY_DTYPE[image.dtype])
assert np.allclose(image, result)
def test_mask_dropout():
img = np.random.randint(0, 256, [50, 10], np.uint8)
mask = np.ones([50, 10], dtype=np.long)
aug = A.MaskDropout(p=1)
result = aug(image=img, mask=mask)
assert np.all(result["image"] == 0)
assert np.all(result["mask"] == 0)
img = np.random.randint(0, 256, [50, 10], np.uint8)
mask = np.zeros([50, 10], dtype=np.long)
aug = A.MaskDropout(p=1)
result = aug(image=img, mask=mask)
assert np.all(result["image"] == img)
assert np.all(result["mask"] == 0)
@pytest.mark.parametrize(
"image", [np.random.randint(0, 256, [256, 320, 3], np.uint8), np.random.random([256, 320, 3]).astype(np.float32)]
)
def test_grid_dropout_mask(image):
mask = np.ones([256, 320], dtype=np.uint8)
aug = A.GridDropout(p=1, mask_fill_value=0)
result = aug(image=image, mask=mask)
assert result["image"].sum() < image.sum()
assert result["image"].shape == image.shape
assert result["mask"].sum() < mask.sum()
assert result["mask"].shape == mask.shape
mask = np.zeros([256, 320], dtype=np.uint8)
aug = A.GridDropout(p=1, mask_fill_value=0)
result = aug(image=image, mask=mask)
assert result["image"].sum() < image.sum()
assert np.all(result["mask"] == 0)
mask = np.random.randint(0, 10, [256, 320], np.uint8)
aug = A.GridDropout(p=1, mask_fill_value=100)
result = aug(image=image, mask=mask)
assert result["image"].sum() < image.sum()
assert result["mask"].sum() > mask.sum()
mask = np.ones([256, 320], dtype=np.uint8)
aug = A.GridDropout(p=1, mask_fill_value=None)
result = aug(image=image, mask=mask)
assert result["image"].sum() < image.sum()
assert result["mask"].sum() == mask.sum()
@pytest.mark.parametrize(
["ratio", "holes_number_x", "holes_number_y", "unit_size_min", "unit_size_max", "shift_x", "shift_y"],
[
(0.00001, 10, 10, 100, 100, 50, 50),
(0.9, 100, None, 200, None, 0, 0),
(0.4556, 10, 20, None, 200, 0, 0),
(0.00004, None, None, 2, 100, None, None),
],
)
def test_grid_dropout_params(ratio, holes_number_x, holes_number_y, unit_size_min, unit_size_max, shift_x, shift_y):
img = np.random.randint(0, 256, [256, 320], np.uint8)
aug = A.GridDropout(
ratio=ratio,
unit_size_min=unit_size_min,
unit_size_max=unit_size_max,
holes_number_x=holes_number_x,
holes_number_y=holes_number_y,
shift_x=shift_x,
shift_y=shift_y,
random_offset=False,
fill_value=0,
p=1,
)
result = aug(image=img)["image"]
assert result.sum() < img.sum()
assert result.shape == img.shape
params = aug.get_params_dependent_on_targets({"image": img})
holes = params["holes"]
assert len(holes[0]) == 4
if shift_x:
assert holes[0][0] == shift_x
else:
assert holes[0][0] == 0
if shift_y:
assert holes[0][1] == shift_y
else:
assert holes[0][1] == 0
if unit_size_min and unit_size_max:
assert max(1, unit_size_min * ratio) <= (holes[0][2] - holes[0][0]) <= min(max(1, unit_size_max * ratio), 256)
elif holes_number_x and holes_number_y:
assert (holes[0][2] - holes[0][0]) == max(1, int(ratio * 320 // holes_number_x))
assert (holes[0][3] - holes[0][1]) == max(1, int(ratio * 256 // holes_number_y))
def test_gauss_noise_incorrect_var_limit_type():
with pytest.raises(TypeError) as exc_info:
A.GaussNoise(var_limit={"low": 70, "high": 90})
message = "Expected var_limit type to be one of (int, float, tuple, list), got <class 'dict'>"
assert str(exc_info.value) == message
@pytest.mark.parametrize(
["blur_limit", "sigma", "result_blur", "result_sigma"],
[
[[0, 0], [1, 1], 0, 1],
[[1, 1], [0, 0], 1, 0],
[[1, 1], [1, 1], 1, 1],
[[0, 0], [0, 0], 3, 0],
[[0, 3], [0, 0], 3, 0],
[[0, 3], [0.1, 0.1], 3, 0.1],
],
)
def test_gaus_blur_limits(blur_limit, sigma, result_blur, result_sigma):
img = np.zeros([100, 100, 3], dtype=np.uint8)
aug = A.Compose([A.GaussianBlur(blur_limit=blur_limit, sigma_limit=sigma, p=1)])
res = aug(image=img)["image"]
assert np.allclose(res, F.gaussian_blur(img, result_blur, result_sigma))
@pytest.mark.parametrize(
["brightness", "contrast", "saturation", "hue"],
[
[1, 1, 1, 0],
[0.123, 1, 1, 0],
[1.321, 1, 1, 0],
[1, 0.234, 1, 0],
[1, 1.432, 1, 0],
[1, 1, 0.345, 0],
[1, 1, 1.543, 0],
],
)
def test_color_jitter(brightness, contrast, saturation, hue):
np.random.seed(0)
img = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
pil_image = Image.fromarray(img)
transform = A.Compose(
[
A.ColorJitter(
brightness=[brightness, brightness],
contrast=[contrast, contrast],
saturation=[saturation, saturation],
hue=[hue, hue],
p=1,
)
]
)
pil_transform = ColorJitter(
brightness=[brightness, brightness],
contrast=[contrast, contrast],
saturation=[saturation, saturation],
hue=[hue, hue],
)
res1 = transform(image=img)["image"]
res2 = np.array(pil_transform(pil_image))
_max = np.abs(res1.astype(np.int16) - res2.astype(np.int16)).max()
assert _max <= 2, "Max: {}".format(_max)
@pytest.mark.parametrize(
["brightness", "contrast", "saturation", "hue"],
[
[1, 1, 1, 0],
[0.123, 1, 1, 0],
[1.321, 1, 1, 0],
[1, 0.234, 1, 0],
[1, 1.432, 1, 0],
[1, 1, 0.345, 0],
[1, 1, 1.543, 0],
[1, 1, 1, 0.456],
[1, 1, 1, -0.432],
],
)
def test_color_jitter_float_uint8_equal(brightness, contrast, saturation, hue):
img = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
transform = A.Compose(
[
A.ColorJitter(
brightness=[brightness, brightness],
contrast=[contrast, contrast],
saturation=[saturation, saturation],
hue=[hue, hue],
p=1,
)
]
)
res1 = transform(image=img)["image"]
res2 = (transform(image=img.astype(np.float32) / 255.0)["image"] * 255).astype(np.uint8)
_max = np.abs(res1.astype(np.int16) - res2.astype(np.int16)).max()
if hue != 0:
assert _max <= 10, "Max: {}".format(_max)
else:
assert _max <= 2, "Max: {}".format(_max)
@pytest.mark.parametrize(["hue", "sat", "val"], [[13, 17, 23], [14, 18, 24], [131, 143, 151], [132, 144, 152]])
def test_hue_saturation_value_float_uint8_equal(hue, sat, val):
img = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
for i in range(2):
sign = 1 if i == 0 else -1
for i in range(4):
if i == 0:
_hue = hue * sign
_sat = 0
_val = 0
elif i == 1:
_hue = 0
_sat = sat * sign
_val = 0
elif i == 2:
_hue = 0
_sat = 0
_val = val * sign
else:
_hue = hue * sign
_sat = sat * sign
_val = val * sign
t1 = A.Compose(
[
A.HueSaturationValue(
hue_shift_limit=[_hue, _hue], sat_shift_limit=[_sat, _sat], val_shift_limit=[_val, _val], p=1
)
]
)
t2 = A.Compose(
[
A.HueSaturationValue(
hue_shift_limit=[_hue / 180 * 360, _hue / 180 * 360],
sat_shift_limit=[_sat / 255, _sat / 255],
val_shift_limit=[_val / 255, _val / 255],
p=1,
)
]
)
res1 = t1(image=img)["image"]
res2 = (t2(image=img.astype(np.float32) / 255.0)["image"] * 255).astype(np.uint8)
_max = np.abs(res1.astype(np.int) - res2).max()
assert _max <= 10, "Max value: {}".format(_max)
def test_shift_scale_separate_shift_x_shift_y(image, mask):
aug = A.ShiftScaleRotate(shift_limit=(0.3, 0.3), shift_limit_y=(0.4, 0.4), scale_limit=0, rotate_limit=0, p=1)
data = aug(image=image, mask=mask)
expected_image = FGeometric.shift_scale_rotate(
image, angle=0, scale=1, dx=0.3, dy=0.4, interpolation=cv2.INTER_LINEAR, border_mode=cv2.BORDER_REFLECT_101
)
expected_mask = FGeometric.shift_scale_rotate(
mask, angle=0, scale=1, dx=0.3, dy=0.4, interpolation=cv2.INTER_NEAREST, border_mode=cv2.BORDER_REFLECT_101
)
assert np.array_equal(data["image"], expected_image)
assert np.array_equal(data["mask"], expected_mask)
@pytest.mark.parametrize(["val_uint8"], [[0], [1], [128], [255]])
def test_glass_blur_float_uint8_diff_less_than_two(val_uint8):
x_uint8 = np.zeros((5, 5)).astype(np.uint8)
x_uint8[2, 2] = val_uint8
x_float32 = np.zeros((5, 5)).astype(np.float32)
x_float32[2, 2] = val_uint8 / 255.0
glassblur = A.GlassBlur(always_apply=True, max_delta=1)
np.random.seed(0)
blur_uint8 = glassblur(image=x_uint8)["image"]
np.random.seed(0)
blur_float32 = glassblur(image=x_float32)["image"]
diff = np.abs(blur_uint8 - blur_float32 * 255)
assert np.all(diff <= 2.0)
@pytest.mark.parametrize(
["img_dtype", "px", "percent", "pad_mode", "pad_cval", "keep_size"],
[
[np.uint8, 10, None, cv2.BORDER_CONSTANT, 0, True],
[np.uint8, -10, None, cv2.BORDER_CONSTANT, 0, True],
[np.uint8, 10, None, cv2.BORDER_CONSTANT, 0, False],
[np.uint8, -10, None, cv2.BORDER_CONSTANT, 0, False],
[np.uint8, None, 0.1, cv2.BORDER_CONSTANT, 0, True],
[np.uint8, None, -0.1, cv2.BORDER_CONSTANT, 0, True],
[np.uint8, None, 0.1, cv2.BORDER_CONSTANT, 0, False],
[np.uint8, None, -0.1, cv2.BORDER_CONSTANT, 0, False],
[np.float32, None, 0.1, cv2.BORDER_CONSTANT, 0, False],
[np.float32, None, -0.1, cv2.BORDER_CONSTANT, 0, False],
[np.uint8, None, 0.1, cv2.BORDER_WRAP, 0, False],
[np.uint8, None, 0.1, cv2.BORDER_REPLICATE, 0, False],
[np.uint8, None, 0.1, cv2.BORDER_REFLECT101, 0, False],
],
)
def test_compare_crop_and_pad(img_dtype, px, percent, pad_mode, pad_cval, keep_size):
h, w, c = 100, 100, 3
mode_mapping = {
cv2.BORDER_CONSTANT: "constant",
cv2.BORDER_REPLICATE: "edge",
cv2.BORDER_REFLECT101: "reflect",
cv2.BORDER_WRAP: "wrap",
}
pad_mode_iaa = mode_mapping[pad_mode]
bbox_params = A.BboxParams(format="pascal_voc")
keypoint_params = A.KeypointParams(format="xy", remove_invisible=False)
keypoints = np.random.randint(0, min(h, w), [10, 2])
bboxes = []
for i in range(10):
x1, y1 = np.random.randint(0, min(h, w) - 2, 2)
x2 = np.random.randint(x1 + 1, w - 1)
y2 = np.random.randint(y1 + 1, h - 1)
bboxes.append([x1, y1, x2, y2, 0])
transform_albu = A.Compose(
[
A.CropAndPad(
px=px,
percent=percent,
pad_mode=pad_mode,
pad_cval=pad_cval,
keep_size=keep_size,
p=1,
interpolation=cv2.INTER_AREA
if (px is not None and px < 0) or (percent is not None and percent < 0)
else cv2.INTER_LINEAR,
)
],
bbox_params=bbox_params,
keypoint_params=keypoint_params,
)
transform_iaa = A.Compose(
[A.IAACropAndPad(px=px, percent=percent, pad_mode=pad_mode_iaa, pad_cval=pad_cval, keep_size=keep_size, p=1)],
bbox_params=bbox_params,
keypoint_params=keypoint_params,
)
if img_dtype == np.uint8:
img = np.random.randint(0, 256, (h, w, c), dtype=np.uint8)
else:
img = np.random.random((h, w, c)).astype(img_dtype)
res_albu = transform_albu(image=img, keypoints=keypoints, bboxes=bboxes)
res_iaa = transform_iaa(image=img, keypoints=keypoints, bboxes=bboxes)
for key, item in res_albu.items():
if key == "bboxes":
bboxes = np.array(res_iaa[key])
h = bboxes[:, 3] - bboxes[:, 1]
w = bboxes[:, 2] - bboxes[:, 0]
res_iaa[key] = bboxes[(h > 0) & (w > 0)]
assert np.allclose(item, res_iaa[key]), f"{key} are not equal"
def test_perspective_keep_size():
h, w = 100, 100
img = np.zeros([h, w, 3], dtype=np.uint8)
h, w = img.shape[:2]
bboxes = []
for _ in range(10):
x1 = np.random.randint(0, w - 1)
y1 = np.random.randint(0, h - 1)
x2 = np.random.randint(x1 + 1, w)
y2 = np.random.randint(y1 + 1, h)
bboxes.append([x1, y1, x2, y2])
keypoints = [(np.random.randint(0, w), np.random.randint(0, h), np.random.random()) for _ in range(10)]
transform_1 = A.Compose(
[A.Perspective(keep_size=True, p=1)],
keypoint_params=A.KeypointParams("xys"),
bbox_params=A.BboxParams("pascal_voc", label_fields=["labels"]),
)
transform_2 = A.Compose(
[A.Perspective(keep_size=False, p=1), A.Resize(h, w)],
keypoint_params=A.KeypointParams("xys"),
bbox_params=A.BboxParams("pascal_voc", label_fields=["labels"]),
)
set_seed()
res_1 = transform_1(image=img, bboxes=bboxes, keypoints=keypoints, labels=[0] * len(bboxes))
set_seed()
res_2 = transform_2(image=img, bboxes=bboxes, keypoints=keypoints, labels=[0] * len(bboxes))
assert np.allclose(res_1["bboxes"], res_2["bboxes"])
assert np.allclose(res_1["keypoints"], res_2["keypoints"])
| true | true |
1c33f7ec4d42c8baa799ae0b07df4c8afb649cf3 | 12,196 | py | Python | ml-agents-envs/mlagents/envs/brain.py | alexcercos/ML-Agents | c096c36b0348e3673b687499e17891cd35168939 | [
"Apache-2.0"
] | 1 | 2019-12-29T13:40:16.000Z | 2019-12-29T13:40:16.000Z | ml-agents-envs/mlagents/envs/brain.py | alexcercos/ML-Agents | c096c36b0348e3673b687499e17891cd35168939 | [
"Apache-2.0"
] | null | null | null | ml-agents-envs/mlagents/envs/brain.py | alexcercos/ML-Agents | c096c36b0348e3673b687499e17891cd35168939 | [
"Apache-2.0"
] | 2 | 2020-08-16T14:18:16.000Z | 2022-03-18T12:22:54.000Z | import logging
import numpy as np
import io
from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
from mlagents.envs.timers import hierarchical_timer, timed
from typing import Dict, List, NamedTuple, Optional
from PIL import Image
logger = logging.getLogger("mlagents.envs")
class CameraResolution(NamedTuple):
height: int
width: int
num_channels: int
@property
def gray_scale(self) -> bool:
return self.num_channels == 1
class BrainParameters:
def __init__(
self,
brain_name: str,
vector_observation_space_size: int,
num_stacked_vector_observations: int,
camera_resolutions: List[CameraResolution],
vector_action_space_size: List[int],
vector_action_descriptions: List[str],
vector_action_space_type: int,
):
"""
Contains all brain-specific parameters.
"""
self.brain_name = brain_name
self.vector_observation_space_size = vector_observation_space_size
self.num_stacked_vector_observations = num_stacked_vector_observations
self.number_visual_observations = len(camera_resolutions)
self.camera_resolutions = camera_resolutions
self.vector_action_space_size = vector_action_space_size
self.vector_action_descriptions = vector_action_descriptions
self.vector_action_space_type = ["discrete", "continuous"][
vector_action_space_type
]
def __str__(self):
return """Unity brain name: {}
Number of Visual Observations (per agent): {}
Vector Observation space size (per agent): {}
Number of stacked Vector Observation: {}
Vector Action space type: {}
Vector Action space size (per agent): {}
Vector Action descriptions: {}""".format(
self.brain_name,
str(self.number_visual_observations),
str(self.vector_observation_space_size),
str(self.num_stacked_vector_observations),
self.vector_action_space_type,
str(self.vector_action_space_size),
", ".join(self.vector_action_descriptions),
)
@staticmethod
def from_proto(
brain_param_proto: BrainParametersProto, agent_info: AgentInfoProto
) -> "BrainParameters":
"""
Converts brain parameter proto to BrainParameter object.
:param brain_param_proto: protobuf object.
:return: BrainParameter object.
"""
resolutions = [
CameraResolution(x.shape[0], x.shape[1], x.shape[2])
for x in agent_info.compressed_observations
]
brain_params = BrainParameters(
brain_param_proto.brain_name,
brain_param_proto.vector_observation_size,
brain_param_proto.num_stacked_vector_observations,
resolutions,
list(brain_param_proto.vector_action_size),
list(brain_param_proto.vector_action_descriptions),
brain_param_proto.vector_action_space_type,
)
return brain_params
class BrainInfo:
def __init__(
self,
visual_observation,
vector_observation,
text_observations,
memory=None,
reward=None,
agents=None,
local_done=None,
vector_action=None,
text_action=None,
max_reached=None,
action_mask=None,
custom_observations=None,
):
"""
Describes experience at current step of all agents linked to a brain.
"""
self.visual_observations = visual_observation
self.vector_observations = vector_observation
self.text_observations = text_observations
self.memories = memory
self.rewards = reward
self.local_done = local_done
self.max_reached = max_reached
self.agents = agents
self.previous_vector_actions = vector_action
self.previous_text_actions = text_action
self.action_masks = action_mask
self.custom_observations = custom_observations
def merge(self, other):
for i in range(len(self.visual_observations)):
self.visual_observations[i].extend(other.visual_observations[i])
self.vector_observations = np.append(
self.vector_observations, other.vector_observations, axis=0
)
self.text_observations.extend(other.text_observations)
self.memories = self.merge_memories(
self.memories, other.memories, self.agents, other.agents
)
self.rewards = safe_concat_lists(self.rewards, other.rewards)
self.local_done = safe_concat_lists(self.local_done, other.local_done)
self.max_reached = safe_concat_lists(self.max_reached, other.max_reached)
self.agents = safe_concat_lists(self.agents, other.agents)
self.previous_vector_actions = safe_concat_np_ndarray(
self.previous_vector_actions, other.previous_vector_actions
)
self.previous_text_actions = safe_concat_lists(
self.previous_text_actions, other.previous_text_actions
)
self.action_masks = safe_concat_np_ndarray(
self.action_masks, other.action_masks
)
self.custom_observations = safe_concat_lists(
self.custom_observations, other.custom_observations
)
@staticmethod
def merge_memories(m1, m2, agents1, agents2):
if len(m1) == 0 and len(m2) != 0:
m1 = np.zeros((len(agents1), m2.shape[1]))
elif len(m2) == 0 and len(m1) != 0:
m2 = np.zeros((len(agents2), m1.shape[1]))
elif m2.shape[1] > m1.shape[1]:
new_m1 = np.zeros((m1.shape[0], m2.shape[1]))
new_m1[0 : m1.shape[0], 0 : m1.shape[1]] = m1
return np.append(new_m1, m2, axis=0)
elif m1.shape[1] > m2.shape[1]:
new_m2 = np.zeros((m2.shape[0], m1.shape[1]))
new_m2[0 : m2.shape[0], 0 : m2.shape[1]] = m2
return np.append(m1, new_m2, axis=0)
return np.append(m1, m2, axis=0)
@staticmethod
@timed
def process_pixels(image_bytes: bytes, gray_scale: bool) -> np.ndarray:
"""
Converts byte array observation image into numpy array, re-sizes it,
and optionally converts it to grey scale
:param gray_scale: Whether to convert the image to grayscale.
:param image_bytes: input byte array corresponding to image
:return: processed numpy array of observation from environment
"""
with hierarchical_timer("image_decompress"):
image_bytearray = bytearray(image_bytes)
image = Image.open(io.BytesIO(image_bytearray))
# Normally Image loads lazily, this forces it to do loading in the timer scope.
image.load()
s = np.array(image) / 255.0
if gray_scale:
s = np.mean(s, axis=2)
s = np.reshape(s, [s.shape[0], s.shape[1], 1])
return s
@staticmethod
def from_agent_proto(
worker_id: int,
agent_info_list: List[AgentInfoProto],
brain_params: BrainParameters,
) -> "BrainInfo":
"""
Converts list of agent infos to BrainInfo.
"""
vis_obs: List[np.ndarray] = []
for i in range(brain_params.number_visual_observations):
obs = [
BrainInfo.process_pixels(
x.compressed_observations[i].data,
brain_params.camera_resolutions[i].gray_scale,
)
for x in agent_info_list
]
vis_obs += [obs]
if len(agent_info_list) == 0:
memory_size = 0
else:
memory_size = max(len(x.memories) for x in agent_info_list)
if memory_size == 0:
memory = np.zeros((0, 0))
else:
[
x.memories.extend([0] * (memory_size - len(x.memories)))
for x in agent_info_list
]
memory = np.array([list(x.memories) for x in agent_info_list])
total_num_actions = sum(brain_params.vector_action_space_size)
mask_actions = np.ones((len(agent_info_list), total_num_actions))
for agent_index, agent_info in enumerate(agent_info_list):
if agent_info.action_mask is not None:
if len(agent_info.action_mask) == total_num_actions:
mask_actions[agent_index, :] = [
0 if agent_info.action_mask[k] else 1
for k in range(total_num_actions)
]
if any(np.isnan(x.reward) for x in agent_info_list):
logger.warning(
"An agent had a NaN reward for brain " + brain_params.brain_name
)
if len(agent_info_list) == 0:
vector_obs = np.zeros(
(
0,
brain_params.vector_observation_space_size
* brain_params.num_stacked_vector_observations,
)
)
else:
stacked_obs = []
has_nan = False
has_inf = False
for x in agent_info_list:
np_obs = np.array(x.stacked_vector_observation)
# Check for NaNs or infs in the observations
# If there's a NaN in the observations, the dot() result will be NaN
# If there's an Inf (either sign) then the result will be Inf
# See https://stackoverflow.com/questions/6736590/fast-check-for-nan-in-numpy for background
# Note that a very large values (larger than sqrt(float_max)) will result in an Inf value here
# This is OK though, worst case it results in an unnecessary (but harmless) nan_to_num call.
d = np.dot(np_obs, np_obs)
has_nan = has_nan or np.isnan(d)
has_inf = has_inf or not np.isfinite(d)
stacked_obs.append(np_obs)
vector_obs = np.array(stacked_obs)
# In we have any NaN or Infs, use np.nan_to_num to replace these with finite values
if has_nan or has_inf:
vector_obs = np.nan_to_num(vector_obs)
if has_nan:
logger.warning(
f"An agent had a NaN observation for brain {brain_params.brain_name}"
)
agents = [f"${worker_id}-{x.id}" for x in agent_info_list]
brain_info = BrainInfo(
visual_observation=vis_obs,
vector_observation=vector_obs,
text_observations=[x.text_observation for x in agent_info_list],
memory=memory,
reward=[x.reward if not np.isnan(x.reward) else 0 for x in agent_info_list],
agents=agents,
local_done=[x.done for x in agent_info_list],
vector_action=np.array([x.stored_vector_actions for x in agent_info_list]),
text_action=[list(x.stored_text_actions) for x in agent_info_list],
max_reached=[x.max_step_reached for x in agent_info_list],
custom_observations=[x.custom_observation for x in agent_info_list],
action_mask=mask_actions,
)
return brain_info
def safe_concat_lists(l1: Optional[List], l2: Optional[List]) -> Optional[List]:
if l1 is None:
if l2 is None:
return None
else:
return l2.copy()
else:
if l2 is None:
return l1.copy()
else:
copy = l1.copy()
copy.extend(l2)
return copy
def safe_concat_np_ndarray(
a1: Optional[np.ndarray], a2: Optional[np.ndarray]
) -> Optional[np.ndarray]:
if a1 is not None and a1.size != 0:
if a2 is not None and a2.size != 0:
return np.append(a1, a2, axis=0)
else:
return a1.copy()
elif a2 is not None and a2.size != 0:
return a2.copy()
return None
# Renaming of dictionary of brain name to BrainInfo for clarity
AllBrainInfo = Dict[str, BrainInfo]
| 38.594937 | 110 | 0.616678 | import logging
import numpy as np
import io
from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
from mlagents.envs.timers import hierarchical_timer, timed
from typing import Dict, List, NamedTuple, Optional
from PIL import Image
logger = logging.getLogger("mlagents.envs")
class CameraResolution(NamedTuple):
height: int
width: int
num_channels: int
@property
def gray_scale(self) -> bool:
return self.num_channels == 1
class BrainParameters:
def __init__(
self,
brain_name: str,
vector_observation_space_size: int,
num_stacked_vector_observations: int,
camera_resolutions: List[CameraResolution],
vector_action_space_size: List[int],
vector_action_descriptions: List[str],
vector_action_space_type: int,
):
self.brain_name = brain_name
self.vector_observation_space_size = vector_observation_space_size
self.num_stacked_vector_observations = num_stacked_vector_observations
self.number_visual_observations = len(camera_resolutions)
self.camera_resolutions = camera_resolutions
self.vector_action_space_size = vector_action_space_size
self.vector_action_descriptions = vector_action_descriptions
self.vector_action_space_type = ["discrete", "continuous"][
vector_action_space_type
]
def __str__(self):
return """Unity brain name: {}
Number of Visual Observations (per agent): {}
Vector Observation space size (per agent): {}
Number of stacked Vector Observation: {}
Vector Action space type: {}
Vector Action space size (per agent): {}
Vector Action descriptions: {}""".format(
self.brain_name,
str(self.number_visual_observations),
str(self.vector_observation_space_size),
str(self.num_stacked_vector_observations),
self.vector_action_space_type,
str(self.vector_action_space_size),
", ".join(self.vector_action_descriptions),
)
@staticmethod
def from_proto(
brain_param_proto: BrainParametersProto, agent_info: AgentInfoProto
) -> "BrainParameters":
resolutions = [
CameraResolution(x.shape[0], x.shape[1], x.shape[2])
for x in agent_info.compressed_observations
]
brain_params = BrainParameters(
brain_param_proto.brain_name,
brain_param_proto.vector_observation_size,
brain_param_proto.num_stacked_vector_observations,
resolutions,
list(brain_param_proto.vector_action_size),
list(brain_param_proto.vector_action_descriptions),
brain_param_proto.vector_action_space_type,
)
return brain_params
class BrainInfo:
def __init__(
self,
visual_observation,
vector_observation,
text_observations,
memory=None,
reward=None,
agents=None,
local_done=None,
vector_action=None,
text_action=None,
max_reached=None,
action_mask=None,
custom_observations=None,
):
self.visual_observations = visual_observation
self.vector_observations = vector_observation
self.text_observations = text_observations
self.memories = memory
self.rewards = reward
self.local_done = local_done
self.max_reached = max_reached
self.agents = agents
self.previous_vector_actions = vector_action
self.previous_text_actions = text_action
self.action_masks = action_mask
self.custom_observations = custom_observations
def merge(self, other):
for i in range(len(self.visual_observations)):
self.visual_observations[i].extend(other.visual_observations[i])
self.vector_observations = np.append(
self.vector_observations, other.vector_observations, axis=0
)
self.text_observations.extend(other.text_observations)
self.memories = self.merge_memories(
self.memories, other.memories, self.agents, other.agents
)
self.rewards = safe_concat_lists(self.rewards, other.rewards)
self.local_done = safe_concat_lists(self.local_done, other.local_done)
self.max_reached = safe_concat_lists(self.max_reached, other.max_reached)
self.agents = safe_concat_lists(self.agents, other.agents)
self.previous_vector_actions = safe_concat_np_ndarray(
self.previous_vector_actions, other.previous_vector_actions
)
self.previous_text_actions = safe_concat_lists(
self.previous_text_actions, other.previous_text_actions
)
self.action_masks = safe_concat_np_ndarray(
self.action_masks, other.action_masks
)
self.custom_observations = safe_concat_lists(
self.custom_observations, other.custom_observations
)
@staticmethod
def merge_memories(m1, m2, agents1, agents2):
if len(m1) == 0 and len(m2) != 0:
m1 = np.zeros((len(agents1), m2.shape[1]))
elif len(m2) == 0 and len(m1) != 0:
m2 = np.zeros((len(agents2), m1.shape[1]))
elif m2.shape[1] > m1.shape[1]:
new_m1 = np.zeros((m1.shape[0], m2.shape[1]))
new_m1[0 : m1.shape[0], 0 : m1.shape[1]] = m1
return np.append(new_m1, m2, axis=0)
elif m1.shape[1] > m2.shape[1]:
new_m2 = np.zeros((m2.shape[0], m1.shape[1]))
new_m2[0 : m2.shape[0], 0 : m2.shape[1]] = m2
return np.append(m1, new_m2, axis=0)
return np.append(m1, m2, axis=0)
@staticmethod
@timed
def process_pixels(image_bytes: bytes, gray_scale: bool) -> np.ndarray:
with hierarchical_timer("image_decompress"):
image_bytearray = bytearray(image_bytes)
image = Image.open(io.BytesIO(image_bytearray))
image.load()
s = np.array(image) / 255.0
if gray_scale:
s = np.mean(s, axis=2)
s = np.reshape(s, [s.shape[0], s.shape[1], 1])
return s
@staticmethod
def from_agent_proto(
worker_id: int,
agent_info_list: List[AgentInfoProto],
brain_params: BrainParameters,
) -> "BrainInfo":
vis_obs: List[np.ndarray] = []
for i in range(brain_params.number_visual_observations):
obs = [
BrainInfo.process_pixels(
x.compressed_observations[i].data,
brain_params.camera_resolutions[i].gray_scale,
)
for x in agent_info_list
]
vis_obs += [obs]
if len(agent_info_list) == 0:
memory_size = 0
else:
memory_size = max(len(x.memories) for x in agent_info_list)
if memory_size == 0:
memory = np.zeros((0, 0))
else:
[
x.memories.extend([0] * (memory_size - len(x.memories)))
for x in agent_info_list
]
memory = np.array([list(x.memories) for x in agent_info_list])
total_num_actions = sum(brain_params.vector_action_space_size)
mask_actions = np.ones((len(agent_info_list), total_num_actions))
for agent_index, agent_info in enumerate(agent_info_list):
if agent_info.action_mask is not None:
if len(agent_info.action_mask) == total_num_actions:
mask_actions[agent_index, :] = [
0 if agent_info.action_mask[k] else 1
for k in range(total_num_actions)
]
if any(np.isnan(x.reward) for x in agent_info_list):
logger.warning(
"An agent had a NaN reward for brain " + brain_params.brain_name
)
if len(agent_info_list) == 0:
vector_obs = np.zeros(
(
0,
brain_params.vector_observation_space_size
* brain_params.num_stacked_vector_observations,
)
)
else:
stacked_obs = []
has_nan = False
has_inf = False
for x in agent_info_list:
np_obs = np.array(x.stacked_vector_observation)
# If there's an Inf (either sign) then the result will be Inf
d = np.dot(np_obs, np_obs)
has_nan = has_nan or np.isnan(d)
has_inf = has_inf or not np.isfinite(d)
stacked_obs.append(np_obs)
vector_obs = np.array(stacked_obs)
if has_nan or has_inf:
vector_obs = np.nan_to_num(vector_obs)
if has_nan:
logger.warning(
f"An agent had a NaN observation for brain {brain_params.brain_name}"
)
agents = [f"${worker_id}-{x.id}" for x in agent_info_list]
brain_info = BrainInfo(
visual_observation=vis_obs,
vector_observation=vector_obs,
text_observations=[x.text_observation for x in agent_info_list],
memory=memory,
reward=[x.reward if not np.isnan(x.reward) else 0 for x in agent_info_list],
agents=agents,
local_done=[x.done for x in agent_info_list],
vector_action=np.array([x.stored_vector_actions for x in agent_info_list]),
text_action=[list(x.stored_text_actions) for x in agent_info_list],
max_reached=[x.max_step_reached for x in agent_info_list],
custom_observations=[x.custom_observation for x in agent_info_list],
action_mask=mask_actions,
)
return brain_info
def safe_concat_lists(l1: Optional[List], l2: Optional[List]) -> Optional[List]:
if l1 is None:
if l2 is None:
return None
else:
return l2.copy()
else:
if l2 is None:
return l1.copy()
else:
copy = l1.copy()
copy.extend(l2)
return copy
def safe_concat_np_ndarray(
a1: Optional[np.ndarray], a2: Optional[np.ndarray]
) -> Optional[np.ndarray]:
if a1 is not None and a1.size != 0:
if a2 is not None and a2.size != 0:
return np.append(a1, a2, axis=0)
else:
return a1.copy()
elif a2 is not None and a2.size != 0:
return a2.copy()
return None
AllBrainInfo = Dict[str, BrainInfo]
| true | true |
1c33f8afcd2c14d682697633b0b5b0150094b3ef | 956 | py | Python | olympicvaxinfo/olympicvaxinfo/urls.py | mueslimak3r/olympicvax | 279bb5eda99d34b20477c613471c1ddcbd9dc968 | [
"MIT"
] | null | null | null | olympicvaxinfo/olympicvaxinfo/urls.py | mueslimak3r/olympicvax | 279bb5eda99d34b20477c613471c1ddcbd9dc968 | [
"MIT"
] | null | null | null | olympicvaxinfo/olympicvaxinfo/urls.py | mueslimak3r/olympicvax | 279bb5eda99d34b20477c613471c1ddcbd9dc968 | [
"MIT"
] | null | null | null | """olympicvaxinfo URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.1/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path
from . import views
urlpatterns = [
path('admin/', admin.site.urls),
path("", views.blog_index, name="blog_index"),
path("<int:pk>/", views.blog_detail, name="blog_detail"),
path("<category>/", views.blog_category, name="blog_category"),
] | 38.24 | 77 | 0.703975 | from django.contrib import admin
from django.urls import path
from . import views
urlpatterns = [
path('admin/', admin.site.urls),
path("", views.blog_index, name="blog_index"),
path("<int:pk>/", views.blog_detail, name="blog_detail"),
path("<category>/", views.blog_category, name="blog_category"),
] | true | true |
1c33f9078d1d81292a0bc042bd045df08b0b6362 | 12,732 | py | Python | models/faster_rcnn.py | hvkwak/simple-faster-rcnn-pytorch | 3ea84a789c91ea8d403637026b4a5add19e5343a | [
"MIT"
] | null | null | null | models/faster_rcnn.py | hvkwak/simple-faster-rcnn-pytorch | 3ea84a789c91ea8d403637026b4a5add19e5343a | [
"MIT"
] | null | null | null | models/faster_rcnn.py | hvkwak/simple-faster-rcnn-pytorch | 3ea84a789c91ea8d403637026b4a5add19e5343a | [
"MIT"
] | null | null | null | import os
import sys
import torch
import torchvision
import numpy as np
from torch import nn
from torch.nn import functional as F
# from models.utils.nms import non_maximum_suppression
from models.utils.bbox_tools import loc2bbox
from utils.array_tool import tonumpy, totensor
from data.dataset import preprocess
from utils.util import read_image
from utils.config import opt
class FasterRCNN(nn.Module):
"""Base class for Faster R-CNN.
This is a base class for Faster R-CNN links supporting object detection
API [#]_. The following three stages constitute Faster R-CNN.
1. **Feature extraction**: Images are taken and their \
feature maps are calculated.
2. **Region Proposal Networks**: Given the feature maps calculated in \
the previous stage, produce set of RoIs around objects.
3. **Localization and Classification Heads**: Using feature maps that \
belong to the proposed RoIs, classify the categories of the objects \
in the RoIs and improve localizations.
Each stage is carried out by one of the callable
:class:`torch.nn.Module` objects :obj:`feature`, :obj:`rpn` and :obj:`head`.
There are two functions :meth:`predict` and :meth:`__call__` to conduct
object detection.
:meth:`predict` takes images and returns bounding boxes that are converted
to image coordinates. This will be useful for a scenario when
Faster R-CNN is treated as a black box function, for instance.
:meth:`__call__` is provided for a scnerario when intermediate outputs
are needed, for instance, for training and debugging.
Links that support obejct detection API have method :meth:`predict` with
the same interface. Please refer to :meth:`predict` for
further details.
.. [#] Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. \
Faster R-CNN: Towards Real-Time Object Detection with \
Region Proposal Networks. NIPS 2015.
Args:
extractor (nn.Module): A module that takes a BCHW image
array and returns feature maps.
rpn (nn.Module): A module that has the same interface as
:class:`model.region_proposal_network.RegionProposalNetwork`.
Please refer to the documentation found there.
head (nn.Module): A module that takes
a BCHW variable, RoIs and batch indices for RoIs. This returns class
dependent localization paramters and class scores.
loc_normalize_mean (tuple of four floats): Mean values of
localization estimates.
loc_normalize_std (tupler of four floats): Standard deviation
of localization estimates.
"""
def __init__(self, extractor, rpn, head,
loc_normalize_mean = (0., 0., 0., 0.),
loc_normalize_std = (0.1, 0.1, 0.2, 0.2)):
# in Python3, inheritance and initialize like this:
super().__init__()
self.extractor = extractor
self.rpn = rpn
self.head = head
# mean and std
self.loc_normalize_mean = loc_normalize_mean
self.loc_normalize_std = loc_normalize_std
self.use_preset('evaluate')
# demo_image
self.demo_image = ""
@property
def n_class(self):
# Total number of classes including the background.
return self.head.n_class
def forward(self, x, scale=1.):
"""Forward Faster R-CNN.
Scaling paramter :obj:`scale` is used by RPN to determine the
threshold to select small objects, which are going to be
rejected irrespective of their confidence scores.
Here are notations used.
* :math:`N` is the number of batch size
* :math:`R'` is the total number of RoIs produced across batches. \
Given :math:`R_i` proposed RoIs from the :math:`i` th image, \
:math:`R' = \\sum _{i=1} ^ N R_i`.
* :math:`L` is the number of classes excluding the background.
Classes are ordered by the background, the first class, ..., and
the :math:`L` th class.
Args:
x (autograd.Variable): 4D image variable.
scale (float): Amount of scaling applied to the raw image
during preprocessing.
Returns:
Variable, Variable, array, array:
Returns tuple of four values listed below.
* **roi_cls_locs**: Offsets and scalings for the proposed RoIs. \
Its shape is :math:`(R', (L + 1) \\times 4)`.
* **roi_scores**: Class predictions for the proposed RoIs. \
Its shape is :math:`(R', L + 1)`.
* **rois**: RoIs proposed by RPN. Its shape is \
:math:`(R', 4)`.
* **roi_indices**: Batch indices of RoIs. Its shape is \
:math:`(R',)`.
"""
img_size = x.shape[2:]
h = self.extractor(x)
# rpn_locs, rpn_scores, rois, roi_indices, anchor = self.rpn(h, img_size, scale)
# rpn_locs, rpn_scores, anchors are obsolete
_, _, rois, roi_indices, _ = self.rpn(h, img_size, scale)
# visualize RPN results to see if they are working correctly:
# visualize_RPN(rois, self.scale, self.demo_image)
# feed forward weiter:
roi_cls_locs, roi_scores = self.head(h, rois, roi_indices)
return roi_cls_locs, roi_scores, rois, roi_indices
def use_preset(self, preset):
"""Use the given preset during prediction.
This method changes values of :obj:`self.nms_thresh` and
:obj:`self.score_thresh`. These values are a threshold value
used for non maximum suppression and a threshold value
to discard low confidence proposals in :meth:`predict`,
respectively.
If the attributes need to be changed to something
other than the values provided in the presets, please modify
them by directly accessing the public attributes.
Args:
preset ({'visualize', 'evaluate'): A string to determine the
preset to use.
"""
if preset == 'visualize':
self.nms_thresh = 0.3
self.score_thresh = 0.9
elif preset == 'evaluate':
self.nms_thresh = 0.1 # 0.2
self.score_thresh = 0.9 # 0.05
else:
raise ValueError('preset must be visualize or evaluate')
def _suppress(self, raw_cls_bbox, raw_prob):
# non maximum suppresion before final predictions
bbox = list()
label = list()
score = list()
# masks = list()
# skip cls_id = 0 because it is the background class
for l in range(1, self.n_class):
cls_bbox_l = raw_cls_bbox.reshape((-1, self.n_class, 4))[:, l, :]
prob_l = raw_prob[:, l]
mask = prob_l > self.score_thresh
cls_bbox_l = cls_bbox_l[mask]
prob_l = prob_l[mask]
keep = torchvision.ops.nms(torch.from_numpy(cls_bbox_l), torch.from_numpy(prob_l), self.nms_thresh)
# mask = np.where(mask)[0]
# import ipdb;ipdb.set_trace()
keep = keep.numpy()
bbox.append(cls_bbox_l[keep])
# The labels are in [0, self.n_class - 2].
label.append((l - 1) * np.ones((len(keep),)))
score.append(prob_l[keep])
# masks.append(mask[keep])
bbox = np.concatenate(bbox, axis=0).astype(np.float32)
label = np.concatenate(label, axis=0).astype(np.int32)
score = np.concatenate(score, axis=0).astype(np.float32)
# masks = np.concatenate(masks, axis = 0)
return bbox, label, score
@torch.no_grad()
def predict(self, imgs, sizes=None, visualize=False):
"""Detect objects from images.
This method predicts objects for each image.
Args:
imgs (iterable of numpy.ndarray): Arrays holding images.
All images are in CHW and RGB format
and the range of their value is :math:`[0, 255]`.
Returns:
tuple of lists:
This method returns a tuple of three lists,
:obj:`(bboxes, labels, scores)`.
* **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
where :math:`R` is the number of bounding boxes in a image. \
Each bouding box is organized by \
:math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
in the second axis.
* **labels** : A list of integer arrays of shape :math:`(R,)`. \
Each value indicates the class of the bounding box. \
Values are in range :math:`[0, L - 1]`, where :math:`L` is the \
number of the foreground classes.
* **scores** : A list of float arrays of shape :math:`(R,)`. \
Each value indicates how confident the prediction is.
"""
self.eval()
if visualize:
self.use_preset('visualize') # Visualize mode
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img, scale = preprocess(tonumpy(img))
self.scale = scale
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
# create output lists
bboxes = list()
labels = list()
scores = list()
masks = list()
for img, size in zip(prepared_imgs, sizes):
# change it to tensor
# [None] addes up one more dimension
img = totensor(img[None]).float()
# scale factor
scale = img.shape[3] / size[1]
# fast forward the image
# img -> (extractor+rpn+head) -> roi_cls_loc, roi_scores, rois
roi_cls_loc, roi_scores, rois, roi_indices = self(img, scale=scale)
# NOTE:
# rois.shape = (300, 4)
# where 4 corresponds to (y1, x1, y2, x2)
# x in [0, 600], y in [0, 800]
# We are assuming that batch size is 1.
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
# change rois to tensor
roi = totensor(rois) / scale
# check the codes below.
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
mean = torch.Tensor(self.loc_normalize_mean). \
repeat(self.n_class)[None]
std = torch.Tensor(self.loc_normalize_std). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(tonumpy(roi).reshape((-1, 4)),
tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = totensor(cls_bbox)
# change the form (N, 4)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clamp in range of [0, size[0]]
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = tonumpy(F.softmax(totensor(roi_score), dim=1))
# change tensors to numpy
raw_cls_bbox = tonumpy(cls_bbox)
raw_prob = tonumpy(prob)
# non maximum suppression
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
# masks.append(mask)
self.use_preset('evaluate')
self.train() # change it back to train mode.
return bboxes, labels, scores
def visualize_RPN(rois, scale, image):
# Visualize RPN results
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
## load image
image_name = image
img1 = Image.open('/home/hyobin/Documents/in-facedemo/facerecognition/PyFaceRecClient/simple-faster-rcnn-pytorch/'+image_name)
# img1 = read_image(os.path.dirname(os.path.abspath(__file__))+'/demo.jpg')
fig, ax = plt.subplots(1)
ax.imshow(img1)
# visualize top images
for i in range(10):
y1, x1, y2, x2 = rois[i, :]
y1, x1, y2, x2 = y1/scale, x1/scale, y2/scale, x2/scale
h = y2 - y1
w = x2 - x1
rect = patches.Rectangle((x1,y1),w,h,linewidth=1,edgecolor='r',facecolor='none')
ax.add_patch(rect)
plt.show() | 39.175385 | 130 | 0.592366 | import os
import sys
import torch
import torchvision
import numpy as np
from torch import nn
from torch.nn import functional as F
from models.utils.bbox_tools import loc2bbox
from utils.array_tool import tonumpy, totensor
from data.dataset import preprocess
from utils.util import read_image
from utils.config import opt
class FasterRCNN(nn.Module):
def __init__(self, extractor, rpn, head,
loc_normalize_mean = (0., 0., 0., 0.),
loc_normalize_std = (0.1, 0.1, 0.2, 0.2)):
super().__init__()
self.extractor = extractor
self.rpn = rpn
self.head = head
self.loc_normalize_mean = loc_normalize_mean
self.loc_normalize_std = loc_normalize_std
self.use_preset('evaluate')
self.demo_image = ""
@property
def n_class(self):
return self.head.n_class
def forward(self, x, scale=1.):
img_size = x.shape[2:]
h = self.extractor(x)
_, _, rois, roi_indices, _ = self.rpn(h, img_size, scale)
roi_cls_locs, roi_scores = self.head(h, rois, roi_indices)
return roi_cls_locs, roi_scores, rois, roi_indices
def use_preset(self, preset):
if preset == 'visualize':
self.nms_thresh = 0.3
self.score_thresh = 0.9
elif preset == 'evaluate':
self.nms_thresh = 0.1
self.score_thresh = 0.9
else:
raise ValueError('preset must be visualize or evaluate')
def _suppress(self, raw_cls_bbox, raw_prob):
bbox = list()
label = list()
score = list()
for l in range(1, self.n_class):
cls_bbox_l = raw_cls_bbox.reshape((-1, self.n_class, 4))[:, l, :]
prob_l = raw_prob[:, l]
mask = prob_l > self.score_thresh
cls_bbox_l = cls_bbox_l[mask]
prob_l = prob_l[mask]
keep = torchvision.ops.nms(torch.from_numpy(cls_bbox_l), torch.from_numpy(prob_l), self.nms_thresh)
keep = keep.numpy()
bbox.append(cls_bbox_l[keep])
label.append((l - 1) * np.ones((len(keep),)))
score.append(prob_l[keep])
bbox = np.concatenate(bbox, axis=0).astype(np.float32)
label = np.concatenate(label, axis=0).astype(np.int32)
score = np.concatenate(score, axis=0).astype(np.float32)
return bbox, label, score
@torch.no_grad()
def predict(self, imgs, sizes=None, visualize=False):
self.eval()
if visualize:
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img, scale = preprocess(tonumpy(img))
self.scale = scale
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = list()
scores = list()
masks = list()
for img, size in zip(prepared_imgs, sizes):
img = totensor(img[None]).float()
scale = img.shape[3] / size[1]
roi_cls_loc, roi_scores, rois, roi_indices = self(img, scale=scale)
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = totensor(rois) / scale
mean = torch.Tensor(self.loc_normalize_mean). \
repeat(self.n_class)[None]
std = torch.Tensor(self.loc_normalize_std). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(tonumpy(roi).reshape((-1, 4)),
tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = tonumpy(F.softmax(totensor(roi_score), dim=1))
raw_cls_bbox = tonumpy(cls_bbox)
raw_prob = tonumpy(prob)
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores
def visualize_RPN(rois, scale, image):
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
ame = image
img1 = Image.open('/home/hyobin/Documents/in-facedemo/facerecognition/PyFaceRecClient/simple-faster-rcnn-pytorch/'+image_name)
fig, ax = plt.subplots(1)
ax.imshow(img1)
for i in range(10):
y1, x1, y2, x2 = rois[i, :]
y1, x1, y2, x2 = y1/scale, x1/scale, y2/scale, x2/scale
h = y2 - y1
w = x2 - x1
rect = patches.Rectangle((x1,y1),w,h,linewidth=1,edgecolor='r',facecolor='none')
ax.add_patch(rect)
plt.show() | true | true |
1c33f9ae72ee75467eb64f9ff3a3a2bc0a89a5fb | 905 | py | Python | interlecture/interauth/migrations/0001_initial.py | afriestad/interlecture | 56d3d086ed6d0fd0de599120d12f88d6d1da2271 | [
"MIT"
] | null | null | null | interlecture/interauth/migrations/0001_initial.py | afriestad/interlecture | 56d3d086ed6d0fd0de599120d12f88d6d1da2271 | [
"MIT"
] | null | null | null | interlecture/interauth/migrations/0001_initial.py | afriestad/interlecture | 56d3d086ed6d0fd0de599120d12f88d6d1da2271 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# Generated by Django 1.10.5 on 2017-04-25 18:48
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='UserActivation',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('activation_key', models.CharField(max_length=128)),
('key_expires', models.DateTimeField()),
('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='profile', to=settings.AUTH_USER_MODEL)),
],
),
]
| 31.206897 | 145 | 0.649724 |
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='UserActivation',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('activation_key', models.CharField(max_length=128)),
('key_expires', models.DateTimeField()),
('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='profile', to=settings.AUTH_USER_MODEL)),
],
),
]
| true | true |
1c33f9b24d3204ce768b252cf5589524fd7c303b | 1,562 | py | Python | configs/detection/_base_/models/slowonly_r50_nl.py | Naoki-Wake/mmaction2 | a2032605db82509744a18d993c94a06feb1efd15 | [
"Apache-2.0"
] | null | null | null | configs/detection/_base_/models/slowonly_r50_nl.py | Naoki-Wake/mmaction2 | a2032605db82509744a18d993c94a06feb1efd15 | [
"Apache-2.0"
] | null | null | null | configs/detection/_base_/models/slowonly_r50_nl.py | Naoki-Wake/mmaction2 | a2032605db82509744a18d993c94a06feb1efd15 | [
"Apache-2.0"
] | null | null | null | # model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
pretrained2d=False,
lateral=False,
num_stages=4,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1),
norm_cfg=dict(type='BN3d', requires_grad=True),
non_local=((0, 0, 0), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 0, 0)),
non_local_cfg=dict(
sub_sample=True,
use_scale=True,
norm_cfg=dict(type='BN3d', requires_grad=True),
mode='embedded_gaussian')),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2048,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.00)))
| 30.627451 | 75 | 0.508963 |
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
pretrained2d=False,
lateral=False,
num_stages=4,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1),
norm_cfg=dict(type='BN3d', requires_grad=True),
non_local=((0, 0, 0), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 0, 0)),
non_local_cfg=dict(
sub_sample=True,
use_scale=True,
norm_cfg=dict(type='BN3d', requires_grad=True),
mode='embedded_gaussian')),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2048,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.00)))
| true | true |
1c33f9b3da0355f5366dab67a8602e113eeb6c9c | 863 | py | Python | belleflopt/migrations/0030_auto_20200228_2058.py | ucd-cws/eflows_optimization | 2eb9f13a042ab81541488358ad0724555a5d57fc | [
"MIT"
] | 2 | 2020-04-19T04:05:51.000Z | 2021-04-19T02:47:40.000Z | belleflopt/migrations/0030_auto_20200228_2058.py | ucd-cws/eflows_optimization | 2eb9f13a042ab81541488358ad0724555a5d57fc | [
"MIT"
] | 7 | 2019-08-31T05:57:30.000Z | 2019-11-27T23:58:13.000Z | belleflopt/migrations/0030_auto_20200228_2058.py | ucd-cws/eflows_optimization | 2eb9f13a042ab81541488358ad0724555a5d57fc | [
"MIT"
] | null | null | null | # Generated by Django 2.2.4 on 2020-02-29 04:58
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('belleflopt', '0029_modelrun_description'),
]
operations = [
migrations.AddField(
model_name='modelrun',
name='water_year',
field=models.SmallIntegerField(default=2010),
preserve_default=False,
),
migrations.AlterField(
model_name='dailyflow',
name='model_run',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='daily_flows', to='belleflopt.ModelRun'),
),
migrations.AlterUniqueTogether(
name='segmentpresence',
unique_together={('stream_segment', 'species')},
),
]
| 28.766667 | 135 | 0.618772 |
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('belleflopt', '0029_modelrun_description'),
]
operations = [
migrations.AddField(
model_name='modelrun',
name='water_year',
field=models.SmallIntegerField(default=2010),
preserve_default=False,
),
migrations.AlterField(
model_name='dailyflow',
name='model_run',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='daily_flows', to='belleflopt.ModelRun'),
),
migrations.AlterUniqueTogether(
name='segmentpresence',
unique_together={('stream_segment', 'species')},
),
]
| true | true |
1c33f9c7f6d84650c468e676ac804f8fae83c447 | 1,200 | py | Python | explorebg/questions/models.py | bvoytash/Quiz-Application | 279a029b8e40513642bf002387f813d680a74ed7 | [
"MIT"
] | null | null | null | explorebg/questions/models.py | bvoytash/Quiz-Application | 279a029b8e40513642bf002387f813d680a74ed7 | [
"MIT"
] | null | null | null | explorebg/questions/models.py | bvoytash/Quiz-Application | 279a029b8e40513642bf002387f813d680a74ed7 | [
"MIT"
] | null | null | null | import random
from django.contrib.auth import get_user_model
from django.db import models
UserModel = get_user_model()
class Question(models.Model):
text = models.CharField(max_length=500)
user = models.ForeignKey(
UserModel,
on_delete=models.CASCADE,
)
def get_answer_quiz(self):
answers = [ans for ans in self.answer_set.all()]
random.shuffle(answers)
return answers
def get_answer(self):
return self.answer_set.all()
def __str__(self):
return self.text
class Answer(models.Model):
text = models.CharField(max_length=200)
correct = models.BooleanField(default=False)
question = models.ForeignKey(Question, on_delete=models.CASCADE)
def __str__(self):
return f"question: {self.question.text} answer: {self.text}, correct: {self.correct}"
class Like(models.Model):
question = models.ForeignKey(Question, on_delete=models.CASCADE)
user = models.ForeignKey(
UserModel,
on_delete=models.CASCADE,
)
class Code(models.Model):
text = models.CharField(max_length=10)
user = models.ForeignKey(
UserModel,
on_delete=models.CASCADE,
)
| 23.076923 | 93 | 0.678333 | import random
from django.contrib.auth import get_user_model
from django.db import models
UserModel = get_user_model()
class Question(models.Model):
text = models.CharField(max_length=500)
user = models.ForeignKey(
UserModel,
on_delete=models.CASCADE,
)
def get_answer_quiz(self):
answers = [ans for ans in self.answer_set.all()]
random.shuffle(answers)
return answers
def get_answer(self):
return self.answer_set.all()
def __str__(self):
return self.text
class Answer(models.Model):
text = models.CharField(max_length=200)
correct = models.BooleanField(default=False)
question = models.ForeignKey(Question, on_delete=models.CASCADE)
def __str__(self):
return f"question: {self.question.text} answer: {self.text}, correct: {self.correct}"
class Like(models.Model):
question = models.ForeignKey(Question, on_delete=models.CASCADE)
user = models.ForeignKey(
UserModel,
on_delete=models.CASCADE,
)
class Code(models.Model):
text = models.CharField(max_length=10)
user = models.ForeignKey(
UserModel,
on_delete=models.CASCADE,
)
| true | true |
1c33f9e9244a342d5f565b8b316868b728c73720 | 74 | py | Python | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/commentsRemoval/data/out_6.py | JetBrains-Research/ast-transformations | 0ab408af3275b520cc87a473f418c4b4dfcb0284 | [
"MIT"
] | 8 | 2021-01-19T21:15:54.000Z | 2022-02-23T19:16:25.000Z | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/commentsRemoval/data/out_6.py | JetBrains-Research/ast-transformations | 0ab408af3275b520cc87a473f418c4b4dfcb0284 | [
"MIT"
] | 4 | 2020-11-17T14:28:25.000Z | 2022-02-24T07:54:28.000Z | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/commentsRemoval/data/out_6.py | nbirillo/ast-transformations | 717706765a2da29087a0de768fc851698886dd65 | [
"MIT"
] | 1 | 2022-02-23T19:16:30.000Z | 2022-02-23T19:16:30.000Z | def main():
b = 5
b = 510
def foo():
pass
a = 5
| 8.222222 | 14 | 0.337838 | def main():
b = 5
b = 510
def foo():
pass
a = 5
| true | true |
1c33f9f8be2a7dfe451fcc83bd92fdf584bcdc77 | 14,611 | py | Python | static/paddlex/tools/x2seg.py | cheneyveron/PaddleX | 86f73fc6a66b12c638f642524bfd1cf730e26c4b | [
"Apache-2.0"
] | 3,655 | 2020-03-28T09:19:50.000Z | 2022-03-31T13:28:39.000Z | static/paddlex/tools/x2seg.py | cheneyveron/PaddleX | 86f73fc6a66b12c638f642524bfd1cf730e26c4b | [
"Apache-2.0"
] | 829 | 2020-03-28T04:03:18.000Z | 2022-03-31T14:34:30.000Z | static/paddlex/tools/x2seg.py | cheneyveron/PaddleX | 86f73fc6a66b12c638f642524bfd1cf730e26c4b | [
"Apache-2.0"
] | 738 | 2020-03-28T03:56:46.000Z | 2022-03-31T13:11:03.000Z | #!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cv2
import uuid
import json
import os
import os.path as osp
import shutil
import numpy as np
import PIL.Image
from .base import MyEncoder, is_pic, get_encoding
import math
class X2Seg(object):
def __init__(self):
self.labels2ids = {'_background_': 0}
def shapes_to_label(self, img_shape, shapes, label_name_to_value):
# 该函数基于https://github.com/wkentaro/labelme/blob/master/labelme/utils/shape.py实现。
def shape_to_mask(img_shape,
points,
shape_type=None,
line_width=10,
point_size=5):
mask = np.zeros(img_shape[:2], dtype=np.uint8)
mask = PIL.Image.fromarray(mask)
draw = PIL.ImageDraw.Draw(mask)
xy = [tuple(point) for point in points]
if shape_type == 'circle':
assert len(
xy) == 2, 'Shape of shape_type=circle must have 2 points'
(cx, cy), (px, py) = xy
d = math.sqrt((cx - px)**2 + (cy - py)**2)
draw.ellipse(
[cx - d, cy - d, cx + d, cy + d], outline=1, fill=1)
elif shape_type == 'rectangle':
assert len(
xy) == 2, 'Shape of shape_type=rectangle must have 2 points'
draw.rectangle(xy, outline=1, fill=1)
elif shape_type == 'line':
assert len(
xy) == 2, 'Shape of shape_type=line must have 2 points'
draw.line(xy=xy, fill=1, width=line_width)
elif shape_type == 'linestrip':
draw.line(xy=xy, fill=1, width=line_width)
elif shape_type == 'point':
assert len(
xy) == 1, 'Shape of shape_type=point must have 1 points'
cx, cy = xy[0]
r = point_size
draw.ellipse(
[cx - r, cy - r, cx + r, cy + r], outline=1, fill=1)
else:
assert len(xy) > 2, 'Polygon must have points more than 2'
draw.polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
return mask
cls = np.zeros(img_shape[:2], dtype=np.int32)
ins = np.zeros_like(cls)
instances = []
for shape in shapes:
points = shape['points']
label = shape['label']
group_id = shape.get('group_id')
if group_id is None:
group_id = uuid.uuid1()
shape_type = shape.get('shape_type', None)
cls_name = label
instance = (cls_name, group_id)
if instance not in instances:
instances.append(instance)
ins_id = instances.index(instance) + 1
cls_id = label_name_to_value[cls_name]
mask = shape_to_mask(img_shape[:2], points, shape_type)
cls[mask] = cls_id
ins[mask] = ins_id
return cls, ins
def get_color_map_list(self, num_classes):
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
return color_map
def convert(self, image_dir, json_dir, dataset_save_dir):
"""转换。
Args:
image_dir (str): 图像文件存放的路径。
json_dir (str): 与每张图像对应的json文件的存放路径。
dataset_save_dir (str): 转换后数据集存放路径。
"""
assert osp.exists(image_dir), "The image folder does not exist!"
assert osp.exists(json_dir), "The json folder does not exist!"
if not osp.exists(dataset_save_dir):
os.makedirs(dataset_save_dir)
# Convert the image files.
new_image_dir = osp.join(dataset_save_dir, "JPEGImages")
if osp.exists(new_image_dir):
raise Exception(
"The directory {} is already exist, please remove the directory first".
format(new_image_dir))
os.makedirs(new_image_dir)
for img_name in os.listdir(image_dir):
if is_pic(img_name):
shutil.copyfile(
osp.join(image_dir, img_name),
osp.join(new_image_dir, img_name))
# Convert the json files.
png_dir = osp.join(dataset_save_dir, "Annotations")
if osp.exists(png_dir):
shutil.rmtree(png_dir)
os.makedirs(png_dir)
self.get_labels2ids(new_image_dir, json_dir)
self.json2png(new_image_dir, json_dir, png_dir)
# Generate the labels.txt
ids2labels = {v: k for k, v in self.labels2ids.items()}
with open(osp.join(dataset_save_dir, 'labels.txt'), 'w') as fw:
for i in range(len(ids2labels)):
fw.write(ids2labels[i] + '\n')
class JingLing2Seg(X2Seg):
"""将使用标注精灵标注的数据集转换为Seg数据集。
"""
def __init__(self):
super(JingLing2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
if 'outputs' in json_info:
for output in json_info['outputs']['object']:
cls_name = output['name']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def json2png(self, image_dir, json_dir, png_dir):
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
data_shapes = []
if 'outputs' in json_info:
for output in json_info['outputs']['object']:
if 'polygon' in output.keys():
polygon = output['polygon']
name = output['name']
points = []
for i in range(1, int(len(polygon) / 2) + 1):
points.append([
polygon['x' + str(i)], polygon['y' + str(
i)]
])
shape = {
'label': name,
'points': points,
'shape_type': 'polygon'
}
data_shapes.append(shape)
if 'size' not in json_info:
continue
img_shape = (json_info['size']['height'],
json_info['size']['width'],
json_info['size']['depth'])
lbl, _ = self.shapes_to_label(
img_shape=img_shape,
shapes=data_shapes,
label_name_to_value=self.labels2ids, )
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
class LabelMe2Seg(X2Seg):
"""将使用LabelMe标注的数据集转换为Seg数据集。
"""
def __init__(self):
super(LabelMe2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
for shape in json_info['shapes']:
cls_name = shape['label']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def json2png(self, image_dir, json_dir, png_dir):
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
img_file = osp.join(image_dir, img_name)
img = np.asarray(PIL.Image.open(img_file))
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
lbl, _ = self.shapes_to_label(
img_shape=img.shape,
shapes=json_info['shapes'],
label_name_to_value=self.labels2ids, )
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
class EasyData2Seg(X2Seg):
"""将使用EasyData标注的分割数据集转换为Seg数据集。
"""
def __init__(self):
super(EasyData2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
for shape in json_info["labels"]:
cls_name = shape['name']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def mask2polygon(self, mask, label):
contours, hierarchy = cv2.findContours(
(mask).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
segmentation = []
for contour in contours:
contour_list = contour.flatten().tolist()
if len(contour_list) > 4:
points = []
for i in range(0, len(contour_list), 2):
points.append([contour_list[i], contour_list[i + 1]])
shape = {
'label': label,
'points': points,
'shape_type': 'polygon'
}
segmentation.append(shape)
return segmentation
def json2png(self, image_dir, json_dir, png_dir):
from pycocotools.mask import decode
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
img_file = osp.join(image_dir, img_name)
img = np.asarray(PIL.Image.open(img_file))
img_h = img.shape[0]
img_w = img.shape[1]
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
data_shapes = []
for shape in json_info['labels']:
mask_dict = {}
mask_dict['size'] = [img_h, img_w]
mask_dict['counts'] = shape['mask'].encode()
mask = decode(mask_dict)
polygon = self.mask2polygon(mask, shape["name"])
data_shapes.extend(polygon)
lbl, _ = self.shapes_to_label(
img_shape=img.shape,
shapes=data_shapes,
label_name_to_value=self.labels2ids, )
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
| 42.228324 | 88 | 0.524263 |
import cv2
import uuid
import json
import os
import os.path as osp
import shutil
import numpy as np
import PIL.Image
from .base import MyEncoder, is_pic, get_encoding
import math
class X2Seg(object):
def __init__(self):
self.labels2ids = {'_background_': 0}
def shapes_to_label(self, img_shape, shapes, label_name_to_value):
def shape_to_mask(img_shape,
points,
shape_type=None,
line_width=10,
point_size=5):
mask = np.zeros(img_shape[:2], dtype=np.uint8)
mask = PIL.Image.fromarray(mask)
draw = PIL.ImageDraw.Draw(mask)
xy = [tuple(point) for point in points]
if shape_type == 'circle':
assert len(
xy) == 2, 'Shape of shape_type=circle must have 2 points'
(cx, cy), (px, py) = xy
d = math.sqrt((cx - px)**2 + (cy - py)**2)
draw.ellipse(
[cx - d, cy - d, cx + d, cy + d], outline=1, fill=1)
elif shape_type == 'rectangle':
assert len(
xy) == 2, 'Shape of shape_type=rectangle must have 2 points'
draw.rectangle(xy, outline=1, fill=1)
elif shape_type == 'line':
assert len(
xy) == 2, 'Shape of shape_type=line must have 2 points'
draw.line(xy=xy, fill=1, width=line_width)
elif shape_type == 'linestrip':
draw.line(xy=xy, fill=1, width=line_width)
elif shape_type == 'point':
assert len(
xy) == 1, 'Shape of shape_type=point must have 1 points'
cx, cy = xy[0]
r = point_size
draw.ellipse(
[cx - r, cy - r, cx + r, cy + r], outline=1, fill=1)
else:
assert len(xy) > 2, 'Polygon must have points more than 2'
draw.polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
return mask
cls = np.zeros(img_shape[:2], dtype=np.int32)
ins = np.zeros_like(cls)
instances = []
for shape in shapes:
points = shape['points']
label = shape['label']
group_id = shape.get('group_id')
if group_id is None:
group_id = uuid.uuid1()
shape_type = shape.get('shape_type', None)
cls_name = label
instance = (cls_name, group_id)
if instance not in instances:
instances.append(instance)
ins_id = instances.index(instance) + 1
cls_id = label_name_to_value[cls_name]
mask = shape_to_mask(img_shape[:2], points, shape_type)
cls[mask] = cls_id
ins[mask] = ins_id
return cls, ins
def get_color_map_list(self, num_classes):
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
return color_map
def convert(self, image_dir, json_dir, dataset_save_dir):
assert osp.exists(image_dir), "The image folder does not exist!"
assert osp.exists(json_dir), "The json folder does not exist!"
if not osp.exists(dataset_save_dir):
os.makedirs(dataset_save_dir)
new_image_dir = osp.join(dataset_save_dir, "JPEGImages")
if osp.exists(new_image_dir):
raise Exception(
"The directory {} is already exist, please remove the directory first".
format(new_image_dir))
os.makedirs(new_image_dir)
for img_name in os.listdir(image_dir):
if is_pic(img_name):
shutil.copyfile(
osp.join(image_dir, img_name),
osp.join(new_image_dir, img_name))
png_dir = osp.join(dataset_save_dir, "Annotations")
if osp.exists(png_dir):
shutil.rmtree(png_dir)
os.makedirs(png_dir)
self.get_labels2ids(new_image_dir, json_dir)
self.json2png(new_image_dir, json_dir, png_dir)
ids2labels = {v: k for k, v in self.labels2ids.items()}
with open(osp.join(dataset_save_dir, 'labels.txt'), 'w') as fw:
for i in range(len(ids2labels)):
fw.write(ids2labels[i] + '\n')
class JingLing2Seg(X2Seg):
def __init__(self):
super(JingLing2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
if 'outputs' in json_info:
for output in json_info['outputs']['object']:
cls_name = output['name']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def json2png(self, image_dir, json_dir, png_dir):
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
data_shapes = []
if 'outputs' in json_info:
for output in json_info['outputs']['object']:
if 'polygon' in output.keys():
polygon = output['polygon']
name = output['name']
points = []
for i in range(1, int(len(polygon) / 2) + 1):
points.append([
polygon['x' + str(i)], polygon['y' + str(
i)]
])
shape = {
'label': name,
'points': points,
'shape_type': 'polygon'
}
data_shapes.append(shape)
if 'size' not in json_info:
continue
img_shape = (json_info['size']['height'],
json_info['size']['width'],
json_info['size']['depth'])
lbl, _ = self.shapes_to_label(
img_shape=img_shape,
shapes=data_shapes,
label_name_to_value=self.labels2ids, )
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
class LabelMe2Seg(X2Seg):
def __init__(self):
super(LabelMe2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
for shape in json_info['shapes']:
cls_name = shape['label']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def json2png(self, image_dir, json_dir, png_dir):
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
img_file = osp.join(image_dir, img_name)
img = np.asarray(PIL.Image.open(img_file))
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
lbl, _ = self.shapes_to_label(
img_shape=img.shape,
shapes=json_info['shapes'],
label_name_to_value=self.labels2ids, )
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
class EasyData2Seg(X2Seg):
def __init__(self):
super(EasyData2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
for shape in json_info["labels"]:
cls_name = shape['name']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def mask2polygon(self, mask, label):
contours, hierarchy = cv2.findContours(
(mask).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
segmentation = []
for contour in contours:
contour_list = contour.flatten().tolist()
if len(contour_list) > 4:
points = []
for i in range(0, len(contour_list), 2):
points.append([contour_list[i], contour_list[i + 1]])
shape = {
'label': label,
'points': points,
'shape_type': 'polygon'
}
segmentation.append(shape)
return segmentation
def json2png(self, image_dir, json_dir, png_dir):
from pycocotools.mask import decode
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(osp.join(image_dir, img_name))
continue
img_file = osp.join(image_dir, img_name)
img = np.asarray(PIL.Image.open(img_file))
img_h = img.shape[0]
img_w = img.shape[1]
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
data_shapes = []
for shape in json_info['labels']:
mask_dict = {}
mask_dict['size'] = [img_h, img_w]
mask_dict['counts'] = shape['mask'].encode()
mask = decode(mask_dict)
polygon = self.mask2polygon(mask, shape["name"])
data_shapes.extend(polygon)
lbl, _ = self.shapes_to_label(
img_shape=img.shape,
shapes=data_shapes,
label_name_to_value=self.labels2ids, )
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
| true | true |
1c33fa15ddbf9c5dfc357e4226f51b2734c6f579 | 738 | py | Python | nodes/List/GetTaskRenderListIndex.py | atticus-lv/RenderNode | 8a4797a2186b76fedebc5d634cff298e69089474 | [
"Apache-2.0"
] | 17 | 2021-11-21T09:26:55.000Z | 2022-03-09T06:56:01.000Z | nodes/List/GetTaskRenderListIndex.py | atticus-lv/RenderNode | 8a4797a2186b76fedebc5d634cff298e69089474 | [
"Apache-2.0"
] | 1 | 2021-12-05T13:02:48.000Z | 2021-12-06T08:02:34.000Z | nodes/List/GetTaskRenderListIndex.py | atticus-lv/RenderNode | 8a4797a2186b76fedebc5d634cff298e69089474 | [
"Apache-2.0"
] | 4 | 2021-11-23T14:49:34.000Z | 2021-12-30T15:04:58.000Z | import bpy
from bpy.props import *
from ...nodes.BASE.node_base import RenderNodeBase
class RenderNodeGetListIndex(RenderNodeBase):
"""A simple input node"""
bl_idname = 'RenderNodeGetListIndex'
bl_label = 'Get List Index'
def init(self, context):
self.create_output('RenderNodeSocketInt', "index", 'Index')
def process(self,context,id,path):
node = self.id_data.nodes.get(bpy.context.window_manager.rsn_active_list)
if not node or node.bl_idname != 'RenderNodeTaskRenderListNode': return
self.outputs[0].set_value(node.active_index)
def register():
bpy.utils.register_class(RenderNodeGetListIndex)
def unregister():
bpy.utils.unregister_class(RenderNodeGetListIndex)
| 26.357143 | 81 | 0.730352 | import bpy
from bpy.props import *
from ...nodes.BASE.node_base import RenderNodeBase
class RenderNodeGetListIndex(RenderNodeBase):
bl_idname = 'RenderNodeGetListIndex'
bl_label = 'Get List Index'
def init(self, context):
self.create_output('RenderNodeSocketInt', "index", 'Index')
def process(self,context,id,path):
node = self.id_data.nodes.get(bpy.context.window_manager.rsn_active_list)
if not node or node.bl_idname != 'RenderNodeTaskRenderListNode': return
self.outputs[0].set_value(node.active_index)
def register():
bpy.utils.register_class(RenderNodeGetListIndex)
def unregister():
bpy.utils.unregister_class(RenderNodeGetListIndex)
| true | true |
1c33fa1cca0ea17ed709ee6bbe64293dc43fa107 | 10,437 | py | Python | das_decennial/programs/schema/attributes/hhtype.py | p-b-j/uscb-das-container-public | 7f7ba44055da15d13b191180249e656e1bd398c6 | [
"MIT"
] | 1 | 2021-11-13T01:35:31.000Z | 2021-11-13T01:35:31.000Z | das_decennial/programs/schema/attributes/hhtype.py | p-b-j/uscb-das-container-public | 7f7ba44055da15d13b191180249e656e1bd398c6 | [
"MIT"
] | 1 | 2021-10-30T00:48:45.000Z | 2021-11-01T23:33:46.000Z | das_decennial/programs/schema/attributes/hhtype.py | p-b-j/uscb-das-container-public | 7f7ba44055da15d13b191180249e656e1bd398c6 | [
"MIT"
] | null | null | null | from programs.schema.attributes.abstractattribute import AbstractAttribute
from constants import CC
class HHTypeAttr(AbstractAttribute):
@staticmethod
def getName():
return CC.ATTR_HHTYPE
@staticmethod
def getLevels():
return {
'Married opposite-sex with own children under 18, under 6 yrs only' : [0],
'Married opposite-sex with own children under 18, between 6 and 17 only': [1],
'Married opposite-sex with own children under 18, both ranges' : [2],
'Married opposite-sex no own children under 18' : [3],
'Married same-sex with own children only under 6 yrs' : [4],
'Married same-sex with own children between 6 and 17' : [5],
'Married same-sex with own children in both ranges' : [6],
'Married same-sex no own children under 18' : [7],
'Cohabiting opposite-sex with own children only under 6 yrs' : [8],
'Cohabiting opposite-sex with own children between 6 and 17' : [9],
'Cohabiting opposite-sex with own children in both ranges' : [10],
'Cohabiting opposite-sex with relatives, no own children under 18' : [11],
'Cohabiting opposite-sex without relatives, no own children under 18' : [12],
'Cohabiting same-sex with own children only under 6 yrs' : [13],
'Cohabiting same-sex with own children between 6 and 17' : [14],
'Cohabiting same-sex with own children in both ranges' : [15],
'Cohabiting same-sex with relatives, no own children under 18' : [16],
'Cohabiting same-sex without relatives, no own children under 18' : [17],
'No spouse or partner, alone' : [18],
'No spouse or partner with own children under 6' : [19],
'No spouse or partner with own children between 6 and 17.' : [20],
'No spouse or partner with own children in both ranges' : [21],
'No spouse or partner living with relatives but no own children' : [22],
'No spouse or partner and no relatives, not alone.' : [23]
}
@staticmethod
def recodeHHtypeOwnChildrenUnderSix():
name = CC.HHTYPE_OWNCHILD_UNDERSIX
groupings = {
"Householder has own child under 6" : [0, 2, 4, 6, 8, 10, 13, 15, 19, 21]
}
return name, groupings
@staticmethod
def recodeHHtypeOwnChildUnder18():
name = CC.HHTYPE_OWNCHILD_UNDER18
groupings = {
"Householder has own child under 18" : [0, 1, 2, 4, 5, 6, 8, 9, 10, 13, 14, 15, 19, 20, 21]
}
return name, groupings
@staticmethod
def recodeFamily():
name = CC.HHTYPE_FAMILY
groupings = {
"Family": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 19, 20, 21, 22]
}
return name, groupings
@staticmethod
def recodeNonfamily():
name = CC.HHTYPE_NONFAMILY
groupings = {
"Non-Family": [12, 17, 18, 23]
}
return name, groupings
@staticmethod
def recodeMarriedFamily():
name = CC.HHTYPE_FAMILY_MARRIED
groupings = {
"Married Family": list(range(0,8))
}
return name, groupings
@staticmethod
def recodeOtherFamily():
name = CC.HHTYPE_FAMILY_OTHER
groupings = {
"Other Family": [8, 9, 10, 11, 13, 14, 15, 16, 19, 20, 21, 22]
}
return name, groupings
@staticmethod
def recodeHHtypeAlone():
name = CC.HHTYPE_ALONE
groupings = {
"Alone": [18]
}
return name, groupings
@staticmethod
def recodeHHtypeNotAlone():
""" everything but living alone """
name = CC.HHTYPE_NOT_ALONE
groupings = {
"Not alone": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23]
}
return name, groupings
@staticmethod
def recodeNonfamilyNotAlone():
""" Nonfamily, not alone """
name = CC.HHTYPE_NONFAMILY_NOT_ALONE
groupings = {
"Nonfamily, Not alone": [12, 17, 23]
}
return name, groupings
@staticmethod
def recodeHHtypeNotSize2():
name = CC.HHTYPE_NOT_SIZE_TWO
groupings = {
"Types inconsistent with size 2": [0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 13, 14, 15, 16, 18, 21]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeTwoWithChild():
name = CC.HHTYPE_SIZE_TWO_WITH_CHILD
groupings = {
"Size 2, householder with child" : [19, 20]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeTwoCouple():
name = CC.HHTYPE_SIZE_TWO_COUPLE
groupings = {
"Size 2, householder with spouse/unmarried partner" : [3, 7, 12, 17]
}
return name, groupings
@staticmethod
def recodeHHtypeNotSizeThree():
name = CC.HHTYPE_NOT_SIZE_THREE
groupings = {
"Type not consistent with size 3": [2, 6, 10, 15, 18]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeThreeNotMulti():
name = CC.HHTYPE_SIZE_THREE_NOT_MULTI
groupings = {
"Size 3, can't be multi": [0, 1, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 16, 17, 21, 23]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeThreeWithTwoChildren():
name = CC.HHTYPE_SIZE_THREE_WITH_TWO_CHILDREN
groupings = {
"Size 3, two children": [21]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeThreeCoupleWithOneChild():
name = CC.HHTYPE_SIZE_THREE_COUPLE_WITH_ONE_CHILD
groupings = {
"Size 3, couple with one child": [0, 1, 4, 5, 8, 9, 13, 14]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeFourNotMulti():
name = CC.HHTYPE_SIZE_FOUR_NOT_MULTI
groupings = {
"Size 4, can't be multigen": [2, 6, 10, 12, 15, 17, 23]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeFourCoupleWithTwoChildren():
name = CC.HHTYPE_SIZE_FOUR_COUPLE_WITH_TWO_CHILDREN
groupings = {
"Size 4, couple with two children": [2, 6, 10, 15]
}
return name, groupings
@staticmethod
def recodeHHtypeNotMulti():
name = CC.HHTYPE_NOT_MULTI
groupings = {
"Can never be multigen regardless of size" : [12, 17, 18, 23]
}
return name, groupings
@staticmethod
def recodeFamilyMarriedWithChildrenIndicator():
name = CC.HHTYPE_FAMILY_MARRIED_WITH_CHILDREN_INDICATOR
groupings = {
"Married without own children under 18": [3, 7],
"Married with own children under 18" : [0, 1, 2, 4, 5, 6]
}
return name, groupings
@staticmethod
def recodeFamilyOtherWithChildrenIndicator():
name = CC.HHTYPE_FAMILY_OTHER_WITH_CHILDREN_INDICATOR
groupings = {
"Other family without own children under 18": [11, 16],
"Other family with own children under 18" : [8, 9, 10, 13, 14, 15]
}
return name, groupings
@staticmethod
def recodeHHtypeCohabiting():
name = CC.HHTYPE_COHABITING
groupings = {
"Cohabiting": list(range(8, 18))
}
return name, groupings
@staticmethod
def recodeHHtypeCohabitingWithChildrenIndicator():
name = CC.HHTYPE_COHABITING_WITH_CHILDREN_INDICATOR
groupings = {
"Cohabiting without own children under 18" : [11, 12, 16, 17],
"Cohabiting with own children under 18" : [8, 9, 10, 13, 14, 15]
}
return name, groupings
@staticmethod
def recodeHHtypeNoSpouseOrPartner():
name = CC.HHTYPE_NO_SPOUSE_OR_PARTNER
groupings = {
"No spouse or partner": list(range(18, 24))
}
return name, groupings
@staticmethod
def recodeHHtypeNoSpouseOrPartnerLevels():
name = CC.HHTYPE_NO_SPOUSE_OR_PARTNER_LEVELS
groupings = {
"Alone": [18],
"With own children": [19, 20, 21],
"With relatives, no own children": [22],
"No relatives, not alone": [23]
}
return name, groupings
@staticmethod
def recodeFamilyMarriedWithChildrenLevels():
name = CC.HHTYPE_FAMILY_MARRIED_WITH_CHILDREN_LEVELS
groupings = {
"Married with children under 6 only" : [0,4],
"Married with children 6 to 17 years only" : [1,5],
"Married with children under 6 years and 6 to 17 years": [2,6]
}
return name, groupings
@staticmethod
def recodeFamilyOtherWithChildrenLevels():
name = CC.HHTYPE_FAMILY_OTHER_WITH_CHILDREN_LEVELS
groupings = {
"Other family with children under 6 only" : [8,13,19],
"Other family with children 6 to 17 years only" : [9,14,20],
"Other family with children under 6 years and 6 to 17 years": [10,15,21]
}
return name, groupings
@staticmethod
def recodeHHtypeCoupleLevels():
name = CC.HHTYPE_COUPLE_LEVELS
groupings = {
"Married": list(range(8)),
"Unmarried Partner": list(range(8,18)),
"All others": list(range(18,24))
}
return name, groupings
@staticmethod
def recodeHHtypeOppositeSexLevels():
name = CC.HHTYPE_OPPOSITE_SEX_LEVELS
groupings = {
"Married Opposite": list(range(4)),
"Partner Opposite": list(range(8,13))
}
return name, groupings
@staticmethod
def recodeHHtypeSameSexLevels():
name = CC.HHTYPE_SAME_SEX_LEVELS
groupings = {
"Married Same": list(range(4,8)),
"Partner Same": list(range(13,18))
}
return name, groupings
| 35.379661 | 107 | 0.562326 | from programs.schema.attributes.abstractattribute import AbstractAttribute
from constants import CC
class HHTypeAttr(AbstractAttribute):
@staticmethod
def getName():
return CC.ATTR_HHTYPE
@staticmethod
def getLevels():
return {
'Married opposite-sex with own children under 18, under 6 yrs only' : [0],
'Married opposite-sex with own children under 18, between 6 and 17 only': [1],
'Married opposite-sex with own children under 18, both ranges' : [2],
'Married opposite-sex no own children under 18' : [3],
'Married same-sex with own children only under 6 yrs' : [4],
'Married same-sex with own children between 6 and 17' : [5],
'Married same-sex with own children in both ranges' : [6],
'Married same-sex no own children under 18' : [7],
'Cohabiting opposite-sex with own children only under 6 yrs' : [8],
'Cohabiting opposite-sex with own children between 6 and 17' : [9],
'Cohabiting opposite-sex with own children in both ranges' : [10],
'Cohabiting opposite-sex with relatives, no own children under 18' : [11],
'Cohabiting opposite-sex without relatives, no own children under 18' : [12],
'Cohabiting same-sex with own children only under 6 yrs' : [13],
'Cohabiting same-sex with own children between 6 and 17' : [14],
'Cohabiting same-sex with own children in both ranges' : [15],
'Cohabiting same-sex with relatives, no own children under 18' : [16],
'Cohabiting same-sex without relatives, no own children under 18' : [17],
'No spouse or partner, alone' : [18],
'No spouse or partner with own children under 6' : [19],
'No spouse or partner with own children between 6 and 17.' : [20],
'No spouse or partner with own children in both ranges' : [21],
'No spouse or partner living with relatives but no own children' : [22],
'No spouse or partner and no relatives, not alone.' : [23]
}
@staticmethod
def recodeHHtypeOwnChildrenUnderSix():
name = CC.HHTYPE_OWNCHILD_UNDERSIX
groupings = {
"Householder has own child under 6" : [0, 2, 4, 6, 8, 10, 13, 15, 19, 21]
}
return name, groupings
@staticmethod
def recodeHHtypeOwnChildUnder18():
name = CC.HHTYPE_OWNCHILD_UNDER18
groupings = {
"Householder has own child under 18" : [0, 1, 2, 4, 5, 6, 8, 9, 10, 13, 14, 15, 19, 20, 21]
}
return name, groupings
@staticmethod
def recodeFamily():
name = CC.HHTYPE_FAMILY
groupings = {
"Family": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 19, 20, 21, 22]
}
return name, groupings
@staticmethod
def recodeNonfamily():
name = CC.HHTYPE_NONFAMILY
groupings = {
"Non-Family": [12, 17, 18, 23]
}
return name, groupings
@staticmethod
def recodeMarriedFamily():
name = CC.HHTYPE_FAMILY_MARRIED
groupings = {
"Married Family": list(range(0,8))
}
return name, groupings
@staticmethod
def recodeOtherFamily():
name = CC.HHTYPE_FAMILY_OTHER
groupings = {
"Other Family": [8, 9, 10, 11, 13, 14, 15, 16, 19, 20, 21, 22]
}
return name, groupings
@staticmethod
def recodeHHtypeAlone():
name = CC.HHTYPE_ALONE
groupings = {
"Alone": [18]
}
return name, groupings
@staticmethod
def recodeHHtypeNotAlone():
name = CC.HHTYPE_NOT_ALONE
groupings = {
"Not alone": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23]
}
return name, groupings
@staticmethod
def recodeNonfamilyNotAlone():
name = CC.HHTYPE_NONFAMILY_NOT_ALONE
groupings = {
"Nonfamily, Not alone": [12, 17, 23]
}
return name, groupings
@staticmethod
def recodeHHtypeNotSize2():
name = CC.HHTYPE_NOT_SIZE_TWO
groupings = {
"Types inconsistent with size 2": [0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 13, 14, 15, 16, 18, 21]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeTwoWithChild():
name = CC.HHTYPE_SIZE_TWO_WITH_CHILD
groupings = {
"Size 2, householder with child" : [19, 20]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeTwoCouple():
name = CC.HHTYPE_SIZE_TWO_COUPLE
groupings = {
"Size 2, householder with spouse/unmarried partner" : [3, 7, 12, 17]
}
return name, groupings
@staticmethod
def recodeHHtypeNotSizeThree():
name = CC.HHTYPE_NOT_SIZE_THREE
groupings = {
"Type not consistent with size 3": [2, 6, 10, 15, 18]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeThreeNotMulti():
name = CC.HHTYPE_SIZE_THREE_NOT_MULTI
groupings = {
"Size 3, can't be multi": [0, 1, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 16, 17, 21, 23]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeThreeWithTwoChildren():
name = CC.HHTYPE_SIZE_THREE_WITH_TWO_CHILDREN
groupings = {
"Size 3, two children": [21]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeThreeCoupleWithOneChild():
name = CC.HHTYPE_SIZE_THREE_COUPLE_WITH_ONE_CHILD
groupings = {
"Size 3, couple with one child": [0, 1, 4, 5, 8, 9, 13, 14]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeFourNotMulti():
name = CC.HHTYPE_SIZE_FOUR_NOT_MULTI
groupings = {
"Size 4, can't be multigen": [2, 6, 10, 12, 15, 17, 23]
}
return name, groupings
@staticmethod
def recodeHHtypeSizeFourCoupleWithTwoChildren():
name = CC.HHTYPE_SIZE_FOUR_COUPLE_WITH_TWO_CHILDREN
groupings = {
"Size 4, couple with two children": [2, 6, 10, 15]
}
return name, groupings
@staticmethod
def recodeHHtypeNotMulti():
name = CC.HHTYPE_NOT_MULTI
groupings = {
"Can never be multigen regardless of size" : [12, 17, 18, 23]
}
return name, groupings
@staticmethod
def recodeFamilyMarriedWithChildrenIndicator():
name = CC.HHTYPE_FAMILY_MARRIED_WITH_CHILDREN_INDICATOR
groupings = {
"Married without own children under 18": [3, 7],
"Married with own children under 18" : [0, 1, 2, 4, 5, 6]
}
return name, groupings
@staticmethod
def recodeFamilyOtherWithChildrenIndicator():
name = CC.HHTYPE_FAMILY_OTHER_WITH_CHILDREN_INDICATOR
groupings = {
"Other family without own children under 18": [11, 16],
"Other family with own children under 18" : [8, 9, 10, 13, 14, 15]
}
return name, groupings
@staticmethod
def recodeHHtypeCohabiting():
name = CC.HHTYPE_COHABITING
groupings = {
"Cohabiting": list(range(8, 18))
}
return name, groupings
@staticmethod
def recodeHHtypeCohabitingWithChildrenIndicator():
name = CC.HHTYPE_COHABITING_WITH_CHILDREN_INDICATOR
groupings = {
"Cohabiting without own children under 18" : [11, 12, 16, 17],
"Cohabiting with own children under 18" : [8, 9, 10, 13, 14, 15]
}
return name, groupings
@staticmethod
def recodeHHtypeNoSpouseOrPartner():
name = CC.HHTYPE_NO_SPOUSE_OR_PARTNER
groupings = {
"No spouse or partner": list(range(18, 24))
}
return name, groupings
@staticmethod
def recodeHHtypeNoSpouseOrPartnerLevels():
name = CC.HHTYPE_NO_SPOUSE_OR_PARTNER_LEVELS
groupings = {
"Alone": [18],
"With own children": [19, 20, 21],
"With relatives, no own children": [22],
"No relatives, not alone": [23]
}
return name, groupings
@staticmethod
def recodeFamilyMarriedWithChildrenLevels():
name = CC.HHTYPE_FAMILY_MARRIED_WITH_CHILDREN_LEVELS
groupings = {
"Married with children under 6 only" : [0,4],
"Married with children 6 to 17 years only" : [1,5],
"Married with children under 6 years and 6 to 17 years": [2,6]
}
return name, groupings
@staticmethod
def recodeFamilyOtherWithChildrenLevels():
name = CC.HHTYPE_FAMILY_OTHER_WITH_CHILDREN_LEVELS
groupings = {
"Other family with children under 6 only" : [8,13,19],
"Other family with children 6 to 17 years only" : [9,14,20],
"Other family with children under 6 years and 6 to 17 years": [10,15,21]
}
return name, groupings
@staticmethod
def recodeHHtypeCoupleLevels():
name = CC.HHTYPE_COUPLE_LEVELS
groupings = {
"Married": list(range(8)),
"Unmarried Partner": list(range(8,18)),
"All others": list(range(18,24))
}
return name, groupings
@staticmethod
def recodeHHtypeOppositeSexLevels():
name = CC.HHTYPE_OPPOSITE_SEX_LEVELS
groupings = {
"Married Opposite": list(range(4)),
"Partner Opposite": list(range(8,13))
}
return name, groupings
@staticmethod
def recodeHHtypeSameSexLevels():
name = CC.HHTYPE_SAME_SEX_LEVELS
groupings = {
"Married Same": list(range(4,8)),
"Partner Same": list(range(13,18))
}
return name, groupings
| true | true |
1c33fbacc173c5443bc0886f17a8de69e63f17c2 | 196 | py | Python | example/myapp/models.py | shamanu4/django-fine-uploader | 8b53fdbaa27f749bf103b77d168fdd5e02def4e5 | [
"MIT"
] | null | null | null | example/myapp/models.py | shamanu4/django-fine-uploader | 8b53fdbaa27f749bf103b77d168fdd5e02def4e5 | [
"MIT"
] | null | null | null | example/myapp/models.py | shamanu4/django-fine-uploader | 8b53fdbaa27f749bf103b77d168fdd5e02def4e5 | [
"MIT"
] | null | null | null | from __future__ import unicode_literals
from django.db import models
class FineFile(models.Model):
fine_file = models.FileField()
def __str__(self):
return self.fine_file.name
| 17.818182 | 39 | 0.739796 | from __future__ import unicode_literals
from django.db import models
class FineFile(models.Model):
fine_file = models.FileField()
def __str__(self):
return self.fine_file.name
| true | true |
1c33fc85f24467f19a7d8f96bb4425aba7affc44 | 24,545 | py | Python | src/garage/torch/modules/gaussian_mlp_module.py | waldow90/garage | 1ea04b8b90d2da0d7da10e8604a144018b61b81c | [
"MIT"
] | 1 | 2020-02-19T00:01:29.000Z | 2020-02-19T00:01:29.000Z | src/garage/torch/modules/gaussian_mlp_module.py | Ashutosh-Adhikari/garage | 482a26a07d46091f878c41b582f1478588e397ff | [
"MIT"
] | null | null | null | src/garage/torch/modules/gaussian_mlp_module.py | Ashutosh-Adhikari/garage | 482a26a07d46091f878c41b582f1478588e397ff | [
"MIT"
] | 1 | 2020-02-13T12:05:35.000Z | 2020-02-13T12:05:35.000Z | """GaussianMLPModule."""
import abc
import torch
from torch import nn
from torch.distributions import Normal
from torch.distributions.independent import Independent
from garage.torch.modules.mlp_module import MLPModule
from garage.torch.modules.multi_headed_mlp_module import MultiHeadedMLPModule
class GaussianMLPBaseModule(nn.Module):
"""Base of GaussianMLPModel.
