max_stars_repo_path stringlengths 3 269 | max_stars_repo_name stringlengths 4 119 | max_stars_count int64 0 191k | id stringlengths 1 7 | content stringlengths 6 1.05M | score float64 0.23 5.13 | int_score int64 0 5 |
|---|---|---|---|---|---|---|
apigee/backups/commands.py | mdelotavo/apigee-cli | 5 | 12767451 | import click
from apigee import console
from apigee.auth import common_auth_options, gen_auth
from apigee.backups.backups import Backups
# from apigee.cls import OptionEatAll
from apigee.prefix import common_prefix_options
from apigee.silent import common_silent_options
from apigee.verbose import common_verbose_options
APIS_CHOICES = {
'apis',
'keyvaluemaps',
'targetservers',
'caches',
'developers',
'apiproducts',
'apps',
'userroles',
}
@click.group(help='Download configuration files from Apigee that can later be restored.')
def backups():
pass
def _take_snapshot(
username,
password,
mfa_secret,
token,
zonename,
org,
profile,
target_directory,
prefix,
environments,
apis,
**kwargs
):
if not isinstance(apis, set):
apis = set(apis)
Backups(
gen_auth(username, password, mfa_secret, token, zonename),
org,
target_directory,
prefix=prefix,
fs_write=True,
apis=apis,
environments=list(environments),
).take_snapshot()
@backups.command(
help='Downloads and generates local snapshots of specified Apigee resources e.g. API proxies, KVMs, target servers, etc.'
)
@common_auth_options
@common_prefix_options
@common_silent_options
@common_verbose_options
@click.option(
'--target-directory',
type=click.Path(exists=False, dir_okay=True, file_okay=False, resolve_path=False),
required=True,
)
@click.option(
'--apis',
type=click.Choice(APIS_CHOICES, case_sensitive=False),
multiple=True,
default=APIS_CHOICES,
show_default=True,
)
# @click.option('--apis', metavar='LIST', cls=OptionEatAll, default=APIS_CHOICES, show_default=True, help='')
# @click.option(
# '-e', '--environments', metavar='LIST', cls=OptionEatAll, default=['test', 'prod'], help=''
# )
@click.option(
'-e', '--environments', multiple=True, show_default=True, default=['test', 'prod'], help=''
)
def take_snapshot(*args, **kwargs):
_take_snapshot(*args, **kwargs)
| 1.9375 | 2 |
dlchord2/parser/chord_parser.py | anime-song/DLChord-2 | 0 | 12767452 | from dlchord2 import const
class ChordParseData(object):
def __init__(self, root_text, bass_text, quality_text):
self.__root_text = root_text
self.__bass_text = bass_text
self.__quality_text = quality_text
@property
def root_text(self):
return self.__root_text
@property
def bass_text(self):
return self.__bass_text
@property
def quality_text(self):
return self.__quality_text
class ChordParser(object):
"""
コードをルート、クオリティ、ベースに分解するクラス。
"""
def parse(self, chord_text):
split_text = chord_text.split("/")
root_text = chord_text[0]
bass_text = ""
accidentals_text = ""
if len(split_text) > 1:
bass_text = split_text[1]
res = split_text[0][1:]
for i in range(len(res)):
if res[i] != const.ACCIDENTALS_SHARP and res[i] != const.ACCIDENTALS_FLAT:
break
accidentals_text += res[i]
root_text += accidentals_text
quality_text = res[len(accidentals_text):]
# ベースがない場合はルート音がベースになります
if bass_text == "":
bass_text = root_text
return ChordParseData(root_text, bass_text, quality_text)
| 3.140625 | 3 |
tests/table_data.py | bws428/ambiance | 18 | 12767453 | <gh_stars>10-100
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Author: <NAME>
# ===== LAYER CHANGES =====
# OK --> -5.0e3
# *OK -2500
# *OK --> 0.00e3
# *OK 2000
# *OK --> 11.0e3
# *OK 15000
# *OK --> 20.0e3
# *OK 25000
# *OK --> 32.0e3
# *OK 41_000
# *OK --> 47.0e3
# *OK 50_000
# *OK --> 51.0e3
# *OK 61_000
# *OK --> 71.0e3
# *OK 75_000
# *OK --> 80.0e3
from collections import defaultdict
import random
import numpy as np
class TableData:
"""
Tabularised data from standard atmosphere
"""
PROPERTY_NAMES = [
# Table 1
"H", # Geopotential height
"temperature",
"temperature_in_celsius",
"pressure",
"density",
"grav_accel",
# Table 2
"speed_of_sound",
"dynamic_viscosity",
"kinematic_viscosity",
"thermal_conductivity",
# Table 3
"pressure_scale_height",
"specific_weight",
"number_density",
"mean_particle_speed",
"collision_frequency",
"mean_free_path",
]
def __init__(self):
self.property_dict = {}
def add_entry(self, h, entry):
# Dictionary: h (geometric height) --> property dict
self.property_dict[h] = entry
def get_vectors(self, return_random=False, list_len=10):
"""
Return test data in vector form
"""
height = []
properties = defaultdict(list)
# -------------------------------------------------------------------
# Random list
# -------------------------------------------------------------------
if return_random:
for _ in range(list_len):
h, entry_dict = random.choice(list(self.property_dict.items()))
height.append(h)
for prop_name, value in entry_dict.items():
properties[prop_name].append(value)
# -------------------------------------------------------------------
# Sorted
# -------------------------------------------------------------------
else:
for h, entry_dict in self.property_dict.items():
height.append(h)
for prop_name, value in entry_dict.items():
properties[prop_name].append(value)
return height, properties
def get_matrices(self, return_random=False, shape=(3, 3)):
"""
Return test data in matrix form
"""
n_elem = np.prod(shape) # Number of elements in matrix
height, properties = self.get_vectors(return_random=return_random)
height = np.reshape(height[0:n_elem], shape)
properties_new = {}
for prop_name, value in properties.items():
value = np.reshape(value[0:n_elem], shape)
properties_new[prop_name] = value
return height, properties_new
table_data = TableData()
table_data.add_entry(
h=-5000,
entry={
"H": -5004,
"temperature": 320.676,
"temperature_in_celsius": 47.526,
"pressure": 1.77762e5,
"density": 1.93113,
"grav_accel": 9.8221,
# ----------
"speed_of_sound": 358.986,
"dynamic_viscosity": 1.9422e-5,
"kinematic_viscosity": 1.0058e-5,
"thermal_conductivity": 2.7861e-2,
# ----------
"pressure_scale_height": 9371.8,
"specific_weight": 1.8968e1,
"number_density": 4.0154e25,
"mean_particle_speed": 484.15,
"collision_frequency": 1.1507e10,
"mean_free_path": 4.2075e-8,
}
)
table_data.add_entry(
h=-4996,
entry={
"H": -5000,
"temperature": 320.650,
"temperature_in_celsius": 47.500,
"pressure": 1.77687e5,
"density": 1.93047,
"grav_accel": 9.8221,
# ----------
"speed_of_sound": 358.972,
"dynamic_viscosity": 1.9421e-5,
"kinematic_viscosity": 1.0060e-5,
"thermal_conductivity": 2.7859e-2,
# ----------
"pressure_scale_height": 9371.1,
"specific_weight": 1.8961e1,
"number_density": 4.0140e25,
"mean_particle_speed": 484.14,
"collision_frequency": 1.1503e10,
"mean_free_path": 4.2089e-8,
}
)
table_data.add_entry(
h=-2500,
entry={
"H": -2501,
"temperature": 304.406,
"temperature_in_celsius": 31.265,
"pressure": 1.35205e5,
"density": 1.54731,
"grav_accel": 9.8144,
# ----------
"speed_of_sound": 349.761,
"dynamic_viscosity": 1.8668e-5,
"kinematic_viscosity": 1.2065e-5,
"thermal_conductivity": 2.6611e-2,
# ----------
"pressure_scale_height": 8903.3,
"specific_weight": 1.5186e1,
"number_density": 3.2173e25,
"mean_particle_speed": 471.71,
"collision_frequency": 8.9830e9,
"mean_free_path": 5.2512e-8,
}
)
table_data.add_entry(
h=0,
entry={
"H": 0,
"temperature": 288.15,
"temperature_in_celsius": 15,
"pressure": 1.01325e5,
"density": 1.225,
"grav_accel": 9.80665,
# ----------
"speed_of_sound": 340.294,
"dynamic_viscosity": 1.7894e-5,
"kinematic_viscosity": 1.4607e-5,
"thermal_conductivity": 2.5343e-2,
# ----------
"pressure_scale_height": 8434.5,
"specific_weight": 1.2013e1,
"number_density": 2.5471e25,
"mean_particle_speed": 458.94,
"collision_frequency": 6.9193e9,
"mean_free_path": 6.6328e-8,
}
)
table_data.add_entry(
h=1000,
entry={
"H": 1000,
"temperature": 281.651,
"temperature_in_celsius": 8.501,
"pressure": 8.98763e4,
"density": 1.11166,
"grav_accel": 9.8036,
# ----------
"speed_of_sound": 336.435,
"dynamic_viscosity": 1.7579e-5,
"kinematic_viscosity": 1.5813e-5,
"thermal_conductivity": 2.4830e-2,
# ----------
"pressure_scale_height": 8246.9,
"specific_weight": 1.0898e1,
"number_density": 2.3115e25,
"mean_particle_speed": 453.74,
"collision_frequency": 6.2079e9,
"mean_free_path": 7.3090e-8,
}
)
table_data.add_entry(
h=2000,
entry={
"H": 1999,
"temperature": 275.154,
"temperature_in_celsius": 2.004,
"pressure": 7.95014e4,
"density": 1.00655,
"grav_accel": 9.8005,
# ----------
"speed_of_sound": 332.532,
"dynamic_viscosity": 1.7260e-5,
"kinematic_viscosity": 1.7147e-5,
"thermal_conductivity": 2.4314e-2,
# ----------
"pressure_scale_height": 8059.2,
"specific_weight": 9.8647,
"number_density": 2.0929e25,
"mean_particle_speed": 448.48,
"collision_frequency": 5.5558e9,
"mean_free_path": 8.0723e-8,
}
)
table_data.add_entry(
h=11_000,
entry={
"H": 10981,
"temperature": 216.774,
"temperature_in_celsius": -56.376,
"pressure": 2.26999e4,
"density": 3.64801e-1,
"grav_accel": 9.7728,
# ----------
"speed_of_sound": 295.154,
"dynamic_viscosity": 1.4223e-5,
"kinematic_viscosity": 3.8988e-5,
"thermal_conductivity": 1.9528e-2,
# ----------
"pressure_scale_height": 6367.2,
"specific_weight": 3.5651,
"number_density": 7.5853e24,
"mean_particle_speed": 398.07,
"collision_frequency": 1.7872e9,
"mean_free_path": 2.2273e-7,
}
)
table_data.add_entry(
h=11_019,
entry={
"H": 11000,
"temperature": 216.650,
"temperature_in_celsius": -56.500,
"pressure": 2.26320e4,
"density": 3.63918e-1,
"grav_accel": 9.7727,
# ----------
"speed_of_sound": 295.069,
"dynamic_viscosity": 1.4216e-5,
"kinematic_viscosity": 3.9064e-5,
"thermal_conductivity": 1.9518e-2,
# ----------
"pressure_scale_height": 6363.6,
"specific_weight": 3.5565,
"number_density": 7.5669e24,
"mean_particle_speed": 397.95,
"collision_frequency": 1.7824e9,
"mean_free_path": 2.2327e-7,
}
)
table_data.add_entry(
h=15_000,
entry={
"H": 14965,
"temperature": 216.650,
"temperature_in_celsius": -56.500,
"pressure": 1.21118e4,
"density": 1.94755e-1,
"grav_accel": 9.7605,
# ----------
"speed_of_sound": 295.069,
"dynamic_viscosity": 1.4216e-5,
"kinematic_viscosity": 7.2995e-5,
"thermal_conductivity": 1.9518e-2,
# ----------
"pressure_scale_height": 6371.6,
"specific_weight": 1.9009,
"number_density": 4.0495e24,
"mean_particle_speed": 397.95,
"collision_frequency": 9.5386e8,
"mean_free_path": 4.1720e-7,
}
)
table_data.add_entry(
h=20_000,
entry={
"H": 19937,
"temperature": 216.650,
"temperature_in_celsius": -56.500,
"pressure": 5.52929e3,
"density": 8.89097e-2,
"grav_accel": 9.7452,
# ----------
"speed_of_sound": 295.069,
"dynamic_viscosity": 1.4216e-5,
"kinematic_viscosity": 1.5989e-4,
"thermal_conductivity": 1.9518e-2,
# ----------
"pressure_scale_height": 6381.6,
"specific_weight": 8.6645e-1,
"number_density": 1.8487e24,
"mean_particle_speed": 397.95,
"collision_frequency": 4.3546e8,
"mean_free_path": 9.1387e-7,
}
)
table_data.add_entry(
h=20_063,
entry={
"H": 20000,
"temperature": 216.650,
"temperature_in_celsius": -56.500,
"pressure": 5.47487e3,
"density": 8.80345e-2,
"grav_accel": 9.7450,
# ----------
"speed_of_sound": 295.069,
"dynamic_viscosity": 1.4216e-5,
"kinematic_viscosity": 1.6148e-4,
"thermal_conductivity": 1.9518e-2,
# ----------
"pressure_scale_height": 6381.7,
"specific_weight": 8.5790e-1,
"number_density": 1.8305e24,
"mean_particle_speed": 397.95,
"collision_frequency": 4.3117e8,
"mean_free_path": 9.2295e-7,
}
)
table_data.add_entry(
h=25_000,
entry={
"H": 24902,
"temperature": 221.552,
"temperature_in_celsius": -51.598,
"pressure": 2.54921e3,
"density": 4.00837e-2,
"grav_accel": 9.7300,
# ----------
"speed_of_sound": 298.389,
"dynamic_viscosity": 1.4484e-5,
"kinematic_viscosity": 3.6135e-4,
"thermal_conductivity": 1.9930e-2,
# ----------
"pressure_scale_height": 6536.2,
"specific_weight": 3.9001e-1,
"number_density": 8.3346e23,
"mean_particle_speed": 402.43,
"collision_frequency": 1.9853e8,
"mean_free_path": 2.0270e-6,
}
)
table_data.add_entry(
h=32_162,
entry={
"H": 32000,
"temperature": 228.650,
"temperature_in_celsius": -44.500,
"pressure": 8.68014e2,
"density": 1.32249e-2,
"grav_accel": 9.7082,
# ----------
"speed_of_sound": 303.131,
"dynamic_viscosity": 1.4868e-5,
"kinematic_viscosity": 1.1242e-3,
"thermal_conductivity": 2.0523e-2,
# ----------
"pressure_scale_height": 6760.8,
"specific_weight": 1.2839e-1,
"number_density": 2.7499e23,
"mean_particle_speed": 408.82,
"collision_frequency": 6.6542e7,
"mean_free_path": 6.1438e-6,
}
)
table_data.add_entry(
h=41_266,
entry={
"H": 41000,
"temperature": 253.850,
"temperature_in_celsius": -19.300,
"pressure": 2.42394e2,
"density": 3.32646e-3,
"grav_accel": 9.6806,
# ----------
"speed_of_sound": 319.399,
"dynamic_viscosity": 1.6189e-5,
"kinematic_viscosity": 4.8668e-3,
"thermal_conductivity": 2.2599e-2,
# ----------
"pressure_scale_height": 7527.3,
"specific_weight": 3.2202e-2,
"number_density": 6.9167e22,
"mean_particle_speed": 430.76,
"collision_frequency": 1.7636e7,
"mean_free_path": 2.4426e-5,
}
)
table_data.add_entry(
h=47_350,
entry={
"H": 47000,
"temperature": 270.650,
"temperature_in_celsius": -2.500,
"pressure": 1.10906e2,
"density": 1.42752e-3,
"grav_accel": 9.6622,
# ----------
"speed_of_sound": 329.799,
"dynamic_viscosity": 1.7037e-5,
"kinematic_viscosity": 1.1934e-2,
"thermal_conductivity": 2.3954e-2,
# ----------
"pressure_scale_height": 8040.7,
"specific_weight": 1.3793e-2,
"number_density": 2.9683e22,
"mean_particle_speed": 444.79,
"collision_frequency": 7.8146e6,
"mean_free_path": 5.6918e-5,
}
)
table_data.add_entry(
h=50_396,
entry={
"H": 50000,
"temperature": 270.650,
"temperature_in_celsius": -2.500,
"pressure": 7.59443e1,
"density": 9.77519e-4,
"grav_accel": 9.6530,
# ----------
"speed_of_sound": 329.799,
"dynamic_viscosity": 1.7037e-5,
"kinematic_viscosity": 1.7429e-2,
"thermal_conductivity": 2.3954e-2,
# ----------
"pressure_scale_height": 8048.4,
"specific_weight": 9.4360e-3,
"number_density": 2.0326e22,
"mean_particle_speed": 444.79,
"collision_frequency": 5.3512e6,
"mean_free_path": 8.3120e-5,
}
)
table_data.add_entry(
h=51_412,
entry={
"H": 51000,
"temperature": 270.650,
"temperature_in_celsius": -2.500,
"pressure": 6.69384e1,
"density": 8.61600e-4,
"grav_accel": 9.6499,
# ----------
"speed_of_sound": 329.799,
"dynamic_viscosity": 1.7037e-5,
"kinematic_viscosity": 1.9773e-2,
"thermal_conductivity": 2.3954e-2,
# ----------
"pressure_scale_height": 8050.9,
"specific_weight": 8.3144e-3,
"number_density": 1.7915e22,
"mean_particle_speed": 444.79,
"collision_frequency": 4.7166e6,
"mean_free_path": 9.4303e-5,
}
)
table_data.add_entry(
h=61_591,
entry={
"H": 61000,
"temperature": 242.650,
"temperature_in_celsius": -30.500,
"pressure": 1.76605e1,
"density": 2.53548e-4,
"grav_accel": 9.6193,
# ----------
"speed_of_sound": 312.274,
"dynamic_viscosity": 1.5610e-5,
"kinematic_viscosity": 6.1565e-2,
"thermal_conductivity": 2.1683e-2,
# ----------
"pressure_scale_height": 7241.0,
"specific_weight": 2.4390e-3,
"number_density": 5.2720e21,
"mean_particle_speed": 421.15,
"collision_frequency": 1.3142e6,
"mean_free_path": 3.2046e-4,
}
)
table_data.add_entry(
h=71_802,
entry={
"H": 71000,
"temperature": 214.650,
"temperature_in_celsius": -58.500,
"pressure": 3.95639e0,
"density": 6.42105e-5,
"grav_accel": 9.5888,
# ----------
"speed_of_sound": 293.704,
"dynamic_viscosity": 1.4106e-5,
"kinematic_viscosity": 2.1968e-1,
"thermal_conductivity": 1.9349e-2,
# ----------
"pressure_scale_height": 6425.8,
"specific_weight": 6.1570e-4,
"number_density": 1.3351e21,
"mean_particle_speed": 396.11,
"collision_frequency": 3.1303e5,
"mean_free_path": 1.2654e-3,
}
)
table_data.add_entry(
h=75_895,
entry={
"H": 75000,
"temperature": 206.650,
"temperature_in_celsius": -66.500,
"pressure": 2.06790e0,
"density": 3.48604e-5,
"grav_accel": 9.5766,
# ----------
"speed_of_sound": 288.179,
"dynamic_viscosity": 1.3661e-5,
"kinematic_viscosity": 3.9188e-1,
"thermal_conductivity": 1.8671e-2,
# ----------
"pressure_scale_height": 6194.2,
"specific_weight": 3.3384e-4,
"number_density": 7.2485e20,
"mean_particle_speed": 388.66,
"collision_frequency": 1.6675e5,
"mean_free_path": 2.3308e-3,
}
)
table_data.add_entry(
h=81_020,
entry={
"H": 80000,
"temperature": 196.650,
"temperature_in_celsius": -76.500,
"pressure": 8.86272e-1,
"density": 1.57004e-5,
"grav_accel": 9.5614,
# ----------
"speed_of_sound": 281.120,
"dynamic_viscosity": 1.3095e-5,
"kinematic_viscosity": 8.3402e-1,
"thermal_conductivity": 1.7817e-2,
# ----------
"pressure_scale_height": 5903.9,
"specific_weight": 1.5012e-4,
"number_density": 3.2646e20,
"mean_particle_speed": 379.14,
"collision_frequency": 7.3262e4,
"mean_free_path": 5.1751e-3,
}
)
| 2.6875 | 3 |
src/tryme.py | RandalJBarnes/AkeyaaGIS | 0 | 12767454 | <reponame>RandalJBarnes/AkeyaaGIS<filename>src/tryme.py
import arcpy
import akeyaa
__version__ = "02 July 2020"
#-------------------------------------------------------------------------------
# The constants CWIGDB and CTYGDB are specific paths on the local machine.
# These paths must be modified to point to the gdb's as installed on the local
# machine. Similarly, DESTINATION is the local path to the output table.
#-------------------------------------------------------------------------------
# Minnesota County Well Index (CWI). Obtained from the Minnesota Department
# of Health. The CWI is administered by the Minnesota Geological Survey.
# https://www.mngs.umn.edu/cwi.html
# This gdb uses 'NAD 1983 UTM zone 15N' (EPSG:26915).
CWIGDB = r"D:\Google Drive\CWI\CWI_20200420\water_well_information.gdb"
# County Boundaries, Minnesota. Obtained from the Minnesota Geospatial Commons.
# https://gisdata.mn.gov/dataset/bdry-counties-in-minnesota
# This gdb uses 'NAD 1983 UTM zone 15N' (EPSG:26915).
CTYGDB = r"D:\Google Drive\GIS\fgdb_bdry_counties_in_minnesota\bdry_counties_in_minnesota.gdb"
# Path and filename prefix for the feature class, .hdr, and .flt files.
BASE_FILENAME = r"D:\Google Drive\Projects\AkeyaaGIS\data\dakota"
# -----------------------------------------------------------------------------
def main():
polygon, xyz = get_dakota_county_data()
akeyaa_array = akeyaa.run_akeyaa(
polygon,
xyz,
radius=3000,
required=25,
spacing=1000,
base_filename=BASE_FILENAME
)
# -----------------------------------------------------------------------------
def get_dakota_county_data():
"""Get the polygon and well data from Dakota County.