Args:
input_dim (int): Input dimension of the model.
output_dim (int): Output dimension of the model.
hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for mean. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a torch.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
torch.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
torch.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a torch.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
torch.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
torch.Tensor.
learn_std (bool): Is std trainable.
init_std (float): Initial value for std.
(plain value - not log or exponentiated).
std_hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for std. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
min_std (float): If not None, the std is at least the value of min_std,
to avoid numerical issues (plain value - not log or exponentiated).
max_std (float): If not None, the std is at most the value of max_std,
to avoid numerical issues (plain value - not log or exponentiated).
std_hidden_nonlinearity (callable): Nonlinearity for each hidden layer
in the std network.
std_hidden_w_init (callable): Initializer function for the weight
of hidden layer (s).
std_hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s).
std_output_nonlinearity (callable): Activation function for output
dense layer in the std network. It should return a torch.Tensor.
Set it to None to maintain a linear activation.
std_output_w_init (callable): Initializer function for the weight
of output dense layer(s) in the std network.
std_parameterization (str): How the std should be parametrized. There
are two options:
- exp: the logarithm of the std will be stored, and applied a
exponential transformation.
- softplus: the std will be computed as log(1+exp(x)).
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
input_dim,
output_dim,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
hidden_w_init=nn.init.xavier_uniform_,
hidden_b_init=nn.init.zeros_,
output_nonlinearity=None,
output_w_init=nn.init.xavier_uniform_,
output_b_init=nn.init.zeros_,
learn_std=True,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=torch.tanh,
std_hidden_w_init=nn.init.xavier_uniform_,
std_hidden_b_init=nn.init.zeros_,
std_output_nonlinearity=None,
std_output_w_init=nn.init.xavier_uniform_,
std_parameterization='exp',
layer_normalization=False):
super().__init__()
self._input_dim = input_dim
self._hidden_sizes = hidden_sizes
self._action_dim = output_dim
self._learn_std = learn_std
self._std_hidden_sizes = std_hidden_sizes
self._min_std = min_std
self._max_std = max_std
self._std_hidden_nonlinearity = std_hidden_nonlinearity
self._std_hidden_w_init = std_hidden_w_init
self._std_hidden_b_init = std_hidden_b_init
self._std_output_nonlinearity = std_output_nonlinearity
self._std_output_w_init = std_output_w_init
self._std_parameterization = std_parameterization
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._layer_normalization = layer_normalization
if self._std_parameterization not in ('exp', 'softplus'):
raise NotImplementedError
init_std_param = torch.Tensor([init_std]).log()
if self._learn_std:
self._init_std = torch.nn.Parameter(init_std_param)
else:
self._init_std = init_std_param
self._min_std_param = self._max_std_param = None
if min_std is not None:
self._min_std_param = torch.Tensor([min_std]).log()
if max_std is not None:
self._max_std_param = torch.Tensor([max_std]).log()
@abc.abstractmethod
def _get_mean_and_log_std(self, *inputs):
pass
def forward(self, *inputs):
"""Forward method.
Args:
*inputs: Input to the module.
Returns:
torch.Tensor: Module output.
"""
mean, log_std_uncentered = self._get_mean_and_log_std(*inputs)
if self._min_std_param or self._max_std_param:
log_std_uncentered = log_std_uncentered.clamp(
min=self._to_scalar_if_not_none(self._min_std_param),
max=self._to_scalar_if_not_none(self._max_std_param))
if self._std_parameterization == 'exp':
std = log_std_uncentered.exp()
else:
std = log_std_uncentered.exp().exp().add(1.).log()
dist = Independent(Normal(mean, std), 1)
return dist
# pylint: disable=no-self-use
def _to_scalar_if_not_none(self, tensor):
"""Convert torch.Tensor of a single value to a Python number.
Args:
tensor (torch.Tensor): A torch.Tensor of a single value.
Returns:
float: The value of tensor.
"""
return None if tensor is None else tensor.item()
class GaussianMLPModule(GaussianMLPBaseModule):
"""GaussianMLPModule that mean and std share the same network.
Args:
input_dim (int): Input dimension of the model.
output_dim (int): Output dimension of the model.
hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for mean. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a torch.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
torch.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
torch.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a torch.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
torch.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
torch.Tensor.
learn_std (bool): Is std trainable.
init_std (float): Initial value for std.
(plain value - not log or exponentiated).
min_std (float): If not None, the std is at least the value of min_std,
to avoid numerical issues (plain value - not log or exponentiated).
max_std (float): If not None, the std is at most the value of max_std,
to avoid numerical issues (plain value - not log or exponentiated).
std_parameterization (str): How the std should be parametrized. There
are two options:
- exp: the logarithm of the std will be stored, and applied a
exponential transformation
- softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
input_dim,
output_dim,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
hidden_w_init=nn.init.xavier_uniform_,
hidden_b_init=nn.init.zeros_,
output_nonlinearity=None,
output_w_init=nn.init.xavier_uniform_,
output_b_init=nn.init.zeros_,
learn_std=True,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_parameterization='exp',
layer_normalization=False):
super(GaussianMLPModule,
self).__init__(input_dim=input_dim,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization)
self._mean_module = MLPModule(
input_dim=self._input_dim,
output_dim=self._action_dim,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
layer_normalization=self._layer_normalization)
def _get_mean_and_log_std(self, *inputs):
"""Get mean and std of Gaussian distribution given inputs.
Args:
*inputs: Input to the module.
Returns:
tuple:
* mean (torch.Tensor): The mean of Gaussian distribution.
* std (torch.Tensor): The variance of Gaussian distribution.
"""
assert len(inputs) == 1
mean = self._mean_module(*inputs)
broadcast_shape = list(inputs[0].shape[:-1]) + [self._action_dim]
uncentered_log_std = torch.zeros(*broadcast_shape) + self._init_std
return mean, uncentered_log_std
class GaussianMLPIndependentStdModule(GaussianMLPBaseModule):
"""GaussianMLPModule which has two different mean and std network.
Args:
input_dim (int): Input dimension of the model.
output_dim (int): Output dimension of the model.
hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for mean. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a torch.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
torch.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
torch.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a torch.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
torch.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
torch.Tensor.
learn_std (bool): Is std trainable.
init_std (float): Initial value for std.
(plain value - not log or exponentiated).
min_std (float): If not None, the std is at least the value of min_std,
to avoid numerical issues (plain value - not log or exponentiated).
max_std (float): If not None, the std is at most the value of max_std,
to avoid numerical issues (plain value - not log or exponentiated).
std_hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for std. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
std_hidden_nonlinearity (callable): Nonlinearity for each hidden layer
in the std network.
std_hidden_w_init (callable): Initializer function for the weight
of hidden layer (s).
std_hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s).
std_output_nonlinearity (callable): Activation function for output
dense layer in the std network. It should return a torch.Tensor.
Set it to None to maintain a linear activation.
std_output_w_init (callable): Initializer function for the weight
of output dense layer(s) in the std network.
std_parameterization (str): How the std should be parametrized. There
are two options:
- exp: the logarithm of the std will be stored, and applied a
exponential transformation
- softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
input_dim,
output_dim,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
hidden_w_init=nn.init.xavier_uniform_,
hidden_b_init=nn.init.zeros_,
output_nonlinearity=None,
output_w_init=nn.init.xavier_uniform_,
output_b_init=nn.init.zeros_,
learn_std=True,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=torch.tanh,
std_hidden_w_init=nn.init.xavier_uniform_,
std_hidden_b_init=nn.init.zeros_,
std_output_nonlinearity=None,
std_output_w_init=nn.init.xavier_uniform_,
std_parameterization='exp',
layer_normalization=False):
super(GaussianMLPIndependentStdModule,
self).__init__(input_dim=input_dim,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_hidden_sizes=std_hidden_sizes,
std_hidden_nonlinearity=std_hidden_nonlinearity,
std_hidden_w_init=std_hidden_w_init,
std_hidden_b_init=std_hidden_b_init,
std_output_nonlinearity=std_output_nonlinearity,
std_output_w_init=std_output_w_init,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization)
self._mean_module = MLPModule(
input_dim=self._input_dim,
output_dim=self._action_dim,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
layer_normalization=self._layer_normalization)
self._log_std_module = MLPModule(
input_dim=self._input_dim,
output_dim=self._action_dim,
hidden_sizes=self._std_hidden_sizes,
hidden_nonlinearity=self._std_hidden_nonlinearity,
hidden_w_init=self._std_hidden_w_init,
hidden_b_init=self._std_hidden_b_init,
output_nonlinearity=self._std_output_nonlinearity,
output_w_init=self._std_output_w_init,
output_b_init=self._init_std_b,
layer_normalization=self._layer_normalization)
def _init_std_b(self, b):
"""Default bias initialization function.
Args:
b (torch.Tensor): The bias tensor.
Returns:
torch.Tensor: The bias tensor itself.
"""
return nn.init.constant_(b, self._init_std.item())
def _get_mean_and_log_std(self, *inputs):
"""Get mean and std of Gaussian distribution given inputs.
Args:
*inputs: Input to the module.
Returns:
tuple:
* mean (torch.Tensor): The mean of Gaussian distribution.
* std (torch.Tensor): The variance of Gaussian distribution.
"""
return self._mean_module(*inputs), self._log_std_module(*inputs)
class GaussianMLPTwoHeadedModule(GaussianMLPBaseModule):
"""GaussianMLPModule which has only one mean network.
Args:
input_dim (int): Input dimension of the model.
output_dim (int): Output dimension of the model.
hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for mean. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a torch.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
torch.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
torch.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a torch.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
torch.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
torch.Tensor.
learn_std (bool): Is std trainable.
init_std (float): Initial value for std.
(plain value - not log or exponentiated).
min_std (float): If not None, the std is at least the value of min_std,
to avoid numerical issues (plain value - not log or exponentiated).
max_std (float): If not None, the std is at most the value of max_std,
to avoid numerical issues (plain value - not log or exponentiated).
std_parameterization (str): How the std should be parametrized. There
are two options:
- exp: the logarithm of the std will be stored, and applied a
exponential transformation
- softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
input_dim,
output_dim,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
hidden_w_init=nn.init.xavier_uniform_,
hidden_b_init=nn.init.zeros_,
output_nonlinearity=None,
output_w_init=nn.init.xavier_uniform_,
output_b_init=nn.init.zeros_,
learn_std=True,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_parameterization='exp',
layer_normalization=False):
super(GaussianMLPTwoHeadedModule,
self).__init__(input_dim=input_dim,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization)
self._shared_mean_log_std_network = MultiHeadedMLPModule(
n_heads=2,
input_dim=self._input_dim,
output_dims=self._action_dim,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearities=self._output_nonlinearity,
output_w_inits=self._output_w_init,
output_b_inits=[
nn.init.zeros_,
lambda x: nn.init.constant_(x, self._init_std.item())
],
layer_normalization=self._layer_normalization)
def _get_mean_and_log_std(self, *inputs):
"""Get mean and std of Gaussian distribution given inputs.
Args:
*inputs: Input to the module.
Returns:
tuple:
* mean (torch.Tensor): The mean of Gaussian distribution.
* std (torch.Tensor): The variance of Gaussian distribution.
"""
return self._shared_mean_log_std_network(*inputs)
| 45.369686 | 79 | 0.612508 | import abc
import torch
from torch import nn
from torch.distributions import Normal
from torch.distributions.independent import Independent
from garage.torch.modules.mlp_module import MLPModule
from garage.torch.modules.multi_headed_mlp_module import MultiHeadedMLPModule
class GaussianMLPBaseModule(nn.Module):
def __init__(self,
input_dim,
output_dim,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
hidden_w_init=nn.init.xavier_uniform_,
hidden_b_init=nn.init.zeros_,
output_nonlinearity=None,
output_w_init=nn.init.xavier_uniform_,
output_b_init=nn.init.zeros_,
learn_std=True,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=torch.tanh,
std_hidden_w_init=nn.init.xavier_uniform_,
std_hidden_b_init=nn.init.zeros_,
std_output_nonlinearity=None,
std_output_w_init=nn.init.xavier_uniform_,
std_parameterization='exp',
layer_normalization=False):
super().__init__()
self._input_dim = input_dim
self._hidden_sizes = hidden_sizes
self._action_dim = output_dim
self._learn_std = learn_std
self._std_hidden_sizes = std_hidden_sizes
self._min_std = min_std
self._max_std = max_std
self._std_hidden_nonlinearity = std_hidden_nonlinearity
self._std_hidden_w_init = std_hidden_w_init
self._std_hidden_b_init = std_hidden_b_init
self._std_output_nonlinearity = std_output_nonlinearity
self._std_output_w_init = std_output_w_init
self._std_parameterization = std_parameterization
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._layer_normalization = layer_normalization
if self._std_parameterization not in ('exp', 'softplus'):
raise NotImplementedError
init_std_param = torch.Tensor([init_std]).log()
if self._learn_std:
self._init_std = torch.nn.Parameter(init_std_param)
else:
self._init_std = init_std_param
self._min_std_param = self._max_std_param = None
if min_std is not None:
self._min_std_param = torch.Tensor([min_std]).log()
if max_std is not None:
self._max_std_param = torch.Tensor([max_std]).log()
@abc.abstractmethod
def _get_mean_and_log_std(self, *inputs):
pass
def forward(self, *inputs):
mean, log_std_uncentered = self._get_mean_and_log_std(*inputs)
if self._min_std_param or self._max_std_param:
log_std_uncentered = log_std_uncentered.clamp(
min=self._to_scalar_if_not_none(self._min_std_param),
max=self._to_scalar_if_not_none(self._max_std_param))
if self._std_parameterization == 'exp':
std = log_std_uncentered.exp()
else:
std = log_std_uncentered.exp().exp().add(1.).log()
dist = Independent(Normal(mean, std), 1)
return dist
def _to_scalar_if_not_none(self, tensor):
return None if tensor is None else tensor.item()
class GaussianMLPModule(GaussianMLPBaseModule):
def __init__(self,
input_dim,
output_dim,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
hidden_w_init=nn.init.xavier_uniform_,
hidden_b_init=nn.init.zeros_,
output_nonlinearity=None,
output_w_init=nn.init.xavier_uniform_,
output_b_init=nn.init.zeros_,
learn_std=True,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_parameterization='exp',
layer_normalization=False):
super(GaussianMLPModule,
self).__init__(input_dim=input_dim,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization)
self._mean_module = MLPModule(
input_dim=self._input_dim,
output_dim=self._action_dim,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
layer_normalization=self._layer_normalization)
def _get_mean_and_log_std(self, *inputs):
assert len(inputs) == 1
mean = self._mean_module(*inputs)
broadcast_shape = list(inputs[0].shape[:-1]) + [self._action_dim]
uncentered_log_std = torch.zeros(*broadcast_shape) + self._init_std
return mean, uncentered_log_std
class GaussianMLPIndependentStdModule(GaussianMLPBaseModule):
def __init__(self,
input_dim,
output_dim,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
hidden_w_init=nn.init.xavier_uniform_,
hidden_b_init=nn.init.zeros_,
output_nonlinearity=None,
output_w_init=nn.init.xavier_uniform_,
output_b_init=nn.init.zeros_,
learn_std=True,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=torch.tanh,
std_hidden_w_init=nn.init.xavier_uniform_,
std_hidden_b_init=nn.init.zeros_,
std_output_nonlinearity=None,
std_output_w_init=nn.init.xavier_uniform_,
std_parameterization='exp',
layer_normalization=False):
super(GaussianMLPIndependentStdModule,
self).__init__(input_dim=input_dim,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_hidden_sizes=std_hidden_sizes,
std_hidden_nonlinearity=std_hidden_nonlinearity,
std_hidden_w_init=std_hidden_w_init,
std_hidden_b_init=std_hidden_b_init,
std_output_nonlinearity=std_output_nonlinearity,
std_output_w_init=std_output_w_init,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization)
self._mean_module = MLPModule(
input_dim=self._input_dim,
output_dim=self._action_dim,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
layer_normalization=self._layer_normalization)
self._log_std_module = MLPModule(
input_dim=self._input_dim,
output_dim=self._action_dim,
hidden_sizes=self._std_hidden_sizes,
hidden_nonlinearity=self._std_hidden_nonlinearity,
hidden_w_init=self._std_hidden_w_init,
hidden_b_init=self._std_hidden_b_init,
output_nonlinearity=self._std_output_nonlinearity,
output_w_init=self._std_output_w_init,
output_b_init=self._init_std_b,
layer_normalization=self._layer_normalization)
def _init_std_b(self, b):
return nn.init.constant_(b, self._init_std.item())
def _get_mean_and_log_std(self, *inputs):
return self._mean_module(*inputs), self._log_std_module(*inputs)
class GaussianMLPTwoHeadedModule(GaussianMLPBaseModule):
def __init__(self,
input_dim,
output_dim,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
hidden_w_init=nn.init.xavier_uniform_,
hidden_b_init=nn.init.zeros_,
output_nonlinearity=None,
output_w_init=nn.init.xavier_uniform_,
output_b_init=nn.init.zeros_,
learn_std=True,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_parameterization='exp',
layer_normalization=False):
super(GaussianMLPTwoHeadedModule,
self).__init__(input_dim=input_dim,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization)
self._shared_mean_log_std_network = MultiHeadedMLPModule(
n_heads=2,
input_dim=self._input_dim,
output_dims=self._action_dim,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearities=self._output_nonlinearity,
output_w_inits=self._output_w_init,
output_b_inits=[
nn.init.zeros_,
lambda x: nn.init.constant_(x, self._init_std.item())
],
layer_normalization=self._layer_normalization)
def _get_mean_and_log_std(self, *inputs):
return self._shared_mean_log_std_network(*inputs)
| true | true |
1c33fcc9c8a2484f4594c3812636622d2fa80cda | 4,296 | py | Python | lm_eval/tasks/pile.py | ucinlp/lm-evaluation-harness | 52bb90e2a161dc7c5d93478406c4cfe489caf2b2 | [
"MIT"
] | null | null | null | lm_eval/tasks/pile.py | ucinlp/lm-evaluation-harness | 52bb90e2a161dc7c5d93478406c4cfe489caf2b2 | [
"MIT"
] | null | null | null | lm_eval/tasks/pile.py | ucinlp/lm-evaluation-harness | 52bb90e2a161dc7c5d93478406c4cfe489caf2b2 | [
"MIT"
] | null | null | null | """
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://arxiv.org/pdf/2101.00027.pdf
The Pile is a 825 GiB diverse, open source language modelling data set that consists
of 22 smaller, high-quality datasets combined together. To score well on Pile
BPB (bits per byte), a model must be able to understand many disparate domains
including books, github repositories, webpages, chat logs, and medical, physics,
math, computer science, and philosophy papers.
Homepage: https://pile.eleuther.ai/
"""
import os
import lm_dataformat
import abc
import numpy as np
from lm_eval.base import rf, PerplexityTask
from ..metrics import mean, matthews_corrcoef, f1_score
from ..utils import general_detokenize
from best_download import download_file
_CITATION = """
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
"""
class PilePerplexityTask(PerplexityTask, abc.ABC):
VERSION = 1
PILE_SET_NAME = None
VAL_PATH = 'data/pile/val.jsonl.zst'
TEST_PATH = 'data/pile/test.jsonl.zst'
def download(self):
# TODO: separate pile val/test out by component so we don't have to scan the entire file once per set
if not os.path.exists("data/pile/test.jsonl.zst"):
# todo use new best_download fallback api
os.makedirs("data/pile/", exist_ok=True)
download_file("http://eaidata.bmk.sh/data/pile/val.jsonl.zst", local_file=self.VAL_PATH, expected_checksum="264c875d8bbd355d8daa9d032b75fd8fb91606218bb84dd1155b203fcd5fab92")
download_file("http://eaidata.bmk.sh/data/pile/test.jsonl.zst", local_file=self.TEST_PATH, expected_checksum="0bb28c52d0b5596d389bf179ce2d43bf7f7ffae76b0d2d20b180c97f62e0975e")
def validation_docs(self):
rdr = lm_dataformat.Reader(self.VAL_PATH)
for doc, metadata in rdr.stream_data(get_meta=True):
if metadata["pile_set_name"] == self.PILE_SET_NAME:
yield doc
def test_docs(self):
rdr = lm_dataformat.Reader(self.TEST_PATH)
for doc, metadata in rdr.stream_data(get_meta=True):
if metadata["pile_set_name"] == self.PILE_SET_NAME:
yield doc
def has_validation_docs(self):
return True
def has_test_docs(self):
return True
class PileArxiv(PilePerplexityTask):
PILE_SET_NAME = "ArXiv"
class PileBooks3(PilePerplexityTask):
PILE_SET_NAME = "Books3"
class PileBookCorpus2(PilePerplexityTask):
PILE_SET_NAME = "BookCorpus2"
class PileDmMathematics(PilePerplexityTask):
PILE_SET_NAME = "DM Mathematics"
class PileEnron(PilePerplexityTask):
PILE_SET_NAME = "Enron Emails"
class PileEuroparl(PilePerplexityTask):
PILE_SET_NAME = "EuroParl"
class PileFreeLaw(PilePerplexityTask):
PILE_SET_NAME = "FreeLaw"
class PileGithub(PilePerplexityTask):
PILE_SET_NAME = "Github"
class PileGutenberg(PilePerplexityTask):
PILE_SET_NAME = "Gutenberg (PG-19)"
class PileHackernews(PilePerplexityTask):
PILE_SET_NAME = "HackerNews"
class PileNIHExporter(PilePerplexityTask):
PILE_SET_NAME = "NIH ExPorter"
class PileOpenSubtitles(PilePerplexityTask):
PILE_SET_NAME = "OpenSubtitles"
class PileOpenWebText2(PilePerplexityTask):
PILE_SET_NAME = "OpenWebText2"
class PilePhilPapers(PilePerplexityTask):
PILE_SET_NAME = "PhilPapers"
class PilePileCc(PilePerplexityTask):
PILE_SET_NAME = "Pile-CC"
class PilePubmedAbstracts(PilePerplexityTask):
PILE_SET_NAME = "PubMed Abstracts"
class PilePubmedCentral(PilePerplexityTask):
PILE_SET_NAME = "PubMed Central"
class PileStackExchange(PilePerplexityTask):
PILE_SET_NAME = "StackExchange"
class PileUspto(PilePerplexityTask):
PILE_SET_NAME = "USPTO Backgrounds"
class PileUbuntuIrc(PilePerplexityTask):
PILE_SET_NAME = "Ubuntu IRC"
class PileWikipedia(PilePerplexityTask):
PILE_SET_NAME = "Wikipedia (en)"
class PileYoutubeSubtitles(PilePerplexityTask):
PILE_SET_NAME = "YoutubeSubtitles"
| 27.896104 | 221 | 0.744646 | import os
import lm_dataformat
import abc
import numpy as np
from lm_eval.base import rf, PerplexityTask
from ..metrics import mean, matthews_corrcoef, f1_score
from ..utils import general_detokenize
from best_download import download_file
_CITATION = """
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
"""
class PilePerplexityTask(PerplexityTask, abc.ABC):
VERSION = 1
PILE_SET_NAME = None
VAL_PATH = 'data/pile/val.jsonl.zst'
TEST_PATH = 'data/pile/test.jsonl.zst'
def download(self):
if not os.path.exists("data/pile/test.jsonl.zst"):
# todo use new best_download fallback api
os.makedirs("data/pile/", exist_ok=True)
download_file("http://eaidata.bmk.sh/data/pile/val.jsonl.zst", local_file=self.VAL_PATH, expected_checksum="264c875d8bbd355d8daa9d032b75fd8fb91606218bb84dd1155b203fcd5fab92")
download_file("http://eaidata.bmk.sh/data/pile/test.jsonl.zst", local_file=self.TEST_PATH, expected_checksum="0bb28c52d0b5596d389bf179ce2d43bf7f7ffae76b0d2d20b180c97f62e0975e")
def validation_docs(self):
rdr = lm_dataformat.Reader(self.VAL_PATH)
for doc, metadata in rdr.stream_data(get_meta=True):
if metadata["pile_set_name"] == self.PILE_SET_NAME:
yield doc
def test_docs(self):
rdr = lm_dataformat.Reader(self.TEST_PATH)
for doc, metadata in rdr.stream_data(get_meta=True):
if metadata["pile_set_name"] == self.PILE_SET_NAME:
yield doc
def has_validation_docs(self):
return True
def has_test_docs(self):
return True
class PileArxiv(PilePerplexityTask):
PILE_SET_NAME = "ArXiv"
class PileBooks3(PilePerplexityTask):
PILE_SET_NAME = "Books3"
class PileBookCorpus2(PilePerplexityTask):
PILE_SET_NAME = "BookCorpus2"
class PileDmMathematics(PilePerplexityTask):
PILE_SET_NAME = "DM Mathematics"
class PileEnron(PilePerplexityTask):
PILE_SET_NAME = "Enron Emails"
class PileEuroparl(PilePerplexityTask):
PILE_SET_NAME = "EuroParl"
class PileFreeLaw(PilePerplexityTask):
PILE_SET_NAME = "FreeLaw"
class PileGithub(PilePerplexityTask):
PILE_SET_NAME = "Github"
class PileGutenberg(PilePerplexityTask):
PILE_SET_NAME = "Gutenberg (PG-19)"
class PileHackernews(PilePerplexityTask):
PILE_SET_NAME = "HackerNews"
class PileNIHExporter(PilePerplexityTask):
PILE_SET_NAME = "NIH ExPorter"
class PileOpenSubtitles(PilePerplexityTask):
PILE_SET_NAME = "OpenSubtitles"
class PileOpenWebText2(PilePerplexityTask):
PILE_SET_NAME = "OpenWebText2"
class PilePhilPapers(PilePerplexityTask):
PILE_SET_NAME = "PhilPapers"
class PilePileCc(PilePerplexityTask):
PILE_SET_NAME = "Pile-CC"
class PilePubmedAbstracts(PilePerplexityTask):
PILE_SET_NAME = "PubMed Abstracts"
class PilePubmedCentral(PilePerplexityTask):
PILE_SET_NAME = "PubMed Central"
class PileStackExchange(PilePerplexityTask):
PILE_SET_NAME = "StackExchange"
class PileUspto(PilePerplexityTask):
PILE_SET_NAME = "USPTO Backgrounds"
class PileUbuntuIrc(PilePerplexityTask):
PILE_SET_NAME = "Ubuntu IRC"
class PileWikipedia(PilePerplexityTask):
PILE_SET_NAME = "Wikipedia (en)"
class PileYoutubeSubtitles(PilePerplexityTask):
PILE_SET_NAME = "YoutubeSubtitles"
| true | true |
1c33fd7758a8b0634c999edfd18b304199a911dc | 75,657 | py | Python | sdk/python/pulumi_azure_native/compute/v20200930/outputs.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | 31 | 2020-09-21T09:41:01.000Z | 2021-02-26T13:21:59.000Z | sdk/python/pulumi_azure_native/compute/v20200930/outputs.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | 231 | 2020-09-21T09:38:45.000Z | 2021-03-01T11:16:03.000Z | sdk/python/pulumi_azure_native/compute/v20200930/outputs.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | 4 | 2020-09-29T14:14:59.000Z | 2021-02-10T20:38:16.000Z | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from . import outputs
from ._enums import *
__all__ = [
'CreationDataResponse',
'DataDiskImageEncryptionResponse',
'DisallowedResponse',
'DiskSkuResponse',
'EncryptionImagesResponse',
'EncryptionResponse',
'EncryptionSetIdentityResponse',
'EncryptionSettingsCollectionResponse',
'EncryptionSettingsElementResponse',
'ExtendedLocationResponse',
'GalleryApplicationVersionPublishingProfileResponse',
'GalleryArtifactVersionSourceResponse',
'GalleryDataDiskImageResponse',
'GalleryIdentifierResponse',
'GalleryImageFeatureResponse',
'GalleryImageIdentifierResponse',
'GalleryImageVersionPublishingProfileResponse',
'GalleryImageVersionStorageProfileResponse',
'GalleryOSDiskImageResponse',
'ImageDiskReferenceResponse',
'ImagePurchasePlanResponse',
'KeyForDiskEncryptionSetResponse',
'KeyVaultAndKeyReferenceResponse',
'KeyVaultAndSecretReferenceResponse',
'OSDiskImageEncryptionResponse',
'PrivateEndpointConnectionResponse',
'PrivateEndpointResponse',
'PrivateLinkServiceConnectionStateResponse',
'PurchasePlanResponse',
'RecommendedMachineConfigurationResponse',
'RegionalReplicationStatusResponse',
'ReplicationStatusResponse',
'ResourceRangeResponse',
'ShareInfoElementResponse',
'SharingProfileGroupResponse',
'SharingProfileResponse',
'SnapshotSkuResponse',
'SourceVaultResponse',
'TargetRegionResponse',
'UserArtifactManageResponse',
'UserArtifactSourceResponse',
]
@pulumi.output_type
class CreationDataResponse(dict):
"""
Data used when creating a disk.
"""
def __init__(__self__, *,
create_option: str,
source_unique_id: str,
gallery_image_reference: Optional['outputs.ImageDiskReferenceResponse'] = None,
image_reference: Optional['outputs.ImageDiskReferenceResponse'] = None,
logical_sector_size: Optional[int] = None,
source_resource_id: Optional[str] = None,
source_uri: Optional[str] = None,
storage_account_id: Optional[str] = None,
upload_size_bytes: Optional[float] = None):
"""
Data used when creating a disk.
:param str create_option: This enumerates the possible sources of a disk's creation.
:param str source_unique_id: If this field is set, this is the unique id identifying the source of this resource.
:param 'ImageDiskReferenceResponseArgs' gallery_image_reference: Required if creating from a Gallery Image. The id of the ImageDiskReference will be the ARM id of the shared galley image version from which to create a disk.
:param 'ImageDiskReferenceResponseArgs' image_reference: Disk source information.
:param int logical_sector_size: Logical sector size in bytes for Ultra disks. Supported values are 512 ad 4096. 4096 is the default.
:param str source_resource_id: If createOption is Copy, this is the ARM id of the source snapshot or disk.
:param str source_uri: If createOption is Import, this is the URI of a blob to be imported into a managed disk.
:param str storage_account_id: Required if createOption is Import. The Azure Resource Manager identifier of the storage account containing the blob to import as a disk.
:param float upload_size_bytes: If createOption is Upload, this is the size of the contents of the upload including the VHD footer. This value should be between 20972032 (20 MiB + 512 bytes for the VHD footer) and 35183298347520 bytes (32 TiB + 512 bytes for the VHD footer).
"""
pulumi.set(__self__, "create_option", create_option)
pulumi.set(__self__, "source_unique_id", source_unique_id)
if gallery_image_reference is not None:
pulumi.set(__self__, "gallery_image_reference", gallery_image_reference)
if image_reference is not None:
pulumi.set(__self__, "image_reference", image_reference)
if logical_sector_size is not None:
pulumi.set(__self__, "logical_sector_size", logical_sector_size)
if source_resource_id is not None:
pulumi.set(__self__, "source_resource_id", source_resource_id)
if source_uri is not None:
pulumi.set(__self__, "source_uri", source_uri)
if storage_account_id is not None:
pulumi.set(__self__, "storage_account_id", storage_account_id)
if upload_size_bytes is not None:
pulumi.set(__self__, "upload_size_bytes", upload_size_bytes)
@property
@pulumi.getter(name="createOption")
def create_option(self) -> str:
"""
This enumerates the possible sources of a disk's creation.
"""
return pulumi.get(self, "create_option")
@property
@pulumi.getter(name="sourceUniqueId")
def source_unique_id(self) -> str:
"""
If this field is set, this is the unique id identifying the source of this resource.
"""
return pulumi.get(self, "source_unique_id")
@property
@pulumi.getter(name="galleryImageReference")
def gallery_image_reference(self) -> Optional['outputs.ImageDiskReferenceResponse']:
"""
Required if creating from a Gallery Image. The id of the ImageDiskReference will be the ARM id of the shared galley image version from which to create a disk.
"""
return pulumi.get(self, "gallery_image_reference")
@property
@pulumi.getter(name="imageReference")
def image_reference(self) -> Optional['outputs.ImageDiskReferenceResponse']:
"""
Disk source information.
"""
return pulumi.get(self, "image_reference")
@property
@pulumi.getter(name="logicalSectorSize")
def logical_sector_size(self) -> Optional[int]:
"""
Logical sector size in bytes for Ultra disks. Supported values are 512 ad 4096. 4096 is the default.
"""
return pulumi.get(self, "logical_sector_size")
@property
@pulumi.getter(name="sourceResourceId")
def source_resource_id(self) -> Optional[str]:
"""
If createOption is Copy, this is the ARM id of the source snapshot or disk.
"""
return pulumi.get(self, "source_resource_id")
@property
@pulumi.getter(name="sourceUri")
def source_uri(self) -> Optional[str]:
"""
If createOption is Import, this is the URI of a blob to be imported into a managed disk.
"""
return pulumi.get(self, "source_uri")
@property
@pulumi.getter(name="storageAccountId")
def storage_account_id(self) -> Optional[str]:
"""
Required if createOption is Import. The Azure Resource Manager identifier of the storage account containing the blob to import as a disk.
"""
return pulumi.get(self, "storage_account_id")
@property
@pulumi.getter(name="uploadSizeBytes")
def upload_size_bytes(self) -> Optional[float]:
"""
If createOption is Upload, this is the size of the contents of the upload including the VHD footer. This value should be between 20972032 (20 MiB + 512 bytes for the VHD footer) and 35183298347520 bytes (32 TiB + 512 bytes for the VHD footer).
"""
return pulumi.get(self, "upload_size_bytes")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class DataDiskImageEncryptionResponse(dict):
"""
Contains encryption settings for a data disk image.
"""
def __init__(__self__, *,
lun: int,
disk_encryption_set_id: Optional[str] = None):
"""
Contains encryption settings for a data disk image.
:param int lun: This property specifies the logical unit number of the data disk. This value is used to identify data disks within the Virtual Machine and therefore must be unique for each data disk attached to the Virtual Machine.
:param str disk_encryption_set_id: A relative URI containing the resource ID of the disk encryption set.
"""
pulumi.set(__self__, "lun", lun)
if disk_encryption_set_id is not None:
pulumi.set(__self__, "disk_encryption_set_id", disk_encryption_set_id)
@property
@pulumi.getter
def lun(self) -> int:
"""
This property specifies the logical unit number of the data disk. This value is used to identify data disks within the Virtual Machine and therefore must be unique for each data disk attached to the Virtual Machine.
"""
return pulumi.get(self, "lun")
@property
@pulumi.getter(name="diskEncryptionSetId")
def disk_encryption_set_id(self) -> Optional[str]:
"""
A relative URI containing the resource ID of the disk encryption set.
"""
return pulumi.get(self, "disk_encryption_set_id")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class DisallowedResponse(dict):
"""
Describes the disallowed disk types.
"""
def __init__(__self__, *,
disk_types: Optional[Sequence[str]] = None):
"""
Describes the disallowed disk types.
:param Sequence[str] disk_types: A list of disk types.
"""
if disk_types is not None:
pulumi.set(__self__, "disk_types", disk_types)
@property
@pulumi.getter(name="diskTypes")
def disk_types(self) -> Optional[Sequence[str]]:
"""
A list of disk types.
"""
return pulumi.get(self, "disk_types")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class DiskSkuResponse(dict):
"""
The disks sku name. Can be Standard_LRS, Premium_LRS, StandardSSD_LRS, or UltraSSD_LRS.
"""
def __init__(__self__, *,
tier: str,
name: Optional[str] = None):
"""
The disks sku name. Can be Standard_LRS, Premium_LRS, StandardSSD_LRS, or UltraSSD_LRS.
:param str tier: The sku tier.
:param str name: The sku name.
"""
pulumi.set(__self__, "tier", tier)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter
def tier(self) -> str:
"""
The sku tier.
"""
return pulumi.get(self, "tier")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
The sku name.
"""
return pulumi.get(self, "name")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionImagesResponse(dict):
"""
Optional. Allows users to provide customer managed keys for encrypting the OS and data disks in the gallery artifact.
"""
def __init__(__self__, *,
data_disk_images: Optional[Sequence['outputs.DataDiskImageEncryptionResponse']] = None,
os_disk_image: Optional['outputs.OSDiskImageEncryptionResponse'] = None):
"""
Optional. Allows users to provide customer managed keys for encrypting the OS and data disks in the gallery artifact.
:param Sequence['DataDiskImageEncryptionResponseArgs'] data_disk_images: A list of encryption specifications for data disk images.
:param 'OSDiskImageEncryptionResponseArgs' os_disk_image: Contains encryption settings for an OS disk image.
"""
if data_disk_images is not None:
pulumi.set(__self__, "data_disk_images", data_disk_images)
if os_disk_image is not None:
pulumi.set(__self__, "os_disk_image", os_disk_image)
@property
@pulumi.getter(name="dataDiskImages")
def data_disk_images(self) -> Optional[Sequence['outputs.DataDiskImageEncryptionResponse']]:
"""
A list of encryption specifications for data disk images.
"""
return pulumi.get(self, "data_disk_images")
@property
@pulumi.getter(name="osDiskImage")
def os_disk_image(self) -> Optional['outputs.OSDiskImageEncryptionResponse']:
"""
Contains encryption settings for an OS disk image.
"""
return pulumi.get(self, "os_disk_image")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionResponse(dict):
"""
Encryption at rest settings for disk or snapshot
"""
def __init__(__self__, *,
disk_encryption_set_id: Optional[str] = None,
type: Optional[str] = None):
"""
Encryption at rest settings for disk or snapshot
:param str disk_encryption_set_id: ResourceId of the disk encryption set to use for enabling encryption at rest.
:param str type: The type of key used to encrypt the data of the disk.
"""
if disk_encryption_set_id is not None:
pulumi.set(__self__, "disk_encryption_set_id", disk_encryption_set_id)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter(name="diskEncryptionSetId")
def disk_encryption_set_id(self) -> Optional[str]:
"""
ResourceId of the disk encryption set to use for enabling encryption at rest.
"""
return pulumi.get(self, "disk_encryption_set_id")
@property
@pulumi.getter
def type(self) -> Optional[str]:
"""
The type of key used to encrypt the data of the disk.
"""
return pulumi.get(self, "type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionSetIdentityResponse(dict):
"""
The managed identity for the disk encryption set. It should be given permission on the key vault before it can be used to encrypt disks.
"""
def __init__(__self__, *,
principal_id: str,
tenant_id: str,
type: Optional[str] = None):
"""
The managed identity for the disk encryption set. It should be given permission on the key vault before it can be used to encrypt disks.
:param str principal_id: The object id of the Managed Identity Resource. This will be sent to the RP from ARM via the x-ms-identity-principal-id header in the PUT request if the resource has a systemAssigned(implicit) identity
:param str tenant_id: The tenant id of the Managed Identity Resource. This will be sent to the RP from ARM via the x-ms-client-tenant-id header in the PUT request if the resource has a systemAssigned(implicit) identity
:param str type: The type of Managed Identity used by the DiskEncryptionSet. Only SystemAssigned is supported for new creations. Disk Encryption Sets can be updated with Identity type None during migration of subscription to a new Azure Active Directory tenant; it will cause the encrypted resources to lose access to the keys.
"""
pulumi.set(__self__, "principal_id", principal_id)
pulumi.set(__self__, "tenant_id", tenant_id)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter(name="principalId")
def principal_id(self) -> str:
"""
The object id of the Managed Identity Resource. This will be sent to the RP from ARM via the x-ms-identity-principal-id header in the PUT request if the resource has a systemAssigned(implicit) identity
"""
return pulumi.get(self, "principal_id")
@property
@pulumi.getter(name="tenantId")
def tenant_id(self) -> str:
"""
The tenant id of the Managed Identity Resource. This will be sent to the RP from ARM via the x-ms-client-tenant-id header in the PUT request if the resource has a systemAssigned(implicit) identity
"""
return pulumi.get(self, "tenant_id")
@property
@pulumi.getter
def type(self) -> Optional[str]:
"""
The type of Managed Identity used by the DiskEncryptionSet. Only SystemAssigned is supported for new creations. Disk Encryption Sets can be updated with Identity type None during migration of subscription to a new Azure Active Directory tenant; it will cause the encrypted resources to lose access to the keys.
"""
return pulumi.get(self, "type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionSettingsCollectionResponse(dict):
"""
Encryption settings for disk or snapshot
"""
def __init__(__self__, *,
enabled: bool,
encryption_settings: Optional[Sequence['outputs.EncryptionSettingsElementResponse']] = None,
encryption_settings_version: Optional[str] = None):
"""
Encryption settings for disk or snapshot
:param bool enabled: Set this flag to true and provide DiskEncryptionKey and optional KeyEncryptionKey to enable encryption. Set this flag to false and remove DiskEncryptionKey and KeyEncryptionKey to disable encryption. If EncryptionSettings is null in the request object, the existing settings remain unchanged.
:param Sequence['EncryptionSettingsElementResponseArgs'] encryption_settings: A collection of encryption settings, one for each disk volume.
:param str encryption_settings_version: Describes what type of encryption is used for the disks. Once this field is set, it cannot be overwritten. '1.0' corresponds to Azure Disk Encryption with AAD app.'1.1' corresponds to Azure Disk Encryption.
"""
pulumi.set(__self__, "enabled", enabled)
if encryption_settings is not None:
pulumi.set(__self__, "encryption_settings", encryption_settings)
if encryption_settings_version is not None:
pulumi.set(__self__, "encryption_settings_version", encryption_settings_version)
@property
@pulumi.getter
def enabled(self) -> bool:
"""
Set this flag to true and provide DiskEncryptionKey and optional KeyEncryptionKey to enable encryption. Set this flag to false and remove DiskEncryptionKey and KeyEncryptionKey to disable encryption. If EncryptionSettings is null in the request object, the existing settings remain unchanged.
"""
return pulumi.get(self, "enabled")
@property
@pulumi.getter(name="encryptionSettings")
def encryption_settings(self) -> Optional[Sequence['outputs.EncryptionSettingsElementResponse']]:
"""
A collection of encryption settings, one for each disk volume.
"""
return pulumi.get(self, "encryption_settings")
@property
@pulumi.getter(name="encryptionSettingsVersion")
def encryption_settings_version(self) -> Optional[str]:
"""
Describes what type of encryption is used for the disks. Once this field is set, it cannot be overwritten. '1.0' corresponds to Azure Disk Encryption with AAD app.'1.1' corresponds to Azure Disk Encryption.
"""
return pulumi.get(self, "encryption_settings_version")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionSettingsElementResponse(dict):
"""
Encryption settings for one disk volume.
"""
def __init__(__self__, *,
disk_encryption_key: Optional['outputs.KeyVaultAndSecretReferenceResponse'] = None,
key_encryption_key: Optional['outputs.KeyVaultAndKeyReferenceResponse'] = None):
"""
Encryption settings for one disk volume.
:param 'KeyVaultAndSecretReferenceResponseArgs' disk_encryption_key: Key Vault Secret Url and vault id of the disk encryption key
:param 'KeyVaultAndKeyReferenceResponseArgs' key_encryption_key: Key Vault Key Url and vault id of the key encryption key. KeyEncryptionKey is optional and when provided is used to unwrap the disk encryption key.
"""
if disk_encryption_key is not None:
pulumi.set(__self__, "disk_encryption_key", disk_encryption_key)
if key_encryption_key is not None:
pulumi.set(__self__, "key_encryption_key", key_encryption_key)
@property
@pulumi.getter(name="diskEncryptionKey")
def disk_encryption_key(self) -> Optional['outputs.KeyVaultAndSecretReferenceResponse']:
"""
Key Vault Secret Url and vault id of the disk encryption key
"""
return pulumi.get(self, "disk_encryption_key")
@property
@pulumi.getter(name="keyEncryptionKey")
def key_encryption_key(self) -> Optional['outputs.KeyVaultAndKeyReferenceResponse']:
"""
Key Vault Key Url and vault id of the key encryption key. KeyEncryptionKey is optional and when provided is used to unwrap the disk encryption key.
"""
return pulumi.get(self, "key_encryption_key")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ExtendedLocationResponse(dict):
"""
The complex type of the extended location.
"""
def __init__(__self__, *,
name: Optional[str] = None,
type: Optional[str] = None):
"""
The complex type of the extended location.
:param str name: The name of the extended location.
:param str type: The type of the extended location.
"""
if name is not None:
pulumi.set(__self__, "name", name)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
The name of the extended location.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def type(self) -> Optional[str]:
"""
The type of the extended location.
"""
return pulumi.get(self, "type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryApplicationVersionPublishingProfileResponse(dict):
"""
The publishing profile of a gallery image version.
"""
def __init__(__self__, *,
published_date: str,
source: 'outputs.UserArtifactSourceResponse',
enable_health_check: Optional[bool] = None,
end_of_life_date: Optional[str] = None,
exclude_from_latest: Optional[bool] = None,
manage_actions: Optional['outputs.UserArtifactManageResponse'] = None,
replica_count: Optional[int] = None,
storage_account_type: Optional[str] = None,
target_regions: Optional[Sequence['outputs.TargetRegionResponse']] = None):
"""
The publishing profile of a gallery image version.
:param str published_date: The timestamp for when the gallery image version is published.
:param 'UserArtifactSourceResponseArgs' source: The source image from which the Image Version is going to be created.
:param bool enable_health_check: Optional. Whether or not this application reports health.
:param str end_of_life_date: The end of life date of the gallery image version. This property can be used for decommissioning purposes. This property is updatable.
:param bool exclude_from_latest: If set to true, Virtual Machines deployed from the latest version of the Image Definition won't use this Image Version.
:param int replica_count: The number of replicas of the Image Version to be created per region. This property would take effect for a region when regionalReplicaCount is not specified. This property is updatable.
:param str storage_account_type: Specifies the storage account type to be used to store the image. This property is not updatable.
:param Sequence['TargetRegionResponseArgs'] target_regions: The target regions where the Image Version is going to be replicated to. This property is updatable.
"""
pulumi.set(__self__, "published_date", published_date)
pulumi.set(__self__, "source", source)
if enable_health_check is not None:
pulumi.set(__self__, "enable_health_check", enable_health_check)
if end_of_life_date is not None:
pulumi.set(__self__, "end_of_life_date", end_of_life_date)
if exclude_from_latest is not None:
pulumi.set(__self__, "exclude_from_latest", exclude_from_latest)
if manage_actions is not None:
pulumi.set(__self__, "manage_actions", manage_actions)
if replica_count is not None:
pulumi.set(__self__, "replica_count", replica_count)
if storage_account_type is not None:
pulumi.set(__self__, "storage_account_type", storage_account_type)
if target_regions is not None:
pulumi.set(__self__, "target_regions", target_regions)
@property
@pulumi.getter(name="publishedDate")
def published_date(self) -> str:
"""
The timestamp for when the gallery image version is published.
"""
return pulumi.get(self, "published_date")
@property
@pulumi.getter
def source(self) -> 'outputs.UserArtifactSourceResponse':
"""
The source image from which the Image Version is going to be created.
"""
return pulumi.get(self, "source")
@property
@pulumi.getter(name="enableHealthCheck")
def enable_health_check(self) -> Optional[bool]:
"""
Optional. Whether or not this application reports health.
"""
return pulumi.get(self, "enable_health_check")
@property
@pulumi.getter(name="endOfLifeDate")
def end_of_life_date(self) -> Optional[str]:
"""
The end of life date of the gallery image version. This property can be used for decommissioning purposes. This property is updatable.
"""
return pulumi.get(self, "end_of_life_date")
@property
@pulumi.getter(name="excludeFromLatest")
def exclude_from_latest(self) -> Optional[bool]:
"""
If set to true, Virtual Machines deployed from the latest version of the Image Definition won't use this Image Version.
"""
return pulumi.get(self, "exclude_from_latest")
@property
@pulumi.getter(name="manageActions")
def manage_actions(self) -> Optional['outputs.UserArtifactManageResponse']:
return pulumi.get(self, "manage_actions")
@property
@pulumi.getter(name="replicaCount")
def replica_count(self) -> Optional[int]:
"""
The number of replicas of the Image Version to be created per region. This property would take effect for a region when regionalReplicaCount is not specified. This property is updatable.
"""
return pulumi.get(self, "replica_count")
@property
@pulumi.getter(name="storageAccountType")
def storage_account_type(self) -> Optional[str]:
"""
Specifies the storage account type to be used to store the image. This property is not updatable.
"""
return pulumi.get(self, "storage_account_type")
@property
@pulumi.getter(name="targetRegions")
def target_regions(self) -> Optional[Sequence['outputs.TargetRegionResponse']]:
"""
The target regions where the Image Version is going to be replicated to. This property is updatable.
"""
return pulumi.get(self, "target_regions")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryArtifactVersionSourceResponse(dict):
"""
The gallery artifact version source.
"""
def __init__(__self__, *,
id: Optional[str] = None,
uri: Optional[str] = None):
"""
The gallery artifact version source.
:param str id: The id of the gallery artifact version source. Can specify a disk uri, snapshot uri, user image or storage account resource.
:param str uri: The uri of the gallery artifact version source. Currently used to specify vhd/blob source.
"""
if id is not None:
pulumi.set(__self__, "id", id)
if uri is not None:
pulumi.set(__self__, "uri", uri)
@property
@pulumi.getter
def id(self) -> Optional[str]:
"""
The id of the gallery artifact version source. Can specify a disk uri, snapshot uri, user image or storage account resource.
"""
return pulumi.get(self, "id")
@property
@pulumi.getter
def uri(self) -> Optional[str]:
"""
The uri of the gallery artifact version source. Currently used to specify vhd/blob source.
"""
return pulumi.get(self, "uri")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryDataDiskImageResponse(dict):
"""
This is the data disk image.
"""
def __init__(__self__, *,
lun: int,
size_in_gb: int,
host_caching: Optional[str] = None,
source: Optional['outputs.GalleryArtifactVersionSourceResponse'] = None):
"""
This is the data disk image.
:param int lun: This property specifies the logical unit number of the data disk. This value is used to identify data disks within the Virtual Machine and therefore must be unique for each data disk attached to the Virtual Machine.
:param int size_in_gb: This property indicates the size of the VHD to be created.
:param str host_caching: The host caching of the disk. Valid values are 'None', 'ReadOnly', and 'ReadWrite'
:param 'GalleryArtifactVersionSourceResponseArgs' source: The gallery artifact version source.
"""
pulumi.set(__self__, "lun", lun)
pulumi.set(__self__, "size_in_gb", size_in_gb)
if host_caching is not None:
pulumi.set(__self__, "host_caching", host_caching)
if source is not None:
pulumi.set(__self__, "source", source)
@property
@pulumi.getter
def lun(self) -> int:
"""
This property specifies the logical unit number of the data disk. This value is used to identify data disks within the Virtual Machine and therefore must be unique for each data disk attached to the Virtual Machine.
"""
return pulumi.get(self, "lun")
@property
@pulumi.getter(name="sizeInGB")
def size_in_gb(self) -> int:
"""
This property indicates the size of the VHD to be created.
"""
return pulumi.get(self, "size_in_gb")
@property
@pulumi.getter(name="hostCaching")
def host_caching(self) -> Optional[str]:
"""
The host caching of the disk. Valid values are 'None', 'ReadOnly', and 'ReadWrite'
"""
return pulumi.get(self, "host_caching")
@property
@pulumi.getter
def source(self) -> Optional['outputs.GalleryArtifactVersionSourceResponse']:
"""
The gallery artifact version source.
"""
return pulumi.get(self, "source")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryIdentifierResponse(dict):
"""
Describes the gallery unique name.
"""
def __init__(__self__, *,
unique_name: str):
"""
Describes the gallery unique name.
:param str unique_name: The unique name of the Shared Image Gallery. This name is generated automatically by Azure.
"""
pulumi.set(__self__, "unique_name", unique_name)
@property
@pulumi.getter(name="uniqueName")
def unique_name(self) -> str:
"""
The unique name of the Shared Image Gallery. This name is generated automatically by Azure.
"""
return pulumi.get(self, "unique_name")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryImageFeatureResponse(dict):
"""
A feature for gallery image.
"""
def __init__(__self__, *,
name: Optional[str] = None,
value: Optional[str] = None):
"""
A feature for gallery image.
:param str name: The name of the gallery image feature.
:param str value: The value of the gallery image feature.
"""
if name is not None:
pulumi.set(__self__, "name", name)
if value is not None:
pulumi.set(__self__, "value", value)
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
The name of the gallery image feature.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def value(self) -> Optional[str]:
"""
The value of the gallery image feature.
"""
return pulumi.get(self, "value")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryImageIdentifierResponse(dict):
"""
This is the gallery image definition identifier.
"""
def __init__(__self__, *,
offer: str,
publisher: str,
sku: str):
"""
This is the gallery image definition identifier.
:param str offer: The name of the gallery image definition offer.
:param str publisher: The name of the gallery image definition publisher.
:param str sku: The name of the gallery image definition SKU.
"""
pulumi.set(__self__, "offer", offer)
pulumi.set(__self__, "publisher", publisher)
pulumi.set(__self__, "sku", sku)
@property
@pulumi.getter
def offer(self) -> str:
"""
The name of the gallery image definition offer.
"""
return pulumi.get(self, "offer")
@property
@pulumi.getter
def publisher(self) -> str:
"""
The name of the gallery image definition publisher.
"""
return pulumi.get(self, "publisher")
@property
@pulumi.getter
def sku(self) -> str:
"""
The name of the gallery image definition SKU.
"""
return pulumi.get(self, "sku")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryImageVersionPublishingProfileResponse(dict):
"""
The publishing profile of a gallery image Version.
"""
def __init__(__self__, *,
published_date: str,
end_of_life_date: Optional[str] = None,
exclude_from_latest: Optional[bool] = None,
replica_count: Optional[int] = None,
storage_account_type: Optional[str] = None,
target_regions: Optional[Sequence['outputs.TargetRegionResponse']] = None):
"""
The publishing profile of a gallery image Version.
:param str published_date: The timestamp for when the gallery image version is published.
:param str end_of_life_date: The end of life date of the gallery image version. This property can be used for decommissioning purposes. This property is updatable.
:param bool exclude_from_latest: If set to true, Virtual Machines deployed from the latest version of the Image Definition won't use this Image Version.
:param int replica_count: The number of replicas of the Image Version to be created per region. This property would take effect for a region when regionalReplicaCount is not specified. This property is updatable.
:param str storage_account_type: Specifies the storage account type to be used to store the image. This property is not updatable.
:param Sequence['TargetRegionResponseArgs'] target_regions: The target regions where the Image Version is going to be replicated to. This property is updatable.
"""
pulumi.set(__self__, "published_date", published_date)
if end_of_life_date is not None:
pulumi.set(__self__, "end_of_life_date", end_of_life_date)
if exclude_from_latest is not None:
pulumi.set(__self__, "exclude_from_latest", exclude_from_latest)
if replica_count is not None:
pulumi.set(__self__, "replica_count", replica_count)
if storage_account_type is not None:
pulumi.set(__self__, "storage_account_type", storage_account_type)
if target_regions is not None:
pulumi.set(__self__, "target_regions", target_regions)
@property
@pulumi.getter(name="publishedDate")
def published_date(self) -> str:
"""
The timestamp for when the gallery image version is published.
"""
return pulumi.get(self, "published_date")
@property
@pulumi.getter(name="endOfLifeDate")
def end_of_life_date(self) -> Optional[str]:
"""
The end of life date of the gallery image version. This property can be used for decommissioning purposes. This property is updatable.
"""
return pulumi.get(self, "end_of_life_date")
@property
@pulumi.getter(name="excludeFromLatest")
def exclude_from_latest(self) -> Optional[bool]:
"""
If set to true, Virtual Machines deployed from the latest version of the Image Definition won't use this Image Version.
"""
return pulumi.get(self, "exclude_from_latest")
@property
@pulumi.getter(name="replicaCount")
def replica_count(self) -> Optional[int]:
"""
The number of replicas of the Image Version to be created per region. This property would take effect for a region when regionalReplicaCount is not specified. This property is updatable.
"""
return pulumi.get(self, "replica_count")
@property
@pulumi.getter(name="storageAccountType")
def storage_account_type(self) -> Optional[str]:
"""
Specifies the storage account type to be used to store the image. This property is not updatable.
"""
return pulumi.get(self, "storage_account_type")
@property
@pulumi.getter(name="targetRegions")
def target_regions(self) -> Optional[Sequence['outputs.TargetRegionResponse']]:
"""
The target regions where the Image Version is going to be replicated to. This property is updatable.
"""
return pulumi.get(self, "target_regions")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryImageVersionStorageProfileResponse(dict):
"""
This is the storage profile of a Gallery Image Version.
"""
def __init__(__self__, *,
data_disk_images: Optional[Sequence['outputs.GalleryDataDiskImageResponse']] = None,
os_disk_image: Optional['outputs.GalleryOSDiskImageResponse'] = None,
source: Optional['outputs.GalleryArtifactVersionSourceResponse'] = None):
"""
This is the storage profile of a Gallery Image Version.
:param Sequence['GalleryDataDiskImageResponseArgs'] data_disk_images: A list of data disk images.
:param 'GalleryOSDiskImageResponseArgs' os_disk_image: This is the OS disk image.
:param 'GalleryArtifactVersionSourceResponseArgs' source: The gallery artifact version source.
"""
if data_disk_images is not None:
pulumi.set(__self__, "data_disk_images", data_disk_images)
if os_disk_image is not None:
pulumi.set(__self__, "os_disk_image", os_disk_image)
if source is not None:
pulumi.set(__self__, "source", source)
@property
@pulumi.getter(name="dataDiskImages")
def data_disk_images(self) -> Optional[Sequence['outputs.GalleryDataDiskImageResponse']]:
"""
A list of data disk images.
"""
return pulumi.get(self, "data_disk_images")
@property
@pulumi.getter(name="osDiskImage")
def os_disk_image(self) -> Optional['outputs.GalleryOSDiskImageResponse']:
"""
This is the OS disk image.
"""
return pulumi.get(self, "os_disk_image")
@property
@pulumi.getter
def source(self) -> Optional['outputs.GalleryArtifactVersionSourceResponse']:
"""
The gallery artifact version source.
"""
return pulumi.get(self, "source")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryOSDiskImageResponse(dict):
"""
This is the OS disk image.
"""
def __init__(__self__, *,
size_in_gb: int,
host_caching: Optional[str] = None,
source: Optional['outputs.GalleryArtifactVersionSourceResponse'] = None):
"""
This is the OS disk image.
:param int size_in_gb: This property indicates the size of the VHD to be created.
:param str host_caching: The host caching of the disk. Valid values are 'None', 'ReadOnly', and 'ReadWrite'
:param 'GalleryArtifactVersionSourceResponseArgs' source: The gallery artifact version source.
"""
pulumi.set(__self__, "size_in_gb", size_in_gb)
if host_caching is not None:
pulumi.set(__self__, "host_caching", host_caching)
if source is not None:
pulumi.set(__self__, "source", source)
@property
@pulumi.getter(name="sizeInGB")
def size_in_gb(self) -> int:
"""
This property indicates the size of the VHD to be created.
"""
return pulumi.get(self, "size_in_gb")
@property
@pulumi.getter(name="hostCaching")
def host_caching(self) -> Optional[str]:
"""
The host caching of the disk. Valid values are 'None', 'ReadOnly', and 'ReadWrite'
"""
return pulumi.get(self, "host_caching")
@property
@pulumi.getter
def source(self) -> Optional['outputs.GalleryArtifactVersionSourceResponse']:
"""
The gallery artifact version source.
"""
return pulumi.get(self, "source")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ImageDiskReferenceResponse(dict):
"""
The source image used for creating the disk.
"""
def __init__(__self__, *,
id: str,
lun: Optional[int] = None):
"""
The source image used for creating the disk.
:param str id: A relative uri containing either a Platform Image Repository or user image reference.
:param int lun: If the disk is created from an image's data disk, this is an index that indicates which of the data disks in the image to use. For OS disks, this field is null.
"""
pulumi.set(__self__, "id", id)
if lun is not None:
pulumi.set(__self__, "lun", lun)
@property
@pulumi.getter
def id(self) -> str:
"""
A relative uri containing either a Platform Image Repository or user image reference.
"""
return pulumi.get(self, "id")
@property
@pulumi.getter
def lun(self) -> Optional[int]:
"""
If the disk is created from an image's data disk, this is an index that indicates which of the data disks in the image to use. For OS disks, this field is null.
"""
return pulumi.get(self, "lun")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ImagePurchasePlanResponse(dict):
"""
Describes the gallery image definition purchase plan. This is used by marketplace images.
"""
def __init__(__self__, *,
name: Optional[str] = None,
product: Optional[str] = None,
publisher: Optional[str] = None):
"""
Describes the gallery image definition purchase plan. This is used by marketplace images.
:param str name: The plan ID.
:param str product: The product ID.
:param str publisher: The publisher ID.
"""
if name is not None:
pulumi.set(__self__, "name", name)
if product is not None:
pulumi.set(__self__, "product", product)
if publisher is not None:
pulumi.set(__self__, "publisher", publisher)
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
The plan ID.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def product(self) -> Optional[str]:
"""
The product ID.
"""
return pulumi.get(self, "product")
@property
@pulumi.getter
def publisher(self) -> Optional[str]:
"""
The publisher ID.
"""
return pulumi.get(self, "publisher")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class KeyForDiskEncryptionSetResponse(dict):
"""
Key Vault Key Url to be used for server side encryption of Managed Disks and Snapshots
"""
def __init__(__self__, *,
key_url: str,
source_vault: Optional['outputs.SourceVaultResponse'] = None):
"""
Key Vault Key Url to be used for server side encryption of Managed Disks and Snapshots
:param str key_url: Fully versioned Key Url pointing to a key in KeyVault
:param 'SourceVaultResponseArgs' source_vault: Resource id of the KeyVault containing the key or secret. This property is optional and cannot be used if the KeyVault subscription is not the same as the Disk Encryption Set subscription.
"""
pulumi.set(__self__, "key_url", key_url)
if source_vault is not None:
pulumi.set(__self__, "source_vault", source_vault)
@property
@pulumi.getter(name="keyUrl")
def key_url(self) -> str:
"""
Fully versioned Key Url pointing to a key in KeyVault
"""
return pulumi.get(self, "key_url")
@property
@pulumi.getter(name="sourceVault")
def source_vault(self) -> Optional['outputs.SourceVaultResponse']:
"""
Resource id of the KeyVault containing the key or secret. This property is optional and cannot be used if the KeyVault subscription is not the same as the Disk Encryption Set subscription.
"""
return pulumi.get(self, "source_vault")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class KeyVaultAndKeyReferenceResponse(dict):
"""
Key Vault Key Url and vault id of KeK, KeK is optional and when provided is used to unwrap the encryptionKey
"""
def __init__(__self__, *,
key_url: str,
source_vault: 'outputs.SourceVaultResponse'):
"""
Key Vault Key Url and vault id of KeK, KeK is optional and when provided is used to unwrap the encryptionKey
:param str key_url: Url pointing to a key or secret in KeyVault
:param 'SourceVaultResponseArgs' source_vault: Resource id of the KeyVault containing the key or secret
"""
pulumi.set(__self__, "key_url", key_url)
pulumi.set(__self__, "source_vault", source_vault)
@property
@pulumi.getter(name="keyUrl")
def key_url(self) -> str:
"""
Url pointing to a key or secret in KeyVault
"""
return pulumi.get(self, "key_url")
@property
@pulumi.getter(name="sourceVault")
def source_vault(self) -> 'outputs.SourceVaultResponse':
"""
Resource id of the KeyVault containing the key or secret
"""
return pulumi.get(self, "source_vault")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class KeyVaultAndSecretReferenceResponse(dict):
"""
Key Vault Secret Url and vault id of the encryption key
"""
def __init__(__self__, *,
secret_url: str,
source_vault: 'outputs.SourceVaultResponse'):
"""
Key Vault Secret Url and vault id of the encryption key
:param str secret_url: Url pointing to a key or secret in KeyVault
:param 'SourceVaultResponseArgs' source_vault: Resource id of the KeyVault containing the key or secret
"""
pulumi.set(__self__, "secret_url", secret_url)
pulumi.set(__self__, "source_vault", source_vault)
@property
@pulumi.getter(name="secretUrl")
def secret_url(self) -> str:
"""
Url pointing to a key or secret in KeyVault
"""
return pulumi.get(self, "secret_url")
@property
@pulumi.getter(name="sourceVault")
def source_vault(self) -> 'outputs.SourceVaultResponse':
"""
Resource id of the KeyVault containing the key or secret
"""
return pulumi.get(self, "source_vault")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class OSDiskImageEncryptionResponse(dict):
"""
Contains encryption settings for an OS disk image.
"""
def __init__(__self__, *,
disk_encryption_set_id: Optional[str] = None):
"""
Contains encryption settings for an OS disk image.
:param str disk_encryption_set_id: A relative URI containing the resource ID of the disk encryption set.
"""
if disk_encryption_set_id is not None:
pulumi.set(__self__, "disk_encryption_set_id", disk_encryption_set_id)
@property
@pulumi.getter(name="diskEncryptionSetId")
def disk_encryption_set_id(self) -> Optional[str]:
"""
A relative URI containing the resource ID of the disk encryption set.
"""
return pulumi.get(self, "disk_encryption_set_id")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class PrivateEndpointConnectionResponse(dict):
"""
The Private Endpoint Connection resource.
"""
def __init__(__self__, *,
id: str,
name: str,
private_link_service_connection_state: 'outputs.PrivateLinkServiceConnectionStateResponse',
provisioning_state: str,
type: str,
private_endpoint: Optional['outputs.PrivateEndpointResponse'] = None):
"""
The Private Endpoint Connection resource.
:param str id: private endpoint connection Id
:param str name: private endpoint connection name
:param 'PrivateLinkServiceConnectionStateResponseArgs' private_link_service_connection_state: A collection of information about the state of the connection between DiskAccess and Virtual Network.
:param str provisioning_state: The provisioning state of the private endpoint connection resource.
:param str type: private endpoint connection type
:param 'PrivateEndpointResponseArgs' private_endpoint: The resource of private end point.
"""
pulumi.set(__self__, "id", id)
pulumi.set(__self__, "name", name)
pulumi.set(__self__, "private_link_service_connection_state", private_link_service_connection_state)
pulumi.set(__self__, "provisioning_state", provisioning_state)
pulumi.set(__self__, "type", type)
if private_endpoint is not None:
pulumi.set(__self__, "private_endpoint", private_endpoint)
@property
@pulumi.getter
def id(self) -> str:
"""
private endpoint connection Id
"""
return pulumi.get(self, "id")
@property
@pulumi.getter
def name(self) -> str:
"""
private endpoint connection name
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="privateLinkServiceConnectionState")
def private_link_service_connection_state(self) -> 'outputs.PrivateLinkServiceConnectionStateResponse':
"""
A collection of information about the state of the connection between DiskAccess and Virtual Network.
"""
return pulumi.get(self, "private_link_service_connection_state")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> str:
"""
The provisioning state of the private endpoint connection resource.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter
def type(self) -> str:
"""
private endpoint connection type
"""
return pulumi.get(self, "type")
@property
@pulumi.getter(name="privateEndpoint")
def private_endpoint(self) -> Optional['outputs.PrivateEndpointResponse']:
"""
The resource of private end point.
"""
return pulumi.get(self, "private_endpoint")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class PrivateEndpointResponse(dict):
"""
The Private Endpoint resource.
"""
def __init__(__self__, *,
id: str):
"""
The Private Endpoint resource.
:param str id: The ARM identifier for Private Endpoint
"""
pulumi.set(__self__, "id", id)
@property
@pulumi.getter
def id(self) -> str:
"""
The ARM identifier for Private Endpoint
"""
return pulumi.get(self, "id")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class PrivateLinkServiceConnectionStateResponse(dict):
"""
A collection of information about the state of the connection between service consumer and provider.
"""
def __init__(__self__, *,
actions_required: Optional[str] = None,
description: Optional[str] = None,
status: Optional[str] = None):
"""
A collection of information about the state of the connection between service consumer and provider.
:param str actions_required: A message indicating if changes on the service provider require any updates on the consumer.
:param str description: The reason for approval/rejection of the connection.
:param str status: Indicates whether the connection has been Approved/Rejected/Removed by the owner of the service.
"""
if actions_required is not None:
pulumi.set(__self__, "actions_required", actions_required)
if description is not None:
pulumi.set(__self__, "description", description)
if status is not None:
pulumi.set(__self__, "status", status)
@property
@pulumi.getter(name="actionsRequired")
def actions_required(self) -> Optional[str]:
"""
A message indicating if changes on the service provider require any updates on the consumer.
"""
return pulumi.get(self, "actions_required")
@property
@pulumi.getter
def description(self) -> Optional[str]:
"""
The reason for approval/rejection of the connection.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter
def status(self) -> Optional[str]:
"""
Indicates whether the connection has been Approved/Rejected/Removed by the owner of the service.
"""
return pulumi.get(self, "status")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class PurchasePlanResponse(dict):
"""
Used for establishing the purchase context of any 3rd Party artifact through MarketPlace.
"""
def __init__(__self__, *,
name: str,
product: str,
publisher: str,
promotion_code: Optional[str] = None):
"""
Used for establishing the purchase context of any 3rd Party artifact through MarketPlace.
:param str name: The plan ID.
:param str product: Specifies the product of the image from the marketplace. This is the same value as Offer under the imageReference element.
:param str publisher: The publisher ID.
:param str promotion_code: The Offer Promotion Code.
"""
pulumi.set(__self__, "name", name)
pulumi.set(__self__, "product", product)
pulumi.set(__self__, "publisher", publisher)
if promotion_code is not None:
pulumi.set(__self__, "promotion_code", promotion_code)
@property
@pulumi.getter
def name(self) -> str:
"""
The plan ID.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def product(self) -> str:
"""
Specifies the product of the image from the marketplace. This is the same value as Offer under the imageReference element.
"""
return pulumi.get(self, "product")
@property
@pulumi.getter
def publisher(self) -> str:
"""
The publisher ID.
"""
return pulumi.get(self, "publisher")
@property
@pulumi.getter(name="promotionCode")
def promotion_code(self) -> Optional[str]:
"""
The Offer Promotion Code.
"""
return pulumi.get(self, "promotion_code")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class RecommendedMachineConfigurationResponse(dict):
"""
The properties describe the recommended machine configuration for this Image Definition. These properties are updatable.
"""
def __init__(__self__, *,
memory: Optional['outputs.ResourceRangeResponse'] = None,
v_cpus: Optional['outputs.ResourceRangeResponse'] = None):
"""
The properties describe the recommended machine configuration for this Image Definition. These properties are updatable.
:param 'ResourceRangeResponseArgs' memory: Describes the resource range.
:param 'ResourceRangeResponseArgs' v_cpus: Describes the resource range.
"""
if memory is not None:
pulumi.set(__self__, "memory", memory)
if v_cpus is not None:
pulumi.set(__self__, "v_cpus", v_cpus)
@property
@pulumi.getter
def memory(self) -> Optional['outputs.ResourceRangeResponse']:
"""
Describes the resource range.
"""
return pulumi.get(self, "memory")
@property
@pulumi.getter(name="vCPUs")
def v_cpus(self) -> Optional['outputs.ResourceRangeResponse']:
"""
Describes the resource range.
"""
return pulumi.get(self, "v_cpus")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class RegionalReplicationStatusResponse(dict):
"""
This is the regional replication status.
"""
def __init__(__self__, *,
details: str,
progress: int,
region: str,
state: str):
"""
This is the regional replication status.
:param str details: The details of the replication status.
:param int progress: It indicates progress of the replication job.
:param str region: The region to which the gallery image version is being replicated to.
:param str state: This is the regional replication state.
"""
pulumi.set(__self__, "details", details)
pulumi.set(__self__, "progress", progress)
pulumi.set(__self__, "region", region)
pulumi.set(__self__, "state", state)
@property
@pulumi.getter
def details(self) -> str:
"""
The details of the replication status.
"""
return pulumi.get(self, "details")
@property
@pulumi.getter
def progress(self) -> int:
"""
It indicates progress of the replication job.
"""
return pulumi.get(self, "progress")
@property
@pulumi.getter
def region(self) -> str:
"""
The region to which the gallery image version is being replicated to.
"""
return pulumi.get(self, "region")
@property
@pulumi.getter
def state(self) -> str:
"""
This is the regional replication state.
"""
return pulumi.get(self, "state")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ReplicationStatusResponse(dict):
"""
This is the replication status of the gallery image version.
"""
def __init__(__self__, *,
aggregated_state: str,
summary: Sequence['outputs.RegionalReplicationStatusResponse']):
"""
This is the replication status of the gallery image version.
:param str aggregated_state: This is the aggregated replication status based on all the regional replication status flags.
:param Sequence['RegionalReplicationStatusResponseArgs'] summary: This is a summary of replication status for each region.
"""
pulumi.set(__self__, "aggregated_state", aggregated_state)
pulumi.set(__self__, "summary", summary)
@property
@pulumi.getter(name="aggregatedState")
def aggregated_state(self) -> str:
"""
This is the aggregated replication status based on all the regional replication status flags.
"""
return pulumi.get(self, "aggregated_state")
@property
@pulumi.getter
def summary(self) -> Sequence['outputs.RegionalReplicationStatusResponse']:
"""
This is a summary of replication status for each region.
"""
return pulumi.get(self, "summary")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ResourceRangeResponse(dict):
"""
Describes the resource range.
"""
def __init__(__self__, *,
max: Optional[int] = None,
min: Optional[int] = None):
"""
Describes the resource range.
:param int max: The maximum number of the resource.
:param int min: The minimum number of the resource.
"""
if max is not None:
pulumi.set(__self__, "max", max)
if min is not None:
pulumi.set(__self__, "min", min)
@property
@pulumi.getter
def max(self) -> Optional[int]:
"""
The maximum number of the resource.
"""
return pulumi.get(self, "max")
@property
@pulumi.getter
def min(self) -> Optional[int]:
"""
The minimum number of the resource.
"""
return pulumi.get(self, "min")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ShareInfoElementResponse(dict):
def __init__(__self__, *,
vm_uri: str):
"""
:param str vm_uri: A relative URI containing the ID of the VM that has the disk attached.
"""
pulumi.set(__self__, "vm_uri", vm_uri)
@property
@pulumi.getter(name="vmUri")
def vm_uri(self) -> str:
"""
A relative URI containing the ID of the VM that has the disk attached.
"""
return pulumi.get(self, "vm_uri")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class SharingProfileGroupResponse(dict):
"""
Group of the gallery sharing profile
"""
def __init__(__self__, *,
ids: Optional[Sequence[str]] = None,
type: Optional[str] = None):
"""
Group of the gallery sharing profile
:param Sequence[str] ids: A list of subscription/tenant ids the gallery is aimed to be shared to.
:param str type: This property allows you to specify the type of sharing group. <br><br> Possible values are: <br><br> **Subscriptions** <br><br> **AADTenants**
"""
if ids is not None:
pulumi.set(__self__, "ids", ids)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter
def ids(self) -> Optional[Sequence[str]]:
"""
A list of subscription/tenant ids the gallery is aimed to be shared to.
"""
return pulumi.get(self, "ids")
@property
@pulumi.getter
def type(self) -> Optional[str]:
"""
This property allows you to specify the type of sharing group. <br><br> Possible values are: <br><br> **Subscriptions** <br><br> **AADTenants**
"""
return pulumi.get(self, "type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class SharingProfileResponse(dict):
"""
Profile for gallery sharing to subscription or tenant
"""
def __init__(__self__, *,
groups: Sequence['outputs.SharingProfileGroupResponse'],
permissions: Optional[str] = None):
"""
Profile for gallery sharing to subscription or tenant
:param Sequence['SharingProfileGroupResponseArgs'] groups: A list of sharing profile groups.
:param str permissions: This property allows you to specify the permission of sharing gallery. <br><br> Possible values are: <br><br> **Private** <br><br> **Groups**
"""
pulumi.set(__self__, "groups", groups)
if permissions is not None:
pulumi.set(__self__, "permissions", permissions)
@property
@pulumi.getter
def groups(self) -> Sequence['outputs.SharingProfileGroupResponse']:
"""
A list of sharing profile groups.
"""
return pulumi.get(self, "groups")
@property
@pulumi.getter
def permissions(self) -> Optional[str]:
"""
This property allows you to specify the permission of sharing gallery. <br><br> Possible values are: <br><br> **Private** <br><br> **Groups**
"""
return pulumi.get(self, "permissions")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class SnapshotSkuResponse(dict):
"""
The snapshots sku name. Can be Standard_LRS, Premium_LRS, or Standard_ZRS. This is an optional parameter for incremental snapshot and the default behavior is the SKU will be set to the same sku as the previous snapshot
"""
def __init__(__self__, *,
tier: str,
name: Optional[str] = None):
"""
The snapshots sku name. Can be Standard_LRS, Premium_LRS, or Standard_ZRS. This is an optional parameter for incremental snapshot and the default behavior is the SKU will be set to the same sku as the previous snapshot
:param str tier: The sku tier.
:param str name: The sku name.
"""
pulumi.set(__self__, "tier", tier)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter
def tier(self) -> str:
"""
The sku tier.
"""
return pulumi.get(self, "tier")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
The sku name.
"""
return pulumi.get(self, "name")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class SourceVaultResponse(dict):
"""
The vault id is an Azure Resource Manager Resource id in the form /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.KeyVault/vaults/{vaultName}
"""
def __init__(__self__, *,
id: Optional[str] = None):
"""
The vault id is an Azure Resource Manager Resource id in the form /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.KeyVault/vaults/{vaultName}
:param str id: Resource Id
"""
if id is not None:
pulumi.set(__self__, "id", id)
@property
@pulumi.getter
def id(self) -> Optional[str]:
"""
Resource Id
"""
return pulumi.get(self, "id")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class TargetRegionResponse(dict):
"""
Describes the target region information.
"""
def __init__(__self__, *,
name: str,
encryption: Optional['outputs.EncryptionImagesResponse'] = None,
regional_replica_count: Optional[int] = None,
storage_account_type: Optional[str] = None):
"""
Describes the target region information.
:param str name: The name of the region.
:param 'EncryptionImagesResponseArgs' encryption: Optional. Allows users to provide customer managed keys for encrypting the OS and data disks in the gallery artifact.
:param int regional_replica_count: The number of replicas of the Image Version to be created per region. This property is updatable.
:param str storage_account_type: Specifies the storage account type to be used to store the image. This property is not updatable.
"""
pulumi.set(__self__, "name", name)
if encryption is not None:
pulumi.set(__self__, "encryption", encryption)
if regional_replica_count is not None:
pulumi.set(__self__, "regional_replica_count", regional_replica_count)
if storage_account_type is not None:
pulumi.set(__self__, "storage_account_type", storage_account_type)
@property
@pulumi.getter
def name(self) -> str:
"""
The name of the region.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def encryption(self) -> Optional['outputs.EncryptionImagesResponse']:
"""
Optional. Allows users to provide customer managed keys for encrypting the OS and data disks in the gallery artifact.
"""
return pulumi.get(self, "encryption")
@property
@pulumi.getter(name="regionalReplicaCount")
def regional_replica_count(self) -> Optional[int]:
"""
The number of replicas of the Image Version to be created per region. This property is updatable.
"""
return pulumi.get(self, "regional_replica_count")
@property
@pulumi.getter(name="storageAccountType")
def storage_account_type(self) -> Optional[str]:
"""
Specifies the storage account type to be used to store the image. This property is not updatable.
"""
return pulumi.get(self, "storage_account_type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class UserArtifactManageResponse(dict):
def __init__(__self__, *,
install: str,
remove: str,
update: Optional[str] = None):
"""
:param str install: Required. The path and arguments to install the gallery application. This is limited to 4096 characters.
:param str remove: Required. The path and arguments to remove the gallery application. This is limited to 4096 characters.
:param str update: Optional. The path and arguments to update the gallery application. If not present, then update operation will invoke remove command on the previous version and install command on the current version of the gallery application. This is limited to 4096 characters.
"""
pulumi.set(__self__, "install", install)
pulumi.set(__self__, "remove", remove)
if update is not None:
pulumi.set(__self__, "update", update)
@property
@pulumi.getter
def install(self) -> str:
"""
Required. The path and arguments to install the gallery application. This is limited to 4096 characters.
"""
return pulumi.get(self, "install")
@property
@pulumi.getter
def remove(self) -> str:
"""
Required. The path and arguments to remove the gallery application. This is limited to 4096 characters.
"""
return pulumi.get(self, "remove")
@property
@pulumi.getter
def update(self) -> Optional[str]:
"""
Optional. The path and arguments to update the gallery application. If not present, then update operation will invoke remove command on the previous version and install command on the current version of the gallery application. This is limited to 4096 characters.
"""
return pulumi.get(self, "update")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class UserArtifactSourceResponse(dict):
"""
The source image from which the Image Version is going to be created.
"""
def __init__(__self__, *,
media_link: str,
default_configuration_link: Optional[str] = None):
"""
The source image from which the Image Version is going to be created.
:param str media_link: Required. The mediaLink of the artifact, must be a readable storage page blob.
:param str default_configuration_link: Optional. The defaultConfigurationLink of the artifact, must be a readable storage page blob.
"""
pulumi.set(__self__, "media_link", media_link)
if default_configuration_link is not None:
pulumi.set(__self__, "default_configuration_link", default_configuration_link)
@property
@pulumi.getter(name="mediaLink")
def media_link(self) -> str:
"""
Required. The mediaLink of the artifact, must be a readable storage page blob.
"""
return pulumi.get(self, "media_link")
@property
@pulumi.getter(name="defaultConfigurationLink")
def default_configuration_link(self) -> Optional[str]:
"""
Optional. The defaultConfigurationLink of the artifact, must be a readable storage page blob.
"""
return pulumi.get(self, "default_configuration_link")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
| 38.95829 | 335 | 0.657718 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from . import outputs
from ._enums import *
__all__ = [
'CreationDataResponse',
'DataDiskImageEncryptionResponse',
'DisallowedResponse',
'DiskSkuResponse',
'EncryptionImagesResponse',
'EncryptionResponse',
'EncryptionSetIdentityResponse',
'EncryptionSettingsCollectionResponse',
'EncryptionSettingsElementResponse',
'ExtendedLocationResponse',
'GalleryApplicationVersionPublishingProfileResponse',
'GalleryArtifactVersionSourceResponse',
'GalleryDataDiskImageResponse',
'GalleryIdentifierResponse',
'GalleryImageFeatureResponse',
'GalleryImageIdentifierResponse',
'GalleryImageVersionPublishingProfileResponse',
'GalleryImageVersionStorageProfileResponse',
'GalleryOSDiskImageResponse',
'ImageDiskReferenceResponse',
'ImagePurchasePlanResponse',
'KeyForDiskEncryptionSetResponse',
'KeyVaultAndKeyReferenceResponse',
'KeyVaultAndSecretReferenceResponse',
'OSDiskImageEncryptionResponse',
'PrivateEndpointConnectionResponse',
'PrivateEndpointResponse',
'PrivateLinkServiceConnectionStateResponse',
'PurchasePlanResponse',
'RecommendedMachineConfigurationResponse',
'RegionalReplicationStatusResponse',
'ReplicationStatusResponse',
'ResourceRangeResponse',
'ShareInfoElementResponse',
'SharingProfileGroupResponse',
'SharingProfileResponse',
'SnapshotSkuResponse',
'SourceVaultResponse',
'TargetRegionResponse',
'UserArtifactManageResponse',
'UserArtifactSourceResponse',
]
@pulumi.output_type
class CreationDataResponse(dict):
def __init__(__self__, *,
create_option: str,
source_unique_id: str,
gallery_image_reference: Optional['outputs.ImageDiskReferenceResponse'] = None,
image_reference: Optional['outputs.ImageDiskReferenceResponse'] = None,
logical_sector_size: Optional[int] = None,
source_resource_id: Optional[str] = None,
source_uri: Optional[str] = None,
storage_account_id: Optional[str] = None,
upload_size_bytes: Optional[float] = None):
pulumi.set(__self__, "create_option", create_option)
pulumi.set(__self__, "source_unique_id", source_unique_id)
if gallery_image_reference is not None:
pulumi.set(__self__, "gallery_image_reference", gallery_image_reference)
if image_reference is not None:
pulumi.set(__self__, "image_reference", image_reference)
if logical_sector_size is not None:
pulumi.set(__self__, "logical_sector_size", logical_sector_size)
if source_resource_id is not None:
pulumi.set(__self__, "source_resource_id", source_resource_id)
if source_uri is not None:
pulumi.set(__self__, "source_uri", source_uri)
if storage_account_id is not None:
pulumi.set(__self__, "storage_account_id", storage_account_id)
if upload_size_bytes is not None:
pulumi.set(__self__, "upload_size_bytes", upload_size_bytes)
@property
@pulumi.getter(name="createOption")
def create_option(self) -> str:
return pulumi.get(self, "create_option")
@property
@pulumi.getter(name="sourceUniqueId")
def source_unique_id(self) -> str:
return pulumi.get(self, "source_unique_id")
@property
@pulumi.getter(name="galleryImageReference")
def gallery_image_reference(self) -> Optional['outputs.ImageDiskReferenceResponse']:
return pulumi.get(self, "gallery_image_reference")
@property
@pulumi.getter(name="imageReference")
def image_reference(self) -> Optional['outputs.ImageDiskReferenceResponse']:
return pulumi.get(self, "image_reference")
@property
@pulumi.getter(name="logicalSectorSize")
def logical_sector_size(self) -> Optional[int]:
return pulumi.get(self, "logical_sector_size")
@property
@pulumi.getter(name="sourceResourceId")
def source_resource_id(self) -> Optional[str]:
return pulumi.get(self, "source_resource_id")
@property
@pulumi.getter(name="sourceUri")
def source_uri(self) -> Optional[str]:
return pulumi.get(self, "source_uri")
@property
@pulumi.getter(name="storageAccountId")
def storage_account_id(self) -> Optional[str]:
return pulumi.get(self, "storage_account_id")
@property
@pulumi.getter(name="uploadSizeBytes")
def upload_size_bytes(self) -> Optional[float]:
return pulumi.get(self, "upload_size_bytes")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class DataDiskImageEncryptionResponse(dict):
def __init__(__self__, *,
lun: int,
disk_encryption_set_id: Optional[str] = None):
pulumi.set(__self__, "lun", lun)
if disk_encryption_set_id is not None:
pulumi.set(__self__, "disk_encryption_set_id", disk_encryption_set_id)
@property
@pulumi.getter
def lun(self) -> int:
return pulumi.get(self, "lun")
@property
@pulumi.getter(name="diskEncryptionSetId")
def disk_encryption_set_id(self) -> Optional[str]:
return pulumi.get(self, "disk_encryption_set_id")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class DisallowedResponse(dict):
def __init__(__self__, *,
disk_types: Optional[Sequence[str]] = None):
if disk_types is not None:
pulumi.set(__self__, "disk_types", disk_types)
@property
@pulumi.getter(name="diskTypes")
def disk_types(self) -> Optional[Sequence[str]]:
return pulumi.get(self, "disk_types")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class DiskSkuResponse(dict):
def __init__(__self__, *,
tier: str,
name: Optional[str] = None):
pulumi.set(__self__, "tier", tier)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter
def tier(self) -> str:
return pulumi.get(self, "tier")
@property
@pulumi.getter
def name(self) -> Optional[str]:
return pulumi.get(self, "name")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionImagesResponse(dict):
def __init__(__self__, *,
data_disk_images: Optional[Sequence['outputs.DataDiskImageEncryptionResponse']] = None,
os_disk_image: Optional['outputs.OSDiskImageEncryptionResponse'] = None):
if data_disk_images is not None:
pulumi.set(__self__, "data_disk_images", data_disk_images)
if os_disk_image is not None:
pulumi.set(__self__, "os_disk_image", os_disk_image)
@property
@pulumi.getter(name="dataDiskImages")
def data_disk_images(self) -> Optional[Sequence['outputs.DataDiskImageEncryptionResponse']]:
return pulumi.get(self, "data_disk_images")
@property
@pulumi.getter(name="osDiskImage")
def os_disk_image(self) -> Optional['outputs.OSDiskImageEncryptionResponse']:
return pulumi.get(self, "os_disk_image")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionResponse(dict):
def __init__(__self__, *,
disk_encryption_set_id: Optional[str] = None,
type: Optional[str] = None):
if disk_encryption_set_id is not None:
pulumi.set(__self__, "disk_encryption_set_id", disk_encryption_set_id)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter(name="diskEncryptionSetId")
def disk_encryption_set_id(self) -> Optional[str]:
return pulumi.get(self, "disk_encryption_set_id")
@property
@pulumi.getter
def type(self) -> Optional[str]:
return pulumi.get(self, "type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionSetIdentityResponse(dict):
def __init__(__self__, *,
principal_id: str,
tenant_id: str,
type: Optional[str] = None):
pulumi.set(__self__, "principal_id", principal_id)
pulumi.set(__self__, "tenant_id", tenant_id)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter(name="principalId")
def principal_id(self) -> str:
return pulumi.get(self, "principal_id")
@property
@pulumi.getter(name="tenantId")
def tenant_id(self) -> str:
return pulumi.get(self, "tenant_id")
@property
@pulumi.getter
def type(self) -> Optional[str]:
return pulumi.get(self, "type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionSettingsCollectionResponse(dict):
def __init__(__self__, *,
enabled: bool,
encryption_settings: Optional[Sequence['outputs.EncryptionSettingsElementResponse']] = None,
encryption_settings_version: Optional[str] = None):
pulumi.set(__self__, "enabled", enabled)
if encryption_settings is not None:
pulumi.set(__self__, "encryption_settings", encryption_settings)
if encryption_settings_version is not None:
pulumi.set(__self__, "encryption_settings_version", encryption_settings_version)
@property
@pulumi.getter
def enabled(self) -> bool:
return pulumi.get(self, "enabled")
@property
@pulumi.getter(name="encryptionSettings")
def encryption_settings(self) -> Optional[Sequence['outputs.EncryptionSettingsElementResponse']]:
return pulumi.get(self, "encryption_settings")
@property
@pulumi.getter(name="encryptionSettingsVersion")
def encryption_settings_version(self) -> Optional[str]:
return pulumi.get(self, "encryption_settings_version")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class EncryptionSettingsElementResponse(dict):
def __init__(__self__, *,
disk_encryption_key: Optional['outputs.KeyVaultAndSecretReferenceResponse'] = None,
key_encryption_key: Optional['outputs.KeyVaultAndKeyReferenceResponse'] = None):
if disk_encryption_key is not None:
pulumi.set(__self__, "disk_encryption_key", disk_encryption_key)
if key_encryption_key is not None:
pulumi.set(__self__, "key_encryption_key", key_encryption_key)
@property
@pulumi.getter(name="diskEncryptionKey")
def disk_encryption_key(self) -> Optional['outputs.KeyVaultAndSecretReferenceResponse']:
return pulumi.get(self, "disk_encryption_key")
@property
@pulumi.getter(name="keyEncryptionKey")
def key_encryption_key(self) -> Optional['outputs.KeyVaultAndKeyReferenceResponse']:
return pulumi.get(self, "key_encryption_key")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ExtendedLocationResponse(dict):
def __init__(__self__, *,
name: Optional[str] = None,
type: Optional[str] = None):
if name is not None:
pulumi.set(__self__, "name", name)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter
def name(self) -> Optional[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter
def type(self) -> Optional[str]:
return pulumi.get(self, "type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryApplicationVersionPublishingProfileResponse(dict):
def __init__(__self__, *,
published_date: str,
source: 'outputs.UserArtifactSourceResponse',
enable_health_check: Optional[bool] = None,
end_of_life_date: Optional[str] = None,
exclude_from_latest: Optional[bool] = None,
manage_actions: Optional['outputs.UserArtifactManageResponse'] = None,
replica_count: Optional[int] = None,
storage_account_type: Optional[str] = None,
target_regions: Optional[Sequence['outputs.TargetRegionResponse']] = None):
pulumi.set(__self__, "published_date", published_date)
pulumi.set(__self__, "source", source)
if enable_health_check is not None:
pulumi.set(__self__, "enable_health_check", enable_health_check)
if end_of_life_date is not None:
pulumi.set(__self__, "end_of_life_date", end_of_life_date)
if exclude_from_latest is not None:
pulumi.set(__self__, "exclude_from_latest", exclude_from_latest)
if manage_actions is not None:
pulumi.set(__self__, "manage_actions", manage_actions)
if replica_count is not None:
pulumi.set(__self__, "replica_count", replica_count)
if storage_account_type is not None:
pulumi.set(__self__, "storage_account_type", storage_account_type)
if target_regions is not None:
pulumi.set(__self__, "target_regions", target_regions)
@property
@pulumi.getter(name="publishedDate")
def published_date(self) -> str:
return pulumi.get(self, "published_date")
@property
@pulumi.getter
def source(self) -> 'outputs.UserArtifactSourceResponse':
return pulumi.get(self, "source")
@property
@pulumi.getter(name="enableHealthCheck")
def enable_health_check(self) -> Optional[bool]:
return pulumi.get(self, "enable_health_check")
@property
@pulumi.getter(name="endOfLifeDate")
def end_of_life_date(self) -> Optional[str]:
return pulumi.get(self, "end_of_life_date")
@property
@pulumi.getter(name="excludeFromLatest")
def exclude_from_latest(self) -> Optional[bool]:
return pulumi.get(self, "exclude_from_latest")
@property
@pulumi.getter(name="manageActions")
def manage_actions(self) -> Optional['outputs.UserArtifactManageResponse']:
return pulumi.get(self, "manage_actions")
@property
@pulumi.getter(name="replicaCount")
def replica_count(self) -> Optional[int]:
return pulumi.get(self, "replica_count")
@property
@pulumi.getter(name="storageAccountType")
def storage_account_type(self) -> Optional[str]:
return pulumi.get(self, "storage_account_type")
@property
@pulumi.getter(name="targetRegions")
def target_regions(self) -> Optional[Sequence['outputs.TargetRegionResponse']]:
return pulumi.get(self, "target_regions")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryArtifactVersionSourceResponse(dict):
def __init__(__self__, *,
id: Optional[str] = None,
uri: Optional[str] = None):
if id is not None:
pulumi.set(__self__, "id", id)
if uri is not None:
pulumi.set(__self__, "uri", uri)
@property
@pulumi.getter
def id(self) -> Optional[str]:
return pulumi.get(self, "id")
@property
@pulumi.getter
def uri(self) -> Optional[str]:
return pulumi.get(self, "uri")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryDataDiskImageResponse(dict):
def __init__(__self__, *,
lun: int,
size_in_gb: int,
host_caching: Optional[str] = None,
source: Optional['outputs.GalleryArtifactVersionSourceResponse'] = None):
pulumi.set(__self__, "lun", lun)
pulumi.set(__self__, "size_in_gb", size_in_gb)
if host_caching is not None:
pulumi.set(__self__, "host_caching", host_caching)
if source is not None:
pulumi.set(__self__, "source", source)
@property
@pulumi.getter
def lun(self) -> int:
return pulumi.get(self, "lun")
@property
@pulumi.getter(name="sizeInGB")
def size_in_gb(self) -> int:
return pulumi.get(self, "size_in_gb")
@property
@pulumi.getter(name="hostCaching")
def host_caching(self) -> Optional[str]:
return pulumi.get(self, "host_caching")
@property
@pulumi.getter
def source(self) -> Optional['outputs.GalleryArtifactVersionSourceResponse']:
return pulumi.get(self, "source")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryIdentifierResponse(dict):
def __init__(__self__, *,
unique_name: str):
pulumi.set(__self__, "unique_name", unique_name)
@property
@pulumi.getter(name="uniqueName")
def unique_name(self) -> str:
return pulumi.get(self, "unique_name")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryImageFeatureResponse(dict):
def __init__(__self__, *,
name: Optional[str] = None,
value: Optional[str] = None):
if name is not None:
pulumi.set(__self__, "name", name)
if value is not None:
pulumi.set(__self__, "value", value)
@property
@pulumi.getter
def name(self) -> Optional[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter
def value(self) -> Optional[str]:
return pulumi.get(self, "value")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryImageIdentifierResponse(dict):
def __init__(__self__, *,
offer: str,
publisher: str,
sku: str):
pulumi.set(__self__, "offer", offer)
pulumi.set(__self__, "publisher", publisher)
pulumi.set(__self__, "sku", sku)
@property
@pulumi.getter
def offer(self) -> str:
return pulumi.get(self, "offer")
@property
@pulumi.getter
def publisher(self) -> str:
return pulumi.get(self, "publisher")
@property
@pulumi.getter
def sku(self) -> str:
return pulumi.get(self, "sku")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryImageVersionPublishingProfileResponse(dict):
def __init__(__self__, *,
published_date: str,
end_of_life_date: Optional[str] = None,
exclude_from_latest: Optional[bool] = None,
replica_count: Optional[int] = None,
storage_account_type: Optional[str] = None,
target_regions: Optional[Sequence['outputs.TargetRegionResponse']] = None):
pulumi.set(__self__, "published_date", published_date)
if end_of_life_date is not None:
pulumi.set(__self__, "end_of_life_date", end_of_life_date)
if exclude_from_latest is not None:
pulumi.set(__self__, "exclude_from_latest", exclude_from_latest)
if replica_count is not None:
pulumi.set(__self__, "replica_count", replica_count)
if storage_account_type is not None:
pulumi.set(__self__, "storage_account_type", storage_account_type)
if target_regions is not None:
pulumi.set(__self__, "target_regions", target_regions)
@property
@pulumi.getter(name="publishedDate")
def published_date(self) -> str:
return pulumi.get(self, "published_date")
@property
@pulumi.getter(name="endOfLifeDate")
def end_of_life_date(self) -> Optional[str]:
return pulumi.get(self, "end_of_life_date")
@property
@pulumi.getter(name="excludeFromLatest")
def exclude_from_latest(self) -> Optional[bool]:
return pulumi.get(self, "exclude_from_latest")
@property
@pulumi.getter(name="replicaCount")
def replica_count(self) -> Optional[int]:
return pulumi.get(self, "replica_count")
@property
@pulumi.getter(name="storageAccountType")
def storage_account_type(self) -> Optional[str]:
return pulumi.get(self, "storage_account_type")
@property
@pulumi.getter(name="targetRegions")
def target_regions(self) -> Optional[Sequence['outputs.TargetRegionResponse']]:
return pulumi.get(self, "target_regions")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryImageVersionStorageProfileResponse(dict):
def __init__(__self__, *,
data_disk_images: Optional[Sequence['outputs.GalleryDataDiskImageResponse']] = None,
os_disk_image: Optional['outputs.GalleryOSDiskImageResponse'] = None,
source: Optional['outputs.GalleryArtifactVersionSourceResponse'] = None):
if data_disk_images is not None:
pulumi.set(__self__, "data_disk_images", data_disk_images)
if os_disk_image is not None:
pulumi.set(__self__, "os_disk_image", os_disk_image)
if source is not None:
pulumi.set(__self__, "source", source)
@property
@pulumi.getter(name="dataDiskImages")
def data_disk_images(self) -> Optional[Sequence['outputs.GalleryDataDiskImageResponse']]:
return pulumi.get(self, "data_disk_images")
@property
@pulumi.getter(name="osDiskImage")
def os_disk_image(self) -> Optional['outputs.GalleryOSDiskImageResponse']:
return pulumi.get(self, "os_disk_image")
@property
@pulumi.getter
def source(self) -> Optional['outputs.GalleryArtifactVersionSourceResponse']:
return pulumi.get(self, "source")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class GalleryOSDiskImageResponse(dict):
def __init__(__self__, *,
size_in_gb: int,
host_caching: Optional[str] = None,
source: Optional['outputs.GalleryArtifactVersionSourceResponse'] = None):
pulumi.set(__self__, "size_in_gb", size_in_gb)
if host_caching is not None:
pulumi.set(__self__, "host_caching", host_caching)
if source is not None:
pulumi.set(__self__, "source", source)
@property
@pulumi.getter(name="sizeInGB")
def size_in_gb(self) -> int:
return pulumi.get(self, "size_in_gb")
@property
@pulumi.getter(name="hostCaching")
def host_caching(self) -> Optional[str]:
return pulumi.get(self, "host_caching")
@property
@pulumi.getter
def source(self) -> Optional['outputs.GalleryArtifactVersionSourceResponse']:
return pulumi.get(self, "source")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ImageDiskReferenceResponse(dict):
def __init__(__self__, *,
id: str,
lun: Optional[int] = None):
pulumi.set(__self__, "id", id)
if lun is not None:
pulumi.set(__self__, "lun", lun)
@property
@pulumi.getter
def id(self) -> str:
return pulumi.get(self, "id")
@property
@pulumi.getter
def lun(self) -> Optional[int]:
return pulumi.get(self, "lun")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ImagePurchasePlanResponse(dict):
def __init__(__self__, *,
name: Optional[str] = None,
product: Optional[str] = None,
publisher: Optional[str] = None):
if name is not None:
pulumi.set(__self__, "name", name)
if product is not None:
pulumi.set(__self__, "product", product)
if publisher is not None:
pulumi.set(__self__, "publisher", publisher)
@property
@pulumi.getter
def name(self) -> Optional[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter
def product(self) -> Optional[str]:
return pulumi.get(self, "product")
@property
@pulumi.getter
def publisher(self) -> Optional[str]:
return pulumi.get(self, "publisher")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class KeyForDiskEncryptionSetResponse(dict):
def __init__(__self__, *,
key_url: str,
source_vault: Optional['outputs.SourceVaultResponse'] = None):
pulumi.set(__self__, "key_url", key_url)
if source_vault is not None:
pulumi.set(__self__, "source_vault", source_vault)
@property
@pulumi.getter(name="keyUrl")
def key_url(self) -> str:
return pulumi.get(self, "key_url")
@property
@pulumi.getter(name="sourceVault")
def source_vault(self) -> Optional['outputs.SourceVaultResponse']:
return pulumi.get(self, "source_vault")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class KeyVaultAndKeyReferenceResponse(dict):
def __init__(__self__, *,
key_url: str,
source_vault: 'outputs.SourceVaultResponse'):
pulumi.set(__self__, "key_url", key_url)
pulumi.set(__self__, "source_vault", source_vault)
@property
@pulumi.getter(name="keyUrl")
def key_url(self) -> str:
return pulumi.get(self, "key_url")
@property
@pulumi.getter(name="sourceVault")
def source_vault(self) -> 'outputs.SourceVaultResponse':
return pulumi.get(self, "source_vault")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class KeyVaultAndSecretReferenceResponse(dict):
def __init__(__self__, *,
secret_url: str,
source_vault: 'outputs.SourceVaultResponse'):
pulumi.set(__self__, "secret_url", secret_url)
pulumi.set(__self__, "source_vault", source_vault)
@property
@pulumi.getter(name="secretUrl")
def secret_url(self) -> str:
return pulumi.get(self, "secret_url")
@property
@pulumi.getter(name="sourceVault")
def source_vault(self) -> 'outputs.SourceVaultResponse':
return pulumi.get(self, "source_vault")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class OSDiskImageEncryptionResponse(dict):
def __init__(__self__, *,
disk_encryption_set_id: Optional[str] = None):
if disk_encryption_set_id is not None:
pulumi.set(__self__, "disk_encryption_set_id", disk_encryption_set_id)
@property
@pulumi.getter(name="diskEncryptionSetId")
def disk_encryption_set_id(self) -> Optional[str]:
return pulumi.get(self, "disk_encryption_set_id")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class PrivateEndpointConnectionResponse(dict):
def __init__(__self__, *,
id: str,
name: str,
private_link_service_connection_state: 'outputs.PrivateLinkServiceConnectionStateResponse',
provisioning_state: str,
type: str,
private_endpoint: Optional['outputs.PrivateEndpointResponse'] = None):
pulumi.set(__self__, "id", id)
pulumi.set(__self__, "name", name)
pulumi.set(__self__, "private_link_service_connection_state", private_link_service_connection_state)
pulumi.set(__self__, "provisioning_state", provisioning_state)
pulumi.set(__self__, "type", type)
if private_endpoint is not None:
pulumi.set(__self__, "private_endpoint", private_endpoint)
@property
@pulumi.getter
def id(self) -> str:
return pulumi.get(self, "id")
@property
@pulumi.getter
def name(self) -> str:
return pulumi.get(self, "name")
@property
@pulumi.getter(name="privateLinkServiceConnectionState")
def private_link_service_connection_state(self) -> 'outputs.PrivateLinkServiceConnectionStateResponse':
return pulumi.get(self, "private_link_service_connection_state")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> str:
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter
def type(self) -> str:
return pulumi.get(self, "type")
@property
@pulumi.getter(name="privateEndpoint")
def private_endpoint(self) -> Optional['outputs.PrivateEndpointResponse']:
return pulumi.get(self, "private_endpoint")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class PrivateEndpointResponse(dict):
def __init__(__self__, *,
id: str):
pulumi.set(__self__, "id", id)
@property
@pulumi.getter
def id(self) -> str:
return pulumi.get(self, "id")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class PrivateLinkServiceConnectionStateResponse(dict):
def __init__(__self__, *,
actions_required: Optional[str] = None,
description: Optional[str] = None,
status: Optional[str] = None):
if actions_required is not None:
pulumi.set(__self__, "actions_required", actions_required)
if description is not None:
pulumi.set(__self__, "description", description)
if status is not None:
pulumi.set(__self__, "status", status)
@property
@pulumi.getter(name="actionsRequired")
def actions_required(self) -> Optional[str]:
return pulumi.get(self, "actions_required")
@property
@pulumi.getter
def description(self) -> Optional[str]:
return pulumi.get(self, "description")
@property
@pulumi.getter
def status(self) -> Optional[str]:
return pulumi.get(self, "status")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class PurchasePlanResponse(dict):
def __init__(__self__, *,
name: str,
product: str,
publisher: str,
promotion_code: Optional[str] = None):
pulumi.set(__self__, "name", name)
pulumi.set(__self__, "product", product)
pulumi.set(__self__, "publisher", publisher)
if promotion_code is not None:
pulumi.set(__self__, "promotion_code", promotion_code)
@property
@pulumi.getter
def name(self) -> str:
return pulumi.get(self, "name")
@property
@pulumi.getter
def product(self) -> str:
return pulumi.get(self, "product")
@property
@pulumi.getter
def publisher(self) -> str:
return pulumi.get(self, "publisher")
@property
@pulumi.getter(name="promotionCode")
def promotion_code(self) -> Optional[str]:
return pulumi.get(self, "promotion_code")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class RecommendedMachineConfigurationResponse(dict):
def __init__(__self__, *,
memory: Optional['outputs.ResourceRangeResponse'] = None,
v_cpus: Optional['outputs.ResourceRangeResponse'] = None):
if memory is not None:
pulumi.set(__self__, "memory", memory)
if v_cpus is not None:
pulumi.set(__self__, "v_cpus", v_cpus)
@property
@pulumi.getter
def memory(self) -> Optional['outputs.ResourceRangeResponse']:
return pulumi.get(self, "memory")
@property
@pulumi.getter(name="vCPUs")
def v_cpus(self) -> Optional['outputs.ResourceRangeResponse']:
return pulumi.get(self, "v_cpus")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class RegionalReplicationStatusResponse(dict):
def __init__(__self__, *,
details: str,
progress: int,
region: str,
state: str):
pulumi.set(__self__, "details", details)
pulumi.set(__self__, "progress", progress)
pulumi.set(__self__, "region", region)
pulumi.set(__self__, "state", state)
@property
@pulumi.getter
def details(self) -> str:
return pulumi.get(self, "details")
@property
@pulumi.getter
def progress(self) -> int:
return pulumi.get(self, "progress")
@property
@pulumi.getter
def region(self) -> str:
return pulumi.get(self, "region")
@property
@pulumi.getter
def state(self) -> str:
return pulumi.get(self, "state")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ReplicationStatusResponse(dict):
def __init__(__self__, *,
aggregated_state: str,
summary: Sequence['outputs.RegionalReplicationStatusResponse']):
pulumi.set(__self__, "aggregated_state", aggregated_state)
pulumi.set(__self__, "summary", summary)
@property
@pulumi.getter(name="aggregatedState")
def aggregated_state(self) -> str:
return pulumi.get(self, "aggregated_state")
@property
@pulumi.getter
def summary(self) -> Sequence['outputs.RegionalReplicationStatusResponse']:
return pulumi.get(self, "summary")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ResourceRangeResponse(dict):
def __init__(__self__, *,
max: Optional[int] = None,
min: Optional[int] = None):
if max is not None:
pulumi.set(__self__, "max", max)
if min is not None:
pulumi.set(__self__, "min", min)
@property
@pulumi.getter
def max(self) -> Optional[int]:
return pulumi.get(self, "max")
@property
@pulumi.getter
def min(self) -> Optional[int]:
return pulumi.get(self, "min")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ShareInfoElementResponse(dict):
def __init__(__self__, *,
vm_uri: str):
pulumi.set(__self__, "vm_uri", vm_uri)
@property
@pulumi.getter(name="vmUri")
def vm_uri(self) -> str:
return pulumi.get(self, "vm_uri")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class SharingProfileGroupResponse(dict):
def __init__(__self__, *,
ids: Optional[Sequence[str]] = None,
type: Optional[str] = None):
if ids is not None:
pulumi.set(__self__, "ids", ids)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter
def ids(self) -> Optional[Sequence[str]]:
return pulumi.get(self, "ids")
@property
@pulumi.getter
def type(self) -> Optional[str]:
return pulumi.get(self, "type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class SharingProfileResponse(dict):
def __init__(__self__, *,
groups: Sequence['outputs.SharingProfileGroupResponse'],
permissions: Optional[str] = None):
pulumi.set(__self__, "groups", groups)
if permissions is not None:
pulumi.set(__self__, "permissions", permissions)
@property
@pulumi.getter
def groups(self) -> Sequence['outputs.SharingProfileGroupResponse']:
return pulumi.get(self, "groups")
@property
@pulumi.getter
def permissions(self) -> Optional[str]:
return pulumi.get(self, "permissions")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class SnapshotSkuResponse(dict):
def __init__(__self__, *,
tier: str,
name: Optional[str] = None):
pulumi.set(__self__, "tier", tier)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter
def tier(self) -> str:
return pulumi.get(self, "tier")
@property
@pulumi.getter
def name(self) -> Optional[str]:
return pulumi.get(self, "name")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class SourceVaultResponse(dict):
def __init__(__self__, *,
id: Optional[str] = None):
if id is not None:
pulumi.set(__self__, "id", id)
@property
@pulumi.getter
def id(self) -> Optional[str]:
return pulumi.get(self, "id")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class TargetRegionResponse(dict):
def __init__(__self__, *,
name: str,
encryption: Optional['outputs.EncryptionImagesResponse'] = None,
regional_replica_count: Optional[int] = None,
storage_account_type: Optional[str] = None):
pulumi.set(__self__, "name", name)
if encryption is not None:
pulumi.set(__self__, "encryption", encryption)
if regional_replica_count is not None:
pulumi.set(__self__, "regional_replica_count", regional_replica_count)
if storage_account_type is not None:
pulumi.set(__self__, "storage_account_type", storage_account_type)
@property
@pulumi.getter
def name(self) -> str:
return pulumi.get(self, "name")
@property
@pulumi.getter
def encryption(self) -> Optional['outputs.EncryptionImagesResponse']:
return pulumi.get(self, "encryption")
@property
@pulumi.getter(name="regionalReplicaCount")
def regional_replica_count(self) -> Optional[int]:
return pulumi.get(self, "regional_replica_count")
@property
@pulumi.getter(name="storageAccountType")
def storage_account_type(self) -> Optional[str]:
return pulumi.get(self, "storage_account_type")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class UserArtifactManageResponse(dict):
def __init__(__self__, *,
install: str,
remove: str,
update: Optional[str] = None):
pulumi.set(__self__, "install", install)
pulumi.set(__self__, "remove", remove)
if update is not None:
pulumi.set(__self__, "update", update)
@property
@pulumi.getter
def install(self) -> str:
return pulumi.get(self, "install")
@property
@pulumi.getter
def remove(self) -> str:
return pulumi.get(self, "remove")
@property
@pulumi.getter
def update(self) -> Optional[str]:
return pulumi.get(self, "update")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class UserArtifactSourceResponse(dict):
def __init__(__self__, *,
media_link: str,
default_configuration_link: Optional[str] = None):
pulumi.set(__self__, "media_link", media_link)
if default_configuration_link is not None:
pulumi.set(__self__, "default_configuration_link", default_configuration_link)
@property
@pulumi.getter(name="mediaLink")
def media_link(self) -> str:
return pulumi.get(self, "media_link")
@property
@pulumi.getter(name="defaultConfigurationLink")
def default_configuration_link(self) -> Optional[str]:
return pulumi.get(self, "default_configuration_link")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
| true | true |
1c33fdb7cffa7a94b8db97932f625aeac2f2911b | 4,987 | py | Python | helper_nodes/display_poses.py | apl-ocean-engineering/visual_odom | 6e88c8d5a098585f7b12e4934f47494414824b4d | [
"MIT"
] | 1 | 2020-11-22T20:09:53.000Z | 2020-11-22T20:09:53.000Z | helper_nodes/display_poses.py | apl-ocean-engineering/stereo_visual_odom | 6e88c8d5a098585f7b12e4934f47494414824b4d | [
"MIT"
] | null | null | null | helper_nodes/display_poses.py | apl-ocean-engineering/stereo_visual_odom | 6e88c8d5a098585f7b12e4934f47494414824b4d | [
"MIT"
] | null | null | null | import matplotlib.pyplot as plt
import copy
import numpy as np
import argparse
def get_eucld_error(x1, x2, y1, y2, z1, z2):
error = []
for i in range(len(x1)):
p1 = np.array([x1[i], y1[i], z1[i]])
p2 = np.array([x2[i], y2[i], z2[i]])
error.append(float(np.sum(np.subtract(p1, p2))))
return error
def get_data(f, ignore_count=[]):
count = 0
idx = []
time = []
x = []
y = []
z = []
roll = []
pitch = []
yaw = []
for line in f:
idx.append(count)
count += 1
time.append(line.split(',')[0])
x.append(float(line.split(',')[1]))
y.append(float(line.split(',')[2]))
z.append(float(line.split(',')[3]))
roll.append(float(line.split(',')[4]))
pitch.append(float(line.split(',')[5]))
yaw.append(float(line.split(',')[6]))
return idx, time, x, y, z, roll, pitch, yaw
def check_val(l1, l2, idx, ind, max_val, min_val):
if l1[ind] > max_val or l2[ind] > max_val:
l1.pop(ind)
l2.pop(ind)
idx.pop(ind)
elif l1[ind] < min_val or l2[ind] < min_val:
l1.pop(ind)
l2.pop(ind)
idx.pop(ind)
else:
ind += 1
return l1, l2, idx, ind
def check_error_val(error, idx, ind, max_val, min_val):
if error[ind] > max_val:
error.pop(ind)
idx.pop(ind)
elif error[ind] < min_val:
error.pop(ind)
idx.pop(ind)
else:
ind += 1
return error, idx, ind
def plot(idx_list, v1, v2, v3=None, title=" ",
label1="1", label2="2", label3="3", dump=False, y_min=None,
y_max=None):
fig1, ax1 = plt.subplots()
ax1.scatter(idx_list, v1, c='k', label=label1)
ax1.scatter(idx_list, v2, c='g', label=label2)
if v3 is not None:
ax1.scatter(idx_list, v3, c='b', label=label3)
ax1.set_title(title)
if y_min is not None and y_max is not None:
ax1.set_ylim((y_min, y_max))
ax1.legend()
if dump:
fig1.savefig(title + ".png")
def plot_error(idx_list, error, title=" ",
dump=False, y_min=None, y_max=None):
fig1, ax1 = plt.subplots()
ax1.scatter(idx_list, error, c='k')
ax1.set_title(title)
if y_min is not None and y_max is not None:
ax1.set_ylim((y_min, y_max))
if dump:
fig1.savefig(title + ".png")
def integrate(lst):
total = 0
for v in lst:
total += v
return total
def main(fname1, fname2, display_integration=False, fname3 = None):
print(fname1, fname2, fname3)
f1 = open(fname1, 'r')
f2 = open(fname2, 'r')
if fname3 is not None:
f3 = open(fname3, 'r')
idx1, time1, x1, y1, z1, roll1, pitch1, yaw1 = get_data(f1)
idx2, time2, x2, y2, z2, roll2, pitch2, yaw2 = get_data(f2)
if fname3 is not None:
idx3, time3, x3, y3, z3, roll3, pitch3, yaw3 = get_data(f3)
error = get_eucld_error(x1, x2, y1, y2, z1, z2)
idx_x = copy.deepcopy(idx1)
idx_y = copy.deepcopy(idx1)
idx_z = copy.deepcopy(idx1)
idx_error = copy.deepcopy(idx1)
# i = 0
# while i < len(x1):
# x1, x2, idx_x, i = check_val(x1, x2, idx_x, i, 100.0, -100.0)
# i = 0
# while i < len(y1):
# y1, y2, idx_y, i = check_val(y1, y2, idx_y, i, 100.0, -100.0)
# i = 0
# while i < len(z1):
# z1, z2, idx_z, i = check_val(z1, z2, idx_z, i, 100.0, -100.0)
# i = 0
# while i < len(error):
# error, idx_error, i = check_error_val(error,
# idx_error, i, 100.0, -100.0)
if display_integration:
print('X')
print(integrate(x1), integrate(x2))
print('Y')
print(integrate(y1), integrate(y2))
print('Z')
print(integrate(z1), integrate(z2))
print('Error')
print(integrate(error)/len(error))
plot(idx_x, x1, x2, v3=x3, title="x",
label1=fname1.split('/')[-1].replace('.txt', ''),
label2=fname2.split('/')[-1].replace('.txt', ''),
label3=fname3.split('/')[-1].replace('.txt', ''),
dump=True)
plot(idx_y, y1, y2, v3=y3, title="y",
label1=fname1.split('/')[-1].replace('.txt', ''),
label2=fname2.split('/')[-1].replace('.txt', ''),
label3=fname3.split('/')[-1].replace('.txt', ''),
dump=True)
plot(idx_z, z1, z2, v3=z3, title="z",
label1=fname1.split('/')[-1].replace('.txt', ''),
label2=fname2.split('/')[-1].replace('.txt', ''),
label3=fname3.split('/')[-1].replace('.txt', ''),
dump=True)
plot_error(idx_error, error)
plt.show()
if __name__ == '__main__':
parser = argparse.ArgumentParser('Display twist')
parser.add_argument('file1')
parser.add_argument('file2')
parser.add_argument('--file3')
parser.add_argument('--show_final_pose', type=bool, default=False)
args = parser.parse_args()
main(args.file1, args.file2, args.show_final_pose, fname3=args.file3)
| 28.175141 | 76 | 0.548626 | import matplotlib.pyplot as plt
import copy
import numpy as np
import argparse
def get_eucld_error(x1, x2, y1, y2, z1, z2):
error = []
for i in range(len(x1)):
p1 = np.array([x1[i], y1[i], z1[i]])
p2 = np.array([x2[i], y2[i], z2[i]])
error.append(float(np.sum(np.subtract(p1, p2))))
return error
def get_data(f, ignore_count=[]):
count = 0
idx = []
time = []
x = []
y = []
z = []
roll = []
pitch = []
yaw = []
for line in f:
idx.append(count)
count += 1
time.append(line.split(',')[0])
x.append(float(line.split(',')[1]))
y.append(float(line.split(',')[2]))
z.append(float(line.split(',')[3]))
roll.append(float(line.split(',')[4]))
pitch.append(float(line.split(',')[5]))
yaw.append(float(line.split(',')[6]))
return idx, time, x, y, z, roll, pitch, yaw
def check_val(l1, l2, idx, ind, max_val, min_val):
if l1[ind] > max_val or l2[ind] > max_val:
l1.pop(ind)
l2.pop(ind)
idx.pop(ind)
elif l1[ind] < min_val or l2[ind] < min_val:
l1.pop(ind)
l2.pop(ind)
idx.pop(ind)
else:
ind += 1
return l1, l2, idx, ind
def check_error_val(error, idx, ind, max_val, min_val):
if error[ind] > max_val:
error.pop(ind)
idx.pop(ind)
elif error[ind] < min_val:
error.pop(ind)
idx.pop(ind)
else:
ind += 1
return error, idx, ind
def plot(idx_list, v1, v2, v3=None, title=" ",
label1="1", label2="2", label3="3", dump=False, y_min=None,
y_max=None):
fig1, ax1 = plt.subplots()
ax1.scatter(idx_list, v1, c='k', label=label1)
ax1.scatter(idx_list, v2, c='g', label=label2)
if v3 is not None:
ax1.scatter(idx_list, v3, c='b', label=label3)
ax1.set_title(title)
if y_min is not None and y_max is not None:
ax1.set_ylim((y_min, y_max))
ax1.legend()
if dump:
fig1.savefig(title + ".png")
def plot_error(idx_list, error, title=" ",
dump=False, y_min=None, y_max=None):
fig1, ax1 = plt.subplots()
ax1.scatter(idx_list, error, c='k')
ax1.set_title(title)
if y_min is not None and y_max is not None:
ax1.set_ylim((y_min, y_max))
if dump:
fig1.savefig(title + ".png")
def integrate(lst):
total = 0
for v in lst:
total += v
return total
def main(fname1, fname2, display_integration=False, fname3 = None):
print(fname1, fname2, fname3)
f1 = open(fname1, 'r')
f2 = open(fname2, 'r')
if fname3 is not None:
f3 = open(fname3, 'r')
idx1, time1, x1, y1, z1, roll1, pitch1, yaw1 = get_data(f1)
idx2, time2, x2, y2, z2, roll2, pitch2, yaw2 = get_data(f2)
if fname3 is not None:
idx3, time3, x3, y3, z3, roll3, pitch3, yaw3 = get_data(f3)
error = get_eucld_error(x1, x2, y1, y2, z1, z2)
idx_x = copy.deepcopy(idx1)
idx_y = copy.deepcopy(idx1)
idx_z = copy.deepcopy(idx1)
idx_error = copy.deepcopy(idx1)
if display_integration:
print('X')
print(integrate(x1), integrate(x2))
print('Y')
print(integrate(y1), integrate(y2))
print('Z')
print(integrate(z1), integrate(z2))
print('Error')
print(integrate(error)/len(error))
plot(idx_x, x1, x2, v3=x3, title="x",
label1=fname1.split('/')[-1].replace('.txt', ''),
label2=fname2.split('/')[-1].replace('.txt', ''),
label3=fname3.split('/')[-1].replace('.txt', ''),
dump=True)
plot(idx_y, y1, y2, v3=y3, title="y",
label1=fname1.split('/')[-1].replace('.txt', ''),
label2=fname2.split('/')[-1].replace('.txt', ''),
label3=fname3.split('/')[-1].replace('.txt', ''),
dump=True)
plot(idx_z, z1, z2, v3=z3, title="z",
label1=fname1.split('/')[-1].replace('.txt', ''),
label2=fname2.split('/')[-1].replace('.txt', ''),
label3=fname3.split('/')[-1].replace('.txt', ''),
dump=True)
plot_error(idx_error, error)
plt.show()
if __name__ == '__main__':
parser = argparse.ArgumentParser('Display twist')
parser.add_argument('file1')
parser.add_argument('file2')
parser.add_argument('--file3')
parser.add_argument('--show_final_pose', type=bool, default=False)
args = parser.parse_args()
main(args.file1, args.file2, args.show_final_pose, fname3=args.file3)
| true | true |
1c33fdc9d99aa7d333404785340880afd23b60d3 | 3,678 | py | Python | models/plain_lstm.py | TalkToTheGAN/RelaxTextGAN | 6d0846392c8a1267eaa103dd70492cb80024079e | [
"Apache-2.0"
] | 3 | 2019-05-30T03:40:38.000Z | 2021-04-12T06:50:41.000Z | models/plain_lstm.py | TalkToTheGAN/RelaxTextGAN | 6d0846392c8a1267eaa103dd70492cb80024079e | [
"Apache-2.0"
] | 1 | 2020-06-15T12:27:56.000Z | 2020-06-15T12:27:56.000Z | models/plain_lstm.py | TalkToTheGAN/RelaxTextGAN | 6d0846392c8a1267eaa103dd70492cb80024079e | [
"Apache-2.0"
] | null | null | null | import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class PlainLSTM(nn.Module):
"""PlainLSTM """
def __init__(self, vocab_size, emb_dim, hidden_dim, use_cuda=False):
super(PlainLSTM, self).__init__()
self.vocab_size = vocab_size
self.emb_dim = emb_dim
self.hidden_dim = hidden_dim
self.use_cuda = use_cuda
self.emb = nn.Embedding(vocab_size, emb_dim)
self.lstm = nn.LSTM(emb_dim, hidden_dim, batch_first=True)
self.lin = nn.Linear(hidden_dim, vocab_size)
self.log_softmax = nn.LogSoftmax(dim=1)
self.init_params()
def forward(self, x):
emb = self.emb(x) # emb dim: (batch_size, seq_len, emb_dim)
h0, c0 = self.init_hidden(x.size(0))
# input to lstm dimensions: (batch, seq_len, input_size)
output, (h, c) = self.lstm(emb, (h0, c0)) # output dim = (batch_size x seq_len, x hidden_dim)
seq_len = output.size()[1]
batch_size = output.size()[0]
pred = self.log_softmax(self.lin(output.contiguous().view(-1, self.hidden_dim)))
pred = pred.view(batch_size, seq_len, self.vocab_size)
return pred
def step(self, x, h, c):
"""
Args:
x: (batch_size, 1), sequence of tokens generated by lstm
h: (1, batch_size, hidden_dim), lstm hidden state
c: (1, batch_size, hidden_dim), lstm cell state
"""
emb = self.emb(x)
output, (h, c) = self.lstm(emb, (h, c))
pred = F.softmax(self.lin(output.view(-1, self.hidden_dim)), dim=1)
return pred, h, c
def init_hidden(self, batch_size):
h = Variable(torch.zeros((1, batch_size, self.hidden_dim)))
c = Variable(torch.zeros((1, batch_size, self.hidden_dim)))
if self.use_cuda:
h, c = h.cuda(), c.cuda()
return h, c
def init_params(self):
for param in self.parameters():
param.data.uniform_(-0.05, 0.05)
def sample(self, batch_size, seq_len, x=None):
res = []
flag = False # whether sample from zero
if x is None:
flag = True
if flag:
x = Variable(torch.zeros((batch_size, 1)).long())
if self.use_cuda:
x = x.cuda()
h, c = self.init_hidden(batch_size)
samples = []
if flag:
for i in range(seq_len):
output, h, c = self.step(x, h, c)
x = output.multinomial(1)
samples.append(x)
else:
given_len = x.size(1)
lis = x.chunk(x.size(1), dim=1)
for i in range(given_len):
output, h, c = self.step(lis[i], h, c)
samples.append(lis[i])
x = output.multinomial(1)
for i in range(given_len, seq_len):
samples.append(x)
output, h, c = self.step(x, h, c)
x = output.multinomial(1)
output = torch.cat(samples, dim=1)
return output
def test_sample(self, batch_size, seq_len, vocab_size):
big_list = []
x = Variable(torch.zeros((batch_size, 1)).long())
h, c = self.init_hidden(batch_size)
for i in range(seq_len):
output, h, c = self.step(x, h, c)
g = Variable(torch.zeros(output.size()))
# print(g.size())
g.data[:,0] = 1 # R*pij
# output.backward(g)
big_list.append((output, g))
for p, g in big_list:
p.backward(g)
return output
| 32.263158 | 104 | 0.54894 | import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class PlainLSTM(nn.Module):
def __init__(self, vocab_size, emb_dim, hidden_dim, use_cuda=False):
super(PlainLSTM, self).__init__()
self.vocab_size = vocab_size
self.emb_dim = emb_dim
self.hidden_dim = hidden_dim
self.use_cuda = use_cuda
self.emb = nn.Embedding(vocab_size, emb_dim)
self.lstm = nn.LSTM(emb_dim, hidden_dim, batch_first=True)
self.lin = nn.Linear(hidden_dim, vocab_size)
self.log_softmax = nn.LogSoftmax(dim=1)
self.init_params()
def forward(self, x):
emb = self.emb(x)
h0, c0 = self.init_hidden(x.size(0))
output, (h, c) = self.lstm(emb, (h0, c0))
seq_len = output.size()[1]
batch_size = output.size()[0]
pred = self.log_softmax(self.lin(output.contiguous().view(-1, self.hidden_dim)))
pred = pred.view(batch_size, seq_len, self.vocab_size)
return pred
def step(self, x, h, c):
emb = self.emb(x)
output, (h, c) = self.lstm(emb, (h, c))
pred = F.softmax(self.lin(output.view(-1, self.hidden_dim)), dim=1)
return pred, h, c
def init_hidden(self, batch_size):
h = Variable(torch.zeros((1, batch_size, self.hidden_dim)))
c = Variable(torch.zeros((1, batch_size, self.hidden_dim)))
if self.use_cuda:
h, c = h.cuda(), c.cuda()
return h, c
def init_params(self):
for param in self.parameters():
param.data.uniform_(-0.05, 0.05)
def sample(self, batch_size, seq_len, x=None):
res = []
flag = False
if x is None:
flag = True
if flag:
x = Variable(torch.zeros((batch_size, 1)).long())
if self.use_cuda:
x = x.cuda()
h, c = self.init_hidden(batch_size)
samples = []
if flag:
for i in range(seq_len):
output, h, c = self.step(x, h, c)
x = output.multinomial(1)
samples.append(x)
else:
given_len = x.size(1)
lis = x.chunk(x.size(1), dim=1)
for i in range(given_len):
output, h, c = self.step(lis[i], h, c)
samples.append(lis[i])
x = output.multinomial(1)
for i in range(given_len, seq_len):
samples.append(x)
output, h, c = self.step(x, h, c)
x = output.multinomial(1)
output = torch.cat(samples, dim=1)
return output
def test_sample(self, batch_size, seq_len, vocab_size):
big_list = []
x = Variable(torch.zeros((batch_size, 1)).long())
h, c = self.init_hidden(batch_size)
for i in range(seq_len):
output, h, c = self.step(x, h, c)
g = Variable(torch.zeros(output.size()))
g.data[:,0] = 1
big_list.append((output, g))
for p, g in big_list:
p.backward(g)
return output
| true | true |
1c33fe2b2b38a27cad60e2b1413c587bdf493fa1 | 317 | py | Python | venv/Lib/site-packages/nipype/interfaces/minc/testdata.py | richung99/digitizePlots | 6b408c820660a415a289726e3223e8f558d3e18b | [
"MIT"
] | 585 | 2015-01-12T16:06:47.000Z | 2022-03-26T14:51:08.000Z | nipype/interfaces/minc/testdata.py | tamires-consulting/nipype | b7879d75a63b6500b2e7d2c3eba5aa7670339274 | [
"Apache-2.0"
] | 2,329 | 2015-01-01T09:56:41.000Z | 2022-03-30T14:24:49.000Z | nipype/interfaces/minc/testdata.py | tamires-consulting/nipype | b7879d75a63b6500b2e7d2c3eba5aa7670339274 | [
"Apache-2.0"
] | 487 | 2015-01-20T01:04:52.000Z | 2022-03-21T21:22:47.000Z | # -*- coding: utf-8 -*-
import os
from ...testing import example_data
minc2Dfile = example_data("minc_test_2D_00.mnc")
minc3Dfile = example_data("minc_test_3D_00.mnc")
nlp_config = example_data("minc_nlp.conf")
def nonempty_minc_data(i, shape="2D"):
return example_data("minc_test_%s_%.2d.mnc" % (shape, i))
| 22.642857 | 61 | 0.728707 |
import os
from ...testing import example_data
minc2Dfile = example_data("minc_test_2D_00.mnc")
minc3Dfile = example_data("minc_test_3D_00.mnc")
nlp_config = example_data("minc_nlp.conf")
def nonempty_minc_data(i, shape="2D"):
return example_data("minc_test_%s_%.2d.mnc" % (shape, i))
| true | true |
1c33fe6b373149f67b58af2338d67477648d458f | 6,134 | py | Python | server_normal_Flask_beautiful/routes/routes_user.py | KiwiShow/PythonWeb | a489bc2ab16f06f7cc4524bab6b45b2653bfb1bd | [
"MIT"
] | 7 | 2018-02-24T13:41:21.000Z | 2022-02-06T04:59:13.000Z | server_normal_Flask_beautiful/routes/routes_user.py | KiwiShow/PythonWeb | a489bc2ab16f06f7cc4524bab6b45b2653bfb1bd | [
"MIT"
] | 6 | 2018-02-25T11:50:42.000Z | 2021-12-13T19:55:13.000Z | server_normal_Flask_beautiful/routes/routes_user.py | KiwiShow/PythonWeb | a489bc2ab16f06f7cc4524bab6b45b2653bfb1bd | [
"MIT"
] | 1 | 2018-03-01T02:43:15.000Z | 2018-03-01T02:43:15.000Z | from utils import log
from config import gg, image_file_dir
from routes import (
current_user,
login_required,
)
from flask import (
request,
Blueprint,
render_template,
redirect,
url_for,
session,
make_response,
send_from_directory,
abort,
flash,
)
from models.user import User
from models.board import Board
from models.mail import Mail
main = Blueprint('user', __name__)
@main.route('/', methods=['GET'])
def index():
return render_template('blog/blog_index.html')
@main.route('/admin', methods=['GET'])
@login_required
def admin():
"""
只有用户id为1的用户有权限
:return: 返回所有用户的信息
"""
user = current_user()
User.check_admin()
print('from admin before', gg.csrf_tokens)
gg.reset_value(user.id)
print('from admin after', gg.csrf_tokens)
return render_template('user/admin.html', token=gg.token[user.id], mails=Mail.find_all(), user=user, users=User.find_all(), boards=Board.find_all())
@main.route('/admin/edit/<int:user_id>', methods=['GET'])
@login_required
def admin_edit(user_id):
"""
只有用户id为1的用户有权限，输入需要修改的id和password
:return: 返回修改过的所有用户的信息
"""
user = current_user()
if User.check_token():
User.check_admin()
u = User.find(user_id)
return render_template('user/admin_edit.html', token=gg.token[user.id], user=user, u=u)
@main.route('/admin/update', methods=['POST'])
@login_required
def admin_update():
"""
只有用户id为1的用户有权限，输入需要修改的id和password
:return: 返回修改过的所有用户的信息
"""
if User.check_token():
User.check_admin()
form = request.form
User.update(form)
return redirect(url_for('.admin'))
# 增加一个register的路由函数
@main.route('/admin/register', methods=['POST'])
def admin_register():
"""
允许GET是因为在地址栏输入地址转到register页面需要
POST是因为在register页面输入账号密码点击register按钮需要
主要的bug是转到register页面和register页面都都是同一个路由函数
:return: 返回register页面，并显示所有用户信息
"""
if User.check_token():
User.check_admin()
form = request.form
if User.validate_register(form):
return redirect(url_for('.admin'))
@main.route('/admin/delete/<int:user_id>')
@login_required
def user_delete(user_id):
if User.check_token():
User.check_admin()
User.remove(user_id)
return redirect(url_for('.admin'))
# 所有用户上传头像,先存在本地得到路径之后上传至七牛云，并删除本地图片
@main.route('/add_image', methods=['POST'])
@login_required
def add_img():
user = current_user()
if User.check_token():
file = request.files['avatar']
user.save_and_up(file)
return redirect(url_for('.user_setting', id=user.id, token=gg.token[user.id]))
# web后端上传头像，后续可以改成Nginx+图床
# 本地只有default.png一张图片
@main.route('/uploads/<filename>')
@login_required
def uploads(filename):
return send_from_directory(image_file_dir, filename)
# 在知乎console输入
# var c = document.cookie
# var img = `<img src='http://localhost:4000/hack?cookie=${c}'>`
# document.body.innerHTML += img
@main.route('/hack')
def hack():
# xss 攻击的后台
cookie = request.args.get('cookie')
print('cookie', cookie)
# 增加一个可以看到任意user的路由函数
# 不需要check token，CRUD中除了查不需要验证token，但是需要传递token
# 需要传递 u: 我想要看的用户 和 user: current_user()
@main.route('/user/<int:id>')
# @login_required
def user_detail(id):
user = current_user()
u = User.find(id)
if u is None:
abort(404)
if user is not None:
# 保证每次调用index函数时清空gg,保证每次调用index函数时都有新的token可用
print('from profile before', gg.csrf_tokens)
gg.reset_value(user.id)
print('from profile after', gg.csrf_tokens)
return render_template('user/profile.html', u=u, token=gg.token[user.id], user=user)
return render_template('user/profile.html', u=u, user=user)
# update方法需要重新写，统一到model父类中
# 增加一个在setting页面update的路由函数
@main.route('/user/update', methods=['POST'])
@login_required
def user_update():
user = current_user()
if User.check_token():
form = request.form
user.password_update(form)
return redirect(url_for('user.user_setting', id=user.id, token=gg.token[user.id]))
# 增加一个去setting页面的路由函数
@main.route('/setting')
@login_required
def user_setting():
user = current_user()
if user is not None:
# 保证每次调用index函数时清空gg,保证每次调用index函数时都有新的token可用
print('from setting before', gg.csrf_tokens)
gg.reset_value(user.id)
print('from setting after', gg.csrf_tokens)
return render_template('user/setting.html', user=user, token=gg.token[user.id], bid=-1)
# GET 去 登陆 页面， POST 提交表单
@main.route('/login', methods=['GET', 'POST'])
def user_login():
form = request.form
log('from route_login --> cookies: ', request.cookies)
# ImmutableMultiDict([])是什么鬼？
if form.get('username', None):
if User.validate_login(form):
u = User.find_by(username=form.get('username'))
print('from signin before', session)
session['user_id'] = u.id
print('from signin after', session)
return redirect(url_for('tweet.index'))
else:
flash('账号密码输入错误，请核对后再输入')
return redirect(url_for('.user_login'))
else:
return render_template('user/login.html')
# GET 去 注册 页面， POST 提交表单
@main.route('/register', methods=['GET', 'POST'])
def user_register():
"""
允许GET是因为在地址栏输入地址转到register页面需要
POST是因为在register页面输入账号密码点击register按钮需要
主要的bug是转到register页面和register页面都都是同一个路由函数
:return: 返回register页面，并显示所有用户信息
"""
form = request.form
if form.get('username', None):
if User.validate_register(form):
return redirect(url_for('user.user_login'))
else:
flash('用户名和密码长度必须大于2，请核对后再输入')
return redirect(url_for('.user_register'))
else:
return render_template('user/register.html')
@main.route('/signout')
def user_signout():
"""
在session中删除当前登录的user_id
:return: 返回login页面
"""
if User.check_token():
print('from signout before', session)
session.pop('user_id')
print('from signout after', session)
return redirect(url_for('tweet.index'))
| 27.141593 | 152 | 0.662048 | from utils import log
from config import gg, image_file_dir
from routes import (
current_user,
login_required,
)
from flask import (
request,
Blueprint,
render_template,
redirect,
url_for,
session,
make_response,
send_from_directory,
abort,
flash,
)
from models.user import User
from models.board import Board
from models.mail import Mail
main = Blueprint('user', __name__)
@main.route('/', methods=['GET'])
def index():
return render_template('blog/blog_index.html')
@main.route('/admin', methods=['GET'])
@login_required
def admin():
user = current_user()
User.check_admin()
print('from admin before', gg.csrf_tokens)
gg.reset_value(user.id)
print('from admin after', gg.csrf_tokens)
return render_template('user/admin.html', token=gg.token[user.id], mails=Mail.find_all(), user=user, users=User.find_all(), boards=Board.find_all())
@main.route('/admin/edit/<int:user_id>', methods=['GET'])
@login_required
def admin_edit(user_id):
user = current_user()
if User.check_token():
User.check_admin()
u = User.find(user_id)
return render_template('user/admin_edit.html', token=gg.token[user.id], user=user, u=u)
@main.route('/admin/update', methods=['POST'])
@login_required
def admin_update():
if User.check_token():
User.check_admin()
form = request.form
User.update(form)
return redirect(url_for('.admin'))
@main.route('/admin/register', methods=['POST'])
def admin_register():
if User.check_token():
User.check_admin()
form = request.form
if User.validate_register(form):
return redirect(url_for('.admin'))
@main.route('/admin/delete/<int:user_id>')
@login_required
def user_delete(user_id):
if User.check_token():
User.check_admin()
User.remove(user_id)
return redirect(url_for('.admin'))
@main.route('/add_image', methods=['POST'])
@login_required
def add_img():
user = current_user()
if User.check_token():
file = request.files['avatar']
user.save_and_up(file)
return redirect(url_for('.user_setting', id=user.id, token=gg.token[user.id]))
@main.route('/uploads/<filename>')
@login_required
def uploads(filename):
return send_from_directory(image_file_dir, filename)
@main.route('/hack')
def hack():
cookie = request.args.get('cookie')
print('cookie', cookie)
@main.route('/user/<int:id>')
def user_detail(id):
user = current_user()
u = User.find(id)
if u is None:
abort(404)
if user is not None:
print('from profile before', gg.csrf_tokens)
gg.reset_value(user.id)
print('from profile after', gg.csrf_tokens)
return render_template('user/profile.html', u=u, token=gg.token[user.id], user=user)
return render_template('user/profile.html', u=u, user=user)
@main.route('/user/update', methods=['POST'])
@login_required
def user_update():
user = current_user()
if User.check_token():
form = request.form
user.password_update(form)
return redirect(url_for('user.user_setting', id=user.id, token=gg.token[user.id]))
@main.route('/setting')
@login_required
def user_setting():
user = current_user()
if user is not None:
print('from setting before', gg.csrf_tokens)
gg.reset_value(user.id)
print('from setting after', gg.csrf_tokens)
return render_template('user/setting.html', user=user, token=gg.token[user.id], bid=-1)
@main.route('/login', methods=['GET', 'POST'])
def user_login():
form = request.form
log('from route_login --> cookies: ', request.cookies)
if form.get('username', None):
if User.validate_login(form):
u = User.find_by(username=form.get('username'))
print('from signin before', session)
session['user_id'] = u.id
print('from signin after', session)
return redirect(url_for('tweet.index'))
else:
flash('账号密码输入错误，请核对后再输入')
return redirect(url_for('.user_login'))
else:
return render_template('user/login.html')
@main.route('/register', methods=['GET', 'POST'])
def user_register():
form = request.form
if form.get('username', None):
if User.validate_register(form):
return redirect(url_for('user.user_login'))
else:
flash('用户名和密码长度必须大于2，请核对后再输入')
return redirect(url_for('.user_register'))
else:
return render_template('user/register.html')
@main.route('/signout')
def user_signout():
if User.check_token():
print('from signout before', session)
session.pop('user_id')
print('from signout after', session)
return redirect(url_for('tweet.index'))
| true | true |
1c33ff10010a98d8a99a1b158c70877e9d59678f | 997 | py | Python | setup.py | brandonvfx/flask-lambda | 29bb0d728037af076019cfbe398e084cd58821bb | [
"Apache-2.0"
] | 6 | 2018-10-16T13:34:26.000Z | 2020-06-15T22:20:15.000Z | setup.py | brandonvfx/flask-lambda | 29bb0d728037af076019cfbe398e084cd58821bb | [
"Apache-2.0"
] | 2 | 2019-07-08T09:22:25.000Z | 2020-12-16T12:47:22.000Z | setup.py | brandonvfx/flask-lambda | 29bb0d728037af076019cfbe398e084cd58821bb | [
"Apache-2.0"
] | 5 | 2018-12-20T14:07:14.000Z | 2021-05-15T02:14:29.000Z | from setuptools import setup, find_packages
with open('README.rst') as f:
long_description = f.read()
setup(
name='flask-lambda-support',
version='0.1.5',
description='Python 3.6+ module to make Flask compatible with AWS Lambda',
long_description=long_description,
keywords='flask aws amazon lambda',
author='Jochen Van de Velde',
author_email='jochen.vandevelde@cloudway.be',
url='https://github.com/becloudway/flask-lambda',
license='Apache License, Version 2.0',
packages=find_packages(),
py_modules=['flask_lambda'],
install_requires=['Flask>=0.10'],
classifiers=[
'Development Status :: 5 - Production/Stable',
'Programming Language :: Python',
'Environment :: Console',
'Operating System :: POSIX',
'Programming Language :: Python :: 3.6',
'Intended Audience :: Developers',
'Natural Language :: English',
'License :: OSI Approved :: Apache Software License',
]
)
| 32.16129 | 78 | 0.652959 | from setuptools import setup, find_packages
with open('README.rst') as f:
long_description = f.read()
setup(
name='flask-lambda-support',
version='0.1.5',
description='Python 3.6+ module to make Flask compatible with AWS Lambda',
long_description=long_description,
keywords='flask aws amazon lambda',
author='Jochen Van de Velde',
author_email='jochen.vandevelde@cloudway.be',
url='https://github.com/becloudway/flask-lambda',
license='Apache License, Version 2.0',
packages=find_packages(),
py_modules=['flask_lambda'],
install_requires=['Flask>=0.10'],
classifiers=[
'Development Status :: 5 - Production/Stable',
'Programming Language :: Python',
'Environment :: Console',
'Operating System :: POSIX',
'Programming Language :: Python :: 3.6',
'Intended Audience :: Developers',
'Natural Language :: English',
'License :: OSI Approved :: Apache Software License',
]
)
| true | true |
1c33ff237e9a5bed9d42985f51dcd2d5a656d2e9 | 174 | py | Python | Unit 2/2.8/2.8.4 Beaded Bracelet.py | shashwat73/cse | 60e49307e57105cf9916c7329f53f891c5e81fdb | [
"MIT"
] | 1 | 2021-04-08T14:02:49.000Z | 2021-04-08T14:02:49.000Z | Unit 2/2.8/2.8.4 Beaded Bracelet.py | shashwat73/cse | 60e49307e57105cf9916c7329f53f891c5e81fdb | [
"MIT"
] | null | null | null | Unit 2/2.8/2.8.4 Beaded Bracelet.py | shashwat73/cse | 60e49307e57105cf9916c7329f53f891c5e81fdb | [
"MIT"
] | null | null | null | speed(0)
def make_bead():
forward(100)
pendown()
circle(10)
penup()
backward(100)
penup()
for i in range(36):
make_bead()
left(10)
| 12.428571 | 20 | 0.528736 | speed(0)
def make_bead():
forward(100)
pendown()
circle(10)
penup()
backward(100)
penup()
for i in range(36):
make_bead()
left(10)
| true | true |
1c33ffe24ec1e81536cab928d5fd3f8679749726 | 6,856 | py | Python | cardea/fhir/Media.py | sarahmish/Cardea | 85c4246c12178e6d1b9cc12eb39c264f3c20f3e9 | [
"MIT"
] | 69 | 2021-01-28T22:25:10.000Z | 2022-03-15T00:23:33.000Z | cardea/fhir/Media.py | sarahmish/Cardea | 85c4246c12178e6d1b9cc12eb39c264f3c20f3e9 | [
"MIT"
] | 30 | 2018-08-29T12:45:23.000Z | 2019-12-24T11:08:12.000Z | cardea/fhir/Media.py | sarahmish/Cardea | 85c4246c12178e6d1b9cc12eb39c264f3c20f3e9 | [
"MIT"
] | 14 | 2021-03-24T01:21:25.000Z | 2022-03-12T11:53:40.000Z | from .fhirbase import fhirbase
class Media(fhirbase):
"""
A photo, video, or audio recording acquired or used in healthcare. The
actual content may be inline or provided by direct reference.
Args:
resourceType: This is a Media resource
identifier: Identifiers associated with the image - these may include
identifiers for the image itself, identifiers for the context of its
collection (e.g. series ids) and context ids such as accession numbers
or other workflow identifiers.
basedOn: A procedure that is fulfilled in whole or in part by the
creation of this media.
type: Whether the media is a photo (still image), an audio recording,
or a video recording.
subtype: Details of the type of the media - usually, how it was
acquired (what type of device). If images sourced from a DICOM system,
are wrapped in a Media resource, then this is the modality.
view: The name of the imaging view e.g. Lateral or Antero-posterior
(AP).
subject: Who/What this Media is a record of.
context: The encounter or episode of care that establishes the context
for this media.
occurrenceDateTime: The date and time(s) at which the media was
collected.
occurrencePeriod: The date and time(s) at which the media was
collected.
operator: The person who administered the collection of the image.
reasonCode: Describes why the event occurred in coded or textual form.
bodySite: Indicates the site on the subject's body where the media was
collected (i.e. the target site).
device: The device used to collect the media.
height: Height of the image in pixels (photo/video).
width: Width of the image in pixels (photo/video).
frames: The number of frames in a photo. This is used with a
multi-page fax, or an imaging acquisition context that takes multiple
slices in a single image, or an animated gif. If there is more than
one frame, this SHALL have a value in order to alert interface
software that a multi-frame capable rendering widget is required.
duration: The duration of the recording in seconds - for audio and
video.
content: The actual content of the media - inline or by direct
reference to the media source file.
note: Comments made about the media by the performer, subject or other
participants.
"""
__name__ = 'Media'
def __init__(self, dict_values=None):
self.resourceType = 'Media'
# type: str
# possible values: Media
self.basedOn = None
# type: list
# reference to Reference: identifier
self.type = None
# type: str
# possible values: photo, video, audio
self.subtype = None
# reference to CodeableConcept
self.view = None
# reference to CodeableConcept
self.subject = None
# reference to Reference: identifier
self.context = None
# reference to Reference: identifier
self.occurrenceDateTime = None
# type: str
self.occurrencePeriod = None
# reference to Period
self.operator = None
# reference to Reference: identifier
self.reasonCode = None
# type: list
# reference to CodeableConcept
self.bodySite = None
# reference to CodeableConcept
self.device = None
# reference to Reference: identifier
self.height = None
# type: int
self.width = None
# type: int
self.frames = None
# type: int
self.duration = None
# type: int
self.content = None
# reference to Attachment
self.note = None
# type: list
# reference to Annotation
self.identifier = None
# type: list
# reference to Identifier
if dict_values:
self.set_attributes(dict_values)
self.assert_type()
def assert_type(self):
if self.type is not None:
for value in self.type:
if value is not None and value.lower() not in [
'photo', 'video', 'audio']:
raise ValueError('"{}" does not match possible values: {}'.format(
value, 'photo, video, audio'))
def get_relationships(self):
return [
{'parent_entity': 'Period',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'occurrencePeriod'},
{'parent_entity': 'Annotation',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'note'},
{'parent_entity': 'CodeableConcept',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'subtype'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'subject'},
{'parent_entity': 'Attachment',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'content'},
{'parent_entity': 'CodeableConcept',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'bodySite'},
{'parent_entity': 'Identifier',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'identifier'},
{'parent_entity': 'CodeableConcept',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'reasonCode'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'operator'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'basedOn'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'context'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'device'},
{'parent_entity': 'CodeableConcept',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'view'},
]
| 34.109453 | 86 | 0.575117 | from .fhirbase import fhirbase
class Media(fhirbase):
__name__ = 'Media'
def __init__(self, dict_values=None):
self.resourceType = 'Media'
self.basedOn = None
self.type = None
self.subtype = None
self.view = None
self.subject = None
self.context = None
self.occurrenceDateTime = None
self.occurrencePeriod = None
self.operator = None
self.reasonCode = None
self.bodySite = None
self.device = None
self.height = None
self.width = None
self.frames = None
self.duration = None
self.content = None
self.note = None
self.identifier = None
if dict_values:
self.set_attributes(dict_values)
self.assert_type()
def assert_type(self):
if self.type is not None:
for value in self.type:
if value is not None and value.lower() not in [
'photo', 'video', 'audio']:
raise ValueError('"{}" does not match possible values: {}'.format(
value, 'photo, video, audio'))
def get_relationships(self):
return [
{'parent_entity': 'Period',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'occurrencePeriod'},
{'parent_entity': 'Annotation',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'note'},
{'parent_entity': 'CodeableConcept',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'subtype'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'subject'},
{'parent_entity': 'Attachment',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'content'},
{'parent_entity': 'CodeableConcept',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'bodySite'},
{'parent_entity': 'Identifier',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'identifier'},
{'parent_entity': 'CodeableConcept',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'reasonCode'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'operator'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'basedOn'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'context'},
{'parent_entity': 'Reference',
'parent_variable': 'identifier',
'child_entity': 'Media',
'child_variable': 'device'},
{'parent_entity': 'CodeableConcept',
'parent_variable': 'object_id',
'child_entity': 'Media',
'child_variable': 'view'},
]
| true | true |
1c340161e4b6f7a3fc08face097e2d2cd16fb14c | 735 | py | Python | chap03/author-manager/src/api/config/config.py | matadorchw/rest_flask | b5b643d72e63e654f2d893621158e2e5db870b15 | [
"MIT"
] | 2 | 2020-10-21T14:04:42.000Z | 2020-10-21T14:05:01.000Z | chap03/author-manager/src/api/config/config.py | matadorchw/rest_flask | b5b643d72e63e654f2d893621158e2e5db870b15 | [
"MIT"
] | null | null | null | chap03/author-manager/src/api/config/config.py | matadorchw/rest_flask | b5b643d72e63e654f2d893621158e2e5db870b15 | [
"MIT"
] | null | null | null | class Config(object):
DEBUG = False
TESTING = False
SQLALCHEMY_TRACK_MODIFICATIONS = False
class ProductionConfig(Config):
SQLALCHEMY_DATABASE_URI = ''
class DevelopmentConfig(Config):
DEBUG = True
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://root:123456@localhost:3306/flaskrest'
SECRET_KEY = 'sunshine'
SECURITY_PASSWORD_SALT = 'dawn'
SQLALCHEMY_ECHO = False
MAIL_DEFAULT_SENDER = 'menglj@we-wins.com'
MAIL_SERVER = 'smtp.263.net'
MAIL_PORT = 25
MAIL_USE_TLS = True
MAIL_USERNAME = 'menglj@we-wins.com'
MAIL_PASSWORD = 'Lte5563'
UPLOAD_FOLDER = 'images'
class TestingConfig(Config):
TESTING = True
SQLALCHEMY_DATABASE_URI = ''
SQLALCHEMY_ECHO = False
| 22.96875 | 84 | 0.706122 | class Config(object):
DEBUG = False
TESTING = False
SQLALCHEMY_TRACK_MODIFICATIONS = False
class ProductionConfig(Config):
SQLALCHEMY_DATABASE_URI = ''
class DevelopmentConfig(Config):
DEBUG = True
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://root:123456@localhost:3306/flaskrest'
SECRET_KEY = 'sunshine'
SECURITY_PASSWORD_SALT = 'dawn'
SQLALCHEMY_ECHO = False
MAIL_DEFAULT_SENDER = 'menglj@we-wins.com'
MAIL_SERVER = 'smtp.263.net'
MAIL_PORT = 25
MAIL_USE_TLS = True
MAIL_USERNAME = 'menglj@we-wins.com'
MAIL_PASSWORD = 'Lte5563'
UPLOAD_FOLDER = 'images'
class TestingConfig(Config):
TESTING = True
SQLALCHEMY_DATABASE_URI = ''
SQLALCHEMY_ECHO = False
| true | true |
1c3402eecc1efe7833e69b853785cb075e895c8e | 132 | py | Python | blogmods/models/__init__.py | stonescar/multi-user-blog | a402dafde1f7d94031129638aa072ce39223e80e | [
"MIT"
] | null | null | null | blogmods/models/__init__.py | stonescar/multi-user-blog | a402dafde1f7d94031129638aa072ce39223e80e | [
"MIT"
] | null | null | null | blogmods/models/__init__.py | stonescar/multi-user-blog | a402dafde1f7d94031129638aa072ce39223e80e | [
"MIT"
] | null | null | null | from database import Database
from users import Users
from posts import Posts
from comments import Comments
from votes import Votes
| 22 | 29 | 0.848485 | from database import Database
from users import Users
from posts import Posts
from comments import Comments
from votes import Votes
| true | true |
1c3404f181a064281760dfa67f303c16422cd5c3 | 529 | py | Python | commands/upgrader/commands/blockdata.py | Red-Teapot/mc-commandblock-1.13-update | 64106e1ecb5adca2aff1eeb3a1fcc11486940000 | [
"MIT"
] | 1 | 2020-07-27T16:53:26.000Z | 2020-07-27T16:53:26.000Z | commands/upgrader/commands/blockdata.py | Red-Teapot/mc-commandblock-1.13-update | 64106e1ecb5adca2aff1eeb3a1fcc11486940000 | [
"MIT"
] | 5 | 2019-01-02T14:21:32.000Z | 2019-07-07T05:39:39.000Z | commands/upgrader/commands/blockdata.py | Red-Teapot/mc-commandblock-1.13-update | 64106e1ecb5adca2aff1eeb3a1fcc11486940000 | [
"MIT"
] | null | null | null | from commands.pre_1_13.cmdex import CMDEx
from commands.upgrader.utils import command_upgrader_base
CMDEXS = [
CMDEx('blockdata {coordinate:x} {coordinate:y} {coordinate:z} {nbtstr:nbt}'),
]
def __upgrade(order, props):
result = 'data merge block '
result += str(props['x']) + ' ' + str(props['y']) + ' ' + str(props['z']) + ' '