This function has one purpose only: to create a test case. This function
will be discarded after we hook into ArcGIS Pro.
"""
CTY_SOURCE = CTYGDB + r"\mn_county_boundaries" # County boundaries
ALLWELLS = CWIGDB + r"\allwells" # MN county well index
C5WL = CWIGDB + r"\C5WL" # MN static water levels
# Get the Dakota County polygon.
source = CTY_SOURCE
what = ["CTY_NAME", "CTY_FIPS", "SHAPE@"]
where = "(CTY_FIPS = 37)"
results = []
with arcpy.da.SearchCursor(source, what, where) as cursor:
for row in cursor:
results.append(row)
dakota_polygon = results[0][2]
# Get the Dakota County wells.
in_table = arcpy.AddJoin_management(ALLWELLS, "RELATEID", C5WL, "RELATEID", False)
dakota_table = arcpy.SelectLayerByLocation_management(in_table, 'WITHIN', dakota_polygon)
field_names = ["allwells.SHAPE", "C5WL.MEAS_ELEV"]
where_clause = (
"(C5WL.MEAS_ELEV is not NULL) AND "
"(allwells.AQUIFER is not NULL) AND "
"(allwells.UTME is not NULL) AND "
"(allwells.UTMN is not NULL)"
)
dakota_wells = []
with arcpy.da.SearchCursor(dakota_table, field_names, where_clause) as cursor:
for row in cursor:
dakota_wells.append((row[0][0], row[0][1], row[1]))
return dakota_polygon, dakota_wells
# -----------------------------------------------------------------------------
if __name__ == "__main__":
# execute only if run as a script
main()
| 1.65625 | 2 |
dynadb/migrations/0075_auto_20170125_1954.py | GPCRmd/GPCRmd | 3 | 12767455 | # -*- coding: utf-8 -*-
# Generated by Django 1.9.2 on 2017-01-25 18:54
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):
dependencies = [
('dynadb', '0074_auto_20170125_1926'),
]
operations = [
migrations.AlterField(
model_name='dyndbsubmission',
name='user_id',
field=models.ForeignKey(blank=True, db_column='user_id', null=True, on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL),
),
]
| 1.695313 | 2 |
pytest_unittest_discovery_scenarios/copy_scenario.py | d3r3kk/vscode-python-extras | 0 | 12767456 | <filename>pytest_unittest_discovery_scenarios/copy_scenario.py
import os
import shutil
import sys
import tarfile
NEVER_COPY_DIRNAMES = [
".pytest_cache",
"__pycache__",
".venv-pytest_37",
".venv-pytest_36",
".git",
]
def compress_scenario(scenario_name, scenario_folder):
tarfile_folder, _ = os.path.split(scenario_folder)
tarfile_path = os.path.join(tarfile_folder, f'{scenario_name}.tar.gz')
archive = tarfile.open(tarfile_path, 'w:gz')
archive.add(scenario_folder, arcname=scenario_name)
def do_not_copy(src, names):
ignore_names = []
for copy_path in names:
_, src_name = os.path.split(copy_path)
if src_name in NEVER_COPY_DIRNAMES:
ignore_names.append(src_name)
return ignore_names
def create_scenario(scenario_name, dir_to_copy):
copy_to_root, _ = os.path.split(dir_to_copy)
copy_to_path = os.path.join(copy_to_root, scenario_name)
shutil.copytree(
dir_to_copy, copy_to_path, symlinks=False,
ignore=do_not_copy)
compress_scenario(scenario_name, copy_to_path)
shutil.rmtree(copy_to_path)
def main():
if len(sys.argv) != 3:
print("Try again, but give me a scenario name and a dir name.")
else:
scenario_name = sys.argv[1]
src_folder = os.path.realpath(sys.argv[2])
create_scenario(scenario_name, src_folder)
if __name__ == "__main__":
main()
| 2.34375 | 2 |
python/022_Generate_Parentheses.py | JerryCatLeung/leetcode | 0 | 12767457 | # class Solution(object):
# def generateParenthesis(self, n):
# """
# :type n: int
# :rtype: List[str]
# """
class Solution(object):
def generateParenthesis(self, n):
if n == 1:
return ['()']
last_list = self.generateParenthesis(n - 1)
res = []
for t in last_list:
curr = t + ')'
for index in range(len(curr)):
if curr[index] == ')':
res.append(curr[:index] + '(' + curr[index:])
return list(set(res))
# def generateParenthesis(self, n):
# def generate(leftnum, rightnum, s, result):
# if leftnum == 0 and rightnum == 0:
# result.append(s)
# if leftnum > 0:
# generate(leftnum - 1, rightnum, s + '(', result)
# if rightnum > 0 and leftnum < rightnum:
# generate(leftnum, rightnum - 1, s + ')', result)
#
# result = []
# s = ''
# generate(n, n, s, result)
# return result
| 3.421875 | 3 |
docs_Ismail_Geles/benchmark/utils/format.py | isgeles/SMARTS | 554 | 12767458 | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
def pretty_dict(d, indent=0):
"""Pretty the output format of a dictionary.
Parameters
----------
d
dict, the input dictionary instance.
indent
int, indent level, non-negative.
Returns
-------
res
str, the output string
"""
res = ""
for k, v in d.items():
res += "\t" * indent + str(k)
if isinstance(v, dict):
res += "\n" + pretty_dict(v, indent + 1)
else:
res += ": " + str(v) + "\n"
return res
| 2.34375 | 2 |
aioairtable/helpers.py | gleb-chipiga/aioairtable | 0 | 12767459 | <gh_stars>0
import json
from functools import partial
from typing import Final
__all__ = ('json_dumps', 'get_python_version', 'get_software', 'debug')
json_dumps: Final = partial(json.dumps, ensure_ascii=False)
def get_python_version() -> str:
from sys import version_info as version
return f'{version.major}.{version.minor}.{version.micro}'
def get_software() -> str:
from . import __version__
return f'Python/{get_python_version()} aioairtable/{__version__}'
def debug() -> bool:
return __debug__
| 2.171875 | 2 |
test_script.py | martinagvilas/CompEnv-Ex1 | 0 | 12767460 | <gh_stars>0
from __future__ import print_function
a = 5
b = 2
print("{0} divided by {1} gives".format(a, b), a / b) | 2.78125 | 3 |
modeling/trans/__init__.py | WenmuZhou/crnn.pytorch | 46 | 12767461 | <reponame>WenmuZhou/crnn.pytorch
# -*- coding: utf-8 -*-
# @Time : 2020/6/17 10:59
# @Author : zhoujun
from .TPS import TPS
__all__ = ['build_trans']
support_trans = ['TPS', 'None']
def build_trans(trans_name, **kwargs):
assert trans_name in support_trans, f'all support head is {support_trans}'
if trans_name == 'None':
return None
head = eval(trans_name)(**kwargs)
return head
| 2.234375 | 2 |
twitcher/__init__.py | edwinmosong/twitcher | 1 | 12767462 | <filename>twitcher/__init__.py
# twitcher project.
# <NAME>
"""
twitcher - A simpleTwitchTV API wrapper.
This python package provides ease of use and accessibility to many, if not all,
of TwitchTV's features.
"""
import json
import requests
import helper
import streams
__version__ = '0.0.1'
BASE_URL = 'https://api.twitch.tv/kraken'
GET_API_PATHS = {
'streams': '/streams/?',
'streams_channel': '/streams/%s/?',
'streams_featured': '/streams/featured?',
'streams_summary': '/streams/summary?',
'channel': '/channels/%s?',
'channel_videos': '/channels/%s/videos?',
'channel_follows': '/channels/%s/follows?'
}
class Twitcher(object):
"""
TwitchTV client that is used to make and send requests. This class wraps
requests and returns objects.
"""
def __init__(self):
self.rest_helper = helper.RESTHelper()
def get_stream_info(self, game='', channel='', limit=25, offset=0,
embeddable='false', hls='false'):
"""
Returns a StreamInfoHelper object for the requested query.
https://github.com/justintv/Twitch-API/blob/master/v2_resources/
streams.md#get-streamschannel
:params: Default Description
------------------------------------------------------------------
game '' Streams categorized under game.
channel '' Streams from a comma-separated list of channels.
Name are case-sensitive.
limit 25 Maximum number of objects in array. Max is 100
offset 0 Object offset for pagination.
embeddable 'false' If set to true, returns streams that can be
embedded.
hls 'false' If set to true, only returns using HLS.
"""
if channel:
endpoint = GET_API_PATHS['streams_channel'] % channel
else:
endpoint = GET_API_PATHS['streams']
params = dict([('game', game), ('channel', channel), ('limit', limit),
('offset', offset), ('embeddable', embeddable),
('hls', hls)])
stream_info = self.rest_helper.request(endpoint=endpoint, params=params)
return streams.StreamInfoHelper(stream_info, params=params)
def get_featured_streams(self, limit=25, offset=0, hls='false'):
"""
Retrieves a StreamInfoHelper for featured streams
"""
endpoint = GET_API_PATHS['streams_featured']
params = dict([('limit', limit), ('offset', offset), ('hls', hls)])
stream_info = self.rest_helper.request(endpoint=endpoint, params=params)
return streams.StreamInfoHelper(stream_info, params=params)
| 2.609375 | 3 |
mmskeleton/deprecated/datasets/recognition.py | fserracant/mmskeleton | 1,347 | 12767463 | import os
import numpy as np
import json
import torch
from .utils import skeleton
class SkeletonDataset(torch.utils.data.Dataset):
""" Feeder for skeleton-based action recognition
Arguments:
data_path: the path to data folder
random_choose: If true, randomly choose a portion of the input sequence
random_move: If true, randomly perfrom affine transformation
window_size: The length of the output sequence
repeat: times of repeating the dataset
data_subscripts: subscript expression of einsum operation.
In the default case, the shape of output data is `(channel, vertex, frames, person)`.
To permute the shape to `(channel, frames, vertex, person)`,
set `data_subscripts` to 'cvfm->cfvm'.
"""
def __init__(self,
data_dir,
random_choose=False,
random_move=False,
window_size=-1,
num_track=1,
data_subscripts=None,
repeat=1):
self.data_dir = data_dir
self.random_choose = random_choose
self.random_move = random_move
self.window_size = window_size
self.num_track = num_track
self.data_subscripts = data_subscripts
self.files = [
os.path.join(self.data_dir, f) for f in os.listdir(self.data_dir)
] * repeat
def __len__(self):
return len(self.files)
def __getitem__(self, index):
with open(self.files[index]) as f:
data = json.load(f)
resolution = data['info']['resolution']
category_id = data['category_id']
annotations = data['annotations']
num_frame = data['info']['num_frame']
num_keypoints = data['info']['num_keypoints']
channel = data['info']['keypoint_channels']
num_channel = len(channel)
# get data
data = np.zeros(
(num_channel, num_keypoints, num_frame, self.num_track),
dtype=np.float32)
for a in annotations:
person_id = a['id'] if a['person_id'] is None else a['person_id']
frame_index = a['frame_index']
if person_id < self.num_track and frame_index < num_frame:
data[:, :, frame_index, person_id] = np.array(
a['keypoints']).transpose()
# normalization
if self.normalization:
for i, c in enumerate(channel):
if c == 'x':
data[i] = data[i] / resolution[0] - 0.5
if c == 'y':
data[i] = data[i] / resolution[1] - 0.5
if c == 'score' or c == 'visibility':
mask = (data[i] == 0)
for j in range(num_channel):
if c != j:
data[j][mask] = 0
# permute
if self.data_subscripts is not None:
data = np.einsum(self.data_subscripts, data)
# augmentation
if self.random_choose:
data = skeleton.random_choose(data, self.window_size)
elif self.window_size > 0:
data = skeleton.auto_pading(data, self.window_size)
if self.random_move:
data = skeleton.random_move(data)
return data, category_id | 2.609375 | 3 |
winton_kafka_streams/processor/_context.py | szczeles/winton-kafka-streams | 0 | 12767464 | <filename>winton_kafka_streams/processor/_context.py
"""
Processor context is the link to kafka from individual processor objects
"""
import logging
import functools
from .._error import KafkaStreamsError
log = logging.getLogger(__name__)
def _raiseIfNullRecord(fn):
@functools.wraps(fn)
def _inner(*args, **kwargs):
if args[0].currentRecord is None:
raise KafkaStreamsError(f"Record cannot be unset when retrieving {fn.__name__}")
return fn(*args, **kwargs)
return _inner
class Context:
"""
Processor context object
"""
def __init__(self, _state_stores):
self.currentNode = None
self.currentRecord = None
self._state_stores = _state_stores
def send(self, topic, key, obj):
"""
Send the key value-pair to a Kafka topic
"""
print(f"Send {obj} to {topic}")
pass
def schedule(self, timestamp):
"""
Schedule the punctuation function call
"""
pass
@property
@_raiseIfNullRecord
def offset(self):
return self.currentRecord.offset()
@property
@_raiseIfNullRecord
def partition(self):
return self.currentRecord.partition()
@property
@_raiseIfNullRecord
def timestamp(self):
return self.currentRecord.timestamp()
@property
@_raiseIfNullRecord
def topic(self):
return self.currentRecord.topic()
def get_store(self, name):
if not self.currentNode:
raise KafkaStreamsError("Access of state from unknown node")
# TODO: Need to check for a global state here
# This is the reason that processors access store through context
if name not in self.currentNode.state_stores:
raise KafkaStreamsError(f"Processor {currentNode.name} does not have access to store {name}")
if name not in self._state_stores:
raise KafkaStreamsError(f"Store {name} is not found")
return self._state_stores[name]
| 2.59375 | 3 |
feature_extraction.py | navtejsingh/epilepsypred | 1 | 12767465 | <filename>feature_extraction.py
"""
Extract Features from EEG time series
-------------------------------------
Peak in Band (PIB) features are extracted from EEG time series data.
Six PIB per minute are calculated. This results in 960 features for
10 minute of EEG recording in 16 channels.
Reference
---------
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0081920
"""
import numpy as np
from glob import glob
import scipy.io as _scio
import scipy.signal as _scsg
import multiprocessing as mp
def readdata(matfile):
"""
Routine to read input MATLAB matrix as store in memory.
Parameters
----------
matfile : string
MATLAB binary file
Returns
-------
Tuple of
eeg_record : numpy array
EEG time series
eeg_record_length_min : int
EEG time series length in minutes
eeg_sampling_frequency : float
EEG time series sampling frequency
"""
try:
data = _scio.loadmat(matfile)
except IOError:
raise IOError("Error opening MATLAB matrix " + matfile)
# Get the length of data for each channel
data_key = ""
for key in data.iterkeys():
if type(data[key]) == np.ndarray:
data_key = key
# Copy data for the channel and return it back
eeg_record = np.copy(data[data_key]["data"][0,0])
eeg_record_length_min = int(data[data_key]["data_length_sec"]/60.)
eeg_sampling_frequency = float(data[data_key]["sampling_frequency"])
del data
return (eeg_record, eeg_record_length_min, eeg_sampling_frequency)
def determine_pib(X, eeg_sampling):
"""
Calculate power in bands (PIB).
Parameters
----------
X : numpy arrat
Time series
eeg_sampling : float
EEG sampling frquency
Returns
-------
pib : numpy array
Power in band (6 elements)
"""
freq, Pxx = _scsg.welch(X, fs = eeg_sampling, noverlap = None, scaling = "density")
pib = np.zeros(6)
# delta band (0.1-4hz)
ipos = (freq >= 0.1) & (freq < 4.0)
pib[0] = np.trapz(Pxx[ipos], freq[ipos])
# theta band (4-8hz)
ipos = (freq >= 4.0) & (freq < 8.0)
pib[1] = np.trapz(Pxx[ipos], freq[ipos])
# alpha band (8-12hz)
ipos = (freq >= 8.0) & (freq < 12.0)
pib[2] = np.trapz(Pxx[ipos], freq[ipos])
# beta band (12-30hz)
ipos = (freq >= 12.0) & (freq < 30.0)
pib[3] = np.trapz(Pxx[ipos], freq[ipos])
# low-gamma band (30-70hz)
ipos = (freq >= 30.0) & (freq < 70.0)
pib[4] = np.trapz(Pxx[ipos], freq[ipos])
# high-gamma band (70-180hz)
ipos = (freq >= 70.0) & (freq < 180.0)
pib[5] = np.trapz(Pxx[ipos], freq[ipos])
return pib
def worker(eegfile):
"""
Multiprocessing pool worker function. Extracts features from EEG time
series.
Parameters
----------
eegfile : EEG recording file name
Returns
-------
feature_arr : numpy array
Extracted feature array
"""
print "Processing file : ", eegfile
# Read the MATLAB binary file and extract data
eeg_record, egg_length_min, eeg_sampling = readdata(eegfile)
n_channels = eeg_record.shape[0]
feature_arr = np.zeros((1, 6 * n_channels * egg_length_min))
# Extract features. Features are sum of power in power spectrum of
# time series. Summed power in 6 bands ==> delta(0.1-4hz), theta(4-8hz),
# alpha(8-12hz),beta(12-30hz), low-gamma(30-70hz), high-gamma(70-180hz)
# This is done for 1 minute segment of the time series.
start, stop = 0, 6
for channel in range(n_channels):
time_chunks = np.array_split(eeg_record[channel], egg_length_min)
# iterate through the chunks and calculate summed spectral power of
# the band. This will give 6 features/1 minute for 1 channel or 96/minute
# for 16 channels. This will give us 960 features for 1 dataset
for chunk in time_chunks:
feature_arr[0,start:stop] = determine_pib(chunk, eeg_sampling)
start, stop = stop, stop + 6
return feature_arr
def main(args):
"""
Main function
Parameters
----------
args : list
List of EEG recording file names
feature_arr : numpy array
Consolidated array of features for the training set
"""
# Determine number of input files
nfiles = len(args)
# Create an array to store extracted features
feature_arr = np.zeros((nfiles, 960))
# Number of cpus
n_cpus = mp.cpu_count()
# Create a pool of worker functions
pool = mp.Pool(n_cpus)
result = pool.map(worker, args)
# Feature array
for i in range(len(result)):
nrecs = result[i].shape[1]
feature_arr[i,:nrecs] = result[i]
del result
return feature_arr
if __name__ == "__main__":
preictal_files = glob("../Dog_1/*preictal_segment*.mat")
feature_arr_1 = main(preictal_files)
interictal_files = glob("../Dog_1/*interictal_segment*.mat")
feature_arr_2 = main(interictal_files)
X = np.vstack((feature_arr_1, feature_arr_2))
y = np.concatenate((np.ones(feature_arr_1.shape[0]), np.zeros(feature_arr_2.shape[0])))
np.savez("../Dog_1/Dog_1_features.npz", X = X, y = y) | 2.96875 | 3 |
rastervision_pytorch_learner/rastervision/pytorch_learner/learner_pipeline.py | theoway/raster-vision | 1,577 | 12767466 | from rastervision.pipeline.pipeline import Pipeline
from rastervision.pytorch_learner import LearnerConfig
class LearnerPipeline(Pipeline):
"""Simple Pipeline that is a wrapper around Learner.main()
This supports the ability to use the pytorch_learner package to train models using
the RV pipeline package and its runner functionality without the rest of RV.
"""
commands = ['train']
gpu_commands = ['train']
def train(self):
learner_cfg: LearnerConfig = self.config.learner
learner = learner_cfg.build(learner_cfg, self.tmp_dir)
learner.main()
| 2.625 | 3 |
books/booksdatasource.py | KristinA64/cs257 | 0 | 12767467 | <gh_stars>0
#!/usr/bin/env python3
'''
booksdatasource.py
<NAME>, 21 September 2021
For use in the 'books' assignment at the beginning of Carleton's
CS 257 Software Design class, Fall 2021.
<NAME> and <NAME>, 2 October 2021
'''
import csv
class Author:
def __init__(self, surname='', given_name='', birth_year=None, death_year=None):
self.surname = surname
self.given_name = given_name
self.birth_year = birth_year
self.death_year = death_year
def __eq__(self, other):
''' For simplicity, we're going to assume that no two authors have the same name. '''
return self.surname == other.surname and self.given_name == other.given_name
class Book:
def __init__(self, title='', publication_year=None, authors=[]):
''' Note that the self.authors instance variable is a list of
references to Author objects. '''
self.title = title
self.publication_year = publication_year
self.authors = authors
def __eq__(self, other):
''' We're going to make the excessively simplifying assumption that
no two books have the same title, so 'same title' is the same
thing as 'same book'. '''
return self.title == other.title
class BooksDataSource:
#Initalizes the books and authors lists by parsing through the csv file
def __init__(self, books_csv_file_name):
''' The books CSV file format looks like this:
title,publication_year,author_description
For example:
All Clear,2010,<NAME> (1945-)
'<NAME>',1934,<NAME> (1881-1975)
This __init__ method parses the specified CSV file and creates
suitable instance variables for the BooksDataSource object containing
a collection of Author objects and a collection of Book objects.
'''
with open(books_csv_file_name, 'r') as csv_file:
reader = csv.reader(csv_file)
self.all_books = []
self.all_authors = []
for row in reader:
title = row[0]
year = int(row[1])
book_authors = []
multiple_authors = row[2].split('and')
for each_author in multiple_authors:
names = each_author.split()
temp_author = self.parse_authors(names)
already_added = False
for ex_author in self.all_authors:
if ex_author == temp_author:
book_authors.append(temp_author)
already_added = True
if already_added == False:
added_author = temp_author
self.all_authors.append(added_author)
book_authors.append(added_author)
self.all_books.append(Book(title, year, book_authors))
def parse_authors(self, names):
''' Helper function which initializes the given name, surname, birth
year, and death year of an author given the list of strings: name.
Returns a temporary Author object.
'''
if '(' in names[2]:
given_name = names[0]
surname = names[1]
years = names[2].split('-')
else:
given_name = names[0] + ' ' + names[1]
surname = names[2]
years = names[3].split('-')
if len(years) == 2:
birth_year = years[0].replace('(','')
death_year = years[1].replace(')','')
else:
birth_year = years[0].replace('(','')
death_year = None
temp_author = Author(surname, given_name, birth_year, death_year)
return temp_author
def authors(self, search_text=None):
''' Returns a list of all the Author objects in this data source whose names contain
(case-insensitively) the search text. If search_text is None, then this method
returns all of the Author objects. In either case, the returned list is sorted
by surname, breaking ties using given name (e.g. <NAME> comes before <NAME>).
'''
if search_text is not None:
filtered_authors = list(filter(lambda author: (search_text.lower() in author.given_name.lower()) or (search_text.lower() in author.surname.lower()), self.all_authors))
filtered_authors = sorted(filtered_authors, key=lambda author: (author.surname, author.given_name))
return filtered_authors
else:
filtered_authors = sorted(self.all_authors, key=lambda author: (author.surname, author.given_name))
return filtered_authors
def books(self, search_text=None, sort_by='title'):
''' Returns a list of all the Book objects in this data source whose
titles contain (case-insensitively) search_text. If search_text is None,
then this method returns all of the books objects.
The list of books is sorted in an order depending on the sort_by parameter:
'year' -- sorts by publication_year, breaking ties with (case-insenstive) title
'title' -- sorts by (case-insensitive) title, breaking ties with publication_year
default -- same as 'title' (that is, if sort_by is anything other than 'year'
or 'title', just do the same thing you would do for 'title')
'''
if search_text is not None:
filtered_books = list(filter(lambda book: search_text.lower() in book.title.lower(), self.all_books))
if sort_by == 'year':
filtered_books = sorted(filtered_books, key=lambda book: (book.publication_year, book.title))
else:
filtered_books = sorted(filtered_books, key=lambda book: (book.title, book.publication_year))
return filtered_books
else:
return self.all_books
def display_books(self, book_list):
''' Prints all book titles, publication years, and author information
in a given list to the terminal window.
'''
if book_list is None:
return
for book in book_list:
format_book = book.title + ' ' + str(book.publication_year)
print(format_book)
self.display_authors(book.authors)
def display_authors(self, author_list):
''' Prints all book author information for each author in a given
list to the terminal window.
'''
if author_list is None:
return
for author in author_list:
if author.death_year:
format_author = author.given_name + ' ' + author.surname + '(' + author.birth_year + '-' + author.death_year + ')'
else:
format_author = author.given_name + ' ' + author.surname + '(' + author.birth_year + '-)'
print(format_author)
def books_between_years(self, start_year=None, end_year=None):
''' Returns a list of all the Book objects in this data source whose publication
years are between start_year and end_year, inclusive. The list is sorted
by publication year, breaking ties by title (e.g. Neverwhere 1996 should
come before Thief of Time 1996).
If start_year is None, then any book published before or during end_year
should be included. If end_year is None, then any book published after or
during start_year should be included. If both are None, then all books
should be included.
'''
if (start_year is not None) and (end_year is not None):
filtered_books = list(filter(lambda book: (book.publication_year >= start_year) and (book.publication_year<= end_year), self.all_books))
filtered_books = sorted(filtered_books, key=lambda book: (book.publication_year, book.title))
return filtered_books
elif start_year is not None:
filtered_books = list(filter(lambda book: book.publication_year>= start_year, self.all_books))
filtered_books = sorted(filtered_books, key=lambda book: (book.publication_year, book.title))
return filtered_books
elif end_year is not None:
filtered_books = list(filter(lambda book: book.publication_year<= end_year, self.all_books))
filtered_books = sorted(filtered_books, key=lambda book: (book.publication_year, book.title))
return filtered_books
else:
return self.all_books
| 4.28125 | 4 |
args/asr_train_arg_parser.py | HikaruHotta/M2M-VC-CycleGAN | 5 | 12767468 | <filename>args/asr_train_arg_parser.py
"""
Arguments for training ASR model.
Inherits BaseArgParser.
"""
from args.train_arg_parser import TrainArgParser
class ASRTrainArgParser(TrainArgParser):
"""
Class which implements an argument parser for args used only in train mode.
It inherits BaseArgParser.
"""
def __init__(self):
super(ASRTrainArgParser, self).__init__()
self.isTrain = True
self.parser.add_argument(
'--dropout', type=float, default=0.1, help='Dropout rate.')
self.parser.add_argument(
'--gamma', type=float, default=0.99, help='Annealing rate for LR scheduler.')
# Model args
self.parser.add_argument(
'--n_cnn_layers', type=int, default=3, help='Numer of CNN layers.')
self.parser.add_argument(
'--n_rnn_layers', type=int, default=5, help='Number of RNN layers')
self.parser.add_argument(
'--rnn_dim', type=int, default=512, help='Dimensionality of RNN')
self.parser.add_argument(
'--n_class', type=int, default=29, help='Number of output classes.')
self.parser.add_argument(
'--n_feats', type=int, default=128, help='Number of features.')
self.parser.add_argument(
'--stride', type=int, default=2, help='Conv2D kernel stride.')
self.parser.add_argument(
'--pretrained_ckpt_path', type=str,
default=None,
help='Model pretrained on Librispeech.')
self.parser.add_argument('--librispeech', default=False, action='store_true',
help=('Train with libirspeech dataset.'))
| 2.953125 | 3 |
migrations/versions/2023b1841f34_initial_revision.py | wlsouza/pydiscordbot | 0 | 12767469 | """initial revision
Revision ID: 2023b1841f34
Revises:
Create Date: 2021-07-21 18:53:29.086785
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '<KEY>'
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
pass
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
pass
# ### end Alembic commands ###
| 1.226563 | 1 |
python/fate_arch/common/__init__.py | QuantumA/FATE | 715 | 12767470 | <filename>python/fate_arch/common/__init__.py
from fate_arch.common._types import FederatedMode, FederatedCommunicationType, EngineType, CoordinationProxyService, \
CoordinationCommunicationProtocol
from fate_arch.common._types import BaseType, Party, DTable
| 1.234375 | 1 |
src/zignalz/cli/log_server.py | rscohn2/sensepy | 0 | 12767471 | # SPDX-FileCopyrightText: 2020 <NAME>
#
# SPDX-License-Identifier: MIT
import logging
logger = logging.getLogger(__name__)
def add_parser(subparsers):
parser = subparsers.add_parser('log-server', help='Log server')
subparsers = parser.add_subparsers(dest='cmd')
subparsers.required = True
sync_parser = subparsers.add_parser(
'sync', help='Sync logs to local machine'
)
sync_parser.set_defaults(func=sync)
def sync():
pass
| 2.296875 | 2 |
src/rpi/stream.py | CI-Warrior/picoastal | 0 | 12767472 | <filename>src/rpi/stream.py
"""
# SCRIPT : stream.py
# POURPOSE : Stream the camera to the screen.
# AUTHOR : <NAME>
# DATE : 14/04/2021
# VERSION : 1.0
"""
# system
import os
from time import sleep
# arguments
import json
import argparse
# PiCamera
from picamera import PiCamera
from picamera.array import PiRGBArray
# OpenCV
import cv2
def set_camera_parameters(cfg):
"""
Set camera parameters.
All values come from the dict generated from the JSON file.
:param cfg: JSON instance.
:type cam: dict
:return: None
:rtype: None
"""
# set camera resolution [width x height]
camera = PiCamera()
camera.resolution = cfg["stream"]["resolution"]
# set camera frame rate [Hz]
camera.framerate = cfg["stream"]["framerate"]
# exposure mode
camera.exposure_mode = cfg["exposure"]["mode"]
if cfg["exposure"]["set_iso"]:
camera.iso = cfg["exposure"]["iso"]
return camera
def run_single_camera(cfg):
"""
Capture frames and display them on the screen
"""
# set camera parameters
camera = set_camera_parameters(cfg)
# read the data
rawCapture = PiRGBArray(camera)
# warm-up the camera
print(" -- warming up the camera --")
sleep(2)
print(" -- starting now --")
# capture frames from the camera
for frame in camera.capture_continuous(
rawCapture, format="bgr", use_video_port=True):
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
image = frame.array
# show the frame
cv2.imshow("Camera stream - press 'q' to quit.", image)
key = cv2.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
def main():
# verify if the configuraton file exists
# if it does, then read it
# else, stop
inp = args.config[0]
if os.path.isfile(inp):
with open(inp, "r") as f:
cfg = json.load(f)
print("\nConfiguration file found, continue...")
else:
raise IOError("No such file or directory \"{}\"".format(inp))
# start the stream
print("\nStreaming the camera")
run_single_camera(cfg)
print("Stream has ended.")
if __name__ == "__main__":
# Argument parser
parser = argparse.ArgumentParser()
# input configuration file
parser.add_argument("--configuration-file", "-cfg", "-i",
nargs=1,
action="store",
dest="config",
required=True,
help="Configuration JSON file.",)
args = parser.parse_args()
# call the main program
main() | 3.296875 | 3 |
us/tests/test_us.py | Juh10/python-us | 346 | 12767473 | <reponame>Juh10/python-us<filename>us/tests/test_us.py
from itertools import chain
import jellyfish # type: ignore
import pytest # type: ignore
import pytz
import us
# attribute
def test_attribute():
for state in us.STATES_AND_TERRITORIES:
assert state == getattr(us.states, state.abbr)
def test_valid_timezones():
for state in us.STATES_AND_TERRITORIES:
if state.capital:
assert pytz.timezone(state.capital_tz)
for tz in state.time_zones:
assert pytz.timezone(tz)
# During migration from SQLite to Python classes, a duplicate
# time zone had been found
assert len(state.time_zones) == len(set(state.time_zones))
# maryland lookup
def test_fips():
assert us.states.lookup("24") == us.states.MD
assert us.states.lookup("51") != us.states.MD
def test_abbr():
assert us.states.lookup("MD") == us.states.MD
assert us.states.lookup("md") == us.states.MD
assert us.states.lookup("VA") != us.states.MD
assert us.states.lookup("va") != us.states.MD
def test_name():
assert us.states.lookup("Maryland") == us.states.MD
assert us.states.lookup("maryland") == us.states.MD
assert us.states.lookup("Maryland", field="name") == us.states.MD
assert us.states.lookup("maryland", field="name") is None
assert us.states.lookup("murryland") == us.states.MD
assert us.states.lookup("Virginia") != us.states.MD
# lookups
def test_abbr_lookup():
for state in us.STATES:
assert us.states.lookup(state.abbr) == state
def test_fips_lookup():
for state in us.STATES:
assert us.states.lookup(state.fips) == state
def test_name_lookup():
for state in us.STATES:
assert us.states.lookup(state.name) == state
def test_obsolete_lookup():
for state in us.OBSOLETE:
assert us.states.lookup(state.name) is None
# test metaphone
def test_jellyfish_metaphone():
for state in chain(us.STATES_AND_TERRITORIES, us.OBSOLETE):
assert state.name_metaphone == jellyfish.metaphone(state.name)
# mappings
def test_mapping():
states = us.STATES[:5]
assert us.states.mapping("abbr", "fips", states=states) == dict(
(s.abbr, s.fips) for s in states
)
def test_obsolete_mapping():
mapping = us.states.mapping("abbr", "fips")
for state in us.states.OBSOLETE:
assert state.abbr not in mapping
def test_custom_mapping():
mapping = us.states.mapping("abbr", "fips", states=[us.states.DC, us.states.MD])
assert len(mapping) == 2
assert "DC" in mapping
assert "MD" in mapping
# known bugs
def test_kentucky_uppercase():
assert us.states.lookup("kentucky") == us.states.KY
assert us.states.lookup("KENTUCKY") == us.states.KY
def test_wayoming():
assert us.states.lookup("Wyoming") == us.states.WY
assert us.states.lookup("Wayoming") is None
def test_dc():
assert us.states.DC not in us.STATES
assert us.states.lookup("DC") == us.states.DC
assert us.states.lookup("District of Columbia") == us.states.DC
assert "DC" in us.states.mapping("abbr", "name")
# shapefiles
@pytest.mark.skip
def test_head():
import requests
for state in us.STATES_AND_TERRITORIES:
for url in state.shapefile_urls().values():
resp = requests.head(url)
assert resp.status_code == 200
# counts
def test_obsolete():
assert len(us.OBSOLETE) == 3
def test_states():
assert len(us.STATES) == 50
def test_territories():
assert len(us.TERRITORIES) == 5
def test_contiguous():
# Lower 48
assert len(us.STATES_CONTIGUOUS) == 48
def test_continental():
# Lower 48 + Alaska
assert len(us.STATES_CONTINENTAL) == 49
def test_dc():
assert us.states.DC not in us.STATES
| 2.53125 | 3 |
UI/res.py | YongLiuLab/BrainRadiomicsTools | 10 | 12767474 | <reponame>YongLiuLab/BrainRadiomicsTools
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.11.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qt_resource_name = b"\
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qt_resource_struct_v1 = b"\
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qt_resource_struct_v2 = b"\
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qt_version = [int(v) for v in QtCore.qVersion().split('.')]
if qt_version < [5, 8, 0]:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| 1.046875 | 1 |
D01/OP-pembagian2.py | shdx8/dtwrhs | 0 | 12767475 | print(10/3)
print(10//3)
print()
kue = 16
anak = 4
kuePerAnak = kue // anak
print ("Setiap anak akan mendapatkan kue sebanyak ", kuePerAnak)
| 3.234375 | 3 |
examples/tree_pretrain/utils/summary.py | jiakai0419/Curvature-Learning-Framework | 86 | 12767476 | <reponame>jiakai0419/Curvature-Learning-Framework
# Copyright (C) 2016-2021 Alibaba Group Holding Limited
#
# 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.
# ==============================================================================
"""Summary Util"""
import tensorflow as tf
def summary_tensor(name, sim):
"""Summary tensor.
Args:
name (str): The prefix name.
sim (tensor): The tensor to statistic.
"""
tf.summary.histogram(name + '_hist', sim)
tf.summary.scalar(name + '_min', tf.reduce_min(sim))
tf.summary.scalar(name + '_max', tf.reduce_max(sim))
tf.summary.scalar(name + '_mean', tf.reduce_mean(sim))
def summary_curvature(name, sim):
"""Summary curvature.
Args:
name (str): The prefix name.
sim (tensor): The tensor to statistic.
"""
tf.summary.scalar(name + '_min', tf.reduce_min(sim))
tf.summary.scalar(name + '_max', tf.reduce_max(sim))
tf.summary.scalar(name + '_mean', tf.reduce_mean(sim))
| 2.3125 | 2 |
Aula 06/[Exercicio 07] .py | IsaacPSilva/LetsCode | 0 | 12767477 | '''7. Faça uma função que sorteia 10 números aleatórios entre 0 e 100 e retorna o maior entre eles.'''
import random
def sorteio():
maior = 0
menor = 0
for i in range(10):
x = random.randint(0,100)
if i==1:
menor = x
if x<menor:
menor = x
if x>maior:
maior = x
print('Menor: ', menor)
print('Maior: ', maior)
sorteio() | 3.8125 | 4 |
ai_semiconductors/tests/test_uncertainty_rfr.py | xiaofx2/ai_semiconductors | 4 | 12767478 | import sys
import uncertainty_rfr
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
import pandas.api.types as ptypes
sys.path.append("../")
df_test = pd.read_csv('./xiaofeng_lasso/unittest_dummy.csv', nrows=5)
X_test, y_test = uncertainty_rfr.descriptors_outputs(df_test, d_start=5,
o=0)
def test_uncertainty_rfr_qfr():
'''
Test function for uncertainty_rfr_qfr. Checks values in actual are 0 when
true_y = False, and that the output df has the correct number of rows.
'''
df_test = pd.read_csv('./xiaofeng_lasso/unittest_dummy.csv')
X = df_test.iloc[range(3)]
err_df_test = \
uncertainty_rfr.uncertainty_rfr_qfr(df_test, X[X.columns[5:]],
Y='none', true_y=False, o=0,
d_start=5)
assert err_df_test['actual'][0] == err_df_test['actual'][1], \
'with true_y = False, all values in "actual" should be equal (0.0)'
assert len(err_df_test) == len(X), \
'length of predicting df should equal length of output df'
def test_descriptors_outputs():
'''
Test function for descriptors_outputs. Checks the shape of X, and checks
that the correct type of value (numeric) is in the columns.