# TODO Maybe upgrade NBT stuff?
result += str(props['nbt'])
return result
def upgrade(command: str):
return command_upgrader_base.upgrade(CMDEXS, command, __upgrade) | 26.45 | 83 | 0.669187 | from commands.pre_1_13.cmdex import CMDEx
from commands.upgrader.utils import command_upgrader_base
CMDEXS = [
CMDEx('blockdata {coordinate:x} {coordinate:y} {coordinate:z} {nbtstr:nbt}'),
]
def __upgrade(order, props):
result = 'data merge block '
result += str(props['x']) + ' ' + str(props['y']) + ' ' + str(props['z']) + ' '
result += str(props['nbt'])
return result
def upgrade(command: str):
return command_upgrader_base.upgrade(CMDEXS, command, __upgrade) | true | true |
1c3404f28196271ef1b09445916e0d69195037e1 | 16,628 | py | Python | utils.py | creol-io/machine-manager | 01108f0c26c15f515c1d9d3361f1c1a27c03d8ab | [
"Apache-2.0"
] | null | null | null | utils.py | creol-io/machine-manager | 01108f0c26c15f515c1d9d3361f1c1a27c03d8ab | [
"Apache-2.0"
] | null | null | null | utils.py | creol-io/machine-manager | 01108f0c26c15f515c1d9d3361f1c1a27c03d8ab | [
"Apache-2.0"
] | null | null | null | """
Copyright 2019 Cartesi Pte. Ltd.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
"""
import subprocess
import logging
import logging.config
import logging.handlers
import traceback
import grpc
import json
import time
import os
import cartesi_machine_pb2_grpc
import cartesi_machine_pb2
import machine_manager_pb2
LOG_FILENAME = "manager.log"
RUN_CYCLES_BATCH_SIZE = 10**7
UNIX = "unix"
TCP = "tcp"
SOCKET_TYPE = UNIX
def get_new_logger(name):
return logging.getLogger(name)
def configure_log(logger):
logger.setLevel(logging.DEBUG)
#Setting format
formatter = logging.Formatter('%(asctime)s %(thread)d %(levelname)-s %(name)s %(lineno)s - %(funcName)s: %(message)s')
#File rotation log handler
rotating_file_handler = logging.handlers.RotatingFileHandler(
LOG_FILENAME, maxBytes=2**20, backupCount=5)
rotating_file_handler.setFormatter(formatter)
rotating_file_handler.setLevel(logging.DEBUG)
#Stream log handler
stream_handler = logging.StreamHandler()
stream_handler.setLevel(logging.DEBUG)
stream_handler.setFormatter(formatter)
logger.addHandler(rotating_file_handler)
logger.addHandler(stream_handler)
return logger
def new_cartesi_machine_server(session_id, manager_address):
LOGGER.info("Creating a cartesi machine server with session_id '{}'".format(session_id))
cmd_line = ["/opt/cartesi/bin/cartesi-machine-server", "-t", SOCKET_TYPE, "-s", session_id, "-m", manager_address]
LOGGER.debug("Executing {}".format(" ".join(cmd_line)))
proc = None
try:
proc = subprocess.Popen(cmd_line, stderr=subprocess.PIPE, stdout=subprocess.PIPE, env=os.environ)
out, err = proc.communicate()
LOGGER.debug("\nStdout:\n{}\nStderr:\n{}".format(out.decode("utf-8"), err.decode("utf-8")))
except Exception as e:
err_msg = "Cartesi machine server creation process failed for session_id '{}'".format(session_id)
LOGGER.info(err_msg)
if (proc):
out, err = proc.communicate()
LOGGER.debug("\nStdout:\n{}\nStderr:\n{}".format(out.decode("utf-8"), err.decode("utf-8")))
raise CartesiMachineServerException(err_msg)
if (proc.returncode == 0):
LOGGER.info("Cartesi machine server creation process returned for session_id '{}'".format(session_id))
LOGGER.debug("\nStdout:\n{}\nStderr:\n{}".format(out.decode("utf-8"), err.decode("utf-8")))
else:
err_msg = "Cartesi machine server creation process returned non-zero code for session_id '{}'".format(session_id)
LOGGER.error(err_msg)
LOGGER.error("\nStdout:\n{}\nStderr:\n{}".format(out.decode("utf-8"), err.decode("utf-8")))
raise CartesiMachineServerException(err_msg)
def new_machine(session_id, address, machine_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.Machine(machine_req)
LOGGER.debug("Cartesi machine created for session_id '{}'".format(session_id))
def shutdown_cartesi_machine_server(session_id, address):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.Shutdown(cartesi_machine_pb2.Void())
LOGGER.debug("Cartesi machine server shutdown for session_id '{}'".format(session_id))
def get_machine_hash(session_id, address):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
LOGGER.debug("Asking for cartesi machine root hash for session_id '{}'".format(session_id))
response = stub.GetRootHash(cartesi_machine_pb2.Void())
LOGGER.debug("Cartesi machine root hash retrieved for session_id '{}'".format(session_id))
return response.hash
def create_machine_snapshot(session_id, address):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
stub.Snapshot(cartesi_machine_pb2.Void())
LOGGER.debug("Cartesi machine snapshot created for session_id '{}'".format(session_id))
def rollback_machine(session_id, address):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
stub.Rollback(cartesi_machine_pb2.Void())
LOGGER.debug("Cartesi machine rolledback for session_id '{}'".format(session_id))
def run_machine(session_id, session_context, desired_cycle):
''' This function must be called only when the lock for the given session
is held by the caller
'''
current_cycle = session_context.cycle
LOGGER.debug("Current cycle: {}\nDesired cycle: {}".format(current_cycle, desired_cycle))
if (desired_cycle < current_cycle):
raise ValueError("The given desired_cycle must not be smaller than the current_cycle")
response = None
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, session_context.address))
with grpc.insecure_channel(session_context.address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
#Setting cycle for run batch
target_cycle = session_context.cycle + RUN_CYCLES_BATCH_SIZE
#If it`s beyond the desired cycle, truncate
if (target_cycle > desired_cycle):
target_cycle = desired_cycle
#Run loop
while (True):
#Run
LOGGER.debug("Running cartesi machine for session id {} with target cycle of {}, current cycle is {}".format(session_id, target_cycle, session_context.cycle))
response = stub.Run(cartesi_machine_pb2.RunRequest(limit=target_cycle))
#Update tracked cycle and updated_at timestamp in the session context
session_context.cycle = response.mcycle
session_context.updated_at = time.time()
LOGGER.debug("Updated cycle of session '{}' to {}".format(session_id, response.mcycle))
#Checking if machine halted
if response.iflags_h:
#Storing the halting cycle in session context to use in progress calculations
session_context.halt_cycle = session_context.cycle
LOGGER.debug("Session {} halted with payload {}".format(session_id, int.from_bytes(response.tohost.to_bytes(8, 'big')[2:], byteorder='big')))
break
#Checking if the machine yielded
elif response.iflags_y:
#Parsing tohost to see if a progress command was given
#The command is the second byte in the tohost 8bytes register
cmd = response.tohost.to_bytes(8, 'big')[1]
payload = int.from_bytes(response.tohost.to_bytes(8, 'big')[2:], byteorder='big')
if (cmd==0):
#It was a progress command, storing the progress
session_context.app_progress = payload
LOGGER.debug("New progress for session {}: {}".format(session_id, payload))
else:
#Wasn't a progress command, just logging
LOGGER.debug("Session {} yielded with command {} and payload {}".format(session_id, cmd, payload))
else:
#The machine reached the target_cycle, setting next one if it wasn't the desired cycle
if target_cycle == desired_cycle:
#It was, break the loop
break
#It wasn't, set the next target cycle
target_cycle += RUN_CYCLES_BATCH_SIZE
#If it`s beyond the desired cycle, truncate
if (target_cycle > desired_cycle):
target_cycle = desired_cycle
LOGGER.debug("Cartesi machine ran for session_id '{}' and desired final cycle of {}, current cycle is {}".format(session_id, desired_cycle, session_context.cycle))
return response
def step_machine(session_id, address, step_params):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.Step(step_params)
LOGGER.debug("Cartesi machine step complete for session_id '{}'".format(session_id))
return response.log
def store_machine(session_id, address, store_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.Store(store_req)
LOGGER.debug("Stored Cartesi machine for session_id '{}', desired directory '{}'".format(session_id, store_req.directory))
return response
def read_machine_memory(session_id, address, read_mem_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.ReadMemory(read_mem_req)
LOGGER.debug("Cartesi machine memory read for session_id '{}', desired mem address {} and length {}".format(session_id, read_mem_req.address, read_mem_req.length))
return response
def write_machine_memory(session_id, address, write_mem_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.WriteMemory(write_mem_req)
LOGGER.debug("Cartesi machine memory written for session_id '{}', desired mem address {} and data {}".format(session_id, write_mem_req.address, write_mem_req.data))
return response
def get_machine_proof(session_id, address, proof_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.GetProof(proof_req)
LOGGER.debug("Got Cartesi machine proof for session_id '{}', desired mem address {} and log2_size {}".format(session_id, proof_req.address, proof_req.log2_size))
return response
def make_session_run_result(summaries, hashes):
return machine_manager_pb2.SessionRunResponse(result=machine_manager_pb2.SessionRunResult(summaries=summaries, hashes=hashes))
def make_session_step_result(access_log):
return machine_manager_pb2.SessionStepResponse(log=access_log)
def make_session_read_memory_result(read_mem_resp):
return machine_manager_pb2.SessionReadMemoryResponse(read_content=read_mem_resp)
class CycleException(Exception):
pass
class CartesiMachineServerException(Exception):
pass
def validate_cycles(values):
last_value = None
#Checking if at least one value was passed
if values:
for value in values:
if (value < 0):
raise CycleException("Positive values expected, first offending value: {}".format(value))
if last_value:
if value < last_value:
raise CycleException("Provide cycle values in crescent order, received {} after {}".format(value, last_value))
last_value = value
else:
raise CycleException("Provide a cycle value")
#Debugging functions
def dump_step_response_to_json(access_log):
access_log_dict = {'accesses':[], 'notes':[], 'brackets':[]}
for note in access_log.log.notes:
access_log_dict['notes'].append(note)
for bracket in access_log.log.brackets:
access_log_dict['brackets'].append(
{
'type':
cartesi_machine_pb2._BRACKETNOTE_BRACKETNOTETYPE.values_by_number[bracket.type].name,
'where': bracket.where,
'text' : bracket.text
})
for access in access_log.log.accesses:
access_dict = {
'read': "0x{}".format(access.read.data.hex()),
'written' : "0x{}".format(access.written.data.hex()),
'operation' : cartesi_machine_pb2._ACCESSOPERATION.values_by_number[access.operation].name,
'proof' : {
'address': access.proof.address,
'log2_size': access.proof.log2_size,
'target_hash': "0x{}".format(access.proof.target_hash.data.hex()),
'root_hash': "0x{}".format(access.proof.root_hash.data.hex()),
'sibling_hashes' : []
}
}
for sibling in access.proof.sibling_hashes:
access_dict['proof']['sibling_hashes'].append("0x{}".format(sibling.data.hex()))
access_log_dict['accesses'].append(access_dict)
return json.dumps(access_log_dict, indent=4, sort_keys=True)
def dump_step_response_to_file(access_log, open_dump_file):
json_dump = dump_step_response_to_json(access_log)
open_dump_file.write("\n\n" + '#'*80 + json_dump)
def dump_run_response_to_json(run_resp):
resp_dict = None
#Checking which of the oneof fields were set
oneof_fieldname = run_resp.WhichOneof("run_oneof")
if oneof_fieldname == "result":
resp_dict = {"summaries": [], "hashes": []}
for val in run_resp.result.summaries:
resp_dict["summaries"].append({
'tohost': val.tohost,
'mcycle': val.mcycle
})
for val in run_resp.result.hashes:
resp_dict["hashes"].append("0x{}".format(val.data.hex()))
elif oneof_fieldname == "progress":
resp_dict = {
"progress": run_resp.progress.progress,
"application_progress": run_resp.progress.application_progress,
"updated_at": run_resp.progress.updated_at,
"cycle": run_resp.progress.cycle
}
return json.dumps(resp_dict, indent=4, sort_keys=True)
def dump_run_response_to_file(run_resp, open_dump_file):
json_dump = dump_run_response_to_json(run_resp)
open_dump_file.write("\n\n" + '#'*80 + json_dump)
def dump_get_proof_response_to_json(proof_resp):
proof = proof_resp.proof
resp_dict = {
'proof': {
'address': proof.address,
'log2_size': proof.log2_size,
'target_hash': "0x{}".format(proof.target_hash.data.hex()),
'root_hash': "0x{}".format(proof.root_hash.data.hex()),
'sibling_hashes' : []
}
}
for sibling in proof.sibling_hashes:
resp_dict['proof']['sibling_hashes'].append("0x{}".format(sibling.data.hex()))
return json.dumps(resp_dict, indent=4, sort_keys=True)
def dump_read_mem_response_to_json(read_mem_resp):
resp_dict = {"data": "0x{}".format(read_mem_resp.read_content.data.hex())}
return json.dumps(resp_dict, indent=4, sort_keys=True)
def dump_write_mem_response_to_json(write_mem_resp):
return json.dumps("{}".format(write_mem_resp), indent=4, sort_keys=True)
#Initializing log
LOGGER = get_new_logger(__name__)
LOGGER = configure_log(LOGGER)
| 45.062331 | 172 | 0.674886 |
import subprocess
import logging
import logging.config
import logging.handlers
import traceback
import grpc
import json
import time
import os
import cartesi_machine_pb2_grpc
import cartesi_machine_pb2
import machine_manager_pb2
LOG_FILENAME = "manager.log"
RUN_CYCLES_BATCH_SIZE = 10**7
UNIX = "unix"
TCP = "tcp"
SOCKET_TYPE = UNIX
def get_new_logger(name):
return logging.getLogger(name)
def configure_log(logger):
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s %(thread)d %(levelname)-s %(name)s %(lineno)s - %(funcName)s: %(message)s')
rotating_file_handler = logging.handlers.RotatingFileHandler(
LOG_FILENAME, maxBytes=2**20, backupCount=5)
rotating_file_handler.setFormatter(formatter)
rotating_file_handler.setLevel(logging.DEBUG)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(logging.DEBUG)
stream_handler.setFormatter(formatter)
logger.addHandler(rotating_file_handler)
logger.addHandler(stream_handler)
return logger
def new_cartesi_machine_server(session_id, manager_address):
LOGGER.info("Creating a cartesi machine server with session_id '{}'".format(session_id))
cmd_line = ["/opt/cartesi/bin/cartesi-machine-server", "-t", SOCKET_TYPE, "-s", session_id, "-m", manager_address]
LOGGER.debug("Executing {}".format(" ".join(cmd_line)))
proc = None
try:
proc = subprocess.Popen(cmd_line, stderr=subprocess.PIPE, stdout=subprocess.PIPE, env=os.environ)
out, err = proc.communicate()
LOGGER.debug("\nStdout:\n{}\nStderr:\n{}".format(out.decode("utf-8"), err.decode("utf-8")))
except Exception as e:
err_msg = "Cartesi machine server creation process failed for session_id '{}'".format(session_id)
LOGGER.info(err_msg)
if (proc):
out, err = proc.communicate()
LOGGER.debug("\nStdout:\n{}\nStderr:\n{}".format(out.decode("utf-8"), err.decode("utf-8")))
raise CartesiMachineServerException(err_msg)
if (proc.returncode == 0):
LOGGER.info("Cartesi machine server creation process returned for session_id '{}'".format(session_id))
LOGGER.debug("\nStdout:\n{}\nStderr:\n{}".format(out.decode("utf-8"), err.decode("utf-8")))
else:
err_msg = "Cartesi machine server creation process returned non-zero code for session_id '{}'".format(session_id)
LOGGER.error(err_msg)
LOGGER.error("\nStdout:\n{}\nStderr:\n{}".format(out.decode("utf-8"), err.decode("utf-8")))
raise CartesiMachineServerException(err_msg)
def new_machine(session_id, address, machine_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.Machine(machine_req)
LOGGER.debug("Cartesi machine created for session_id '{}'".format(session_id))
def shutdown_cartesi_machine_server(session_id, address):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.Shutdown(cartesi_machine_pb2.Void())
LOGGER.debug("Cartesi machine server shutdown for session_id '{}'".format(session_id))
def get_machine_hash(session_id, address):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
LOGGER.debug("Asking for cartesi machine root hash for session_id '{}'".format(session_id))
response = stub.GetRootHash(cartesi_machine_pb2.Void())
LOGGER.debug("Cartesi machine root hash retrieved for session_id '{}'".format(session_id))
return response.hash
def create_machine_snapshot(session_id, address):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
stub.Snapshot(cartesi_machine_pb2.Void())
LOGGER.debug("Cartesi machine snapshot created for session_id '{}'".format(session_id))
def rollback_machine(session_id, address):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
stub.Rollback(cartesi_machine_pb2.Void())
LOGGER.debug("Cartesi machine rolledback for session_id '{}'".format(session_id))
def run_machine(session_id, session_context, desired_cycle):
current_cycle = session_context.cycle
LOGGER.debug("Current cycle: {}\nDesired cycle: {}".format(current_cycle, desired_cycle))
if (desired_cycle < current_cycle):
raise ValueError("The given desired_cycle must not be smaller than the current_cycle")
response = None
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, session_context.address))
with grpc.insecure_channel(session_context.address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
target_cycle = session_context.cycle + RUN_CYCLES_BATCH_SIZE
if (target_cycle > desired_cycle):
target_cycle = desired_cycle
while (True):
LOGGER.debug("Running cartesi machine for session id {} with target cycle of {}, current cycle is {}".format(session_id, target_cycle, session_context.cycle))
response = stub.Run(cartesi_machine_pb2.RunRequest(limit=target_cycle))
session_context.cycle = response.mcycle
session_context.updated_at = time.time()
LOGGER.debug("Updated cycle of session '{}' to {}".format(session_id, response.mcycle))
if response.iflags_h:
session_context.halt_cycle = session_context.cycle
LOGGER.debug("Session {} halted with payload {}".format(session_id, int.from_bytes(response.tohost.to_bytes(8, 'big')[2:], byteorder='big')))
break
elif response.iflags_y:
cmd = response.tohost.to_bytes(8, 'big')[1]
payload = int.from_bytes(response.tohost.to_bytes(8, 'big')[2:], byteorder='big')
if (cmd==0):
session_context.app_progress = payload
LOGGER.debug("New progress for session {}: {}".format(session_id, payload))
else:
LOGGER.debug("Session {} yielded with command {} and payload {}".format(session_id, cmd, payload))
else:
#The machine reached the target_cycle, setting next one if it wasn't the desired cycle
if target_cycle == desired_cycle:
break
target_cycle += RUN_CYCLES_BATCH_SIZE
#If it`s beyond the desired cycle, truncate
if (target_cycle > desired_cycle):
target_cycle = desired_cycle
LOGGER.debug("Cartesi machine ran for session_id '{}' and desired final cycle of {}, current cycle is {}".format(session_id, desired_cycle, session_context.cycle))
return response
def step_machine(session_id, address, step_params):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.Step(step_params)
LOGGER.debug("Cartesi machine step complete for session_id '{}'".format(session_id))
return response.log
def store_machine(session_id, address, store_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.Store(store_req)
LOGGER.debug("Stored Cartesi machine for session_id '{}', desired directory '{}'".format(session_id, store_req.directory))
return response
def read_machine_memory(session_id, address, read_mem_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.ReadMemory(read_mem_req)
LOGGER.debug("Cartesi machine memory read for session_id '{}', desired mem address {} and length {}".format(session_id, read_mem_req.address, read_mem_req.length))
return response
def write_machine_memory(session_id, address, write_mem_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.WriteMemory(write_mem_req)
LOGGER.debug("Cartesi machine memory written for session_id '{}', desired mem address {} and data {}".format(session_id, write_mem_req.address, write_mem_req.data))
return response
def get_machine_proof(session_id, address, proof_req):
LOGGER.debug("Connecting to cartesi machine server from session '{}' in address '{}'".format(session_id, address))
with grpc.insecure_channel(address) as channel:
stub = cartesi_machine_pb2_grpc.MachineStub(channel)
response = stub.GetProof(proof_req)
LOGGER.debug("Got Cartesi machine proof for session_id '{}', desired mem address {} and log2_size {}".format(session_id, proof_req.address, proof_req.log2_size))
return response
def make_session_run_result(summaries, hashes):
return machine_manager_pb2.SessionRunResponse(result=machine_manager_pb2.SessionRunResult(summaries=summaries, hashes=hashes))
def make_session_step_result(access_log):
return machine_manager_pb2.SessionStepResponse(log=access_log)
def make_session_read_memory_result(read_mem_resp):
return machine_manager_pb2.SessionReadMemoryResponse(read_content=read_mem_resp)
class CycleException(Exception):
pass
class CartesiMachineServerException(Exception):
pass
def validate_cycles(values):
last_value = None
#Checking if at least one value was passed
if values:
for value in values:
if (value < 0):
raise CycleException("Positive values expected, first offending value: {}".format(value))
if last_value:
if value < last_value:
raise CycleException("Provide cycle values in crescent order, received {} after {}".format(value, last_value))
last_value = value
else:
raise CycleException("Provide a cycle value")
#Debugging functions
def dump_step_response_to_json(access_log):
access_log_dict = {'accesses':[], 'notes':[], 'brackets':[]}
for note in access_log.log.notes:
access_log_dict['notes'].append(note)
for bracket in access_log.log.brackets:
access_log_dict['brackets'].append(
{
'type':
cartesi_machine_pb2._BRACKETNOTE_BRACKETNOTETYPE.values_by_number[bracket.type].name,
'where': bracket.where,
'text' : bracket.text
})
for access in access_log.log.accesses:
access_dict = {
'read': "0x{}".format(access.read.data.hex()),
'written' : "0x{}".format(access.written.data.hex()),
'operation' : cartesi_machine_pb2._ACCESSOPERATION.values_by_number[access.operation].name,
'proof' : {
'address': access.proof.address,
'log2_size': access.proof.log2_size,
'target_hash': "0x{}".format(access.proof.target_hash.data.hex()),
'root_hash': "0x{}".format(access.proof.root_hash.data.hex()),
'sibling_hashes' : []
}
}
for sibling in access.proof.sibling_hashes:
access_dict['proof']['sibling_hashes'].append("0x{}".format(sibling.data.hex()))
access_log_dict['accesses'].append(access_dict)
return json.dumps(access_log_dict, indent=4, sort_keys=True)
def dump_step_response_to_file(access_log, open_dump_file):
json_dump = dump_step_response_to_json(access_log)
open_dump_file.write("\n\n" + '
def dump_run_response_to_json(run_resp):
resp_dict = None
#Checking which of the oneof fields were set
oneof_fieldname = run_resp.WhichOneof("run_oneof")
if oneof_fieldname == "result":
resp_dict = {"summaries": [], "hashes": []}
for val in run_resp.result.summaries:
resp_dict["summaries"].append({
'tohost': val.tohost,
'mcycle': val.mcycle
})
for val in run_resp.result.hashes:
resp_dict["hashes"].append("0x{}".format(val.data.hex()))
elif oneof_fieldname == "progress":
resp_dict = {
"progress": run_resp.progress.progress,
"application_progress": run_resp.progress.application_progress,
"updated_at": run_resp.progress.updated_at,
"cycle": run_resp.progress.cycle
}
return json.dumps(resp_dict, indent=4, sort_keys=True)
def dump_run_response_to_file(run_resp, open_dump_file):
json_dump = dump_run_response_to_json(run_resp)
open_dump_file.write("\n\n" + '
def dump_get_proof_response_to_json(proof_resp):
proof = proof_resp.proof
resp_dict = {
'proof': {
'address': proof.address,
'log2_size': proof.log2_size,
'target_hash': "0x{}".format(proof.target_hash.data.hex()),
'root_hash': "0x{}".format(proof.root_hash.data.hex()),
'sibling_hashes' : []
}
}
for sibling in proof.sibling_hashes:
resp_dict['proof']['sibling_hashes'].append("0x{}".format(sibling.data.hex()))
return json.dumps(resp_dict, indent=4, sort_keys=True)
def dump_read_mem_response_to_json(read_mem_resp):
resp_dict = {"data": "0x{}".format(read_mem_resp.read_content.data.hex())}
return json.dumps(resp_dict, indent=4, sort_keys=True)
def dump_write_mem_response_to_json(write_mem_resp):
return json.dumps("{}".format(write_mem_resp), indent=4, sort_keys=True)
#Initializing log
LOGGER = get_new_logger(__name__)
LOGGER = configure_log(LOGGER)
| true | true |
1c34061109ca350266eae9e1a19f7c8cd8d30559 | 150 | py | Python | terrascript/resource/dnsimple.py | amlodzianowski/python-terrascript | 1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49 | [
"BSD-2-Clause"
] | null | null | null | terrascript/resource/dnsimple.py | amlodzianowski/python-terrascript | 1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49 | [
"BSD-2-Clause"
] | null | null | null | terrascript/resource/dnsimple.py | amlodzianowski/python-terrascript | 1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49 | [
"BSD-2-Clause"
] | null | null | null | # terrascript/resource/dnsimple.py
import terrascript
class dnsimple_record(terrascript.Resource):
pass
__all__ = [
"dnsimple_record",
]
| 11.538462 | 44 | 0.74 |
import terrascript
class dnsimple_record(terrascript.Resource):
pass
__all__ = [
"dnsimple_record",
]
| true | true |
1c34064210bf3bb84e61b0d3413700caef45e132 | 2,375 | py | Python | app/kaznlplib/tokenization/tokhmm.py | n1EzeR/reviews_tazalau | 973b0a8ad1c4f54ad13e767424cf3d42fb1a0bbf | [
"CC0-1.0"
] | null | null | null | app/kaznlplib/tokenization/tokhmm.py | n1EzeR/reviews_tazalau | 973b0a8ad1c4f54ad13e767424cf3d42fb1a0bbf | [
"CC0-1.0"
] | 1 | 2021-06-02T00:47:32.000Z | 2021-06-02T00:47:32.000Z | app/kaznlplib/tokenization/tokhmm.py | n1EzeR/reviews_tazalau | 973b0a8ad1c4f54ad13e767424cf3d42fb1a0bbf | [
"CC0-1.0"
] | null | null | null | # -*- coding: UTF-8 -*-
import re
from kaznlp.models.hmm import HMM_DI
# character processing regex with replacements
CPREX = {
# uppercase mathcer and replacer
re.compile(u"[A-ZА-ЯЁӘІҢҒҮҰҚӨҺ]", re.U): "CAP",
# lowercase mathcer and replacer
re.compile(u"[a-zа-яёәіңғүұқөһ]", re.U): "LOW",
# sentence-final punctuation matcher and replacer
re.compile(u"[\.\?\!]", re.U): "SFL",
# spaces (tab, whitespace, new line, carrier) matcher and replacer
re.compile(u"\s", re.U): "SPC",
# digit matcher and replacer
re.compile(u"\d", re.U): "DIG",
}
class TokenizerHMM:
def __init__(self, implementation=HMM_DI, model=None):
self.hmm = implementation()
if model:
self.hmm.load_model(model)
def get_sequence(slef, txt):
ret = []
for c in txt:
for rex, rep in CPREX.items():
if rex.match(c):
c = rep
break
ret.append(c)
return ret
def tokenize(self, txt, lower=False):
ret = []
curr_sen = []
curr_tok = []
for i, label in enumerate(self.hmm.generate(self.get_sequence(txt))):
char = txt[i]
if label == "S":
if curr_tok:
curr_tok = "".join(curr_tok)
curr_tok = curr_tok.lower() if lower else curr_tok
curr_sen.append(curr_tok)
if curr_sen:
ret.append(curr_sen)
curr_sen = []
curr_tok = [char]
elif label == "T":
if curr_tok:
curr_tok = "".join(curr_tok)
curr_tok = curr_tok.lower() if lower else curr_tok
curr_sen.append(curr_tok)
curr_tok = [char]
elif label == "I":
curr_tok.append(char)
elif label == "O":
if curr_tok:
curr_tok = "".join(curr_tok)
curr_tok = curr_tok.lower() if lower else curr_tok
curr_sen.append(curr_tok)
curr_tok = []
if curr_tok:
curr_tok = "".join(curr_tok)
curr_tok = curr_tok.lower() if lower else curr_tok
curr_sen.append(curr_tok)
ret.append(curr_sen)
return ret
| 32.534247 | 77 | 0.507368 |
import re
from kaznlp.models.hmm import HMM_DI
CPREX = {
re.compile(u"[A-ZА-ЯЁӘІҢҒҮҰҚӨҺ]", re.U): "CAP",
re.compile(u"[a-zа-яёәіңғүұқөһ]", re.U): "LOW",
re.compile(u"[\.\?\!]", re.U): "SFL",
re.compile(u"\s", re.U): "SPC",
re.compile(u"\d", re.U): "DIG",
}
class TokenizerHMM:
def __init__(self, implementation=HMM_DI, model=None):
self.hmm = implementation()
if model:
self.hmm.load_model(model)
def get_sequence(slef, txt):
ret = []
for c in txt:
for rex, rep in CPREX.items():
if rex.match(c):
c = rep
break
ret.append(c)
return ret
def tokenize(self, txt, lower=False):
ret = []
curr_sen = []
curr_tok = []
for i, label in enumerate(self.hmm.generate(self.get_sequence(txt))):
char = txt[i]
if label == "S":
if curr_tok:
curr_tok = "".join(curr_tok)
curr_tok = curr_tok.lower() if lower else curr_tok
curr_sen.append(curr_tok)
if curr_sen:
ret.append(curr_sen)
curr_sen = []
curr_tok = [char]
elif label == "T":
if curr_tok:
curr_tok = "".join(curr_tok)
curr_tok = curr_tok.lower() if lower else curr_tok
curr_sen.append(curr_tok)
curr_tok = [char]
elif label == "I":
curr_tok.append(char)
elif label == "O":
if curr_tok:
curr_tok = "".join(curr_tok)
curr_tok = curr_tok.lower() if lower else curr_tok
curr_sen.append(curr_tok)
curr_tok = []
if curr_tok:
curr_tok = "".join(curr_tok)
curr_tok = curr_tok.lower() if lower else curr_tok
curr_sen.append(curr_tok)
ret.append(curr_sen)
return ret
| true | true |
1c34065aa7e70f2a9d46f68e522f48e8db1adb12 | 25,852 | py | Python | userbot/modules/scrapers.py | Saksham033/PaperplaneExtended | 1480e25bcd2e012ba1e2d78c1ba29a3cbc449a23 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null | userbot/modules/scrapers.py | Saksham033/PaperplaneExtended | 1480e25bcd2e012ba1e2d78c1ba29a3cbc449a23 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null | userbot/modules/scrapers.py | Saksham033/PaperplaneExtended | 1480e25bcd2e012ba1e2d78c1ba29a3cbc449a23 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null | # Copyright (C) 2019 The Raphielscape Company LLC.
#
# Licensed under the Raphielscape Public License, Version 1.c (the "License");
# you may not use this file except in compliance with the License.
#
""" Userbot module containing various scrapers. """
import os
import time
import asyncio
import shutil
from bs4 import BeautifulSoup
import re
from time import sleep
from html import unescape
from re import findall
from selenium import webdriver
from urllib.parse import quote_plus
from urllib.error import HTTPError
from selenium.webdriver.support.ui import Select
from selenium.webdriver.chrome.options import Options
from wikipedia import summary
from wikipedia.exceptions import DisambiguationError, PageError
import asyncurban
from requests import get
from search_engine_parser import GoogleSearch
from google_images_download import google_images_download
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from googletrans import LANGUAGES, Translator
from gtts import gTTS
from gtts.lang import tts_langs
from emoji import get_emoji_regexp
from youtube_dl import YoutubeDL
from youtube_dl.utils import (DownloadError, ContentTooShortError,
ExtractorError, GeoRestrictedError,
MaxDownloadsReached, PostProcessingError,
UnavailableVideoError, XAttrMetadataError)
from asyncio import sleep
from userbot import CMD_HELP, BOTLOG, BOTLOG_CHATID, YOUTUBE_API_KEY, CHROME_DRIVER, GOOGLE_CHROME_BIN
from userbot.events import register
from telethon.tl.types import DocumentAttributeAudio
from userbot.modules.upload_download import progress, humanbytes, time_formatter
CARBONLANG = "auto"
TTS_LANG = "en"
TRT_LANG = "en"
@register(outgoing=True, pattern="^.crblang (.*)")
async def setlang(prog):
global CARBONLANG
CARBONLANG = prog.pattern_match.group(1)
await prog.edit(f"Language for carbon.now.sh set to {CARBONLANG}")
@register(outgoing=True, pattern="^.carbon")
async def carbon_api(e):
""" A Wrapper for carbon.now.sh """
await e.edit("`Processing..`")
CARBON = 'https://carbon.now.sh/?l={lang}&code={code}'
global CARBONLANG
textx = await e.get_reply_message()
pcode = e.text
if pcode[8:]:
pcode = str(pcode[8:])
elif textx:
pcode = str(textx.message) # Importing message to module
code = quote_plus(pcode) # Converting to urlencoded
await e.edit("`Processing..\n25%`")
if os.path.isfile("./carbon.png"):
os.remove("./carbon.png")
url = CARBON.format(code=code, lang=CARBONLANG)
chrome_options = Options()
chrome_options.add_argument("--headless")
chrome_options.binary_location = GOOGLE_CHROME_BIN
chrome_options.add_argument("--window-size=1920x1080")
chrome_options.add_argument("--disable-dev-shm-usage")
chrome_options.add_argument("--no-sandbox")
chrome_options.add_argument("--disable-gpu")
prefs = {'download.default_directory': './'}
chrome_options.add_experimental_option('prefs', prefs)
driver = webdriver.Chrome(executable_path=CHROME_DRIVER,
options=chrome_options)
driver.get(url)
await e.edit("`Processing..\n50%`")
download_path = './'
driver.command_executor._commands["send_command"] = (
"POST", '/session/$sessionId/chromium/send_command')
params = {
'cmd': 'Page.setDownloadBehavior',
'params': {
'behavior': 'allow',
'downloadPath': download_path
}
}
command_result = driver.execute("send_command", params)
driver.find_element_by_xpath("//button[contains(text(),'Export')]").click()
driver.find_element_by_xpath("//button[contains(text(),'4x')]").click()
driver.find_element_by_xpath("//button[contains(text(),'PNG')]").click()
await e.edit("`Processing..\n75%`")
# Waiting for downloading
while not os.path.isfile("./carbon.png"):
await sleep(0.5)
await e.edit("`Processing..\n100%`")
file = './carbon.png'
await e.edit("`Uploading..`")
await e.client.send_file(
e.chat_id,
file,
caption="Made using [Carbon](https://carbon.now.sh/about/),\
\na project by [Dawn Labs](https://dawnlabs.io/)",
force_document=True,
reply_to=e.message.reply_to_msg_id,
)
os.remove('./carbon.png')
driver.quit()
# Removing carbon.png after uploading
await e.delete() # Deleting msg
@register(outgoing=True, pattern="^.img (.*)")
async def img_sampler(event):
""" For .img command, search and return images matching the query. """
await event.edit("Processing...")
query = event.pattern_match.group(1)
lim = findall(r"lim=\d+", query)
try:
lim = lim[0]
lim = lim.replace("lim=", "")
query = query.replace("lim=" + lim[0], "")
except IndexError:
lim = 3
response = google_images_download.googleimagesdownload()
# creating list of arguments
arguments = {
"keywords": query,
"limit": lim,
"format": "jpg",
"no_directory": "no_directory"
}
# passing the arguments to the function
paths = response.download(arguments)
lst = paths[0][query]
await event.client.send_file(
await event.client.get_input_entity(event.chat_id), lst)
shutil.rmtree(os.path.dirname(os.path.abspath(lst[0])))
await event.delete()
@register(outgoing=True, pattern="^.currency (.*)")
async def moni(event):
input_str = event.pattern_match.group(1)
input_sgra = input_str.split(" ")
if len(input_sgra) == 3:
try:
number = float(input_sgra[0])
currency_from = input_sgra[1].upper()
currency_to = input_sgra[2].upper()
request_url = "https://api.exchangeratesapi.io/latest?base={}".format(
currency_from)
current_response = get(request_url).json()
if currency_to in current_response["rates"]:
current_rate = float(current_response["rates"][currency_to])
rebmun = round(number * current_rate, 2)
await event.edit("{} {} = {} {}".format(
number, currency_from, rebmun, currency_to))
else:
await event.edit(
"`This seems to be some alien currency, which I can't convert right now.`"
)
except Exception as e:
await event.edit(str(e))
else:
await event.edit("`Invalid syntax.`")
return
@register(outgoing=True, pattern=r"^.google (.*)")
async def gsearch(q_event):
""" For .google command, do a Google search. """
match = q_event.pattern_match.group(1)
page = findall(r"page=\d+", match)
try:
page = page[0]
page = page.replace("page=", "")
match = match.replace("page=" + page[0], "")
except IndexError:
page = 1
search_args = (str(match), int(page))
gsearch = GoogleSearch()
gresults = await gsearch.async_search(*search_args)
msg = ""
for i in range(len(gresults["links"])):
try:
title = gresults["titles"][i]
link = gresults["links"][i]
desc = gresults["descriptions"][i]
msg += f"[{title}]({link})\n`{desc}`\n\n"
except IndexError:
break
await q_event.edit("**Search Query:**\n`" + match + "`\n\n**Results:**\n" +
msg,
link_preview=False)
if BOTLOG:
await q_event.client.send_message(
BOTLOG_CHATID,
"Google Search query `" + match + "` was executed successfully",
)
@register(outgoing=True, pattern=r"^.wiki (.*)")
async def wiki(wiki_q):
""" For .wiki command, fetch content from Wikipedia. """
match = wiki_q.pattern_match.group(1)
try:
summary(match)
except DisambiguationError as error:
await wiki_q.edit(f"Disambiguated page found.\n\n{error}")
return
except PageError as pageerror:
await wiki_q.edit(f"Page not found.\n\n{pageerror}")
return
result = summary(match)
if len(result) >= 4096:
file = open("output.txt", "w+")
file.write(result)
file.close()
await wiki_q.client.send_file(
wiki_q.chat_id,
"output.txt",
reply_to=wiki_q.id,
caption="`Output too large, sending as file`",
)
if os.path.exists("output.txt"):
os.remove("output.txt")
return
await wiki_q.edit("**Search:**\n`" + match + "`\n\n**Result:**\n" + result)
if BOTLOG:
await wiki_q.client.send_message(
BOTLOG_CHATID, f"Wiki query `{match}` was executed successfully")
@register(outgoing=True, pattern="^.ud (.*)")
async def urban_dict(ud_e):
""" For .ud command, fetch content from Urban Dictionary. """
await ud_e.edit("Processing...")
query = ud_e.pattern_match.group(1)
urban_dict_helper = asyncurban.UrbanDictionary()
try:
urban_def = await urban_dict_helper.get_word(query)
except asyncurban.WordNotFoundError:
await ud_e.edit(f"Sorry, couldn't find any results for: {query}")
return
deflen = sum(len(i) for i in urban_def.definition)
exalen = sum(len(i) for i in urban_def.example)
meanlen = deflen + exalen
if int(meanlen) >= 0:
if int(meanlen) >= 4096:
await ud_e.edit("`Output too large, sending as file.`")
file = open("output.txt", "w+")
file.write("Text: " + query + "\n\nMeaning: " +
urban_def.definition + "\n\n" + "Example: \n" +
urban_def.example)
file.close()
await ud_e.client.send_file(
ud_e.chat_id,
"output.txt",
caption="`Output was too large, sent it as a file.`")
if os.path.exists("output.txt"):
os.remove("output.txt")
await ud_e.delete()
return
await ud_e.edit("Text: **" + query + "**\n\nMeaning: **" +
urban_def.definition + "**\n\n" + "Example: \n__" +
urban_def.example + "__")
if BOTLOG:
await ud_e.client.send_message(
BOTLOG_CHATID, "UrbanDictionary query for `" + query +
"` executed successfully.")
else:
await ud_e.edit("No result found for **" + query + "**")
@register(outgoing=True, pattern=r"^.tts(?: |$)([\s\S]*)")
async def text_to_speech(query):
""" For .tts command, a wrapper for Google Text-to-Speech. """
textx = await query.get_reply_message()
message = query.pattern_match.group(1)
if message:
pass
elif textx:
message = textx.text
else:
await query.edit(
"`Give a text or reply to a message for Text-to-Speech!`")
return
try:
gTTS(message, TTS_LANG)
except AssertionError:
await query.edit(
'The text is empty.\n'
'Nothing left to speak after pre-precessing, tokenizing and cleaning.'
)
return
except ValueError:
await query.edit('Language is not supported.')
return
except RuntimeError:
await query.edit('Error loading the languages dictionary.')
return
tts = gTTS(message, TTS_LANG)
tts.save("k.mp3")
with open("k.mp3", "rb") as audio:
linelist = list(audio)
linecount = len(linelist)
if linecount == 1:
tts = gTTS(message, TTS_LANG)
tts.save("k.mp3")
with open("k.mp3", "r"):
await query.client.send_file(query.chat_id, "k.mp3", voice_note=True)
os.remove("k.mp3")
if BOTLOG:
await query.client.send_message(
BOTLOG_CHATID, "Text to Speech executed successfully !")
await query.delete()
# kanged from Blank-x ;---;
@register(outgoing=True, pattern="^.imdb (.*)")
async def imdb(e):
try:
movie_name = e.pattern_match.group(1)
remove_space = movie_name.split(' ')
final_name = '+'.join(remove_space)
page = get("https://www.imdb.com/find?ref_=nv_sr_fn&q=" + final_name +
"&s=all")
lnk = str(page.status_code)
soup = BeautifulSoup(page.content, 'lxml')
odds = soup.findAll("tr", "odd")
mov_title = odds[0].findNext('td').findNext('td').text
mov_link = "http://www.imdb.com/" + \
odds[0].findNext('td').findNext('td').a['href']
page1 = get(mov_link)
soup = BeautifulSoup(page1.content, 'lxml')
if soup.find('div', 'poster'):
poster = soup.find('div', 'poster').img['src']
else:
poster = ''
if soup.find('div', 'title_wrapper'):
pg = soup.find('div', 'title_wrapper').findNext('div').text
mov_details = re.sub(r'\s+', ' ', pg)
else:
mov_details = ''
credits = soup.findAll('div', 'credit_summary_item')
if len(credits) == 1:
director = credits[0].a.text
writer = 'Not available'
stars = 'Not available'
elif len(credits) > 2:
director = credits[0].a.text
writer = credits[1].a.text
actors = []
for x in credits[2].findAll('a'):
actors.append(x.text)
actors.pop()
stars = actors[0] + ',' + actors[1] + ',' + actors[2]
else:
director = credits[0].a.text
writer = 'Not available'
actors = []
for x in credits[1].findAll('a'):
actors.append(x.text)
actors.pop()
stars = actors[0] + ',' + actors[1] + ',' + actors[2]
if soup.find('div', "inline canwrap"):
story_line = soup.find('div',
"inline canwrap").findAll('p')[0].text
else:
story_line = 'Not available'
info = soup.findAll('div', "txt-block")
if info:
mov_country = []
mov_language = []
for node in info:
a = node.findAll('a')
for i in a:
if "country_of_origin" in i['href']:
mov_country.append(i.text)
elif "primary_language" in i['href']:
mov_language.append(i.text)
if soup.findAll('div', "ratingValue"):
for r in soup.findAll('div', "ratingValue"):
mov_rating = r.strong['title']
else:
mov_rating = 'Not available'
await e.edit('<a href=' + poster + '>​</a>'
'<b>Title : </b><code>' + mov_title + '</code>\n<code>' +
mov_details + '</code>\n<b>Rating : </b><code>' +
mov_rating + '</code>\n<b>Country : </b><code>' +
mov_country[0] + '</code>\n<b>Language : </b><code>' +
mov_language[0] + '</code>\n<b>Director : </b><code>' +
director + '</code>\n<b>Writer : </b><code>' + writer +
'</code>\n<b>Stars : </b><code>' + stars +
'</code>\n<b>IMDB Url : </b>' + mov_link +
'\n<b>Story Line : </b>' + story_line,
link_preview=True,
parse_mode='HTML')
except IndexError:
await e.edit("Plox enter **Valid movie name** kthx")
@register(outgoing=True, pattern=r"^.trt(?: |$)([\s\S]*)")
async def translateme(trans):
""" For .trt command, translate the given text using Google Translate. """
translator = Translator()
textx = await trans.get_reply_message()
message = trans.pattern_match.group(1)
if message:
pass
elif textx:
message = textx.text
else:
await trans.edit("`Give a text or reply to a message to translate!`")
return
try:
reply_text = translator.translate(deEmojify(message), dest=TRT_LANG)
except ValueError:
await trans.edit("Invalid destination language.")
return
source_lan = LANGUAGES[f'{reply_text.src.lower()}']
transl_lan = LANGUAGES[f'{reply_text.dest.lower()}']
reply_text = f"From **{source_lan.title()}**\nTo **{transl_lan.title()}:**\n\n{reply_text.text}"
await trans.edit(reply_text)
if BOTLOG:
await trans.client.send_message(
BOTLOG_CHATID,
f"Translated some {source_lan.title()} stuff to {transl_lan.title()} just now.",
)
@register(pattern="^.lang (trt|tts) (.*)", outgoing=True)
async def lang(value):
""" For .lang command, change the default langauge of userbot scrapers. """
util = value.pattern_match.group(1).lower()
if util == "trt":
scraper = "Translator"
global TRT_LANG
arg = value.pattern_match.group(2).lower()
if arg in LANGUAGES:
TRT_LANG = arg
LANG = LANGUAGES[arg]
else:
await value.edit(
f"`Invalid Language code !!`\n`Available language codes for TRT`:\n\n`{LANGUAGES}`"
)
return
elif util == "tts":
scraper = "Text to Speech"
global TTS_LANG
arg = value.pattern_match.group(2).lower()
if arg in tts_langs():
TTS_LANG = arg
LANG = tts_langs()[arg]
else:
await value.edit(
f"`Invalid Language code !!`\n`Available language codes for TTS`:\n\n`{tts_langs()}`"
)
return
await value.edit(f"`Language for {scraper} changed to {LANG.title()}.`")
if BOTLOG:
await value.client.send_message(
BOTLOG_CHATID,
f"`Language for {scraper} changed to {LANG.title()}.`")
@register(outgoing=True, pattern="^.yt (.*)")
async def yt_search(video_q):
""" For .yt command, do a YouTube search from Telegram. """
query = video_q.pattern_match.group(1)
result = ''
if not YOUTUBE_API_KEY:
await video_q.edit(
"`Error: YouTube API key missing! Add it to environment vars or config.env.`"
)
return
await video_q.edit("```Processing...```")
full_response = await youtube_search(query)
videos_json = full_response[1]
for video in videos_json:
title = f"{unescape(video['snippet']['title'])}"
link = f"https://youtu.be/{video['id']['videoId']}"
result += f"{title}\n{link}\n\n"
reply_text = f"**Search Query:**\n`{query}`\n\n**Results:**\n\n{result}"
await video_q.edit(reply_text)
async def youtube_search(query,
order="relevance",
token=None,
location=None,
location_radius=None):
""" Do a YouTube search. """
youtube = build('youtube',
'v3',
developerKey=YOUTUBE_API_KEY,
cache_discovery=False)
search_response = youtube.search().list(
q=query,
type="video",
pageToken=token,
order=order,
part="id,snippet",
maxResults=10,
location=location,
locationRadius=location_radius).execute()
videos = []
for search_result in search_response.get("items", []):
if search_result["id"]["kind"] == "youtube#video":
videos.append(search_result)
try:
nexttok = search_response["nextPageToken"]
return (nexttok, videos)
except HttpError:
nexttok = "last_page"
return (nexttok, videos)
except KeyError:
nexttok = "KeyError, try again."
return (nexttok, videos)
@register(outgoing=True, pattern=r"^.rip(audio|video) (.*)")
async def download_video(v_url):
""" For .rip command, download media from YouTube and many other sites. """
url = v_url.pattern_match.group(2)
type = v_url.pattern_match.group(1).lower()
await v_url.edit("`Preparing to download...`")
if type == "audio":
opts = {
'format':
'bestaudio',
'addmetadata':
True,
'key':
'FFmpegMetadata',
'writethumbnail':
True,
'prefer_ffmpeg':
True,
'geo_bypass':
True,
'nocheckcertificate':
True,
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'mp3',
'preferredquality': '320',
}],
'outtmpl':
'%(id)s.mp3',
'quiet':
True,
'logtostderr':
False
}
video = False
song = True
elif type == "video":
opts = {
'format':
'best',
'addmetadata':
True,
'key':
'FFmpegMetadata',
'prefer_ffmpeg':
True,
'geo_bypass':
True,
'nocheckcertificate':
True,
'postprocessors': [{
'key': 'FFmpegVideoConvertor',
'preferedformat': 'mp4'
}],
'outtmpl':
'%(id)s.mp4',
'logtostderr':
False,
'quiet':
True
}
song = False
video = True
try:
await v_url.edit("`Fetching data, please wait..`")
with YoutubeDL(opts) as rip:
rip_data = rip.extract_info(url)
except DownloadError as DE:
await v_url.edit(f"`{str(DE)}`")
return
except ContentTooShortError:
await v_url.edit("`The download content was too short.`")
return
except GeoRestrictedError:
await v_url.edit(
"`Video is not available from your geographic location due to geographic restrictions imposed by a website.`"
)
return
except MaxDownloadsReached:
await v_url.edit("`Max-downloads limit has been reached.`")
return
except PostProcessingError:
await v_url.edit("`There was an error during post processing.`")
return
except UnavailableVideoError:
await v_url.edit("`Media is not available in the requested format.`")
return
except XAttrMetadataError as XAME:
await v_url.edit(f"`{XAME.code}: {XAME.msg}\n{XAME.reason}`")
return
except ExtractorError:
await v_url.edit("`There was an error during info extraction.`")
return
except Exception as e:
await v_url.edit(f"{str(type(e)): {str(e)}}")
return
c_time = time.time()
if song:
await v_url.edit(f"`Preparing to upload song:`\
\n**{rip_data['title']}**\
\nby __{rip_data['uploader']}__")
await v_url.client.send_file(
v_url.chat_id,
f"{rip_data['id']}.mp3",
supports_streaming=True,
attributes=[
DocumentAttributeAudio(duration=int(rip_data['duration']),
title=str(rip_data['title']),
performer=str(rip_data['uploader']))
],
progress_callback=lambda d, t: asyncio.get_event_loop(
).create_task(
progress(d, t, v_url, c_time, "Uploading..",
f"{rip_data['title']}.mp3")))
os.remove(f"{rip_data['id']}.mp3")
await v_url.delete()
elif video:
await v_url.edit(f"`Preparing to upload video:`\
\n**{rip_data['title']}**\
\nby __{rip_data['uploader']}__")
await v_url.client.send_file(
v_url.chat_id,
f"{rip_data['id']}.mp4",
supports_streaming=True,
caption=rip_data['title'],
progress_callback=lambda d, t: asyncio.get_event_loop(
).create_task(
progress(d, t, v_url, c_time, "Uploading..",
f"{rip_data['title']}.mp4")))
os.remove(f"{rip_data['id']}.mp4")
await v_url.delete()
def deEmojify(inputString):
""" Remove emojis and other non-safe characters from string """
return get_emoji_regexp().sub(u'', inputString)
CMD_HELP.update({
'img':
'.img <search_query>\
\nUsage: Does an image search on Google and shows 5 images.'
})
CMD_HELP.update({
'currency':
'.currency <amount> <from> <to>\
\nUsage: Converts various currencies for you.'
})
CMD_HELP.update({
'carbon':
'.carbon <text> [or reply]\
\nUsage: Beautify your code using carbon.now.sh\nUse .crblang <text> to set language for your code.'
})
CMD_HELP.update(
{'google': '.google <query>\
\nUsage: Does a search on Google.'})
CMD_HELP.update(
{'wiki': '.wiki <query>\
\nUsage: Does a search on Wikipedia.'})
CMD_HELP.update(
{'ud': '.ud <query>\
\nUsage: Does a search on Urban Dictionary.'})
CMD_HELP.update({
'tts':
'.tts <text> [or reply]\
\nUsage: Translates text to speech for the language which is set.\nUse .lang tts <language code> to set language for tts. (Default is English.)'
})
CMD_HELP.update({
'trt':
'.trt <text> [or reply]\
\nUsage: Translates text to the language which is set.\nUse .lang trt <language code> to set language for trt. (Default is English)'
})
CMD_HELP.update({'yt': '.yt <text>\
\nUsage: Does a YouTube search.'})
CMD_HELP.update(
{"imdb": ".imdb <movie-name>\nShows movie info and other stuff."})
CMD_HELP.update({
'rip':
'.ripaudio <url> or ripvideo <url>\
\nUsage: Download videos and songs from YouTube (and [many other sites](https://ytdl-org.github.io/youtube-dl/supportedsites.html)).'
})
| 35.268759 | 152 | 0.572567 |
import os
import time
import asyncio
import shutil
from bs4 import BeautifulSoup
import re
from time import sleep
from html import unescape
from re import findall
from selenium import webdriver
from urllib.parse import quote_plus
from urllib.error import HTTPError
from selenium.webdriver.support.ui import Select
from selenium.webdriver.chrome.options import Options
from wikipedia import summary
from wikipedia.exceptions import DisambiguationError, PageError
import asyncurban
from requests import get
from search_engine_parser import GoogleSearch
from google_images_download import google_images_download
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from googletrans import LANGUAGES, Translator
from gtts import gTTS
from gtts.lang import tts_langs
from emoji import get_emoji_regexp
from youtube_dl import YoutubeDL
from youtube_dl.utils import (DownloadError, ContentTooShortError,
ExtractorError, GeoRestrictedError,
MaxDownloadsReached, PostProcessingError,
UnavailableVideoError, XAttrMetadataError)
from asyncio import sleep
from userbot import CMD_HELP, BOTLOG, BOTLOG_CHATID, YOUTUBE_API_KEY, CHROME_DRIVER, GOOGLE_CHROME_BIN
from userbot.events import register
from telethon.tl.types import DocumentAttributeAudio
from userbot.modules.upload_download import progress, humanbytes, time_formatter
CARBONLANG = "auto"
TTS_LANG = "en"
TRT_LANG = "en"
@register(outgoing=True, pattern="^.crblang (.*)")
async def setlang(prog):
global CARBONLANG
CARBONLANG = prog.pattern_match.group(1)
await prog.edit(f"Language for carbon.now.sh set to {CARBONLANG}")
@register(outgoing=True, pattern="^.carbon")
async def carbon_api(e):
await e.edit("`Processing..`")
CARBON = 'https://carbon.now.sh/?l={lang}&code={code}'
global CARBONLANG
textx = await e.get_reply_message()
pcode = e.text
if pcode[8:]:
pcode = str(pcode[8:])
elif textx:
pcode = str(textx.message)
code = quote_plus(pcode)
await e.edit("`Processing..\n25%`")
if os.path.isfile("./carbon.png"):
os.remove("./carbon.png")
url = CARBON.format(code=code, lang=CARBONLANG)
chrome_options = Options()
chrome_options.add_argument("--headless")
chrome_options.binary_location = GOOGLE_CHROME_BIN
chrome_options.add_argument("--window-size=1920x1080")
chrome_options.add_argument("--disable-dev-shm-usage")
chrome_options.add_argument("--no-sandbox")
chrome_options.add_argument("--disable-gpu")
prefs = {'download.default_directory': './'}
chrome_options.add_experimental_option('prefs', prefs)
driver = webdriver.Chrome(executable_path=CHROME_DRIVER,
options=chrome_options)
driver.get(url)
await e.edit("`Processing..\n50%`")
download_path = './'
driver.command_executor._commands["send_command"] = (
"POST", '/session/$sessionId/chromium/send_command')
params = {
'cmd': 'Page.setDownloadBehavior',
'params': {
'behavior': 'allow',
'downloadPath': download_path
}
}
command_result = driver.execute("send_command", params)
driver.find_element_by_xpath("//button[contains(text(),'Export')]").click()
driver.find_element_by_xpath("//button[contains(text(),'4x')]").click()
driver.find_element_by_xpath("//button[contains(text(),'PNG')]").click()
await e.edit("`Processing..\n75%`")
while not os.path.isfile("./carbon.png"):
await sleep(0.5)
await e.edit("`Processing..\n100%`")
file = './carbon.png'
await e.edit("`Uploading..`")
await e.client.send_file(
e.chat_id,
file,
caption="Made using [Carbon](https://carbon.now.sh/about/),\
\na project by [Dawn Labs](https://dawnlabs.io/)",
force_document=True,
reply_to=e.message.reply_to_msg_id,
)
os.remove('./carbon.png')
driver.quit()
await e.delete()
@register(outgoing=True, pattern="^.img (.*)")
async def img_sampler(event):
await event.edit("Processing...")
query = event.pattern_match.group(1)
lim = findall(r"lim=\d+", query)
try:
lim = lim[0]
lim = lim.replace("lim=", "")
query = query.replace("lim=" + lim[0], "")
except IndexError:
lim = 3
response = google_images_download.googleimagesdownload()
arguments = {
"keywords": query,
"limit": lim,
"format": "jpg",
"no_directory": "no_directory"
}
paths = response.download(arguments)
lst = paths[0][query]
await event.client.send_file(
await event.client.get_input_entity(event.chat_id), lst)
shutil.rmtree(os.path.dirname(os.path.abspath(lst[0])))
await event.delete()
@register(outgoing=True, pattern="^.currency (.*)")
async def moni(event):
input_str = event.pattern_match.group(1)
input_sgra = input_str.split(" ")
if len(input_sgra) == 3:
try:
number = float(input_sgra[0])
currency_from = input_sgra[1].upper()
currency_to = input_sgra[2].upper()
request_url = "https://api.exchangeratesapi.io/latest?base={}".format(
currency_from)
current_response = get(request_url).json()
if currency_to in current_response["rates"]:
current_rate = float(current_response["rates"][currency_to])
rebmun = round(number * current_rate, 2)
await event.edit("{} {} = {} {}".format(
number, currency_from, rebmun, currency_to))
else:
await event.edit(
"`This seems to be some alien currency, which I can't convert right now.`"
)
except Exception as e:
await event.edit(str(e))
else:
await event.edit("`Invalid syntax.`")
return
@register(outgoing=True, pattern=r"^.google (.*)")
async def gsearch(q_event):
match = q_event.pattern_match.group(1)
page = findall(r"page=\d+", match)
try:
page = page[0]
page = page.replace("page=", "")
match = match.replace("page=" + page[0], "")
except IndexError:
page = 1
search_args = (str(match), int(page))
gsearch = GoogleSearch()
gresults = await gsearch.async_search(*search_args)
msg = ""
for i in range(len(gresults["links"])):
try:
title = gresults["titles"][i]
link = gresults["links"][i]
desc = gresults["descriptions"][i]
msg += f"[{title}]({link})\n`{desc}`\n\n"
except IndexError:
break
await q_event.edit("**Search Query:**\n`" + match + "`\n\n**Results:**\n" +
msg,
link_preview=False)
if BOTLOG:
await q_event.client.send_message(
BOTLOG_CHATID,
"Google Search query `" + match + "` was executed successfully",
)
@register(outgoing=True, pattern=r"^.wiki (.*)")
async def wiki(wiki_q):
match = wiki_q.pattern_match.group(1)
try:
summary(match)
except DisambiguationError as error:
await wiki_q.edit(f"Disambiguated page found.\n\n{error}")
return
except PageError as pageerror:
await wiki_q.edit(f"Page not found.\n\n{pageerror}")
return
result = summary(match)
if len(result) >= 4096:
file = open("output.txt", "w+")
file.write(result)
file.close()
await wiki_q.client.send_file(
wiki_q.chat_id,
"output.txt",
reply_to=wiki_q.id,
caption="`Output too large, sending as file`",
)
if os.path.exists("output.txt"):
os.remove("output.txt")
return
await wiki_q.edit("**Search:**\n`" + match + "`\n\n**Result:**\n" + result)
if BOTLOG:
await wiki_q.client.send_message(
BOTLOG_CHATID, f"Wiki query `{match}` was executed successfully")
@register(outgoing=True, pattern="^.ud (.*)")
async def urban_dict(ud_e):
await ud_e.edit("Processing...")
query = ud_e.pattern_match.group(1)
urban_dict_helper = asyncurban.UrbanDictionary()
try:
urban_def = await urban_dict_helper.get_word(query)
except asyncurban.WordNotFoundError:
await ud_e.edit(f"Sorry, couldn't find any results for: {query}")
return
deflen = sum(len(i) for i in urban_def.definition)
exalen = sum(len(i) for i in urban_def.example)
meanlen = deflen + exalen
if int(meanlen) >= 0:
if int(meanlen) >= 4096:
await ud_e.edit("`Output too large, sending as file.`")
file = open("output.txt", "w+")
file.write("Text: " + query + "\n\nMeaning: " +
urban_def.definition + "\n\n" + "Example: \n" +
urban_def.example)
file.close()
await ud_e.client.send_file(
ud_e.chat_id,
"output.txt",
caption="`Output was too large, sent it as a file.`")
if os.path.exists("output.txt"):
os.remove("output.txt")
await ud_e.delete()
return
await ud_e.edit("Text: **" + query + "**\n\nMeaning: **" +
urban_def.definition + "**\n\n" + "Example: \n__" +
urban_def.example + "__")
if BOTLOG:
await ud_e.client.send_message(
BOTLOG_CHATID, "UrbanDictionary query for `" + query +
"` executed successfully.")
else:
await ud_e.edit("No result found for **" + query + "**")
@register(outgoing=True, pattern=r"^.tts(?: |$)([\s\S]*)")
async def text_to_speech(query):
textx = await query.get_reply_message()
message = query.pattern_match.group(1)
if message:
pass
elif textx:
message = textx.text
else:
await query.edit(
"`Give a text or reply to a message for Text-to-Speech!`")
return
try:
gTTS(message, TTS_LANG)
except AssertionError:
await query.edit(
'The text is empty.\n'
'Nothing left to speak after pre-precessing, tokenizing and cleaning.'
)
return
except ValueError:
await query.edit('Language is not supported.')
return
except RuntimeError:
await query.edit('Error loading the languages dictionary.')
return
tts = gTTS(message, TTS_LANG)
tts.save("k.mp3")
with open("k.mp3", "rb") as audio:
linelist = list(audio)
linecount = len(linelist)
if linecount == 1:
tts = gTTS(message, TTS_LANG)
tts.save("k.mp3")
with open("k.mp3", "r"):
await query.client.send_file(query.chat_id, "k.mp3", voice_note=True)
os.remove("k.mp3")
if BOTLOG:
await query.client.send_message(
BOTLOG_CHATID, "Text to Speech executed successfully !")
await query.delete()
@register(outgoing=True, pattern="^.imdb (.*)")
async def imdb(e):
try:
movie_name = e.pattern_match.group(1)
remove_space = movie_name.split(' ')
final_name = '+'.join(remove_space)
page = get("https://www.imdb.com/find?ref_=nv_sr_fn&q=" + final_name +
"&s=all")
lnk = str(page.status_code)
soup = BeautifulSoup(page.content, 'lxml')
odds = soup.findAll("tr", "odd")
mov_title = odds[0].findNext('td').findNext('td').text
mov_link = "http://www.imdb.com/" + \
odds[0].findNext('td').findNext('td').a['href']
page1 = get(mov_link)
soup = BeautifulSoup(page1.content, 'lxml')
if soup.find('div', 'poster'):
poster = soup.find('div', 'poster').img['src']
else:
poster = ''
if soup.find('div', 'title_wrapper'):
pg = soup.find('div', 'title_wrapper').findNext('div').text
mov_details = re.sub(r'\s+', ' ', pg)
else:
mov_details = ''
credits = soup.findAll('div', 'credit_summary_item')
if len(credits) == 1:
director = credits[0].a.text
writer = 'Not available'
stars = 'Not available'
elif len(credits) > 2:
director = credits[0].a.text
writer = credits[1].a.text
actors = []
for x in credits[2].findAll('a'):
actors.append(x.text)
actors.pop()
stars = actors[0] + ',' + actors[1] + ',' + actors[2]
else:
director = credits[0].a.text
writer = 'Not available'
actors = []
for x in credits[1].findAll('a'):
actors.append(x.text)
actors.pop()
stars = actors[0] + ',' + actors[1] + ',' + actors[2]
if soup.find('div', "inline canwrap"):
story_line = soup.find('div',
"inline canwrap").findAll('p')[0].text
else:
story_line = 'Not available'
info = soup.findAll('div', "txt-block")
if info:
mov_country = []
mov_language = []
for node in info:
a = node.findAll('a')
for i in a:
if "country_of_origin" in i['href']:
mov_country.append(i.text)
elif "primary_language" in i['href']:
mov_language.append(i.text)
if soup.findAll('div', "ratingValue"):
for r in soup.findAll('div', "ratingValue"):
mov_rating = r.strong['title']
else:
mov_rating = 'Not available'
await e.edit('<a href=' + poster + '>​</a>'
'<b>Title : </b><code>' + mov_title + '</code>\n<code>' +
mov_details + '</code>\n<b>Rating : </b><code>' +
mov_rating + '</code>\n<b>Country : </b><code>' +
mov_country[0] + '</code>\n<b>Language : </b><code>' +
mov_language[0] + '</code>\n<b>Director : </b><code>' +
director + '</code>\n<b>Writer : </b><code>' + writer +
'</code>\n<b>Stars : </b><code>' + stars +
'</code>\n<b>IMDB Url : </b>' + mov_link +
'\n<b>Story Line : </b>' + story_line,
link_preview=True,
parse_mode='HTML')
except IndexError:
await e.edit("Plox enter **Valid movie name** kthx")
@register(outgoing=True, pattern=r"^.trt(?: |$)([\s\S]*)")
async def translateme(trans):
translator = Translator()
textx = await trans.get_reply_message()
message = trans.pattern_match.group(1)
if message:
pass
elif textx:
message = textx.text
else:
await trans.edit("`Give a text or reply to a message to translate!`")
return
try:
reply_text = translator.translate(deEmojify(message), dest=TRT_LANG)
except ValueError:
await trans.edit("Invalid destination language.")
return
source_lan = LANGUAGES[f'{reply_text.src.lower()}']
transl_lan = LANGUAGES[f'{reply_text.dest.lower()}']
reply_text = f"From **{source_lan.title()}**\nTo **{transl_lan.title()}:**\n\n{reply_text.text}"
await trans.edit(reply_text)
if BOTLOG:
await trans.client.send_message(
BOTLOG_CHATID,
f"Translated some {source_lan.title()} stuff to {transl_lan.title()} just now.",
)
@register(pattern="^.lang (trt|tts) (.*)", outgoing=True)
async def lang(value):
util = value.pattern_match.group(1).lower()
if util == "trt":
scraper = "Translator"
global TRT_LANG
arg = value.pattern_match.group(2).lower()
if arg in LANGUAGES:
TRT_LANG = arg
LANG = LANGUAGES[arg]
else:
await value.edit(
f"`Invalid Language code !!`\n`Available language codes for TRT`:\n\n`{LANGUAGES}`"
)
return
elif util == "tts":
scraper = "Text to Speech"
global TTS_LANG
arg = value.pattern_match.group(2).lower()
if arg in tts_langs():
TTS_LANG = arg
LANG = tts_langs()[arg]
else:
await value.edit(
f"`Invalid Language code !!`\n`Available language codes for TTS`:\n\n`{tts_langs()}`"
)
return
await value.edit(f"`Language for {scraper} changed to {LANG.title()}.`")
if BOTLOG:
await value.client.send_message(
BOTLOG_CHATID,
f"`Language for {scraper} changed to {LANG.title()}.`")
@register(outgoing=True, pattern="^.yt (.*)")
async def yt_search(video_q):
query = video_q.pattern_match.group(1)
result = ''
if not YOUTUBE_API_KEY:
await video_q.edit(
"`Error: YouTube API key missing! Add it to environment vars or config.env.`"
)
return
await video_q.edit("```Processing...```")
full_response = await youtube_search(query)
videos_json = full_response[1]
for video in videos_json:
title = f"{unescape(video['snippet']['title'])}"
link = f"https://youtu.be/{video['id']['videoId']}"
result += f"{title}\n{link}\n\n"
reply_text = f"**Search Query:**\n`{query}`\n\n**Results:**\n\n{result}"
await video_q.edit(reply_text)
async def youtube_search(query,
order="relevance",
token=None,
location=None,
location_radius=None):
youtube = build('youtube',
'v3',
developerKey=YOUTUBE_API_KEY,
cache_discovery=False)
search_response = youtube.search().list(
q=query,
type="video",
pageToken=token,
order=order,
part="id,snippet",
maxResults=10,
location=location,
locationRadius=location_radius).execute()
videos = []
for search_result in search_response.get("items", []):
if search_result["id"]["kind"] == "youtube#video":
videos.append(search_result)
try:
nexttok = search_response["nextPageToken"]
return (nexttok, videos)
except HttpError:
nexttok = "last_page"
return (nexttok, videos)
except KeyError:
nexttok = "KeyError, try again."
return (nexttok, videos)
@register(outgoing=True, pattern=r"^.rip(audio|video) (.*)")
async def download_video(v_url):
url = v_url.pattern_match.group(2)
type = v_url.pattern_match.group(1).lower()
await v_url.edit("`Preparing to download...`")
if type == "audio":
opts = {
'format':
'bestaudio',
'addmetadata':
True,
'key':
'FFmpegMetadata',
'writethumbnail':
True,
'prefer_ffmpeg':
True,
'geo_bypass':
True,
'nocheckcertificate':
True,
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'mp3',
'preferredquality': '320',
}],
'outtmpl':
'%(id)s.mp3',
'quiet':
True,
'logtostderr':
False
}
video = False
song = True
elif type == "video":
opts = {
'format':
'best',
'addmetadata':
True,
'key':
'FFmpegMetadata',
'prefer_ffmpeg':
True,
'geo_bypass':
True,
'nocheckcertificate':
True,
'postprocessors': [{
'key': 'FFmpegVideoConvertor',
'preferedformat': 'mp4'
}],
'outtmpl':
'%(id)s.mp4',
'logtostderr':
False,
'quiet':
True
}
song = False
video = True
try:
await v_url.edit("`Fetching data, please wait..`")
with YoutubeDL(opts) as rip:
rip_data = rip.extract_info(url)
except DownloadError as DE:
await v_url.edit(f"`{str(DE)}`")
return
except ContentTooShortError:
await v_url.edit("`The download content was too short.`")
return
except GeoRestrictedError:
await v_url.edit(
"`Video is not available from your geographic location due to geographic restrictions imposed by a website.`"
)
return
except MaxDownloadsReached:
await v_url.edit("`Max-downloads limit has been reached.`")
return
except PostProcessingError:
await v_url.edit("`There was an error during post processing.`")
return
except UnavailableVideoError:
await v_url.edit("`Media is not available in the requested format.`")
return
except XAttrMetadataError as XAME:
await v_url.edit(f"`{XAME.code}: {XAME.msg}\n{XAME.reason}`")
return
except ExtractorError:
await v_url.edit("`There was an error during info extraction.`")
return
except Exception as e:
await v_url.edit(f"{str(type(e)): {str(e)}}")
return
c_time = time.time()
if song:
await v_url.edit(f"`Preparing to upload song:`\
\n**{rip_data['title']}**\
\nby __{rip_data['uploader']}__")
await v_url.client.send_file(
v_url.chat_id,
f"{rip_data['id']}.mp3",
supports_streaming=True,
attributes=[
DocumentAttributeAudio(duration=int(rip_data['duration']),
title=str(rip_data['title']),
performer=str(rip_data['uploader']))
],
progress_callback=lambda d, t: asyncio.get_event_loop(
).create_task(
progress(d, t, v_url, c_time, "Uploading..",
f"{rip_data['title']}.mp3")))
os.remove(f"{rip_data['id']}.mp3")
await v_url.delete()
elif video:
await v_url.edit(f"`Preparing to upload video:`\
\n**{rip_data['title']}**\
\nby __{rip_data['uploader']}__")
await v_url.client.send_file(
v_url.chat_id,
f"{rip_data['id']}.mp4",
supports_streaming=True,
caption=rip_data['title'],
progress_callback=lambda d, t: asyncio.get_event_loop(
).create_task(
progress(d, t, v_url, c_time, "Uploading..",
f"{rip_data['title']}.mp4")))
os.remove(f"{rip_data['id']}.mp4")
await v_url.delete()
def deEmojify(inputString):
return get_emoji_regexp().sub(u'', inputString)
CMD_HELP.update({
'img':
'.img <search_query>\
\nUsage: Does an image search on Google and shows 5 images.'
})
CMD_HELP.update({
'currency':
'.currency <amount> <from> <to>\
\nUsage: Converts various currencies for you.'
})
CMD_HELP.update({
'carbon':
'.carbon <text> [or reply]\
\nUsage: Beautify your code using carbon.now.sh\nUse .crblang <text> to set language for your code.'
})
CMD_HELP.update(
{'google': '.google <query>\
\nUsage: Does a search on Google.'})
CMD_HELP.update(
{'wiki': '.wiki <query>\
\nUsage: Does a search on Wikipedia.'})
CMD_HELP.update(
{'ud': '.ud <query>\
\nUsage: Does a search on Urban Dictionary.'})
CMD_HELP.update({
'tts':
'.tts <text> [or reply]\
\nUsage: Translates text to speech for the language which is set.\nUse .lang tts <language code> to set language for tts. (Default is English.)'
})
CMD_HELP.update({
'trt':
'.trt <text> [or reply]\
\nUsage: Translates text to the language which is set.\nUse .lang trt <language code> to set language for trt. (Default is English)'
})
CMD_HELP.update({'yt': '.yt <text>\
\nUsage: Does a YouTube search.'})
CMD_HELP.update(
{"imdb": ".imdb <movie-name>\nShows movie info and other stuff."})
CMD_HELP.update({
'rip':
'.ripaudio <url> or ripvideo <url>\
\nUsage: Download videos and songs from YouTube (and [many other sites](https://ytdl-org.github.io/youtube-dl/supportedsites.html)).'
})
| true | true |
1c34067ecf84e3d35f17b9865cf821ee9efe0073 | 7,473 | py | Python | account/tests/test_password.py | AHDCreative/django_user_accounts | 5ab37c5298123189a29fb4c7048ea6a69e2509ff | [
"MIT"
] | null | null | null | account/tests/test_password.py | AHDCreative/django_user_accounts | 5ab37c5298123189a29fb4c7048ea6a69e2509ff | [
"MIT"
] | null | null | null | account/tests/test_password.py | AHDCreative/django_user_accounts | 5ab37c5298123189a29fb4c7048ea6a69e2509ff | [
"MIT"
] | null | null | null | import datetime
import django
from django.contrib.auth.hashers import check_password, make_password
from django.contrib.auth.models import User
from django.test import TestCase, modify_settings, override_settings
import pytz
from account.compat import reverse
from account.models import PasswordExpiry, PasswordHistory
from account.utils import check_password_expired
def middleware_kwarg(value):
if django.VERSION >= (1, 10):
kwarg = "MIDDLEWARE"
else:
kwarg = "MIDDLEWARE_CLASSES"
return {kwarg: value}
@override_settings(
ACCOUNT_PASSWORD_USE_HISTORY=True
)
@modify_settings(
**middleware_kwarg({
"append": "account.middleware.ExpiredPasswordMiddleware"
})
)
class PasswordExpirationTestCase(TestCase):
def setUp(self):
self.username = "user1"
self.email = "user1@example.com"
self.password = "changeme"
self.user = User.objects.create_user(
self.username,
email=self.email,
password=self.password,
)
# create PasswordExpiry for user
self.expiry = PasswordExpiry.objects.create(
user=self.user,
expiry=60, # password expires after sixty seconds
)
# create PasswordHistory for user
self.history = PasswordHistory.objects.create(
user=self.user,
password=make_password(self.password)
)
def test_signup(self):
"""
Ensure new user has one PasswordHistory and no PasswordExpiry.
"""
email = "foobar@example.com"
password = "bar"
post_data = {
"username": "foo",
"password": password,
"password_confirm": password,
"email": email,
}
response = self.client.post(reverse("account_signup"), post_data)
self.assertEqual(response.status_code, 302)
user = User.objects.get(email=email)
self.assertFalse(hasattr(user, "password_expiry"))
latest_history = user.password_history.latest("timestamp")
self.assertTrue(latest_history)
# verify password is not expired
self.assertFalse(check_password_expired(user))
# verify raw password matches encrypted password in history
self.assertTrue(check_password(password, latest_history.password))
def test_get_not_expired(self):
"""
Ensure authenticated user can retrieve account settings page
without "password change" redirect.
"""
self.client.login(username=self.username, password=self.password)
# get account settings page (could be any application page)
response = self.client.get(reverse("account_settings"))
self.assertEquals(response.status_code, 200)
def test_get_expired(self):
"""
Ensure authenticated user is redirected to change password
when retrieving account settings page if password is expired.
"""
# set PasswordHistory timestamp in past so password is expired.
self.history.timestamp = datetime.datetime.now(tz=pytz.UTC) - datetime.timedelta(days=1, seconds=self.expiry.expiry)
self.history.save()
self.client.login(username=self.username, password=self.password)
# get account settings page (could be any application page)
url_name = "account_settings"
response = self.client.get(reverse(url_name))
# verify desired page is set as "?next=" in redirect URL
redirect_url = "{}?next={}".format(reverse("account_password"), url_name)
self.assertRedirects(response, redirect_url)
def test_password_expiration_reset(self):
"""
Ensure changing password results in new PasswordHistory.
"""
history_count = self.user.password_history.count()
# get login
self.client.login(username=self.username, password=self.password)
# post new password to reset PasswordHistory
new_password = "lynyrdskynyrd"
post_data = {
"password_current": self.password,
"password_new": new_password,
"password_new_confirm": new_password,
}
self.client.post(
reverse("account_password"),
post_data
)
# Should see one more history entry for this user
self.assertEquals(self.user.password_history.count(), history_count + 1)
latest = PasswordHistory.objects.latest("timestamp")
self.assertTrue(latest != self.history)
self.assertTrue(latest.timestamp > self.history.timestamp)
@modify_settings(
**middleware_kwarg({
"append": "account.middleware.ExpiredPasswordMiddleware"
})
)
class ExistingUserNoHistoryTestCase(TestCase):
"""
Tests where user has no PasswordHistory.
"""
def setUp(self):
self.username = "user1"
self.email = "user1@example.com"
self.password = "changeme"
self.user = User.objects.create_user(
self.username,
email=self.email,
password=self.password,
)
def test_get_no_history(self):
"""
Ensure authenticated user without password history can retrieve
account settings page without "password change" redirect.
"""
self.client.login(username=self.username, password=self.password)
with override_settings(
ACCOUNT_PASSWORD_USE_HISTORY=True
):
# get account settings page (could be any application page)
response = self.client.get(reverse("account_settings"))
self.assertEquals(response.status_code, 200)
def test_password_expiration_reset(self):
"""
Ensure changing password results in new PasswordHistory,
even when no PasswordHistory exists.
"""
history_count = self.user.password_history.count()
# get login
self.client.login(username=self.username, password=self.password)
# post new password to reset PasswordHistory
new_password = "lynyrdskynyrd"
post_data = {
"password_current": self.password,
"password_new": new_password,
"password_new_confirm": new_password,
}
with override_settings(
ACCOUNT_PASSWORD_USE_HISTORY=True
):
self.client.post(
reverse("account_password"),
post_data
)
# Should see one more history entry for this user
self.assertEquals(self.user.password_history.count(), history_count + 1)
def test_password_reset(self):
"""
Ensure changing password results in NO new PasswordHistory
when ACCOUNT_PASSWORD_USE_HISTORY == False.
"""
# get login
self.client.login(username=self.username, password=self.password)
# post new password to reset PasswordHistory
new_password = "lynyrdskynyrd"
post_data = {
"password_current": self.password,
"password_new": new_password,
"password_new_confirm": new_password,
}
with override_settings(
ACCOUNT_PASSWORD_USE_HISTORY=False
):
self.client.post(
reverse("account_password"),
post_data
)
# history count should be zero
self.assertEquals(self.user.password_history.count(), 0) | 34.597222 | 124 | 0.63977 | import datetime
import django
from django.contrib.auth.hashers import check_password, make_password
from django.contrib.auth.models import User
from django.test import TestCase, modify_settings, override_settings
import pytz
from account.compat import reverse
from account.models import PasswordExpiry, PasswordHistory
from account.utils import check_password_expired
def middleware_kwarg(value):
if django.VERSION >= (1, 10):
kwarg = "MIDDLEWARE"
else:
kwarg = "MIDDLEWARE_CLASSES"
return {kwarg: value}
@override_settings(
ACCOUNT_PASSWORD_USE_HISTORY=True
)
@modify_settings(
**middleware_kwarg({
"append": "account.middleware.ExpiredPasswordMiddleware"
})
)
class PasswordExpirationTestCase(TestCase):
def setUp(self):
self.username = "user1"
self.email = "user1@example.com"
self.password = "changeme"
self.user = User.objects.create_user(
self.username,
email=self.email,
password=self.password,
)
self.expiry = PasswordExpiry.objects.create(
user=self.user,
expiry=60,
)
self.history = PasswordHistory.objects.create(
user=self.user,
password=make_password(self.password)
)
def test_signup(self):
email = "foobar@example.com"
password = "bar"
post_data = {
"username": "foo",
"password": password,
"password_confirm": password,
"email": email,
}
response = self.client.post(reverse("account_signup"), post_data)
self.assertEqual(response.status_code, 302)
user = User.objects.get(email=email)
self.assertFalse(hasattr(user, "password_expiry"))
latest_history = user.password_history.latest("timestamp")
self.assertTrue(latest_history)
self.assertFalse(check_password_expired(user))
self.assertTrue(check_password(password, latest_history.password))
def test_get_not_expired(self):
self.client.login(username=self.username, password=self.password)
response = self.client.get(reverse("account_settings"))
self.assertEquals(response.status_code, 200)
def test_get_expired(self):
self.history.timestamp = datetime.datetime.now(tz=pytz.UTC) - datetime.timedelta(days=1, seconds=self.expiry.expiry)
self.history.save()
self.client.login(username=self.username, password=self.password)
url_name = "account_settings"
response = self.client.get(reverse(url_name))
redirect_url = "{}?next={}".format(reverse("account_password"), url_name)
self.assertRedirects(response, redirect_url)
def test_password_expiration_reset(self):
history_count = self.user.password_history.count()
self.client.login(username=self.username, password=self.password)
new_password = "lynyrdskynyrd"
post_data = {
"password_current": self.password,
"password_new": new_password,
"password_new_confirm": new_password,
}
self.client.post(
reverse("account_password"),
post_data
)
self.assertEquals(self.user.password_history.count(), history_count + 1)
latest = PasswordHistory.objects.latest("timestamp")
self.assertTrue(latest != self.history)
self.assertTrue(latest.timestamp > self.history.timestamp)
@modify_settings(
**middleware_kwarg({
"append": "account.middleware.ExpiredPasswordMiddleware"
})
)
class ExistingUserNoHistoryTestCase(TestCase):
def setUp(self):
self.username = "user1"
self.email = "user1@example.com"
self.password = "changeme"
self.user = User.objects.create_user(
self.username,
email=self.email,
password=self.password,
)
def test_get_no_history(self):
self.client.login(username=self.username, password=self.password)
with override_settings(
ACCOUNT_PASSWORD_USE_HISTORY=True
):
response = self.client.get(reverse("account_settings"))
self.assertEquals(response.status_code, 200)
def test_password_expiration_reset(self):
history_count = self.user.password_history.count()
self.client.login(username=self.username, password=self.password)
new_password = "lynyrdskynyrd"
post_data = {
"password_current": self.password,
"password_new": new_password,
"password_new_confirm": new_password,
}
with override_settings(
ACCOUNT_PASSWORD_USE_HISTORY=True
):
self.client.post(
reverse("account_password"),
post_data
)
self.assertEquals(self.user.password_history.count(), history_count + 1)
def test_password_reset(self):
self.client.login(username=self.username, password=self.password)
new_password = "lynyrdskynyrd"
post_data = {
"password_current": self.password,
"password_new": new_password,
"password_new_confirm": new_password,
}
with override_settings(
ACCOUNT_PASSWORD_USE_HISTORY=False
):
self.client.post(
reverse("account_password"),
post_data
)
self.assertEquals(self.user.password_history.count(), 0) | true | true |
1c3406c0154dcae2ad9030f6a2382d96e6ce407b | 13,130 | py | Python | extra_foam/special_suite/tests/test_gotthard.py | ebadkamil/EXtra-foam | 8e58143040c788dc70ea98ea5adc1fb63b7cfe0d | [
"BSD-3-Clause"
] | 7 | 2019-11-27T09:31:37.000Z | 2022-02-12T21:28:49.000Z | extra_foam/special_suite/tests/test_gotthard.py | ebadkamil/EXtra-foam | 8e58143040c788dc70ea98ea5adc1fb63b7cfe0d | [
"BSD-3-Clause"
] | 172 | 2019-12-03T07:56:02.000Z | 2022-03-25T15:46:45.000Z | extra_foam/special_suite/tests/test_gotthard.py | ebadkamil/EXtra-foam | 8e58143040c788dc70ea98ea5adc1fb63b7cfe0d | [
"BSD-3-Clause"
] | 9 | 2019-11-27T09:32:38.000Z | 2022-01-05T09:56:10.000Z | import unittest
from unittest.mock import MagicMock, patch, PropertyMock
from collections import Counter
import pytest
import numpy as np
from xarray import DataArray
from PyQt5.QtCore import Qt
from PyQt5.QtTest import QSignalSpy, QTest
from extra_foam.pipeline.tests import _RawDataMixin
from extra_foam.special_suite import logger, mkQApp
from extra_foam.special_suite.gotthard_proc import GotthardProcessor
from extra_foam.special_suite.gotthard_w import (
GotthardWindow, GotthardImageView, GotthardAvgPlot, GotthardPulsePlot,
GotthardHist
)
from extra_foam.special_suite.special_analysis_base import (
ProcessingError
)
from . import _SpecialSuiteWindowTestBase, _SpecialSuiteProcessorTestBase
app = mkQApp()
logger.setLevel('INFO')
class TestGotthardWindow(_SpecialSuiteWindowTestBase):
_window_type = GotthardWindow
@staticmethod
def data4visualization(n_pulses=4):
"""Override."""
return {
"x": None,
"spectrum": np.arange(10 * n_pulses).reshape(n_pulses, 10),
"spectrum_ma": np.arange(10 * n_pulses).reshape(n_pulses, 10),
"spectrum_mean": np.arange(10),
"spectrum_ma_mean": np.arange(10),
"poi_index": 0,
"hist": (np.arange(5), np.arange(5), 1, 1, 1),
}
def testWindow(self):
win = self._win
self.assertEqual(4, len(win._plot_widgets_st))
counter = Counter()
for key in win._plot_widgets_st:
counter[key.__class__] += 1
self.assertEqual(1, counter[GotthardImageView])
self.assertEqual(1, counter[GotthardAvgPlot])
self.assertEqual(1, counter[GotthardPulsePlot])
self.assertEqual(1, counter[GotthardHist])
self._check_update_plots()
def testCtrl(self):
from extra_foam.special_suite.gotthard_w import _DEFAULT_N_BINS, _DEFAULT_BIN_RANGE
win = self._win
ctrl_widget = win._ctrl_widget_st
proc = win._worker_st
# test default values
self.assertTrue(proc._output_channel)
self.assertEqual(slice(None, None), proc._pulse_slicer)
self.assertEqual(0, proc._poi_index)
self.assertEqual(1, proc.__class__._raw_ma.window)
self.assertEqual(0, proc._scale)
self.assertEqual(0, proc._offset)
self.assertTupleEqual(tuple(float(v) for v in _DEFAULT_BIN_RANGE.split(',')),
proc._bin_range)
self.assertEqual(int(_DEFAULT_N_BINS), proc._n_bins)
self.assertFalse(proc._hist_over_ma)
# test set new values
widget = ctrl_widget.output_ch_le
widget.clear()
QTest.keyClicks(widget, "new/output/channel")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual("new/output/channel", proc._output_channel)
widget = ctrl_widget.pulse_slicer_le
widget.clear()
QTest.keyClicks(widget, "::2")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(slice(None, None, 2), proc._pulse_slicer)
widget = ctrl_widget.poi_index_le
widget.clear()
QTest.keyClicks(widget, "120")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(0, proc._poi_index) # maximum is 119 and one can still type "120"
widget.clear()
QTest.keyClicks(widget, "119")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(119, proc._poi_index)
widget = ctrl_widget.ma_window_le
widget.clear()
QTest.keyClicks(widget, "9")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(9, proc.__class__._raw_ma.window)
widget = ctrl_widget.scale_le
widget.clear()
QTest.keyClicks(widget, "0.002")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(0.002, proc._scale)
widget.clear()
QTest.keyClicks(widget, "-1")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(1, proc._scale) # cannot enter '-'
widget = ctrl_widget.offset_le
widget.clear()
QTest.keyClicks(widget, "-0.18")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(-0.18, proc._offset)
widget = ctrl_widget.bin_range_le
widget.clear()
QTest.keyClicks(widget, "-1.0, 1.0")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertTupleEqual((-1.0, 1.0), proc._bin_range)
widget = ctrl_widget.n_bins_le
widget.clear()
QTest.keyClicks(widget, "1000")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(100, proc._n_bins) # maximum is 999 and one can not put the 3rd 0 in
widget.clear()
QTest.keyClicks(widget, "999")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(999, proc._n_bins)
ctrl_widget.hist_over_ma_cb.setChecked(True)
self.assertTrue(proc._hist_over_ma)
class TestGotthardProcessor(_RawDataMixin, _SpecialSuiteProcessorTestBase):
@pytest.fixture(autouse=True)
def setUp(self):
self._proc = GotthardProcessor(object(), object())
self._proc._output_channel = "gotthard:output"
self._adc = np.random.randint(0, 100, size=(4, 4), dtype=np.uint16)
def _get_data(self, tid, times=1):
# data, meta
return self._gen_data(tid, {
"gotthard:output": [
("data.adc", times * self._adc),
("data.3d", np.ones((4, 2, 2)))
]})
def testPreProcessing(self):
proc = self._proc
data = self._get_data(12345)
with pytest.raises(ProcessingError, match="actual 3D"):
with patch.object(GotthardProcessor, "_ppt",
new_callable=PropertyMock, create=True, return_value="data.3d"):
proc.process(data)
with pytest.raises(ProcessingError, match="out of boundary"):
proc._poi_index = 100
processed = proc.process(data)
assert processed is None
# test not raise
proc._poi_index = 3
proc.process(data)
with pytest.raises(ProcessingError, match="out of boundary"):
# test with slicer
proc._pulse_slicer = slice(None, None, 2)
proc.process(data)
@patch("extra_foam.special_suite.special_analysis_base.QThreadWorker._loadRunDirectoryST")
def testLoadDarkRun(self, load_run):
proc = self._proc
load_run.return_value = None
# nothing should happen
proc.onLoadDarkRun("run/path")
data_collection = MagicMock()
load_run.return_value = data_collection
with patch.object(proc.log, "error") as error:
# get_array returns a wrong shape
data_collection.get_array.return_value = DataArray(np.random.randn(4, 3))
proc.onLoadDarkRun("run/path")
error.assert_called_once()
assert "Data must be a 3D array" in error.call_args[0][0]
error.reset_mock()
# get_array returns a correct shape
data_collection.get_array.return_value = DataArray(np.random.randn(4, 3, 2))
with patch.object(proc.log, "info") as info:
proc.onLoadDarkRun("run/path")
info.assert_called_once()
assert "Found dark data with shape" in info.call_args[0][0]
error.assert_not_called()
def testProcessingWhenRecordingDark(self):
from extra_foam.special_suite.gotthard_proc import _PIXEL_DTYPE
proc = self._proc
assert 2147483647 == proc.__class__._dark_ma.window
proc._recording_dark_st = True
proc._subtract_dark_st = True # take no effect
proc._poi_index = 0
adc_gt = self._adc.astype(_PIXEL_DTYPE)
adc_gt2 = 2.0 * self._adc
adc_gt_avg = 1.5 * self._adc
# 1st train
processed = proc.process(self._get_data(12345))
np.testing.assert_array_almost_equal(adc_gt, proc._dark_ma)
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), proc._dark_mean_ma)
assert 0 == processed["poi_index"]
np.testing.assert_array_almost_equal(adc_gt, processed["spectrum"])
np.testing.assert_array_almost_equal(adc_gt, processed["spectrum_ma"])
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), processed["spectrum_mean"])
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), processed["spectrum_ma_mean"])
assert np.mean(adc_gt) == processed["hist"][2]
# 2nd train
processed = proc.process(self._get_data(12346, 2))
np.testing.assert_array_almost_equal(adc_gt_avg, proc._dark_ma)
np.testing.assert_array_almost_equal(np.mean(adc_gt_avg, axis=0), proc._dark_mean_ma)
assert 0 == processed["poi_index"]
np.testing.assert_array_almost_equal(adc_gt2, processed["spectrum"])
np.testing.assert_array_almost_equal(adc_gt_avg, processed["spectrum_ma"])
np.testing.assert_array_almost_equal(np.mean(adc_gt2, axis=0), processed["spectrum_mean"])
np.testing.assert_array_almost_equal(np.mean(adc_gt_avg, axis=0), processed["spectrum_ma_mean"])
assert np.mean(adc_gt2) == processed["hist"][2]
# 3nd train
proc._hist_over_ma = True
processed = proc.process(self._get_data(12347, 3))
np.testing.assert_array_almost_equal(adc_gt2, proc._dark_ma)
np.testing.assert_array_almost_equal(np.mean(adc_gt2, axis=0), proc._dark_mean_ma)
assert np.mean(adc_gt2) == processed["hist"][2]
# reset
proc.reset()
assert proc._dark_ma is None
@pytest.mark.parametrize("subtract_dark", [(True, ), (False,)])
def testProcessing(self, subtract_dark):
from extra_foam.special_suite.gotthard_proc import _PIXEL_DTYPE
proc = self._proc
proc._recording_dark = False
proc._poi_index = 1
proc._scale = 0.1
proc._offset = 0.2
proc._subtract_dark = subtract_dark
offset = np.ones(self._adc.shape[1]).astype(np.float32)
proc._dark_mean_ma = offset
proc._hist_over_ma = False
adc_gt = self._adc.astype(_PIXEL_DTYPE)
adc_gt2 = 2.0 * self._adc
adc_gt_avg = 1.5 * self._adc
if subtract_dark:
adc_gt -= offset
adc_gt2 -= offset
adc_gt_avg -= offset
# 1st train
processed = proc.process(self._get_data(12345))
self._check_processed_data_structure(processed)
assert 1 == processed["poi_index"]
np.testing.assert_array_almost_equal(adc_gt, processed["spectrum"])
np.testing.assert_array_almost_equal(adc_gt, processed["spectrum_ma"])
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), processed["spectrum_mean"])
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), processed["spectrum_ma_mean"])
assert np.mean(adc_gt) == processed["hist"][2]
# 2nd train
proc.__class__._raw_ma.window = 3
processed = proc.process(self._get_data(12346, 2))
assert 1 == processed["poi_index"]
np.testing.assert_array_almost_equal(adc_gt2, processed["spectrum"])
np.testing.assert_array_almost_equal(adc_gt_avg, processed["spectrum_ma"])
np.testing.assert_array_almost_equal(np.mean(adc_gt2, axis=0), processed["spectrum_mean"])
np.testing.assert_array_almost_equal(np.mean(adc_gt_avg, axis=0), processed["spectrum_ma_mean"])
assert np.mean(adc_gt2) == processed["hist"][2]
# 3nd train
proc._hist_over_ma = True
processed = proc.process(self._get_data(12347, 3))
assert np.mean(adc_gt2) == processed["hist"][2]
# reset
proc.reset()
assert proc._raw_ma is None
def testCalibration(self):
proc = self._proc
processed = proc.process(self._get_data(12345))
assert processed["x"] is None
proc._scale = 0.1
proc._offset = 0.2
processed = proc.process(self._get_data(12345))
np.testing.assert_array_almost_equal(np.arange(len(self._adc)) * 0.1 - 0.2, processed['x'])
def testPulseSlicerChange(self):
proc = self._proc
del proc._dark_ma
proc._dark_mean_ma = None
proc._pulse_slicer = slice(None, None)
proc.onPulseSlicerChanged([None, 4])
assert proc._dark_mean_ma is None
proc._dark_ma = np.random.randn(4, 2)
proc.onPulseSlicerChanged([None, None, 2])
# test _dark_mean_ma was re-calculated
np.testing.assert_array_almost_equal(np.mean(proc._dark_ma[::2], axis=0), proc._dark_mean_ma)
def testRemoveDark(self):
proc = self._proc
proc._dark_ma = np.ones((2, 2))
proc._dark_mean_ma = np.ones((2, 2))
proc.onRemoveDark()
assert proc._dark_ma is None
assert proc._dark_mean_ma is None
def _check_processed_data_structure(self, ret):
"""Override."""