'''
X_test, y_test = uncertainty_rfr.descriptors_outputs(df_test, d_start=5,
o=0)
assert X_test.shape[1] == 5, \
'array shape is incorrect. should be ({}, 7), got ({}, {})'\
.format(X_test.shape[0], X_test.shape[0], X_test.shape[1])
assert all(ptypes.is_numeric_dtype(X_test[col]) for col in
list(X_test[X_test.columns[:]])), \
'data type in columns is of incorrect type, must be numeric'
assert ptypes.is_numeric_dtype(y_test), \
'data type in columns is of incorrect type, must be numeric'
def test_traintest():
'''
Test function for traintest. Checks that the length of X_train and
y_train are the same.
'''
train_idx_test = np.array([0, 1, 2])
test_idx_test = np.array([3, 4])
X_train_test, y_train_test = \
uncertainty_rfr.traintest(X_test, y_test, train_idx_test,
test_idx_test)
assert X_train_test.shape[0] == y_train_test.shape[0], \
'X_train and y_train datapoints do not have the same num of values'
def test_predict_append():
'''
Test function for predict_append. Checks that the func appends one value
at a time, and that the output is a list.
'''
df_test2 = df_test[df_test.columns[:7]]
X_test, y_test = uncertainty_rfr.descriptors_outputs(df_test2, d_start=5,
o=0)
clf_test = RandomForestRegressor(random_state=130)
clf_test.fit(X_test, y_test)
N_arr_test = np.array([[3.98069889, 0.38048415],
[-0.78001682, 0.20058657]])
n_test = 0
preds_test = []
preds_test = uncertainty_rfr.predict_append(clf_test, N_arr_test, n_test,
preds_test)
assert len(preds_test) == 1, \
'preds_test needs to be length 1. Got {}'.format(len(preds_test))
assert isinstance(preds_test, list), \
'preds_test needs to be a list, got {}'.format(type(preds_test))
def test_dft_points():
'''
Test functino for dft_points. Checks that when true_y = True, the output
array is equal to Y_test, adn when true_y = False the output arry is the
same length as N_arr_test.
'''
Y_test = [3, 5]
N_arr_test = np.array([[3.98069889, 0.38048415],
[-0.78001682, 0.20058657]])
Y_arr_test = uncertainty_rfr.dft_points(True, Y_test, N_arr_test)
Y_arr_test2 = uncertainty_rfr.dft_points(False, Y_test, N_arr_test)
assert Y_arr_test[0] == Y_test[0], \
'Y_arr_test got unexpected result. Expected np.array([3,5]), got{}'.\
format(Y_arr_test)
assert len(Y_arr_test2) == N_arr_test.shape[0], \
'length of Y_arr_test2 should be equal to the number of rows of \
N_arr_test. Got Y_arr: {}, N_arr {}'.\
format(len(Y_arr_test2), N_arr_test.shape[0])
def test_uncert_table():
'''
Test function for uncert_table. Checks that the columns in the df are in
the correct place, the length of the output dataframe the correct
length, and that the last three columns in the output df are numeric.
'''
N_test = df_test[df_test.columns[5:]].iloc[[0, 1]]
X = df_test.iloc[[0, 1]]
Y_arr_test = np.array([3, 5])
pred_desc_test = pd.DataFrame(data={'mean': [1, 2], 'std': [3, 4]}).T
err_df = uncertainty_rfr.uncert_table(N_test, X, 1, 2, 3, 4,
Y_arr_test, pred_desc_test)
assert err_df.columns[0] == 'Type', \
'first column got unexpected value {}, should be Type'.\
format(err_df.columns[0])
assert len(err_df) == len(X), \
'arrays must all be the same length'
assert all(ptypes.is_numeric_dtype(err_df[col]) for col in
list(err_df[err_df.columns[4:]])), \
'columns "true val", "mean", and "std" are of wrong type, should be\
numeric values.'
def test_uncertainty_rfr_cv():
'''
Test function for undertainty_rfr_cv. Checks that the prediction df has
as many rows as folds in cv. In the output df it checks that "true val"
values are 0 when true_y = False, and checks that values in "AB" are of
type string.
'''
X = df_test.iloc[[0, 1]]
Y = 'none'
d_start, x_start = 5, 5
o = 0
folds_test = 2
pred_df_test, err_df_test = \
uncertainty_rfr.uncertainty_rfr_cv(df_test, X, Y, o, d_start, x_start,
folds=folds_test)
assert pred_df_test.shape[0] == folds_test, \
'Number of row in pred_df_test array should equal number of folds, \
expected {}, got {}'.format(folds_test, pred_df_test.shape[0])
assert err_df_test[err_df_test.columns[4]][0] == 0.0, \
'Expected 0.0 in "true val" with true_y set to false, instead got a \
different val'
assert isinstance(err_df_test['AB'][1], str), \
'Expected string in column "AB", got {}'.format(type(
err_df_test['AB'][1]))
def test_largest_uncertainty():
'''
test function for largest_uncertainty. checks that that length of the
df is equal to the num of values it was asked to return, and that the
output idx are a list.
'''
df = pd.DataFrame(data={'err_int': [1, 2, 3], 'std_dev': [4, 5, 6]})
num_vals = 2
larg, idx = uncertainty_rfr.largest_uncertainty(df, num_vals, 'std_dev')
assert len(larg) == num_vals, \
'number of rows in the output df should equal the number of values\
the func called to return'
assert isinstance(idx, list), \
'expected idx to be list, got {}'.format(type(idx))
| 2.8125 | 3 |
o_doh.py | Black-Blade/smartfon_no_ads | 0 | 12767479 | <reponame>Black-Blade/smartfon_no_ads
#!/usr/bin/env python3
#/***************************************************************************//**
# @file o_doh.py
#
# @author Black-Blade
# @brief o_doh.py
# @date 17.01.2021
# @version 0.0.1 Doxygen style eingebaut und erstellen dieser File
# @see https://tools.ietf.org/html/rfc8484
#*******************************************************************************/
#pip3 install requests
import urllib3
import base64
from urllib.request import urlretrieve
from urllib.parse import quote
# IMPORT MY STANDRT CLASS
from log import logging
from config import Config
if __name__ == "__main__":
quit()
class OUTPUT_DOH:
#/*******************************************************************************
# @author Black-Blade
# @brief Constructor of OUTPUT_DOH
# @date 10.03.2021
# @param [dohserver(String()]
# @return
# @version 0.0.1 Doxygen style eingebaut und erstellen dieser File
# @see
# *******************************************************************************/
def __init__(self,dohserver=None):
logging.debug ("")
if dohserver is None:
server = Config.O_DOHSERVER
else:
server = dohserver
if server=="cloudflare-dns-post":
self._url ="https://cloudflare-dns.com/dns-query"
self._method ="POST"
self._base64 =False
self._headers={
'Accept': 'application/dns-udpwireformat',
'Content-type': 'application/dns-udpwireformat'
}
if server=="cloudflare-dns-get":
self._url= "https://cloudflare-dns.com/dns-query?ct=application/dns-udpwireformat&dns="
self._method ="GET"
self._base64 =True
self._headers={
'Accept': 'application/dns-udpwireformat',
'Content-type': 'application/dns-udpwireformat'
}
if server=="cleanbrowsing-family-get":
self._url ="https://doh.cleanbrowsing.org/doh/family-filter/?ct&dns="
self._method="GET"
self._base64=True
self._headers={
'Accept': 'application/dns-udpwireformat',
'Content-type': 'application/dns-udpwireformat'
}
if server=="cleanbrowsing-adult-get":
self._url ="https://doh.cleanbrowsing.org/doh/adult-filter/?ct&dns="
self._method="GET"
self._base64=True
self._headers={
'Accept': 'application/dns-udpwireformat',
'Content-type': 'application/dns-udpwireformat'
}
if server=="cleanbrowsing-security-get":
self._url ="https://doh.cleanbrowsing.org/doh/security-filter/?ct&dns="
self._method="GET"
self._base64=True
self._headers={
'Accept': 'application/dns-udpwireformat',
'Content-type': 'application/dns-udpwireformat'
}
if server=="ffmuc-post":
self._url ="https://doh.ffmuc.net/dns-query"
self._method="POST"
self._base64=False
self._headers={
'Accept': 'application/dns-udpwireformat',
'Content-type': 'application/dns-udpwireformat'
}
if server=="google-post":
self._url ="https://dns.google/dns-query"
self._method="POST"
self._base64=False
self._headers = {
'Accept': 'application/dns-message',
'Content-type': 'application/dns-message'
}
if server=="google-get":
self._url ="https://dns.google/dns-query?ct/?ct&dns="
self._method="GET"
self._base64=True
self._headers = {
'Accept': 'application/dns-message',
'Content-type': 'application/dns-message'
}
if server=="digitale-gesellschaft-post":
self._url ="https://dns.digitale-gesellschaft.ch/dns-query"
self._method="POST"
self._base64=False
self._headers = {
'Accept': 'application/dns-udpwireformat',
'Content-type': 'application/dns-udpwireformat'
}
#/*******************************************************************************
# @author Black-Blade
# @brief Deconstructor of OUTPUT_DOH
# @date 06.03.2021
# @param
# @return
# @version 0.0.1 Doxygen style eingebaut und erstellen dieser File
# @see
# *******************************************************************************/
def __del__(self):
logging.debug ("")
#/*******************************************************************************
# @author Black-Blade
# @brief Send data to extern DNS DOH server
# @date 06.03.2021
# @param [txdata (data to dns server)]
# @return [rxdata (data from dns server)]
# @version 0.0.1 Doxygen style eingebaut und erstellen dieser File
# @see https://tools.ietf.org/html/rfc8484
# *******************************************************************************/
def send(self,txdata):
logging.debug ("")
rxdata =None
r= None
#Add 4 byte for DOH PROTOKOLL
transactionid= txdata[0:2]
txdata = b'\xab\xcd\x01\x00' +txdata[4:]
if self._base64==True:
txdata = base64.b64encode(txdata).decode("utf-8")
txdata = txdata.replace("=", "")
txdata = quote(txdata)
try:
http = urllib3.PoolManager()
if self._method=="POST":
r = http.request(self._method, self._url,
headers=self._headers,
body=txdata)
if self._method=="GET":
url= self._url+ txdata
r = http.request(self._method,url,
headers=self._headers)
if r.status == 200:
# clear the first 2 byte for DOH PROTOKLL
rxdata =r.data[2:]
rxdata = transactionid+rxdata
http.clear()
except OSError as err:
logging.error("OS error: {0}".format(err))
return rxdata
| 1.757813 | 2 |
frappe-bench/apps/erpnext/erpnext/accounts/doctype/c_form/c_form.py | Semicheche/foa_frappe_docker | 0 | 12767480 | # Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors
# License: GNU General Public License v3. See license.txt
from __future__ import unicode_literals
import frappe
from frappe.utils import flt
from frappe import _
from frappe.model.document import Document
class CForm(Document):
def validate(self):
"""Validate invoice that c-form is applicable
and no other c-form is received for that"""
for d in self.get('invoices'):
if d.invoice_no:
inv = frappe.db.sql("""select c_form_applicable, c_form_no from
`tabSales Invoice` where name = %s and docstatus = 1""", d.invoice_no)
if inv and inv[0][0] != 'Yes':
frappe.throw(_("C-form is not applicable for Invoice: {0}".format(d.invoice_no)))
elif inv and inv[0][1] and inv[0][1] != self.name:
frappe.throw(_("""Invoice {0} is tagged in another C-form: {1}.
If you want to change C-form no for this invoice,
please remove invoice no from the previous c-form and then try again"""\
.format(d.invoice_no, inv[0][1])))
elif not inv:
frappe.throw(_("Row {0}: Invoice {1} is invalid, it might be cancelled / does not exist. \
Please enter a valid Invoice".format(d.idx, d.invoice_no)))
def on_update(self):
""" Update C-Form No on invoices"""
self.set_total_invoiced_amount()
def on_submit(self):
self.set_cform_in_sales_invoices()
def before_cancel(self):
# remove cform reference
frappe.db.sql("""update `tabSales Invoice` set c_form_no=null where c_form_no=%s""", self.name)
def set_cform_in_sales_invoices(self):
inv = [d.invoice_no for d in self.get('invoices')]
if inv:
frappe.db.sql("""update `tabSales Invoice` set c_form_no=%s, modified=%s where name in (%s)""" %
('%s', '%s', ', '.join(['%s'] * len(inv))), tuple([self.name, self.modified] + inv))
frappe.db.sql("""update `tabSales Invoice` set c_form_no = null, modified = %s
where name not in (%s) and ifnull(c_form_no, '') = %s""" %
('%s', ', '.join(['%s']*len(inv)), '%s'), tuple([self.modified] + inv + [self.name]))
else:
frappe.throw(_("Please enter atleast 1 invoice in the table"))
def set_total_invoiced_amount(self):
total = sum([flt(d.grand_total) for d in self.get('invoices')])
frappe.db.set(self, 'total_invoiced_amount', total)
def get_invoice_details(self, invoice_no):
""" Pull details from invoices for referrence """
if invoice_no:
inv = frappe.db.get_value("Sales Invoice", invoice_no,
["posting_date", "territory", "base_net_total", "base_grand_total"], as_dict=True)
return {
'invoice_date' : inv.posting_date,
'territory' : inv.territory,
'net_total' : inv.base_net_total,
'grand_total' : inv.base_grand_total
}
| 2.0625 | 2 |
stubs.min/Autodesk/Revit/DB/__init___parts/ExportUnit.py | hdm-dt-fb/ironpython-stubs | 1 | 12767481 | <gh_stars>1-10
class ExportUnit(Enum,IComparable,IFormattable,IConvertible):
"""
An enumerated type listing possible target units for CAD Export.
enum ExportUnit,values: Centimeter (4),Default (0),Foot (2),Inch (1),Meter (5),Millimeter (3)
"""
def __eq__(self,*args):
""" x.__eq__(y) <==> x==yx.__eq__(y) <==> x==yx.__eq__(y) <==> x==y """
pass
def __format__(self,*args):
""" __format__(formattable: IFormattable,format: str) -> str """
pass
def __ge__(self,*args):
pass
def __gt__(self,*args):
pass
def __init__(self,*args):
""" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """
pass
def __le__(self,*args):
pass
def __lt__(self,*args):
pass
def __ne__(self,*args):
pass
def __reduce_ex__(self,*args):
pass
def __str__(self,*args):
pass
Centimeter=None
Default=None
Foot=None
Inch=None
Meter=None
Millimeter=None
value__=None
| 2.328125 | 2 |
art/estimators/classification/query_efficient_bb.py | h3yin/ART-with-amortized-attacks | 0 | 12767482 | # MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Provides black-box gradient estimation using NES.
"""
import logging
from typing import List, Optional, Tuple, Union, TYPE_CHECKING
import numpy as np
from scipy.stats import entropy
from art.estimators.estimator import BaseEstimator
from art.estimators.classification.classifier import ClassifierMixin, ClassifierLossGradients
from art.utils import clip_and_round
if TYPE_CHECKING:
from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
logger = logging.getLogger(__name__)
import itertools
class QueryEfficientGradientEstimationClassifier(ClassifierLossGradients, ClassifierMixin, BaseEstimator):
"""
Implementation of Query-Efficient Black-box Adversarial Examples. The attack approximates the gradient by
maximizing the loss function over samples drawn from random Gaussian noise around the input.
| Paper link: https://arxiv.org/abs/1712.07113
"""
estimator_params = ["num_basis", "sigma", "round_samples"]
def __init__(
self,
classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
num_basis: int,
sigma: float,
round_samples: float = 0.0,
) -> None:
"""
:param classifier: An instance of a classification estimator whose loss_gradient is being approximated.
:param num_basis: The number of samples to draw to approximate the gradient.
:param sigma: Scaling on the Gaussian noise N(0,1).
:param round_samples: The resolution of the input domain to round the data to, e.g., 1.0, or 1/255. Set to 0 to
disable.
"""
super().__init__(model=classifier.model, clip_values=classifier.clip_values)
# pylint: disable=E0203
self._classifier = classifier
self.num_basis = num_basis
self.sigma = sigma
self.round_samples = round_samples
self._nb_classes = self._classifier.nb_classes
@property
def input_shape(self) -> Tuple[int, ...]:
"""
Return the shape of one input sample.
:return: Shape of one input sample.
"""
return self._classifier.input_shape # type: ignore
def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray: # pylint: disable=W0221
"""
Perform prediction of the classifier for input `x`. Rounds results first.
:param x: Features in array of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
:param batch_size: Size of batches.
:return: Array of predictions of shape `(nb_inputs, nb_classes)`.
"""
return self._classifier.predict(clip_and_round(x, self.clip_values, self.round_samples), batch_size=batch_size)
def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
"""
Fit the classifier using the training data `(x, y)`.
:param x: Features in array of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
:param y: Target values (class labels in classification) in array of shape (nb_samples, nb_classes) in
one-hot encoding format.
:param kwargs: Dictionary of framework-specific arguments.
"""
raise NotImplementedError
def _generate_samples(self, x: np.ndarray, epsilon_map: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""
Generate samples around the current image.
:param x: Sample input with shape as expected by the model.
:param epsilon_map: Samples drawn from search space.
:return: Two arrays of new input samples to approximate gradient.
"""
minus = clip_and_round(
np.repeat(x, self.num_basis, axis=0) - epsilon_map,
self.clip_values,
self.round_samples,
)
plus = clip_and_round(
np.repeat(x, self.num_basis, axis=0) + epsilon_map,
self.clip_values,
self.round_samples,
)
return minus, plus
def class_gradient(self, x: np.ndarray, label: Union[int, List[int], None] = None, **kwargs) -> np.ndarray:
"""
Compute per-class derivatives w.r.t. `x`.
:param x: Input with shape as expected by the classifier's model.
:param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
output is computed for all samples. If multiple values as provided, the first dimension should
match the batch size of `x`, and each value will be used as target for its corresponding sample in
`x`. If `None`, then gradients for all classes will be computed for each sample.
:return: Array of gradients of input features w.r.t. each class in the form
`(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
`(batch_size, 1, input_shape)` when `label` parameter is specified.
"""
raise NotImplementedError
def _generate_sample_i(self, x: np.ndarray, epsilon_map: np.ndarray, i: int) -> Tuple[np.ndarray, np.ndarray]:
minus = clip_and_round(
x - epsilon_map[i],
self.clip_values,
self.round_samples,
)
plus = clip_and_round(
x + epsilon_map[i],
self.clip_values,
self.round_samples,
)
return minus, plus
def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
if self.amortized_attack:
return self.loss_gradient_new_efficient(x, y)
#return self.loss_gradient_new(x, y)
else:
return self.loss_gradient_old(x, y)
#return self.loss_gradient_new(x, y)
#return self.loss_gradient_new_efficient(x, y)
def loss_gradient_new_efficient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:param y: Correct labels, one-vs-rest encoding.
:return: Array of gradients of the same shape as `x`.
"""
epsilon_map = self.sigma * np.random.normal(size=([self.num_basis] + list(self.input_shape)))
#print(epsilon_map.shape)
#print(epsilon_map.reshape(self.num_basis, -1).shape)
grads = [0.0] * len(x)
#print('eps map shape', epsilon_map.shape)
#print('epsmap 11', epsilon_map[11])
#batch over multiple examples
reps_per_batch = 10
reps = epsilon_map.shape[0]
for jb in range(0, reps, reps_per_batch):
minus_preds = []
len_x = len(x)
pm_len = 2*len_x*reps_per_batch
minuses_pluses = [None]*pm_len
for b in range(reps_per_batch):
j = jb + b
#print('j', j, 'b', b)
if j >= reps:
b -= 1
#print('b after dec', b)
break
for i in range(len(x)):
minus, plus = self._generate_sample_i(x[i : i + 1], epsilon_map, j)
#print('j', j)
#print('minus i', i + b*2*len_x, 'plus i', i + len_x + b*2*len_x)
minuses_pluses[i + b*2*len_x] = minus
minuses_pluses[i + len_x + b*2*len_x] = plus
#print('b after loop', b)
if jb + reps_per_batch > reps:
#print(minuses_pluses[:(b+1)*2*len_x])
#print(minuses_pluses[(b+1)*2*len_x:])
minuses_pluses = minuses_pluses[:(b+1)*2*len_x]
#print('len(minuses_pluses)', len(minuses_pluses))
minuses_pluses = np.array(minuses_pluses)
minuses_pluses = np.squeeze(minuses_pluses, 1)
#print(minuses_pluses.shape)
pm_preds = self.predict(minuses_pluses, batch_size=4000)
#minus_preds, plus_preds = np.split(pm_preds, 2)
#print('num pm preds', pm_preds.shape)
#print('b', b+1)
rounds = np.split(pm_preds, b+1)
#print('len(rounds)', len(rounds))
for rn, r in enumerate(rounds):
minus_preds, plus_preds = np.split(r, 2)
#print(minus_preds.shape, plus_preds.shape)
j = jb + rn
for i, (mp, pp) in enumerate(zip(minus_preds, plus_preds)):
new_y_minus = entropy(y[i], mp)
new_y_plus = entropy(y[i], pp)
one_grad = epsilon_map[j] * (new_y_plus - new_y_minus)
grads[i] += one_grad
for i in range(len(grads)):
grads[i] = grads[i] / (self.num_basis * self.sigma)
grads = self._apply_preprocessing_gradient(x, np.array(grads))
return grads
def loss_gradient_new(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:param y: Correct labels, one-vs-rest encoding.
:return: Array of gradients of the same shape as `x`.
"""
epsilon_map = self.sigma * np.random.normal(size=([self.num_basis] + list(self.input_shape)))
#print(epsilon_map.shape)
#print(epsilon_map.reshape(self.num_basis, -1).shape)
grads = [0.0] * len(x)
for j in range(epsilon_map.shape[0]):
minus_preds = []
#plus_preds = []
pluses = []
minus = None
plus = None
for r in range(2):
for i in range(len(x)):
if r == 0:
minus, plus = self._generate_sample_i(x[i : i + 1], epsilon_map, j)
minus_preds.append(self.predict(minus)[0])
pluses.append(plus)
else:
plus_pred = self.predict(pluses[i])[0]
new_y_minus = entropy(y[i], minus_preds[i])
new_y_plus = entropy(y[i], plus_pred)
one_grad = epsilon_map[j] * (new_y_plus - new_y_minus)
grads[i] += one_grad
#for j in range(epsilon_map.shape[0]):
# for i in range(len(x)):
# minus, plus = self._generate_sample_i(x[i : i + 1], epsilon_map, j)
# pred = self.predict(np.concatenate((minus, plus)))
# new_y_minus = entropy(y[i], pred[0])
# new_y_plus = entropy(y[i], pred[1])
# one_grad = epsilon_map[j] * (new_y_plus - new_y_minus)
# grads[i] += one_grad
# #pluses = [self._generate_sample_i(x[i : i + 1], epsilon_map, j)[1][0] for i in range(len(x))]
# #plus_preds = self.predict(pluses)
# #print('plus_preds.shape', plus_preds.shape)
# #print(len(pluses))
# #minuses = [self._generate_sample_i(x[i : i + 1], epsilon_map, j)[0][0] for i in range(len(x))]
# #minus_preds = self.predict(minuses)
# #print('minus_preds.shape', minus_preds.shape)
# #for i in range(len(x)):
# # grads[i] += epsilon_map[j] * (plus_preds[i] - minus_preds[i])
for i in range(len(grads)):
grads[i] = grads[i]* 2/self.num_basis / (2 * self.sigma)
grads = self._apply_preprocessing_gradient(x, np.array(grads))
return grads
def loss_gradient_old(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
#new_grads = self.loss_gradient_new(x, y)
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:param y: Correct labels, one-vs-rest encoding.
:return: Array of gradients of the same shape as `x`.
"""
epsilon_map = self.sigma * np.random.normal(size=([self.num_basis] + list(self.input_shape)))
#print(epsilon_map.shape)
#print(epsilon_map.reshape(self.num_basis, -1).shape)
grads = []
for i in range(len(x)):
#print('i', i)
minus, plus = self._generate_samples(x[i : i + 1], epsilon_map)
#print('shape', minus.shape, plus.shape)
# Vectorized; small tests weren't faster
# ent_vec = np.vectorize(lambda p: entropy(y[i], p), signature='(n)->()')
# new_y_minus = ent_vec(self.predict(minus))
# new_y_plus = ent_vec(self.predict(plus))
# Vanilla
new_y_minus = np.array([entropy(y[i], p) for p in self.predict(minus, batch_size=4000)])
new_y_plus = np.array([entropy(y[i], p) for p in self.predict(plus, batch_size=4000)])
#print('term1 shape', epsilon_map.reshape(self.num_basis, -1).shape)
#print('term2 shape', ((new_y_plus - new_y_minus).reshape(self.num_basis, -1) / (2 * self.sigma)).shape)
query_efficient_grad = 2 * np.mean(
np.multiply(
epsilon_map.reshape(self.num_basis, -1),
(new_y_plus - new_y_minus).reshape(self.num_basis, -1) / (2 * self.sigma),
).reshape([-1] + list(self.input_shape)),
axis=0,
)
grads.append(query_efficient_grad)
grads = self._apply_preprocessing_gradient(x, np.array(grads))
#print('old grads', grads)
#print('new grads', new_grads)
#print('equal', grads == new_grads)
return grads
def get_activations(self, x: np.ndarray, layer: Union[int, str], batch_size: int) -> np.ndarray:
"""
Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
`nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
calling `layer_names`.
:param x: Input for computing the activations.
:param layer: Layer for computing the activations.
:param batch_size: Size of batches.
:return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
"""
raise NotImplementedError
def save(self, filename: str, path: Optional[str] = None) -> None:
"""
Save a model to file specific to the backend framework.
:param filename: Name of the file where to save the model.
:param path: Path of the directory where to save the model. If no path is specified, the model will be stored in
the default data location of ART at `ART_DATA_PATH`.
"""
raise NotImplementedError
| 1.546875 | 2 |
tests/scripts_question_utils_unittest.py | Chad-Ballay/devops-interview-questions | 1 | 12767483 | <gh_stars>1-10
import unittest
from pathlib import Path
from typing import List
from scripts.question_utils import get_answered_questions, get_question_list
def open_test_case_file(n: int) -> List[bytes]:
tests_path = Path(__file__).parent.joinpath()
with open(f'{tests_path}/testcases/testcase{n}.md', 'rb') as f:
file_list = [line.rstrip() for line in f.readlines()]
return file_list
class QuestionCount(unittest.TestCase):
def test_case_1(self):
raw_list = open_test_case_file(1)
question_list = get_question_list(raw_list)
answers = get_answered_questions(question_list)
self.assertEqual(len(question_list), 11)
self.assertEqual(len(answers), 3)
def test_case_2(self):
raw_list = open_test_case_file(2)
question_list = get_question_list(raw_list)
answers = get_answered_questions(question_list)
self.assertEqual(len(question_list), 16)
self.assertEqual(len(answers), 11)
| 2.84375 | 3 |
Exercicies/ex021.py | sthe-eduarda/Curso-de-python | 0 | 12767484 | <reponame>sthe-eduarda/Curso-de-python
import pygame
pygame.init()
pygame.mixer.music.load('nome do arquivo')
pygame.mix.music.play()
pygame.event.wait() | 2.515625 | 3 |
tests/pytests/functional/netapi/rest_cherrypy/test_out_formats.py | racooper/salt | 0 | 12767485 | import pytest
from salt.ext.tornado.httpclient import HTTPError
@pytest.fixture
def app(app):
app.wsgi_application.config["global"]["tools.hypermedia_out.on"] = True
return app
async def test_default_accept(http_client, content_type_map):
response = await http_client.fetch("/", method="GET")
assert response.headers["Content-Type"] == content_type_map["json"]
async def test_unsupported_accept(http_client):
with pytest.raises(HTTPError) as exc:
await http_client.fetch(
"/", method="GET", headers={"Accept": "application/ms-word"}
)
assert exc.value.code == 406
async def test_json_out(http_client, content_type_map):
response = await http_client.fetch(
"/", method="GET", headers={"Accept": content_type_map["json"]}
)
assert response.headers["Content-Type"] == content_type_map["json"]
async def test_yaml_out(http_client, content_type_map):
response = await http_client.fetch(
"/", method="GET", headers={"Accept": content_type_map["yaml"]}
)
assert response.headers["Content-Type"] == content_type_map["yaml"]
| 2.09375 | 2 |
taller 5/punto 7.py | 9juandromero7/controlrepeticion | 0 | 12767486 | while True:
X, M = map(int, input().split())
if X == 0 and M == 0:
break
Y = X * M
print(Y) | 3.359375 | 3 |
3.16.py | donghufeng/learn_mxnet | 0 | 12767487 | <reponame>donghufeng/learn_mxnet
# %%
import d2lzh as d2l
from mxnet import autograd, init, nd, gluon
from mxnet.gluon import data as gdata, loss as gloss, nn
import numpy as np
import pandas as pd
# %%
filename = './data/kaggle_house_pred_{}.csv'
train_data = pd.read_csv(filename.format('train'))
test_data = pd.read_csv(filename.format('test'))
# %%
all_feature = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))
# %%
numeric_feature = all_feature.dtypes[all_feature.dtypes != 'object'].index
all_feature[numeric_feature] = all_feature[numeric_feature].apply(
lambda x: (x - x.mean()) / x.std())
all_feature[numeric_feature] = all_feature[numeric_feature].fillna(0)
# %%
all_feature = pd.get_dummies(all_feature, dummy_na=True)
# %%
n_train = train_data.shape[0]
train_features = nd.array(all_feature[:n_train].values)
test_features = nd.array(all_feature[n_train:].values)
train_labels = nd.array(train_data.SalePrice.values).reshape((-1, 1))
# %%
# Method 1
loss = gloss.L2Loss()
def get_net():
net = nn.Sequential()
net.add(nn.Dense(1))
net.initialize()
return net
def log_rmse(net, feature, labels):
clipped_preds = nd.clip(net(feature), 1, float('inf'))
rmse = nd.sqrt(2 * loss(clipped_preds.log(), labels.log()).mean())
return rmse.asscalar()
def train(net, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size):
train_ls, test_ls = [], []
train_iter = gdata.DataLoader(gdata.ArrayDataset(
train_features, train_labels), batch_size, shuffle=True)
trainer = gluon.Trainer(net.collect_params(), 'adam', {
'learning_rate': learning_rate, 'wd': weight_decay})
for epoch in range(num_epochs):
for X, y in train_iter:
with autograd.record():
l = loss(net(X), y)
l.backward()
trainer.step(batch_size)
train_ls.append(log_rmse(net, train_features, train_labels))
if test_labels is not None:
test_ls.append(log_rmse(net, test_features, test_labels))
return train_ls, test_ls
def get_k_fold_data(k, i, X, y):
assert k > 1
fold_size = X.shape[0] // k
X_train, y_train = None, None
for j in range(k):
idx = slice(j * fold_size, (j + 1) * fold_size)
X_part, y_part = X[idx, :], y[idx]
if j == i:
X_valid, y_valid = X_part, y_part
elif X_train is None:
X_train, y_train = X_part, y_part
else:
X_train = nd.concat(X_train, X_part, dim=0)
y_train = nd.concat(y_train, y_part, dim=0)
return X_train, y_train, X_valid, y_valid
def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay, batch_size):
train_l_sum, valid_l_sum = 0.0, 0.0
for i in range(k):
data = get_k_fold_data(k, i, X_train, y_train)
net = get_net()
train_ls, valid_ls = train(
net, *data, num_epochs, learning_rate, weight_decay, batch_size)
train_l_sum += train_ls[-1]
valid_l_sum += valid_ls[-1]
if i == 0:
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse',
range(1, num_epochs + 1), valid_ls, ['train', 'valid'])
print('fold %d, train rmse %f, valid rmse %f' %
(i, train_ls[-1], valid_ls[-1]))
return train_l_sum / k, valid_l_sum / k
k, num_epochs, lr, weight_decay, batch_size = 5, 100, 5, 0, 64
train_l, valid_l = k_fold(k, train_features, train_labels,
num_epochs, lr, weight_decay, batch_size)
print('%d-fold validation: avg train rmse %f, avg valid rmse %f' %
(k, train_l, valid_l))
# %%
def train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size):
net = get_net()
train_ls, _ = train(net, train_features, train_labels,
None, None, num_epochs, lr, weight_decay, batch_size)
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse')
print('train rmse %f' % train_ls[-1])
preds = net(test_features).asnumpy()
test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)
submission.to_csv('submission.csv', index=False)
train_and_pred(train_features, test_features, train_labels,
test_data, num_epochs, lr, weight_decay, batch_size)
# %%
| 2.46875 | 2 |
SVMToCoreML/venv/Lib/site-packages/coremltools/proto/DictVectorizer_pb2.py | blueXstar597/HandwritingRecognition | 1 | 12767488 | <filename>SVMToCoreML/venv/Lib/site-packages/coremltools/proto/DictVectorizer_pb2.py
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: DictVectorizer.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
import DataStructures_pb2 as DataStructures__pb2
FeatureTypes__pb2 = DataStructures__pb2.FeatureTypes__pb2
from DataStructures_pb2 import *
DESCRIPTOR = _descriptor.FileDescriptor(
name='DictVectorizer.proto',
package='CoreML.Specification',
syntax='proto3',
serialized_pb=_b('\n\x14\x44ictVectorizer.proto\x12\x14\x43oreML.Specification\x1a\x14\x44\x61taStructures.proto\"\x8f\x01\n\x0e\x44ictVectorizer\x12;\n\rstringToIndex\x18\x01 \x01(\x0b\x32\".CoreML.Specification.StringVectorH\x00\x12\x39\n\x0cint64ToIndex\x18\x02 \x01(\x0b\x32!.CoreML.Specification.Int64VectorH\x00\x42\x05\n\x03MapB\x02H\x03P\x00\x62\x06proto3')
,
dependencies=[DataStructures__pb2.DESCRIPTOR,],
public_dependencies=[DataStructures__pb2.DESCRIPTOR,])
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
_DICTVECTORIZER = _descriptor.Descriptor(
name='DictVectorizer',
full_name='CoreML.Specification.DictVectorizer',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='stringToIndex', full_name='CoreML.Specification.DictVectorizer.stringToIndex', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='int64ToIndex', full_name='CoreML.Specification.DictVectorizer.int64ToIndex', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='Map', full_name='CoreML.Specification.DictVectorizer.Map',
index=0, containing_type=None, fields=[]),
],
serialized_start=69,
serialized_end=212,
)
_DICTVECTORIZER.fields_by_name['stringToIndex'].message_type = DataStructures__pb2._STRINGVECTOR
_DICTVECTORIZER.fields_by_name['int64ToIndex'].message_type = DataStructures__pb2._INT64VECTOR
_DICTVECTORIZER.oneofs_by_name['Map'].fields.append(
_DICTVECTORIZER.fields_by_name['stringToIndex'])
_DICTVECTORIZER.fields_by_name['stringToIndex'].containing_oneof = _DICTVECTORIZER.oneofs_by_name['Map']
_DICTVECTORIZER.oneofs_by_name['Map'].fields.append(
_DICTVECTORIZER.fields_by_name['int64ToIndex'])
_DICTVECTORIZER.fields_by_name['int64ToIndex'].containing_oneof = _DICTVECTORIZER.oneofs_by_name['Map']
DESCRIPTOR.message_types_by_name['DictVectorizer'] = _DICTVECTORIZER
DictVectorizer = _reflection.GeneratedProtocolMessageType('DictVectorizer', (_message.Message,), dict(
DESCRIPTOR = _DICTVECTORIZER,
__module__ = 'DictVectorizer_pb2'
# @@protoc_insertion_point(class_scope:CoreML.Specification.DictVectorizer)
))
_sym_db.RegisterMessage(DictVectorizer)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('H\003'))
# @@protoc_insertion_point(module_scope)
| 1.429688 | 1 |
autodiff/examples/decorators.py | gwtaylor/pyautodiff | 59 | 12767489 | from autodiff import function, gradient
@function
def f(x):
return x ** 2
@gradient
def g(x):
return x ** 2
if __name__ == '__main__':
print 'x = 20'
print 'f(x) = {0}'.format(f(20.0))
print 'f\'(x) = {0}'.format(g(20.0))
| 3.359375 | 3 |
ciphey/basemods/Decoders/base64_url.py | AlexandruValeanu/Ciphey | 9,908 | 12767490 | import base64
from typing import Dict, Optional
from ciphey.iface import Config, Decoder, ParamSpec, T, U, registry
@registry.register
class Base64_url(Decoder[str]):
def decode(self, ctext: T) -> Optional[U]:
"""
Performs Base64 URL decoding
"""
ctext_padding = ctext + "=" * (4 - len(ctext) % 4)
try:
return base64.urlsafe_b64decode(ctext_padding).decode("utf-8")
except Exception:
return None
@staticmethod
def priority() -> float:
# Not expected to show up often, but also very fast to check.
return 0.05
def __init__(self, config: Config):
super().__init__(config)
@staticmethod
def getParams() -> Optional[Dict[str, ParamSpec]]:
return None
@staticmethod
def getTarget() -> str:
return "base64_url"
| 2.765625 | 3 |
tests/test_basics.py | chnlkx/straxen | 0 | 12767491 | import numpy as np
import straxen
import tempfile
import os
import unittest
import shutil
import uuid
test_run_id_1T = '180423_1021'
class TestBasics(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
temp_folder = uuid.uuid4().hex
# Keep one temp dir because we don't want to download the data every time.
cls.tempdir = os.path.join(tempfile.gettempdir(), temp_folder)
assert not os.path.exists(cls.tempdir)
print("Downloading test data (if needed)")
st = straxen.contexts.demo()
cls.run_id = test_run_id_1T
cls.st = st
@classmethod
def tearDownClass(cls):
# Make sure to only cleanup this dir after we have done all the tests
if os.path.exists(cls.tempdir):
shutil.rmtree(cls.tempdir)
def test_run_selection(self):
st = self.st
# Ignore strax-internal warnings
st.set_context_config({'free_options': tuple(st.config.keys())})
run_df = st.select_runs(available='raw_records')
print(run_df)
run_id = run_df.iloc[0]['name']
assert run_id == test_run_id_1T
def test_processing(self):
st = self.st
df = st.get_df(self.run_id, 'event_info')
assert len(df) > 0
assert 'cs1' in df.columns
assert df['cs1'].sum() > 0
assert not np.all(np.isnan(df['x'].values))
def test_get_livetime_sec(self):
st = self.st
events = st.get_array(self.run_id, 'peaks')
straxen.get_livetime_sec(st, test_run_id_1T, things=events)
def test_mini_analysis(self):
@straxen.mini_analysis(requires=('raw_records',))
def count_rr(raw_records):
return len(raw_records)
n = self.st.count_rr(self.run_id)
assert n > 100
| 2.265625 | 2 |
spectrum.py | ttarhan/pixel-audio-visualizer | 0 | 12767492 | <reponame>ttarhan/pixel-audio-visualizer
import numpy as np
from matplotlib import pyplot as plt
from scipy import fftpack as fft
COLORS = [
(0, 0, 255),
(0, 255, 255),
(0, 255, 0),
(255, 255, 0),
(255, 128, 0),
(255, 0, 0),
(255, 0, 255)
]
class AudioSpectrum(object):
def __init__(self, sampleinfo, startchannel, ledcount, lowfreq, highfreq, energylow, energyhigh, reverse):
self.startchannel = startchannel
self.ledcount = ledcount
self.frequencies = fft.rfftfreq(sampleinfo.chunk, 1/sampleinfo.rate)
self.energylow = energylow
self.energyhigh = energyhigh
self.energyrange = self.energyhigh - self.energylow
self.reverse = reverse
bins = np.linspace(lowfreq, highfreq, self.ledcount)
binned = np.digitize(self.frequencies, bins)
# Digitize returns a bin after our last bin for out-of-range items; chop it off
self.binned = binned[binned < self.ledcount]