data_gt = TestGotthardWindow.data4visualization().keys()
assert set(ret.keys()) == set(data_gt)
| 37.947977 | 104 | 0.651942 | import unittest
from unittest.mock import MagicMock, patch, PropertyMock
from collections import Counter
import pytest
import numpy as np
from xarray import DataArray
from PyQt5.QtCore import Qt
from PyQt5.QtTest import QSignalSpy, QTest
from extra_foam.pipeline.tests import _RawDataMixin
from extra_foam.special_suite import logger, mkQApp
from extra_foam.special_suite.gotthard_proc import GotthardProcessor
from extra_foam.special_suite.gotthard_w import (
GotthardWindow, GotthardImageView, GotthardAvgPlot, GotthardPulsePlot,
GotthardHist
)
from extra_foam.special_suite.special_analysis_base import (
ProcessingError
)
from . import _SpecialSuiteWindowTestBase, _SpecialSuiteProcessorTestBase
app = mkQApp()
logger.setLevel('INFO')
class TestGotthardWindow(_SpecialSuiteWindowTestBase):
_window_type = GotthardWindow
@staticmethod
def data4visualization(n_pulses=4):
return {
"x": None,
"spectrum": np.arange(10 * n_pulses).reshape(n_pulses, 10),
"spectrum_ma": np.arange(10 * n_pulses).reshape(n_pulses, 10),
"spectrum_mean": np.arange(10),
"spectrum_ma_mean": np.arange(10),
"poi_index": 0,
"hist": (np.arange(5), np.arange(5), 1, 1, 1),
}
def testWindow(self):
win = self._win
self.assertEqual(4, len(win._plot_widgets_st))
counter = Counter()
for key in win._plot_widgets_st:
counter[key.__class__] += 1
self.assertEqual(1, counter[GotthardImageView])
self.assertEqual(1, counter[GotthardAvgPlot])
self.assertEqual(1, counter[GotthardPulsePlot])
self.assertEqual(1, counter[GotthardHist])
self._check_update_plots()
def testCtrl(self):
from extra_foam.special_suite.gotthard_w import _DEFAULT_N_BINS, _DEFAULT_BIN_RANGE
win = self._win
ctrl_widget = win._ctrl_widget_st
proc = win._worker_st
self.assertTrue(proc._output_channel)
self.assertEqual(slice(None, None), proc._pulse_slicer)
self.assertEqual(0, proc._poi_index)
self.assertEqual(1, proc.__class__._raw_ma.window)
self.assertEqual(0, proc._scale)
self.assertEqual(0, proc._offset)
self.assertTupleEqual(tuple(float(v) for v in _DEFAULT_BIN_RANGE.split(',')),
proc._bin_range)
self.assertEqual(int(_DEFAULT_N_BINS), proc._n_bins)
self.assertFalse(proc._hist_over_ma)
widget = ctrl_widget.output_ch_le
widget.clear()
QTest.keyClicks(widget, "new/output/channel")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual("new/output/channel", proc._output_channel)
widget = ctrl_widget.pulse_slicer_le
widget.clear()
QTest.keyClicks(widget, "::2")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(slice(None, None, 2), proc._pulse_slicer)
widget = ctrl_widget.poi_index_le
widget.clear()
QTest.keyClicks(widget, "120")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(0, proc._poi_index)
widget.clear()
QTest.keyClicks(widget, "119")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(119, proc._poi_index)
widget = ctrl_widget.ma_window_le
widget.clear()
QTest.keyClicks(widget, "9")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(9, proc.__class__._raw_ma.window)
widget = ctrl_widget.scale_le
widget.clear()
QTest.keyClicks(widget, "0.002")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(0.002, proc._scale)
widget.clear()
QTest.keyClicks(widget, "-1")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(1, proc._scale)
widget = ctrl_widget.offset_le
widget.clear()
QTest.keyClicks(widget, "-0.18")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(-0.18, proc._offset)
widget = ctrl_widget.bin_range_le
widget.clear()
QTest.keyClicks(widget, "-1.0, 1.0")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertTupleEqual((-1.0, 1.0), proc._bin_range)
widget = ctrl_widget.n_bins_le
widget.clear()
QTest.keyClicks(widget, "1000")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(100, proc._n_bins)
widget.clear()
QTest.keyClicks(widget, "999")
QTest.keyPress(widget, Qt.Key_Enter)
self.assertEqual(999, proc._n_bins)
ctrl_widget.hist_over_ma_cb.setChecked(True)
self.assertTrue(proc._hist_over_ma)
class TestGotthardProcessor(_RawDataMixin, _SpecialSuiteProcessorTestBase):
@pytest.fixture(autouse=True)
def setUp(self):
self._proc = GotthardProcessor(object(), object())
self._proc._output_channel = "gotthard:output"
self._adc = np.random.randint(0, 100, size=(4, 4), dtype=np.uint16)
def _get_data(self, tid, times=1):
return self._gen_data(tid, {
"gotthard:output": [
("data.adc", times * self._adc),
("data.3d", np.ones((4, 2, 2)))
]})
def testPreProcessing(self):
proc = self._proc
data = self._get_data(12345)
with pytest.raises(ProcessingError, match="actual 3D"):
with patch.object(GotthardProcessor, "_ppt",
new_callable=PropertyMock, create=True, return_value="data.3d"):
proc.process(data)
with pytest.raises(ProcessingError, match="out of boundary"):
proc._poi_index = 100
processed = proc.process(data)
assert processed is None
proc._poi_index = 3
proc.process(data)
with pytest.raises(ProcessingError, match="out of boundary"):
proc._pulse_slicer = slice(None, None, 2)
proc.process(data)
@patch("extra_foam.special_suite.special_analysis_base.QThreadWorker._loadRunDirectoryST")
def testLoadDarkRun(self, load_run):
proc = self._proc
load_run.return_value = None
proc.onLoadDarkRun("run/path")
data_collection = MagicMock()
load_run.return_value = data_collection
with patch.object(proc.log, "error") as error:
data_collection.get_array.return_value = DataArray(np.random.randn(4, 3))
proc.onLoadDarkRun("run/path")
error.assert_called_once()
assert "Data must be a 3D array" in error.call_args[0][0]
error.reset_mock()
data_collection.get_array.return_value = DataArray(np.random.randn(4, 3, 2))
with patch.object(proc.log, "info") as info:
proc.onLoadDarkRun("run/path")
info.assert_called_once()
assert "Found dark data with shape" in info.call_args[0][0]
error.assert_not_called()
def testProcessingWhenRecordingDark(self):
from extra_foam.special_suite.gotthard_proc import _PIXEL_DTYPE
proc = self._proc
assert 2147483647 == proc.__class__._dark_ma.window
proc._recording_dark_st = True
proc._subtract_dark_st = True
proc._poi_index = 0
adc_gt = self._adc.astype(_PIXEL_DTYPE)
adc_gt2 = 2.0 * self._adc
adc_gt_avg = 1.5 * self._adc
processed = proc.process(self._get_data(12345))
np.testing.assert_array_almost_equal(adc_gt, proc._dark_ma)
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), proc._dark_mean_ma)
assert 0 == processed["poi_index"]
np.testing.assert_array_almost_equal(adc_gt, processed["spectrum"])
np.testing.assert_array_almost_equal(adc_gt, processed["spectrum_ma"])
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), processed["spectrum_mean"])
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), processed["spectrum_ma_mean"])
assert np.mean(adc_gt) == processed["hist"][2]
processed = proc.process(self._get_data(12346, 2))
np.testing.assert_array_almost_equal(adc_gt_avg, proc._dark_ma)
np.testing.assert_array_almost_equal(np.mean(adc_gt_avg, axis=0), proc._dark_mean_ma)
assert 0 == processed["poi_index"]
np.testing.assert_array_almost_equal(adc_gt2, processed["spectrum"])
np.testing.assert_array_almost_equal(adc_gt_avg, processed["spectrum_ma"])
np.testing.assert_array_almost_equal(np.mean(adc_gt2, axis=0), processed["spectrum_mean"])
np.testing.assert_array_almost_equal(np.mean(adc_gt_avg, axis=0), processed["spectrum_ma_mean"])
assert np.mean(adc_gt2) == processed["hist"][2]
proc._hist_over_ma = True
processed = proc.process(self._get_data(12347, 3))
np.testing.assert_array_almost_equal(adc_gt2, proc._dark_ma)
np.testing.assert_array_almost_equal(np.mean(adc_gt2, axis=0), proc._dark_mean_ma)
assert np.mean(adc_gt2) == processed["hist"][2]
proc.reset()
assert proc._dark_ma is None
@pytest.mark.parametrize("subtract_dark", [(True, ), (False,)])
def testProcessing(self, subtract_dark):
from extra_foam.special_suite.gotthard_proc import _PIXEL_DTYPE
proc = self._proc
proc._recording_dark = False
proc._poi_index = 1
proc._scale = 0.1
proc._offset = 0.2
proc._subtract_dark = subtract_dark
offset = np.ones(self._adc.shape[1]).astype(np.float32)
proc._dark_mean_ma = offset
proc._hist_over_ma = False
adc_gt = self._adc.astype(_PIXEL_DTYPE)
adc_gt2 = 2.0 * self._adc
adc_gt_avg = 1.5 * self._adc
if subtract_dark:
adc_gt -= offset
adc_gt2 -= offset
adc_gt_avg -= offset
processed = proc.process(self._get_data(12345))
self._check_processed_data_structure(processed)
assert 1 == processed["poi_index"]
np.testing.assert_array_almost_equal(adc_gt, processed["spectrum"])
np.testing.assert_array_almost_equal(adc_gt, processed["spectrum_ma"])
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), processed["spectrum_mean"])
np.testing.assert_array_almost_equal(np.mean(adc_gt, axis=0), processed["spectrum_ma_mean"])
assert np.mean(adc_gt) == processed["hist"][2]
proc.__class__._raw_ma.window = 3
processed = proc.process(self._get_data(12346, 2))
assert 1 == processed["poi_index"]
np.testing.assert_array_almost_equal(adc_gt2, processed["spectrum"])
np.testing.assert_array_almost_equal(adc_gt_avg, processed["spectrum_ma"])
np.testing.assert_array_almost_equal(np.mean(adc_gt2, axis=0), processed["spectrum_mean"])
np.testing.assert_array_almost_equal(np.mean(adc_gt_avg, axis=0), processed["spectrum_ma_mean"])
assert np.mean(adc_gt2) == processed["hist"][2]
proc._hist_over_ma = True
processed = proc.process(self._get_data(12347, 3))
assert np.mean(adc_gt2) == processed["hist"][2]
proc.reset()
assert proc._raw_ma is None
def testCalibration(self):
proc = self._proc
processed = proc.process(self._get_data(12345))
assert processed["x"] is None
proc._scale = 0.1
proc._offset = 0.2
processed = proc.process(self._get_data(12345))
np.testing.assert_array_almost_equal(np.arange(len(self._adc)) * 0.1 - 0.2, processed['x'])
def testPulseSlicerChange(self):
proc = self._proc
del proc._dark_ma
proc._dark_mean_ma = None
proc._pulse_slicer = slice(None, None)
proc.onPulseSlicerChanged([None, 4])
assert proc._dark_mean_ma is None
proc._dark_ma = np.random.randn(4, 2)
proc.onPulseSlicerChanged([None, None, 2])
np.testing.assert_array_almost_equal(np.mean(proc._dark_ma[::2], axis=0), proc._dark_mean_ma)
def testRemoveDark(self):
proc = self._proc
proc._dark_ma = np.ones((2, 2))
proc._dark_mean_ma = np.ones((2, 2))
proc.onRemoveDark()
assert proc._dark_ma is None
assert proc._dark_mean_ma is None
def _check_processed_data_structure(self, ret):
data_gt = TestGotthardWindow.data4visualization().keys()
assert set(ret.keys()) == set(data_gt)
| true | true |
1c3406df759000e6ee4b53c4ee71e09130029fa6 | 3,304 | py | Python | openstack/network/v2/certificate.py | IamFive/sdk-python | 223b04f90477f7de0f00b3e652d8672ba73271c8 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | openstack/network/v2/certificate.py | IamFive/sdk-python | 223b04f90477f7de0f00b3e652d8672ba73271c8 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | openstack/network/v2/certificate.py | IamFive/sdk-python | 223b04f90477f7de0f00b3e652d8672ba73271c8 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from openstack.network import network_service
from openstack import resource2
class Certificate(resource2.Resource):
resource_key = 'certificate'
resources_key = 'certificates'
base_path = '/lbaas/certificates'
service = network_service.NetworkService()
allow_create = True
allow_get = True
allow_update = True
allow_delete = True
allow_list = True
# SSL certificate ID
id = resource2.Body("id")
# SSL certificate name.
# Value range: a string of 0-64 characters in length,
# consisting of Chinese, English letters, numbers, "_" or "-"
name = resource2.Body("name")
# SSL certificate description.
# Value range: A string of 0-128 characters in length.
# The string cannot contain two characters, "<" and ">".
# Chinese characters must be UTF-8 or unicode encoded
description = resource2.Body("description")
# Certificate type.
# Ranges:
# server
# client: The value is reserved. It is not enabled yet.
# Default: server
type = resource2.Body("type")
# The domain name signed by the server certificate.
# Value range: A string of 0-100 characters in length.
# A string can only contain English letters, digits, "-", or ".".
# It must begin or end with a letter or number.
# This field is valid only when type is server.
domain = resource2.Body("domain")
# Server-side private key in PEM format.
# Format: The private key is in PEM format.
# This field is valid only when type is server and is mandatory
private_key = resource2.Body("private_key")
# Server-side public key in PEM format
certificate = resource2.Body("certificate")
# creat time
create_time = resource2.Body("create_time")
# update time
update_time = resource2.Body("update_time")
def _translate_response(self, response, has_body=True):
"""Given a KSA response, inflate this instance with its data
DELETE operations don't return a body, so only try to work
with a body when has_body is True.
This method updates attributes that correspond to headers
and body on this instance and clears the dirty set.
"""
if has_body:
body = response.json()
if self.resource_key and self.resource_key in body and isinstance(body[self.resource_key], dict):
body = body[self.resource_key]
body = self._filter_component(body, self._body_mapping())
self._body.attributes.update(body)
self._body.clean()
headers = self._filter_component(response.headers,
self._header_mapping())
self._header.attributes.update(headers)
self._header.clean()
| 39.807229 | 109 | 0.681901 |
from openstack.network import network_service
from openstack import resource2
class Certificate(resource2.Resource):
resource_key = 'certificate'
resources_key = 'certificates'
base_path = '/lbaas/certificates'
service = network_service.NetworkService()
allow_create = True
allow_get = True
allow_update = True
allow_delete = True
allow_list = True
id = resource2.Body("id")
name = resource2.Body("name")
description = resource2.Body("description")
type = resource2.Body("type")
domain = resource2.Body("domain")
private_key = resource2.Body("private_key")
certificate = resource2.Body("certificate")
create_time = resource2.Body("create_time")
update_time = resource2.Body("update_time")
def _translate_response(self, response, has_body=True):
if has_body:
body = response.json()
if self.resource_key and self.resource_key in body and isinstance(body[self.resource_key], dict):
body = body[self.resource_key]
body = self._filter_component(body, self._body_mapping())
self._body.attributes.update(body)
self._body.clean()
headers = self._filter_component(response.headers,
self._header_mapping())
self._header.attributes.update(headers)
self._header.clean()
| true | true |
1c3407eb73e0eefc84b416e84e029f9171d5bc7d | 1,522 | py | Python | bioinformatics_stronghold/tests/test_revc.py | nathaliagg/my_rosalind_answers | f5c95c63051360a34e2b599648d4d57cbaf693a8 | [
"MIT"
] | null | null | null | bioinformatics_stronghold/tests/test_revc.py | nathaliagg/my_rosalind_answers | f5c95c63051360a34e2b599648d4d57cbaf693a8 | [
"MIT"
] | null | null | null | bioinformatics_stronghold/tests/test_revc.py | nathaliagg/my_rosalind_answers | f5c95c63051360a34e2b599648d4d57cbaf693a8 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
"""tests for revc .py"""
import os
import re
from subprocess import getstatusoutput
prg = '../revc.py'
good_input = 'test_data/good_input_revc.txt'
bad_input = 'test_data/bad_input_revc.txt'
good_output = 'ACCGGGTTTT'
# --------------------------------------------------
def test_exists():
"""Exists"""
assert os.path.isfile(prg)
# --------------------------------------------------
def test_usage():
"""Usage"""
for flag in ['-h', '--help']:
rv, out = getstatusoutput(f'{prg} {flag}')
assert rv == 0
assert re.match("usage", out, re.IGNORECASE)
# --------------------------------------------------
def test_no_args():
"""Output when no args are provided"""
rv, out = getstatusoutput(f'{prg}')
assert rv != 0
error_string = 'following arguments are required: FILE'
assert re.findall(error_string, out, re.IGNORECASE)
# --------------------------------------------------
def test_bad_args():
"""Test with a bad string == other than A, T, C, G"""
rv, out = getstatusoutput(f'{prg} "{bad_input}"')
assert rv != 0
error_string = "Bad nucleotide sequence. Only ATCG allowed."
assert re.findall(error_string, out, re.IGNORECASE)
# --------------------------------------------------
def test_good_output():
"""Test output with a good string"""
rv, out = getstatusoutput(f'{prg} "{good_input}"')
assert rv == 0
assert out == good_output
# --------------------------------------------------
| 24.15873 | 64 | 0.508541 |
import os
import re
from subprocess import getstatusoutput
prg = '../revc.py'
good_input = 'test_data/good_input_revc.txt'
bad_input = 'test_data/bad_input_revc.txt'
good_output = 'ACCGGGTTTT'
def test_exists():
assert os.path.isfile(prg)
def test_usage():
for flag in ['-h', '--help']:
rv, out = getstatusoutput(f'{prg} {flag}')
assert rv == 0
assert re.match("usage", out, re.IGNORECASE)
def test_no_args():
rv, out = getstatusoutput(f'{prg}')
assert rv != 0
error_string = 'following arguments are required: FILE'
assert re.findall(error_string, out, re.IGNORECASE)
def test_bad_args():
rv, out = getstatusoutput(f'{prg} "{bad_input}"')
assert rv != 0
error_string = "Bad nucleotide sequence. Only ATCG allowed."
assert re.findall(error_string, out, re.IGNORECASE)
def test_good_output():
rv, out = getstatusoutput(f'{prg} "{good_input}"')
assert rv == 0
assert out == good_output
| true | true |
1c3407ecd357dfc5ca2a544cbb74615755d2699b | 1,696 | py | Python | src/study/config.py | ppmzhang2/gpt3-study | 1c4e34238301e06da8cbda23eb4e473567e15c80 | [
"MIT"
] | null | null | null | src/study/config.py | ppmzhang2/gpt3-study | 1c4e34238301e06da8cbda23eb4e473567e15c80 | [
"MIT"
] | null | null | null | src/study/config.py | ppmzhang2/gpt3-study | 1c4e34238301e06da8cbda23eb4e473567e15c80 | [
"MIT"
] | null | null | null | """project config"""
import os
import sys
from logging.config import dictConfig
basedir = os.path.abspath(os.path.dirname(__file__))
class Config:
# pylint: disable=too-few-public-methods
"""default config"""
# token
OPENAI_KEY = os.getenv("OPENAI_KEY", "")
# logging
LOG_LEVEL = "WARNING"
LOG_LINE_FORMAT = "%(asctime)s %(levelname)-5s %(threadName)s: %(message)s"
LOG_DATETIME_FORMAT = "%Y/%m/%d %H:%M:%S"
TOKEN_BPE = os.path.join(basedir, 'tokenizer/vocab.bpe')
TOKEN_ID = os.path.join(basedir, 'tokenizer/encoder.json')
TOKEN_ALPHABET = os.path.join(basedir, 'tokenizer/alphabet_utf8.json')
@classmethod
def configure_logger(cls, root_module_name):
"""configure logging"""
dictConfig({
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"stdout_formatter": {
"format": cls.LOG_LINE_FORMAT,
"datefmt": cls.LOG_DATETIME_FORMAT,
},
},
"handlers": {
"stdout_handler": {
"level": cls.LOG_LEVEL,
"formatter": "stdout_formatter",
"class": "logging.StreamHandler",
"stream": sys.stdout,
},
},
"loggers": {
root_module_name: {
"handlers": ["stdout_handler"],
"level": cls.LOG_LEVEL,
"propagate": True,
},
},
})
class TestConfig(Config):
# pylint: disable=too-few-public-methods
"""testing config"""
LOG_LEVEL = "DEBUG"
| 29.241379 | 79 | 0.524175 | import os
import sys
from logging.config import dictConfig
basedir = os.path.abspath(os.path.dirname(__file__))
class Config:
OPENAI_KEY = os.getenv("OPENAI_KEY", "")
LOG_LEVEL = "WARNING"
LOG_LINE_FORMAT = "%(asctime)s %(levelname)-5s %(threadName)s: %(message)s"
LOG_DATETIME_FORMAT = "%Y/%m/%d %H:%M:%S"
TOKEN_BPE = os.path.join(basedir, 'tokenizer/vocab.bpe')
TOKEN_ID = os.path.join(basedir, 'tokenizer/encoder.json')
TOKEN_ALPHABET = os.path.join(basedir, 'tokenizer/alphabet_utf8.json')
@classmethod
def configure_logger(cls, root_module_name):
dictConfig({
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"stdout_formatter": {
"format": cls.LOG_LINE_FORMAT,
"datefmt": cls.LOG_DATETIME_FORMAT,
},
},
"handlers": {
"stdout_handler": {
"level": cls.LOG_LEVEL,
"formatter": "stdout_formatter",
"class": "logging.StreamHandler",
"stream": sys.stdout,
},
},
"loggers": {
root_module_name: {
"handlers": ["stdout_handler"],
"level": cls.LOG_LEVEL,
"propagate": True,
},
},
})
class TestConfig(Config):
LOG_LEVEL = "DEBUG"
| true | true |
1c340895d8ff7e0e155b6637d09d7adf5472fe3b | 8,420 | py | Python | notes/mainwithfacade.py | yeezysmem/notes.io | 9ca7c2251f0fc4e38b6f50e0f0411ebf5ee132da | [
"MIT"
] | 1 | 2020-12-02T14:31:02.000Z | 2020-12-02T14:31:02.000Z | notes/mainwithfacade.py | yeezysmem/notes.io | 9ca7c2251f0fc4e38b6f50e0f0411ebf5ee132da | [
"MIT"
] | null | null | null | notes/mainwithfacade.py | yeezysmem/notes.io | 9ca7c2251f0fc4e38b6f50e0f0411ebf5ee132da | [
"MIT"
] | null | null | null | import time
from prettytable import PrettyTable
# from PIL import Image
import tkinter as t
from tkinter.filedialog import askopenfilename
import webbrowser
from sqlalchemy import create_engine
from sqlalchemy.orm.session import sessionmaker
engine = create_engine('sqlite:///DataBaseForNotes.db' , echo=False)
from sqlalchemy import Column, Integer, ForeignKey, String
# from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base
base = declarative_base()
# class Note_Container(base):
# __tablename__= "notes_container"
# id = Column(Integer, primary_key=True)
# name = Column(String)
# sub_note = relationship("Note")
class Note(base):
__tablename__="note"
id = Column(Integer, primary_key=True)
name = Column(String)
data = Column(String)
type = Column(String)
time = Column(String)
# container_id = Column(ForeignKey('notes_container.id'))
# note = relationship("Note_Container")
__mapper_args__ = {'polymorphic_identity': 'note'}
def get_time(self):
self.time = time.ctime()
def set_data(self, data):
self.data = data
def print(self):
print(self.data)
def delete_note(self):
session = sessionmaker(bind=engine)()
print("Enter the name of note you want to delete:")
title = input()
q = session.query(Note).filter_by(name=title).first()
session.delete(q)
session.commit()
class Table_Note(Note):
__tablename__="tablenote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
def set_type(self):
self.type = "table"
def create_table(self):
self.data = PrettyTable()
return self.data
def insert_field_names(self, names):
self.data.field_names = names
def insert_rows(self, rows):
self.data.add_row(rows)
def insert_column(self, new_table):
column = input().split()
new_table.add_column(column)
def create_tablenote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = Table_Note()
New_Note.name = name
New_Note.set_type()
New_Note.create_table()
New_Note.get_time()
print("Enter the fields names for your table:")
field_names = input().split()
New_Note.insert_field_names(field_names)
print("Enter the rows of you table")
rows = input().split()
New_Note.insert_rows(rows)
table_string = New_Note.data.get_string()
New_Note.data = table_string
session.add(New_Note)
session.commit()
def show_tablenote(self):
session = sessionmaker(bind=engine)()
print("Enter the name of table you want to show:")
title = input()
q = session.query(Table_Note).filter_by(name=title)
other_note = q.first()
other_note.print()
class Text_Note(Note):
__tablename__ = "textnote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
def set_type(self):
self.type = "text"
def create_textnote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = Text_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter the text of your note:")
text = input()
New_Note.set_data(text)
session.add(New_Note)
session.commit()
class List_Note(Note):
__tablename__ = "listnote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
type = "list"
list_heading = Column(String)
listt = Column(String)
def set_type(self):
self.type = "list"
def create_list_heading(self, heading):
self.list_heading = heading
def add_element(self, string):
self.listt.append(string)
def print(self):
print(self.list_heading)
for i in range(len(self.listt)):
print(i+1,".", self.listt[i])
def create_listnote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = List_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter the list heading:")
heading = input()
New_Note.create_list_heading(heading)
print("Enter the amount of items in list:")
num = int(input())
for i in range(num):
print("Enter list item:")
item = input()
New_Note.add_element(item)
session.add(New_Note)
session.commit()
class Image_Note(Note):
__tablename__ = "imagenote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
path = Column(String)
def set_type(self):
self.type = "image"
def set_path(self):
self.path = input()
# def print(self):
# self.data.show()
def import_pict_binary(self):
f = open(self.path, "rb")
pict_binary = f.read()
self.data = pict_binary
def create_imagenote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = Image_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter the path to your image:")
New_Note.import_pict_binary()
session.add(New_Note)
session.commit()
class File_Note(Note):
__tablename__ = "filenote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
path = Column(String)
def set_type(self):
self.type = "file"
def import_file_binary(self):
self.path = input()
f = open(self.path, "rb")
file_binary = f.read()
self.data = file_binary
def create_filenote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = File_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter the path to your file:")
New_Note.import_file_binary()
session.add(New_Note)
session.commit()
class Link_Note(Note):
__tablename__ = "linknote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
def set_type(self):
self.type = "link"
def new_URL(self):
self.data = input()
def follow_the_link(self):
webbrowser.open_new(self.data)
def create_linknote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = Link_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter a link to save in note:")
New_Note.new_URL()
session.add(New_Note)
session.commit()
base.metadata.create_all(engine)
# def show_all_notes():
# session = sessionmaker(bind=engine)()
# q = session.query(Note).all()
# for i in range(len(q)):
#
# def edit_note():
# pass
class Facade(object):
def __init__(self):
self._note = Note()
self._tablenote = Table_Note()
self._textnote = Text_Note()
self._listnote = List_Note()
self._imagenote = Image_Note()
self._filenote = File_Note()
self._linknote = Link_Note()
def subsystem(self):
# self._tablenote.create_tablenote()
# self._textnote.create_textnote()
# self._listnote.create_listnote()
# self._imagenote.create_imagenote()
# self._filenote.create_filenote()
# self._tablenote.show_tablenote()
self._linknote.create_linknote()
self._linknote.new_URL()
self._linknote.follow_the_link()
# Клиентская часть
if __name__ == "__main__":
facade = Facade()
facade.subsystem()
# /Users/vadimarko/Desktop/Exams.png
# create_tablenote()
# show_tablenote()
# class Note_Container():
# # def __init__(self, name):
# # self.name = name
# # self.objects = []
# #
# # def add_object(self, object):
# # self.objects.append(object)
| 23.852691 | 68 | 0.610095 | import time
from prettytable import PrettyTable
import tkinter as t
from tkinter.filedialog import askopenfilename
import webbrowser
from sqlalchemy import create_engine
from sqlalchemy.orm.session import sessionmaker
engine = create_engine('sqlite:///DataBaseForNotes.db' , echo=False)
from sqlalchemy import Column, Integer, ForeignKey, String
from sqlalchemy.ext.declarative import declarative_base
base = declarative_base()
class Note(base):
__tablename__="note"
id = Column(Integer, primary_key=True)
name = Column(String)
data = Column(String)
type = Column(String)
time = Column(String)
__mapper_args__ = {'polymorphic_identity': 'note'}
def get_time(self):
self.time = time.ctime()
def set_data(self, data):
self.data = data
def print(self):
print(self.data)
def delete_note(self):
session = sessionmaker(bind=engine)()
print("Enter the name of note you want to delete:")
title = input()
q = session.query(Note).filter_by(name=title).first()
session.delete(q)
session.commit()
class Table_Note(Note):
__tablename__="tablenote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
def set_type(self):
self.type = "table"
def create_table(self):
self.data = PrettyTable()
return self.data
def insert_field_names(self, names):
self.data.field_names = names
def insert_rows(self, rows):
self.data.add_row(rows)
def insert_column(self, new_table):
column = input().split()
new_table.add_column(column)
def create_tablenote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = Table_Note()
New_Note.name = name
New_Note.set_type()
New_Note.create_table()
New_Note.get_time()
print("Enter the fields names for your table:")
field_names = input().split()
New_Note.insert_field_names(field_names)
print("Enter the rows of you table")
rows = input().split()
New_Note.insert_rows(rows)
table_string = New_Note.data.get_string()
New_Note.data = table_string
session.add(New_Note)
session.commit()
def show_tablenote(self):
session = sessionmaker(bind=engine)()
print("Enter the name of table you want to show:")
title = input()
q = session.query(Table_Note).filter_by(name=title)
other_note = q.first()
other_note.print()
class Text_Note(Note):
__tablename__ = "textnote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
def set_type(self):
self.type = "text"
def create_textnote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = Text_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter the text of your note:")
text = input()
New_Note.set_data(text)
session.add(New_Note)
session.commit()
class List_Note(Note):
__tablename__ = "listnote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
type = "list"
list_heading = Column(String)
listt = Column(String)
def set_type(self):
self.type = "list"
def create_list_heading(self, heading):
self.list_heading = heading
def add_element(self, string):
self.listt.append(string)
def print(self):
print(self.list_heading)
for i in range(len(self.listt)):
print(i+1,".", self.listt[i])
def create_listnote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = List_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter the list heading:")
heading = input()
New_Note.create_list_heading(heading)
print("Enter the amount of items in list:")
num = int(input())
for i in range(num):
print("Enter list item:")
item = input()
New_Note.add_element(item)
session.add(New_Note)
session.commit()
class Image_Note(Note):
__tablename__ = "imagenote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
path = Column(String)
def set_type(self):
self.type = "image"
def set_path(self):
self.path = input()
def import_pict_binary(self):
f = open(self.path, "rb")
pict_binary = f.read()
self.data = pict_binary
def create_imagenote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = Image_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter the path to your image:")
New_Note.import_pict_binary()
session.add(New_Note)
session.commit()
class File_Note(Note):
__tablename__ = "filenote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
path = Column(String)
def set_type(self):
self.type = "file"
def import_file_binary(self):
self.path = input()
f = open(self.path, "rb")
file_binary = f.read()
self.data = file_binary
def create_filenote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = File_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter the path to your file:")
New_Note.import_file_binary()
session.add(New_Note)
session.commit()
class Link_Note(Note):
__tablename__ = "linknote"
id = Column(Integer, ForeignKey('note.id'), primary_key=True)
def set_type(self):
self.type = "link"
def new_URL(self):
self.data = input()
def follow_the_link(self):
webbrowser.open_new(self.data)
def create_linknote(self):
print("Enter a name for new note:")
name = input()
session = sessionmaker(bind=engine)()
New_Note = Link_Note()
New_Note.name = name
New_Note.set_type()
New_Note.get_time()
print("Enter a link to save in note:")
New_Note.new_URL()
session.add(New_Note)
session.commit()
base.metadata.create_all(engine)
class Facade(object):
def __init__(self):
self._note = Note()
self._tablenote = Table_Note()
self._textnote = Text_Note()
self._listnote = List_Note()
self._imagenote = Image_Note()
self._filenote = File_Note()
self._linknote = Link_Note()
def subsystem(self):
self._linknote.create_linknote()
self._linknote.new_URL()
self._linknote.follow_the_link()
if __name__ == "__main__":
facade = Facade()
facade.subsystem()
| true | true |
1c340923dc4ec14bb7428534234c2587080e5110 | 7,169 | py | Python | src/read_actual_data.py | gdalle/PartiallyObservedVectorAutoRegressions | 28c9d34d7b6e45679e442721daf4946867fd5fb0 | [
"MIT"
] | null | null | null | src/read_actual_data.py | gdalle/PartiallyObservedVectorAutoRegressions | 28c9d34d7b6e45679e442721daf4946867fd5fb0 | [
"MIT"
] | null | null | null | src/read_actual_data.py | gdalle/PartiallyObservedVectorAutoRegressions | 28c9d34d7b6e45679e442721daf4946867fd5fb0 | [
"MIT"
] | null | null | null | import itertools
import os
import zipfile
from joblib import Parallel, delayed
import pandas as pd
from tqdm.notebook import tqdm
# Constants
YEARS = ["2018", "2019", "2020", "2021"]
MONTHS = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
DATA_COLUMNS = {
"BETRIEBSTAG": ("date", "string"),
"FAHRT_BEZEICHNER": ("trip_id", "category"),
"BETREIBER_ID": ("agency_id", "category"),
"BETREIBER_ABK": ("agency_short_name", "category"),
"BETREIBER_NAME": ("agency_name", "category"),
"PRODUKT_ID": ("transportation_type", "category"),
"LINIEN_ID": ("line_id", "category"),
"LINIEN_TEXT": ("line_name", "category"),
"UMLAUF_ID": ("circuit_transfer", "category"),
"VERKEHRSMITTEL_TEXT": ("transportation_subtype", "category"),
"ZUSATZFAHRT_TF": ("unplanned_trip", "category"),
"FAELLT_AUS_TF": ("cancelled_trip", "category"),
"BPUIC": ("stop_id", "category"),
"HALTESTELLEN_NAME": ("stop_name_unofficial", "category"),
"ANKUNFTSZEIT": ("arrival_time_planned", "string"),
"AN_PROGNOSE": ("arrival_time_real", "string"),
"AN_PROGNOSE_STATUS": ("arrival_time_status", "category"),
"ABFAHRTSZEIT": ("departure_time_planned", "string"),
"AB_PROGNOSE": ("departure_time_real", "string"),
"AB_PROGNOSE_STATUS": ("departure_time_status", "category"),
"DURCHFAHRT_TF": ("skipped_stop", "category"),
}
AGENCY_NAMES = ["Verkehrsbetriebe Zürich", "Verkehrsbetriebe Zürich INFO+"]
TRANSPORTATION_TYPES = ["Tram"]
# Utils
def concat_preserving_categorical(dfs):
"""Concatenate while preserving categorical columns."""
columns, dtypes = dfs[0].columns, dfs[0].dtypes
res = pd.DataFrame()
for c in tqdm(columns, desc="Concatenation "):
if str(dtypes[c]) == "category":
res[c] = pd.api.types.union_categoricals(
[df[c].astype("category") for df in dfs]
)
else:
res[c] = pd.concat([df[c] for df in dfs])
return res
# Read CSV files
def read_day_csv(daily_csv_path):
"""Read daily csv in the right format."""
try:
data = pd.read_csv(
daily_csv_path,
sep=";",
dtype={c: DATA_COLUMNS[c][1] for c in DATA_COLUMNS.keys()},
)
except UnicodeDecodeError:
print("Skipped (UTF-8 error): ", daily_csv_path)
return None
# Rename columns
data = data.rename(
mapper={c: DATA_COLUMNS[c][0] for c in DATA_COLUMNS.keys()}, axis=1
)
# Convert datetime columns
for timecol in ["date"]:
data[timecol] = pd.to_datetime(
data[timecol], format="%d.%m.%Y", errors="coerce"
)
for timecol in ["arrival_time_planned", "departure_time_planned"]:
data[timecol] = pd.to_datetime(
data[timecol], format="%d.%m.%Y %H:%M", errors="coerce"
)
for timecol in ["arrival_time_real", "departure_time_real"]:
data[timecol] = pd.to_datetime(
data[timecol], format="%d.%m.%Y %H:%M:%S", errors="coerce"
)
# Translate columns in German
for status_col in ["arrival_time_status", "departure_time_status"]:
data[status_col] = (
data[status_col]
.replace(
{
"PROGNOSE": "Forecast",
"GESCHAETZT": "Estimated",
"UNBEKANNT": "Unknown",
"REAL": "Real",
}
)
.fillna("Forecast")
.astype("category")
)
data["transportation_type"] = (
data["transportation_type"]
.replace(
{
"Zug": "Train",
"Bus": "Bus",
"BUS": "Bus",
"Schiff": "Boat",
"Tram": "Tram",
}
)
.fillna("Unknown")
.astype("category")
)
return data
# A pyramid of decompression and recompression
def unzip_single_month_store_days(
monthly_zip_dir_path, monthly_zip_name, daily_parquet_dir_path
):
"""Read a single zipped month full of csv and split it into parquet days."""
monthly_zip_path = os.path.join(monthly_zip_dir_path, monthly_zip_name)
with zipfile.ZipFile(monthly_zip_path) as monthly_zip_file:
# Loop over all days of the month
for daily_csv_name in monthly_zip_file.namelist():
# Skip additional files
if ".csv" not in daily_csv_name:
continue
# Open and parse csv
with monthly_zip_file.open(daily_csv_name, "r") as daily_csv_file:
daily_data = read_day_csv(daily_csv_file)
# Filter
if daily_data is not None:
daily_data = daily_data[
daily_data["agency_name"].isin(AGENCY_NAMES)
& daily_data["transportation_type"].isin(TRANSPORTATION_TYPES)
]
# Save as parquet file
parquet_file_name = daily_csv_name.split("/")[-1][:10] + ".parquet"
daily_data.to_parquet(
os.path.join(daily_parquet_dir_path, parquet_file_name)
)
def unzip_months_store_days(
monthly_zip_dir_path,
daily_parquet_dir_path,
):
"""Read all zipped months full of csv and split them into parquet days."""
monthly_zip_names = [
name
for name in sorted(os.listdir(monthly_zip_dir_path))
if name.startswith("19_") or name.startswith("18_")
]
Parallel(n_jobs=6)(
delayed(unzip_single_month_store_days)(
monthly_zip_dir_path, monthly_zip_name, daily_parquet_dir_path
)
for monthly_zip_name in tqdm(
monthly_zip_names, desc="Decompressing months for 2018-2019"
)
)
def read_days_store_months(daily_parquet_dir_path, monthly_parquet_dir_path):
"""Read parquet days and put them together into months."""
for (year, month) in list(itertools.product(YEARS, MONTHS)):
yearmonth = "{}-{}".format(year, month)
daily_files = sorted(
[file for file in os.listdir(daily_parquet_dir_path) if yearmonth in file]
)
if daily_files:
monthly_data_list = []
for date in tqdm(daily_files, desc="Reading " + yearmonth):
daily_data = pd.read_parquet(os.path.join(daily_parquet_dir_path, date))
monthly_data_list.append(daily_data)
monthly_data = concat_preserving_categorical(monthly_data_list)
monthly_data.to_parquet(
os.path.join(monthly_parquet_dir_path, yearmonth + ".parquet")
)
def read_months_return_full(
monthly_parquet_dir_path, years=[2018, 2019]
):
"""Read parquet months and put them together into a full dataframe."""
data = concat_preserving_categorical(
[
pd.read_parquet(os.path.join(monthly_parquet_dir_path, monthly_file))
for monthly_file in tqdm(
sorted(os.listdir(monthly_parquet_dir_path)), desc="Reading files "
)
if any(str(year) in monthly_file for year in years)
]
)
return data
| 35.490099 | 88 | 0.600921 | import itertools
import os
import zipfile
from joblib import Parallel, delayed
import pandas as pd
from tqdm.notebook import tqdm
YEARS = ["2018", "2019", "2020", "2021"]
MONTHS = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
DATA_COLUMNS = {
"BETRIEBSTAG": ("date", "string"),
"FAHRT_BEZEICHNER": ("trip_id", "category"),
"BETREIBER_ID": ("agency_id", "category"),
"BETREIBER_ABK": ("agency_short_name", "category"),
"BETREIBER_NAME": ("agency_name", "category"),
"PRODUKT_ID": ("transportation_type", "category"),
"LINIEN_ID": ("line_id", "category"),
"LINIEN_TEXT": ("line_name", "category"),
"UMLAUF_ID": ("circuit_transfer", "category"),
"VERKEHRSMITTEL_TEXT": ("transportation_subtype", "category"),
"ZUSATZFAHRT_TF": ("unplanned_trip", "category"),
"FAELLT_AUS_TF": ("cancelled_trip", "category"),
"BPUIC": ("stop_id", "category"),
"HALTESTELLEN_NAME": ("stop_name_unofficial", "category"),
"ANKUNFTSZEIT": ("arrival_time_planned", "string"),
"AN_PROGNOSE": ("arrival_time_real", "string"),
"AN_PROGNOSE_STATUS": ("arrival_time_status", "category"),
"ABFAHRTSZEIT": ("departure_time_planned", "string"),
"AB_PROGNOSE": ("departure_time_real", "string"),
"AB_PROGNOSE_STATUS": ("departure_time_status", "category"),
"DURCHFAHRT_TF": ("skipped_stop", "category"),
}
AGENCY_NAMES = ["Verkehrsbetriebe Zürich", "Verkehrsbetriebe Zürich INFO+"]
TRANSPORTATION_TYPES = ["Tram"]
def concat_preserving_categorical(dfs):
columns, dtypes = dfs[0].columns, dfs[0].dtypes
res = pd.DataFrame()
for c in tqdm(columns, desc="Concatenation "):
if str(dtypes[c]) == "category":
res[c] = pd.api.types.union_categoricals(
[df[c].astype("category") for df in dfs]
)
else:
res[c] = pd.concat([df[c] for df in dfs])
return res
def read_day_csv(daily_csv_path):
try:
data = pd.read_csv(
daily_csv_path,
sep=";",
dtype={c: DATA_COLUMNS[c][1] for c in DATA_COLUMNS.keys()},
)
except UnicodeDecodeError:
print("Skipped (UTF-8 error): ", daily_csv_path)
return None
data = data.rename(
mapper={c: DATA_COLUMNS[c][0] for c in DATA_COLUMNS.keys()}, axis=1
)
for timecol in ["date"]:
data[timecol] = pd.to_datetime(
data[timecol], format="%d.%m.%Y", errors="coerce"
)
for timecol in ["arrival_time_planned", "departure_time_planned"]:
data[timecol] = pd.to_datetime(
data[timecol], format="%d.%m.%Y %H:%M", errors="coerce"
)
for timecol in ["arrival_time_real", "departure_time_real"]:
data[timecol] = pd.to_datetime(
data[timecol], format="%d.%m.%Y %H:%M:%S", errors="coerce"
)
for status_col in ["arrival_time_status", "departure_time_status"]:
data[status_col] = (
data[status_col]
.replace(
{
"PROGNOSE": "Forecast",
"GESCHAETZT": "Estimated",
"UNBEKANNT": "Unknown",
"REAL": "Real",
}
)
.fillna("Forecast")
.astype("category")
)
data["transportation_type"] = (
data["transportation_type"]
.replace(
{
"Zug": "Train",
"Bus": "Bus",
"BUS": "Bus",
"Schiff": "Boat",
"Tram": "Tram",
}
)
.fillna("Unknown")
.astype("category")
)
return data
def unzip_single_month_store_days(
monthly_zip_dir_path, monthly_zip_name, daily_parquet_dir_path
):
monthly_zip_path = os.path.join(monthly_zip_dir_path, monthly_zip_name)
with zipfile.ZipFile(monthly_zip_path) as monthly_zip_file:
for daily_csv_name in monthly_zip_file.namelist():
if ".csv" not in daily_csv_name:
continue
with monthly_zip_file.open(daily_csv_name, "r") as daily_csv_file:
daily_data = read_day_csv(daily_csv_file)
if daily_data is not None:
daily_data = daily_data[
daily_data["agency_name"].isin(AGENCY_NAMES)
& daily_data["transportation_type"].isin(TRANSPORTATION_TYPES)
]
parquet_file_name = daily_csv_name.split("/")[-1][:10] + ".parquet"
daily_data.to_parquet(
os.path.join(daily_parquet_dir_path, parquet_file_name)
)
def unzip_months_store_days(
monthly_zip_dir_path,
daily_parquet_dir_path,
):
monthly_zip_names = [
name
for name in sorted(os.listdir(monthly_zip_dir_path))
if name.startswith("19_") or name.startswith("18_")
]
Parallel(n_jobs=6)(
delayed(unzip_single_month_store_days)(
monthly_zip_dir_path, monthly_zip_name, daily_parquet_dir_path
)
for monthly_zip_name in tqdm(
monthly_zip_names, desc="Decompressing months for 2018-2019"
)
)
def read_days_store_months(daily_parquet_dir_path, monthly_parquet_dir_path):
for (year, month) in list(itertools.product(YEARS, MONTHS)):
yearmonth = "{}-{}".format(year, month)
daily_files = sorted(
[file for file in os.listdir(daily_parquet_dir_path) if yearmonth in file]
)
if daily_files:
monthly_data_list = []
for date in tqdm(daily_files, desc="Reading " + yearmonth):
daily_data = pd.read_parquet(os.path.join(daily_parquet_dir_path, date))
monthly_data_list.append(daily_data)
monthly_data = concat_preserving_categorical(monthly_data_list)
monthly_data.to_parquet(
os.path.join(monthly_parquet_dir_path, yearmonth + ".parquet")
)
def read_months_return_full(
monthly_parquet_dir_path, years=[2018, 2019]
):
data = concat_preserving_categorical(
[
pd.read_parquet(os.path.join(monthly_parquet_dir_path, monthly_file))
for monthly_file in tqdm(
sorted(os.listdir(monthly_parquet_dir_path)), desc="Reading files "
)
if any(str(year) in monthly_file for year in years)
]
)
return data
| true | true |
1c340959bfcf319ad547abc637ac8cd4909c2e99 | 667 | py | Python | LeetCode/347 Top K Frequent Elements.py | gesuwen/Algorithms | 0c9cf4412d76f8b69ef68cc80636323f5a0e5786 | [
"MIT"
] | null | null | null | LeetCode/347 Top K Frequent Elements.py | gesuwen/Algorithms | 0c9cf4412d76f8b69ef68cc80636323f5a0e5786 | [
"MIT"
] | null | null | null | LeetCode/347 Top K Frequent Elements.py | gesuwen/Algorithms | 0c9cf4412d76f8b69ef68cc80636323f5a0e5786 | [
"MIT"
] | null | null | null | # Hash Table; Heap
# Given a non-empty array of integers, return the k most frequent elements.
#
# Example 1:
#
# Input: nums = [1,1,1,2,2,3], k = 2
# Output: [1,2]
# Example 2:
#
# Input: nums = [1], k = 1
# Output: [1]
# Note:
#
# You may assume k is always valid, 1 ≤ k ≤ number of unique elements.
# Your algorithm's time complexity must be better than O(n log n), where n is the array's size.
class Solution:
def topKFrequent(self, nums, k):
"""
:type nums: List[int]
:type k: int
:rtype: List[int]
"""
numsDictKCommon = collections.Counter(nums).most_common(k)
return [x[0] for x in numsDictKCommon]
| 25.653846 | 95 | 0.610195 |
class Solution:
def topKFrequent(self, nums, k):
numsDictKCommon = collections.Counter(nums).most_common(k)
return [x[0] for x in numsDictKCommon]
| true | true |
1c3409b40d34943b071152c0968e0e9175ed142c | 405 | py | Python | exproject/cli.py | yatin-darbar/exproject | 5cac45a28dc71d83215dabe33211dd758b83191a | [
"MIT"
] | null | null | null | exproject/cli.py | yatin-darbar/exproject | 5cac45a28dc71d83215dabe33211dd758b83191a | [
"MIT"
] | null | null | null | exproject/cli.py | yatin-darbar/exproject | 5cac45a28dc71d83215dabe33211dd758b83191a | [
"MIT"
] | null | null | null | """Console script for exproject."""
import sys
import click
@click.command()
def main(args=None):
"""Console script for exproject."""
click.echo("Replace this message by putting your code into "
"exproject.cli.main")
click.echo("See click documentation at https://click.palletsprojects.com/")
return 0
if __name__ == "__main__":
sys.exit(main()) # pragma: no cover
| 23.823529 | 79 | 0.666667 | import sys
import click
@click.command()
def main(args=None):
click.echo("Replace this message by putting your code into "
"exproject.cli.main")
click.echo("See click documentation at https://click.palletsprojects.com/")
return 0
if __name__ == "__main__":
sys.exit(main())
| true | true |
1c3409fa0f2cac32d7f09305197d2875ed50d8f8 | 1,325 | py | Python | aiotdlib/api/functions/request_qr_code_authentication.py | jraylan/aiotdlib | 4528fcfca7c5c69b54a878ce6ce60e934a2dcc73 | [
"MIT"
] | 37 | 2021-05-04T10:41:41.000Z | 2022-03-30T13:48:05.000Z | aiotdlib/api/functions/request_qr_code_authentication.py | jraylan/aiotdlib | 4528fcfca7c5c69b54a878ce6ce60e934a2dcc73 | [
"MIT"
] | 13 | 2021-07-17T19:54:51.000Z | 2022-02-26T06:50:00.000Z | aiotdlib/api/functions/request_qr_code_authentication.py | jraylan/aiotdlib | 4528fcfca7c5c69b54a878ce6ce60e934a2dcc73 | [
"MIT"
] | 7 | 2021-09-22T21:27:11.000Z | 2022-02-20T02:33:19.000Z | # =============================================================================== #
# #
# This file has been generated automatically!! Do not change this manually! #
# #
# =============================================================================== #
from __future__ import annotations
from pydantic import Field
from ..base_object import BaseObject
class RequestQrCodeAuthentication(BaseObject):
"""
Requests QR code authentication by scanning a QR code on another logged in device. Works only when the current authorization state is authorizationStateWaitPhoneNumber, or if there is no pending authentication query and the current authorization state is authorizationStateWaitCode, authorizationStateWaitRegistration, or authorizationStateWaitPassword
:param other_user_ids: List of user identifiers of other users currently using the application
:type other_user_ids: :class:`list[int]`
"""
ID: str = Field("requestQrCodeAuthentication", alias="@type")
other_user_ids: list[int]
@staticmethod
def read(q: dict) -> RequestQrCodeAuthentication:
return RequestQrCodeAuthentication.construct(**q)
| 47.321429 | 356 | 0.578113 | from __future__ import annotations
from pydantic import Field
from ..base_object import BaseObject
class RequestQrCodeAuthentication(BaseObject):
ID: str = Field("requestQrCodeAuthentication", alias="@type")
other_user_ids: list[int]
@staticmethod
def read(q: dict) -> RequestQrCodeAuthentication:
return RequestQrCodeAuthentication.construct(**q)
| true | true |
1c340a57c3769de9448e4dae1c6352d7f424212f | 15,644 | py | Python | PaddleCV/PaddleGAN/trainer/Pix2pix.py | heavengate/models | f05c910f8a8e3105de8c2f1d81e83ca00d2c7ec7 | [
"Apache-2.0"
] | 2 | 2021-06-11T06:48:20.000Z | 2021-09-02T10:23:07.000Z | PaddleCV/PaddleGAN/trainer/Pix2pix.py | heavengate/models | f05c910f8a8e3105de8c2f1d81e83ca00d2c7ec7 | [
"Apache-2.0"
] | null | null | null | PaddleCV/PaddleGAN/trainer/Pix2pix.py | heavengate/models | f05c910f8a8e3105de8c2f1d81e83ca00d2c7ec7 | [
"Apache-2.0"
] | 1 | 2019-08-27T11:19:09.000Z | 2019-08-27T11:19:09.000Z | #copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from network.Pix2pix_network import Pix2pix_model
from util import utility
import paddle.fluid as fluid
from paddle.fluid import profiler
import sys
import time
import numpy as np
class GTrainer():
def __init__(self, input_A, input_B, cfg, step_per_epoch):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
model = Pix2pix_model()
self.fake_B = model.network_G(input_A, "generator", cfg=cfg)
self.fake_B.persistable = True
self.infer_program = self.program.clone()
AB = fluid.layers.concat([input_A, self.fake_B], 1)
self.pred = model.network_D(AB, "discriminator", cfg)
if cfg.gan_mode == "lsgan":
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred,
shape=self.pred.shape,
value=1,
dtype='float32')
self.g_loss_gan = fluid.layers.reduce_mean(
fluid.layers.square(
fluid.layers.elementwise_sub(
x=self.pred, y=ones)))
elif cfg.gan_mode == "vanilla":
pred_shape = self.pred.shape
self.pred = fluid.layers.reshape(
self.pred,
[-1, pred_shape[1] * pred_shape[2] * pred_shape[3]],
inplace=True)
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred,
shape=self.pred.shape,
value=1,
dtype='float32')
self.g_loss_gan = fluid.layers.mean(
fluid.layers.sigmoid_cross_entropy_with_logits(
x=self.pred, label=ones))
else:
raise NotImplementedError("gan_mode {} is not support!".format(
cfg.gan_mode))
self.g_loss_L1 = fluid.layers.reduce_mean(
fluid.layers.abs(
fluid.layers.elementwise_sub(
x=input_B, y=self.fake_B))) * cfg.lambda_L1
self.g_loss = fluid.layers.elementwise_add(self.g_loss_L1,
self.g_loss_gan)
lr = cfg.learning_rate
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith(
"generator"):
vars.append(var.name)
self.param = vars
if cfg.epoch <= 100:
optimizer = fluid.optimizer.Adam(
learning_rate=lr, beta1=0.5, beta2=0.999, name="net_G")
else:
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=[99 * step_per_epoch] + [
x * step_per_epoch
for x in range(100, cfg.epoch - 1)
],
values=[lr] + [
lr * (1.0 - (x - 99.0) / 101.0)
for x in range(100, cfg.epoch)
]),
beta1=0.5,
beta2=0.999,
name="net_G")
optimizer.minimize(self.g_loss, parameter_list=vars)
class DTrainer():
def __init__(self, input_A, input_B, fake_B, cfg, step_per_epoch):
self.program = fluid.default_main_program().clone()
lr = cfg.learning_rate
with fluid.program_guard(self.program):
model = Pix2pix_model()
self.real_AB = fluid.layers.concat([input_A, input_B], 1)
self.fake_AB = fluid.layers.concat([input_A, fake_B], 1)
self.pred_real = model.network_D(
self.real_AB, "discriminator", cfg=cfg)
self.pred_fake = model.network_D(
self.fake_AB, "discriminator", cfg=cfg)
if cfg.gan_mode == "lsgan":
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred_real,
shape=self.pred_real.shape,
value=1,
dtype='float32')
self.d_loss_real = fluid.layers.reduce_mean(
fluid.layers.square(
fluid.layers.elementwise_sub(
x=self.pred_real, y=ones)))
self.d_loss_fake = fluid.layers.reduce_mean(
fluid.layers.square(x=self.pred_fake))
elif cfg.gan_mode == "vanilla":
pred_shape = self.pred_real.shape
self.pred_real = fluid.layers.reshape(
self.pred_real,
[-1, pred_shape[1] * pred_shape[2] * pred_shape[3]],
inplace=True)
self.pred_fake = fluid.layers.reshape(
self.pred_fake,
[-1, pred_shape[1] * pred_shape[2] * pred_shape[3]],
inplace=True)
zeros = fluid.layers.fill_constant_batch_size_like(
input=self.pred_fake,
shape=self.pred_fake.shape,
value=0,
dtype='float32')
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred_real,
shape=self.pred_real.shape,
value=1,
dtype='float32')
self.d_loss_real = fluid.layers.mean(
fluid.layers.sigmoid_cross_entropy_with_logits(
x=self.pred_real, label=ones))
self.d_loss_fake = fluid.layers.mean(
fluid.layers.sigmoid_cross_entropy_with_logits(
x=self.pred_fake, label=zeros))
else:
raise NotImplementedError("gan_mode {} is not support!".format(
cfg.gan_mode))
self.d_loss = 0.5 * (self.d_loss_real + self.d_loss_fake)
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith(
"discriminator"):
vars.append(var.name)
self.param = vars
if cfg.epoch <= 100:
optimizer = fluid.optimizer.Adam(
learning_rate=lr, beta1=0.5, beta2=0.999, name="net_D")
else:
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=[99 * step_per_epoch] + [
x * step_per_epoch
for x in range(100, cfg.epoch - 1)
],
values=[lr] + [
lr * (1.0 - (x - 99.0) / 101.0)
for x in range(100, cfg.epoch)
]),
beta1=0.5,
beta2=0.999,
name="net_D")
optimizer.minimize(self.d_loss, parameter_list=vars)
class Pix2pix(object):
def add_special_args(self, parser):
parser.add_argument(
'--net_G',
type=str,
default="unet_256",
help="Choose the Pix2pix generator's network, choose in [resnet_9block|resnet_6block|unet_128|unet_256]"
)
parser.add_argument(
'--net_D',
type=str,
default="basic",
help="Choose the Pix2pix discriminator's network, choose in [basic|nlayers|pixel]"
)
parser.add_argument(
'--d_nlayers',
type=int,
default=3,
help="only used when Pix2pix discriminator is nlayers")
parser.add_argument(
'--enable_ce',
action='store_true',
help="if set, run the tasks with continuous evaluation logs")
return parser
def __init__(self,
cfg=None,
train_reader=None,
test_reader=None,
batch_num=1,
id2name=None):
self.cfg = cfg
self.train_reader = train_reader
self.test_reader = test_reader
self.batch_num = batch_num
self.id2name = id2name
def build_model(self):
data_shape = [None, 3, self.cfg.crop_size, self.cfg.crop_size]
input_A = fluid.data(name='input_A', shape=data_shape, dtype='float32')
input_B = fluid.data(name='input_B', shape=data_shape, dtype='float32')
input_fake = fluid.data(
name='input_fake', shape=data_shape, dtype='float32')
# used for continuous evaluation
if self.cfg.enable_ce:
fluid.default_startup_program().random_seed = 90
loader = fluid.io.DataLoader.from_generator(
feed_list=[input_A, input_B],
capacity=4,
iterable=True,
use_double_buffer=True)
gen_trainer = GTrainer(input_A, input_B, self.cfg, self.batch_num)
dis_trainer = DTrainer(input_A, input_B, input_fake, self.cfg,
self.batch_num)
# prepare environment
place = fluid.CUDAPlace(0) if self.cfg.use_gpu else fluid.CPUPlace()
loader.set_batch_generator(
self.train_reader,
places=fluid.cuda_places()
if self.cfg.use_gpu else fluid.cpu_places())
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if self.cfg.init_model:
utility.init_checkpoints(self.cfg, exe, gen_trainer, "net_G")
utility.init_checkpoints(self.cfg, exe, dis_trainer, "net_D")
### memory optim
build_strategy = fluid.BuildStrategy()
gen_trainer_program = fluid.CompiledProgram(
gen_trainer.program).with_data_parallel(
loss_name=gen_trainer.g_loss.name,
build_strategy=build_strategy)
dis_trainer_program = fluid.CompiledProgram(
dis_trainer.program).with_data_parallel(
loss_name=dis_trainer.d_loss.name,
build_strategy=build_strategy)
t_time = 0
total_train_batch = 0 # used for benchmark
for epoch_id in range(self.cfg.epoch):
batch_id = 0
for tensor in loader():
if self.cfg.max_iter and total_train_batch == self.cfg.max_iter: # used for benchmark
return
s_time = time.time()
# optimize the generator network
g_loss_gan, g_loss_l1, fake_B_tmp = exe.run(
gen_trainer_program,
fetch_list=[
gen_trainer.g_loss_gan, gen_trainer.g_loss_L1,
gen_trainer.fake_B
],
feed=tensor)
devices_num = utility.get_device_num(self.cfg)
fake_per_device = int(len(fake_B_tmp) / devices_num)
for dev in range(devices_num):
tensor[dev]['input_fake'] = fake_B_tmp[dev * fake_per_device : (dev+1) * fake_per_device]
# optimize the discriminator network
d_loss_real, d_loss_fake = exe.run(dis_trainer_program,
fetch_list=[
dis_trainer.d_loss_real,
dis_trainer.d_loss_fake
],
feed=tensor)
batch_time = time.time() - s_time
t_time += batch_time
if batch_id % self.cfg.print_freq == 0:
print("epoch{}: batch{}: \n\
g_loss_gan: {}; g_loss_l1: {}; \n\
d_loss_real: {}; d_loss_fake: {}; \n\
Batch_time_cost: {}"
.format(epoch_id, batch_id, g_loss_gan[0], g_loss_l1[
0], d_loss_real[0], d_loss_fake[0], batch_time))
sys.stdout.flush()
batch_id += 1
total_train_batch += 1 # used for benchmark
# profiler tools
if self.cfg.profile and epoch_id == 0 and batch_id == self.cfg.print_freq:
profiler.reset_profiler()
elif self.cfg.profile and epoch_id == 0 and batch_id == self.cfg.print_freq + 5:
return
if self.cfg.run_test:
image_name = fluid.data(
name='image_name',
shape=[None, self.cfg.batch_size],
dtype="int32")
test_loader = fluid.io.DataLoader.from_generator(
feed_list=[input_A, input_B, image_name],
capacity=4,
iterable=True,
use_double_buffer=True)
test_loader.set_batch_generator(
self.test_reader,
places=fluid.cuda_places()
if self.cfg.use_gpu else fluid.cpu_places())
test_program = gen_trainer.infer_program
utility.save_test_image(
epoch_id,
self.cfg,
exe,
place,
test_program,
gen_trainer,
test_loader,
A_id2name=self.id2name)
if self.cfg.save_checkpoints:
utility.checkpoints(epoch_id, self.cfg, exe, gen_trainer,
"net_G")
utility.checkpoints(epoch_id, self.cfg, exe, dis_trainer,
"net_D")
if self.cfg.enable_ce:
device_num = fluid.core.get_cuda_device_count(
) if self.cfg.use_gpu else 1
print("kpis\tpix2pix_g_loss_gan_card{}\t{}".format(device_num,
g_loss_gan[0]))
print("kpis\tpix2pix_g_loss_l1_card{}\t{}".format(device_num,
g_loss_l1[0]))
print("kpis\tpix2pix_d_loss_real_card{}\t{}".format(device_num,
d_loss_real[0]))
print("kpis\tpix2pix_d_loss_fake_card{}\t{}".format(device_num,
d_loss_fake[0]))
print("kpis\tpix2pix_Batch_time_cost_card{}\t{}".format(device_num,
batch_time))
| 43.576602 | 116 | 0.510483 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from network.Pix2pix_network import Pix2pix_model
from util import utility
import paddle.fluid as fluid
from paddle.fluid import profiler
import sys
import time
import numpy as np
class GTrainer():
def __init__(self, input_A, input_B, cfg, step_per_epoch):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
model = Pix2pix_model()
self.fake_B = model.network_G(input_A, "generator", cfg=cfg)
self.fake_B.persistable = True
self.infer_program = self.program.clone()
AB = fluid.layers.concat([input_A, self.fake_B], 1)
self.pred = model.network_D(AB, "discriminator", cfg)
if cfg.gan_mode == "lsgan":
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred,
shape=self.pred.shape,
value=1,
dtype='float32')
self.g_loss_gan = fluid.layers.reduce_mean(
fluid.layers.square(
fluid.layers.elementwise_sub(
x=self.pred, y=ones)))
elif cfg.gan_mode == "vanilla":
pred_shape = self.pred.shape
self.pred = fluid.layers.reshape(
self.pred,
[-1, pred_shape[1] * pred_shape[2] * pred_shape[3]],
inplace=True)
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred,
shape=self.pred.shape,
value=1,
dtype='float32')
self.g_loss_gan = fluid.layers.mean(
fluid.layers.sigmoid_cross_entropy_with_logits(
x=self.pred, label=ones))
else:
raise NotImplementedError("gan_mode {} is not support!".format(
cfg.gan_mode))
self.g_loss_L1 = fluid.layers.reduce_mean(
fluid.layers.abs(
fluid.layers.elementwise_sub(
x=input_B, y=self.fake_B))) * cfg.lambda_L1
self.g_loss = fluid.layers.elementwise_add(self.g_loss_L1,
self.g_loss_gan)
lr = cfg.learning_rate
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith(
"generator"):
vars.append(var.name)
self.param = vars
if cfg.epoch <= 100:
optimizer = fluid.optimizer.Adam(
learning_rate=lr, beta1=0.5, beta2=0.999, name="net_G")
else:
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=[99 * step_per_epoch] + [
x * step_per_epoch
for x in range(100, cfg.epoch - 1)
],
values=[lr] + [
lr * (1.0 - (x - 99.0) / 101.0)
for x in range(100, cfg.epoch)
]),
beta1=0.5,
beta2=0.999,
name="net_G")
optimizer.minimize(self.g_loss, parameter_list=vars)
class DTrainer():
def __init__(self, input_A, input_B, fake_B, cfg, step_per_epoch):
self.program = fluid.default_main_program().clone()
lr = cfg.learning_rate
with fluid.program_guard(self.program):
model = Pix2pix_model()
self.real_AB = fluid.layers.concat([input_A, input_B], 1)
self.fake_AB = fluid.layers.concat([input_A, fake_B], 1)
self.pred_real = model.network_D(
self.real_AB, "discriminator", cfg=cfg)
self.pred_fake = model.network_D(
self.fake_AB, "discriminator", cfg=cfg)
if cfg.gan_mode == "lsgan":
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred_real,
shape=self.pred_real.shape,
value=1,
dtype='float32')
self.d_loss_real = fluid.layers.reduce_mean(
fluid.layers.square(
fluid.layers.elementwise_sub(
x=self.pred_real, y=ones)))
self.d_loss_fake = fluid.layers.reduce_mean(
fluid.layers.square(x=self.pred_fake))
elif cfg.gan_mode == "vanilla":
pred_shape = self.pred_real.shape
self.pred_real = fluid.layers.reshape(
self.pred_real,
[-1, pred_shape[1] * pred_shape[2] * pred_shape[3]],
inplace=True)
self.pred_fake = fluid.layers.reshape(
self.pred_fake,
[-1, pred_shape[1] * pred_shape[2] * pred_shape[3]],
inplace=True)
zeros = fluid.layers.fill_constant_batch_size_like(
input=self.pred_fake,
shape=self.pred_fake.shape,
value=0,
dtype='float32')
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred_real,
shape=self.pred_real.shape,
value=1,
dtype='float32')
self.d_loss_real = fluid.layers.mean(
fluid.layers.sigmoid_cross_entropy_with_logits(
x=self.pred_real, label=ones))
self.d_loss_fake = fluid.layers.mean(
fluid.layers.sigmoid_cross_entropy_with_logits(
x=self.pred_fake, label=zeros))
else:
raise NotImplementedError("gan_mode {} is not support!".format(
cfg.gan_mode))
self.d_loss = 0.5 * (self.d_loss_real + self.d_loss_fake)
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith(
"discriminator"):
vars.append(var.name)
self.param = vars
if cfg.epoch <= 100:
optimizer = fluid.optimizer.Adam(
learning_rate=lr, beta1=0.5, beta2=0.999, name="net_D")
else:
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=[99 * step_per_epoch] + [
x * step_per_epoch
for x in range(100, cfg.epoch - 1)
],
values=[lr] + [
lr * (1.0 - (x - 99.0) / 101.0)
for x in range(100, cfg.epoch)
]),
beta1=0.5,
beta2=0.999,
name="net_D")
optimizer.minimize(self.d_loss, parameter_list=vars)
class Pix2pix(object):
def add_special_args(self, parser):
parser.add_argument(
'--net_G',
type=str,
default="unet_256",
help="Choose the Pix2pix generator's network, choose in [resnet_9block|resnet_6block|unet_128|unet_256]"
)
parser.add_argument(
'--net_D',
type=str,
default="basic",
help="Choose the Pix2pix discriminator's network, choose in [basic|nlayers|pixel]"
)
parser.add_argument(
'--d_nlayers',
type=int,
default=3,
help="only used when Pix2pix discriminator is nlayers")
parser.add_argument(
'--enable_ce',
action='store_true',
help="if set, run the tasks with continuous evaluation logs")
return parser
def __init__(self,
cfg=None,
train_reader=None,
test_reader=None,
batch_num=1,
id2name=None):
self.cfg = cfg
self.train_reader = train_reader
self.test_reader = test_reader
self.batch_num = batch_num
self.id2name = id2name
def build_model(self):
data_shape = [None, 3, self.cfg.crop_size, self.cfg.crop_size]
input_A = fluid.data(name='input_A', shape=data_shape, dtype='float32')
input_B = fluid.data(name='input_B', shape=data_shape, dtype='float32')
input_fake = fluid.data(
name='input_fake', shape=data_shape, dtype='float32')
if self.cfg.enable_ce:
fluid.default_startup_program().random_seed = 90
loader = fluid.io.DataLoader.from_generator(
feed_list=[input_A, input_B],
capacity=4,
iterable=True,
use_double_buffer=True)
gen_trainer = GTrainer(input_A, input_B, self.cfg, self.batch_num)
dis_trainer = DTrainer(input_A, input_B, input_fake, self.cfg,
self.batch_num)
place = fluid.CUDAPlace(0) if self.cfg.use_gpu else fluid.CPUPlace()
loader.set_batch_generator(
self.train_reader,
places=fluid.cuda_places()
if self.cfg.use_gpu else fluid.cpu_places())
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if self.cfg.init_model:
utility.init_checkpoints(self.cfg, exe, gen_trainer, "net_G")
utility.init_checkpoints(self.cfg, exe, dis_trainer, "net_D")
id.BuildStrategy()
gen_trainer_program = fluid.CompiledProgram(
gen_trainer.program).with_data_parallel(
loss_name=gen_trainer.g_loss.name,
build_strategy=build_strategy)
dis_trainer_program = fluid.CompiledProgram(
dis_trainer.program).with_data_parallel(
loss_name=dis_trainer.d_loss.name,
build_strategy=build_strategy)
t_time = 0
total_train_batch = 0
for epoch_id in range(self.cfg.epoch):
batch_id = 0
for tensor in loader():
if self.cfg.max_iter and total_train_batch == self.cfg.max_iter:
return
s_time = time.time()
g_loss_gan, g_loss_l1, fake_B_tmp = exe.run(
gen_trainer_program,
fetch_list=[
gen_trainer.g_loss_gan, gen_trainer.g_loss_L1,
gen_trainer.fake_B
],
feed=tensor)
devices_num = utility.get_device_num(self.cfg)
fake_per_device = int(len(fake_B_tmp) / devices_num)
for dev in range(devices_num):
tensor[dev]['input_fake'] = fake_B_tmp[dev * fake_per_device : (dev+1) * fake_per_device]
d_loss_real, d_loss_fake = exe.run(dis_trainer_program,
fetch_list=[
dis_trainer.d_loss_real,
dis_trainer.d_loss_fake
],
feed=tensor)
batch_time = time.time() - s_time
t_time += batch_time
if batch_id % self.cfg.print_freq == 0:
print("epoch{}: batch{}: \n\
g_loss_gan: {}; g_loss_l1: {}; \n\
d_loss_real: {}; d_loss_fake: {}; \n\
Batch_time_cost: {}"
.format(epoch_id, batch_id, g_loss_gan[0], g_loss_l1[
0], d_loss_real[0], d_loss_fake[0], batch_time))
sys.stdout.flush()
batch_id += 1
total_train_batch += 1
if self.cfg.profile and epoch_id == 0 and batch_id == self.cfg.print_freq:
profiler.reset_profiler()
elif self.cfg.profile and epoch_id == 0 and batch_id == self.cfg.print_freq + 5:
return
if self.cfg.run_test:
image_name = fluid.data(
name='image_name',
shape=[None, self.cfg.batch_size],
dtype="int32")
test_loader = fluid.io.DataLoader.from_generator(
feed_list=[input_A, input_B, image_name],
capacity=4,
iterable=True,
use_double_buffer=True)
test_loader.set_batch_generator(
self.test_reader,
places=fluid.cuda_places()
if self.cfg.use_gpu else fluid.cpu_places())
test_program = gen_trainer.infer_program
utility.save_test_image(
epoch_id,
self.cfg,
exe,
place,
test_program,
gen_trainer,
test_loader,
A_id2name=self.id2name)
if self.cfg.save_checkpoints:
utility.checkpoints(epoch_id, self.cfg, exe, gen_trainer,
"net_G")
utility.checkpoints(epoch_id, self.cfg, exe, dis_trainer,
"net_D")
if self.cfg.enable_ce:
device_num = fluid.core.get_cuda_device_count(
) if self.cfg.use_gpu else 1
print("kpis\tpix2pix_g_loss_gan_card{}\t{}".format(device_num,
g_loss_gan[0]))
print("kpis\tpix2pix_g_loss_l1_card{}\t{}".format(device_num,
g_loss_l1[0]))
print("kpis\tpix2pix_d_loss_real_card{}\t{}".format(device_num,
d_loss_real[0]))
print("kpis\tpix2pix_d_loss_fake_card{}\t{}".format(device_num,
d_loss_fake[0]))
print("kpis\tpix2pix_Batch_time_cost_card{}\t{}".format(device_num,
batch_time))
| true | true |
1c340a6352846a6f94a05e23657cc0dadbe2b9f6 | 24,811 | py | Python | test/functional/feature_csv_activation.py | Dollar-coin/Dollar | 4b84e5d14408f3985d527aaccac21472b47c91d5 | [
"MIT"
] | 1 | 2021-02-06T22:18:29.000Z | 2021-02-06T22:18:29.000Z | test/functional/feature_csv_activation.py | Dollar-coin/Dollar | 4b84e5d14408f3985d527aaccac21472b47c91d5 | [
"MIT"
] | 1 | 2021-02-07T00:57:29.000Z | 2021-02-07T10:22:29.000Z | test/functional/feature_csv_activation.py | Dollar-coin/Dollar | 4b84e5d14408f3985d527aaccac21472b47c91d5 | [
"MIT"
] | 1 | 2021-02-26T22:29:45.000Z | 2021-02-26T22:29:45.000Z | #!/usr/bin/env python3
# Copyright (c) 2015-2018 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Test activation of the first version bits soft fork.
This soft fork will activate the following BIPS:
BIP 68 - nSequence relative lock times
BIP 112 - CHECKSEQUENCEVERIFY
BIP 113 - MedianTimePast semantics for nLockTime
regtest lock-in with 108/144 block signalling
activation after a further 144 blocks
mine 82 blocks whose coinbases will be used to generate inputs for our tests
mine 61 blocks to transition from DEFINED to STARTED
mine 144 blocks only 100 of which are signaling readiness in order to fail to change state this period
mine 144 blocks with 108 signaling and verify STARTED->LOCKED_IN
mine 140 blocks and seed block chain with the 82 inputs will use for our tests at height 572
mine 3 blocks and verify still at LOCKED_IN and test that enforcement has not triggered
mine 1 block and test that enforcement has triggered (which triggers ACTIVE)
Test BIP 113 is enforced
Mine 4 blocks so next height is 580 and test BIP 68 is enforced for time and height
Mine 1 block so next height is 581 and test BIP 68 now passes time but not height
Mine 1 block so next height is 582 and test BIP 68 now passes time and height
Test that BIP 112 is enforced
Various transactions will be used to test that the BIPs rules are not enforced before the soft fork activates
And that after the soft fork activates transactions pass and fail as they should according to the rules.
For each BIP, transactions of versions 1 and 2 will be tested.
----------------
BIP 113:
bip113tx - modify the nLocktime variable
BIP 68:
bip68txs - 16 txs with nSequence relative locktime of 10 with various bits set as per the relative_locktimes below
BIP 112:
bip112txs_vary_nSequence - 16 txs with nSequence relative_locktimes of 10 evaluated against 10 OP_CSV OP_DROP
bip112txs_vary_nSequence_9 - 16 txs with nSequence relative_locktimes of 9 evaluated against 10 OP_CSV OP_DROP
bip112txs_vary_OP_CSV - 16 txs with nSequence = 10 evaluated against varying {relative_locktimes of 10} OP_CSV OP_DROP
bip112txs_vary_OP_CSV_9 - 16 txs with nSequence = 9 evaluated against varying {relative_locktimes of 10} OP_CSV OP_DROP
bip112tx_special - test negative argument to OP_CSV
"""
from decimal import Decimal
from itertools import product
from io import BytesIO
import time
from test_framework.blocktools import create_coinbase, create_block, create_transaction
from test_framework.messages import ToHex, CTransaction
from test_framework.mininode import P2PDataStore
from test_framework.script import (
CScript,
OP_CHECKSEQUENCEVERIFY,
OP_DROP,
)
from test_framework.test_framework import DollarTestFramework
from test_framework.util import (
assert_equal,
get_bip9_status,
hex_str_to_bytes,
)
BASE_RELATIVE_LOCKTIME = 10
SEQ_DISABLE_FLAG = 1 << 31
SEQ_RANDOM_HIGH_BIT = 1 << 25
SEQ_TYPE_FLAG = 1 << 22
SEQ_RANDOM_LOW_BIT = 1 << 18
def relative_locktime(sdf, srhb, stf, srlb):
"""Returns a locktime with certain bits set."""
locktime = BASE_RELATIVE_LOCKTIME
if sdf:
locktime |= SEQ_DISABLE_FLAG
if srhb:
locktime |= SEQ_RANDOM_HIGH_BIT
if stf:
locktime |= SEQ_TYPE_FLAG
if srlb:
locktime |= SEQ_RANDOM_LOW_BIT
return locktime
def all_rlt_txs(txs):
return [tx['tx'] for tx in txs]
def sign_transaction(node, unsignedtx):
rawtx = ToHex(unsignedtx)
signresult = node.signrawtransactionwithwallet(rawtx)
tx = CTransaction()
f = BytesIO(hex_str_to_bytes(signresult['hex']))
tx.deserialize(f)
return tx
def create_bip112special(node, input, txversion, address):
tx = create_transaction(node, input, address, amount=Decimal("49.98"))
tx.nVersion = txversion
signtx = sign_transaction(node, tx)
signtx.vin[0].scriptSig = CScript([-1, OP_CHECKSEQUENCEVERIFY, OP_DROP] + list(CScript(signtx.vin[0].scriptSig)))
return signtx
def send_generic_input_tx(node, coinbases, address):
return node.sendrawtransaction(ToHex(sign_transaction(node, create_transaction(node, node.getblock(coinbases.pop())['tx'][0], address, amount=Decimal("49.99")))))
def create_bip68txs(node, bip68inputs, txversion, address, locktime_delta=0):
"""Returns a list of bip68 transactions with different bits set."""
txs = []
assert(len(bip68inputs) >= 16)
for i, (sdf, srhb, stf, srlb) in enumerate(product(*[[True, False]] * 4)):
locktime = relative_locktime(sdf, srhb, stf, srlb)
tx = create_transaction(node, bip68inputs[i], address, amount=Decimal("49.98"))
tx.nVersion = txversion
tx.vin[0].nSequence = locktime + locktime_delta
tx = sign_transaction(node, tx)
tx.rehash()
txs.append({'tx': tx, 'sdf': sdf, 'stf': stf})
return txs
def create_bip112txs(node, bip112inputs, varyOP_CSV, txversion, address, locktime_delta=0):
"""Returns a list of bip68 transactions with different bits set."""
txs = []
assert(len(bip112inputs) >= 16)
for i, (sdf, srhb, stf, srlb) in enumerate(product(*[[True, False]] * 4)):
locktime = relative_locktime(sdf, srhb, stf, srlb)
tx = create_transaction(node, bip112inputs[i], address, amount=Decimal("49.98"))
if (varyOP_CSV): # if varying OP_CSV, nSequence is fixed
tx.vin[0].nSequence = BASE_RELATIVE_LOCKTIME + locktime_delta
else: # vary nSequence instead, OP_CSV is fixed
tx.vin[0].nSequence = locktime + locktime_delta
tx.nVersion = txversion
signtx = sign_transaction(node, tx)
if (varyOP_CSV):
signtx.vin[0].scriptSig = CScript([locktime, OP_CHECKSEQUENCEVERIFY, OP_DROP] + list(CScript(signtx.vin[0].scriptSig)))
else:
signtx.vin[0].scriptSig = CScript([BASE_RELATIVE_LOCKTIME, OP_CHECKSEQUENCEVERIFY, OP_DROP] + list(CScript(signtx.vin[0].scriptSig)))
tx.rehash()
txs.append({'tx': signtx, 'sdf': sdf, 'stf': stf})
return txs
class BIP68_112_113Test(DollarTestFramework):
def set_test_params(self):
self.num_nodes = 1
self.setup_clean_chain = True
self.extra_args = [['-whitelist=127.0.0.1', '-blockversion=4', '-addresstype=legacy']]
def skip_test_if_missing_module(self):
self.skip_if_no_wallet()
def generate_blocks(self, number, version, test_blocks=None):
if test_blocks is None:
test_blocks = []
for i in range(number):
block = self.create_test_block([], version)
test_blocks.append(block)
self.last_block_time += 600
self.tip = block.sha256
self.tipheight += 1
return test_blocks
def create_test_block(self, txs, version=536870912):
block = create_block(self.tip, create_coinbase(self.tipheight + 1), self.last_block_time + 600)
block.nVersion = version
block.vtx.extend(txs)
block.hashMerkleRoot = block.calc_merkle_root()
block.rehash()
block.solve()
return block
def sync_blocks(self, blocks, success=True, reject_code=None, reject_reason=None, request_block=True):
"""Sends blocks to test node. Syncs and verifies that tip has advanced to most recent block.