# How many unique bins did we end up with?
usable_bins = np.unique(self.binned).size
self.led_multiple = int(self.ledcount / usable_bins)
self.led_offset = int((self.ledcount - usable_bins * self.led_multiple) / 2)
self.ln = None
def frame(self, audio, audiofft, dmx):
# dBS = 10 * np.log10(audiofft)
newvalues = list(groupbins(self.binned, audiofft))
maxvalues = [max(m) if len(m) else 0 for m in newvalues]
# maxvalues = [sum(map(lambda x:x*x,m)) if len(m) else 0 for m in newvalues]
buffer = np.full((self.ledcount, 3), 0, dtype = np.uint8)
for (i,v) in enumerate(maxvalues):
if v < self.energylow:
continue
colorindex = (v - self.energylow) / self.energyrange * (len(COLORS) - 1)
colorindex = int(min(colorindex, len(COLORS) - 1))
chan = self.led_offset + i * self.led_multiple
endchan = chan + self.led_multiple - 1
buffer[chan:endchan + 1] = COLORS[colorindex]
dmx[self.startchannel:self.startchannel+self.ledcount] = np.flip(buffer, 0) if self.reverse else buffer
if self.ln is not None:
self.ln.remove()
# self.ln, = plt.plot(self.frequencies,audiofft)
# self.ln, = plt.plot(np.reshape(dmx,-1))
# plt.pause(0.005)
# print(f'Diff: {round(time.time()-lt,4)}')
def groupbins(bins, data):
current = []
curbin = bins[0]
for (i,d) in enumerate(bins):
if d != curbin:
yield current
current = []
curbin = d
current.append(data[i])
yield current | 2.625 | 3 |
rds/client/python/example_postgres/basic_examples/bytea_select.py | BigMountainTiger/aws-cdk-examples | 2 | 12767493 | <gh_stars>1-10
import os
import psycopg2
from pprint import pprint
CONSTR = 'postgres://postgres:<PASSWORD>@database-<EMAIL>-east-<EMAIL>:5432/StudentDB'
def save_to_file(file_id, file_content):
# Create the directory if not exists
directory = './images/files-from-db/'
if not os.path.exists(directory):
os.makedirs(directory)
with open(f'{directory}file-{file_id}.jpg', 'wb') as file:
file.write(file_content)
def connect():
sql = 'select * from public.image_test where id = %s'
try:
conn = psycopg2.connect(CONSTR)
cur = conn.cursor()
cur.execute(sql, [2])
row = cur.fetchone()
cur.close()
file_id = row[0]
file_content = row[1].tobytes()
save_to_file(file_id, file_content)
except (Exception) as error:
print(error)
finally:
if conn is not None:
conn.close()
if __name__ == '__main__':
connect()
print('Done') | 3.25 | 3 |
plugins/coral/src/main.py | ctso/scrypted | 0 | 12767494 | from __future__ import annotations
from asyncio.events import AbstractEventLoop, TimerHandle
from asyncio.futures import Future
from typing import Mapping
from safe_set_result import safe_set_result
import scrypted_sdk
import numpy as np
import re
import tflite_runtime.interpreter as tflite
from pycoral.utils.edgetpu import make_interpreter
from pycoral.utils.edgetpu import list_edge_tpus
from pycoral.utils.edgetpu import run_inference
from pycoral.adapters.common import input_size
from pycoral.adapters import detect
from PIL import Image
import common
import io
import gstreamer
import json
import asyncio
import time
import os
import binascii
from urllib.parse import urlparse
from gi.repository import Gst
import multiprocessing
from third_party.sort import Sort
from scrypted_sdk.types import FFMpegInput, Lock, MediaObject, ObjectDetection, ObjectDetectionModel, ObjectDetectionResult, ObjectDetectionSession, OnOff, ObjectsDetected, ScryptedInterface, ScryptedMimeTypes
def parse_label_contents(contents: str):
lines = contents.splitlines()
ret = {}
for row_number, content in enumerate(lines):
pair = re.split(r'[:\s]+', content.strip(), maxsplit=1)
if len(pair) == 2 and pair[0].strip().isdigit():
ret[int(pair[0])] = pair[1].strip()
else:
ret[row_number] = content.strip()
return ret
class DetectionSession:
id: str
timerHandle: TimerHandle
future: Future
loop: AbstractEventLoop
score_threshold: float
running: bool
def __init__(self) -> None:
self.timerHandle = None
self.future = Future()
self.tracker = Sort()
self.running = False
def cancel(self):
if self.timerHandle:
self.timerHandle.cancel()
self.timerHandle = None
def timedOut(self):
safe_set_result(self.future)
def setTimeout(self, duration: float):
self.cancel()
self.loop.call_later(duration, lambda: self.timedOut())
class CoralPlugin(scrypted_sdk.ScryptedDeviceBase, ObjectDetection):
detection_sessions: Mapping[str, DetectionSession] = {}
session_mutex = multiprocessing.Lock()
def __init__(self, nativeId: str | None = None):
super().__init__(nativeId=nativeId)
labels_contents = scrypted_sdk.zip.open(
'fs/coco_labels.txt').read().decode('utf8')
self.labels = parse_label_contents(labels_contents)
edge_tpus = list_edge_tpus()
if len(edge_tpus):
model = scrypted_sdk.zip.open(
'fs/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite').read()
self.interpreter = make_interpreter(model)
else:
model = scrypted_sdk.zip.open(
'fs/mobilenet_ssd_v2_coco_quant_postprocess.tflite').read()
self.interpreter = tflite.Interpreter(model_content=model)
self.interpreter.allocate_tensors()
self.mutex = multiprocessing.Lock()
async def getInferenceModels(self) -> list[ObjectDetectionModel]:
ret = list[ObjectDetectionModel]()
_, height, width, channels = self.interpreter.get_input_details()[
0]['shape']
d = {
'id': 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu',
'name': '<NAME>',
'classes': list(self.labels.values()),
'inputShape': [int(width), int(height), int(channels)],
}
ret.append(d)
return ret
def create_detection_result(self, objs, size, tracker: Sort = None):
detections = list[ObjectDetectionResult]()
detection_result: ObjectsDetected = {}
detection_result['detections'] = detections
detection_result['inputDimensions'] = size
tracker_detections = []
for obj in objs:
element = [] # np.array([])
element.append(obj.bbox.xmin)
element.append(obj.bbox.ymin)
element.append(obj.bbox.xmax)
element.append(obj.bbox.ymax)
element.append(obj.score) # print('element= ',element)
tracker_detections.append(element)
tracker_detections = np.array(tracker_detections)
trdata = []
trackerFlag = False
if tracker and tracker_detections.any():
trdata = tracker.update(tracker_detections)
trackerFlag = True
if trackerFlag and (np.array(trdata)).size:
for td in trdata:
x0, y0, x1, y1, trackID = td[0].item(), td[1].item(
), td[2].item(), td[3].item(), td[4].item()
overlap = 0
for ob in objs:
dx0, dy0, dx1, dy1 = ob.bbox.xmin, ob.bbox.ymin, ob.bbox.xmax, ob.bbox.ymax
area = (min(dx1, x1)-max(dx0, x0)) * \
(min(dy1, y1)-max(dy0, y0))
if (area > overlap):
overlap = area
obj = ob
detection: ObjectDetectionResult = {}
detection['id'] = str(trackID)
detection['boundingBox'] = (
obj.bbox.xmin, obj.bbox.ymin, obj.bbox.ymax, obj.bbox.ymax)
detection['className'] = self.labels.get(obj.id, obj.id)
detection['score'] = obj.score
detections.append(detection)
else:
for obj in objs:
detection: ObjectDetectionResult = {}
detection['boundingBox'] = (
obj.bbox.xmin, obj.bbox.ymin, obj.bbox.ymax, obj.bbox.ymax)
detection['className'] = self.labels.get(obj.id, obj.id)
detection['score'] = obj.score
detections.append(detection)
return detection_result
def detection_event(self, detection_session: DetectionSession, detection_result: ObjectsDetected, event_buffer: bytes = None):
detection_result['detectionId'] = detection_session.id
detection_result['timestamp'] = int(time.time() * 1000)
asyncio.run_coroutine_threadsafe(self.onDeviceEvent(
ScryptedInterface.ObjectDetection.value, detection_result), loop=detection_session.loop)
def end_session(self, detection_session: DetectionSession):
print('detection ended', detection_session.id)
detection_session.cancel()
with self.session_mutex:
self.detection_sessions.pop(detection_session.id, None)
detection_result: ObjectsDetected = {}
detection_result['running'] = False
self.detection_event(detection_session, detection_result)
async def detectObjects(self, mediaObject: MediaObject, session: ObjectDetectionSession = None) -> ObjectsDetected:
score_threshold = -float('inf')
duration = None
detection_id = None
if session:
detection_id = session.get('detectionId', -float('inf'))
duration = session.get('duration', None)
score_threshold = session.get('minScore', score_threshold)
is_image = mediaObject and mediaObject.mimeType.startswith('image/')
with self.session_mutex:
if not is_image and not detection_id:
detection_id = binascii.b2a_hex(os.urandom(15)).decode('utf8')
if detection_id:
detection_session = self.detection_sessions.get(detection_id, None)
if not duration and not is_image:
if detection_session:
self.end_session(detection_session)
return
elif detection_id and not detection_session:
if not mediaObject:
raise Exception(
'session %s inactive and no mediaObject provided' % detection_id)
detection_session = DetectionSession()
detection_session.id = detection_id
detection_session.score_threshold = score_threshold
loop = asyncio.get_event_loop()
detection_session.loop = loop
self.detection_sessions[detection_id] = detection_session
detection_session.future.add_done_callback(
lambda _: self.end_session(detection_session))
if is_image:
stream = io.BytesIO(bytes(await scrypted_sdk.mediaManager.convertMediaObjectToBuffer(mediaObject, 'image/jpeg')))
image = Image.open(stream)
_, scale = common.set_resized_input(
self.interpreter, image.size, lambda size: image.resize(size, Image.ANTIALIAS))
tracker = None
if detection_session:
tracker = detection_session.tracker
with self.mutex:
self.interpreter.invoke()
objs = detect.get_objects(
self.interpreter, score_threshold=score_threshold, image_scale=scale)
return self.create_detection_result(objs, image.size, tracker = tracker)
new_session = not detection_session.running
if new_session:
detection_session.running = True
detection_session.setTimeout(duration / 1000)
if not new_session:
return
print('detection starting', detection_id)
b = await scrypted_sdk.mediaManager.convertMediaObjectToBuffer(mediaObject, ScryptedMimeTypes.MediaStreamUrl.value)
s = b.decode('utf8')
j: FFMpegInput = json.loads(s)
container = j['container']
videofmt = 'raw'
videosrc = j['url']
if container == 'mpegts' and videosrc.startswith('tcp://'):
parsed_url = urlparse(videosrc)
videofmt = 'gst'
videosrc = 'tcpclientsrc port=%s host=%s ! tsdemux' % (
parsed_url.port, parsed_url.hostname)
size = j['mediaStreamOptions']['video']
inference_size = input_size(self.interpreter)
width, height = inference_size
w, h = (size['width'], size['height'])
scale = min(width / w, height / h)
def user_callback(input_tensor, src_size, inference_box):
with self.mutex:
run_inference(self.interpreter, input_tensor)
objs = detect.get_objects(
self.interpreter, score_threshold=score_threshold, image_scale=(scale, scale))
# (result, mapinfo) = input_tensor.map(Gst.MapFlags.READ)
try:
detection_result = self.create_detection_result(objs,
src_size, detection_session.tracker)
# self.detection_event(detection_session, detection_result, mapinfo.data.tobytes())
self.detection_event(detection_session, detection_result)
if not session or not duration:
safe_set_result(detection_session.future)
finally:
# input_tensor.unmap(mapinfo)
pass
pipeline = gstreamer.run_pipeline(detection_session.future, user_callback,
src_size=(
size['width'], size['height']),
appsink_size=inference_size,
videosrc=videosrc,
videofmt=videofmt)
task = pipeline.run()
asyncio.ensure_future(task)
detection_result: ObjectsDetected = {}
detection_result['detectionId'] = detection_id
detection_result['running'] = True
return detection_result
def create_scrypted_plugin():
return CoralPlugin()
#
| 1.9375 | 2 |
tests/test_utility.py | yahoo/pypirun | 2 | 12767495 | import os
import shutil
import unittest
from pypirun import utility
class TestUtility(unittest.TestCase):
test_key = 'OUROATH_UTILITY'
def tearDown(self):
try:
del os.environ[self.test_key]
except KeyError:
pass
def test__env_bool__default(self):
self.assertTrue(utility.env_bool(self.test_key, True))
self.assertFalse(utility.env_bool(self.test_key, False))
def test__env_bool__true(self):
os.environ[self.test_key] = 'True'
self.assertTrue(utility.env_bool(self.test_key, False))
def test__env_bool__false(self):
os.environ[self.test_key] = 'False'
self.assertFalse(utility.env_bool(self.test_key, True))
def test__which__allow_symlink(self):
result = utility.which('python3', allow_symlink=True)
self.assertEqual(result, shutil.which('python3'))
def test__which__allow_symlink__false(self):
result = utility.which('python3', allow_symlink=False)
| 2.75 | 3 |
app/utils.py | tradaviahe1982/labman-master | 10 | 12767496 | <reponame>tradaviahe1982/labman-master
import logging
import random
import string
from multiprocessing import Process
from flask import render_template
from flask_mail import Message
from log4mongo.handlers import MongoHandler
from PIL import Image
from app import CONFIG, mailer
from app.db import get_db
def rand_str(len, case='any'):
if case == 'any':
str_set = string.ascii_letters + string.digits
elif case == 'lower':
str_set = string.ascii_lowercase + string.digits
elif case == 'upper':
str_set = string.ascii_uppercase + string.digits
else:
raise ValueError('case must be "lower", "upper" or "any"')
return ''.join(random.choice(str_set) for i in range(len))
def get_next_uid():
db = get_db()
res = db.counters.find_one_and_update(
{'_id': 'uid'}, {'$inc': {'next_uid': 1}})
return res['next_uid']
def get_position_name(position_id):
for pid, pname in CONFIG['positions']:
if pid == position_id:
return pname
return None
def get_logger(name):
logging.basicConfig(level=CONFIG['log']['level'])
logger = logging.getLogger(name)
logger.addHandler(MongoHandler(host=CONFIG['db']['ip'],
capped=CONFIG['log']['capped']))
return logger
def crop_square(filename):
img = Image.open(filename)
w, h = img.size
if w == h:
return
elif w > h:
x = round((w - h) / 2)
region = img.crop((x, 0, x + h, h))
else:
y = round((h - w) / 2)
region = img.crop((0, y, w, y + w))
region.save(filename)
def send_mail(subject, member, content):
msg = Message(subject,
body='Hi {},\n\n{}'.format(member.en_name, content),
html=render_template('email.html', to_name=member.en_name, content=content),
recipients=[member.email])
mailer.send(msg)
def async_send_mail(subject, member, content):
p = Process(target=send_mail, args=(subject, member, content))
p.start()
| 1.9375 | 2 |
Lambda/send-telegram-message-2/lambda_function.py | wyutong1997/SmartMedicineDispenser | 5 | 12767497 | <reponame>wyutong1997/SmartMedicineDispenser
import json
import os
from botocore.vendored import requests
def executeCommand(command:str, chat_id:int, text:str)->str:
hyper_url = "http://ec544.hopto.org:7786/"
if command == "patients":
requests.post(hyper_url + "patients", data={"did": chat_id}).json()
#requests.post("https://1eb82cd0e02229fbc16e5923449d0dcf.m.pipedream.net", data={"did": chat_id}).json()
elif command == "prescriptions":
#requests.post(hyper_url + "medlist", data={"did": chat_id, "pid": text.replace('/prescriptions', '').strip()}).json()
requests.post("https://1eb82cd0e02229fbc16e5923449d0dcf.m.pipedream.net", data={"did": chat_id, "pid": text.replace('/prescriptions', '').strip()}).json()
elif command == "progress":
#requests.post(hyper_url + "progress", data={"did": chat_id, "pid": text.replace('/progress', '').strip()}).json()
requests.post("https://1eb82cd0e02229fbc16e5923449d0dcf.m.pipedream.net", data={"did": chat_id, "pid": text.replace('/progress', '').strip()}).json()
elif command == "meddetail":
#requests.post(hyper_url + "meddetail", data={"did": chat_id, "pid": text.replace('/meddetail', '').strip()}).json()
requests.post("https://1eb82cd0e02229fbc16e5923449d0dcf.m.pipedream.net", data={"did": chat_id, "pid": text.replace('/meddetail', '').strip()}).json()
else:
return "Valid bot commands:\n" \
"/patients - name & age of patients\n" \
"/progress - side effects or symptoms this week\n" \
"/prescriptions [patient ID] - list of medications\n" \
"/meddetail [med ID] - details of 1 medication {}".format(command)
return 'ok'
def lambda_handler(event, context):
field_errors = {}
command = ""
if 'message' in event:
message = event['message']
if 'from' in message:
chat_id = str(message['from']['id']).strip()
else:
field_errors['chat_id'] = "Dr's ID missing"
if 'text' in message:
text = message['text']
else:
field_errors['text'] = "Text missing"
if 'entities' in message:
entities = message['entities']
for entity in entities:
if entity['type'] == "bot_command":
command = text[entity['offset']+1:entity['offset']+entity['length']]
telegram_msg = executeCommand(command, chat_id, text)
if telegram_msg != 'ok':
params = {'chat_id': chat_id, 'text': telegram_msg}
telegram_token = os.environ['TELEGRAM_BOT_TOKEN']
api_url = "https://api.telegram.org/bot"+telegram_token+"/"
res = requests.post(api_url + "sendMessage", data=params).json()
if not res["ok"]:
print(res)
return{
"statusCode": 400,
"body": res
}
break
else:
field_errors['message'] = "Message missing"
if field_errors:
raise Exception(json.dumps({'field_errors': field_errors}))
return {
"statusCode": 200,
"body": "success"
}
| 2.234375 | 2 |
dao/control/db/migrate_repo/versions/003_add_port.py | Symantec/dao-control | 0 | 12767498 | from sqlalchemy import Column, Table, MetaData, Index
import logging
from sqlalchemy.dialects.mysql.base import DATETIME
from sqlalchemy.dialects.mysql.base import INTEGER
from sqlalchemy.dialects.mysql.base import VARCHAR
LOG = logging.getLogger(__name__)
def upgrade(migrate_engine):
meta = MetaData()
meta.bind = migrate_engine
port = Table(
'port', meta,
Column('created_at', DATETIME),
Column('updated_at', DATETIME),
Column('deleted_at', DATETIME),
Column('deleted', INTEGER(display_width=11)),
Column('key', VARCHAR(length=128)),
Column('id', INTEGER(display_width=11),
primary_key=True, nullable=False),
Column('device_id', VARCHAR(length=32), nullable=False),
Column('rack_name', VARCHAR(length=32), nullable=False),
Column('vlan_tag', INTEGER(display_width=11), nullable=False),
Column('ip', VARCHAR(length=15)),
Column('mac', VARCHAR(length=31)),
)
try:
port.create()
except Exception:
LOG.info(repr(port))
LOG.exception('Exception while creating table.')
raise
indexes = [
Index('port_vlan_tag_idx', port.c.vlan_tag),
Index('port_rack_name_idx', port.c.rack_name)
]
for index in indexes:
index.create(migrate_engine)
def downgrade(migrate_engine):
meta = MetaData()
meta.bind = migrate_engine
table = Table('port', meta, autoload=True)
table.drop()
| 2.234375 | 2 |
tushare_trader/datayes/idx.py | gorf/tushare-trader | 1 | 12767499 | # -*- coding:utf-8 -*-
"""
通联数据
Created on 2015/08/24
@author: <NAME>
@group : waditu
@contact: <EMAIL>
"""
from io import StringIO
import pandas as pd
from tushare.util import vars as vs
from tushare.util.common import Client
from tushare.util import upass as up
class Idx():
def __init__(self, client=None):
if client is None:
self.client = Client(up.get_token())
else:
self.client = client
def Idx(self, secID='', ticker='', field=''):
"""
获取国内外指数的基本要素信息，包括指数名称、指数代码、发布机构、发布日期、基日、基点等。
"""
code, result = self.client.getData(vs.IDX%(secID, ticker, field))
return _ret_data(code, result)
def IdxCons(self, secID='', ticker='', intoDate='', isNew='', field=''):
"""
获取国内外指数的成分构成情况，包括指数成分股名称、成分股代码、入选日期、剔除日期等。
"""
code, result = self.client.getData(vs.IDXCONS%(secID, ticker, intoDate,
intoDate, isNew, field))
return _ret_data(code, result)
def _ret_data(code, result):
if code==200:
result = result.decode('utf-8') if vs.PY3 else result
df = pd.read_csv(StringIO(result))
return df
else:
print(result)
return None
| 2.328125 | 2 |
continuous-umps/try_this_first.py | jemisjoky/Continuous-uMPS | 0 | 12767500 | import torch
# After running `make install` in the torchmps folder, this should work
from torchmps import ProbMPS
# Dummy parameters for the model and data
bond_dim = 13
input_dim = 2
batch_size = 55
sequence_len = 21
complex_params = True
# Verify that you can initialize the model
my_mps = ProbMPS(sequence_len, input_dim, bond_dim, complex_params)
# Verify that a Pytorch optimizer initializes properly
optimizer = torch.optim.Adam(my_mps.parameters())
# Create dummy discrete index data (has to be integer/long type!)
data = torch.randint(high=input_dim, size=(sequence_len, batch_size))
# Verify that the model forward function works on dummy data
log_probs = my_mps(data)
assert log_probs.shape == (batch_size,)
# Verify that backprop works fine, and that gradients are populated
loss = my_mps.loss(data) # <- Negative log likelihood loss
assert all(p.grad is None for p in my_mps.parameters())
# Normally we have to call optimizer.zero_grad before loss.backward, but
# this is just single training run so it doesn't matter
loss.backward()
optimizer.step()
assert all(p.grad is not None for p in my_mps.parameters())
# Congrats, you're ready to start writing the actual training script!
print("Yay, things seem to be working :)")
| 2.984375 | 3 |
examples/example_test/main.py | lcopey/node_editor | 1 | 12767501 | <reponame>lcopey/node_editor
import sys
import os
import inspect
from PyQt5.QtWidgets import *
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..'))
from node_editor.node_editor_window import NodeEditorWindow
from node_editor.utils import loadStylessheet
if __name__ == '__main__':
app = QApplication(sys.argv)
wnd = NodeEditorWindow()
wnd.nodeEditor.addNodes()
module_path = os.path.dirname(inspect.getfile(wnd.__class__))
loadStylessheet(os.path.join(module_path, 'qss\\nodestyle.qss'))
sys.exit(app.exec_())
| 2.203125 | 2 |
tests/vtk_ui/test_vtk_ui_handle_interaction.py | scottwittenburg/vcs | 11 | 12767502 | """
Tests handle's interactivity.