Call with success = False if the tip shouldn't advance to the most recent block."""
self.nodes[0].p2p.send_blocks_and_test(blocks, self.nodes[0], success=success, reject_code=reject_code, reject_reason=reject_reason, request_block=request_block)
def run_test(self):
self.nodes[0].add_p2p_connection(P2PDataStore())
self.log.info("Generate blocks in the past for coinbase outputs.")
long_past_time = int(time.time()) - 600 * 1000 # enough to build up to 1000 blocks 10 minutes apart without worrying about getting into the future
self.nodes[0].setmocktime(long_past_time - 100) # enough so that the generated blocks will still all be before long_past_time
self.coinbase_blocks = self.nodes[0].generate(1 + 16 + 2 * 32 + 1) # 82 blocks generated for inputs
self.nodes[0].setmocktime(0) # set time back to present so yielded blocks aren't in the future as we advance last_block_time
self.tipheight = 82 # height of the next block to build
self.last_block_time = long_past_time
self.tip = int(self.nodes[0].getbestblockhash(), 16)
self.nodeaddress = self.nodes[0].getnewaddress()
self.log.info("Test that the csv softfork is DEFINED")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'defined')
test_blocks = self.generate_blocks(61, 4)
self.sync_blocks(test_blocks)
self.log.info("Advance from DEFINED to STARTED, height = 143")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'started')
self.log.info("Fail to achieve LOCKED_IN")
# 100 out of 144 signal bit 0. Use a variety of bits to simulate multiple parallel softforks
test_blocks = self.generate_blocks(50, 536870913) # 0x20000001 (signalling ready)
test_blocks = self.generate_blocks(20, 4, test_blocks) # 0x00000004 (signalling not)
test_blocks = self.generate_blocks(50, 536871169, test_blocks) # 0x20000101 (signalling ready)
test_blocks = self.generate_blocks(24, 536936448, test_blocks) # 0x20010000 (signalling not)
self.sync_blocks(test_blocks)
self.log.info("Failed to advance past STARTED, height = 287")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'started')
self.log.info("Generate blocks to achieve LOCK-IN")
# 108 out of 144 signal bit 0 to achieve lock-in
# using a variety of bits to simulate multiple parallel softforks
test_blocks = self.generate_blocks(58, 536870913) # 0x20000001 (signalling ready)
test_blocks = self.generate_blocks(26, 4, test_blocks) # 0x00000004 (signalling not)
test_blocks = self.generate_blocks(50, 536871169, test_blocks) # 0x20000101 (signalling ready)
test_blocks = self.generate_blocks(10, 536936448, test_blocks) # 0x20010000 (signalling not)
self.sync_blocks(test_blocks)
self.log.info("Advanced from STARTED to LOCKED_IN, height = 431")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'locked_in')
# Generate 140 more version 4 blocks
test_blocks = self.generate_blocks(140, 4)
self.sync_blocks(test_blocks)
# Inputs at height = 572
#
# Put inputs for all tests in the chain at height 572 (tip now = 571) (time increases by 600s per block)
# Note we reuse inputs for v1 and v2 txs so must test these separately
# 16 normal inputs
bip68inputs = []
for i in range(16):
bip68inputs.append(send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress))
# 2 sets of 16 inputs with 10 OP_CSV OP_DROP (actually will be prepended to spending scriptSig)
bip112basicinputs = []
for j in range(2):
inputs = []
for i in range(16):
inputs.append(send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress))
bip112basicinputs.append(inputs)
# 2 sets of 16 varied inputs with (relative_lock_time) OP_CSV OP_DROP (actually will be prepended to spending scriptSig)
bip112diverseinputs = []
for j in range(2):
inputs = []
for i in range(16):
inputs.append(send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress))
bip112diverseinputs.append(inputs)
# 1 special input with -1 OP_CSV OP_DROP (actually will be prepended to spending scriptSig)
bip112specialinput = send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress)
# 1 normal input
bip113input = send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress)
self.nodes[0].setmocktime(self.last_block_time + 600)
inputblockhash = self.nodes[0].generate(1)[0] # 1 block generated for inputs to be in chain at height 572
self.nodes[0].setmocktime(0)
self.tip = int(inputblockhash, 16)
self.tipheight += 1
self.last_block_time += 600
assert_equal(len(self.nodes[0].getblock(inputblockhash, True)["tx"]), 82 + 1)
# 2 more version 4 blocks
test_blocks = self.generate_blocks(2, 4)
self.sync_blocks(test_blocks)
self.log.info("Not yet advanced to ACTIVE, height = 574 (will activate for block 576, not 575)")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'locked_in')
# Test both version 1 and version 2 transactions for all tests
# BIP113 test transaction will be modified before each use to put in appropriate block time
bip113tx_v1 = create_transaction(self.nodes[0], bip113input, self.nodeaddress, amount=Decimal("49.98"))
bip113tx_v1.vin[0].nSequence = 0xFFFFFFFE
bip113tx_v1.nVersion = 1
bip113tx_v2 = create_transaction(self.nodes[0], bip113input, self.nodeaddress, amount=Decimal("49.98"))
bip113tx_v2.vin[0].nSequence = 0xFFFFFFFE
bip113tx_v2.nVersion = 2
# For BIP68 test all 16 relative sequence locktimes
bip68txs_v1 = create_bip68txs(self.nodes[0], bip68inputs, 1, self.nodeaddress)
bip68txs_v2 = create_bip68txs(self.nodes[0], bip68inputs, 2, self.nodeaddress)
# For BIP112 test:
# 16 relative sequence locktimes of 10 against 10 OP_CSV OP_DROP inputs
bip112txs_vary_nSequence_v1 = create_bip112txs(self.nodes[0], bip112basicinputs[0], False, 1, self.nodeaddress)
bip112txs_vary_nSequence_v2 = create_bip112txs(self.nodes[0], bip112basicinputs[0], False, 2, self.nodeaddress)
# 16 relative sequence locktimes of 9 against 10 OP_CSV OP_DROP inputs
bip112txs_vary_nSequence_9_v1 = create_bip112txs(self.nodes[0], bip112basicinputs[1], False, 1, self.nodeaddress, -1)
bip112txs_vary_nSequence_9_v2 = create_bip112txs(self.nodes[0], bip112basicinputs[1], False, 2, self.nodeaddress, -1)
# sequence lock time of 10 against 16 (relative_lock_time) OP_CSV OP_DROP inputs
bip112txs_vary_OP_CSV_v1 = create_bip112txs(self.nodes[0], bip112diverseinputs[0], True, 1, self.nodeaddress)
bip112txs_vary_OP_CSV_v2 = create_bip112txs(self.nodes[0], bip112diverseinputs[0], True, 2, self.nodeaddress)
# sequence lock time of 9 against 16 (relative_lock_time) OP_CSV OP_DROP inputs
bip112txs_vary_OP_CSV_9_v1 = create_bip112txs(self.nodes[0], bip112diverseinputs[1], True, 1, self.nodeaddress, -1)
bip112txs_vary_OP_CSV_9_v2 = create_bip112txs(self.nodes[0], bip112diverseinputs[1], True, 2, self.nodeaddress, -1)
# -1 OP_CSV OP_DROP input
bip112tx_special_v1 = create_bip112special(self.nodes[0], bip112specialinput, 1, self.nodeaddress)
bip112tx_special_v2 = create_bip112special(self.nodes[0], bip112specialinput, 2, self.nodeaddress)
self.log.info("TESTING")
self.log.info("Pre-Soft Fork Tests. All txs should pass.")
self.log.info("Test version 1 txs")
success_txs = []
# add BIP113 tx and -1 CSV tx
bip113tx_v1.nLockTime = self.last_block_time - 600 * 5 # = MTP of prior block (not <) but < time put on current block
bip113signed1 = sign_transaction(self.nodes[0], bip113tx_v1)
success_txs.append(bip113signed1)
success_txs.append(bip112tx_special_v1)
# add BIP 68 txs
success_txs.extend(all_rlt_txs(bip68txs_v1))
# add BIP 112 with seq=10 txs
success_txs.extend(all_rlt_txs(bip112txs_vary_nSequence_v1))
success_txs.extend(all_rlt_txs(bip112txs_vary_OP_CSV_v1))
# try BIP 112 with seq=9 txs
success_txs.extend(all_rlt_txs(bip112txs_vary_nSequence_9_v1))
success_txs.extend(all_rlt_txs(bip112txs_vary_OP_CSV_9_v1))
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
self.log.info("Test version 2 txs")
success_txs = []
# add BIP113 tx and -1 CSV tx
bip113tx_v2.nLockTime = self.last_block_time - 600 * 5 # = MTP of prior block (not <) but < time put on current block
bip113signed2 = sign_transaction(self.nodes[0], bip113tx_v2)
success_txs.append(bip113signed2)
success_txs.append(bip112tx_special_v2)
# add BIP 68 txs
success_txs.extend(all_rlt_txs(bip68txs_v2))
# add BIP 112 with seq=10 txs
success_txs.extend(all_rlt_txs(bip112txs_vary_nSequence_v2))
success_txs.extend(all_rlt_txs(bip112txs_vary_OP_CSV_v2))
# try BIP 112 with seq=9 txs
success_txs.extend(all_rlt_txs(bip112txs_vary_nSequence_9_v2))
success_txs.extend(all_rlt_txs(bip112txs_vary_OP_CSV_9_v2))
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
# 1 more version 4 block to get us to height 575 so the fork should now be active for the next block
test_blocks = self.generate_blocks(1, 4)
self.sync_blocks(test_blocks)
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'active')
self.log.info("Post-Soft Fork Tests.")
self.log.info("BIP 113 tests")
# BIP 113 tests should now fail regardless of version number if nLockTime isn't satisfied by new rules
bip113tx_v1.nLockTime = self.last_block_time - 600 * 5 # = MTP of prior block (not <) but < time put on current block
bip113signed1 = sign_transaction(self.nodes[0], bip113tx_v1)
bip113tx_v2.nLockTime = self.last_block_time - 600 * 5 # = MTP of prior block (not <) but < time put on current block
bip113signed2 = sign_transaction(self.nodes[0], bip113tx_v2)
for bip113tx in [bip113signed1, bip113signed2]:
self.sync_blocks([self.create_test_block([bip113tx])], success=False)
# BIP 113 tests should now pass if the locktime is < MTP
bip113tx_v1.nLockTime = self.last_block_time - 600 * 5 - 1 # < MTP of prior block
bip113signed1 = sign_transaction(self.nodes[0], bip113tx_v1)
bip113tx_v2.nLockTime = self.last_block_time - 600 * 5 - 1 # < MTP of prior block
bip113signed2 = sign_transaction(self.nodes[0], bip113tx_v2)
for bip113tx in [bip113signed1, bip113signed2]:
self.sync_blocks([self.create_test_block([bip113tx])])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
# Next block height = 580 after 4 blocks of random version
test_blocks = self.generate_blocks(4, 1234)
self.sync_blocks(test_blocks)
self.log.info("BIP 68 tests")
self.log.info("Test version 1 txs - all should still pass")
success_txs = []
success_txs.extend(all_rlt_txs(bip68txs_v1))
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
self.log.info("Test version 2 txs")
# All txs with SEQUENCE_LOCKTIME_DISABLE_FLAG set pass
bip68success_txs = [tx['tx'] for tx in bip68txs_v2 if tx['sdf']]
self.sync_blocks([self.create_test_block(bip68success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
# All txs without flag fail as we are at delta height = 8 < 10 and delta time = 8 * 600 < 10 * 512
bip68timetxs = [tx['tx'] for tx in bip68txs_v2 if not tx['sdf'] and tx['stf']]
for tx in bip68timetxs:
self.sync_blocks([self.create_test_block([tx])], success=False)
bip68heighttxs = [tx['tx'] for tx in bip68txs_v2 if not tx['sdf'] and not tx['stf']]
for tx in bip68heighttxs:
self.sync_blocks([self.create_test_block([tx])], success=False)
# Advance one block to 581
test_blocks = self.generate_blocks(1, 1234)
self.sync_blocks(test_blocks)
# Height txs should fail and time txs should now pass 9 * 600 > 10 * 512
bip68success_txs.extend(bip68timetxs)
self.sync_blocks([self.create_test_block(bip68success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
for tx in bip68heighttxs:
self.sync_blocks([self.create_test_block([tx])], success=False)
# Advance one block to 582
test_blocks = self.generate_blocks(1, 1234)
self.sync_blocks(test_blocks)
# All BIP 68 txs should pass
bip68success_txs.extend(bip68heighttxs)
self.sync_blocks([self.create_test_block(bip68success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
self.log.info("BIP 112 tests")
self.log.info("Test version 1 txs")
# -1 OP_CSV tx should fail
self.sync_blocks([self.create_test_block([bip112tx_special_v1])], success=False)
# If SEQUENCE_LOCKTIME_DISABLE_FLAG is set in argument to OP_CSV, version 1 txs should still pass
success_txs = [tx['tx'] for tx in bip112txs_vary_OP_CSV_v1 if tx['sdf']]
success_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v1 if tx['sdf']]
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
# If SEQUENCE_LOCKTIME_DISABLE_FLAG is unset in argument to OP_CSV, version 1 txs should now fail
fail_txs = all_rlt_txs(bip112txs_vary_nSequence_v1)
fail_txs += all_rlt_txs(bip112txs_vary_nSequence_9_v1)
fail_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v1 if not tx['sdf']]
fail_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v1 if not tx['sdf']]
for tx in fail_txs:
self.sync_blocks([self.create_test_block([tx])], success=False)
self.log.info("Test version 2 txs")
# -1 OP_CSV tx should fail
self.sync_blocks([self.create_test_block([bip112tx_special_v2])], success=False)
# If SEQUENCE_LOCKTIME_DISABLE_FLAG is set in argument to OP_CSV, version 2 txs should pass (all sequence locks are met)
success_txs = [tx['tx'] for tx in bip112txs_vary_OP_CSV_v2 if tx['sdf']]
success_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v2 if tx['sdf']]
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
# SEQUENCE_LOCKTIME_DISABLE_FLAG is unset in argument to OP_CSV for all remaining txs ##
# All txs with nSequence 9 should fail either due to earlier mismatch or failing the CSV check
fail_txs = all_rlt_txs(bip112txs_vary_nSequence_9_v2)
fail_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v2 if not tx['sdf']]
for tx in fail_txs:
self.sync_blocks([self.create_test_block([tx])], success=False)
# If SEQUENCE_LOCKTIME_DISABLE_FLAG is set in nSequence, tx should fail
fail_txs = [tx['tx'] for tx in bip112txs_vary_nSequence_v2 if tx['sdf']]
for tx in fail_txs:
self.sync_blocks([self.create_test_block([tx])], success=False)
# If sequencelock types mismatch, tx should fail
fail_txs = [tx['tx'] for tx in bip112txs_vary_nSequence_v2 if not tx['sdf'] and tx['stf']]
fail_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_v2 if not tx['sdf'] and tx['stf']]
for tx in fail_txs:
self.sync_blocks([self.create_test_block([tx])], success=False)
# Remaining txs should pass, just test masking works properly
success_txs = [tx['tx'] for tx in bip112txs_vary_nSequence_v2 if not tx['sdf'] and not tx['stf']]
success_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_v2 if not tx['sdf'] and not tx['stf']]
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
# Additional test, of checking that comparison of two time types works properly
time_txs = []
for tx in [tx['tx'] for tx in bip112txs_vary_OP_CSV_v2 if not tx['sdf'] and tx['stf']]:
tx.vin[0].nSequence = BASE_RELATIVE_LOCKTIME | SEQ_TYPE_FLAG
signtx = sign_transaction(self.nodes[0], tx)
time_txs.append(signtx)
self.sync_blocks([self.create_test_block(time_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
# TODO: Test empty stack fails
if __name__ == '__main__':
BIP68_112_113Test().main()
| 51.05144 | 169 | 0.696506 |
from decimal import Decimal
from itertools import product
from io import BytesIO
import time
from test_framework.blocktools import create_coinbase, create_block, create_transaction
from test_framework.messages import ToHex, CTransaction
from test_framework.mininode import P2PDataStore
from test_framework.script import (
CScript,
OP_CHECKSEQUENCEVERIFY,
OP_DROP,
)
from test_framework.test_framework import DollarTestFramework
from test_framework.util import (
assert_equal,
get_bip9_status,
hex_str_to_bytes,
)
BASE_RELATIVE_LOCKTIME = 10
SEQ_DISABLE_FLAG = 1 << 31
SEQ_RANDOM_HIGH_BIT = 1 << 25
SEQ_TYPE_FLAG = 1 << 22
SEQ_RANDOM_LOW_BIT = 1 << 18
def relative_locktime(sdf, srhb, stf, srlb):
locktime = BASE_RELATIVE_LOCKTIME
if sdf:
locktime |= SEQ_DISABLE_FLAG
if srhb:
locktime |= SEQ_RANDOM_HIGH_BIT
if stf:
locktime |= SEQ_TYPE_FLAG
if srlb:
locktime |= SEQ_RANDOM_LOW_BIT
return locktime
def all_rlt_txs(txs):
return [tx['tx'] for tx in txs]
def sign_transaction(node, unsignedtx):
rawtx = ToHex(unsignedtx)
signresult = node.signrawtransactionwithwallet(rawtx)
tx = CTransaction()
f = BytesIO(hex_str_to_bytes(signresult['hex']))
tx.deserialize(f)
return tx
def create_bip112special(node, input, txversion, address):
tx = create_transaction(node, input, address, amount=Decimal("49.98"))
tx.nVersion = txversion
signtx = sign_transaction(node, tx)
signtx.vin[0].scriptSig = CScript([-1, OP_CHECKSEQUENCEVERIFY, OP_DROP] + list(CScript(signtx.vin[0].scriptSig)))
return signtx
def send_generic_input_tx(node, coinbases, address):
return node.sendrawtransaction(ToHex(sign_transaction(node, create_transaction(node, node.getblock(coinbases.pop())['tx'][0], address, amount=Decimal("49.99")))))
def create_bip68txs(node, bip68inputs, txversion, address, locktime_delta=0):
txs = []
assert(len(bip68inputs) >= 16)
for i, (sdf, srhb, stf, srlb) in enumerate(product(*[[True, False]] * 4)):
locktime = relative_locktime(sdf, srhb, stf, srlb)
tx = create_transaction(node, bip68inputs[i], address, amount=Decimal("49.98"))
tx.nVersion = txversion
tx.vin[0].nSequence = locktime + locktime_delta
tx = sign_transaction(node, tx)
tx.rehash()
txs.append({'tx': tx, 'sdf': sdf, 'stf': stf})
return txs
def create_bip112txs(node, bip112inputs, varyOP_CSV, txversion, address, locktime_delta=0):
txs = []
assert(len(bip112inputs) >= 16)
for i, (sdf, srhb, stf, srlb) in enumerate(product(*[[True, False]] * 4)):
locktime = relative_locktime(sdf, srhb, stf, srlb)
tx = create_transaction(node, bip112inputs[i], address, amount=Decimal("49.98"))
if (varyOP_CSV):
tx.vin[0].nSequence = BASE_RELATIVE_LOCKTIME + locktime_delta
else:
tx.vin[0].nSequence = locktime + locktime_delta
tx.nVersion = txversion
signtx = sign_transaction(node, tx)
if (varyOP_CSV):
signtx.vin[0].scriptSig = CScript([locktime, OP_CHECKSEQUENCEVERIFY, OP_DROP] + list(CScript(signtx.vin[0].scriptSig)))
else:
signtx.vin[0].scriptSig = CScript([BASE_RELATIVE_LOCKTIME, OP_CHECKSEQUENCEVERIFY, OP_DROP] + list(CScript(signtx.vin[0].scriptSig)))
tx.rehash()
txs.append({'tx': signtx, 'sdf': sdf, 'stf': stf})
return txs
class BIP68_112_113Test(DollarTestFramework):
def set_test_params(self):
self.num_nodes = 1
self.setup_clean_chain = True
self.extra_args = [['-whitelist=127.0.0.1', '-blockversion=4', '-addresstype=legacy']]
def skip_test_if_missing_module(self):
self.skip_if_no_wallet()
def generate_blocks(self, number, version, test_blocks=None):
if test_blocks is None:
test_blocks = []
for i in range(number):
block = self.create_test_block([], version)
test_blocks.append(block)
self.last_block_time += 600
self.tip = block.sha256
self.tipheight += 1
return test_blocks
def create_test_block(self, txs, version=536870912):
block = create_block(self.tip, create_coinbase(self.tipheight + 1), self.last_block_time + 600)
block.nVersion = version
block.vtx.extend(txs)
block.hashMerkleRoot = block.calc_merkle_root()
block.rehash()
block.solve()
return block
def sync_blocks(self, blocks, success=True, reject_code=None, reject_reason=None, request_block=True):
self.nodes[0].p2p.send_blocks_and_test(blocks, self.nodes[0], success=success, reject_code=reject_code, reject_reason=reject_reason, request_block=request_block)
def run_test(self):
self.nodes[0].add_p2p_connection(P2PDataStore())
self.log.info("Generate blocks in the past for coinbase outputs.")
long_past_time = int(time.time()) - 600 * 1000
self.nodes[0].setmocktime(long_past_time - 100)
self.coinbase_blocks = self.nodes[0].generate(1 + 16 + 2 * 32 + 1)
self.nodes[0].setmocktime(0)
self.tipheight = 82 # height of the next block to build
self.last_block_time = long_past_time
self.tip = int(self.nodes[0].getbestblockhash(), 16)
self.nodeaddress = self.nodes[0].getnewaddress()
self.log.info("Test that the csv softfork is DEFINED")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'defined')
test_blocks = self.generate_blocks(61, 4)
self.sync_blocks(test_blocks)
self.log.info("Advance from DEFINED to STARTED, height = 143")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'started')
self.log.info("Fail to achieve LOCKED_IN")
# 100 out of 144 signal bit 0. Use a variety of bits to simulate multiple parallel softforks
test_blocks = self.generate_blocks(50, 536870913) # 0x20000001 (signalling ready)
test_blocks = self.generate_blocks(20, 4, test_blocks) # 0x00000004 (signalling not)
test_blocks = self.generate_blocks(50, 536871169, test_blocks) # 0x20000101 (signalling ready)
test_blocks = self.generate_blocks(24, 536936448, test_blocks) # 0x20010000 (signalling not)
self.sync_blocks(test_blocks)
self.log.info("Failed to advance past STARTED, height = 287")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'started')
self.log.info("Generate blocks to achieve LOCK-IN")
# 108 out of 144 signal bit 0 to achieve lock-in
# using a variety of bits to simulate multiple parallel softforks
test_blocks = self.generate_blocks(58, 536870913) # 0x20000001 (signalling ready)
test_blocks = self.generate_blocks(26, 4, test_blocks) # 0x00000004 (signalling not)
test_blocks = self.generate_blocks(50, 536871169, test_blocks) # 0x20000101 (signalling ready)
test_blocks = self.generate_blocks(10, 536936448, test_blocks) # 0x20010000 (signalling not)
self.sync_blocks(test_blocks)
self.log.info("Advanced from STARTED to LOCKED_IN, height = 431")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'locked_in')
# Generate 140 more version 4 blocks
test_blocks = self.generate_blocks(140, 4)
self.sync_blocks(test_blocks)
# Inputs at height = 572
#
# Put inputs for all tests in the chain at height 572 (tip now = 571) (time increases by 600s per block)
# Note we reuse inputs for v1 and v2 txs so must test these separately
# 16 normal inputs
bip68inputs = []
for i in range(16):
bip68inputs.append(send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress))
# 2 sets of 16 inputs with 10 OP_CSV OP_DROP (actually will be prepended to spending scriptSig)
bip112basicinputs = []
for j in range(2):
inputs = []
for i in range(16):
inputs.append(send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress))
bip112basicinputs.append(inputs)
# 2 sets of 16 varied inputs with (relative_lock_time) OP_CSV OP_DROP (actually will be prepended to spending scriptSig)
bip112diverseinputs = []
for j in range(2):
inputs = []
for i in range(16):
inputs.append(send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress))
bip112diverseinputs.append(inputs)
# 1 special input with -1 OP_CSV OP_DROP (actually will be prepended to spending scriptSig)
bip112specialinput = send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress)
# 1 normal input
bip113input = send_generic_input_tx(self.nodes[0], self.coinbase_blocks, self.nodeaddress)
self.nodes[0].setmocktime(self.last_block_time + 600)
inputblockhash = self.nodes[0].generate(1)[0] # 1 block generated for inputs to be in chain at height 572
self.nodes[0].setmocktime(0)
self.tip = int(inputblockhash, 16)
self.tipheight += 1
self.last_block_time += 600
assert_equal(len(self.nodes[0].getblock(inputblockhash, True)["tx"]), 82 + 1)
# 2 more version 4 blocks
test_blocks = self.generate_blocks(2, 4)
self.sync_blocks(test_blocks)
self.log.info("Not yet advanced to ACTIVE, height = 574 (will activate for block 576, not 575)")
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'locked_in')
# Test both version 1 and version 2 transactions for all tests
# BIP113 test transaction will be modified before each use to put in appropriate block time
bip113tx_v1 = create_transaction(self.nodes[0], bip113input, self.nodeaddress, amount=Decimal("49.98"))
bip113tx_v1.vin[0].nSequence = 0xFFFFFFFE
bip113tx_v1.nVersion = 1
bip113tx_v2 = create_transaction(self.nodes[0], bip113input, self.nodeaddress, amount=Decimal("49.98"))
bip113tx_v2.vin[0].nSequence = 0xFFFFFFFE
bip113tx_v2.nVersion = 2
# For BIP68 test all 16 relative sequence locktimes
bip68txs_v1 = create_bip68txs(self.nodes[0], bip68inputs, 1, self.nodeaddress)
bip68txs_v2 = create_bip68txs(self.nodes[0], bip68inputs, 2, self.nodeaddress)
# For BIP112 test:
# 16 relative sequence locktimes of 10 against 10 OP_CSV OP_DROP inputs
bip112txs_vary_nSequence_v1 = create_bip112txs(self.nodes[0], bip112basicinputs[0], False, 1, self.nodeaddress)
bip112txs_vary_nSequence_v2 = create_bip112txs(self.nodes[0], bip112basicinputs[0], False, 2, self.nodeaddress)
# 16 relative sequence locktimes of 9 against 10 OP_CSV OP_DROP inputs
bip112txs_vary_nSequence_9_v1 = create_bip112txs(self.nodes[0], bip112basicinputs[1], False, 1, self.nodeaddress, -1)
bip112txs_vary_nSequence_9_v2 = create_bip112txs(self.nodes[0], bip112basicinputs[1], False, 2, self.nodeaddress, -1)
# sequence lock time of 10 against 16 (relative_lock_time) OP_CSV OP_DROP inputs
bip112txs_vary_OP_CSV_v1 = create_bip112txs(self.nodes[0], bip112diverseinputs[0], True, 1, self.nodeaddress)
bip112txs_vary_OP_CSV_v2 = create_bip112txs(self.nodes[0], bip112diverseinputs[0], True, 2, self.nodeaddress)
# sequence lock time of 9 against 16 (relative_lock_time) OP_CSV OP_DROP inputs
bip112txs_vary_OP_CSV_9_v1 = create_bip112txs(self.nodes[0], bip112diverseinputs[1], True, 1, self.nodeaddress, -1)
bip112txs_vary_OP_CSV_9_v2 = create_bip112txs(self.nodes[0], bip112diverseinputs[1], True, 2, self.nodeaddress, -1)
# -1 OP_CSV OP_DROP input
bip112tx_special_v1 = create_bip112special(self.nodes[0], bip112specialinput, 1, self.nodeaddress)
bip112tx_special_v2 = create_bip112special(self.nodes[0], bip112specialinput, 2, self.nodeaddress)
self.log.info("TESTING")
self.log.info("Pre-Soft Fork Tests. All txs should pass.")
self.log.info("Test version 1 txs")
success_txs = []
# add BIP113 tx and -1 CSV tx
bip113tx_v1.nLockTime = self.last_block_time - 600 * 5 # = MTP of prior block (not <) but < time put on current block
bip113signed1 = sign_transaction(self.nodes[0], bip113tx_v1)
success_txs.append(bip113signed1)
success_txs.append(bip112tx_special_v1)
# add BIP 68 txs
success_txs.extend(all_rlt_txs(bip68txs_v1))
# add BIP 112 with seq=10 txs
success_txs.extend(all_rlt_txs(bip112txs_vary_nSequence_v1))
success_txs.extend(all_rlt_txs(bip112txs_vary_OP_CSV_v1))
# try BIP 112 with seq=9 txs
success_txs.extend(all_rlt_txs(bip112txs_vary_nSequence_9_v1))
success_txs.extend(all_rlt_txs(bip112txs_vary_OP_CSV_9_v1))
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
self.log.info("Test version 2 txs")
success_txs = []
# add BIP113 tx and -1 CSV tx
bip113tx_v2.nLockTime = self.last_block_time - 600 * 5 # = MTP of prior block (not <) but < time put on current block
bip113signed2 = sign_transaction(self.nodes[0], bip113tx_v2)
success_txs.append(bip113signed2)
success_txs.append(bip112tx_special_v2)
# add BIP 68 txs
success_txs.extend(all_rlt_txs(bip68txs_v2))
# add BIP 112 with seq=10 txs
success_txs.extend(all_rlt_txs(bip112txs_vary_nSequence_v2))
success_txs.extend(all_rlt_txs(bip112txs_vary_OP_CSV_v2))
# try BIP 112 with seq=9 txs
success_txs.extend(all_rlt_txs(bip112txs_vary_nSequence_9_v2))
success_txs.extend(all_rlt_txs(bip112txs_vary_OP_CSV_9_v2))
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
# 1 more version 4 block to get us to height 575 so the fork should now be active for the next block
test_blocks = self.generate_blocks(1, 4)
self.sync_blocks(test_blocks)
assert_equal(get_bip9_status(self.nodes[0], 'csv')['status'], 'active')
self.log.info("Post-Soft Fork Tests.")
self.log.info("BIP 113 tests")
# BIP 113 tests should now fail regardless of version number if nLockTime isn't satisfied by new rules
bip113tx_v1.nLockTime = self.last_block_time - 600 * 5
bip113signed1 = sign_transaction(self.nodes[0], bip113tx_v1)
bip113tx_v2.nLockTime = self.last_block_time - 600 * 5
bip113signed2 = sign_transaction(self.nodes[0], bip113tx_v2)
for bip113tx in [bip113signed1, bip113signed2]:
self.sync_blocks([self.create_test_block([bip113tx])], success=False)
bip113tx_v1.nLockTime = self.last_block_time - 600 * 5 - 1
bip113signed1 = sign_transaction(self.nodes[0], bip113tx_v1)
bip113tx_v2.nLockTime = self.last_block_time - 600 * 5 - 1
bip113signed2 = sign_transaction(self.nodes[0], bip113tx_v2)
for bip113tx in [bip113signed1, bip113signed2]:
self.sync_blocks([self.create_test_block([bip113tx])])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
test_blocks = self.generate_blocks(4, 1234)
self.sync_blocks(test_blocks)
self.log.info("BIP 68 tests")
self.log.info("Test version 1 txs - all should still pass")
success_txs = []
success_txs.extend(all_rlt_txs(bip68txs_v1))
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
self.log.info("Test version 2 txs")
bip68success_txs = [tx['tx'] for tx in bip68txs_v2 if tx['sdf']]
self.sync_blocks([self.create_test_block(bip68success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
bip68timetxs = [tx['tx'] for tx in bip68txs_v2 if not tx['sdf'] and tx['stf']]
for tx in bip68timetxs:
self.sync_blocks([self.create_test_block([tx])], success=False)
bip68heighttxs = [tx['tx'] for tx in bip68txs_v2 if not tx['sdf'] and not tx['stf']]
for tx in bip68heighttxs:
self.sync_blocks([self.create_test_block([tx])], success=False)
test_blocks = self.generate_blocks(1, 1234)
self.sync_blocks(test_blocks)
bip68success_txs.extend(bip68timetxs)
self.sync_blocks([self.create_test_block(bip68success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
for tx in bip68heighttxs:
self.sync_blocks([self.create_test_block([tx])], success=False)
test_blocks = self.generate_blocks(1, 1234)
self.sync_blocks(test_blocks)
bip68success_txs.extend(bip68heighttxs)
self.sync_blocks([self.create_test_block(bip68success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
self.log.info("BIP 112 tests")
self.log.info("Test version 1 txs")
self.sync_blocks([self.create_test_block([bip112tx_special_v1])], success=False)
success_txs = [tx['tx'] for tx in bip112txs_vary_OP_CSV_v1 if tx['sdf']]
success_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v1 if tx['sdf']]
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
fail_txs = all_rlt_txs(bip112txs_vary_nSequence_v1)
fail_txs += all_rlt_txs(bip112txs_vary_nSequence_9_v1)
fail_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v1 if not tx['sdf']]
fail_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v1 if not tx['sdf']]
for tx in fail_txs:
self.sync_blocks([self.create_test_block([tx])], success=False)
self.log.info("Test version 2 txs")
self.sync_blocks([self.create_test_block([bip112tx_special_v2])], success=False)
success_txs = [tx['tx'] for tx in bip112txs_vary_OP_CSV_v2 if tx['sdf']]
success_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v2 if tx['sdf']]
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
fail_txs = all_rlt_txs(bip112txs_vary_nSequence_9_v2)
fail_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_9_v2 if not tx['sdf']]
for tx in fail_txs:
self.sync_blocks([self.create_test_block([tx])], success=False)
fail_txs = [tx['tx'] for tx in bip112txs_vary_nSequence_v2 if tx['sdf']]
for tx in fail_txs:
self.sync_blocks([self.create_test_block([tx])], success=False)
fail_txs = [tx['tx'] for tx in bip112txs_vary_nSequence_v2 if not tx['sdf'] and tx['stf']]
fail_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_v2 if not tx['sdf'] and tx['stf']]
for tx in fail_txs:
self.sync_blocks([self.create_test_block([tx])], success=False)
success_txs = [tx['tx'] for tx in bip112txs_vary_nSequence_v2 if not tx['sdf'] and not tx['stf']]
success_txs += [tx['tx'] for tx in bip112txs_vary_OP_CSV_v2 if not tx['sdf'] and not tx['stf']]
self.sync_blocks([self.create_test_block(success_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
time_txs = []
for tx in [tx['tx'] for tx in bip112txs_vary_OP_CSV_v2 if not tx['sdf'] and tx['stf']]:
tx.vin[0].nSequence = BASE_RELATIVE_LOCKTIME | SEQ_TYPE_FLAG
signtx = sign_transaction(self.nodes[0], tx)
time_txs.append(signtx)
self.sync_blocks([self.create_test_block(time_txs)])
self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash())
if __name__ == '__main__':
BIP68_112_113Test().main()
| true | true |
1c340b1f2cbd9486c82f411e8186992ec5ca287f | 3,885 | py | Python | edb/edgeql/compiler/options.py | sfermigier/edgedb | 13aff7004aa682777287157dea52642c374967e8 | [
"Apache-2.0"
] | 7,302 | 2018-05-10T18:36:31.000Z | 2022-03-31T17:49:36.000Z | edb/edgeql/compiler/options.py | sfermigier/edgedb | 13aff7004aa682777287157dea52642c374967e8 | [
"Apache-2.0"
] | 1,602 | 2018-05-10T17:45:38.000Z | 2022-03-31T23:46:19.000Z | edb/edgeql/compiler/options.py | sfermigier/edgedb | 13aff7004aa682777287157dea52642c374967e8 | [
"Apache-2.0"
] | 236 | 2018-05-13T14:15:29.000Z | 2022-03-29T19:39:19.000Z | #
# This source file is part of the EdgeDB open source project.
#
# Copyright 2008-present MagicStack Inc. and the EdgeDB authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""EdgeQL compiler options."""
from __future__ import annotations
from typing import *
from dataclasses import dataclass, field as dc_field
if TYPE_CHECKING:
from edb.schema import functions as s_func
from edb.schema import objects as s_obj
from edb.schema import name as s_name
from edb.schema import types as s_types
from edb.schema import pointers as s_pointers
@dataclass
class GlobalCompilerOptions:
"""Compiler toggles that affect compilation as a whole."""
#: Whether to allow the expression to be of a generic type.
allow_generic_type_output: bool = False
#: Allow writing to protected pointers in INSERT.
allow_writing_protected_pointers: bool = False
#: Whether to apply various query rewrites, including access policy.
apply_query_rewrites: bool = True
#: Enables constant folding optimization (enabled by default).
constant_folding: bool = True
#: Force types of all parameters to std::json
json_parameters: bool = False
#: Use material types for pointer targets in schema views.
schema_view_mode: bool = False
#: Whether to track which subexpressions reference each schema object.
track_schema_ref_exprs: bool = False
#: If the expression is being processed in the context of a certain
#: schema object, i.e. a constraint expression, or a pointer default,
#: this contains the type of the schema object.
schema_object_context: Optional[Type[s_obj.Object]] = None
#: When compiling a function body, specifies function parameter
#: definitions.
func_params: Optional[s_func.ParameterLikeList] = None
#: The name that can be used in a "DML is disallowed in ..."
#: error. When this is not None, any DML should cause an error.
in_ddl_context_name: Optional[str] = None
#: Sometimes (like in queries compiled form GraphQL) it may be OK
#: to contain DML in the top-level shape computables.
allow_top_level_shape_dml: bool = False
@dataclass
class CompilerOptions(GlobalCompilerOptions):
#: Module name aliases.
modaliases: Mapping[Optional[str], str] = dc_field(default_factory=dict)
#: External symbol table.
anchors: Mapping[str, Any] = dc_field(default_factory=dict)
#: The symbol to assume as the prefix for abbreviated paths.
path_prefix_anchor: Optional[str] = None
#: Module to put derived schema objects to.
derived_target_module: Optional[str] = None
#: The name to use for the top-level type variant.
result_view_name: Optional[s_name.QualName] = None
#: If > 0, Inject implicit LIMIT to every SELECT query.
implicit_limit: int = 0
#: Include id property in every shape implicitly.
implicit_id_in_shapes: bool = False
#: Include __tid__ computable (.__type__.id) in every shape implicitly.
implicit_tid_in_shapes: bool = False
#: Include __tname__ computable (.__type__.name) in every shape implicitly.
implicit_tname_in_shapes: bool = False
#: A set of schema types and links that should be treated
#: as singletons in the context of this compilation.
singletons: FrozenSet[Union[s_types.Type, s_pointers.Pointer]] = (
frozenset())
| 34.380531 | 79 | 0.732046 |
from __future__ import annotations
from typing import *
from dataclasses import dataclass, field as dc_field
if TYPE_CHECKING:
from edb.schema import functions as s_func
from edb.schema import objects as s_obj
from edb.schema import name as s_name
from edb.schema import types as s_types
from edb.schema import pointers as s_pointers
@dataclass
class GlobalCompilerOptions:
allow_generic_type_output: bool = False
allow_writing_protected_pointers: bool = False
apply_query_rewrites: bool = True
constant_folding: bool = True
json_parameters: bool = False
schema_view_mode: bool = False
track_schema_ref_exprs: bool = False
schema_object_context: Optional[Type[s_obj.Object]] = None
func_params: Optional[s_func.ParameterLikeList] = None
in_ddl_context_name: Optional[str] = None
allow_top_level_shape_dml: bool = False
@dataclass
class CompilerOptions(GlobalCompilerOptions):
modaliases: Mapping[Optional[str], str] = dc_field(default_factory=dict)
anchors: Mapping[str, Any] = dc_field(default_factory=dict)
path_prefix_anchor: Optional[str] = None
derived_target_module: Optional[str] = None
result_view_name: Optional[s_name.QualName] = None
implicit_limit: int = 0
implicit_id_in_shapes: bool = False
implicit_tid_in_shapes: bool = False
implicit_tname_in_shapes: bool = False
singletons: FrozenSet[Union[s_types.Type, s_pointers.Pointer]] = (
frozenset())
| true | true |
1c340c823663b0eb355585b2deb48e95a2779621 | 182 | py | Python | pyretri/models/__init__.py | dongan-beta/PyRetri | 8756d5d5813a5211b58855373b6c6cd33d7a11f6 | [
"Apache-2.0"
] | 1,063 | 2020-04-21T12:42:05.000Z | 2022-03-31T06:32:50.000Z | pyretri/models/__init__.py | dongan-beta/PyRetri | 8756d5d5813a5211b58855373b6c6cd33d7a11f6 | [
"Apache-2.0"
] | 39 | 2020-05-07T07:24:19.000Z | 2022-02-02T23:49:23.000Z | pyretri/models/__init__.py | dongan-beta/PyRetri | 8756d5d5813a5211b58855373b6c6cd33d7a11f6 | [
"Apache-2.0"
] | 174 | 2020-04-26T04:33:11.000Z | 2022-03-17T02:58:45.000Z | # -*- coding: utf-8 -*-
from yacs.config import CfgNode
from .config import get_model_cfg
from .builder import build_model
__all__ = [
'get_model_cfg',
'build_model',
]
| 13 | 33 | 0.686813 |
from yacs.config import CfgNode
from .config import get_model_cfg
from .builder import build_model
__all__ = [
'get_model_cfg',
'build_model',
]
| true | true |
1c340cac809b176c6009ba616e4eda9d096d8527 | 722 | py | Python | nazgul/messages/google_chat.py | avara1986/nazgul | 21e32262acac2acda0232c3eb71b8aaa292e63b5 | [
"Apache-2.0"
] | 1 | 2019-06-17T20:28:24.000Z | 2019-06-17T20:28:24.000Z | nazgul/messages/google_chat.py | avara1986/nazgul | 21e32262acac2acda0232c3eb71b8aaa292e63b5 | [
"Apache-2.0"
] | null | null | null | nazgul/messages/google_chat.py | avara1986/nazgul | 21e32262acac2acda0232c3eb71b8aaa292e63b5 | [
"Apache-2.0"
] | null | null | null | from nazgul.message import DriverMessage
from nazgul.response import GoogleChatResponse
class Message(DriverMessage):
response = GoogleChatResponse
use_ids = True
def set_values(self):
self.user_id = self.msg["user"]["name"]
self.user_name = self.users.get(self.user_id, {}).get("name", False)
if not self.user_name:
self.user_name = self.msg["user"]["displayName"]
if self.users.get(self.user_id, {}).get("alias", False):
self.user_id = self.users[self.user_id]["alias"]
self.text = self.msg["message"]["text"]
def is_valid_msg(self):
return self.msg.get("message", False) and self.msg.get("message", {}).get("text", False)
| 34.380952 | 96 | 0.641274 | from nazgul.message import DriverMessage
from nazgul.response import GoogleChatResponse
class Message(DriverMessage):
response = GoogleChatResponse
use_ids = True
def set_values(self):
self.user_id = self.msg["user"]["name"]
self.user_name = self.users.get(self.user_id, {}).get("name", False)
if not self.user_name:
self.user_name = self.msg["user"]["displayName"]
if self.users.get(self.user_id, {}).get("alias", False):
self.user_id = self.users[self.user_id]["alias"]
self.text = self.msg["message"]["text"]
def is_valid_msg(self):
return self.msg.get("message", False) and self.msg.get("message", {}).get("text", False)
| true | true |
1c340da185a24d00ef78622ff2aacf41ea532c61 | 4,453 | py | Python | tools/face/__make_cs6_split_annot.py | AruniRC/detectron-self-train | a5d0edc51aeab92b953948ef2401294e87efb719 | [
"MIT"
] | 128 | 2019-04-12T17:06:27.000Z | 2022-02-26T10:24:43.000Z | tools/face/__make_cs6_split_annot.py | AruniRC/detectron-self-train | a5d0edc51aeab92b953948ef2401294e87efb719 | [
"MIT"
] | 15 | 2019-06-12T03:55:48.000Z | 2021-03-12T07:09:53.000Z | tools/face/__make_cs6_split_annot.py | AruniRC/detectron-self-train | a5d0edc51aeab92b953948ef2401294e87efb719 | [
"MIT"
] | 24 | 2019-04-12T17:06:30.000Z | 2021-07-12T12:38:20.000Z |
"""
Create ground-truth annotation files for IJBC-style evaluation on CS6.
By default the "val" split is considered, using validation videos listed in
data/CS6/list_video_val.txt.
NOTE: create symlink to "/mnt/nfs/work1/elm/arunirc/Data/CS6_annots/" at "data/CS6_annots".
Usage:
srun --pty --mem 50000 python tools/face/make_cs6_split_annot.py --split val
Output files:
data/CS6_annots
cs6_annot_eval_imlist_val.txt
cs6_annot_eval_val.txt
"""
from __future__ import absolute_import
from __future__ import division
import matplotlib
matplotlib.use('Agg')
import sys
sys.path.append('./tools')
import _init_paths
import numpy as np
import os
import argparse
import os.path as osp
import time
from six.moves import xrange
import utils.face_utils as face_utils
GT_VIDEO_LIST = 'data/CS6/list_video_%s.txt'
GT_ANNOT_DIR = '/mnt/nfs/work1/elm/arunirc/Data/CS6_annots/video_annots'
DET_DIR = '/mnt/nfs/work1/elm/arunirc/Research/face-faster-rcnn-ohem/output/CS6/dets-resnet101-baseline-frames'
NUM_IM_VID = 20 # number of images to be sampled per video (for subset creation)
DEBUG = False
def parse_args():
parser = argparse.ArgumentParser(description='Creating CS6 ground truth data')
parser.add_argument(
'--gt_dir', help='Path to CS6 ground-truth',
default=GT_ANNOT_DIR
)
parser.add_argument(
'--split', help='Split (train, val, test)',
default='val'
)
parser.add_argument(
'--video_list', help='Path to CS6 videos listed in split',
default=GT_VIDEO_LIST
)
# parser.add_argument(
# '--subset', help='Create a subset for quick eval', action='store_true',
# default=False
# )
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
args.video_list = args.video_list % args.split
np.random.seed(0)
# -----------------------------------------------------------------------------------
# Data setup
# -----------------------------------------------------------------------------------
# Ground truth
vid_list = np.loadtxt(args.video_list, dtype=str)
# Outputs
gt_out_dir = osp.dirname(args.gt_dir)
gt_out_file = osp.join(gt_out_dir, 'cs6_annot_eval_%s.txt' % args.split)
gt_imlist_file = osp.join(gt_out_dir, 'cs6_annot_eval_imlist_%s.txt' % args.split)
# -----------------------------------------------------------------------------------
# Eval-format ground-truth annots for CS6
# -----------------------------------------------------------------------------------
with open(gt_out_file, 'w') as fid_gt:
with open(gt_imlist_file, 'w') as fid_imlist:
for video_name in vid_list:
# Load ground-truth annots for that video
gt_file = osp.join(args.gt_dir,
video_name.split('.')[0] + '.txt')
gt_annots = face_utils.parse_wider_gt(gt_file)
if len(gt_annots) == 0:
continue # no gt faces in this video
image_list = np.array( list(gt_annots.keys()) )
# # Select a subset of frames, or use all frames (much slower)
# if args.subset:
# assert len(image_list) != 0
# subset_size = min( (NUM_IM_VID, len(image_list)) )
# sel = np.random.randint(len(image_list), size=NUM_IM_VID)
# image_list = image_list[sel]
print('Video annot: %s' % gt_file)
# Output bboxes lists for evaluation
for i, im_name in enumerate(image_list):
# Writing to ground-truth text file
annot = np.array( gt_annots[im_name] )
fid_gt.write(im_name + '\n')
fid_gt.write(str(annot.shape[0]) + '\n')
for j in xrange(annot.shape[0]):
fid_gt.write('%f %f %f %f\n' % ( annot[j, 0], annot[j, 1],
annot[j, 2], annot[j, 3]) )
# Writing image names (order of images must match for imlist and annots)
fid_imlist.write(im_name + '\n')
if ((i + 1) % 100) == 0:
sys.stdout.write('. ')
sys.stdout.flush()
print('\n')
| 32.268116 | 111 | 0.543454 |
from __future__ import absolute_import
from __future__ import division
import matplotlib
matplotlib.use('Agg')
import sys
sys.path.append('./tools')
import _init_paths
import numpy as np
import os
import argparse
import os.path as osp
import time
from six.moves import xrange
import utils.face_utils as face_utils
GT_VIDEO_LIST = 'data/CS6/list_video_%s.txt'
GT_ANNOT_DIR = '/mnt/nfs/work1/elm/arunirc/Data/CS6_annots/video_annots'
DET_DIR = '/mnt/nfs/work1/elm/arunirc/Research/face-faster-rcnn-ohem/output/CS6/dets-resnet101-baseline-frames'
NUM_IM_VID = 20
DEBUG = False
def parse_args():
parser = argparse.ArgumentParser(description='Creating CS6 ground truth data')
parser.add_argument(
'--gt_dir', help='Path to CS6 ground-truth',
default=GT_ANNOT_DIR
)
parser.add_argument(
'--split', help='Split (train, val, test)',
default='val'
)
parser.add_argument(
'--video_list', help='Path to CS6 videos listed in split',
default=GT_VIDEO_LIST
)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
args.video_list = args.video_list % args.split
np.random.seed(0)
vid_list = np.loadtxt(args.video_list, dtype=str)
gt_out_dir = osp.dirname(args.gt_dir)
gt_out_file = osp.join(gt_out_dir, 'cs6_annot_eval_%s.txt' % args.split)
gt_imlist_file = osp.join(gt_out_dir, 'cs6_annot_eval_imlist_%s.txt' % args.split)
with open(gt_out_file, 'w') as fid_gt:
with open(gt_imlist_file, 'w') as fid_imlist:
for video_name in vid_list:
gt_file = osp.join(args.gt_dir,
video_name.split('.')[0] + '.txt')
gt_annots = face_utils.parse_wider_gt(gt_file)
if len(gt_annots) == 0:
continue
image_list = np.array( list(gt_annots.keys()) )
print('Video annot: %s' % gt_file)
for i, im_name in enumerate(image_list):
annot = np.array( gt_annots[im_name] )
fid_gt.write(im_name + '\n')
fid_gt.write(str(annot.shape[0]) + '\n')
for j in xrange(annot.shape[0]):
fid_gt.write('%f %f %f %f\n' % ( annot[j, 0], annot[j, 1],
annot[j, 2], annot[j, 3]) )
fid_imlist.write(im_name + '\n')
if ((i + 1) % 100) == 0:
sys.stdout.write('. ')
sys.stdout.flush()
print('\n')
| true | true |
1c340e1d1cf5c12e33fe0ec46bb8d38eb17ee24d | 1,016 | py | Python | src/OFS/tests/testLockable.py | rbanffy/Zope | ecf6770219052e7c7f8c9634ddf187a1e6280742 | [
"ZPL-2.1"
] | 289 | 2015-01-05T12:38:21.000Z | 2022-03-05T21:20:39.000Z | src/OFS/tests/testLockable.py | rbanffy/Zope | ecf6770219052e7c7f8c9634ddf187a1e6280742 | [
"ZPL-2.1"
] | 732 | 2015-02-09T23:35:57.000Z | 2022-03-31T09:10:13.000Z | src/OFS/tests/testLockable.py | rbanffy/Zope | ecf6770219052e7c7f8c9634ddf187a1e6280742 | [
"ZPL-2.1"
] | 102 | 2015-01-12T14:03:35.000Z | 2022-03-30T11:02:44.000Z | import unittest
from OFS.interfaces import IWriteLock
from zope.interface import implementer
@implementer(IWriteLock)
class LockableResource:
def __init__(self, locked):
self.locked = locked
def wl_isLocked(self):
return self.locked
class UnlockableResource:
pass
class TestUtilFunctions(unittest.TestCase):
def test_wl_isLocked(self):
from OFS.Lockable import wl_isLocked
unlockable = UnlockableResource()
self.assertFalse(wl_isLocked(unlockable))
lockable_unlocked = LockableResource(locked=False)
self.assertFalse(wl_isLocked(lockable_unlocked))
lockable_locked = LockableResource(locked=True)
self.assertTrue(wl_isLocked(lockable_locked))
def test_wl_isLockable(self):
from OFS.Lockable import wl_isLockable
unlockable = UnlockableResource()
self.assertFalse(wl_isLockable(unlockable))
lockable = LockableResource(locked=False)
self.assertTrue(wl_isLockable(lockable))
| 26.736842 | 58 | 0.729331 | import unittest
from OFS.interfaces import IWriteLock
from zope.interface import implementer
@implementer(IWriteLock)
class LockableResource:
def __init__(self, locked):
self.locked = locked
def wl_isLocked(self):
return self.locked
class UnlockableResource:
pass
class TestUtilFunctions(unittest.TestCase):
def test_wl_isLocked(self):
from OFS.Lockable import wl_isLocked
unlockable = UnlockableResource()
self.assertFalse(wl_isLocked(unlockable))
lockable_unlocked = LockableResource(locked=False)
self.assertFalse(wl_isLocked(lockable_unlocked))
lockable_locked = LockableResource(locked=True)
self.assertTrue(wl_isLocked(lockable_locked))
def test_wl_isLockable(self):
from OFS.Lockable import wl_isLockable
unlockable = UnlockableResource()
self.assertFalse(wl_isLockable(unlockable))
lockable = LockableResource(locked=False)
self.assertTrue(wl_isLockable(lockable))
| true | true |
1c340e8db8d7a89fc4a7b2e9665f3d46e309f7d4 | 1,061 | py | Python | git_gopher/DeleteTagRemote.py | derekhamilton/git-gud | 7fd377a39796b0aa1268e7ecda6808e8e45173fe | [
"MIT"
] | 15 | 2019-11-13T20:59:53.000Z | 2020-12-15T05:21:21.000Z | git_gopher/DeleteTagRemote.py | derekhamilton/git-gud | 7fd377a39796b0aa1268e7ecda6808e8e45173fe | [
"MIT"
] | 50 | 2019-10-12T16:57:11.000Z | 2019-10-27T21:03:22.000Z | git_gopher/DeleteTagRemote.py | derekhamilton/git-gud | 7fd377a39796b0aa1268e7ecda6808e8e45173fe | [
"MIT"
] | 1 | 2019-11-14T03:20:21.000Z | 2019-11-14T03:20:21.000Z | from git_gopher.CommandInterface import CommandInterface
from git_gopher.HistoryCommandRunner import HistoryCommandRunner
from git_gopher.GitDataGetter import GitDataGetter
class DeleteTagRemote(CommandInterface):
def __init__(self, hist_command_runner: HistoryCommandRunner, git_data_getter: GitDataGetter):
self._hist_command_runner = hist_command_runner
self._git_data_getter = git_data_getter
def run(self):
remote = self._git_data_getter.get_remote_name(preview='echo "No action is taken until selecting a tag."')
if not remote:
return
tags = self._git_data_getter.get_tag_names_remote(remote, preview='echo "git tag -d {2} && git push "' + remote + ' {2}"')
if tags:
output = ''
for tag in tags:
self._hist_command_runner.run(['git', 'tag', '-d', tag])
self._hist_command_runner.run(['git', 'push', '--delete', remote, tag])
output = output + "\nDeleted tag " + tag + " on " + remote
return output
| 42.44 | 130 | 0.664467 | from git_gopher.CommandInterface import CommandInterface
from git_gopher.HistoryCommandRunner import HistoryCommandRunner
from git_gopher.GitDataGetter import GitDataGetter
class DeleteTagRemote(CommandInterface):
def __init__(self, hist_command_runner: HistoryCommandRunner, git_data_getter: GitDataGetter):
self._hist_command_runner = hist_command_runner
self._git_data_getter = git_data_getter
def run(self):
remote = self._git_data_getter.get_remote_name(preview='echo "No action is taken until selecting a tag."')
if not remote:
return
tags = self._git_data_getter.get_tag_names_remote(remote, preview='echo "git tag -d {2} && git push "' + remote + ' {2}"')
if tags:
output = ''
for tag in tags:
self._hist_command_runner.run(['git', 'tag', '-d', tag])
self._hist_command_runner.run(['git', 'push', '--delete', remote, tag])
output = output + "\nDeleted tag " + tag + " on " + remote
return output
| true | true |
1c34115300559310ebe35a731ce334a5b031a186 | 1,907 | py | Python | deepspeech_pytorch/loss.py | welgazil/DeDTW | 05d46c68122521dfe706736aaff24d6f99807e6e | [
"MIT"
] | null | null | null | deepspeech_pytorch/loss.py | welgazil/DeDTW | 05d46c68122521dfe706736aaff24d6f99807e6e | [
"MIT"
] | null | null | null | deepspeech_pytorch/loss.py | welgazil/DeDTW | 05d46c68122521dfe706736aaff24d6f99807e6e | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 3 11:53:32 2021
@author: louisbard
"""
# import numpy as np
import torch
import torch.nn as nn
# import torch.nn.functional as F
from deepspeech_pytorch.soft_dtw import SoftDTW
from deepspeech_pytorch.gauss import distcos
class DTWLosslabels(nn.Module):
def __init__(self, representation):
super(DTWLosslabels, self).__init__()
self.sdtw = SoftDTW(gamma=1.0, normalize=True, dist="cosine")
self.criterion = nn.MSELoss()
self.representation = representation
def forward(self, TGT, OTH, X, labels):
TGT, OTH, X = TGT.to(torch.float32), OTH.to(torch.float32), X.to(torch.float32)
labels = torch.as_tensor(labels[0], dtype=torch.float)
if self.representation == "gauss":
diff = distcos(OTH, X) - distcos(TGT, X)
loss = self.criterion(diff, labels)
#print(labels, diff, loss)
else:
diff = self.sdtw(OTH, X) - self.sdtw(TGT, X)
#print(diff, labels)
loss = self.criterion(diff, labels)
#print(loss)
return loss
class DTWLosswithoutlabels(nn.Module):
def __init__(self, representation):
super(DTWLosswithoutlabels, self).__init__()
self.sdtw = SoftDTW(gamma=1.0, normalize=True, dist="cosine")
self.representation = representation
def forward(self, TGT, OTH, X, labels):
TGT, OTH, X = TGT.to(torch.float32), OTH.to(torch.float32), X.to(torch.float32)
# labels = torch.as_tensor(labels[0], dtype=torch.float)
if self.representation == "gauss":
loss = distcos(TGT, X) - distcos(OTH, X)
else:
loss = self.sdtw(TGT, X) - self.sdtw(OTH, X) # it is ok to put it this way because we want to minimize this
# otherwise, the delta value is the other way around
return loss
| 32.87931 | 120 | 0.629261 |
import torch
import torch.nn as nn
from deepspeech_pytorch.soft_dtw import SoftDTW
from deepspeech_pytorch.gauss import distcos
class DTWLosslabels(nn.Module):
def __init__(self, representation):
super(DTWLosslabels, self).__init__()
self.sdtw = SoftDTW(gamma=1.0, normalize=True, dist="cosine")
self.criterion = nn.MSELoss()
self.representation = representation
def forward(self, TGT, OTH, X, labels):
TGT, OTH, X = TGT.to(torch.float32), OTH.to(torch.float32), X.to(torch.float32)
labels = torch.as_tensor(labels[0], dtype=torch.float)
if self.representation == "gauss":
diff = distcos(OTH, X) - distcos(TGT, X)
loss = self.criterion(diff, labels)
else:
diff = self.sdtw(OTH, X) - self.sdtw(TGT, X)
loss = self.criterion(diff, labels)
return loss
class DTWLosswithoutlabels(nn.Module):
def __init__(self, representation):
super(DTWLosswithoutlabels, self).__init__()
self.sdtw = SoftDTW(gamma=1.0, normalize=True, dist="cosine")
self.representation = representation
def forward(self, TGT, OTH, X, labels):
TGT, OTH, X = TGT.to(torch.float32), OTH.to(torch.float32), X.to(torch.float32)
if self.representation == "gauss":
loss = distcos(TGT, X) - distcos(OTH, X)
else:
loss = self.sdtw(TGT, X) - self.sdtw(OTH, X)
return loss
| true | true |
1c341224ea77f0c0bf517aac747cfaae30d1066b | 4,287 | py | Python | tools/lib-alert-tree/lib_alert_tree/prometheus.py | SaintLoong/metalk8s | 06fa3a731f35ab0f9ad8d3443fd8f8c4e7037432 | [
"Apache-2.0"
] | 23 | 2018-03-16T09:06:46.000Z | 2018-08-02T00:02:07.000Z | tools/lib-alert-tree/lib_alert_tree/prometheus.py | SaintLoong/metalk8s | 06fa3a731f35ab0f9ad8d3443fd8f8c4e7037432 | [
"Apache-2.0"
] | 131 | 2018-03-13T07:31:34.000Z | 2018-08-02T21:57:18.000Z | tools/lib-alert-tree/lib_alert_tree/prometheus.py | SaintLoong/metalk8s | 06fa3a731f35ab0f9ad8d3443fd8f8c4e7037432 | [
"Apache-2.0"
] | 4 | 2018-04-03T07:18:39.000Z | 2018-07-02T22:56:56.000Z | """Classes for storing and serializing Prometheus alert rules."""
import abc
import operator
import yaml
EXACT_MATCH_LABELS = frozenset(["alertstate", "alertname", "severity"])
class Serializable(metaclass=abc.ABCMeta):
"""Base-class for data serializable into YAML strings."""
@abc.abstractmethod
def serialize(self):
"""Serialize this data container into a dict."""
return {}
def dump(self, out=None):
"""Dump the serialized data in YAML format."""
return yaml.safe_dump(
self.serialize(), stream=out, sort_keys=True, default_flow_style=False
)
def __repr__(self):
return self.dump()
class AlertRule(Serializable):
"""A single alerting rule."""
def __init__(
self,
name,
expr=None,
duration=None,
annotations=None,
labels=None,
severity=None,
summary=None,
):
self.name = name
self.expr = expr
self.duration = duration
self.labels = labels or {}
self.annotations = annotations or {}
if severity:
self.labels["severity"] = severity
if summary:
self.annotations["summary"] = summary
def serialize(self):
for attr in ["expr", "duration"]:
assert (
getattr(self, attr) is not None
), f"Cannot serialize '{self.name}': `{attr}` must not be None"
return {
"alert": self.name,
"expr": self.expr,
"for": self.duration,
"annotations": self.annotations,
"labels": self.labels,
}
def format_labels(self, **updates):
"""Format labels (and optional updates) as a string."""
return ", ".join(
f"{key}='{val}'" if key in EXACT_MATCH_LABELS else f"{key}=~'{val}'"
for key, val in sorted(
dict(self.labels, **updates).items(),
key=operator.itemgetter(0),
)
)
def labels_to_json_path_filters(self, **updates):
"""Build JSON Path filters matching the labels."""
return " && ".join(
f"@.labels.{key} === '{val}'"
if key in EXACT_MATCH_LABELS
else f"@.labels.{key}.match(new RegExp('^(?:{val})$'))"
for key, val in sorted(
dict(self.labels, **updates).items(),
key=operator.itemgetter(0),
)
)
@property
def query(self):
"""The PromQL query for selecting this alert."""
labels_str = self.format_labels(alertname=self.name, alertstate="firing")
return f"ALERTS{{{labels_str}}}"
@property
def child_id(self):
"""A short representation of this alert, for use in annotations."""
return f"{self.name}{{{self.format_labels()}}}"
@property
def child_json_path(self):
"""A JSONPath filter expression for selecting this alert as a child.
This expression will be combined into a full JSONPath query for retrieving
all children of a derived alert, exposed in an annotation for consumption
by clients (such as UIs).
"""
labels_filters = self.labels_to_json_path_filters(alertname=self.name)
return f"({labels_filters})"
class RulesGroup(Serializable):
"""A group of alerting rules."""
def __init__(self, name, rules=None):
self.rules = rules or []
self.name = name
def serialize(self):
return {
"name": self.name,
"rules": [r.serialize() for r in self.rules],
}
class PrometheusRule(Serializable):
"""A complete PrometheusRule custom resource."""
def __init__(self, name, namespace, labels=None, groups=None):
self.name = name
self.namespace = namespace
self.labels = labels or {}
self.groups = groups or []
def serialize(self):
return {
"apiVersion": "monitoring.coreos.com/v1",
"kind": "PrometheusRule",
"metadata": {
"labels": self.labels,
"name": self.name,
"namespace": self.namespace,
},
"spec": {"groups": [g.serialize() for g in self.groups]},
}
| 29.163265 | 82 | 0.563798 |
import abc
import operator
import yaml
EXACT_MATCH_LABELS = frozenset(["alertstate", "alertname", "severity"])
class Serializable(metaclass=abc.ABCMeta):
@abc.abstractmethod
def serialize(self):
return {}
def dump(self, out=None):
return yaml.safe_dump(
self.serialize(), stream=out, sort_keys=True, default_flow_style=False
)
def __repr__(self):
return self.dump()
class AlertRule(Serializable):
def __init__(
self,
name,
expr=None,
duration=None,
annotations=None,
labels=None,
severity=None,
summary=None,
):
self.name = name
self.expr = expr
self.duration = duration
self.labels = labels or {}
self.annotations = annotations or {}
if severity:
self.labels["severity"] = severity
if summary:
self.annotations["summary"] = summary
def serialize(self):
for attr in ["expr", "duration"]:
assert (
getattr(self, attr) is not None
), f"Cannot serialize '{self.name}': `{attr}` must not be None"
return {
"alert": self.name,
"expr": self.expr,
"for": self.duration,
"annotations": self.annotations,
"labels": self.labels,
}
def format_labels(self, **updates):
return ", ".join(
f"{key}='{val}'" if key in EXACT_MATCH_LABELS else f"{key}=~'{val}'"
for key, val in sorted(
dict(self.labels, **updates).items(),
key=operator.itemgetter(0),
)
)
def labels_to_json_path_filters(self, **updates):
return " && ".join(
f"@.labels.{key} === '{val}'"
if key in EXACT_MATCH_LABELS
else f"@.labels.{key}.match(new RegExp('^(?:{val})$'))"
for key, val in sorted(
dict(self.labels, **updates).items(),
key=operator.itemgetter(0),
)
)
@property
def query(self):
labels_str = self.format_labels(alertname=self.name, alertstate="firing")
return f"ALERTS{{{labels_str}}}"
@property
def child_id(self):
return f"{self.name}{{{self.format_labels()}}}"
@property
def child_json_path(self):
labels_filters = self.labels_to_json_path_filters(alertname=self.name)
return f"({labels_filters})"
class RulesGroup(Serializable):
def __init__(self, name, rules=None):
self.rules = rules or []
self.name = name
def serialize(self):
return {
"name": self.name,
"rules": [r.serialize() for r in self.rules],
}
class PrometheusRule(Serializable):
def __init__(self, name, namespace, labels=None, groups=None):
self.name = name
self.namespace = namespace
self.labels = labels or {}
self.groups = groups or []
def serialize(self):
return {
"apiVersion": "monitoring.coreos.com/v1",
"kind": "PrometheusRule",
"metadata": {
"labels": self.labels,
"name": self.name,
"namespace": self.namespace,
},
"spec": {"groups": [g.serialize() for g in self.groups]},
}
| true | true |
1c34131c9f689c78e777d6970a99f16b0f8b4b23 | 512 | py | Python | src/Plot_tools/__init__.py | nishantsule/PyRsw | 753788608a0d227b5c8dc8b863d85bfc3a907310 | [
"MIT"
] | null | null | null | src/Plot_tools/__init__.py | nishantsule/PyRsw | 753788608a0d227b5c8dc8b863d85bfc3a907310 | [
"MIT"
] | null | null | null | src/Plot_tools/__init__.py | nishantsule/PyRsw | 753788608a0d227b5c8dc8b863d85bfc3a907310 | [
"MIT"
] | null | null | null | # Plot_tools
# This module provides the functions used
# for visualizing results from a PyRSW
# simulation
# Import the plotting tools
from Plot_tools.initialize_plots_animsave_1D import initialize_plots_animsave_1D
from Plot_tools.initialize_plots_animsave_2D import initialize_plots_animsave_2D
from Plot_tools.initialize_plots_hov import initialize_plots_hov
from Plot_tools.plot_hov import plot_hov
from Plot_tools.update_hov import update_hov
from Plot_tools.smart_time import smart_time
| 39.384615 | 80 | 0.849609 |
from Plot_tools.initialize_plots_animsave_1D import initialize_plots_animsave_1D
from Plot_tools.initialize_plots_animsave_2D import initialize_plots_animsave_2D
from Plot_tools.initialize_plots_hov import initialize_plots_hov
from Plot_tools.plot_hov import plot_hov
from Plot_tools.update_hov import update_hov
from Plot_tools.smart_time import smart_time
| true | true |
1c34131f5d8c76c9c56c1b80ea4408150a7a4aab | 1,706 | py | Python | ooobuild/lo/uno/x_reference.py | Amourspirit/ooo_uno_tmpl | 64e0c86fd68f24794acc22d63d8d32ae05dd12b8 | [
"Apache-2.0"
] | null | null | null | ooobuild/lo/uno/x_reference.py | Amourspirit/ooo_uno_tmpl | 64e0c86fd68f24794acc22d63d8d32ae05dd12b8 | [
"Apache-2.0"
] | null | null | null | ooobuild/lo/uno/x_reference.py | Amourspirit/ooo_uno_tmpl | 64e0c86fd68f24794acc22d63d8d32ae05dd12b8 | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
#
# Copyright 2022 :Barry-Thomas-Paul: Moss
#
# Licensed under the Apache License, Version 2.0 (the "License")
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http: // www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Interface Class
# this is a auto generated file generated by Cheetah
# Libre Office Version: 7.3
# Namespace: com.sun.star.uno
from abc import abstractmethod
from .x_interface import XInterface as XInterface_8f010a43
class XReference(XInterface_8f010a43):
"""
must be implemented by anyone who holds the adapter on the client side.
See Also:
`API XReference <https://api.libreoffice.org/docs/idl/ref/interfacecom_1_1sun_1_1star_1_1uno_1_1XReference.html>`_
"""
__ooo_ns__: str = 'com.sun.star.uno'
__ooo_full_ns__: str = 'com.sun.star.uno.XReference'
__ooo_type_name__: str = 'interface'
__pyunointerface__: str = 'com.sun.star.uno.XReference'
@abstractmethod
def dispose(self) -> None:
"""
removes all references to the adapter.
This method is called when the adapted object dies. The implementation of the client-side's weak reference must include removal of all references to the adapter. Otherwise, the adapted object will be destroyed, but the adapter will be alive.
"""
__all__ = ['XReference']
| 37.086957 | 249 | 0.732708 |
from abc import abstractmethod
from .x_interface import XInterface as XInterface_8f010a43
class XReference(XInterface_8f010a43):
__ooo_ns__: str = 'com.sun.star.uno'
__ooo_full_ns__: str = 'com.sun.star.uno.XReference'
__ooo_type_name__: str = 'interface'
__pyunointerface__: str = 'com.sun.star.uno.XReference'
@abstractmethod
def dispose(self) -> None:
__all__ = ['XReference']
| true | true |
1c3413eb37c6a11a85248a02655f54824f19eeec | 17,089 | py | Python | tests/test_graceful_reload.py | twu/pypicloud | 1ff40b8bcb2f123acab93248368e114cca123504 | [
"MIT"
] | 336 | 2016-10-05T14:15:23.000Z | 2022-03-17T12:42:10.000Z | tests/test_graceful_reload.py | twu/pypicloud | 1ff40b8bcb2f123acab93248368e114cca123504 | [
"MIT"
] | 215 | 2016-10-03T20:17:09.000Z | 2022-03-29T18:03:46.000Z | tests/test_graceful_reload.py | twu/pypicloud | 1ff40b8bcb2f123acab93248368e114cca123504 | [
"MIT"
] | 104 | 2016-10-03T18:58:26.000Z | 2022-02-10T00:23:28.000Z | """ Tests for gracefully reloading the caches """
import unittest
from datetime import timedelta
import redis
import transaction
from mock import MagicMock
from pyramid.testing import DummyRequest
from sqlalchemy.exc import OperationalError
from pypicloud.cache import RedisCache, SQLCache
from pypicloud.cache.dynamo import DynamoCache, DynamoPackage, PackageSummary
from pypicloud.cache.sql import SQLPackage
from pypicloud.dateutil import utcnow
from pypicloud.storage import IStorage
from pypicloud.util import EnvironSettings
from . import make_package
from .db_utils import get_mysql_url, get_postgres_url, get_sqlite_url
# pylint: disable=W0707
class TestDynamoCache(unittest.TestCase):
"""Tests for the DynamoCache"""
dynamo = None
@classmethod
def setUpClass(cls):
super(TestDynamoCache, cls).setUpClass()
host = cls.dynamo.host[cls.dynamo.host.index("//") + 2 :]
host, port = host.split(":")
settings = {
"pypi.storage": "tests.DummyStorage",
"db.region_name": "us-east-1",
"db.host": host,
"db.port": port,
"db.namespace": "test.",
"db.aws_access_key_id": "",
"db.aws_secret_access_key": "",
"db.graceful_reload": True,
}
cls.kwargs = DynamoCache.configure(settings)
cls.engine = cls.kwargs["engine"]
@classmethod
def tearDownClass(cls):
super(TestDynamoCache, cls).tearDownClass()
cls.engine.delete_schema()
def setUp(self):
super(TestDynamoCache, self).setUp()
self.db = DynamoCache(DummyRequest(), **self.kwargs)
self.storage = self.db.storage = MagicMock(spec=IStorage)
def tearDown(self):
super(TestDynamoCache, self).tearDown()
for model in (DynamoPackage, PackageSummary):
self.engine.scan(model).delete()
def _save_pkgs(self, *pkgs):
"""Save a DynamoPackage to the db"""
for pkg in pkgs:
self.engine.save(pkg)
summary = PackageSummary(pkg)
self.engine.save(summary, overwrite=True)
def test_add_missing(self):
"""Add missing packages to cache"""
keys = [make_package(factory=DynamoPackage)]
self.storage.list.return_value = keys
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, keys)
all_summaries = self.engine.scan(PackageSummary).all()
self.assertEqual(len(all_summaries), 1)
def test_remove_extra(self):
"""Remove extra packages from cache"""
keys = [
make_package(factory=DynamoPackage),
make_package("mypkg2", "1.3.4", factory=DynamoPackage),
]
self.db.save(keys[0])
self.db.save(keys[1])
self.storage.list.return_value = keys[:1]
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, keys[:1])
# It should have removed the summary as well
self.assertEqual(self.engine.scan(PackageSummary).count(), 1)
def test_remove_extra_leave_concurrent(self):
"""Removing extra packages will leave packages that were uploaded concurrently"""
pkgs = [
make_package(factory=DynamoPackage),
make_package("mypkg2", factory=DynamoPackage),
]
self.db.save(pkgs[0])
self.db.save(pkgs[1])
# Return first pkgs[1], then pkgs[1:] because the second time we list
# we will have "uploaded" pkgs[2]
return_values = [lambda: pkgs[1:2], lambda: pkgs[1:]]
def list_storage(factory):
"""mocked method for listing storage packages"""
# The first time we list from storage, concurrently "upload"
# pkgs[2]
if len(return_values) == 2:
pkg = make_package("mypkg3", factory=DynamoPackage)
pkgs.append(pkg)
self.db.save(pkg)
return return_values.pop(0)()
self.storage.list.side_effect = list_storage
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, pkgs[1:])
self.assertEqual(self.engine.scan(PackageSummary).count(), 2)
def test_remove_extra_concurrent_deletes(self):
"""Remove packages from cache that were concurrently deleted"""
pkgs = [
make_package(factory=DynamoPackage),
make_package("mypkg2", factory=DynamoPackage),
]
self.db.save(pkgs[0])
# Return first pkgs[:], then pkgs[:1] because the second time we list
# we will have "deleted" pkgs[1]
return_values = [pkgs[:], pkgs[:1]]
self.storage.list.side_effect = lambda _: return_values.pop(0)
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, pkgs[:1])
self.assertEqual(self.engine.scan(PackageSummary).count(), 1)
def test_add_missing_more_recent(self):
"""If we sync a more recent package, update the summary"""
pkgs = [
make_package(
last_modified=utcnow() - timedelta(hours=1),
factory=DynamoPackage,
),
make_package(version="1.5", factory=DynamoPackage),
]
self.db.save(pkgs[0])
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, pkgs)
summaries = self.db.summary()
self.assertEqual(len(summaries), 1)
summary = summaries[0]
self.assertEqual(summary["last_modified"], pkgs[1].last_modified)
def test_same_package_name_version(self):
"""Storage can have packages with the same name and version (different filename)"""
pkgs = [
make_package(filename="mypkg-1.1-win32.whl", factory=DynamoPackage),
make_package(filename="mypkg-1.1-macosx.whl", factory=DynamoPackage),
make_package(filename="mypkg-1.1-x86_64.whl", factory=DynamoPackage),
]
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, pkgs)
summaries = self.db.summary()
self.assertEqual(len(summaries), 1)
class TestRedisCache(unittest.TestCase):
"""Tests for the RedisCache"""
@classmethod
def setUpClass(cls):
super(TestRedisCache, cls).setUpClass()
settings = {
"pypi.storage": "tests.DummyStorage",
"db.url": "redis://localhost",
"db.graceful_reload": True,
}
cls.kwargs = RedisCache.configure(settings)
cls.redis = cls.kwargs["db"]
try:
cls.redis.flushdb()
except redis.exceptions.ConnectionError:
msg = "Redis not found on port 6379"
setattr(cls, "setUp", lambda cls: unittest.TestCase.skipTest(cls, msg))
@classmethod
def tearDownClass(cls):
super(TestRedisCache, cls).tearDownClass()
def setUp(self):
super(TestRedisCache, self).setUp()
self.db = RedisCache(DummyRequest(), **self.kwargs)
self.storage = self.db.storage = MagicMock(spec=IStorage)
def tearDown(self):
super(TestRedisCache, self).tearDown()
self.redis.flushdb()
def _save_pkgs(self, *pkgs):
"""Save packages to the db"""
pipe = self.redis.pipeline()
for pkg in pkgs:
self.db.save(pkg, pipe)
pipe.execute()
def test_add_missing(self):
"""Add missing packages to cache"""
keys = [make_package()]
self.storage.list.return_value = keys
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, keys)
self.assertEqual(len(self.db.summary()), 1)
def test_remove_extra(self):
"""Remove extra packages from cache"""
keys = [make_package(), make_package("mypkg2", "1.3.4")]
self.db.save(keys[0])
self.db.save(keys[1])
self.storage.list.return_value = keys[:1]
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, keys[:1])
# It should have removed the summary as well
self.assertEqual(len(self.db.summary()), 1)
def test_remove_extra_leave_concurrent(self):
"""Removing extra packages will leave packages that were uploaded concurrently"""
pkgs = [make_package(), make_package("mypkg2")]
self.db.save(pkgs[0])
self.db.save(pkgs[1])
# Return first pkgs[1], then pkgs[1:] because the second time we list
# we will have "uploaded" pkgs[2]
return_values = [lambda: pkgs[1:2], lambda: pkgs[1:]]
def list_storage(factory):
"""mocked method for listing storage packages"""
# The first time we list from storage, concurrently "upload"
# pkgs[2]
if len(return_values) == 2:
pkg = make_package("mypkg3")
pkgs.append(pkg)
self.db.save(pkg)
return return_values.pop(0)()
self.storage.list.side_effect = list_storage
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, pkgs[1:])
self.assertEqual(len(self.db.summary()), 2)
def test_remove_extra_concurrent_deletes(self):
"""Remove packages from cache that were concurrently deleted"""
pkgs = [make_package(), make_package("mypkg2")]
self.db.save(pkgs[0])
# Return first pkgs[:], then pkgs[:1] because the second time we list
# we will have "deleted" pkgs[1]
return_values = [pkgs[:], pkgs[:1]]
self.storage.list.side_effect = lambda _: return_values.pop(0)
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, pkgs[:1])
self.assertEqual(len(self.db.summary()), 1)
def test_add_missing_more_recent(self):
"""If we sync a more recent package, update the summary"""
pkgs = [
make_package(last_modified=utcnow() - timedelta(hours=1)),
make_package(version="1.5"),
]
self.db.save(pkgs[0])
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, pkgs)
summaries = self.db.summary()
self.assertEqual(len(summaries), 1)
summary = summaries[0]
self.assertEqual(summary["last_modified"].hour, pkgs[1].last_modified.hour)
def test_same_package_name_version(self):
"""Storage can have packages with the same name and version (different filename)"""
pkgs = [
make_package(filename="mypkg-1.1-win32.whl"),
make_package(filename="mypkg-1.1-macosx.whl"),
make_package(filename="mypkg-1.1-x86_64.whl"),
]
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, pkgs)
summaries = self.db.summary()
self.assertEqual(len(summaries), 1)
class TestSQLiteCache(unittest.TestCase):
"""Tests for the SQLCache"""
@classmethod
def get_db_url(cls) -> str:
return get_sqlite_url()
@classmethod
def setUpClass(cls):
super(TestSQLiteCache, cls).setUpClass()
db_url = cls.get_db_url()
settings = EnvironSettings(
{
"pypi.storage": "tests.DummyStorage",
"db.url": db_url,
"db.graceful_reload": True,
},
{},
)
try:
cls.kwargs = SQLCache.configure(settings)
except OperationalError:
raise unittest.SkipTest(f"Couldn't connect to database {db_url}")
def setUp(self):
super(TestSQLiteCache, self).setUp()
transaction.begin()
self.request = DummyRequest()
self.request.tm = transaction.manager
self.db = SQLCache(self.request, **self.kwargs)
self.sql = self.db.db
self.storage = self.db.storage = MagicMock(spec=IStorage)
def tearDown(self):
super(TestSQLiteCache, self).tearDown()
transaction.abort()
self.sql.query(SQLPackage).delete()
transaction.commit()
self.request._process_finished_callbacks()
def _make_package(self, *args, **kwargs):
"""Wrapper around make_package"""
# Some SQL dbs are rounding the timestamps (looking at you MySQL >:|
# which is a problem if they round UP to the future, as our
# calculations depend on the timestamps being monotonically increasing.
now = utcnow() - timedelta(seconds=1)
kwargs.setdefault("last_modified", now)
kwargs.setdefault("factory", SQLPackage)
return make_package(*args, **kwargs)
def test_add_missing(self):
"""Add missing packages to cache"""
keys = [self._make_package()]
self.storage.list.return_value = keys
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, keys)
def test_remove_extra(self):
"""Remove extra packages from cache"""
keys = [self._make_package(), self._make_package("mypkg2", "1.3.4")]
self.db.save(keys[0])
self.db.save(keys[1])
self.storage.list.return_value = keys[:1]
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, keys[:1])
def test_remove_extra_leave_concurrent(self):
"""Removing extra packages will leave packages that were uploaded concurrently"""
pkgs = [self._make_package(), self._make_package("mypkg2")]
self.db.save(pkgs[0])
self.db.save(pkgs[1])
# Return first pkgs[1], then pkgs[1:] because the second time we list
# we will have "uploaded" pkgs[2]
return_values = [lambda: pkgs[1:2], lambda: pkgs[1:]]
def list_storage(factory):
"""mocked method for listing storage packages"""
# The first time we list from storage, concurrently "upload"
# pkgs[2]
if len(return_values) == 2:
nowish = utcnow() + timedelta(seconds=1)
pkg = self._make_package("mypkg3", last_modified=nowish)
pkgs.append(pkg)
self.db.save(pkg)
return return_values.pop(0)()
self.storage.list.side_effect = list_storage
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, pkgs[1:])
def test_remove_extra_concurrent_deletes(self):
"""Remove packages from cache that were concurrently deleted"""
pkgs = [self._make_package(), self._make_package("mypkg2")]
self.db.save(pkgs[0])
# Return first pkgs[:], then pkgs[:1] because the second time we list
# we will have "deleted" pkgs[1]
return_values = [pkgs[:], pkgs[:1]]
self.storage.list.side_effect = lambda _: return_values.pop(0)
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, pkgs[:1])
def test_add_missing_more_recent(self):
"""If we sync a more recent package, update the summary"""
pkgs = [
self._make_package(last_modified=utcnow() - timedelta(hours=1)),
self._make_package(version="1.5"),
]
self.db.save(pkgs[0])
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, pkgs)
def test_same_package_name_version(self):
"""Storage can have packages with the same name and version (different filename)"""
pkgs = [
self._make_package(filename="mypkg-1.1-win32.whl"),
self._make_package(filename="mypkg-1.1-macosx.whl"),
self._make_package(filename="mypkg-1.1-x86_64.whl"),
]
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, pkgs)
class TestMySQLCache(TestSQLiteCache):
"""Test the SQLAlchemy cache on a MySQL DB"""
@classmethod
def get_db_url(cls) -> str:
return get_mysql_url()
class TestPostgresCache(TestSQLiteCache):
"""Test the SQLAlchemy cache on a Postgres DB"""
@classmethod
def get_db_url(cls) -> str:
return get_postgres_url()
| 37.069414 | 91 | 0.6259 | import unittest
from datetime import timedelta
import redis
import transaction
from mock import MagicMock
from pyramid.testing import DummyRequest
from sqlalchemy.exc import OperationalError
from pypicloud.cache import RedisCache, SQLCache
from pypicloud.cache.dynamo import DynamoCache, DynamoPackage, PackageSummary
from pypicloud.cache.sql import SQLPackage
from pypicloud.dateutil import utcnow
from pypicloud.storage import IStorage
from pypicloud.util import EnvironSettings
from . import make_package
from .db_utils import get_mysql_url, get_postgres_url, get_sqlite_url
class TestDynamoCache(unittest.TestCase):
dynamo = None
@classmethod
def setUpClass(cls):
super(TestDynamoCache, cls).setUpClass()
host = cls.dynamo.host[cls.dynamo.host.index("//") + 2 :]
host, port = host.split(":")
settings = {
"pypi.storage": "tests.DummyStorage",
"db.region_name": "us-east-1",
"db.host": host,
"db.port": port,
"db.namespace": "test.",
"db.aws_access_key_id": "",
"db.aws_secret_access_key": "",
"db.graceful_reload": True,
}
cls.kwargs = DynamoCache.configure(settings)
cls.engine = cls.kwargs["engine"]
@classmethod
def tearDownClass(cls):
super(TestDynamoCache, cls).tearDownClass()
cls.engine.delete_schema()
def setUp(self):
super(TestDynamoCache, self).setUp()
self.db = DynamoCache(DummyRequest(), **self.kwargs)
self.storage = self.db.storage = MagicMock(spec=IStorage)
def tearDown(self):
super(TestDynamoCache, self).tearDown()
for model in (DynamoPackage, PackageSummary):
self.engine.scan(model).delete()
def _save_pkgs(self, *pkgs):
for pkg in pkgs:
self.engine.save(pkg)
summary = PackageSummary(pkg)
self.engine.save(summary, overwrite=True)
def test_add_missing(self):
keys = [make_package(factory=DynamoPackage)]
self.storage.list.return_value = keys
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, keys)
all_summaries = self.engine.scan(PackageSummary).all()
self.assertEqual(len(all_summaries), 1)
def test_remove_extra(self):
keys = [
make_package(factory=DynamoPackage),
make_package("mypkg2", "1.3.4", factory=DynamoPackage),
]
self.db.save(keys[0])
self.db.save(keys[1])
self.storage.list.return_value = keys[:1]
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, keys[:1])
self.assertEqual(self.engine.scan(PackageSummary).count(), 1)
def test_remove_extra_leave_concurrent(self):
pkgs = [
make_package(factory=DynamoPackage),
make_package("mypkg2", factory=DynamoPackage),
]
self.db.save(pkgs[0])
self.db.save(pkgs[1])
return_values = [lambda: pkgs[1:2], lambda: pkgs[1:]]
def list_storage(factory):
if len(return_values) == 2:
pkg = make_package("mypkg3", factory=DynamoPackage)
pkgs.append(pkg)
self.db.save(pkg)
return return_values.pop(0)()
self.storage.list.side_effect = list_storage
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, pkgs[1:])
self.assertEqual(self.engine.scan(PackageSummary).count(), 2)
def test_remove_extra_concurrent_deletes(self):
pkgs = [
make_package(factory=DynamoPackage),
make_package("mypkg2", factory=DynamoPackage),
]
self.db.save(pkgs[0])
return_values = [pkgs[:], pkgs[:1]]
self.storage.list.side_effect = lambda _: return_values.pop(0)
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, pkgs[:1])
self.assertEqual(self.engine.scan(PackageSummary).count(), 1)
def test_add_missing_more_recent(self):
pkgs = [
make_package(
last_modified=utcnow() - timedelta(hours=1),
factory=DynamoPackage,
),
make_package(version="1.5", factory=DynamoPackage),
]
self.db.save(pkgs[0])
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, pkgs)
summaries = self.db.summary()
self.assertEqual(len(summaries), 1)
summary = summaries[0]
self.assertEqual(summary["last_modified"], pkgs[1].last_modified)
def test_same_package_name_version(self):
pkgs = [
make_package(filename="mypkg-1.1-win32.whl", factory=DynamoPackage),
make_package(filename="mypkg-1.1-macosx.whl", factory=DynamoPackage),
make_package(filename="mypkg-1.1-x86_64.whl", factory=DynamoPackage),
]
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.engine.scan(DynamoPackage).all()
self.assertCountEqual(all_pkgs, pkgs)
summaries = self.db.summary()
self.assertEqual(len(summaries), 1)
class TestRedisCache(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(TestRedisCache, cls).setUpClass()
settings = {
"pypi.storage": "tests.DummyStorage",
"db.url": "redis://localhost",
"db.graceful_reload": True,
}
cls.kwargs = RedisCache.configure(settings)
cls.redis = cls.kwargs["db"]
try:
cls.redis.flushdb()
except redis.exceptions.ConnectionError:
msg = "Redis not found on port 6379"
setattr(cls, "setUp", lambda cls: unittest.TestCase.skipTest(cls, msg))
@classmethod
def tearDownClass(cls):
super(TestRedisCache, cls).tearDownClass()
def setUp(self):
super(TestRedisCache, self).setUp()
self.db = RedisCache(DummyRequest(), **self.kwargs)
self.storage = self.db.storage = MagicMock(spec=IStorage)
def tearDown(self):
super(TestRedisCache, self).tearDown()
self.redis.flushdb()
def _save_pkgs(self, *pkgs):
pipe = self.redis.pipeline()
for pkg in pkgs:
self.db.save(pkg, pipe)
pipe.execute()
def test_add_missing(self):
keys = [make_package()]
self.storage.list.return_value = keys
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, keys)
self.assertEqual(len(self.db.summary()), 1)
def test_remove_extra(self):
keys = [make_package(), make_package("mypkg2", "1.3.4")]
self.db.save(keys[0])
self.db.save(keys[1])
self.storage.list.return_value = keys[:1]
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, keys[:1])
self.assertEqual(len(self.db.summary()), 1)
def test_remove_extra_leave_concurrent(self):
pkgs = [make_package(), make_package("mypkg2")]
self.db.save(pkgs[0])
self.db.save(pkgs[1])
return_values = [lambda: pkgs[1:2], lambda: pkgs[1:]]
def list_storage(factory):
if len(return_values) == 2:
pkg = make_package("mypkg3")
pkgs.append(pkg)
self.db.save(pkg)
return return_values.pop(0)()
self.storage.list.side_effect = list_storage
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, pkgs[1:])
self.assertEqual(len(self.db.summary()), 2)
def test_remove_extra_concurrent_deletes(self):
pkgs = [make_package(), make_package("mypkg2")]
self.db.save(pkgs[0])
return_values = [pkgs[:], pkgs[:1]]
self.storage.list.side_effect = lambda _: return_values.pop(0)
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, pkgs[:1])
self.assertEqual(len(self.db.summary()), 1)
def test_add_missing_more_recent(self):
pkgs = [
make_package(last_modified=utcnow() - timedelta(hours=1)),
make_package(version="1.5"),
]
self.db.save(pkgs[0])
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, pkgs)
summaries = self.db.summary()
self.assertEqual(len(summaries), 1)
summary = summaries[0]
self.assertEqual(summary["last_modified"].hour, pkgs[1].last_modified.hour)
def test_same_package_name_version(self):
pkgs = [
make_package(filename="mypkg-1.1-win32.whl"),
make_package(filename="mypkg-1.1-macosx.whl"),
make_package(filename="mypkg-1.1-x86_64.whl"),
]
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.db._load_all_packages()
self.assertCountEqual(all_pkgs, pkgs)
summaries = self.db.summary()
self.assertEqual(len(summaries), 1)
class TestSQLiteCache(unittest.TestCase):
@classmethod
def get_db_url(cls) -> str:
return get_sqlite_url()
@classmethod
def setUpClass(cls):
super(TestSQLiteCache, cls).setUpClass()
db_url = cls.get_db_url()
settings = EnvironSettings(
{
"pypi.storage": "tests.DummyStorage",
"db.url": db_url,
"db.graceful_reload": True,
},
{},
)
try:
cls.kwargs = SQLCache.configure(settings)
except OperationalError:
raise unittest.SkipTest(f"Couldn't connect to database {db_url}")
def setUp(self):
super(TestSQLiteCache, self).setUp()
transaction.begin()
self.request = DummyRequest()
self.request.tm = transaction.manager
self.db = SQLCache(self.request, **self.kwargs)
self.sql = self.db.db
self.storage = self.db.storage = MagicMock(spec=IStorage)
def tearDown(self):
super(TestSQLiteCache, self).tearDown()
transaction.abort()
self.sql.query(SQLPackage).delete()
transaction.commit()
self.request._process_finished_callbacks()
def _make_package(self, *args, **kwargs):
# Some SQL dbs are rounding the timestamps (looking at you MySQL >:|
# which is a problem if they round UP to the future, as our
# calculations depend on the timestamps being monotonically increasing.
now = utcnow() - timedelta(seconds=1)
kwargs.setdefault("last_modified", now)
kwargs.setdefault("factory", SQLPackage)
return make_package(*args, **kwargs)
def test_add_missing(self):
keys = [self._make_package()]
self.storage.list.return_value = keys
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, keys)
def test_remove_extra(self):
keys = [self._make_package(), self._make_package("mypkg2", "1.3.4")]
self.db.save(keys[0])
self.db.save(keys[1])
self.storage.list.return_value = keys[:1]
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, keys[:1])
def test_remove_extra_leave_concurrent(self):
pkgs = [self._make_package(), self._make_package("mypkg2")]
self.db.save(pkgs[0])
self.db.save(pkgs[1])
# Return first pkgs[1], then pkgs[1:] because the second time we list
# we will have "uploaded" pkgs[2]
return_values = [lambda: pkgs[1:2], lambda: pkgs[1:]]
def list_storage(factory):
# The first time we list from storage, concurrently "upload"
# pkgs[2]
if len(return_values) == 2:
nowish = utcnow() + timedelta(seconds=1)
pkg = self._make_package("mypkg3", last_modified=nowish)
pkgs.append(pkg)
self.db.save(pkg)
return return_values.pop(0)()
self.storage.list.side_effect = list_storage
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, pkgs[1:])
def test_remove_extra_concurrent_deletes(self):
pkgs = [self._make_package(), self._make_package("mypkg2")]
self.db.save(pkgs[0])
# Return first pkgs[:], then pkgs[:1] because the second time we list
# we will have "deleted" pkgs[1]
return_values = [pkgs[:], pkgs[:1]]
self.storage.list.side_effect = lambda _: return_values.pop(0)
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, pkgs[:1])
def test_add_missing_more_recent(self):
pkgs = [
self._make_package(last_modified=utcnow() - timedelta(hours=1)),
self._make_package(version="1.5"),
]
self.db.save(pkgs[0])
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, pkgs)
def test_same_package_name_version(self):
pkgs = [
self._make_package(filename="mypkg-1.1-win32.whl"),
self._make_package(filename="mypkg-1.1-macosx.whl"),
self._make_package(filename="mypkg-1.1-x86_64.whl"),
]
self.storage.list.return_value = pkgs
self.db.reload_from_storage()
all_pkgs = self.sql.query(SQLPackage).all()
self.assertCountEqual(all_pkgs, pkgs)
class TestMySQLCache(TestSQLiteCache):
@classmethod
def get_db_url(cls) -> str:
return get_mysql_url()
class TestPostgresCache(TestSQLiteCache):
@classmethod
def get_db_url(cls) -> str:
return get_postgres_url()
| true | true |
1c34149b9cfd658fb4f97781ad55439fe6439063 | 1,442 | py | Python | Electronic Station/timeConverter12Hto24H.py | amatsuraki/CheckiO-Elementary- | e041ea41b1f5ff59618c810468dec005bb797883 | [
"MIT"
] | null | null | null | Electronic Station/timeConverter12Hto24H.py | amatsuraki/CheckiO-Elementary- | e041ea41b1f5ff59618c810468dec005bb797883 | [
"MIT"
] | null | null | null | Electronic Station/timeConverter12Hto24H.py | amatsuraki/CheckiO-Elementary- | e041ea41b1f5ff59618c810468dec005bb797883 | [
"MIT"
] | null | null | null | #!/usr/bin/python
# -*- Coding: utf-8 -*-
"""
[Time converter 12h to 24h]
You are the modern man who prefers the 24-hour time format. But the 12-hour format is used in some places. Your task is to convert the time from the 12-h format into 24-h by following the next rules:
- the output format should be 'hh:mm'
- if the output hour is less than 10 - write '0' before it. For example: '09:05'
"""
def time_converter(time):
time = time.split(" ")
time0 = time[0].split(":")
hour = int(time0[0])
minute = time0[1]
if time[1] == "a.m.":
if hour is not 12:
hour = "{0:02d}".format(hour)
else:
hour = "00"
else:
if hour is not 12:
hour = "{0:02d}".format(hour + 12)
else:
hour = "12"
return str(hour + ":" + minute)
if __name__ == '__main__':
print("Example:")
print(time_converter('12:30 p.m.'))
#These "asserts" using only for self-checking and not necessary for auto-testing
assert time_converter('12:30 p.m.') == '12:30'
assert time_converter('9:00 a.m.') == '09:00'
assert time_converter('11:15 p.m.') == '23:15'
print("Coding complete? Click 'Check' to earn cool rewards!")
"""
メモ
12:00am = 00:00
12:00pm = 12:00
if time[1] is "a.m.": ->false
if time[1] == "a.m.": ->true
isは同じオブジェクトではなくidが異なるためfalseになる
datetime.strptimeを使うと良い
""" | 28.84 | 200 | 0.574896 |
def time_converter(time):
time = time.split(" ")
time0 = time[0].split(":")
hour = int(time0[0])
minute = time0[1]
if time[1] == "a.m.":
if hour is not 12:
hour = "{0:02d}".format(hour)
else:
hour = "00"
else:
if hour is not 12:
hour = "{0:02d}".format(hour + 12)
else:
hour = "12"
return str(hour + ":" + minute)
if __name__ == '__main__':
print("Example:")
print(time_converter('12:30 p.m.'))
assert time_converter('12:30 p.m.') == '12:30'
assert time_converter('9:00 a.m.') == '09:00'
assert time_converter('11:15 p.m.') == '23:15'
print("Coding complete? Click 'Check' to earn cool rewards!")
| true | true |
1c3415006f05237c1c53f3c865f96ea8235e73ac | 2,684 | py | Python | plot_grid.py | jonasvj/TFDE | c5d25947b28524c7a40626f797ca8c157fa70a53 | [
"MIT"
] | null | null | null | plot_grid.py | jonasvj/TFDE | c5d25947b28524c7a40626f797ca8c157fa70a53 | [
"MIT"
] | null | null | null | plot_grid.py | jonasvj/TFDE | c5d25947b28524c7a40626f797ca8c157fa70a53 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def tt_params(K, M):
return K + 3*M*K**2
def tt_dof(K, M):
return (K-1) + M*K*(K-1) + 2*M*K**2
def bic(n, k, nllh_per_sample):
log_lh = -1*nllh_per_sample*n
return k*np.log(n) - 2*log_lh
def aic(n, k, nllh_per_sample):
log_lh = -1*nllh_per_sample*n
return 2*k - 2*log_lh
def n_params(model, K, M):
if model == 'TT':
return (K-1) + M*K*(K-1) + 2*M*K*K
elif model == 'CP':
return (K-1) + 2*M*K
elif model == 'GMM':
return (K-1) + (2*M + M*(M-1)/2)*K
sizes = {
'power': {'n_train': 1659917, 'n_val': 184435, 'n_test': 204928, 'M': 6},
'gas': {'n_train': 852174, 'n_val': 94685, 'n_test': 105206, 'M': 8},
'hepmass': {'n_train': 315123, 'n_val': 35013, 'n_test': 174987, 'M': 21},
'miniboone': {'n_train': 29556, 'n_val': 3284, 'n_test': 3648, 'M': 43},
'bsds300': {'n_train': 1000000, 'n_val': 50000, 'n_test': 250000, 'M': 63},
'8gaussians': {'n_train': 30000, 'n_val': 30000, 'n_test': 30000, 'M': 2},
'checkerboard': {'n_train': 30000, 'n_val': 30000, 'n_test': 30000, 'M': 2},
'2spirals': {'n_train': 30000, 'n_val': 30000, 'n_test': 30000, 'M': 2}
}
df = pd.read_csv('results/grid_results.txt', index_col=0)
df_gmm = pd.read_csv('results/gmm_results.txt', index_col=0)
df = df.append(df_gmm, ignore_index=True)
df = df[df.optimal_order == 1]
print(df)
# Add new columns
df['M'] = df.apply(lambda row: sizes[row.dataset]['M'], axis=1)
df['dof'] = df.apply(lambda row: n_params(row.model_type, row.K, row.M), axis=1)
datasets = ['hepmass', 'miniboone']
subsample_sizes = [1750, 7000, 28000]
groups = df.groupby(['dataset', 'subsample_size'])
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(24, 12),
sharex='all', sharey='row')
for i, (group, frame) in enumerate(groups):
row_idx = datasets.index(group[0])
col_idx = subsample_sizes.index(group[1])
model_groups = frame.groupby(['model_type'])
for model, model_frame in model_groups:
mean = model_frame.groupby('dof').mean()
sem = model_frame.groupby('dof').sem()
min_ = model_frame.groupby('dof').min()
axes[row_idx, col_idx].errorbar(
mean.index, mean.nllh_test, yerr=sem.nllh_test, fmt='.:',
label=model, alpha=.75, capsize=3, capthick=1)
axes[row_idx, col_idx].set_xlabel('Free parameters')
axes[row_idx, col_idx].set_ylabel(f'Test NLLH per sample ({group[0]})')
axes[row_idx, col_idx].set_title(f'Subsample size: {group[1]}')
axes[row_idx, col_idx].legend()
fig.savefig('plots/' + 'grid_plot.pdf')
plt.close() | 33.974684 | 80 | 0.618107 |
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def tt_params(K, M):
return K + 3*M*K**2
def tt_dof(K, M):
return (K-1) + M*K*(K-1) + 2*M*K**2
def bic(n, k, nllh_per_sample):
log_lh = -1*nllh_per_sample*n
return k*np.log(n) - 2*log_lh
def aic(n, k, nllh_per_sample):
log_lh = -1*nllh_per_sample*n
return 2*k - 2*log_lh
def n_params(model, K, M):
if model == 'TT':
return (K-1) + M*K*(K-1) + 2*M*K*K
elif model == 'CP':
return (K-1) + 2*M*K
elif model == 'GMM':
return (K-1) + (2*M + M*(M-1)/2)*K
sizes = {
'power': {'n_train': 1659917, 'n_val': 184435, 'n_test': 204928, 'M': 6},
'gas': {'n_train': 852174, 'n_val': 94685, 'n_test': 105206, 'M': 8},
'hepmass': {'n_train': 315123, 'n_val': 35013, 'n_test': 174987, 'M': 21},
'miniboone': {'n_train': 29556, 'n_val': 3284, 'n_test': 3648, 'M': 43},
'bsds300': {'n_train': 1000000, 'n_val': 50000, 'n_test': 250000, 'M': 63},
'8gaussians': {'n_train': 30000, 'n_val': 30000, 'n_test': 30000, 'M': 2},
'checkerboard': {'n_train': 30000, 'n_val': 30000, 'n_test': 30000, 'M': 2},
'2spirals': {'n_train': 30000, 'n_val': 30000, 'n_test': 30000, 'M': 2}
}
df = pd.read_csv('results/grid_results.txt', index_col=0)
df_gmm = pd.read_csv('results/gmm_results.txt', index_col=0)
df = df.append(df_gmm, ignore_index=True)
df = df[df.optimal_order == 1]
print(df)
df['M'] = df.apply(lambda row: sizes[row.dataset]['M'], axis=1)
df['dof'] = df.apply(lambda row: n_params(row.model_type, row.K, row.M), axis=1)
datasets = ['hepmass', 'miniboone']
subsample_sizes = [1750, 7000, 28000]
groups = df.groupby(['dataset', 'subsample_size'])
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(24, 12),
sharex='all', sharey='row')
for i, (group, frame) in enumerate(groups):
row_idx = datasets.index(group[0])
col_idx = subsample_sizes.index(group[1])
model_groups = frame.groupby(['model_type'])
for model, model_frame in model_groups:
mean = model_frame.groupby('dof').mean()
sem = model_frame.groupby('dof').sem()
min_ = model_frame.groupby('dof').min()
axes[row_idx, col_idx].errorbar(
mean.index, mean.nllh_test, yerr=sem.nllh_test, fmt='.:',
label=model, alpha=.75, capsize=3, capthick=1)
axes[row_idx, col_idx].set_xlabel('Free parameters')
axes[row_idx, col_idx].set_ylabel(f'Test NLLH per sample ({group[0]})')
axes[row_idx, col_idx].set_title(f'Subsample size: {group[1]}')
axes[row_idx, col_idx].legend()
fig.savefig('plots/' + 'grid_plot.pdf')
plt.close() | true | true |
1c341554ac9b7a0b6cb004b22f6f12d6d712fcb7 | 267 | py | Python | jp.atcoder/abc136/abc136_c/12000304.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | 1 | 2022-02-09T03:06:25.000Z | 2022-02-09T03:06:25.000Z | jp.atcoder/abc136/abc136_c/12000304.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | 1 | 2022-02-05T22:53:18.000Z | 2022-02-09T01:29:30.000Z | jp.atcoder/abc136/abc136_c/12000304.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | null | null | null | import sys
n, *h = map(int, sys.stdin.read().split())
def main():
ans = 'Yes'
for i in range(n - 1, 0, -1):
if h[i-1] > h[i]: h[i-1] -= 1
if h[i-1] > h[i]: ans = 'No'; break
print(ans)
if __name__ == '__main__':
main()
| 19.071429 | 44 | 0.449438 | import sys
n, *h = map(int, sys.stdin.read().split())
def main():
ans = 'Yes'
for i in range(n - 1, 0, -1):
if h[i-1] > h[i]: h[i-1] -= 1
if h[i-1] > h[i]: ans = 'No'; break
print(ans)
if __name__ == '__main__':
main()
| true | true |
1c34161cb6d656bb855f3974b66bc85b05c6f779 | 6,909 | py | Python | test/test_catalog.py | mickybart/openbrokerapi | a2d57334da0061425731adc4e10376f2123f71f1 | [
"MIT"
] | null | null | null | test/test_catalog.py | mickybart/openbrokerapi | a2d57334da0061425731adc4e10376f2123f71f1 | [
"MIT"
] | null | null | null | test/test_catalog.py | mickybart/openbrokerapi | a2d57334da0061425731adc4e10376f2123f71f1 | [
"MIT"
] | null | null | null | import http
from openbrokerapi.catalog import (
ServiceDashboardClient,
ServiceMetadata,
ServicePlan,
ServicePlanCost,
ServicePlanMetaData,
)
from openbrokerapi.service_broker import Service
from test import BrokerTestCase
class CatalogTest(BrokerTestCase):
service = Service(
id="s1",
name="service_name",
description="service_description",
bindable=True,
plans=[ServicePlan(id="p1", name="default", description="plan_description")]
)
def test_catalog_called_with_the_right_values(self):
self.broker.catalog.return_value = self.service
self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header
})
self.assertTrue(self.broker.catalog.called)
def test_catalog_ignores_request_headers(self):
self.broker.catalog.return_value = self.service
self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header,
"unknown": "unknown"
})
self.assertTrue(self.broker.catalog.called)
def test_catalog_returns_200_with_service_information(self):
self.broker.catalog.return_value = Service(
id="s1",
name="service_name",
description="service_description",
bindable=True,
plans=[ServicePlan(
id="p1",
name="default",
description="plan_description",
metadata=ServicePlanMetaData(
displayName="displayName",
bullets=["bullet1"],
costs=[ServicePlanCost(
amount={"requests": 1},
unit="unit"
)]
),
free=True,
bindable=True
)],
tags=['tag1', 'tag2'],
requires=['something'],
metadata=ServiceMetadata(
displayName="displayName",
imageUrl="imageUrl",
longDescription="longDescription",
providerDisplayName="providerDisplayName",
documentationUrl="documentationUrl",
supportUrl="supportUrl"
),
dashboard_client=ServiceDashboardClient(
id="id",
secret="secret",
redirect_uri="redirect_uri"
),
plan_updateable=True
)
response = self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header,
"unknown": "unknown"
})
self.assertEqual(response.status_code, http.HTTPStatus.OK)
self.assertEqual(response.json,
dict(
services=[
dict(id="s1",
name="service_name",
description="service_description",
bindable=True,
plans=[dict(
id="p1",
name="default",
description="plan_description",
metadata=dict(
displayName="displayName",
bullets=["bullet1"],
costs=[dict(
amount={"requests": 1},
unit="unit"
)]
),
free=True,
bindable=True
)],
tags=['tag1', 'tag2'],
requires=['something'],
metadata=dict(
displayName="displayName",
imageUrl="imageUrl",
longDescription="longDescription",
providerDisplayName="providerDisplayName",
documentationUrl="documentationUrl",
supportUrl="supportUrl"
),
dashboard_client=dict(
id="id",
secret="secret",
redirect_uri="redirect_uri"
),
plan_updateable=True
)
]
))
def test_catalog_returns_200_with_minimal_service_information(self):
self.broker.catalog.return_value = self.service
response = self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header,
"unknown": "unknown"
})
self.assertEqual(response.status_code, http.HTTPStatus.OK)
self.assertEqual(response.json,
dict(
services=[
dict(
id="s1",
name="service_name",
description="service_description",
bindable=True,
plan_updateable=False,
plans=[dict(id="p1", name="default", description="plan_description")]
)
]
))
def test_catalog_returns_500_if_error_raised(self):
self.broker.catalog.side_effect = Exception("ERROR")
response = self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header,
"unknown": "unknown"
})
self.assertEqual(response.status_code, http.HTTPStatus.INTERNAL_SERVER_ERROR)
self.assertEqual(response.json,
dict(
description="ERROR"
))
| 38.383333 | 106 | 0.4099 | import http
from openbrokerapi.catalog import (
ServiceDashboardClient,
ServiceMetadata,
ServicePlan,
ServicePlanCost,
ServicePlanMetaData,
)
from openbrokerapi.service_broker import Service
from test import BrokerTestCase
class CatalogTest(BrokerTestCase):
service = Service(
id="s1",
name="service_name",
description="service_description",
bindable=True,
plans=[ServicePlan(id="p1", name="default", description="plan_description")]
)
def test_catalog_called_with_the_right_values(self):
self.broker.catalog.return_value = self.service
self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header
})
self.assertTrue(self.broker.catalog.called)
def test_catalog_ignores_request_headers(self):
self.broker.catalog.return_value = self.service
self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header,
"unknown": "unknown"
})
self.assertTrue(self.broker.catalog.called)
def test_catalog_returns_200_with_service_information(self):
self.broker.catalog.return_value = Service(
id="s1",
name="service_name",
description="service_description",
bindable=True,
plans=[ServicePlan(
id="p1",
name="default",
description="plan_description",
metadata=ServicePlanMetaData(
displayName="displayName",
bullets=["bullet1"],
costs=[ServicePlanCost(
amount={"requests": 1},
unit="unit"
)]
),
free=True,
bindable=True
)],
tags=['tag1', 'tag2'],
requires=['something'],
metadata=ServiceMetadata(
displayName="displayName",
imageUrl="imageUrl",
longDescription="longDescription",
providerDisplayName="providerDisplayName",
documentationUrl="documentationUrl",
supportUrl="supportUrl"
),
dashboard_client=ServiceDashboardClient(
id="id",
secret="secret",
redirect_uri="redirect_uri"
),
plan_updateable=True
)
response = self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header,
"unknown": "unknown"
})
self.assertEqual(response.status_code, http.HTTPStatus.OK)
self.assertEqual(response.json,
dict(
services=[
dict(id="s1",
name="service_name",
description="service_description",
bindable=True,
plans=[dict(
id="p1",
name="default",
description="plan_description",
metadata=dict(
displayName="displayName",
bullets=["bullet1"],
costs=[dict(
amount={"requests": 1},
unit="unit"
)]
),
free=True,
bindable=True
)],
tags=['tag1', 'tag2'],
requires=['something'],
metadata=dict(
displayName="displayName",
imageUrl="imageUrl",
longDescription="longDescription",
providerDisplayName="providerDisplayName",
documentationUrl="documentationUrl",
supportUrl="supportUrl"
),
dashboard_client=dict(
id="id",
secret="secret",
redirect_uri="redirect_uri"
),
plan_updateable=True
)
]
))
def test_catalog_returns_200_with_minimal_service_information(self):
self.broker.catalog.return_value = self.service
response = self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header,
"unknown": "unknown"
})
self.assertEqual(response.status_code, http.HTTPStatus.OK)
self.assertEqual(response.json,
dict(
services=[
dict(
id="s1",
name="service_name",
description="service_description",
bindable=True,
plan_updateable=False,
plans=[dict(id="p1", name="default", description="plan_description")]
)
]
))
def test_catalog_returns_500_if_error_raised(self):
self.broker.catalog.side_effect = Exception("ERROR")
response = self.client.get(
"/v2/catalog",
headers={
'X-Broker-Api-Version': '2.13',
'Authorization': self.auth_header,
"unknown": "unknown"
})
self.assertEqual(response.status_code, http.HTTPStatus.INTERNAL_SERVER_ERROR)
self.assertEqual(response.json,
dict(
description="ERROR"
))
| true | true |
1c3416ab88fda022aac2cf4ef1c028fed8372863 | 427 | py | Python | Problem Solving/Search Algorithms/Data Structure Examples/Stack.py | cholazzzb/A-Study-of-AI | b069d536eb344a363d1b042086926d026afc0360 | [
"MIT"
] | null | null | null | Problem Solving/Search Algorithms/Data Structure Examples/Stack.py | cholazzzb/A-Study-of-AI | b069d536eb344a363d1b042086926d026afc0360 | [
"MIT"
] | null | null | null | Problem Solving/Search Algorithms/Data Structure Examples/Stack.py | cholazzzb/A-Study-of-AI | b069d536eb344a363d1b042086926d026afc0360 | [
"MIT"
] | null | null | null | class Stack(object):
'Stack'
def __init__(self, build):
self.data = [build]
print("Stack :", self.data)
def push(self, new):
self.data.append(new)
print("Stack :", self.data)
def pop(self):
out = self.data.pop(-1)
print("Stack :", self.data)
return out
data = Stack(5)
data.pop()
data.push(3)
data.push(23)
data.push(3)
data.push(234)
data.pop()
| 17.08 | 35 | 0.552693 | class Stack(object):
def __init__(self, build):
self.data = [build]
print("Stack :", self.data)
def push(self, new):
self.data.append(new)
print("Stack :", self.data)
def pop(self):
out = self.data.pop(-1)
print("Stack :", self.data)
return out
data = Stack(5)
data.pop()
data.push(3)
data.push(23)
data.push(3)
data.push(234)
data.pop()
| true | true |
1c34177280393478e3fc7c88fc4d2eed5f5f808e | 524 | py | Python | backend/home/migrations/0002_homme1.py | crowdbotics-dev/retyetuytrd-dev-23670 | ed9bf7a6d019152274452ca82896664771233b81 | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | backend/home/migrations/0002_homme1.py | crowdbotics-dev/retyetuytrd-dev-23670 | ed9bf7a6d019152274452ca82896664771233b81 | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | backend/home/migrations/0002_homme1.py | crowdbotics-dev/retyetuytrd-dev-23670 | ed9bf7a6d019152274452ca82896664771233b81 | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | # Generated by Django 2.2.26 on 2022-03-11 07:27
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
('home', '0001_load_initial_data'),
]
operations = [
migrations.CreateModel(
name='Homme1',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('phone', models.BigIntegerField()),
],
),
]
| 22.782609 | 114 | 0.572519 |
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
('home', '0001_load_initial_data'),
]
operations = [
migrations.CreateModel(
name='Homme1',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('phone', models.BigIntegerField()),
],
),
]
| true | true |
1c3418889fe5c8db858f699a6da7c157d7e3c801 | 8,302 | py | Python | src/m5r_fancy_iterating.py | munnamn/11-Sequences | 8e03697d635fb7121e393402462b1caa341ba45b | [
"MIT"
] | null | null | null | src/m5r_fancy_iterating.py | munnamn/11-Sequences | 8e03697d635fb7121e393402462b1caa341ba45b | [
"MIT"
] | null | null | null | src/m5r_fancy_iterating.py | munnamn/11-Sequences | 8e03697d635fb7121e393402462b1caa341ba45b | [
"MIT"
] | null | null | null | """
This module shows how to ITERATE (i.e. loop) through a SEQUENCE
in ways OTHER than just going thru the sequence from BEGINNING to END.
It also shows how to SELECT items in a sequence, e.g.:
-- the items that are strings
-- the items that are even integers (e.g. 2, 4, 6, ...)
Note that:
-- SELECTING items that ARE even integers
is different from:
-- LOOKING only at items AT even-numbered indices.
Authors: David Mutchler, Vibha Alangar, Matt Boutell, Dave Fisher,
Mark Hays, Amanda Stouder, Aaron Wilkin, their colleagues,
and Nihaar Munnamgi.
""" # DONE: 1. PUT YOUR NAME IN THE ABOVE LINE.
###############################################################################
# DONE: 2. READ the program below and RUN it.
#
# When you have read it, asking questions as needed,
# and you feel that you understand:
# -- how to loop through a sequence
# in ways OTHER than just from BEGINNING to END,
# -- how to SELECT items in sequence
# -- the distinction between:
# -- SELECTING items that ARE even integers and
# -- LOOKING only at items AT even-numbered indices.
# then:
# change the above _TODO to DONE.
###############################################################################
def main():
""" Calls the TEST functions in this module. """
run_test_sum_string_lengths()
run_test_sum_even_integers()
run_test_sum_items_at_even_indices()
###############################################################################
# The TEST functions are further down in the file,
# so that you can focus on the following examples.
###############################################################################
def sum_string_lengths(sequence, m, n):
"""
What comes in:
-- A sequence of strings
-- Integers m and n,
where 0 <= m <= n < length of the sequence
(which ensures that you can safely use m and n as indices)
What goes out:
Returns the sum of the lengths of the strings
at indices m to n, inclusive, with the restriction
that the loop must go thru the sequence BACKWARDS.
Side effects: None.
Examples:
Suppose that sequence is:
['five', 'OK', 'songs', 'roxanne', 'the police', '', 'three']
Then:
-- sum_string_lengths(sequence, 1, 3)
returns the length of 'roxanne'
plus the length of 'songs' plus the length of 'OK',
which is 7 + 5 + 2, which is 14.
-- sum_string_lengths(sequence, 2, 6)
returns the length of 'three'
plus the length of '' plus the length of 'the police'
plus the length of 'roxanne' plus the length of 'songs,
which is 5 + 0 + 10 + 7 + 5, which is 27.
Type hints:
:type sequence: list or tuple (of strings)
:type m: int
:type n: int
"""
# -------------------------------------------------------------------------
# EXAMPLE 1. Iterates through PART of a sequence, BACKWARDS.
# -------------------------------------------------------------------------
total = 0
for k in range(n, m - 1, -1):
s = sequence[k]
total = total + len(s)
return total
# Here is an alternative (there are other alternatives as well):
total = 0
for k in range(m, n + 1):
total = total + len(sequence[m + n - k])
return total
def sum_even_integers(sequence):
"""
What comes in:
-- A sequence
What goes out:
Returns the sum of the items in the sequence that:
-- are integers AND
-- are even.
Side effects: None.
Examples:
sum_even_integers([3, 10, 6, 5, 5, 10])
returns 10 + 6 + 10, which is 26
sum_even_integers([3, 9, 10, 99, 101, 5, 6, 5, 5, 10])
still returns 10 + 6 + 10, which is 26
sum_even_integers(['hello', 3, 10, 6, 'bye', 5, 7.33, 5, 10])
still returns 10 + 6 + 10, which is 26
Type hints:
:type sequence: list or tuple
"""
# -------------------------------------------------------------------------
# EXAMPLE 2. Iterates through a sequence,
# identifying and summing the items that are EVEN INTEGERS.
#
# Note how:
# -- The TYPE function returns the TYPE of its argument.
# -- An integer X is EVEN if the remainder is 0
# when you divide X by 2 and take the remainder.
# -------------------------------------------------------------------------
total = 0
for k in range(len(sequence)):
item = sequence[k]
if type(item) is int:
if item % 2 == 0:
total = total + item
return total
# Here is an alternative (there are other alternatives as well):
total = 0
for k in range(len(sequence)):
if (type(sequence[k]) is int) and (sequence[k] % 2 == 0):
total = total + sequence[k]
return total
def sum_items_at_even_indices(sequence):
"""
What comes in:
-- A sequence of numbers.
What goes out:
Returns the sum of the numbers in the list that:
-- are at EVEN INDICES.
Side effects: None.
Examples:
sum_items_at_even_indices([3, 10, 6, 5, 5, 10])
returns 3 + 6 + 5, which is 14
sum_items_at_even_indices([5.5, 10, 3, 2, 10, 0, 1])
returns 5.5 + 3 + 10 + 1, which is 19.5
Type hints:
:type sequence: list or tuple (of numbers)
"""
# -------------------------------------------------------------------------
# EXAMPLE 3. Iterates through and sums the items in a list
# of numbers that are at even INDICES.
#
# Constrast this example with the previous example.
# -------------------------------------------------------------------------
total = 0
for k in range(0, len(sequence), 2):
total = total + sequence[k]
return total
# Here is a ** BAD alternative ** that computes the right result
# but takes twice as long to do so as needed.
total = 0
for k in range(len(sequence)): # This is a BAD solution
if k % 2 == 0:
total = total + sequence[k]
return total
###############################################################################
# Just TEST functions below here.
###############################################################################
def run_test_sum_string_lengths():
""" Tests the sum_string_lengths function. """
print()
print('--------------------------------------------------')
print('Testing the sum_string_lengths function:')
print('--------------------------------------------------')
seq = ['five', 'OK', 'songs', 'roxanne', 'the police', '', 'three']
total1 = sum_string_lengths(seq, 1, 3)
total2 = sum_string_lengths(seq, 2, 6)
print('Returned, expected:', total1, 14)
print('Returned, expected:', total2, 27)
def run_test_sum_even_integers():
""" Tests the sum_even_integers function. """
print()
print('--------------------------------------------------')
print('Testing the sum_even_integers function:')
print('--------------------------------------------------')
total1 = sum_even_integers([3, 10, 6, 5, 5, 10])
total2 = sum_even_integers(['hello', 3, 10, 6, 'bye', 5, 7.33, 5, 10])
total3 = sum_even_integers([3, 9, 10, 99, 101, 5, 6, 5, 5, 10])
print('Returned, expected:', total1, 26)
print('Returned, expected:', total2, 26)
print('Returned, expected:', total3, 26)
def run_test_sum_items_at_even_indices():
""" Tests the sum_items_at_even_indices function. """
print()
print('--------------------------------------------------')
print('Testing the sum_items_at_even_indices function:')
print('--------------------------------------------------')
total1 = sum_items_at_even_indices([3, 10, 6, 5, 5, 10])
total2 = sum_items_at_even_indices([5.5, 10, 3, 2, 10, 0, 1])
print('Returned, expected:', total1, 14)
print('Returned, expected:', total2, 19.5)
# -----------------------------------------------------------------------------
# Calls main to start the ball rolling.
# -----------------------------------------------------------------------------
main()
| 34.448133 | 79 | 0.503252 | true | true | |
1c3418a9ba18af2f77f23c15c0404c43548e23c4 | 1,446 | py | Python | setup.py | ChaitanyaSinghBisht/PyWhatKit | ff4b876fb8846f3afa4c9a84b1aa1bd676225207 | [
"MIT"
] | 1 | 2021-01-27T11:34:24.000Z | 2021-01-27T11:34:24.000Z | setup.py | AshleyAlexJacob/PyWhatKit | 866f960f6309378959d9a47e1e2d6854952ca273 | [
"MIT"
] | 3 | 2021-09-08T03:19:26.000Z | 2022-03-12T00:58:05.000Z | setup.py | AshleyAlexJacob/PyWhatKit | 866f960f6309378959d9a47e1e2d6854952ca273 | [
"MIT"
] | null | null | null | from distutils.core import setup
import setuptools
def readme():
with open(r'README.md') as f:
README = f.read()
return README
setup(
name = 'pywhatkit',
packages = setuptools.find_packages(),
version = '2.9',
license='MIT',
description = 'pywhatkit is a Python library for Sending whatsapp message at certain time, it has several other features too.',
author = 'Ankit Raj Mahapatra',
author_email = 'ankitrajjitendra816@gmail.com',
url = 'https://github.com/Ankit404butfound/awesomepy',
download_url = 'https://github.com/Ankit404butfound/awesomepy/archive/1.0.tar.gz',
keywords = ['sendwhatmsg', 'info', 'playonyt', 'search','watch_tutorial'],
install_requires=[
'pyautogui',
'beautifulsoup4',
'wikipedia',
'requests',
'Pillow',
'numpy',
'opencv-python',
],
include_package_data=True,
long_description=readme(),
long_description_content_type="text/markdown",
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
],
)
| 32.863636 | 132 | 0.609267 | from distutils.core import setup
import setuptools
def readme():
with open(r'README.md') as f:
README = f.read()
return README
setup(
name = 'pywhatkit',
packages = setuptools.find_packages(),
version = '2.9',
license='MIT',
description = 'pywhatkit is a Python library for Sending whatsapp message at certain time, it has several other features too.',
author = 'Ankit Raj Mahapatra',
author_email = 'ankitrajjitendra816@gmail.com',
url = 'https://github.com/Ankit404butfound/awesomepy',
download_url = 'https://github.com/Ankit404butfound/awesomepy/archive/1.0.tar.gz',
keywords = ['sendwhatmsg', 'info', 'playonyt', 'search','watch_tutorial'],
install_requires=[
'pyautogui',
'beautifulsoup4',
'wikipedia',
'requests',
'Pillow',
'numpy',
'opencv-python',
],
include_package_data=True,
long_description=readme(),
long_description_content_type="text/markdown",
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
],
)
| true | true |
1c341977f01cd73a75c3fd0b665b08c602d34f78 | 1,857 | py | Python | Python-Programs/Sorting Algorithms/pancakeSort.py | naschwin/Simple-Programs | a06e62b7280890cc8e3b9d2dfac9b7fd90706af3 | [
"MIT"
] | null | null | null | Python-Programs/Sorting Algorithms/pancakeSort.py | naschwin/Simple-Programs | a06e62b7280890cc8e3b9d2dfac9b7fd90706af3 | [
"MIT"
] | null | null | null | Python-Programs/Sorting Algorithms/pancakeSort.py | naschwin/Simple-Programs | a06e62b7280890cc8e3b9d2dfac9b7fd90706af3 | [
"MIT"
] | 1 | 2021-10-09T09:51:22.000Z | 2021-10-09T09:51:22.000Z | import pygame, sys, random
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
WIDTH = 720
HEIGHT = 400
win_size = (WIDTH, HEIGHT)
pygame.init()
win = pygame.display.set_mode(win_size)
pygame.display.set_caption('Pancake Sort')
clock = pygame.time.Clock()
#width of the bars
n = 4
w = int(WIDTH/n)
h_arr = []
states = []
for i in range(w):
#height of the bars
height = random.randint(10, HEIGHT)
h_arr.append(height)
states.append(0)
def maps(num, in_min, in_max, out_min, out_max):
return (num - in_min) * (out_max - out_min) / (in_max - in_min) + out_min
def flip(arr, i):
start = 0
while start < i:
temp = arr[start]
arr[start] = arr[i]
arr[i] = temp
start += 1
i -= 1
flag = False
cur_size = len(h_arr)
while True:
win.fill(BLACK)
if flag:
if cur_size > 1:
mi = h_arr.index(max(h_arr[:cur_size]))
states[cur_size - 1] = 2
if mi != cur_size - 1:
flip(h_arr, mi)
flip(h_arr, cur_size - 1)
cur_size -= 1
else:
states[0] = 2
for i in range(len(h_arr)):
# if states[i] == 0:
# color = (255, 0, 0)
# elif states[i] == 2:
# color = (0, 255, 0)
# else:
# color = WHITE
h_ar = maps(h_arr[i], 0, HEIGHT, 20, 255)
pygame.draw.rect(win, (h_ar//3, h_ar, h_ar//4), pygame.Rect(int(i*n), (HEIGHT - h_arr[i])//2, n, h_arr[i]))
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_RETURN:
flag = True
clock.tick(20)
pygame.display.flip()
| 22.925926 | 116 | 0.501885 | import pygame, sys, random
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
WIDTH = 720
HEIGHT = 400
win_size = (WIDTH, HEIGHT)
pygame.init()
win = pygame.display.set_mode(win_size)
pygame.display.set_caption('Pancake Sort')
clock = pygame.time.Clock()
n = 4
w = int(WIDTH/n)
h_arr = []
states = []
for i in range(w):
height = random.randint(10, HEIGHT)
h_arr.append(height)
states.append(0)
def maps(num, in_min, in_max, out_min, out_max):
return (num - in_min) * (out_max - out_min) / (in_max - in_min) + out_min
def flip(arr, i):
start = 0
while start < i:
temp = arr[start]
arr[start] = arr[i]
arr[i] = temp
start += 1
i -= 1
flag = False
cur_size = len(h_arr)
while True:
win.fill(BLACK)
if flag:
if cur_size > 1:
mi = h_arr.index(max(h_arr[:cur_size]))
states[cur_size - 1] = 2
if mi != cur_size - 1:
flip(h_arr, mi)
flip(h_arr, cur_size - 1)
cur_size -= 1
else:
states[0] = 2
for i in range(len(h_arr)):
h_ar = maps(h_arr[i], 0, HEIGHT, 20, 255)
pygame.draw.rect(win, (h_ar//3, h_ar, h_ar//4), pygame.Rect(int(i*n), (HEIGHT - h_arr[i])//2, n, h_arr[i]))
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_RETURN:
flag = True
clock.tick(20)
pygame.display.flip()
| true | true |
1c341a125f820f2c895baafa6db006a93966988e | 6,374 | py | Python | tempest/api/image/base.py | BeenzSyed/tempest | 7a64ee1216d844f6b99928b53f5c665b84cb8719 | [
"Apache-2.0"
] | null | null | null | tempest/api/image/base.py | BeenzSyed/tempest | 7a64ee1216d844f6b99928b53f5c665b84cb8719 | [
"Apache-2.0"
] | null | null | null | tempest/api/image/base.py | BeenzSyed/tempest | 7a64ee1216d844f6b99928b53f5c665b84cb8719 | [
"Apache-2.0"
] | null | null | null | # Copyright 2013 IBM Corp.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import cStringIO as StringIO
from tempest import clients
from tempest.common import isolated_creds
from tempest.common.utils import data_utils
from tempest import exceptions
from tempest.openstack.common import log as logging
import tempest.test
LOG = logging.getLogger(__name__)
class BaseImageTest(tempest.test.BaseTestCase):
"""Base test class for Image API tests."""
@classmethod
def setUpClass(cls):
cls.set_network_resources()
super(BaseImageTest, cls).setUpClass()
cls.created_images = []
cls._interface = 'json'
cls.isolated_creds = isolated_creds.IsolatedCreds(
cls.__name__, network_resources=cls.network_resources)
if not cls.config.service_available.glance:
skip_msg = ("%s skipped as glance is not available" % cls.__name__)
raise cls.skipException(skip_msg)
if cls.config.compute.allow_tenant_isolation:
creds = cls.isolated_creds.get_primary_creds()
username, tenant_name, password = creds
cls.os = clients.Manager(username=username,
password=password,
tenant_name=tenant_name)
else:
cls.os = clients.Manager()
@classmethod
def tearDownClass(cls):
for image_id in cls.created_images:
try:
cls.client.delete_image(image_id)
except exceptions.NotFound:
pass
for image_id in cls.created_images:
cls.client.wait_for_resource_deletion(image_id)
cls.isolated_creds.clear_isolated_creds()
super(BaseImageTest, cls).tearDownClass()
@classmethod
def create_image(cls, **kwargs):
"""Wrapper that returns a test image."""
name = data_utils.rand_name(cls.__name__ + "-instance")
if 'name' in kwargs:
name = kwargs.pop('name')
container_format = kwargs.pop('container_format')
disk_format = kwargs.pop('disk_format')
resp, image = cls.client.create_image(name, container_format,
disk_format, **kwargs)
cls.created_images.append(image['id'])
return resp, image
class BaseV1ImageTest(BaseImageTest):
@classmethod
def setUpClass(cls):
super(BaseV1ImageTest, cls).setUpClass()
cls.client = cls.os.image_client
if not cls.config.image_feature_enabled.api_v1:
msg = "Glance API v1 not supported"
raise cls.skipException(msg)
class BaseV1ImageMembersTest(BaseV1ImageTest):
@classmethod
def setUpClass(cls):
super(BaseV1ImageMembersTest, cls).setUpClass()
if cls.config.compute.allow_tenant_isolation:
creds = cls.isolated_creds.get_alt_creds()
username, tenant_name, password = creds
cls.os_alt = clients.Manager(username=username,
password=password,
tenant_name=tenant_name)
cls.alt_tenant_id = cls.isolated_creds.get_alt_tenant()['id']
else:
cls.os_alt = clients.AltManager()
identity_client = cls._get_identity_admin_client()
cls.alt_tenant_id = identity_client.get_tenant_by_name(
cls.os_alt.tenant_name)['id']
cls.alt_img_cli = cls.os_alt.image_client
def _create_image(self):
image_file = StringIO.StringIO('*' * 1024)
resp, image = self.create_image(container_format='bare',
disk_format='raw',
is_public=False,
data=image_file)
self.assertEqual(201, resp.status)
image_id = image['id']
return image_id
class BaseV2ImageTest(BaseImageTest):
@classmethod
def setUpClass(cls):
super(BaseV2ImageTest, cls).setUpClass()
cls.client = cls.os.image_client_v2
if not cls.config.image_feature_enabled.api_v2:
msg = "Glance API v2 not supported"
raise cls.skipException(msg)
class BaseV2MemeberImageTest(BaseV2ImageTest):
@classmethod
def setUpClass(cls):
super(BaseV2MemeberImageTest, cls).setUpClass()
if cls.config.compute.allow_tenant_isolation:
creds = cls.isolated_creds.get_alt_creds()
username, tenant_name, password = creds
cls.os_alt = clients.Manager(username=username,
password=password,
tenant_name=tenant_name,
interface=cls._interface)
cls.alt_tenant_id = cls.isolated_creds.get_alt_tenant()['id']
else:
cls.os_alt = clients.AltManager()
alt_tenant_name = cls.os_alt.tenant_name
identity_client = cls._get_identity_admin_client()
cls.alt_tenant_id = identity_client.get_tenant_by_name(
alt_tenant_name)['id']
cls.os_img_client = cls.os.image_client_v2
cls.alt_img_client = cls.os_alt.image_client_v2
def _list_image_ids_as_alt(self):
_, image_list = self.alt_img_client.image_list()
image_ids = map(lambda x: x['id'], image_list)
return image_ids
def _create_image(self):
name = data_utils.rand_name('image')
resp, image = self.os_img_client.create_image(name,
container_format='bare',
disk_format='raw')
image_id = image['id']
self.addCleanup(self.os_img_client.delete_image, image_id)
return image_id
| 38.167665 | 79 | 0.617038 |
import cStringIO as StringIO
from tempest import clients
from tempest.common import isolated_creds
from tempest.common.utils import data_utils
from tempest import exceptions
from tempest.openstack.common import log as logging
import tempest.test
LOG = logging.getLogger(__name__)
class BaseImageTest(tempest.test.BaseTestCase):
@classmethod
def setUpClass(cls):
cls.set_network_resources()
super(BaseImageTest, cls).setUpClass()
cls.created_images = []
cls._interface = 'json'
cls.isolated_creds = isolated_creds.IsolatedCreds(
cls.__name__, network_resources=cls.network_resources)
if not cls.config.service_available.glance:
skip_msg = ("%s skipped as glance is not available" % cls.__name__)
raise cls.skipException(skip_msg)
if cls.config.compute.allow_tenant_isolation:
creds = cls.isolated_creds.get_primary_creds()
username, tenant_name, password = creds
cls.os = clients.Manager(username=username,
password=password,
tenant_name=tenant_name)
else:
cls.os = clients.Manager()
@classmethod
def tearDownClass(cls):
for image_id in cls.created_images:
try:
cls.client.delete_image(image_id)
except exceptions.NotFound:
pass
for image_id in cls.created_images:
cls.client.wait_for_resource_deletion(image_id)
cls.isolated_creds.clear_isolated_creds()
super(BaseImageTest, cls).tearDownClass()
@classmethod
def create_image(cls, **kwargs):
name = data_utils.rand_name(cls.__name__ + "-instance")
if 'name' in kwargs:
name = kwargs.pop('name')
container_format = kwargs.pop('container_format')
disk_format = kwargs.pop('disk_format')
resp, image = cls.client.create_image(name, container_format,
disk_format, **kwargs)
cls.created_images.append(image['id'])
return resp, image
class BaseV1ImageTest(BaseImageTest):
@classmethod
def setUpClass(cls):
super(BaseV1ImageTest, cls).setUpClass()
cls.client = cls.os.image_client
if not cls.config.image_feature_enabled.api_v1:
msg = "Glance API v1 not supported"
raise cls.skipException(msg)
class BaseV1ImageMembersTest(BaseV1ImageTest):
@classmethod
def setUpClass(cls):
super(BaseV1ImageMembersTest, cls).setUpClass()
if cls.config.compute.allow_tenant_isolation:
creds = cls.isolated_creds.get_alt_creds()
username, tenant_name, password = creds
cls.os_alt = clients.Manager(username=username,
password=password,
tenant_name=tenant_name)
cls.alt_tenant_id = cls.isolated_creds.get_alt_tenant()['id']
else:
cls.os_alt = clients.AltManager()
identity_client = cls._get_identity_admin_client()
cls.alt_tenant_id = identity_client.get_tenant_by_name(
cls.os_alt.tenant_name)['id']
cls.alt_img_cli = cls.os_alt.image_client
def _create_image(self):
image_file = StringIO.StringIO('*' * 1024)
resp, image = self.create_image(container_format='bare',
disk_format='raw',
is_public=False,
data=image_file)
self.assertEqual(201, resp.status)
image_id = image['id']
return image_id
class BaseV2ImageTest(BaseImageTest):
@classmethod
def setUpClass(cls):
super(BaseV2ImageTest, cls).setUpClass()
cls.client = cls.os.image_client_v2
if not cls.config.image_feature_enabled.api_v2:
msg = "Glance API v2 not supported"
raise cls.skipException(msg)
class BaseV2MemeberImageTest(BaseV2ImageTest):
@classmethod
def setUpClass(cls):
super(BaseV2MemeberImageTest, cls).setUpClass()
if cls.config.compute.allow_tenant_isolation:
creds = cls.isolated_creds.get_alt_creds()
username, tenant_name, password = creds
cls.os_alt = clients.Manager(username=username,
password=password,
tenant_name=tenant_name,
interface=cls._interface)
cls.alt_tenant_id = cls.isolated_creds.get_alt_tenant()['id']
else:
cls.os_alt = clients.AltManager()
alt_tenant_name = cls.os_alt.tenant_name
identity_client = cls._get_identity_admin_client()
cls.alt_tenant_id = identity_client.get_tenant_by_name(
alt_tenant_name)['id']
cls.os_img_client = cls.os.image_client_v2
cls.alt_img_client = cls.os_alt.image_client_v2
def _list_image_ids_as_alt(self):
_, image_list = self.alt_img_client.image_list()
image_ids = map(lambda x: x['id'], image_list)
return image_ids
def _create_image(self):
name = data_utils.rand_name('image')
resp, image = self.os_img_client.create_image(name,
container_format='bare',
disk_format='raw')
image_id = image['id']
self.addCleanup(self.os_img_client.delete_image, image_id)
return image_id
| true | true |
1c341a4678e8e2200ae0b0556e8e280719fa2b93 | 8,103 | py | Python | wrappers/python/virgil_crypto_lib/foundation/aes256_cbc.py | odidev/virgil-crypto-c | 3d5d5cb19fdcf81eab08cdc63647f040117ecbd8 | [
"BSD-3-Clause"
] | 26 | 2018-12-17T13:45:25.000Z | 2022-01-16T20:00:04.000Z | wrappers/python/virgil_crypto_lib/foundation/aes256_cbc.py | odidev/virgil-crypto-c | 3d5d5cb19fdcf81eab08cdc63647f040117ecbd8 | [
"BSD-3-Clause"
] | 4 | 2019-01-03T12:08:52.000Z | 2021-12-02T05:21:13.000Z | wrappers/python/virgil_crypto_lib/foundation/aes256_cbc.py | odidev/virgil-crypto-c | 3d5d5cb19fdcf81eab08cdc63647f040117ecbd8 | [
"BSD-3-Clause"
] | 8 | 2019-01-24T08:22:06.000Z | 2022-02-07T11:37:00.000Z | # Copyright (C) 2015-2021 Virgil Security, Inc.
#
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# (1) Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# (2) Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in
# the documentation and/or other materials provided with the
# distribution.
#
# (3) Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ''AS IS'' AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
# IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# Lead Maintainer: Virgil Security Inc. <support@virgilsecurity.com>
from ctypes import *
from ._c_bridge import VscfAes256Cbc
from ._c_bridge import VscfImplTag
from ._c_bridge import VscfStatus
from virgil_crypto_lib.common._c_bridge import Data
from virgil_crypto_lib.common._c_bridge import Buffer
from .alg import Alg
from .encrypt import Encrypt
from .decrypt import Decrypt
from .cipher_info import CipherInfo
from .cipher import Cipher
class Aes256Cbc(Alg, Encrypt, Decrypt, CipherInfo, Cipher):
"""Implementation of the symmetric cipher AES-256 bit in a CBC mode.
Note, this implementation contains dynamic memory allocations,
this should be improved in the future releases."""
# Cipher nfonce length or IV length in bytes, or 0 if nonce is not required.
NONCE_LEN = 16
# Cipher key length in bytes.
KEY_LEN = 32
# Cipher key length in bits.
KEY_BITLEN = 256
# Cipher block length in bytes.
BLOCK_LEN = 16
def __init__(self):
"""Create underlying C context."""
self._lib_vscf_aes256_cbc = VscfAes256Cbc()
self._c_impl = None
self._ctx = None
self.ctx = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_new()
def __delete__(self, instance):
"""Destroy underlying C context."""
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_delete(self.ctx)
def alg_id(self):
"""Provide algorithm identificator."""
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_alg_id(self.ctx)
return result
def produce_alg_info(self):
"""Produce object with algorithm information and configuration parameters."""
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_produce_alg_info(self.ctx)
instance = VscfImplTag.get_type(result)[0].take_c_ctx(cast(result, POINTER(VscfImplTag.get_type(result)[1])))
return instance
def restore_alg_info(self, alg_info):
"""Restore algorithm configuration from the given object."""
status = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_restore_alg_info(self.ctx, alg_info.c_impl)
VscfStatus.handle_status(status)
def encrypt(self, data):
"""Encrypt given data."""
d_data = Data(data)
out = Buffer(self.encrypted_len(data_len=len(data)))
status = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_encrypt(self.ctx, d_data.data, out.c_buffer)
VscfStatus.handle_status(status)
return out.get_bytes()
def encrypted_len(self, data_len):
"""Calculate required buffer length to hold the encrypted data."""
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_encrypted_len(self.ctx, data_len)
return result
def precise_encrypted_len(self, data_len):
"""Precise length calculation of encrypted data."""
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_precise_encrypted_len(self.ctx, data_len)
return result
def decrypt(self, data):
"""Decrypt given data."""
d_data = Data(data)
out = Buffer(self.decrypted_len(data_len=len(data)))
status = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_decrypt(self.ctx, d_data.data, out.c_buffer)
VscfStatus.handle_status(status)
return out.get_bytes()
def decrypted_len(self, data_len):
"""Calculate required buffer length to hold the decrypted data."""
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_decrypted_len(self.ctx, data_len)
return result
def set_nonce(self, nonce):
"""Setup IV or nonce."""
d_nonce = Data(nonce)
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_set_nonce(self.ctx, d_nonce.data)
def set_key(self, key):
"""Set cipher encryption / decryption key."""
d_key = Data(key)
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_set_key(self.ctx, d_key.data)
def start_encryption(self):
"""Start sequential encryption."""
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_start_encryption(self.ctx)
def start_decryption(self):
"""Start sequential decryption."""
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_start_decryption(self.ctx)
def update(self, data):
"""Process encryption or decryption of the given data chunk."""
d_data = Data(data)
out = Buffer(self.out_len(data_len=len(data)))
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_update(self.ctx, d_data.data, out.c_buffer)
return out.get_bytes()
def out_len(self, data_len):
"""Return buffer length required to hold an output of the methods
"update" or "finish" in an current mode.
Pass zero length to define buffer length of the method "finish"."""
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_out_len(self.ctx, data_len)
return result
def encrypted_out_len(self, data_len):
"""Return buffer length required to hold an output of the methods
"update" or "finish" in an encryption mode.
Pass zero length to define buffer length of the method "finish"."""
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_encrypted_out_len(self.ctx, data_len)
return result
def decrypted_out_len(self, data_len):
"""Return buffer length required to hold an output of the methods
"update" or "finish" in an decryption mode.
Pass zero length to define buffer length of the method "finish"."""
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_decrypted_out_len(self.ctx, data_len)
return result
def finish(self):
"""Accomplish encryption or decryption process."""
out = Buffer(self.out_len(data_len=0))
status = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_finish(self.ctx, out.c_buffer)
VscfStatus.handle_status(status)
return out.get_bytes()
@classmethod
def take_c_ctx(cls, c_ctx):
inst = cls.__new__(cls)
inst._lib_vscf_aes256_cbc = VscfAes256Cbc()
inst.ctx = c_ctx
return inst
@classmethod
def use_c_ctx(cls, c_ctx):
inst = cls.__new__(cls)
inst._lib_vscf_aes256_cbc = VscfAes256Cbc()
inst.ctx = inst._lib_vscf_aes256_cbc.vscf_aes256_cbc_shallow_copy(c_ctx)
return inst
@property
def c_impl(self):
return self._c_impl
@property
def ctx(self):
return self._ctx
@ctx.setter
def ctx(self, value):
self._ctx = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_shallow_copy(value)
self._c_impl = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_impl(self.ctx)
| 40.515 | 117 | 0.710971 |
from ctypes import *
from ._c_bridge import VscfAes256Cbc
from ._c_bridge import VscfImplTag
from ._c_bridge import VscfStatus
from virgil_crypto_lib.common._c_bridge import Data
from virgil_crypto_lib.common._c_bridge import Buffer
from .alg import Alg
from .encrypt import Encrypt
from .decrypt import Decrypt
from .cipher_info import CipherInfo
from .cipher import Cipher
class Aes256Cbc(Alg, Encrypt, Decrypt, CipherInfo, Cipher):
NONCE_LEN = 16
KEY_LEN = 32
KEY_BITLEN = 256
BLOCK_LEN = 16
def __init__(self):
self._lib_vscf_aes256_cbc = VscfAes256Cbc()
self._c_impl = None
self._ctx = None
self.ctx = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_new()
def __delete__(self, instance):
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_delete(self.ctx)
def alg_id(self):
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_alg_id(self.ctx)
return result
def produce_alg_info(self):
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_produce_alg_info(self.ctx)
instance = VscfImplTag.get_type(result)[0].take_c_ctx(cast(result, POINTER(VscfImplTag.get_type(result)[1])))
return instance
def restore_alg_info(self, alg_info):
status = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_restore_alg_info(self.ctx, alg_info.c_impl)
VscfStatus.handle_status(status)
def encrypt(self, data):
d_data = Data(data)
out = Buffer(self.encrypted_len(data_len=len(data)))
status = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_encrypt(self.ctx, d_data.data, out.c_buffer)
VscfStatus.handle_status(status)
return out.get_bytes()
def encrypted_len(self, data_len):
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_encrypted_len(self.ctx, data_len)
return result
def precise_encrypted_len(self, data_len):
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_precise_encrypted_len(self.ctx, data_len)
return result
def decrypt(self, data):
d_data = Data(data)
out = Buffer(self.decrypted_len(data_len=len(data)))
status = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_decrypt(self.ctx, d_data.data, out.c_buffer)
VscfStatus.handle_status(status)
return out.get_bytes()
def decrypted_len(self, data_len):
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_decrypted_len(self.ctx, data_len)
return result
def set_nonce(self, nonce):
d_nonce = Data(nonce)
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_set_nonce(self.ctx, d_nonce.data)
def set_key(self, key):
d_key = Data(key)
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_set_key(self.ctx, d_key.data)
def start_encryption(self):
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_start_encryption(self.ctx)
def start_decryption(self):
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_start_decryption(self.ctx)
def update(self, data):
d_data = Data(data)
out = Buffer(self.out_len(data_len=len(data)))
self._lib_vscf_aes256_cbc.vscf_aes256_cbc_update(self.ctx, d_data.data, out.c_buffer)
return out.get_bytes()
def out_len(self, data_len):
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_out_len(self.ctx, data_len)
return result
def encrypted_out_len(self, data_len):
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_encrypted_out_len(self.ctx, data_len)
return result
def decrypted_out_len(self, data_len):
result = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_decrypted_out_len(self.ctx, data_len)
return result
def finish(self):
out = Buffer(self.out_len(data_len=0))
status = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_finish(self.ctx, out.c_buffer)
VscfStatus.handle_status(status)
return out.get_bytes()
@classmethod
def take_c_ctx(cls, c_ctx):
inst = cls.__new__(cls)
inst._lib_vscf_aes256_cbc = VscfAes256Cbc()
inst.ctx = c_ctx
return inst
@classmethod
def use_c_ctx(cls, c_ctx):
inst = cls.__new__(cls)
inst._lib_vscf_aes256_cbc = VscfAes256Cbc()
inst.ctx = inst._lib_vscf_aes256_cbc.vscf_aes256_cbc_shallow_copy(c_ctx)
return inst
@property
def c_impl(self):
return self._c_impl
@property
def ctx(self):
return self._ctx
@ctx.setter
def ctx(self, value):
self._ctx = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_shallow_copy(value)
self._c_impl = self._lib_vscf_aes256_cbc.vscf_aes256_cbc_impl(self.ctx)
| true | true |
1c341b88dfddbd4f7080f9bdde084e142e7f3d29 | 1,008 | py | Python | code/Solution_0447_numberOfBoomerangs.py | qizhenkang/myLeetCode | cb9edce69567eba9d96ce756507a5a7ac6e74293 | [
"MIT"
] | null | null | null | code/Solution_0447_numberOfBoomerangs.py | qizhenkang/myLeetCode | cb9edce69567eba9d96ce756507a5a7ac6e74293 | [
"MIT"
] | null | null | null | code/Solution_0447_numberOfBoomerangs.py | qizhenkang/myLeetCode | cb9edce69567eba9d96ce756507a5a7ac6e74293 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 13 09:43:47 2021
@author: qizhe
"""
class Solution:
def numberOfBoomerangs(self, points) -> int:
result = 0
for pi in points:
distdict = {}
for pj in points:
dist = (pi[0] - pj[0])**2 + (pi[1] - pj[1])**2
if dist not in distdict:
distdict[dist] = 1
else:
distdict[dist] += 1
for m in distdict.values():
# print(distdict)
result += m*(m-1)
return result
if __name__ == '__main__':
solu = Solution()
input_Str = str('hello')
input_List =[[0,0],[1,0],[2,0]]
# input_List = TreeNode(1)
# input_List.left = TreeNode(2)
# input_List.right = TreeNode(3)
result = solu.numberOfBoomerangs(input_List)
# output_Str = 'result = ' + solu.intToRoman(input_int)
output_Str = ' result = ' + str(result)
print(output_Str) | 24 | 62 | 0.499008 |
class Solution:
def numberOfBoomerangs(self, points) -> int:
result = 0
for pi in points:
distdict = {}
for pj in points:
dist = (pi[0] - pj[0])**2 + (pi[1] - pj[1])**2
if dist not in distdict:
distdict[dist] = 1
else:
distdict[dist] += 1
for m in distdict.values():
result += m*(m-1)
return result
if __name__ == '__main__':
solu = Solution()
input_Str = str('hello')
input_List =[[0,0],[1,0],[2,0]]
result = solu.numberOfBoomerangs(input_List)
output_Str = ' result = ' + str(result)
print(output_Str) | true | true |
1c341bbaa6104c7364e7f7be8428c56c2f7c75d0 | 8,832 | py | Python | sdk/python/pulumi_azure/network/virtual_hub_connection.py | adnang/pulumi-azure | 32360d2f1e41e27d7fdd6522cb26d65e531f279f | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure/network/virtual_hub_connection.py | adnang/pulumi-azure | 32360d2f1e41e27d7fdd6522cb26d65e531f279f | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure/network/virtual_hub_connection.py | adnang/pulumi-azure | 32360d2f1e41e27d7fdd6522cb26d65e531f279f | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import json
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class VirtualHubConnection(pulumi.CustomResource):
hub_to_vitual_network_traffic_allowed: pulumi.Output[bool]
"""
Is the Virtual Hub traffic allowed to transit via the Remote Virtual Network? Changing this forces a new resource to be created.
"""
internet_security_enabled: pulumi.Output[bool]
"""
Should Internet Security be enabled to secure internet traffic? Changing this forces a new resource to be created.
"""
name: pulumi.Output[str]
"""
The Name which should be used for this Connection, which must be unique within the Virtual Hub. Changing this forces a new resource to be created.
"""
remote_virtual_network_id: pulumi.Output[str]
"""
The ID of the Virtual Network which the Virtual Hub should be connected to. Changing this forces a new resource to be created.
"""
virtual_hub_id: pulumi.Output[str]
"""
The ID of the Virtual Hub within which this connection should be created. Changing this forces a new resource to be created.
"""
vitual_network_to_hub_gateways_traffic_allowed: pulumi.Output[bool]
"""
Is Remote Virtual Network traffic allowed to transit the Hub's Virtual Network Gateway's? Changing this forces a new resource to be created.
"""
def __init__(__self__, resource_name, opts=None, hub_to_vitual_network_traffic_allowed=None, internet_security_enabled=None, name=None, remote_virtual_network_id=None, virtual_hub_id=None, vitual_network_to_hub_gateways_traffic_allowed=None, __props__=None, __name__=None, __opts__=None):
"""
Manages a Connection for a Virtual Hub.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_virtual_network = azure.network.VirtualNetwork("exampleVirtualNetwork",
address_spaces=["172.0.0.0/16"],
location=example_resource_group.location,
resource_group_name=example_resource_group.name)
test = azure.network.VirtualWan("test",
resource_group_name=example_resource_group.name,
location=example_resource_group.location)
example_virtual_hub = azure.network.VirtualHub("exampleVirtualHub",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
virtual_wan_id=azurerm_virtual_wan["example"]["id"],
address_prefix="10.0.1.0/24")
example_virtual_hub_connection = azure.network.VirtualHubConnection("exampleVirtualHubConnection",
virtual_hub_id=example_virtual_hub.id,
remote_virtual_network_id=example_virtual_network.id)
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] hub_to_vitual_network_traffic_allowed: Is the Virtual Hub traffic allowed to transit via the Remote Virtual Network? Changing this forces a new resource to be created.
:param pulumi.Input[bool] internet_security_enabled: Should Internet Security be enabled to secure internet traffic? Changing this forces a new resource to be created.
:param pulumi.Input[str] name: The Name which should be used for this Connection, which must be unique within the Virtual Hub. Changing this forces a new resource to be created.
:param pulumi.Input[str] remote_virtual_network_id: The ID of the Virtual Network which the Virtual Hub should be connected to. Changing this forces a new resource to be created.
:param pulumi.Input[str] virtual_hub_id: The ID of the Virtual Hub within which this connection should be created. Changing this forces a new resource to be created.
:param pulumi.Input[bool] vitual_network_to_hub_gateways_traffic_allowed: Is Remote Virtual Network traffic allowed to transit the Hub's Virtual Network Gateway's? Changing this forces a new resource to be created.
"""
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['hub_to_vitual_network_traffic_allowed'] = hub_to_vitual_network_traffic_allowed
__props__['internet_security_enabled'] = internet_security_enabled
__props__['name'] = name
if remote_virtual_network_id is None:
raise TypeError("Missing required property 'remote_virtual_network_id'")
__props__['remote_virtual_network_id'] = remote_virtual_network_id
if virtual_hub_id is None:
raise TypeError("Missing required property 'virtual_hub_id'")
__props__['virtual_hub_id'] = virtual_hub_id
__props__['vitual_network_to_hub_gateways_traffic_allowed'] = vitual_network_to_hub_gateways_traffic_allowed
super(VirtualHubConnection, __self__).__init__(
'azure:network/virtualHubConnection:VirtualHubConnection',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name, id, opts=None, hub_to_vitual_network_traffic_allowed=None, internet_security_enabled=None, name=None, remote_virtual_network_id=None, virtual_hub_id=None, vitual_network_to_hub_gateways_traffic_allowed=None):
"""
Get an existing VirtualHubConnection resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param str id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] hub_to_vitual_network_traffic_allowed: Is the Virtual Hub traffic allowed to transit via the Remote Virtual Network? Changing this forces a new resource to be created.
:param pulumi.Input[bool] internet_security_enabled: Should Internet Security be enabled to secure internet traffic? Changing this forces a new resource to be created.
:param pulumi.Input[str] name: The Name which should be used for this Connection, which must be unique within the Virtual Hub. Changing this forces a new resource to be created.
:param pulumi.Input[str] remote_virtual_network_id: The ID of the Virtual Network which the Virtual Hub should be connected to. Changing this forces a new resource to be created.
:param pulumi.Input[str] virtual_hub_id: The ID of the Virtual Hub within which this connection should be created. Changing this forces a new resource to be created.
:param pulumi.Input[bool] vitual_network_to_hub_gateways_traffic_allowed: Is Remote Virtual Network traffic allowed to transit the Hub's Virtual Network Gateway's? Changing this forces a new resource to be created.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["hub_to_vitual_network_traffic_allowed"] = hub_to_vitual_network_traffic_allowed
__props__["internet_security_enabled"] = internet_security_enabled
__props__["name"] = name
__props__["remote_virtual_network_id"] = remote_virtual_network_id
__props__["virtual_hub_id"] = virtual_hub_id
__props__["vitual_network_to_hub_gateways_traffic_allowed"] = vitual_network_to_hub_gateways_traffic_allowed
return VirtualHubConnection(resource_name, opts=opts, __props__=__props__)
def translate_output_property(self, prop):
return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| 61.762238 | 292 | 0.733809 |
import json
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class VirtualHubConnection(pulumi.CustomResource):
hub_to_vitual_network_traffic_allowed: pulumi.Output[bool]
internet_security_enabled: pulumi.Output[bool]
name: pulumi.Output[str]
remote_virtual_network_id: pulumi.Output[str]
virtual_hub_id: pulumi.Output[str]
vitual_network_to_hub_gateways_traffic_allowed: pulumi.Output[bool]
def __init__(__self__, resource_name, opts=None, hub_to_vitual_network_traffic_allowed=None, internet_security_enabled=None, name=None, remote_virtual_network_id=None, virtual_hub_id=None, vitual_network_to_hub_gateways_traffic_allowed=None, __props__=None, __name__=None, __opts__=None):
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['hub_to_vitual_network_traffic_allowed'] = hub_to_vitual_network_traffic_allowed
__props__['internet_security_enabled'] = internet_security_enabled
__props__['name'] = name
if remote_virtual_network_id is None:
raise TypeError("Missing required property 'remote_virtual_network_id'")
__props__['remote_virtual_network_id'] = remote_virtual_network_id
if virtual_hub_id is None:
raise TypeError("Missing required property 'virtual_hub_id'")
__props__['virtual_hub_id'] = virtual_hub_id
__props__['vitual_network_to_hub_gateways_traffic_allowed'] = vitual_network_to_hub_gateways_traffic_allowed
super(VirtualHubConnection, __self__).__init__(
'azure:network/virtualHubConnection:VirtualHubConnection',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name, id, opts=None, hub_to_vitual_network_traffic_allowed=None, internet_security_enabled=None, name=None, remote_virtual_network_id=None, virtual_hub_id=None, vitual_network_to_hub_gateways_traffic_allowed=None):
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["hub_to_vitual_network_traffic_allowed"] = hub_to_vitual_network_traffic_allowed
__props__["internet_security_enabled"] = internet_security_enabled
__props__["name"] = name
__props__["remote_virtual_network_id"] = remote_virtual_network_id
__props__["virtual_hub_id"] = virtual_hub_id
__props__["vitual_network_to_hub_gateways_traffic_allowed"] = vitual_network_to_hub_gateways_traffic_allowed
return VirtualHubConnection(resource_name, opts=opts, __props__=__props__)
def translate_output_property(self, prop):
return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| true | true |
1c341e35933bcdb0812891953bdb7c49dedc7b37 | 578 | py | Python | text/color/_rgb/_rgba.py | jedhsu/text | 8525b602d304ac571a629104c48703443244545c | [
"Apache-2.0"
] | null | null | null | text/color/_rgb/_rgba.py | jedhsu/text | 8525b602d304ac571a629104c48703443244545c | [
"Apache-2.0"
] | null | null | null | text/color/_rgb/_rgba.py | jedhsu/text | 8525b602d304ac571a629104c48703443244545c | [
"Apache-2.0"
] | null | null | null | """
*Rgba*
The color spectral measure using the RGBA system.