"""
import vcs.vtk_ui
import vtk_ui_test
import decimal
class test_vtk_ui_handle_interaction(vtk_ui_test.vtk_ui_test):
def setUp(self):
super(test_vtk_ui_handle_interaction, self).setUp()
self.h = None
self.h2 = None
def do(self):
self.win.SetSize(100, 100)
self.h = vcs.vtk_ui.Handle(self.inter, (5, 5), clicked=self.clicked, dragged=self.dragged, released=self.released)
self.h.show()
self.mouse_down(5, 5)
self.mouse_move(10, 10)
self.mouse_up(10, 10)
# Test normalized drag provides normalized dx/dy
self.h2 = vcs.vtk_ui.Handle(self.inter, (.3, .3), dragged=self.norm_drag, normalize=True)
self.h2.show()
self.mouse_down(30, 30)
self.mouse_move(40, 40)
self.mouse_up(40, 40)
assert self.passed == 5, "Did not trigger drag on normalized"
self.passed = 0
def norm_drag(self, handle, dx, dy):
assert handle == self.h2, "Normalized passed wrong handle to drag"
assert decimal.Decimal("%f" % dx) == decimal.Decimal("%f" % .1), "DX normalized incorrectly; %f when expecting %f" % (dx, .1)
assert decimal.Decimal("%f" % dy) == decimal.Decimal("%f" % .1), "DY normalized incorrectly; %f when expecting %f" % (dy, .1)
assert self.passed == 4, "Did not trigger released"
self.passed = 5
def clicked(self, handle):
assert handle == self.h, "Clicked received argument that was not the handle"
self.passed = 2
def dragged(self, handle, dx, dy):
assert handle == self.h, "Dragged received argument that was not the handle"
assert dx == 5, "DX was different from expected value"
assert dy == 5, "DY was different from expected value"
assert self.passed == 2, "Did not trigger clicked before dragging"
self.passed = 3
def released(self, handle):
assert handle == self.h, "Released received argument that was not the handle"
assert self.passed == 3, "Did not trigger dragged before released"
self.passed = 4
if __name__ == "__main__":
t = test_vtk_ui_handle_interaction()
t.test()
| 2.46875 | 2 |
scrape-twitter-example.py | kemalcanbora/python-examples | 1 | 12767503 | #!/usr/bin/python3
# scrape twitter example going to web page for twitter profile
# author: <NAME>
# date: 2015 06 02
# Note: MUST USE PYTHON 3 from terminal
import json
import urllib.request, urllib.parse
import random
from bs4 import BeautifulSoup
useragents = ['Mozilla/5.0','Bandicout Broadway 2.4','Carls Crawler Critter 1.0','Dirty Dungeon Diksearch 69','Internet Explorer but better']
# function that returns one random value from a list only
def singlerando(listofterms):
randomed = random.choice(listofterms)
return randomed
def parseT(twitterpage):
soup = BeautifulSoup(twitterpage) # create a new bs4 object from the html data loaded
for script in soup(["script", "style"]): # remove all javascript and stylesheet code
script.extract()
# get text
text = soup.get_text()
tester = soup.find_all("p", class_="tweet-text")
print(tester[1].text)
exit()
def searchT(searchfor):
randomuseragent = singlerando(useragents) # select a random user agent from list
headers = { 'User-Agent' : randomuseragent } # get random header from above
url = 'https://twitter.com/%s' % searchfor # GOOGLE ajax API string
search_response_pre = urllib.request.Request(url,None,headers) # key to get the random headers to work
search_response = urllib.request.urlopen(search_response_pre)
search_results = search_response.read().decode("utf8")
#print(search_results)
parseT(search_results)
# global dictionary list of terms - do not change
diction = []
subset = []
twitteruser = input('Enter twitter user: ')
searchT(twitteruser)
exit()
| 3.5625 | 4 |
odoo_actions/utils.py | catalyst-cloud/adjutant-odoo | 1 | 12767504 | <reponame>catalyst-cloud/adjutant-odoo<filename>odoo_actions/utils.py<gh_stars>1-10
# Copyright (C) 2016 Catalyst IT 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.
from uuid import uuid4
from random import randint
def generate_short_id():
"""
Quick and dirty function to create a vaguely unique short id.
Mainly to be used to append to something to make it unique,
but unique enough to avoid a few subsequent calls making the
same id.
"""
length = 6
uuid = uuid4().hex
clip_range = randint(0, (31-length))
return uuid[clip_range:clip_range+length]
| 1.875 | 2 |
ML/ml.py | slav9n4ik/openhack2020 | 0 | 12767505 | <filename>ML/ml.py
#!/usr/bin/python
import sys
from random import seed
from random import randint
from time import sleep
#seed(1)
print('Argument List:', str(sys.argv))
sleep(15)
print('ID:', randint(0, 999999), ", Value: ", randint(0, 999999)) | 2.34375 | 2 |
module_scikit-learn/logistic_regression.py | robert-g-butler/python_reference_guide | 1 | 12767506 | '''
This script contains examples of Logistic Regression analysis, using the
SciKit-Learn library.
Logistic regression is useful when trying to classify data between 2 binary
groups / labels. For example, a logistic model would be useful to predict if
someone has a disease (1) or does not have a disease (0).
Logistic regression uses the sigmoid function, which can only output between
0 - 1.
'''
import pathlib2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
# Data is from here: https://www.kaggle.com/c/titanic/data
csv_url = ('https://raw.githubusercontent.com/robert-g-butler/python_reference'
'_guide/master/dummy_data/logistic_dummy_data.csv')
df = pd.read_csv(csv_url)
# Explore the data with graphs ------------------------------------------------
df.head()
df.info()
sns.set_style(style='whitegrid')
sns.heatmap(data=df.isna(), cmap='viridis') # yticklabels=False, cbar=False
plt.show()
sns.countplot(x='Survived', data=df, hue='Sex', palette='RdBu_r'); plt.show()
sns.countplot(x='Survived', data=df, hue='Pclass'); plt.show()
sns.distplot(df['Age'].dropna(), bins=30); plt.show() # kde=False
sns.countplot(x='SibSp', data=df); plt.show()
df['Fare'].hist(bins=40, figsize=(10, 4)); plt.show()
# Clean missing values --------------------------------------------------------
# Clean missing Age values. Impute Age by Pclass.
sns.boxplot(x='Pclass', y='Age', data=df); plt.show()
sns.heatmap(data=df.isna(), cmap='viridis'); plt.show()
def impute_age(cols):
age = cols['Age']
pclass = cols['Pclass']
if pd.isna(age):
if pclass == 1:
return 37
elif pclass == 2:
return 29
else:
return 24
else:
return age
df['Age'] = df[['Age', 'Pclass']].apply(func=impute_age, axis=1)
sns.heatmap(data=df.isna(), cmap='viridis'); plt.show()
# Drop the Cabin variable because there are too many missing values.
df.drop(columns='Cabin', axis=1, inplace=True)
sns.heatmap(data=df.isna(), cmap='viridis'); plt.show()
# Drop any remaining rows with missing values.
df.dropna(inplace=True)
sns.heatmap(data=df.isna(), cmap='viridis'); plt.show()
# Update text & categorical columns with numerical data -----------------------
# Get numerical values for each text column.
pd.get_dummies(df['Sex'])
# We must use 'drop_first' to avoid having 1 column perfectly the others.
# This problem is called 'multi-colinearity'.
sex = pd.get_dummies(df['Sex'], drop_first=True)
embarked = pd.get_dummies(df['Embarked'], drop_first=True)
df = pd.concat([df, sex, embarked], axis=1)
df.head()
# Drop columns that are text or aren't useful for prediction.
df.drop(['PassengerId', 'Sex', 'Embarked', 'Name', 'Ticket'], axis=1, inplace=True)
df.head()
# Create the model ------------------------------------------------------------
X = df.drop('Survived', axis=1)
y = df['Survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25)
logmodel = LogisticRegression()
logmodel.fit(X=X_train, y=y_train)
predictions = logmodel.predict(X=X_test)
# Check the prediction accuracy with 'classification_report'
print(metrics.classification_report(y_true=y_test, y_pred=predictions))
# Check the prediction accuracy with a 'confusion_matrix'
metrics.confusion_matrix(y_true=y_test, y_pred=predictions)
| 3.890625 | 4 |
editor.py | Commander07/Magnitude | 6 | 12767507 | import editor
editor.start()
| 1.054688 | 1 |
common/world.py | FrostMiser/Frostica | 1 | 12767508 | from models.tile import Tile
# TILE REFERENCE
# 0 = show covered mountains
# 1 = flat snow
# 2 = water
# 3 = trees
# 4 = mountains
world = {
'x': 0,
'y': 0,
'tiles': []
}
def get_tile_id(x, y):
if y < 0 or x < 0:
tile_id = -1
else:
try:
world_y = world['tiles'][y]
tile_id = world_y[x]
except (ValueError, IndexError, KeyError):
tile_id = -1
return tile_id
def get_tile(tile_id, session):
return session.query(Tile).get(tile_id)
def get_tile_from(x, y, session):
tile_id = get_tile_id(x, y)
return get_tile(tile_id, session)
def get_local_area(pos_x, pos_y, limit=5):
negative_offset_x = pos_x - limit
negative_offset_y = pos_y - limit
positive_offset_x = pos_x + limit
positive_offset_y = pos_y + limit
local_area = {
'x': negative_offset_x,
'y': negative_offset_y,
'tiles': []
}
for y in range(negative_offset_y, positive_offset_y):
y_chunk = []
for x in range(negative_offset_x, positive_offset_x):
tile_id = get_tile_id(x, y)
y_chunk.append(tile_id)
local_area['tiles'].append(y_chunk)
return local_area
| 2.484375 | 2 |
Introduction.py | mudgalsaurabh/IEEE-Python_Workshop-2018 | 3 | 12767509 | <gh_stars>1-10
age = 5+(8%3)-3+(3*10)/2
greetings = "Welcome to IEEE Python Workshop 2018 edition. It's my pleasure to conduct today's workshop for you."
name = "<NAME>"
major = "mechanical engineering"
print(greetings)
print("My name is " + name + ".")
print("I am " + str(age) + " years old and am majoring in " + major + ".")
| 3.453125 | 3 |
glsl/sema.py | yoshou/ratchetwrench | 0 | 12767510 | <filename>glsl/sema.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from ast.node import *
from glsl.symtab import SymbolTable, Symbol
def register_function_proto(node, sym_table, is_constructor=False):
proto = node
ty = sym_table.find_type(proto.type.specifier)
params = []
for param in proto.params:
param_name = param.ident.val if param.ident is not None else None
param_ty = sym_table.find_type(param.type.specifier)
if isinstance(param.type, FullType):
param_quals = param.type.qualifiers
else:
param_quals = []
params.append((param_ty, param_quals, param_name))
name = proto.ident.val
if is_constructor:
name = FuncSignature(name, [param[0] for param in params])
sym = sym_table.register_func(name, ty, params)
return TypedFunctionProto(ty, sym, params, [])
def evaluate_constant_expr(expr):
if isinstance(expr, IntegerConstantExpr):
return expr.val
raise ValueError()
def register_variable(node, sym_table):
from ast.types import ArrayType
ty = sym_table.find_type(
node.type.specifier, node.type.array_specifier)
if isinstance(node.type, FullType):
quals = node.type.qualifiers
else:
quals = []
variables = []
for ident, arr_spec, initializer in node.idents:
ty2 = ty
if arr_spec:
ty2 = ty
for sz in arr_spec.sizes:
ty2 = ArrayType(ty2, evaluate_constant_expr(sz))
var = sym_table.register_var(ident.val, ty2, quals)
assert(ty2)
variables.append([TypedIdentExpr(var, ty2), initializer])
return TypedVariable(ty, variables, [])
def enter_node(node, depth, sym_table: SymbolTable):
from ast.types import ArrayType
if isinstance(node, Function):
proto = register_function_proto(node.proto, sym_table)
param_names = []
for param in node.proto.params:
param_name = param.ident.val if param.ident is not None else None
param_names.append(param_name)
sym_table.push_scope()
params = []
for param_names, (param_ty, param_quals, param_name) in zip(param_names, proto.params):
if param_names is not None:
params.append(sym_table.register_var(
param_names, param_ty, param_quals))
node = TypedFunction(proto, params, node.stmts)
if isinstance(node, FunctionProto):
node = register_function_proto(node, sym_table)
if isinstance(node, Variable):
register_variable(node, sym_table)
if isinstance(node, StructSpecifier):
fields = []
for decl in node.decls:
for declor in decl.declarators:
ty = sym_table.find_type(decl.type.specifier)
fields.append((ty, declor.ident, declor.arrspec))
sym_table.register_composite_type(node.ident, fields)
if isinstance(node, CompoundStmt):
sym_table.push_scope()
if isinstance(node, IfStmt):
sym_table.push_scope()
return node
def is_incr_func_type(t):
return t in ['int', 'uint']
scalar_convertable = [
# (to, from)
('int', 'uint'),
('int', 'bool'),
('int', 'float'),
('int', 'double'),
('uint', 'int'),
('uint', 'bool'),
('uint', 'float'),
('uint', 'double'),
('bool', 'int'),
('bool', 'uint'),
('bool', 'float'),
('bool', 'double'),
('float', 'int'),
('float', 'uint'),
('float', 'bool'),
('float', 'double'),
('double', 'int'),
('double', 'uint'),
('double', 'bool'),
('double', 'float'),
]
implicit_convertable = [
# (to, from)
('uint', 'int'),
('float', 'int'),
('float', 'uint'),
('double', 'int'),
('double', 'uint'),
('double', 'float'),
# TODO: Not implemented. Need additional items.
]
integer_types = [
'int', 'uint'
]
integer_vec_types = [
# TODO: Not implemented.
]
def compute_binary_op_type(op, lhs_type, rhs_type, sym_table):
from ast.types import PrimitiveType, VectorType
lhs_is_vec = isinstance(lhs_type, VectorType)
rhs_is_vec = isinstance(rhs_type, VectorType)
if lhs_is_vec or rhs_is_vec:
if lhs_is_vec and rhs_is_vec:
assert(lhs_type.size == rhs_type.size)
size = lhs_type.size
elif lhs_is_vec:
size = lhs_type.size
else:
size = rhs_type.size
if lhs_is_vec:
lhs_type = lhs_type.elem_ty
if rhs_is_vec:
rhs_type = rhs_type.elem_ty
# Arithematic operators
if op in ['+', '-', '*', '/']:
able_conv_lhs = [to_type for to_type,
from_type in implicit_convertable if from_type == lhs_type.name] + [lhs_type.name]
if rhs_type.name in able_conv_lhs:
if lhs_is_vec or rhs_is_vec:
return VectorType(rhs_type, size)
return rhs_type
able_conv_rhs = [to_type for to_type,
from_type in implicit_convertable if from_type == rhs_type.name] + [rhs_type.name]
if lhs_type.name in able_conv_rhs:
if lhs_is_vec or rhs_is_vec:
return VectorType(lhs_type, size)
return lhs_type
# Bitwise operators
if op in ['&', '^', '|']:
able_conv_lhs = [to_type for to_type,
from_type in implicit_convertable if from_type == lhs_type.name] + [lhs_type.name]
if rhs_type.name in able_conv_lhs:
return rhs_type
able_conv_rhs = [to_type for to_type,
from_type in implicit_convertable if from_type == rhs_type.name] + [rhs_type.name]
if lhs_type.name in able_conv_rhs:
return lhs_type
if op in ['==', '!=', '<', '>', '<=', '>=']:
able_conv_lhs = [to_type for to_type,
from_type in implicit_convertable if from_type == lhs_type.name] + [lhs_type.name]
if rhs_type.name in able_conv_lhs:
return sym_table.find_type('bool')
able_conv_rhs = [to_type for to_type,
from_type in implicit_convertable if from_type == rhs_type.name] + [rhs_type.name]
if lhs_type.name in able_conv_rhs:
return sym_table.find_type('bool')
if op in ['<<', '>>']:
if lhs_type.name in integer_types and rhs_type.name in integer_types:
return lhs_type
if lhs_type.name in integer_vec_types and rhs_type.name in integer_vec_types:
return lhs_type
# Logical operators
if op in ['&&', '^^', '||']:
if lhs_type.name == 'bool' and rhs_type.name == 'bool':
return lhs_type
if op in ['=', '+=', '-=', '*=', '/=']:
able_conv_rhs = [to_type for to_type,
from_type in implicit_convertable if from_type == rhs_type.name] + [rhs_type.name]
if lhs_type.name in able_conv_rhs:
return lhs_type
return None
def is_scalar_convertable(from_type: str, to_type: str):
return (to_type, from_type) in scalar_convertable
def is_implicit_convertable(from_type: str, to_type: str):
return (to_type, from_type) in implicit_convertable
def is_typed_expr(node):
return isinstance(node, (
TypedBinaryOp,
TypedIdentExpr,
IntegerConstantExpr,
FloatingConstantExpr,
TypedUnaryOp,
TypedPostOp,
TypedAccessorOp,
TypedArrayIndexerOp,
TypedFunctionCall))
def is_constructor(name):
return name in [
"float", "vec2", "vec3", "vec4"
]
def mangle_func_name(name):
if name in ["sin", "cos", "abs", "sqrt", "mod", "min", "max"]:
return "glsl_" + name
return name
def exit_node(node, depth, sym_table):
from ast.types import ArrayType
if isinstance(node, TypedFunction):
sym_table.pop_scope()
if isinstance(node, CompoundStmt):
sym_table.pop_scope()
if isinstance(node, IfStmt):
sym_table.pop_scope()
if isinstance(node, IntegerConstantExpr):
ty = sym_table.find_type(node.type.specifier)
assert(ty.name in ["int", "uint"])
node = IntegerConstantExpr(node.val, ty)
if isinstance(node, FloatingConstantExpr):
ty = sym_table.find_type(node.type.specifier)
assert(ty.name in ["float", "double"])
node = FloatingConstantExpr(node.val, ty)
if isinstance(node, Variable):
variables = []
ty = sym_table.find_type(node.type.specifier)
for ident, arr_spec, initializer in node.idents:
ty2 = ty
if arr_spec:
ty2 = ty
for sz in arr_spec.sizes:
ty2 = ArrayType(ty2, evaluate_constant_expr(sz))
var = sym_table.find_var(ident.val)
assert(ty2)
variables.append([TypedIdentExpr(var, ty2), initializer])
node = TypedVariable(ty, variables, [])
if isinstance(node, IdentExpr):
var = sym_table.find_var(node.val)
if var is not None:
assert(var.ty)
typed_node = TypedIdentExpr(var, var.ty)
func_name = mangle_func_name(node.val)
func = sym_table.find_func(func_name)
if func is not None:
typed_node = TypedIdentExpr(func, func.ty)
if typed_node is None:
raise RuntimeError(f"Error: Undefined identity \"{node.val}\"")
node = typed_node
if isinstance(node, BinaryOp):
from ast.types import PrimitiveType, VectorType
assert(is_typed_expr(node.lhs))
assert(is_typed_expr(node.rhs))
lhs = node.lhs
rhs = node.rhs
lhs_type = lhs.type
rhs_type = rhs.type
return_type = compute_binary_op_type(
node.op, lhs_type, rhs_type, sym_table)
if return_type is None:
raise RuntimeError(
f"Error: Undefined operation between types \"{lhs_typename}\" and \"{rhs_typename}\" with op \"{node.op}\"")
ty = sym_table.find_type(return_type.name)
# if lhs_typename != return_type:
# lhs = CastExpr(node.lhs, ty)
# if rhs_typename != return_type:
# rhs = CastExpr(node.rhs, ty)
node = TypedBinaryOp(node.op, lhs, rhs, ty)
if isinstance(node, ConditionalExpr):
assert(is_typed_expr(node.true_expr))
assert(is_typed_expr(node.false_expr))
assert(is_typed_expr(node.cond_expr))
true_expr = node.true_expr
false_expr = node.false_expr
cond_expr = node.cond_expr
assert(true_expr.type == false_expr.type)
assert(cond_expr.type.name == "bool")
ty = sym_table.find_type(true_expr.type.name)
node = TypedConditionalExpr(
node.cond_expr, node.true_expr, node.false_expr, ty)
if isinstance(node, UnaryOp):
assert(is_typed_expr(node.expr))
expr_typename = node.expr.type.name
if node.op in ["++", "--"]:
if not is_incr_func_type(expr_typename):
raise RuntimeError(
f"Error: Not supporting types with increment \"{expr_typename}\"")
node = TypedUnaryOp(node.op, node.expr, node.expr.type)
if isinstance(node, PostOp):
assert(is_typed_expr(node.expr))
expr_typename = node.expr.type.name
if not is_incr_func_type(expr_typename):
raise RuntimeError(
f"Error: Not supporting types with increment \"{expr_typename}\"")
node = TypedPostOp(node.op, node.expr, node.expr.type)
if isinstance(node, ArrayIndexerOp):
assert(is_typed_expr(node.arr))
arr_type = node.arr.type
assert(isinstance(arr_type, ArrayType))
node = TypedArrayIndexerOp(node.arr, node.idx, arr_type.elem_ty)
if isinstance(node, AccessorOp):
assert(is_typed_expr(node.obj))
obj_typename = node.obj.type.name
field_type = sym_table.find_type_and_field(
obj_typename, node.field.val)
if field_type is None:
raise RuntimeError(
f"Error: Invalid field access \"{node.field.val}\" in \"{obj_typename}\"")
node = TypedAccessorOp(node.obj, node.field, field_type[0])
if isinstance(node, FunctionCall):
if isinstance(node.ident, TypedIdentExpr):
func_name = node.ident.val.name
elif isinstance(node.ident, Type):
func_name = node.ident.specifier
else:
raise NotImplementedError()
func_name = mangle_func_name(func_name)
arg_tys = [arg.type for arg in node.params]
if is_constructor(func_name):
func_name = FuncSignature(func_name, arg_tys)
func_def = sym_table.find_func(func_name)
if func_def is None:
raise RuntimeError(
f"Error: The function \"{func_name}\" not defined.")