"""
from dataclasses import dataclass
# from wich.measure.spectral import Spectral
from .red import Red
from .green import Green
from .blue import Blue
__all__ = [
"Rgba",
]
@dataclass
class Rgba(
# Spectral,
# GraphicalColor,
):
red: Red
green: Green
blue: Blue
@classmethod
def create(
cls,
red: int,
green: int,
blue: int,
):
return cls(
Red(red),
Green(green),
Blue(blue),
)
| 12.844444 | 51 | 0.550173 |
from dataclasses import dataclass
from .red import Red
from .green import Green
from .blue import Blue
__all__ = [
"Rgba",
]
@dataclass
class Rgba(
):
red: Red
green: Green
blue: Blue
@classmethod
def create(
cls,
red: int,
green: int,
blue: int,
):
return cls(
Red(red),
Green(green),
Blue(blue),
)
| true | true |
1c341e9953b24c6332cd370640c276a8aedd7d2e | 1,687 | py | Python | kontenjan.py | abkkl/me | a0ba942d02c5d503f977c2db62c21e3083c8e5cb | [
"Apache-2.0"
] | null | null | null | kontenjan.py | abkkl/me | a0ba942d02c5d503f977c2db62c21e3083c8e5cb | [
"Apache-2.0"
] | null | null | null | kontenjan.py | abkkl/me | a0ba942d02c5d503f977c2db62c21e3083c8e5cb | [
"Apache-2.0"
] | null | null | null | import mechanize
from bs4 import BeautifulSoup
import openpyxl
import time
import ctypes
wb = openpyxl.Workbook()
ws = wb.active
print("Course Capacity Checker Created by Ahmet Bakkal 2022\n")
ders = input("Course Code (i.e. END458E):").upper()
crn = input("CRN Number (i.e. 21268):")
def itukont(ders,crn):
browser = mechanize.Browser()
browser.set_handle_robots(False)
browser.open("https://www.sis.itu.edu.tr/TR/ogrenci/ders-programi/ders-programi.php?seviye=LS")
browser.select_form(nr=0)
browser.form["derskodu"]=[ders[:3]]
browser.submit()
soup_table=BeautifulSoup(browser.response().read(),'html.parser')
table = soup_table.find('table')
a = 0
c = 0
for i in table.find_all('tr'):
b = 0
c += 1
if c == 1 or c == 2:
pass
else:
a += 1
for j in i.find_all('td'):
b += 1
if c == 1 or c == 2:
pass
else:
ws.cell(column=b, row=a).value = j.get_text(strip=True)
for row in ws.rows:
if row[0].value == crn:
for cell in row:
if int(row[9].value) > int(row[10].value):
check = "Course is available"
ctypes.windll.user32.MessageBoxW(0,"Course is available, run forest run :)", "Attention from Course Capacity Checker",0x40000)
print(check)
break
else:
check = "Course is full"
print(check)
break
while True:
itukont(ders,crn)
time.sleep(5)
| 27.209677 | 147 | 0.5246 | import mechanize
from bs4 import BeautifulSoup
import openpyxl
import time
import ctypes
wb = openpyxl.Workbook()
ws = wb.active
print("Course Capacity Checker Created by Ahmet Bakkal 2022\n")
ders = input("Course Code (i.e. END458E):").upper()
crn = input("CRN Number (i.e. 21268):")
def itukont(ders,crn):
browser = mechanize.Browser()
browser.set_handle_robots(False)
browser.open("https://www.sis.itu.edu.tr/TR/ogrenci/ders-programi/ders-programi.php?seviye=LS")
browser.select_form(nr=0)
browser.form["derskodu"]=[ders[:3]]
browser.submit()
soup_table=BeautifulSoup(browser.response().read(),'html.parser')
table = soup_table.find('table')
a = 0
c = 0
for i in table.find_all('tr'):
b = 0
c += 1
if c == 1 or c == 2:
pass
else:
a += 1
for j in i.find_all('td'):
b += 1
if c == 1 or c == 2:
pass
else:
ws.cell(column=b, row=a).value = j.get_text(strip=True)
for row in ws.rows:
if row[0].value == crn:
for cell in row:
if int(row[9].value) > int(row[10].value):
check = "Course is available"
ctypes.windll.user32.MessageBoxW(0,"Course is available, run forest run :)", "Attention from Course Capacity Checker",0x40000)
print(check)
break
else:
check = "Course is full"
print(check)
break
while True:
itukont(ders,crn)
time.sleep(5)
| true | true |
1c341ec043178dd20596c680ef1491f1e21f2686 | 12,564 | py | Python | python/ccxt/async_support/base/exchange.py | 1Konto/ccxt | 817eace045a87b441f009c2303827b3df5e1f339 | [
"MIT"
] | null | null | null | python/ccxt/async_support/base/exchange.py | 1Konto/ccxt | 817eace045a87b441f009c2303827b3df5e1f339 | [
"MIT"
] | 1 | 2019-09-19T06:38:21.000Z | 2019-09-19T06:38:21.000Z | python/ccxt/async_support/base/exchange.py | 1Konto/ccxt | 817eace045a87b441f009c2303827b3df5e1f339 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
__version__ = '1.18.1165'
# -----------------------------------------------------------------------------
import asyncio
import concurrent
import socket
import time
import math
import random
import certifi
import aiohttp
import ssl
import sys
import yarl
# -----------------------------------------------------------------------------
from ccxt.async_support.base.throttle import throttle
# -----------------------------------------------------------------------------
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import ExchangeNotAvailable
from ccxt.base.errors import RequestTimeout
from ccxt.base.errors import NotSupported
# -----------------------------------------------------------------------------
from ccxt.base.exchange import Exchange as BaseExchange
# -----------------------------------------------------------------------------
__all__ = [
'BaseExchange',
'Exchange',
]
# -----------------------------------------------------------------------------
class Exchange(BaseExchange):
def __init__(self, config={}):
if 'asyncio_loop' in config:
self.asyncio_loop = config['asyncio_loop']
self.asyncio_loop = self.asyncio_loop or asyncio.get_event_loop()
self.aiohttp_trust_env = config.get('aiohttp_trust_env', self.aiohttp_trust_env)
self.verify = config.get('verify', self.verify)
self.own_session = 'session' not in config
self.cafile = config.get('cafile', certifi.where())
self.open()
super(Exchange, self).__init__(config)
self.init_rest_rate_limiter()
def init_rest_rate_limiter(self):
self.throttle = throttle(self.extend({
'loop': self.asyncio_loop,
}, self.tokenBucket))
def __del__(self):
if self.session is not None:
self.logger.warning(self.id + " requires to release all resources with an explicit call to the .close() coroutine. If you are using the exchange instance with async coroutines, add exchange.close() to your code into a place when you're done with the exchange and don't need the exchange instance anymore (at the end of your async coroutine).")
if sys.version_info >= (3, 5):
async def __aenter__(self):
self.open()
return self
async def __aexit__(self, exc_type, exc, tb):
await self.close()
def open(self):
if self.own_session and self.session is None:
# Create our SSL context object with our CA cert file
context = ssl.create_default_context(cafile=self.cafile) if self.verify else self.verify
# Pass this SSL context to aiohttp and create a TCPConnector
connector = aiohttp.TCPConnector(ssl=context, loop=self.asyncio_loop)
self.session = aiohttp.ClientSession(loop=self.asyncio_loop, connector=connector, trust_env=self.aiohttp_trust_env)
async def close(self):
if self.session is not None:
if self.own_session:
await self.session.close()
self.session = None
async def wait_for_token(self):
while self.rateLimitTokens <= 1:
# if self.verbose:
# print('Waiting for tokens: Exchange: {0}'.format(self.id))
self.add_new_tokens()
seconds_delays = [0.001, 0.005, 0.022, 0.106, 0.5]
delay = random.choice(seconds_delays)
await asyncio.sleep(delay)
self.rateLimitTokens -= 1
def add_new_tokens(self):
# if self.verbose:
# print('Adding new tokens: Exchange: {0}'.format(self.id))
now = time.monotonic()
time_since_update = now - self.rateLimitUpdateTime
new_tokens = math.floor((0.8 * 1000.0 * time_since_update) / self.rateLimit)
if new_tokens > 1:
self.rateLimitTokens = min(self.rateLimitTokens + new_tokens, self.rateLimitMaxTokens)
self.rateLimitUpdateTime = now
async def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""A better wrapper over request for deferred signing"""
if self.enableRateLimit:
await self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method, params, headers, body)
return await self.fetch(request['url'], request['method'], request['headers'], request['body'])
async def fetch(self, url, method='GET', headers=None, body=None):
"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
print("\nRequest:", method, url, headers, body)
self.logger.debug("%s %s, Request: %s %s", method, url, headers, body)
request_body = body
encoded_body = body.encode() if body else None
session_method = getattr(self.session, method.lower())
http_response = None
http_status_code = None
http_status_text = None
json_response = None
try:
async with session_method(yarl.URL(url, encoded=True),
data=encoded_body,
headers=request_headers,
timeout=(self.timeout / 1000),
proxy=self.aiohttp_proxy) as response:
http_response = await response.text()
http_status_code = response.status
http_status_text = response.reason
json_response = self.parse_json(http_response)
headers = response.headers
if self.enableLastHttpResponse:
self.last_http_response = http_response
if self.enableLastResponseHeaders:
self.last_response_headers = headers
if self.enableLastJsonResponse:
self.last_json_response = json_response
if self.verbose:
print("\nResponse:", method, url, http_status_code, headers, http_response)
self.logger.debug("%s %s, Response: %s %s %s", method, url, http_status_code, headers, http_response)
except socket.gaierror as e:
raise ExchangeNotAvailable(method + ' ' + url)
except concurrent.futures._base.TimeoutError as e:
raise RequestTimeout(method + ' ' + url)
except aiohttp.client_exceptions.ClientConnectionError as e:
raise ExchangeNotAvailable(method + ' ' + url)
except aiohttp.client_exceptions.ClientError as e: # base exception class
raise ExchangeError(method + ' ' + url)
self.handle_errors(http_status_code, http_status_text, url, method, headers, http_response, json_response, request_headers, request_body)
self.handle_rest_errors(http_status_code, http_status_text, http_response, url, method)
self.handle_rest_response(http_response, json_response, url, method)
if json_response is not None:
return json_response
return http_response
async def load_markets(self, reload=False, params={}):
if not reload:
if self.markets:
if not self.markets_by_id:
return self.set_markets(self.markets)
return self.markets
currencies = None
if self.has['fetchCurrencies']:
currencies = await self.fetch_currencies()
markets = await self.fetch_markets(params)
return self.set_markets(markets, currencies)
async def fetch_fees(self):
trading = {}
funding = {}
if self.has['fetchTradingFees']:
trading = await self.fetch_trading_fees()
if self.has['fetchFundingFees']:
funding = await self.fetch_funding_fees()
return {
'trading': trading,
'funding': funding,
}
async def load_fees(self, reload=False):
if not reload:
if self.loaded_fees != Exchange.loaded_fees:
return self.loaded_fees
self.loaded_fees = self.deep_extend(self.loaded_fees, await self.fetch_fees())
return self.loaded_fees
async def fetch_markets(self, params={}):
# markets are returned as a list
# currencies are returned as a dict
# this is for historical reasons
# and may be changed for consistency later
return self.to_array(self.markets)
async def fetch_currencies(self, params={}):
# markets are returned as a list
# currencies are returned as a dict
# this is for historical reasons
# and may be changed for consistency later
return self.currencies
async def fetch_status(self, params={}):
if self.has['fetchTime']:
updated = await self.fetch_time(params)
self.status['updated'] = updated
return self.status
async def fetch_order_status(self, id, symbol=None, params={}):
order = await self.fetch_order(id, symbol, params)
return order['status']
async def fetch_partial_balance(self, part, params={}):
balance = await self.fetch_balance(params)
return balance[part]
async def fetch_l2_order_book(self, symbol, limit=None, params={}):
orderbook = await self.fetch_order_book(symbol, limit, params)
return self.extend(orderbook, {
'bids': self.sort_by(self.aggregate(orderbook['bids']), 0, True),
'asks': self.sort_by(self.aggregate(orderbook['asks']), 0),
})
async def perform_order_book_request(self, market, limit=None, params={}):
raise NotSupported(self.id + ' performOrderBookRequest not supported yet')
async def fetch_order_book(self, symbol, limit=None, params={}):
await self.load_markets()
market = self.market(symbol)
orderbook = await self.perform_order_book_request(market, limit, params)
return self.parse_order_book(orderbook, market, limit, params)
async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}):
if not self.has['fetchTrades']:
raise NotSupported('fetch_ohlcv() not implemented yet')
await self.load_markets()
trades = await self.fetch_trades(symbol, since, limit, params)
return self.build_ohlcv(trades, timeframe, since, limit)
async def fetchOHLCV(self, symbol, timeframe='1m', since=None, limit=None, params={}):
return await self.fetch_ohlcv(symbol, timeframe, since, limit, params)
async def fetch_full_tickers(self, symbols=None, params={}):
return await self.fetch_tickers(symbols, params)
async def edit_order(self, id, symbol, *args):
if not self.enableRateLimit:
raise ExchangeError('updateOrder() requires enableRateLimit = true')
await self.cancel_order(id, symbol)
return await self.create_order(symbol, *args)
async def fetch_trading_fees(self, params={}):
raise NotSupported('fetch_trading_fees() not supported yet')
async def fetch_trading_fee(self, symbol, params={}):
if not self.has['fetchTradingFees']:
raise NotSupported('fetch_trading_fee() not supported yet')
return await self.fetch_trading_fees(params)
async def load_trading_limits(self, symbols=None, reload=False, params={}):
if self.has['fetchTradingLimits']:
if reload or not('limitsLoaded' in list(self.options.keys())):
response = await self.fetch_trading_limits(symbols)
for i in range(0, len(symbols)):
symbol = symbols[i]
self.markets[symbol] = self.deep_extend(self.markets[symbol], response[symbol])
self.options['limitsLoaded'] = self.milliseconds()
return self.markets
async def load_accounts(self, reload=False, params={}):
if reload:
self.accounts = await self.fetch_accounts(params)
else:
if self.accounts:
return self.accounts
else:
self.accounts = await self.fetch_accounts(params)
self.accountsById = self.index_by(self.accounts, 'id')
return self.accounts
async def fetch_ticker(self, symbol, params={}):
raise NotSupported('fetch_ticker() not supported yet')
| 41.740864 | 355 | 0.610236 |
__version__ = '1.18.1165'
import asyncio
import concurrent
import socket
import time
import math
import random
import certifi
import aiohttp
import ssl
import sys
import yarl
from ccxt.async_support.base.throttle import throttle
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import ExchangeNotAvailable
from ccxt.base.errors import RequestTimeout
from ccxt.base.errors import NotSupported
from ccxt.base.exchange import Exchange as BaseExchange
__all__ = [
'BaseExchange',
'Exchange',
]
class Exchange(BaseExchange):
def __init__(self, config={}):
if 'asyncio_loop' in config:
self.asyncio_loop = config['asyncio_loop']
self.asyncio_loop = self.asyncio_loop or asyncio.get_event_loop()
self.aiohttp_trust_env = config.get('aiohttp_trust_env', self.aiohttp_trust_env)
self.verify = config.get('verify', self.verify)
self.own_session = 'session' not in config
self.cafile = config.get('cafile', certifi.where())
self.open()
super(Exchange, self).__init__(config)
self.init_rest_rate_limiter()
def init_rest_rate_limiter(self):
self.throttle = throttle(self.extend({
'loop': self.asyncio_loop,
}, self.tokenBucket))
def __del__(self):
if self.session is not None:
self.logger.warning(self.id + " requires to release all resources with an explicit call to the .close() coroutine. If you are using the exchange instance with async coroutines, add exchange.close() to your code into a place when you're done with the exchange and don't need the exchange instance anymore (at the end of your async coroutine).")
if sys.version_info >= (3, 5):
async def __aenter__(self):
self.open()
return self
async def __aexit__(self, exc_type, exc, tb):
await self.close()
def open(self):
if self.own_session and self.session is None:
context = ssl.create_default_context(cafile=self.cafile) if self.verify else self.verify
connector = aiohttp.TCPConnector(ssl=context, loop=self.asyncio_loop)
self.session = aiohttp.ClientSession(loop=self.asyncio_loop, connector=connector, trust_env=self.aiohttp_trust_env)
async def close(self):
if self.session is not None:
if self.own_session:
await self.session.close()
self.session = None
async def wait_for_token(self):
while self.rateLimitTokens <= 1:
self.add_new_tokens()
seconds_delays = [0.001, 0.005, 0.022, 0.106, 0.5]
delay = random.choice(seconds_delays)
await asyncio.sleep(delay)
self.rateLimitTokens -= 1
def add_new_tokens(self):
now = time.monotonic()
time_since_update = now - self.rateLimitUpdateTime
new_tokens = math.floor((0.8 * 1000.0 * time_since_update) / self.rateLimit)
if new_tokens > 1:
self.rateLimitTokens = min(self.rateLimitTokens + new_tokens, self.rateLimitMaxTokens)
self.rateLimitUpdateTime = now
async def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
if self.enableRateLimit:
await self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method, params, headers, body)
return await self.fetch(request['url'], request['method'], request['headers'], request['body'])
async def fetch(self, url, method='GET', headers=None, body=None):
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
print("\nRequest:", method, url, headers, body)
self.logger.debug("%s %s, Request: %s %s", method, url, headers, body)
request_body = body
encoded_body = body.encode() if body else None
session_method = getattr(self.session, method.lower())
http_response = None
http_status_code = None
http_status_text = None
json_response = None
try:
async with session_method(yarl.URL(url, encoded=True),
data=encoded_body,
headers=request_headers,
timeout=(self.timeout / 1000),
proxy=self.aiohttp_proxy) as response:
http_response = await response.text()
http_status_code = response.status
http_status_text = response.reason
json_response = self.parse_json(http_response)
headers = response.headers
if self.enableLastHttpResponse:
self.last_http_response = http_response
if self.enableLastResponseHeaders:
self.last_response_headers = headers
if self.enableLastJsonResponse:
self.last_json_response = json_response
if self.verbose:
print("\nResponse:", method, url, http_status_code, headers, http_response)
self.logger.debug("%s %s, Response: %s %s %s", method, url, http_status_code, headers, http_response)
except socket.gaierror as e:
raise ExchangeNotAvailable(method + ' ' + url)
except concurrent.futures._base.TimeoutError as e:
raise RequestTimeout(method + ' ' + url)
except aiohttp.client_exceptions.ClientConnectionError as e:
raise ExchangeNotAvailable(method + ' ' + url)
except aiohttp.client_exceptions.ClientError as e:
raise ExchangeError(method + ' ' + url)
self.handle_errors(http_status_code, http_status_text, url, method, headers, http_response, json_response, request_headers, request_body)
self.handle_rest_errors(http_status_code, http_status_text, http_response, url, method)
self.handle_rest_response(http_response, json_response, url, method)
if json_response is not None:
return json_response
return http_response
async def load_markets(self, reload=False, params={}):
if not reload:
if self.markets:
if not self.markets_by_id:
return self.set_markets(self.markets)
return self.markets
currencies = None
if self.has['fetchCurrencies']:
currencies = await self.fetch_currencies()
markets = await self.fetch_markets(params)
return self.set_markets(markets, currencies)
async def fetch_fees(self):
trading = {}
funding = {}
if self.has['fetchTradingFees']:
trading = await self.fetch_trading_fees()
if self.has['fetchFundingFees']:
funding = await self.fetch_funding_fees()
return {
'trading': trading,
'funding': funding,
}
async def load_fees(self, reload=False):
if not reload:
if self.loaded_fees != Exchange.loaded_fees:
return self.loaded_fees
self.loaded_fees = self.deep_extend(self.loaded_fees, await self.fetch_fees())
return self.loaded_fees
async def fetch_markets(self, params={}):
return self.to_array(self.markets)
async def fetch_currencies(self, params={}):
return self.currencies
async def fetch_status(self, params={}):
if self.has['fetchTime']:
updated = await self.fetch_time(params)
self.status['updated'] = updated
return self.status
async def fetch_order_status(self, id, symbol=None, params={}):
order = await self.fetch_order(id, symbol, params)
return order['status']
async def fetch_partial_balance(self, part, params={}):
balance = await self.fetch_balance(params)
return balance[part]
async def fetch_l2_order_book(self, symbol, limit=None, params={}):
orderbook = await self.fetch_order_book(symbol, limit, params)
return self.extend(orderbook, {
'bids': self.sort_by(self.aggregate(orderbook['bids']), 0, True),
'asks': self.sort_by(self.aggregate(orderbook['asks']), 0),
})
async def perform_order_book_request(self, market, limit=None, params={}):
raise NotSupported(self.id + ' performOrderBookRequest not supported yet')
async def fetch_order_book(self, symbol, limit=None, params={}):
await self.load_markets()
market = self.market(symbol)
orderbook = await self.perform_order_book_request(market, limit, params)
return self.parse_order_book(orderbook, market, limit, params)
async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}):
if not self.has['fetchTrades']:
raise NotSupported('fetch_ohlcv() not implemented yet')
await self.load_markets()
trades = await self.fetch_trades(symbol, since, limit, params)
return self.build_ohlcv(trades, timeframe, since, limit)
async def fetchOHLCV(self, symbol, timeframe='1m', since=None, limit=None, params={}):
return await self.fetch_ohlcv(symbol, timeframe, since, limit, params)
async def fetch_full_tickers(self, symbols=None, params={}):
return await self.fetch_tickers(symbols, params)
async def edit_order(self, id, symbol, *args):
if not self.enableRateLimit:
raise ExchangeError('updateOrder() requires enableRateLimit = true')
await self.cancel_order(id, symbol)
return await self.create_order(symbol, *args)
async def fetch_trading_fees(self, params={}):
raise NotSupported('fetch_trading_fees() not supported yet')
async def fetch_trading_fee(self, symbol, params={}):
if not self.has['fetchTradingFees']:
raise NotSupported('fetch_trading_fee() not supported yet')
return await self.fetch_trading_fees(params)
async def load_trading_limits(self, symbols=None, reload=False, params={}):
if self.has['fetchTradingLimits']:
if reload or not('limitsLoaded' in list(self.options.keys())):
response = await self.fetch_trading_limits(symbols)
for i in range(0, len(symbols)):
symbol = symbols[i]
self.markets[symbol] = self.deep_extend(self.markets[symbol], response[symbol])
self.options['limitsLoaded'] = self.milliseconds()
return self.markets
async def load_accounts(self, reload=False, params={}):
if reload:
self.accounts = await self.fetch_accounts(params)
else:
if self.accounts:
return self.accounts
else:
self.accounts = await self.fetch_accounts(params)
self.accountsById = self.index_by(self.accounts, 'id')
return self.accounts
async def fetch_ticker(self, symbol, params={}):
raise NotSupported('fetch_ticker() not supported yet')
| true | true |
1c341f0ecec81fd94df513cec1e8797119db1d81 | 195 | py | Python | setup.py | AdamBlomfield/mod_4_project | dad5775535d50ad4d3d9a8cafdc638572d978ca3 | [
"RSA-MD"
] | 2 | 2019-06-18T12:57:42.000Z | 2019-07-10T15:28:48.000Z | setup.py | AdamBlomfield/mod_4_project | dad5775535d50ad4d3d9a8cafdc638572d978ca3 | [
"RSA-MD"
] | null | null | null | setup.py | AdamBlomfield/mod_4_project | dad5775535d50ad4d3d9a8cafdc638572d978ca3 | [
"RSA-MD"
] | null | null | null | from setuptools import find_packages, setup
setup(
name='src',
packages=find_packages(),
version='0.1.0',
description='mod_4_final_project',
author='far',
#license='',
)
| 17.727273 | 43 | 0.651282 | from setuptools import find_packages, setup
setup(
name='src',
packages=find_packages(),
version='0.1.0',
description='mod_4_final_project',
author='far',
)
| true | true |
1c341fc8a17cbd253c660ba3cf5f1ac0c9f71bb8 | 16,537 | py | Python | sans_tools/NotesAppFe.py | Developernation/PythonProjects | d682960d060cb1daede3f2f8e814a5aea05c0ec6 | [
"BSD-3-Clause"
] | 11 | 2022-02-12T23:50:27.000Z | 2022-03-04T01:24:14.000Z | sans_tools/NotesAppFe.py | Developernation/PythonProjects | d682960d060cb1daede3f2f8e814a5aea05c0ec6 | [
"BSD-3-Clause"
] | null | null | null | sans_tools/NotesAppFe.py | Developernation/PythonProjects | d682960d060cb1daede3f2f8e814a5aea05c0ec6 | [
"BSD-3-Clause"
] | 1 | 2022-03-27T22:28:37.000Z | 2022-03-27T22:28:37.000Z | from tkinter.filedialog import askopenfilename
from tkinter.messagebox import showinfo
from NotesApp import SansNotesApp as snp
from datetime import datetime
from tkinter import ttk
import tkinter as tk
import pandas as pd
import os
pd.set_option('display.max_rows', None)
#database connection
notes_db = snp()
notes_db.database_name = 'sans'
notes_db.db_connect_and_cursor()
db_list = notes_db.show_databases()
notes_db.create_table('default_sans_table')
db_tables = notes_db.show_tables()
#first frame
def build_frame(label_text_info,box_width,master_frame,label_width=10):
frame1 = tk.Frame(master=master_frame,relief=border_effects['flat'],width=100, height=10)
text_box1 = tk.Entry(master=frame1, width=box_width, borderwidth=4)
label1 = tk.Label(master=frame1, text=label_text_info,width=label_width)
label1.pack(side='left')
text_box1.pack(side='left')
frame1.pack(fill=tk.X)
return text_box1
#-------------------------------------------------------------------------
border_effects = {
"flat": tk.FLAT,
"sunken": tk.SUNKEN,
"raised": tk.RAISED,
"groove": tk.GROOVE,
"ridge": tk.RIDGE,
}
min_width, min_height = 300,400
label_text = ['Subject:','Topic:','Book:','Page:','Notes:']
window = tk.Tk()
tabControl = ttk.Notebook(window)
window.minsize(min_width, min_height)
window.title('SANS NOTES APP')
#setting defaults for table list
clickedA = tk.StringVar()
clickedA.set(db_tables[0])
clickedB = tk.StringVar()
clickedB.set(db_tables[0])
clickedC = tk.StringVar()
clickedC.set(db_tables[0])
########################################################
#################### Add Data ##########################
########################################################
super_frame_tab1 = ttk.Frame(master=window,relief=border_effects['flat'])
drop_down_frameA = tk.Frame(master=super_frame_tab1,relief=border_effects['flat'],width=50, height=10)
drop_down_labelA = tk.Label( drop_down_frameA , text = "Select Table:" )
drop_down_labelA.pack(side='left')
# Create Dropdown menu
dropA = tk.OptionMenu(drop_down_frameA , clickedA, *db_tables)
dropA.pack(side='left')
drop_down_frameA.pack(fill=tk.X)
frm0 = build_frame(label_text[0],10,super_frame_tab1)
frm1 = build_frame(label_text[1],40,super_frame_tab1)
frm2 = build_frame(label_text[2],5,super_frame_tab1)
frm3 = build_frame(label_text[3],5,super_frame_tab1)
frame3 = tk.Frame(master=super_frame_tab1,relief=border_effects['flat'],width=50, height=10)
inputtxt = tk.Text(master= frame3, height = 5, width = 52,borderwidth=4,relief=border_effects['sunken'])
label2 = tk.Label(master=frame3, text=label_text[4],width=10)
label2.pack(side='left')
inputtxt.pack(side='left')
frame3.pack(fill=tk.X)
def write_dataA():
input_vals = {
'table': clickedA.get().strip(),
'subject':frm0.get().strip(),
'topic':frm1.get().strip(),
'book':frm2.get().strip(),
'page':frm3.get().strip(),
'notes':inputtxt.get("1.0","end-1c").strip(),
}
notes_db.insert_values(
input_vals['table'],
input_vals['subject'],
input_vals['topic'],
input_vals['book'],
input_vals['page'],
input_vals['notes']
)
return input_vals
def add_opt():
dropA['menu'].add_command(label=frm0_tb3.get(), command=tk._setit(clickedA, frm0_tb3.get()))
dropB['menu'].add_command(label=frm0_tb3.get(), command=tk._setit(clickedB, frm0_tb3.get()))
dropC['menu'].add_command(label=frm0_tb3.get(), command=tk._setit(clickedC, frm0_tb3.get()))
global db_tables
db_tables.append(frm0_tb3.get())
def create_table():
notes_db.create_table(frm0_tb3.get().strip())
add_opt()
return True
def remove_item():
r_index1=dropB['menu'].index(frm0_tb3.get())
dropB['menu'].delete(r_index1)
clickedB.set(dropB['menu'].entrycget(0,"label")) # select the first one
r_index2=dropA['menu'].index(frm0_tb3.get())
dropA['menu'].delete(r_index2)
clickedA.set(dropB['menu'].entrycget(0,"label")) # select the first one
r_index3=dropC['menu'].index(frm0_tb3.get())
dropC['menu'].delete(r_index3)
clickedC.set(dropC['menu'].entrycget(0,"label")) # select the first one
return True
def delete_table():
notes_db.drop_table(frm0_tb3.get().strip())
remove_item()
return True
frame5 = tk.Frame(master=super_frame_tab1,relief=border_effects['flat'],width=100, height=10)
label_opt = tk.Label(master=frame5, text='Options',width=10)
Add_Button = tk.Button(master=frame5,
height = 1,
width = 10,
text ="Add Data",
relief=tk.RAISED,
fg = "blue",
command = lambda:write_dataA()
)
frame5.pack(fill=tk.X)
label_opt.pack(side='left')
Add_Button.pack(side='left')
tabControl.add(super_frame_tab1,text='Add Data')
#############################################################
######################## SEARCH DATA TAB ####################
#############################################################
def show_search_data():
Output.delete('1.0', tk.END)
global show_vals
show_vals = {
'table': clickedB.get(),
'subject':frm0_tb2.get(),
'topic':frm1_tb2.get(),
'book':frm2_tb2.get(),
'page':frm3_tb2.get(),
}
global search_data
search_data = notes_db.search_data(
show_vals['table'],
show_vals['subject'],
show_vals['topic'],
show_vals['book'],
show_vals['page'],
strict_search = False
)
Output.insert(tk.END,search_data)
def show_all_table_data():
Output.delete('1.0', tk.END)
global search_data
search_data = notes_db.show_table_data(clickedB.get())
Output.insert(tk.END,search_data)
def show_all_ingest_columns():
Output_tb4.delete('1.0', tk.END)
col_data = None
if filename.endswith('xlsx'):
col_data = list(pd.read_excel(filename).columns)
else:
col_data = list(pd.read_csv(filename).columns)
Output_tb4.insert(tk.END,"""
*********Directions******
1) Map the columns in your file to their respective column in the
table schema by entering them in the spaces above.
2) If you do not want to map a specific column from you file you can
leave the entry blank.
Below are the columns in your file:\n
******************** Ingest Data Column Names *******************
\n\t{}
*****************************************************************
""".format('\n\t'.join(col_data)))
def delete_data():
notes_db.delete_data(
table_name=clickedB.get().strip(),
subject=frm0_tb2.get().strip(),
topic=frm1_tb2.get().strip(),
book=frm2_tb2.get().strip(),
page=frm3_tb2.get().strip(),
)
show_search_data()
def save_to_excel():
save_location = f"{os.path.join(os.path.expanduser('~'),'Downloads','search_data' + datetime.today().strftime('%y%m%d_%H%M%S'))}.xlsx"
search_data.sort_values(by='topic').reset_index(drop=True).to_excel(
save_location
)
showinfo(
title='File Saved',
message="File has been saved to:\n{}".format(save_location)
)
super_frame_tab2 = ttk.Frame(master=window,relief=border_effects['flat'])
drop_down_frameB = tk.Frame(master=super_frame_tab2,relief=border_effects['flat'],width=50, height=10)
drop_down_labelB = tk.Label( drop_down_frameB , text = "Select Table:" )
drop_down_labelB.pack(side='left')
# Create Dropdown menu
dropB = tk.OptionMenu(drop_down_frameB , clickedB, *db_tables)
dropB.pack(side='left')
drop_down_frameB.pack(fill=tk.X)
frm0_tb2 = build_frame(label_text[0],10,super_frame_tab2)
frm1_tb2 = build_frame(label_text[1],40,super_frame_tab2)
frm2_tb2 = build_frame(label_text[2],5,super_frame_tab2)
frm3_tb2 = build_frame(label_text[3],5,super_frame_tab2)
frame0a_tb2 = tk.Frame(master=super_frame_tab2,relief=border_effects['flat'],width=100, height=10)
label_opt2 = tk.Label(master=frame0a_tb2, text='Options:',width=10)
Show_Search_Button = tk.Button(master=frame0a_tb2,
height = 1,
width = 15,
text ="Show Search Data",
relief=tk.RIDGE,
fg = "blue",
command = lambda : show_search_data() )
Search_All_Data = tk.Button(master=frame0a_tb2,
height = 1,
width = 15,
text ="Show All Data",
relief=tk.RIDGE,
fg = "blue",
command = lambda : show_all_table_data())
To_Excel_Button = tk.Button(master=frame0a_tb2,
height = 1,
width = 15,
text ="Save Display To Excel",
relief=tk.RIDGE,
fg = "blue",
command = lambda : save_to_excel())
Delete_Data_Button = tk.Button(master=frame0a_tb2,
height = 1,
width = 15,
text ="Delete Displayed Data",
relief=tk.RIDGE,
fg = "red",
command = lambda : delete_data())
label_opt2.pack(side='left')
Show_Search_Button.pack(side='left')
Search_All_Data.pack(side='left')
To_Excel_Button.pack(side='left')
Delete_Data_Button.pack(side='left')
frame0a_tb2.pack(fill=tk.X)
#------
frame0b_tb2 = tk.Frame(master=super_frame_tab2,relief=border_effects['flat'],width=100, height=10)
###
Output = tk.Text(frame0b_tb2, height = 50,
width = 150,
bg = "light cyan")
###
Output.pack(side='left')
frame0b_tb2.pack(fill=tk.X)
super_frame_tab2.pack(fill=tk.X)
tabControl.add(super_frame_tab2,text='Search Data')
tabControl.pack(expand=1, fill="both",side='right')
############################################################################
#################### Create / Delete Table #################################
############################################################################
super_frame_tab3 = ttk.Frame(master=window,relief=border_effects['flat'])
frm0_tb3 = build_frame('Table Nane:\n *Only letters \n & underscores*',20,super_frame_tab3)
#------
frame0_tb3 = tk.Frame(master=super_frame_tab3,relief=border_effects['flat'],width=100, height=10)
label_opt3 = tk.Label(master=frame0_tb3, text='Options:',width=10)
Create_Button = tk.Button(master=frame0_tb3,
height = 1,
width = 15,
text ="Create Table",
relief=tk.RIDGE,
padx=5,
fg = "blue",
command = create_table
)
Delete_Table = tk.Button(master=frame0_tb3,
height = 1,
width = 10,
text ="Delete Table",
relief=tk.RIDGE,
fg = "red",
command = delete_table
)
label_opt3.pack(side='left')
Create_Button.pack(side='left')
Delete_Table.pack(side='left')
frame0_tb3.pack(fill=tk.X)
tabControl.add(super_frame_tab3,text='Create Table')
tabControl.pack(expand=1, fill="both",side='right')
#############################################################################
####################### Upload Excel File ###################################
#############################################################################
super_frame_tab4 = ttk.Frame(master=window,relief=border_effects['flat'])
directions_frame = tk.Frame(master=super_frame_tab4,relief=border_effects['flat'],width=70, height=50)
directions_text = """"
********** File Upload Directions **********
1) Select a table to upload data or create one in the Create Table tab.
2) Select your file by using the Ingest Data button below
3) Click Show Columns to display the availble columns for mapping to the table schema columns
4) Enter the columns from your file that you would like to map to table schema columns in section below
5) Click the Upload Data button
"""
directions_label = tk.Label(master=directions_frame, text=directions_text ,width=70)
directions_label.pack(side='left')
directions_frame.pack(fill=tk.X)
drop_down_frameC = tk.Frame(master=super_frame_tab4,relief=border_effects['flat'],width=50, height=10)
drop_down_labelC = tk.Label( drop_down_frameC , text = "Select Table:" )
drop_down_labelC.pack(side='left')
# Create Dropdown menu
dropC = tk.OptionMenu(drop_down_frameC , clickedC, *db_tables)
dropC.pack(side='left')
drop_down_frameC.pack(fill=tk.X)
frm0_tb4 = build_frame(f'{label_text[0][:-1]} Column Mapping',10,super_frame_tab4,label_width=20)
frm1_tb4 = build_frame(f'{label_text[1][:-1]} Column Mapping',10,super_frame_tab4,label_width=20)
frm2_tb4 = build_frame(f'{label_text[2][:-1]} Column Mapping',10,super_frame_tab4,label_width=20)
frm3_tb4 = build_frame(f'{label_text[3][:-1]} Column Mapping',10,super_frame_tab4,label_width=20)
frm6_tb4 = build_frame('Notes Column Mapping',10,super_frame_tab4,label_width=20)
def ingest_file_data():
#select file
filetypes = (
('CSV files', '*.csv'),
('Excel files', '*.xlsx')
)
global filename
filename = askopenfilename(
title='Open files',
initialdir='/',
filetypes=filetypes)
showinfo(
title='Selected Files',
message=f'You selected:\n {filename}'
)
def check_len(str_):
return str_ if len(str_) > 0 else None
def upload_data():
Output_tb4.delete('1.0', tk.END)
table = clickedC.get()
subject = frm0_tb4.get()
topic = frm1_tb4.get()
book = frm2_tb4.get()
page = frm3_tb4.get()
notes = frm6_tb4.get()
mapping_vals = {
'subject': check_len(subject),
'topic': check_len(topic),
'book': check_len(book),
'page': check_len(page),
'notes': check_len(notes),
}
notes_db.set_ingest_file(
filename
)
notes_db.rename_df_columns(
topic_column=mapping_vals['topic'],
subject_column=mapping_vals['subject'],
page_column=mapping_vals['page'],
notes_column=mapping_vals['notes'],
book_column=mapping_vals['book']
)
cursor = notes_db.get_cursor()
res = notes_db.build_insert_query(
table,
cursor,
topic_column=mapping_vals['topic'],
subject_column=mapping_vals['subject'],
page_column=mapping_vals['page'],
notes_column=mapping_vals['notes'],
book_column=mapping_vals['book']
)
if res:
showinfo(
title='File Uploaded',
message=f'{filename} was uploaded to {table}'
)
else:
showinfo(
title='File Upload Failure',
message="Please review input colums:\n{}".format(',\n'.join(icols))
)
frm4_tb4 = tk.Frame(master=super_frame_tab4,relief=border_effects['flat'],width=100, height=10)
label_opt4a = tk.Label(master=frm4_tb4, text='Options:',width=10)
Ingest_Button = tk.Button(master=frm4_tb4,
height = 1,
width = 15,
text ="Ingest Data",
relief=tk.RIDGE,
padx=5,
fg = "blue",
command = ingest_file_data
)
Show_Columns_Button = tk.Button(master=frm4_tb4,
height = 1,
width = 15,
text ="Show Columns",
relief=tk.RIDGE,
padx=5,
fg = "green",
command = show_all_ingest_columns
)
Upload_Data_Button = tk.Button(master=frm4_tb4,
height = 1,
width = 15,
text ="Upload Data",
relief=tk.RIDGE,
padx=5,
fg = "orange",
command = upload_data
)
frm5_tb4 = tk.Frame(master=super_frame_tab4,relief=border_effects['flat'],width=100, height=10)
###
Output_tb4 = tk.Text(frm5_tb4, height = 40,
width = 99,
bg = "light green")
label_opt4a.pack(side='left')
Ingest_Button.pack(side='left')
Show_Columns_Button.pack(side='left')
Upload_Data_Button.pack(side='left')
Output_tb4.pack(side='bottom')
frm4_tb4.pack(fill=tk.X)
frm5_tb4.pack(fill=tk.X)
tabControl.add(super_frame_tab4,text='Upload CSV / Excel')
tabControl.pack(expand=1, fill="both",side='right')
##############################################################################
window.mainloop()
notes_db.committ_and_close() | 32.173152 | 138 | 0.600774 | from tkinter.filedialog import askopenfilename
from tkinter.messagebox import showinfo
from NotesApp import SansNotesApp as snp
from datetime import datetime
from tkinter import ttk
import tkinter as tk
import pandas as pd
import os
pd.set_option('display.max_rows', None)
notes_db = snp()
notes_db.database_name = 'sans'
notes_db.db_connect_and_cursor()
db_list = notes_db.show_databases()
notes_db.create_table('default_sans_table')
db_tables = notes_db.show_tables()
def build_frame(label_text_info,box_width,master_frame,label_width=10):
frame1 = tk.Frame(master=master_frame,relief=border_effects['flat'],width=100, height=10)
text_box1 = tk.Entry(master=frame1, width=box_width, borderwidth=4)
label1 = tk.Label(master=frame1, text=label_text_info,width=label_width)
label1.pack(side='left')
text_box1.pack(side='left')
frame1.pack(fill=tk.X)
return text_box1
border_effects = {
"flat": tk.FLAT,
"sunken": tk.SUNKEN,
"raised": tk.RAISED,
"groove": tk.GROOVE,
"ridge": tk.RIDGE,
}
min_width, min_height = 300,400
label_text = ['Subject:','Topic:','Book:','Page:','Notes:']
window = tk.Tk()
tabControl = ttk.Notebook(window)
window.minsize(min_width, min_height)
window.title('SANS NOTES APP')
clickedA = tk.StringVar()
clickedA.set(db_tables[0])
clickedB = tk.StringVar()
clickedB.set(db_tables[0])
clickedC = tk.StringVar()
clickedC.set(db_tables[0])
| true | true |
1c3421c57b17d9a16190870d5ee5157487dc1ffc | 5,474 | py | Python | gluon/_compat.py | zhiyongwang/web2py | 759616e537deb6148b8f32430e214142b4a65261 | [
"BSD-3-Clause"
] | null | null | null | gluon/_compat.py | zhiyongwang/web2py | 759616e537deb6148b8f32430e214142b4a65261 | [
"BSD-3-Clause"
] | null | null | null | gluon/_compat.py | zhiyongwang/web2py | 759616e537deb6148b8f32430e214142b4a65261 | [
"BSD-3-Clause"
] | null | null | null | import sys
import hashlib
import os
PY2 = sys.version_info[0] == 2
_identity = lambda x: x
if PY2:
import cPickle as pickle
from cStringIO import StringIO
import copy_reg as copyreg
from HTMLParser import HTMLParser
import urlparse
from htmlentitydefs import entitydefs, name2codepoint
import __builtin__ as builtin
import thread
import Cookie
import urllib2
import Queue
import ConfigParser as configparser
from email.MIMEBase import MIMEBase
from email.Header import Header
from email import Encoders, Charset
from email.MIMEMultipart import MIMEMultipart
from email.MIMEText import MIMEText
from email.Charset import add_charset, QP as charset_QP
from urllib import FancyURLopener, urlencode, urlopen
from urllib import quote as urllib_quote, unquote as urllib_unquote
from string import maketrans
from types import ClassType
import cgi
import cookielib
reduce = reduce
hashlib_md5 = hashlib.md5
iterkeys = lambda d: d.iterkeys()
itervalues = lambda d: d.itervalues()
iteritems = lambda d: d.iteritems()
integer_types = (int, long)
string_types = (str, unicode)
text_type = unicode
basestring = basestring
xrange = xrange
long = long
unichr = unichr
unicodeT = unicode
def implements_bool(cls):
cls.__nonzero__ = cls.__bool__
del cls.__bool__
return cls
def to_bytes(obj, charset='utf-8', errors='strict'):
if obj is None:
return None
if isinstance(obj, (bytes, bytearray, buffer)):
return bytes(obj)
if isinstance(obj, unicode):
return obj.encode(charset, errors)
raise TypeError('Expected bytes')
def to_native(obj, charset='utf8', errors='strict'):
if obj is None or isinstance(obj, str):
return obj
return obj.encode(charset, errors)
def _local_html_escape(data, quote=False):
s = cgi.escape(data, quote)
return s.replace("'", "'") if quote else s
else:
import pickle
from io import StringIO
import copyreg
from functools import reduce
from html.parser import HTMLParser
from http import cookies as Cookie
from urllib import parse as urlparse
from urllib import request as urllib2
from html.entities import entitydefs, name2codepoint
import builtins as builtin
import _thread as thread
import configparser
import queue as Queue
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email import encoders as Encoders
from email.header import Header
from email.charset import Charset, add_charset, QP as charset_QP
from urllib.request import FancyURLopener, urlopen
from urllib.parse import quote as urllib_quote, unquote as urllib_unquote, urlencode
from http import cookiejar as cookielib
import html
hashlib_md5 = lambda s: hashlib.md5(bytes(s, 'utf8'))
iterkeys = lambda d: iter(d.keys())
itervalues = lambda d: iter(d.values())
iteritems = lambda d: iter(d.items())
integer_types = (int,)
string_types = (str,)
text_type = str
basestring = str
xrange = range
long = int
unichr = chr
unicodeT = str
maketrans = str.maketrans
ClassType = type
implements_iterator = _identity
implements_bool = _identity
def to_bytes(obj, charset='utf-8', errors='strict'):
if obj is None:
return None
if isinstance(obj, (bytes, bytearray, memoryview)):
return bytes(obj)
if isinstance(obj, str):
return obj.encode(charset, errors)
raise TypeError('Expected bytes')
def to_native(obj, charset='utf8', errors='strict'):
if obj is None or isinstance(obj, str):
return obj
return obj.decode(charset, errors)
def _local_html_escape(s, quote=True):
"""
Works with bytes.
Replace special characters "&", "<" and ">" to HTML-safe sequences.
If the optional flag quote is true (the default), the quotation mark
characters, both double quote (") and single quote (') characters are also
translated.
"""
if isinstance(s, str):
return html.escape(s, quote=quote)
s = s.replace(b"&", b"&") # Must be done first!
s = s.replace(b"<", b"<")
s = s.replace(b">", b">")
if quote:
s = s.replace(b'"', b""")
s = s.replace(b'\'', b"'")
return s
def with_metaclass(meta, *bases):
"""Create a base class with a metaclass."""
# This requires a bit of explanation: the basic idea is to make a dummy
# metaclass for one level of class instantiation that replaces itself with
# the actual metaclass.
class metaclass(meta):
__call__ = type.__call__
__init__ = type.__init__
def __new__(cls, name, this_bases, d):
if this_bases is None:
return type.__new__(cls, name, (), d)
return meta(name, bases, d)
return metaclass('temporary_class', None, {})
def to_unicode(obj, charset='utf-8', errors='strict'):
if obj is None:
return None
if not isinstance(obj, bytes):
return text_type(obj)
return obj.decode(charset, errors)
# shortcuts
pjoin = os.path.join
exists = os.path.exists
| 31.641618 | 88 | 0.654183 | import sys
import hashlib
import os
PY2 = sys.version_info[0] == 2
_identity = lambda x: x
if PY2:
import cPickle as pickle
from cStringIO import StringIO
import copy_reg as copyreg
from HTMLParser import HTMLParser
import urlparse
from htmlentitydefs import entitydefs, name2codepoint
import __builtin__ as builtin
import thread
import Cookie
import urllib2
import Queue
import ConfigParser as configparser
from email.MIMEBase import MIMEBase
from email.Header import Header
from email import Encoders, Charset
from email.MIMEMultipart import MIMEMultipart
from email.MIMEText import MIMEText
from email.Charset import add_charset, QP as charset_QP
from urllib import FancyURLopener, urlencode, urlopen
from urllib import quote as urllib_quote, unquote as urllib_unquote
from string import maketrans
from types import ClassType
import cgi
import cookielib
reduce = reduce
hashlib_md5 = hashlib.md5
iterkeys = lambda d: d.iterkeys()
itervalues = lambda d: d.itervalues()
iteritems = lambda d: d.iteritems()
integer_types = (int, long)
string_types = (str, unicode)
text_type = unicode
basestring = basestring
xrange = xrange
long = long
unichr = unichr
unicodeT = unicode
def implements_bool(cls):
cls.__nonzero__ = cls.__bool__
del cls.__bool__
return cls
def to_bytes(obj, charset='utf-8', errors='strict'):
if obj is None:
return None
if isinstance(obj, (bytes, bytearray, buffer)):
return bytes(obj)
if isinstance(obj, unicode):
return obj.encode(charset, errors)
raise TypeError('Expected bytes')
def to_native(obj, charset='utf8', errors='strict'):
if obj is None or isinstance(obj, str):
return obj
return obj.encode(charset, errors)
def _local_html_escape(data, quote=False):
s = cgi.escape(data, quote)
return s.replace("'", "'") if quote else s
else:
import pickle
from io import StringIO
import copyreg
from functools import reduce
from html.parser import HTMLParser
from http import cookies as Cookie
from urllib import parse as urlparse
from urllib import request as urllib2
from html.entities import entitydefs, name2codepoint
import builtins as builtin
import _thread as thread
import configparser
import queue as Queue
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email import encoders as Encoders
from email.header import Header
from email.charset import Charset, add_charset, QP as charset_QP
from urllib.request import FancyURLopener, urlopen
from urllib.parse import quote as urllib_quote, unquote as urllib_unquote, urlencode
from http import cookiejar as cookielib
import html
hashlib_md5 = lambda s: hashlib.md5(bytes(s, 'utf8'))
iterkeys = lambda d: iter(d.keys())
itervalues = lambda d: iter(d.values())
iteritems = lambda d: iter(d.items())
integer_types = (int,)
string_types = (str,)
text_type = str
basestring = str
xrange = range
long = int
unichr = chr
unicodeT = str
maketrans = str.maketrans
ClassType = type
implements_iterator = _identity
implements_bool = _identity
def to_bytes(obj, charset='utf-8', errors='strict'):
if obj is None:
return None
if isinstance(obj, (bytes, bytearray, memoryview)):
return bytes(obj)
if isinstance(obj, str):
return obj.encode(charset, errors)
raise TypeError('Expected bytes')
def to_native(obj, charset='utf8', errors='strict'):
if obj is None or isinstance(obj, str):
return obj
return obj.decode(charset, errors)
def _local_html_escape(s, quote=True):
"""
Works with bytes.
Replace special characters "&", "<" and ">" to HTML-safe sequences.
If the optional flag quote is true (the default), the quotation mark
characters, both double quote (") and single quote (') characters are also
translated.
"""
if isinstance(s, str):
return html.escape(s, quote=quote)
s = s.replace(b"&", b"&") # Must be done first!
s = s.replace(b"<", b"<")
s = s.replace(b">", b">")
if quote:
s = s.replace(b'"', b""")
s = s.replace(b'\'', b"'")
return s
def with_metaclass(meta, *bases):
# This requires a bit of explanation: the basic idea is to make a dummy
# metaclass for one level of class instantiation that replaces itself with
# the actual metaclass.
class metaclass(meta):
__call__ = type.__call__
__init__ = type.__init__
def __new__(cls, name, this_bases, d):
if this_bases is None:
return type.__new__(cls, name, (), d)
return meta(name, bases, d)
return metaclass('temporary_class', None, {})
def to_unicode(obj, charset='utf-8', errors='strict'):
if obj is None:
return None
if not isinstance(obj, bytes):
return text_type(obj)
return obj.decode(charset, errors)
# shortcuts
pjoin = os.path.join
exists = os.path.exists
| true | true |
1c3421ddc32d9035bd1863f644152cdb8cfd4a61 | 6,610 | py | Python | django_sass_finder/finders.py | Tru-Dev/django_sass_finder | 2541e30b1167abd4154ed946aaae7531d3f2a94a | [
"MIT"
] | 1 | 2020-10-24T23:17:18.000Z | 2020-10-24T23:17:18.000Z | django_sass_finder/finders.py | Tru-Dev/django_sass_finder | 2541e30b1167abd4154ed946aaae7531d3f2a94a | [
"MIT"
] | 2 | 2021-09-13T09:41:16.000Z | 2021-09-17T15:56:24.000Z | django_sass_finder/finders.py | Tru-Dev/django_sass_finder | 2541e30b1167abd4154ed946aaae7531d3f2a94a | [
"MIT"
] | 3 | 2021-04-13T18:16:45.000Z | 2021-09-12T12:05:34.000Z | # -*- coding: utf-8 -*-
import stat
from pathlib import Path
import sass
from django.apps import apps
from django.conf import settings
from django.contrib.staticfiles.finders import FileSystemFinder, AppDirectoriesFinder, BaseFinder
from django.core.checks import Error
from django.core.files.storage import FileSystemStorage
__all__ = (
'ScssFinder',
)
class ScssFinder(BaseFinder):
"""
Finds .scss files specified in SCSS_ROOT and SCSS_COMPILE settings with globs.
"""
def _path_is_parent(self, path: Path) -> bool:
try:
self.css_compile_dir.relative_to(path)
return True
except ValueError:
return False
def _path_in_staticfiles(self):
for static_dir in getattr(settings, 'STATICFILES_DIRS', []):
if self._path_is_parent(Path(static_dir).resolve()):
self._serve_static = getattr(settings, 'CSS_SERVE_STATIC', False)
return
def _path_in_appdirectories(self):
if not self.apps_static_checked and apps.apps_ready and self._serve_static:
try:
app_configs = apps.get_app_configs()
for app_config in app_configs:
if self._path_is_parent(Path(app_config.path) / AppDirectoriesFinder.source_dir):
self._serve_static = getattr(settings, 'CSS_SERVE_STATIC', False)
return
finally:
self.apps_static_checked = True
def __init__(self, app_names=None, *args, **kwargs):
self.scss_compile = getattr(settings, 'SCSS_COMPILE', ['**/*.scss'])
self.root = Path(settings.SCSS_ROOT)
self.css_compile_dir = Path(settings.CSS_COMPILE_DIR).resolve()
self.output_style = getattr(settings, 'CSS_STYLE', '')
self.css_map = getattr(settings, 'CSS_MAP', False)
self.include_paths = getattr(settings, 'SCSS_INCLUDE_PATHS', [])
self.storage = FileSystemStorage(location=self.css_compile_dir)
# by default, we serve our own files
self._serve_static = True
# we can check staticfiles immediately
self._path_in_staticfiles()
# apps probably aren't ready yet
self.apps_static_checked = False
self._path_in_appdirectories()
self.source_cache = {}
self.files_cache = {}
@property
def serve_static(self):
self._path_in_appdirectories()
return self._serve_static
def check(self, **kwargs):
"""
Checks if ScssFinder is configured correctly.
SCSS_COMPILE should contain valid files.
"""
errors = []
for scss_item in self.scss_compile:
for _ in self.root.glob(scss_item):
break
else:
errors.append(Error(
f'{scss_item} returned no files in {self.scss_compile}.',
id='sass.E001'
))
return errors
def output_path(self, scss_file, makedirs=False):
# determine where the file will be generated, and ensure path exists if possible
outpath = self.css_compile_dir / scss_file.relative_to(self.root).parent
if makedirs:
outpath.mkdir(parents=True, exist_ok=True)
# add the filename to the output path
return outpath / (scss_file.stem + '.css')
def compile_scss(self):
# search for and compile all scss files
checked = []
self.files_cache.clear()
for scss_item in self.scss_compile:
for scss_file in self.root.glob(scss_item):
try:
scss_stat = scss_file.stat()
except OSError:
continue # usually FileNotFoundError
if not stat.S_ISREG(scss_stat.st_mode):
continue # not is_file()
# mark this as checked
checked.append(scss_file)
# add it to the files cache
outpath = self.output_path(scss_file, makedirs=True)
relpath = outpath.relative_to(self.css_compile_dir)
self.files_cache[relpath.as_posix()] = outpath
try:
cached = self.source_cache[scss_file]
if scss_stat.st_mtime == cached:
continue # unchanged, skip
except KeyError:
pass
mappath = outpath.parent / (outpath.stem + '.map')
# generate the css
with outpath.open('w+') as outfile:
sass_args = {'filename': str(scss_file)}
if self.css_map:
sass_args['source_map_filename'] = str(mappath)
if self.include_paths:
sass_args['include_paths'] = [str(path) for path in self.include_paths]
if self.output_style:
sass_args['output_style'] = self.output_style
result = sass.compile(**sass_args)
if isinstance(result, tuple):
# if source map was requested, sass.compile returns a tuple: result, source map
# we're not really interested in the source map other than generating it
result, _ = result
outfile.write(result)
# add to or update the cache
self.source_cache[scss_file] = scss_stat.st_mtime
# walk the cache and check for any previously present files
removed = [scss_file for scss_file, _ in self.source_cache.items() if scss_file not in checked]
# and remove them from cache and unlink the target files
for scss_file in removed:
del self.source_cache[scss_file]
outpath = self.output_path(scss_file)
try:
outpath.unlink(missing_ok=True)
except OSError:
pass
def find(self, path, all=False):
"""
Run the compiler and see if was collected
"""
self.compile_scss()
if self.serve_static and path in self.files_cache:
path = self.files_cache[path].as_posix()
return [path] if all else path
return []
def list(self, ignore_patterns):
"""
Compile then list the .css files.
"""
self.compile_scss()
if self.serve_static and self.files_cache:
for path, _ in self.files_cache.items():
yield str(path), self.storage
| 38.208092 | 103 | 0.581997 |
import stat
from pathlib import Path
import sass
from django.apps import apps
from django.conf import settings
from django.contrib.staticfiles.finders import FileSystemFinder, AppDirectoriesFinder, BaseFinder
from django.core.checks import Error
from django.core.files.storage import FileSystemStorage
__all__ = (
'ScssFinder',
)
class ScssFinder(BaseFinder):
def _path_is_parent(self, path: Path) -> bool:
try:
self.css_compile_dir.relative_to(path)
return True
except ValueError:
return False
def _path_in_staticfiles(self):
for static_dir in getattr(settings, 'STATICFILES_DIRS', []):
if self._path_is_parent(Path(static_dir).resolve()):
self._serve_static = getattr(settings, 'CSS_SERVE_STATIC', False)
return
def _path_in_appdirectories(self):
if not self.apps_static_checked and apps.apps_ready and self._serve_static:
try:
app_configs = apps.get_app_configs()
for app_config in app_configs:
if self._path_is_parent(Path(app_config.path) / AppDirectoriesFinder.source_dir):
self._serve_static = getattr(settings, 'CSS_SERVE_STATIC', False)
return
finally:
self.apps_static_checked = True
def __init__(self, app_names=None, *args, **kwargs):
self.scss_compile = getattr(settings, 'SCSS_COMPILE', ['**/*.scss'])
self.root = Path(settings.SCSS_ROOT)
self.css_compile_dir = Path(settings.CSS_COMPILE_DIR).resolve()
self.output_style = getattr(settings, 'CSS_STYLE', '')
self.css_map = getattr(settings, 'CSS_MAP', False)
self.include_paths = getattr(settings, 'SCSS_INCLUDE_PATHS', [])
self.storage = FileSystemStorage(location=self.css_compile_dir)
self._serve_static = True
self._path_in_staticfiles()
self.apps_static_checked = False
self._path_in_appdirectories()
self.source_cache = {}
self.files_cache = {}
@property
def serve_static(self):
self._path_in_appdirectories()
return self._serve_static
def check(self, **kwargs):
errors = []
for scss_item in self.scss_compile:
for _ in self.root.glob(scss_item):
break
else:
errors.append(Error(
f'{scss_item} returned no files in {self.scss_compile}.',
id='sass.E001'
))
return errors
def output_path(self, scss_file, makedirs=False):
# determine where the file will be generated, and ensure path exists if possible
outpath = self.css_compile_dir / scss_file.relative_to(self.root).parent
if makedirs:
outpath.mkdir(parents=True, exist_ok=True)
# add the filename to the output path
return outpath / (scss_file.stem + '.css')
def compile_scss(self):
# search for and compile all scss files
checked = []
self.files_cache.clear()
for scss_item in self.scss_compile:
for scss_file in self.root.glob(scss_item):
try:
scss_stat = scss_file.stat()
except OSError:
continue # usually FileNotFoundError
if not stat.S_ISREG(scss_stat.st_mode):
continue # not is_file()
# mark this as checked
checked.append(scss_file)
# add it to the files cache
outpath = self.output_path(scss_file, makedirs=True)
relpath = outpath.relative_to(self.css_compile_dir)
self.files_cache[relpath.as_posix()] = outpath
try:
cached = self.source_cache[scss_file]
if scss_stat.st_mtime == cached:
continue # unchanged, skip
except KeyError:
pass
mappath = outpath.parent / (outpath.stem + '.map')
# generate the css
with outpath.open('w+') as outfile:
sass_args = {'filename': str(scss_file)}
if self.css_map:
sass_args['source_map_filename'] = str(mappath)
if self.include_paths:
sass_args['include_paths'] = [str(path) for path in self.include_paths]
if self.output_style:
sass_args['output_style'] = self.output_style
result = sass.compile(**sass_args)
if isinstance(result, tuple):
# if source map was requested, sass.compile returns a tuple: result, source map
# we're not really interested in the source map other than generating it
result, _ = result
outfile.write(result)
self.source_cache[scss_file] = scss_stat.st_mtime
removed = [scss_file for scss_file, _ in self.source_cache.items() if scss_file not in checked]
for scss_file in removed:
del self.source_cache[scss_file]
outpath = self.output_path(scss_file)
try:
outpath.unlink(missing_ok=True)
except OSError:
pass
def find(self, path, all=False):
self.compile_scss()
if self.serve_static and path in self.files_cache:
path = self.files_cache[path].as_posix()
return [path] if all else path
return []
def list(self, ignore_patterns):
self.compile_scss()
if self.serve_static and self.files_cache:
for path, _ in self.files_cache.items():
yield str(path), self.storage
| true | true |
1c34231126a265909f39756cbb8e3e90cd5157cc | 652 | py | Python | pywikibot/compat/userlib.py | notconfusing/pywikibot-fr-welcome-bot | 6e07b7e74166a47c9425816e79786308df369ac2 | [
"MIT"
] | 1 | 2020-01-03T11:52:01.000Z | 2020-01-03T11:52:01.000Z | pywikibot/compat/userlib.py | notconfusing/pywikibot-fr-welcome-bot | 6e07b7e74166a47c9425816e79786308df369ac2 | [
"MIT"
] | 2 | 2019-11-07T13:46:32.000Z | 2019-11-07T14:20:53.000Z | pywikibot/compat/userlib.py | notconfusing/pywikibot-fr-welcome-bot | 6e07b7e74166a47c9425816e79786308df369ac2 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
WARNING: THIS MODULE EXISTS SOLELY TO PROVIDE BACKWARDS-COMPATIBILITY.
Do not use in new scripts; use the source to find the appropriate
function/method instead.
"""
#
# (C) Pywikibot team, 2008-2018
#
# Distributed under the terms of the MIT license.
#
from __future__ import absolute_import, division, unicode_literals
from pywikibot.page import User
from pywikibot.tools import ModuleDeprecationWrapper
__all__ = ('User',)
wrapper = ModuleDeprecationWrapper(__name__)
wrapper._add_deprecated_attr('User',
replacement_name='pywikibot.User',
since='20141209')
| 26.08 | 70 | 0.707055 |
from __future__ import absolute_import, division, unicode_literals
from pywikibot.page import User
from pywikibot.tools import ModuleDeprecationWrapper
__all__ = ('User',)
wrapper = ModuleDeprecationWrapper(__name__)
wrapper._add_deprecated_attr('User',
replacement_name='pywikibot.User',
since='20141209')
| true | true |
1c34234f58c4cfb7a22a62835900d0f0b2695b61 | 13,645 | py | Python | thrift/compiler/test/fixtures/complex-union/gen-py3lite/module/lite_metadata.py | dgrnbrg-meta/fbthrift | 1d5f0799ef53feeb83425b6c9c79f86aeac7d9ed | [
"Apache-2.0"
] | null | null | null | thrift/compiler/test/fixtures/complex-union/gen-py3lite/module/lite_metadata.py | dgrnbrg-meta/fbthrift | 1d5f0799ef53feeb83425b6c9c79f86aeac7d9ed | [
"Apache-2.0"
] | 1 | 2022-03-03T09:40:25.000Z | 2022-03-03T09:40:25.000Z | thrift/compiler/test/fixtures/complex-union/gen-py3lite/module/lite_metadata.py | dgrnbrg-meta/fbthrift | 1d5f0799ef53feeb83425b6c9c79f86aeac7d9ed | [
"Apache-2.0"
] | null | null | null | #
# Autogenerated by Thrift
#
# DO NOT EDIT
# @generated
#
import apache.thrift.metadata.lite_types as _fbthrift_metadata
# TODO (ffrancet): This general pattern can be optimized by using tuples and dicts
# instead of re-generating thrift structs
def _fbthrift_gen_metadata_struct_ComplexUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.ComplexUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I64_TYPE), name="intValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=5, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="stringValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_list=_fbthrift_metadata.ThriftListType(valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I64_TYPE))), name="intListValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=3, type=_fbthrift_metadata.ThriftType(t_list=_fbthrift_metadata.ThriftListType(valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE))), name="stringListValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=9, type=_fbthrift_metadata.ThriftType(t_map=_fbthrift_metadata.ThriftMapType(keyType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I16_TYPE),valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE))), name="typedefValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=14, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="stringRef", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
# intValue
# stringValue
# intListValue
# stringListValue
# key
# val # typedefValue
# stringRef
return new_struct
def gen_metadata_struct_ComplexUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_ComplexUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
# TODO (ffrancet): This general pattern can be optimized by using tuples and dicts
# instead of re-generating thrift structs
def _fbthrift_gen_metadata_struct_ListUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.ListUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_list=_fbthrift_metadata.ThriftListType(valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I64_TYPE))), name="intListValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=3, type=_fbthrift_metadata.ThriftType(t_list=_fbthrift_metadata.ThriftListType(valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE))), name="stringListValue", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
# intListValue
# stringListValue
return new_struct
def gen_metadata_struct_ListUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_ListUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
# TODO (ffrancet): This general pattern can be optimized by using tuples and dicts
# instead of re-generating thrift structs
def _fbthrift_gen_metadata_struct_DataUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.DataUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_BINARY_TYPE), name="binaryData", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="stringData", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
# binaryData
# stringData
return new_struct
def gen_metadata_struct_DataUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_DataUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
# TODO (ffrancet): This general pattern can be optimized by using tuples and dicts
# instead of re-generating thrift structs
def _fbthrift_gen_metadata_struct_Val(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.Val"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="strVal", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I32_TYPE), name="intVal", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=9, type=_fbthrift_metadata.ThriftType(t_map=_fbthrift_metadata.ThriftMapType(keyType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I16_TYPE),valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE))), name="typedefValue", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=False,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
# strVal
# intVal
# key
# val # typedefValue
return new_struct
def gen_metadata_struct_Val() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_Val(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
# TODO (ffrancet): This general pattern can be optimized by using tuples and dicts
# instead of re-generating thrift structs
def _fbthrift_gen_metadata_struct_ValUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.ValUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_struct=_fbthrift_metadata.ThriftStructType(name="module.Val")), name="v1", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_struct=_fbthrift_metadata.ThriftStructType(name="module.Val")), name="v2", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
new_struct = _fbthrift_gen_metadata_struct_Val(new_struct) # v1
new_struct = _fbthrift_gen_metadata_struct_Val(new_struct) # v2
return new_struct
def gen_metadata_struct_ValUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_ValUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
# TODO (ffrancet): This general pattern can be optimized by using tuples and dicts
# instead of re-generating thrift structs
def _fbthrift_gen_metadata_struct_VirtualComplexUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.VirtualComplexUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="thingOne", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="thingTwo", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
# thingOne
# thingTwo
return new_struct
def gen_metadata_struct_VirtualComplexUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_VirtualComplexUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
# TODO (ffrancet): This general pattern can be optimized by using tuples and dicts
# instead of re-generating thrift structs
def _fbthrift_gen_metadata_struct_NonCopyableStruct(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.NonCopyableStruct"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I64_TYPE), name="num", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=False,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
# num
return new_struct
def gen_metadata_struct_NonCopyableStruct() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_NonCopyableStruct(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
# TODO (ffrancet): This general pattern can be optimized by using tuples and dicts
# instead of re-generating thrift structs
def _fbthrift_gen_metadata_struct_NonCopyableUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.NonCopyableUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_struct=_fbthrift_metadata.ThriftStructType(name="module.NonCopyableStruct")), name="s", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
new_struct = _fbthrift_gen_metadata_struct_NonCopyableStruct(new_struct) # s
return new_struct
def gen_metadata_struct_NonCopyableUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_NonCopyableUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def getThriftModuleMetadata() -> _fbthrift_metadata.ThriftMetadata:
meta = _fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={})
meta = _fbthrift_gen_metadata_struct_ComplexUnion(meta)
meta = _fbthrift_gen_metadata_struct_ListUnion(meta)
meta = _fbthrift_gen_metadata_struct_DataUnion(meta)
meta = _fbthrift_gen_metadata_struct_Val(meta)
meta = _fbthrift_gen_metadata_struct_ValUnion(meta)
meta = _fbthrift_gen_metadata_struct_VirtualComplexUnion(meta)
meta = _fbthrift_gen_metadata_struct_NonCopyableStruct(meta)
meta = _fbthrift_gen_metadata_struct_NonCopyableUnion(meta)
return meta
| 54.36255 | 403 | 0.785196 |
import apache.thrift.metadata.lite_types as _fbthrift_metadata
def _fbthrift_gen_metadata_struct_ComplexUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.ComplexUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I64_TYPE), name="intValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=5, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="stringValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_list=_fbthrift_metadata.ThriftListType(valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I64_TYPE))), name="intListValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=3, type=_fbthrift_metadata.ThriftType(t_list=_fbthrift_metadata.ThriftListType(valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE))), name="stringListValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=9, type=_fbthrift_metadata.ThriftType(t_map=_fbthrift_metadata.ThriftMapType(keyType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I16_TYPE),valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE))), name="typedefValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=14, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="stringRef", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
turn new_struct
def gen_metadata_struct_ComplexUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_ComplexUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def _fbthrift_gen_metadata_struct_ListUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.ListUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_list=_fbthrift_metadata.ThriftListType(valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I64_TYPE))), name="intListValue", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=3, type=_fbthrift_metadata.ThriftType(t_list=_fbthrift_metadata.ThriftListType(valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE))), name="stringListValue", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
return new_struct
def gen_metadata_struct_ListUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_ListUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def _fbthrift_gen_metadata_struct_DataUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.DataUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_BINARY_TYPE), name="binaryData", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="stringData", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
return new_struct
def gen_metadata_struct_DataUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_DataUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def _fbthrift_gen_metadata_struct_Val(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.Val"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="strVal", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I32_TYPE), name="intVal", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=9, type=_fbthrift_metadata.ThriftType(t_map=_fbthrift_metadata.ThriftMapType(keyType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I16_TYPE),valueType=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE))), name="typedefValue", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=False,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
ew_struct
def gen_metadata_struct_Val() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_Val(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def _fbthrift_gen_metadata_struct_ValUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.ValUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_struct=_fbthrift_metadata.ThriftStructType(name="module.Val")), name="v1", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_struct=_fbthrift_metadata.ThriftStructType(name="module.Val")), name="v2", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
new_struct = _fbthrift_gen_metadata_struct_Val(new_struct)
new_struct = _fbthrift_gen_metadata_struct_Val(new_struct)
return new_struct
def gen_metadata_struct_ValUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_ValUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def _fbthrift_gen_metadata_struct_VirtualComplexUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.VirtualComplexUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="thingOne", is_optional=False, structured_annotations=[
]),
_fbthrift_metadata.ThriftField(id=2, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_STRING_TYPE), name="thingTwo", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
return new_struct
def gen_metadata_struct_VirtualComplexUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_VirtualComplexUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def _fbthrift_gen_metadata_struct_NonCopyableStruct(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.NonCopyableStruct"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_primitive=_fbthrift_metadata.ThriftPrimitiveType.THRIFT_I64_TYPE), name="num", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=False,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
return new_struct
def gen_metadata_struct_NonCopyableStruct() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_NonCopyableStruct(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def _fbthrift_gen_metadata_struct_NonCopyableUnion(metadata_struct: _fbthrift_metadata.ThriftMetadata) -> _fbthrift_metadata.ThriftMetadata:
qualified_name = "module.NonCopyableUnion"
if qualified_name in metadata_struct.structs:
return metadata_struct
fields = [
_fbthrift_metadata.ThriftField(id=1, type=_fbthrift_metadata.ThriftType(t_struct=_fbthrift_metadata.ThriftStructType(name="module.NonCopyableStruct")), name="s", is_optional=False, structured_annotations=[
]),
]
struct_dict = dict(metadata_struct.structs)
struct_dict[qualified_name] = _fbthrift_metadata.ThriftStruct(name=qualified_name, fields=fields,
is_union=True,
structured_annotations=[
])
new_struct = metadata_struct(structs=struct_dict)
new_struct = _fbthrift_gen_metadata_struct_NonCopyableStruct(new_struct)
return new_struct
def gen_metadata_struct_NonCopyableUnion() -> _fbthrift_metadata.ThriftMetadata:
return _fbthrift_gen_metadata_struct_NonCopyableUnion(_fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={}))
def getThriftModuleMetadata() -> _fbthrift_metadata.ThriftMetadata:
meta = _fbthrift_metadata.ThriftMetadata(structs={}, enums={}, exceptions={}, services={})
meta = _fbthrift_gen_metadata_struct_ComplexUnion(meta)
meta = _fbthrift_gen_metadata_struct_ListUnion(meta)
meta = _fbthrift_gen_metadata_struct_DataUnion(meta)
meta = _fbthrift_gen_metadata_struct_Val(meta)
meta = _fbthrift_gen_metadata_struct_ValUnion(meta)
meta = _fbthrift_gen_metadata_struct_VirtualComplexUnion(meta)
meta = _fbthrift_gen_metadata_struct_NonCopyableStruct(meta)
meta = _fbthrift_gen_metadata_struct_NonCopyableUnion(meta)
return meta
| true | true |
1c3423996610ed84883be5d37c04c4b1a7904461 | 10,743 | py | Python | src/pystockwatch/pystockwatch.py | UBC-MDS/Pystockwatch | 4c72dae96d392cf2681b043db6e2fd440c936e49 | [
"MIT"
] | null | null | null | src/pystockwatch/pystockwatch.py | UBC-MDS/Pystockwatch | 4c72dae96d392cf2681b043db6e2fd440c936e49 | [
"MIT"
] | 55 | 2022-01-13T08:26:19.000Z | 2022-02-05T11:24:07.000Z | src/pystockwatch/pystockwatch.py | UBC-MDS/Pystockwatch | 4c72dae96d392cf2681b043db6e2fd440c936e49 | [
"MIT"
] | 1 | 2022-01-29T19:33:30.000Z | 2022-01-29T19:33:30.000Z | # authors: Affrin Sultana, Helin Wang, Shi Yan Wang and Pavel Levchenko
# January,2022
# import plotly.express as px
import plotly.graph_objects as go
import altair as alt
import pandas as pd
import numpy as np
import yfinance as yf
import pandas_datareader as pdr
import datetime
import warnings
alt.renderers.enable('altair_viewer')
def percent_change(stock_ticker, start_date, end_date):
"""
Calculates daily percentage change of a stock price within a given period of time
Parameters
----------
stock_ticker : string
Ticker of the stock such as 'AAPL'
start_date : string
Initial date for data extraction
end_date : string
Final date for stock analysis
Returns
--------
DataFrame
A data frame with dates and their corresponding stock price percentage changes.
Examples
--------
>>> percent_change('AAPL', '2017-01-01', '2017-01-10')
Price Change Percentage(%)
Date
2017-01-03 0.000
2017-01-04 -0.112
2017-01-05 0.396
2017-01-06 1.515
2017-01-09 2.445
"""
# Assert ticker input value
ticker = yf.Ticker(stock_ticker)
if(ticker.info["regularMarketPrice"] == None):
raise NameError("You have entered an invalid stock ticker! Try again.")
# Assert start date input value
format = "%Y-%m-%d"
try: datetime.datetime.strptime(start_date, format)
except ValueError:
raise ValueError("You have entered an invalid start date! Try date formatted in YYYY-MM-DD.")
# Assert end date input value
try: datetime.datetime.strptime(end_date, format)
except ValueError:
raise ValueError("You have entered an invalid end date! Try date formatted in YYYY-MM-DD.")
# Assert end date is later than start date
format = "%Y-%m-%d"
if(datetime.datetime.strptime(end_date, format) < datetime.datetime.strptime(start_date, format)):
raise ValueError("You have entered an end date which is earlier than the start date! Try again.")
# Import original dataframe by giving stock ticker, start data and end date
data = yf.download(stock_ticker, start=start_date, end=end_date)
# Only Keep "Adj Close" Price for
data = data.drop(columns={'Open', 'High', 'Low', 'Adj Close', 'Volume'})
# Carry out calculation
for i in range(1,len(data)):
data.iloc[i,:] = round((data.iloc[i,:] - data.iloc[0,:])/data.iloc[0,:]*100, 3)
data.iloc[0,:] = round((data.iloc[0,:] - data.iloc[0,:])/data.iloc[0,:]*100, 3)
# Manipulate column name
data = data.rename(columns={"Close": "Price Change Percentage(%)"})
# Return result
return pd.DataFrame(data)
def profit_viz(stock_ticker, start_date , end_date, benchmark_ticker):
"""
Visualizes trend of a stock price change against the market benchmark within a given period of time
Parameters
----------
stock_ticker : string
Ticker of the stock such as 'AAPL'
start_date : string
Initial date for data extraction
end_date : string
Final date for stock analysis
benchmark_ticker : string
Ticker for benchmark comparison such as 'SP500'
Returns
--------
Altair Chart
A line chart which shows percentage change in stock price and market performance over time
Examples
--------
>>> profit_viz('AAPL', '2015-01-01', '2021-12-31', 'SP500')
"""
ticker = yf.Ticker(stock_ticker)
bench_ticker = yf.Ticker(benchmark_ticker)
try:
# Assert ticker input value
if(ticker.info["regularMarketPrice"] == None):
raise NameError("You have entered an invalid stock ticker! Try again.")
# check data type of input
if type(stock_ticker) != str:
raise TypeError("stock_ticker should be of type string.")
# Assert benchmark ticker input value
if(bench_ticker.info["regularMarketPrice"] == None):
raise NameError("You have entered an invalid benchmark ticker! Try again.")
# check data type of input
if type(benchmark_ticker) != str:
raise TypeError("Bench Mark ticker should be of type string.")
# #check stock ticker and bench mark ticker are not same
# if stock_ticker is bench_ticker:
# raise NameError("Stock Mark ticker should not be same as Bench Ticker.")
# #check stock ticker is not empty
# if not stock_ticker or not bench_ticker:
# raise ValueError("'Tickers' cannot be empty")
# Assert start date input value
format = "%Y-%m-%d"
try: datetime.datetime.strptime(start_date, format)
except ValueError:
raise ValueError("You have entered an invalid start date! Try date formatted in YYYY-MM-DD.")
# Assert end date input value
try: datetime.datetime.strptime(end_date, format)
except ValueError:
raise ValueError("You have entered an invalid end date! Try date formatted in YYYY-MM-DD.")
# Assert end date is later than start date
format = "%Y-%m-%d"
if(datetime.datetime.strptime(end_date, format) < datetime.datetime.strptime(start_date, format)):
raise ValueError("You have entered an end date which is earlier than the start date! Try again.")
except (TypeError, ValueError, NameError) as err:
print(err)
raise
# Code to generate the visualization of profit
try:
stock_profit = percent_change(stock_ticker, start_date, end_date).reset_index()
benchmark_profit = percent_change(benchmark_ticker, start_date, end_date).reset_index()
profit_df = pd.merge(stock_profit, benchmark_profit, on="Date")
profit_df.rename({'Price Change Percentage(%)_x': 'Profit Percent Stock', 'Price Change Percentage(%)_y': 'Profit Percent Benchmark'}, axis=1, inplace=True)
# catch when dataframe is None
except AttributeError:
pass
#Checks if the datatype of data frame is correct
try:
isinstance(profit_df, pd.DataFrame)
except ValueError:
raise ValueError("profit_df is not a pandas dataframe.")
try:
isinstance(stock_profit, pd.DataFrame)
except ValueError:
raise ValueError("stock_profit couldnot be converted to a pandas dataframe.")
try:
isinstance(benchmark_profit, pd.DataFrame)
except ValueError:
raise ValueError("Benchmark_profit couldnot be converted to a pandas dataframe.")
# Code to plot the profit visualization
chart = alt.Chart(profit_df, title='Profit Percent trend of Stock vs Benchmark ticker').mark_line().transform_fold(
fold=['Profit Percent Stock', 'Profit Percent Benchmark'],
as_=['company', 'Profit Percent']
).encode(
x='Date:T',
y = alt.Y('Profit Percent:Q', axis=alt.Axis(format='$.2f')),
color=alt.Color('company:N', scale= alt.Scale(domain=['Profit Percent Stock','Profit Percent Benchmark'], range=['red', 'blue'])),
tooltip=[alt.Tooltip('Profit Percent Stock'),alt.Tooltip('Profit Percent Benchmark')]
)
return chart
def volume_change(stock_ticker, start_date, end_date):
"""
Calculates the daily trading volume change status of a stock within a given period of time
Parameters
----------
stock_ticker : string
Ticker of the stock such as 'AAPL'
start_date : string
Initial date for data extraction
end_date : string
Final date for stock analysis
Returns
--------
DataFrame
A data frame with dates and their corresponding trading volume and changes
Examples
--------
>>> volume_change('AAPL', '2021-01-01', '2022-01-01')
Date Volume Volume_Change
01-01-2021 1000 Nan
01-02-2021 2000 Increase
01-03-2021 3000 Increase
01-04-2021 2500 Decrease
...
12-31-2021 4000 Increase
01-01-2022 5000 Increase
"""
# Assert ticker value
ticker = yf.Ticker(stock_ticker)
if(ticker.info["regularMarketPrice"] == None):
raise NameError("You have entered an invalid stock ticker! Try again.")
# Assert date value
format = "%Y-%m-%d"
try: datetime.datetime.strptime(start_date, format)
except ValueError:
raise ValueError("You have entered an invalid start date! Try again.")
try: datetime.datetime.strptime(end_date, format)
except ValueError:
raise ValueError("You have entered an invalid end date! Try again.")
df = pdr.get_data_yahoo(stock_ticker, start=start_date, end=end_date).reset_index()
# Assert correct data fetched
try:
isinstance(df, pd.DataFrame)
except ValueError:
raise ValueError("Your input can't be converted to a pandas dataframe.")
df["Price_diff"] = df["Close"].diff().to_frame()
df['Price_change'] = np.select([df["Price_diff"] > 0, df["Price_diff"] < 0],
["Increase", "Decrease"], default = np.nan)
return df[["Date", "Volume", "Price_change"]]
def volume_viz(stock_ticker, start_date, end_date):
"""
Visualize the daily trading volume of a stock using bar plot within a given period of time
Parameters
----------
stock_ticker : string
Ticker of the stock such as 'AAPL'
start_date : string
Initial date for data extraction
end_date : string
Final date for stock analysis
Returns
--------
Chart
Create interactive bar plot to view the volume change
Examples
--------
>>> volume_viz('AAPL', '2021-01-01', '2022-01-01')
"""
try:
vdf = volume_change(stock_ticker, start_date, end_date)
# catch when dataframe is None
except AttributeError:
raise AttributeError("Invalid volume change input!")
vdf_increase = vdf.loc[vdf['Price_change']=='Increase']
vdf_decrease = vdf.loc[vdf['Price_change']=='Decrease']
fig = go.Figure()
fig.add_trace(go.Bar(x=vdf_increase['Date'], y=vdf_increase['Volume'],
base=0,
marker_color='green',
name='Price Increase'))
fig.add_trace(go.Bar(x=vdf_decrease['Date'], y=vdf_decrease['Volume'],
base=0,
marker_color='red',
name='Price Decrease'
))
return fig
| 35.572848 | 164 | 0.626361 |
import plotly.graph_objects as go
import altair as alt
import pandas as pd
import numpy as np
import yfinance as yf
import pandas_datareader as pdr
import datetime
import warnings
alt.renderers.enable('altair_viewer')
def percent_change(stock_ticker, start_date, end_date):
ticker = yf.Ticker(stock_ticker)
if(ticker.info["regularMarketPrice"] == None):
raise NameError("You have entered an invalid stock ticker! Try again.")
format = "%Y-%m-%d"
try: datetime.datetime.strptime(start_date, format)
except ValueError:
raise ValueError("You have entered an invalid start date! Try date formatted in YYYY-MM-DD.")
try: datetime.datetime.strptime(end_date, format)
except ValueError:
raise ValueError("You have entered an invalid end date! Try date formatted in YYYY-MM-DD.")
format = "%Y-%m-%d"
if(datetime.datetime.strptime(end_date, format) < datetime.datetime.strptime(start_date, format)):
raise ValueError("You have entered an end date which is earlier than the start date! Try again.")
data = yf.download(stock_ticker, start=start_date, end=end_date)
data = data.drop(columns={'Open', 'High', 'Low', 'Adj Close', 'Volume'})
for i in range(1,len(data)):
data.iloc[i,:] = round((data.iloc[i,:] - data.iloc[0,:])/data.iloc[0,:]*100, 3)
data.iloc[0,:] = round((data.iloc[0,:] - data.iloc[0,:])/data.iloc[0,:]*100, 3)
data = data.rename(columns={"Close": "Price Change Percentage(%)"})
return pd.DataFrame(data)
def profit_viz(stock_ticker, start_date , end_date, benchmark_ticker):
ticker = yf.Ticker(stock_ticker)
bench_ticker = yf.Ticker(benchmark_ticker)
try:
if(ticker.info["regularMarketPrice"] == None):
raise NameError("You have entered an invalid stock ticker! Try again.")
if type(stock_ticker) != str:
raise TypeError("stock_ticker should be of type string.")
if(bench_ticker.info["regularMarketPrice"] == None):
raise NameError("You have entered an invalid benchmark ticker! Try again.")
if type(benchmark_ticker) != str:
raise TypeError("Bench Mark ticker should be of type string.")
etime.strptime(start_date, format)
except ValueError:
raise ValueError("You have entered an invalid start date! Try date formatted in YYYY-MM-DD.")
try: datetime.datetime.strptime(end_date, format)
except ValueError:
raise ValueError("You have entered an invalid end date! Try date formatted in YYYY-MM-DD.")
format = "%Y-%m-%d"
if(datetime.datetime.strptime(end_date, format) < datetime.datetime.strptime(start_date, format)):
raise ValueError("You have entered an end date which is earlier than the start date! Try again.")
except (TypeError, ValueError, NameError) as err:
print(err)
raise
try:
stock_profit = percent_change(stock_ticker, start_date, end_date).reset_index()
benchmark_profit = percent_change(benchmark_ticker, start_date, end_date).reset_index()
profit_df = pd.merge(stock_profit, benchmark_profit, on="Date")
profit_df.rename({'Price Change Percentage(%)_x': 'Profit Percent Stock', 'Price Change Percentage(%)_y': 'Profit Percent Benchmark'}, axis=1, inplace=True)
except AttributeError:
pass
try:
isinstance(profit_df, pd.DataFrame)
except ValueError:
raise ValueError("profit_df is not a pandas dataframe.")
try:
isinstance(stock_profit, pd.DataFrame)
except ValueError:
raise ValueError("stock_profit couldnot be converted to a pandas dataframe.")
try:
isinstance(benchmark_profit, pd.DataFrame)
except ValueError:
raise ValueError("Benchmark_profit couldnot be converted to a pandas dataframe.")
chart = alt.Chart(profit_df, title='Profit Percent trend of Stock vs Benchmark ticker').mark_line().transform_fold(
fold=['Profit Percent Stock', 'Profit Percent Benchmark'],
as_=['company', 'Profit Percent']
).encode(
x='Date:T',
y = alt.Y('Profit Percent:Q', axis=alt.Axis(format='$.2f')),
color=alt.Color('company:N', scale= alt.Scale(domain=['Profit Percent Stock','Profit Percent Benchmark'], range=['red', 'blue'])),
tooltip=[alt.Tooltip('Profit Percent Stock'),alt.Tooltip('Profit Percent Benchmark')]
)
return chart
def volume_change(stock_ticker, start_date, end_date):
ticker = yf.Ticker(stock_ticker)
if(ticker.info["regularMarketPrice"] == None):
raise NameError("You have entered an invalid stock ticker! Try again.")
format = "%Y-%m-%d"
try: datetime.datetime.strptime(start_date, format)
except ValueError:
raise ValueError("You have entered an invalid start date! Try again.")
try: datetime.datetime.strptime(end_date, format)
except ValueError:
raise ValueError("You have entered an invalid end date! Try again.")
df = pdr.get_data_yahoo(stock_ticker, start=start_date, end=end_date).reset_index()
try:
isinstance(df, pd.DataFrame)
except ValueError:
raise ValueError("Your input can't be converted to a pandas dataframe.")
df["Price_diff"] = df["Close"].diff().to_frame()
df['Price_change'] = np.select([df["Price_diff"] > 0, df["Price_diff"] < 0],
["Increase", "Decrease"], default = np.nan)
return df[["Date", "Volume", "Price_change"]]
def volume_viz(stock_ticker, start_date, end_date):
try:
vdf = volume_change(stock_ticker, start_date, end_date)
# catch when dataframe is None
except AttributeError:
raise AttributeError("Invalid volume change input!")
vdf_increase = vdf.loc[vdf['Price_change']=='Increase']
vdf_decrease = vdf.loc[vdf['Price_change']=='Decrease']
fig = go.Figure()
fig.add_trace(go.Bar(x=vdf_increase['Date'], y=vdf_increase['Volume'],
base=0,
marker_color='green',
name='Price Increase'))
fig.add_trace(go.Bar(x=vdf_decrease['Date'], y=vdf_decrease['Volume'],
base=0,
marker_color='red',
name='Price Decrease'
))
return fig
| true | true |
1c3423a4b26e69f635ef04ef776574436569154e | 2,900 | py | Python | npxconv.py | LeeTehi/nnvolterra | d6a084d2f5b4b716423cb4b952cf58a09ea84c23 | [
"MIT"
] | 1 | 2021-11-28T17:16:57.000Z | 2021-11-28T17:16:57.000Z | npxconv.py | LeeTehi/nnvolterra | d6a084d2f5b4b716423cb4b952cf58a09ea84c23 | [
"MIT"
] | null | null | null | npxconv.py | LeeTehi/nnvolterra | d6a084d2f5b4b716423cb4b952cf58a09ea84c23 | [
"MIT"
] | null | null | null | import libnpxconv as libx
import numpy as np
# params for libx (sedt, kernel, srcx, /, padleft, stride)
def conv_ordern(ker, *src, oshape=None, padleft=0, stride=1):
if len(src) == 1:
src = src[0]
if oshape is None:
if isinstance(src, (list, tuple)):
assert np.ndim(ker) == len(src)
oshape = np.min([np.min(s.shape) for s in src])
else:
oshape = np.min(src.shape)
ks = np.max(ker.shape)
if oshape > ks:
oshape = (oshape - ks) // stride + 1
out = np.zeros(oshape, dtype=ker.dtype)
if isinstance(src, list):
src = [np.ascontiguousarray(x) for x in src]
else:
src = np.ascontiguousarray(src)
ker = np.ascontiguousarray(ker)
libx.conv1d_order_n(out, ker, src, padleft, stride)
return out
def outerconv(g, *hx, oshape=None, padleft=0, stride=1):
if isinstance(hx[0], (list, tuple)) and len(hx) == 1:
hx = hx[0]
assert np.ndim(g) == len(hx), \
f"mismatch ndim(g) = {np.ndim(g)}, len(hx) = {len(hx)}"
if oshape is None:
oshape = []
for i, h in enumerate(hx):
gsi = g.shape[i]
for hsi in h.shape:
oshape.append(hsi + stride*gsi-stride)
out = np.zeros(oshape, dtype=g.dtype)
g = np.ascontiguousarray(g)
hx = [np.ascontiguousarray(h) for h in hx]
libx.outerconv_nm(out, g, hx, padleft, stride)
return out
def outerconv_diag(g, *hx, oshape=None, padleft=0, stride=1):
# in the case of passing [x, y, z, ...]
if isinstance(hx[0], (list, tuple)) and len(hx) == 1:
hx = hx[0]
assert np.ndim(g) == 1, "only 1D signal supported"
assert len(hx) >= 1, "length of hx must grater than or equals to 1"
if oshape is None:
oshape = []
for h in hx:
for hsi in h.shape:
oshape.append(hsi+g.shape[0]-1)
out = np.zeros(oshape, dtype=g.dtype)
g = np.ascontiguousarray(g)
hx = [np.ascontiguousarray(h) for h in hx]
libx.outerconv_diagonal_nm(out, g, hx, padleft, stride)
return out
def outerconv_2d(g, h, stride=1):
"""g @ h
Only support Order-1 2-dimensional outer convolution
<-> this equals to convTranspose2D
"""
assert np.ndim(g) == 2 and np.ndim(h) == 2
if stride > 1:
_hs = h.shape
_hp = _hs[0] * (stride - 1), _hs[1] * (stride - 1)
h = np.pad(h, [(0, _hp[0]), (0, _hp[1])])\
.reshape(stride, _hs[0], stride, _hs[1])\
.transpose(1, 0, 3, 2).reshape(stride*_hs[0], stride*_hs[1])
psg = h.shape[1] - 1
psh = g.shape[1] - 1
pg = np.pad(g, [(0, 0), (0, psg)]).reshape(-1)
ph = np.pad(h, [(0, 0), (0, psh)]).reshape(-1)
goh = outerconv(pg, ph, stride=1)
glen = g.shape[0] + h.shape[0]- 1
R = glen * (g.shape[1] + h.shape[1] - 1)
return goh[0:R].reshape(glen, -1)
| 29 | 72 | 0.553103 | import libnpxconv as libx
import numpy as np
def conv_ordern(ker, *src, oshape=None, padleft=0, stride=1):
if len(src) == 1:
src = src[0]
if oshape is None:
if isinstance(src, (list, tuple)):
assert np.ndim(ker) == len(src)
oshape = np.min([np.min(s.shape) for s in src])
else:
oshape = np.min(src.shape)
ks = np.max(ker.shape)
if oshape > ks:
oshape = (oshape - ks) // stride + 1
out = np.zeros(oshape, dtype=ker.dtype)
if isinstance(src, list):
src = [np.ascontiguousarray(x) for x in src]
else:
src = np.ascontiguousarray(src)
ker = np.ascontiguousarray(ker)
libx.conv1d_order_n(out, ker, src, padleft, stride)
return out
def outerconv(g, *hx, oshape=None, padleft=0, stride=1):
if isinstance(hx[0], (list, tuple)) and len(hx) == 1:
hx = hx[0]
assert np.ndim(g) == len(hx), \
f"mismatch ndim(g) = {np.ndim(g)}, len(hx) = {len(hx)}"
if oshape is None:
oshape = []
for i, h in enumerate(hx):
gsi = g.shape[i]
for hsi in h.shape:
oshape.append(hsi + stride*gsi-stride)
out = np.zeros(oshape, dtype=g.dtype)
g = np.ascontiguousarray(g)
hx = [np.ascontiguousarray(h) for h in hx]
libx.outerconv_nm(out, g, hx, padleft, stride)
return out
def outerconv_diag(g, *hx, oshape=None, padleft=0, stride=1):
if isinstance(hx[0], (list, tuple)) and len(hx) == 1:
hx = hx[0]
assert np.ndim(g) == 1, "only 1D signal supported"
assert len(hx) >= 1, "length of hx must grater than or equals to 1"
if oshape is None:
oshape = []
for h in hx:
for hsi in h.shape:
oshape.append(hsi+g.shape[0]-1)
out = np.zeros(oshape, dtype=g.dtype)
g = np.ascontiguousarray(g)
hx = [np.ascontiguousarray(h) for h in hx]
libx.outerconv_diagonal_nm(out, g, hx, padleft, stride)
return out
def outerconv_2d(g, h, stride=1):
assert np.ndim(g) == 2 and np.ndim(h) == 2
if stride > 1:
_hs = h.shape
_hp = _hs[0] * (stride - 1), _hs[1] * (stride - 1)
h = np.pad(h, [(0, _hp[0]), (0, _hp[1])])\
.reshape(stride, _hs[0], stride, _hs[1])\
.transpose(1, 0, 3, 2).reshape(stride*_hs[0], stride*_hs[1])
psg = h.shape[1] - 1
psh = g.shape[1] - 1
pg = np.pad(g, [(0, 0), (0, psg)]).reshape(-1)
ph = np.pad(h, [(0, 0), (0, psh)]).reshape(-1)
goh = outerconv(pg, ph, stride=1)
glen = g.shape[0] + h.shape[0]- 1
R = glen * (g.shape[1] + h.shape[1] - 1)
return goh[0:R].reshape(glen, -1)
| true | true |
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