func_type = func_def.ty.return_ty
func_params = func_def.ty.params
# check parameter
pos = 0
for (param_type, param_quals, param_name), arg in zip(func_params, node.params):
if not (param_type.name == arg.type.name
or is_implicit_convertable(arg.type.name, param_type.name)):
raise RuntimeError(
f"Error: Invalid type \"{arg.type.name}\" found at position \"{pos}\" must be \"{param_type.name}\".")
pos += 1
node = TypedFunctionCall(func_def, node.params, func_type)
return node
class FuncSignature:
def __init__(self, name, param_types):
self.name = name
self.param_types = param_types
def __hash__(self):
return hash((self.name, *self.param_types))
def __eq__(self, other):
if not isinstance(other, FuncSignature):
return False
return self.name == other.name and self.param_types == other.param_types
def __ne__(self, other):
return not self.__eq__(other)
def __str__(self):
return self.name + "_" + str(len(self.param_types))
def setup_buildin_decls(sym_table):
# vec constructor
for i in range(1, 4):
size = i + 1
params = [FunctionParam(Type('float', None), None, None)] * size
proto = FunctionProto(
Type(f'vec{size}', None), Ident(f'vec{size}'), params)
yield register_function_proto(proto, sym_table, True)
params = [FunctionParam(Type('float', None), None, None)]
proto = FunctionProto(
Type(f'vec{size}', None), Ident(f'vec{size}'), params)
yield register_function_proto(proto, sym_table, True)
for i in range(2, 4):
size = i + 1
params = [FunctionParam(Type(f'vec{size-1}', None), None, None),
FunctionParam(Type('float', None), None, None)]
proto = FunctionProto(
Type(f'vec{size}', None), Ident(f'vec{size}'), params)
yield register_function_proto(proto, sym_table, True)
# dot
proto = FunctionProto(Type('float', None), Ident('dot'), [
FunctionParam(Type('vec3', None), None, None),
FunctionParam(Type('vec3', None), None, None)])
yield register_function_proto(proto, sym_table)
# normalize
proto = FunctionProto(Type('vec3', None), Ident('normalize'), [
FunctionParam(Type('vec3', None), None, None)])
yield register_function_proto(proto, sym_table)
# reflect
proto = FunctionProto(Type('vec3', None), Ident('reflect'), [
FunctionParam(Type('vec3', None), None, None),
FunctionParam(Type('vec3', None), None, None)])
yield register_function_proto(proto, sym_table)
# clamp
proto = FunctionProto(Type('float', None), Ident('clamp'), [
FunctionParam(Type('float', None), None, None),
FunctionParam(Type('float', None), None, None),
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# fract
proto = FunctionProto(Type('float', None), Ident('glsl_fract'), [
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# mod
proto = FunctionProto(Type('float', None), Ident('glsl_mod'), [
FunctionParam(Type('float', None), None, None),
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# min
proto = FunctionProto(Type('float', None), Ident('glsl_min'), [
FunctionParam(Type('float', None), None, None),
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# max
proto = FunctionProto(Type('float', None), Ident('glsl_max'), [
FunctionParam(Type('float', None), None, None),
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# sqrt
proto = FunctionProto(Type('float', None), Ident('glsl_sqrt'), [
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# abs
proto = FunctionProto(Type('float', None), Ident('glsl_abs'), [
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# sin
proto = FunctionProto(Type('float', None), Ident('glsl_sin'), [
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# cos
proto = FunctionProto(Type('float', None), Ident('glsl_cos'), [
FunctionParam(Type('float', None), None, None)])
yield register_function_proto(proto, sym_table)
# memoryBarrier
proto = FunctionProto(Type('void', None), Ident('memoryBarrier'), [])
yield register_function_proto(proto, sym_table)
variable = Variable(FullType(["uniform"], 'vec3', None), [
[IdentExpr('gl_FragCoord', ), None, None]])
yield register_variable(variable, sym_table)
variable = Variable(FullType(["uniform"], 'vec4', None), [
[IdentExpr('gl_FragColor', ), None, None]])
yield register_variable(variable, sym_table)
variable = Variable(FullType(["uniform"], 'uvec3', None), [
[IdentExpr('gl_NumWorkGroups', ), None, None]])
yield register_variable(variable, sym_table)
variable = Variable(FullType(["uniform"], 'uvec3', None), [
[IdentExpr('gl_WorkGroupID', ), None, None]])
yield register_variable(variable, sym_table)
variable = Variable(FullType(["uniform"], 'uvec3', None), [
[IdentExpr('gl_LocalInvocationID', ), None, None]])
yield register_variable(variable, sym_table)
variable = Variable(FullType(["uniform"], 'uvec3', None), [
[IdentExpr('gl_GlobalInvocationID', ), None, None]])
yield register_variable(variable, sym_table)
variable = Variable(FullType(["uniform"], 'uint', None), [
[IdentExpr('gl_LocalInvocationIndex', ), None, None]])
yield register_variable(variable, sym_table)
variable = Variable(FullType(["const"], 'uvec3', None), [
[IdentExpr('gl_WorkGroupSize', ), None, None]])
yield register_variable(variable, sym_table)
def semantic_analysis(ast):
sym_table = SymbolTable()
buildin_decls = list(setup_buildin_decls(sym_table))
analyzed = buildin_decls + \
traverse_depth_update(ast, enter_node, exit_node, sym_table)
return (analyzed, sym_table)
| 2.203125 | 2 |
hashes/password-to-ntlm.py | r0kit/Pentesting-Templates | 0 | 12767511 | import argparse
import hashlib
import binascii
def run(args):
h = hashlib.new('md4', args.password.encode('utf-16le')).digest()
print(binascii.hexlify(h).decode('utf-8'))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Make an NTLM hash from a password.')
parser.add_argument('--password', dest='password', type=str, help='the password to hash', required=True)
try:
args = parser.parse_args()
run(args)
except argparse.ArgumentError as e:
print("[!] {}".format(e))
parser.print_usage()
sys.exit(1)
| 3.1875 | 3 |
pyleecan/Functions/Optimization/tournamentDCD.py | IrakozeFD/pyleecan | 95 | 12767512 | <reponame>IrakozeFD/pyleecan
from random import sample, choice
def choseDCD(indiv_1, indiv_2):
"""Chose between two individuals
Parameters
----------
indiv_1 : individual
indiv_2 : individual
Returns
-------
individual selected
"""
if indiv_1.cstr_viol == 0 and indiv_2.cstr_viol > 0: # only indiv_1 feasible
return indiv_1
elif indiv_1.cstr_viol > 0 and indiv_2.cstr_viol == 0: # only indiv_2 feasible
return indiv_2
elif (
indiv_1.cstr_viol > 0 and indiv_2.cstr_viol > 0
): # indiv_1 and indiv_2 unfeasible
# Compare the number of constraint violations
if indiv_1.cstr_viol < indiv_2.cstr_viol:
return indiv_1
elif indiv_1.cstr_viol > indiv_2.cstr_viol:
return indiv_2
else:
return choice([indiv_1, indiv_2])
else: # Both indiv feasible
# Check domination, else check crowding distance, else random choice
if indiv_1.fitness.dominates(indiv_2.fitness):
return indiv_1
elif indiv_2.fitness.dominates(indiv_1.fitness):
return indiv_2
elif indiv_1.fitness.crowding_dist > indiv_2.fitness.crowding_dist:
return indiv_1
elif indiv_1.fitness.crowding_dist < indiv_2.fitness.crowding_dist:
return indiv_2
else:
return choice([indiv_1, indiv_2])
def tournamentDCD(pop, size):
"""Select individuals from the population with a tournament based on the domination and the crowding distance
This function is inspired by DEAP selTournamentDCD function at https://github.com/DEAP/deap/blob/master/deap/tools/emo.py
Parameters
----------
pop : list
list of individuals created with the DEAP toolbox
size : int
number of individual to select
Returns
-------
selection : list
list of individuals selected
"""
if len(pop) % 4 != 0:
raise ValueError("TournamentDCD: pop length must be a multiple of 4")
if size % 4 != 0:
raise ValueError(
"TournamentDCD: number of individuals to select must be a multiple of 4"
)
# Sample the population
indiv_1 = sample(pop, len(pop))
indiv_2 = sample(pop, len(pop))
selection = []
# Select individuals
for i in range(0, size, 4):
selection.append(choseDCD(indiv_1[i], indiv_1[i + 1]))
selection.append(choseDCD(indiv_2[i], indiv_2[i + 1]))
selection.append(choseDCD(indiv_1[i + 2], indiv_1[i + 3]))
selection.append(choseDCD(indiv_2[i + 2], indiv_2[i + 3]))
return selection
| 3.296875 | 3 |
lifelib/projects/savings_gallery/plot_option_value.py | fumitoh/lifelib | 77 | 12767513 | r"""
Monte Carlo vs Black-Scholes-Merton
===========================================
Time values of options and guarantees for various in-the-moneyness
are calculated using Monte Carlo simulations and the Black-Scholes-Merton
pricing formula for European put options.
The Black-Scholes-Merton pricing formula for European put options
can be expressed as below, where
:math:`X` and :math:`S_{0}` correspond to the sum assured
and the initial account value in this example.
.. math::
p=Xe^{-rT}N\left(-d_{2}\right)-S_{0}N\left(-d_{1}\right)
d_{1}=\frac{\ln\left(\frac{S_{0}}{X}\right)+\left(r+\frac{\sigma^{2}}{2}\right)T}{\sigma\sqrt{T}}
d_{2}=d_{1}-\sigma\sqrt{T}
The graph below shows the results obtained from
the Monte Carlo simulations with 10,000 risk neutral scenarios,
and from the Black-Scholes-Merton formula.
Reference: *Options, Futures, and Other Derivatives* by <NAME>
.. seealso::
* :doc:`/libraries/notebooks/savings/savings_example1` notebook in the :mod:`~savings` library
"""
import modelx as mx
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import norm, lognorm
import numpy as np
model = mx.read_model("CashValue_ME_EX1")
proj = model.Projection
proj.model_point_table = proj.model_point_moneyness
monte_carlo = pd.Series(proj.pv_claims_over_av('MATURITY'), index=proj.model_point().index)
monte_carlo = list(np.average(monte_carlo[i]) for i in range(1, 10))
S0 = proj.model_point_table['premium_pp'] * proj.model_point_table['policy_count']
fig, ax = plt.subplots()
ax.scatter(S0, monte_carlo, s= 10, alpha=1, label='Monte Carlo')
ax.scatter(S0, proj.formula_option_put(120), alpha=0.5, label='Black-Scholes-Merton')
ax.legend()
ax.grid(True)
fig.suptitle('TVOG by ITM')
| 3.1875 | 3 |
src/codewars/7-kyu/uglify-word/uglify_word.py | nwthomas/code-challenges | 1 | 12767514 | """
Summary
In this kata, you have to make a function named uglify_word (uglifyWord in Java and Javascript). It accepts a string parameter.
What does the uglify_word do?
It checks the char in the given string from the front with an iteration, in the iteration it does these steps:
There is a flag and it will be started from 1.
Check the current char in the iteration index.
If it is an alphabet character [a-zA-Z] and the flag value is equal to 1, then change this character to upper case.
If it is an alphabet character [a-zA-Z] and the flag value is equal to 0, then change this character to lower case.
Otherwise, if it is not an alphabet character, then set the flag value to 1.
If the current char is an alphabet character, do a boolean not operation to the flag.
After the iteration has done, return the fixed string that might have been changed in such iteration.
Examples
uglify_word("aaa") === "AaA"
uglify_word("AAA") === "AaA"
uglify_word("BbB") === "BbB"
uglify_word("aaa-bbb-ccc") === "AaA-BbB-CcC"
uglify_word("AaA-BbB-CcC") === "AaA-BbB-CcC"
uglify_word("eeee-ffff-gggg") === "EeEe-FfFf-GgGg"
uglify_word("EeEe-FfFf-GgGg") === "EeEe-FfFf-GgGg"
uglify_word("qwe123asdf456zxc") === "QwE123AsDf456ZxC"
uglify_word("Hello World") === "HeLlO WoRlD"
"""
from re import match
def uglify_word(s):
flag = True
final = ""
for char in s:
if match("[a-zA-Z]", char):
final += char.upper() if flag else char.lower()
flag = False if flag else True
else:
final += char
flag = True
return final | 4.4375 | 4 |
flask-appointment-calendar/tests/test_models.py | codyhan94/PMA-scheduler | 1 | 12767515 | <filename>flask-appointment-calendar/tests/test_models.py
# -*- coding: utf-8 -*-
from fbone.user import User
from tests import TestCase
class TestUser(TestCase):
def test_get_current_time(self):
assert User.query.count() == 2
| 2.03125 | 2 |
lib/pylint/checkers/utils.py | willemneal/Docky | 0 | 12767516 | # pylint: disable=W0611
#
# Copyright (c) 2003-2013 LOGILAB S.A. (Paris, FRANCE).
# http://www.logilab.fr/ -- mailto:<EMAIL>
#
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free Software
# Foundation; either version 2 of the License, or (at your option) any later
# version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with
# this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""some functions that may be useful for various checkers
"""
import re
import sys
import string
import astroid
from astroid import scoped_nodes
from logilab.common.compat import builtins
BUILTINS_NAME = builtins.__name__
COMP_NODE_TYPES = astroid.ListComp, astroid.SetComp, astroid.DictComp, astroid.GenExpr
PY3K = sys.version_info[0] == 3
if not PY3K:
EXCEPTIONS_MODULE = "exceptions"
else:
EXCEPTIONS_MODULE = "builtins"
ABC_METHODS = set(('abc.abstractproperty', 'abc.abstractmethod',
'abc.abstractclassmethod', 'abc.abstractstaticmethod'))
class NoSuchArgumentError(Exception):
pass
def is_inside_except(node):
"""Returns true if node is inside the name of an except handler."""
current = node
while current and not isinstance(current.parent, astroid.ExceptHandler):
current = current.parent
return current and current is current.parent.name
def get_all_elements(node):
"""Recursively returns all atoms in nested lists and tuples."""
if isinstance(node, (astroid.Tuple, astroid.List)):
for child in node.elts:
for e in get_all_elements(child):
yield e
else:
yield node
def clobber_in_except(node):
"""Checks if an assignment node in an except handler clobbers an existing
variable.
Returns (True, args for W0623) if assignment clobbers an existing variable,
(False, None) otherwise.
"""
if isinstance(node, astroid.AssAttr):
return (True, (node.attrname, 'object %r' % (node.expr.as_string(),)))
elif isinstance(node, astroid.AssName):
name = node.name
if is_builtin(name):
return (True, (name, 'builtins'))
else:
stmts = node.lookup(name)[1]
if (stmts and not isinstance(stmts[0].ass_type(),
(astroid.Assign, astroid.AugAssign,
astroid.ExceptHandler))):
return (True, (name, 'outer scope (line %s)' % stmts[0].fromlineno))
return (False, None)
def safe_infer(node):
"""return the inferred value for the given node.
Return None if inference failed or if there is some ambiguity (more than
one node has been inferred)
"""
try:
inferit = node.infer()
value = next(inferit)
except astroid.InferenceError:
return
try:
next(inferit)
return # None if there is ambiguity on the inferred node
except astroid.InferenceError:
return # there is some kind of ambiguity
except StopIteration:
return value
def is_super(node):
"""return True if the node is referencing the "super" builtin function
"""
if getattr(node, 'name', None) == 'super' and \
node.root().name == BUILTINS_NAME:
return True
return False
def is_error(node):
"""return true if the function does nothing but raising an exception"""
for child_node in node.get_children():
if isinstance(child_node, astroid.Raise):
return True
return False
def is_raising(body):
"""return true if the given statement node raise an exception"""
for node in body:
if isinstance(node, astroid.Raise):
return True
return False
def is_empty(body):
"""return true if the given node does nothing but 'pass'"""
return len(body) == 1 and isinstance(body[0], astroid.Pass)
builtins = builtins.__dict__.copy()
SPECIAL_BUILTINS = ('__builtins__',) # '__path__', '__file__')
def is_builtin_object(node):
"""Returns True if the given node is an object from the __builtin__ module."""
return node and node.root().name == BUILTINS_NAME
def is_builtin(name): # was is_native_builtin
"""return true if <name> could be considered as a builtin defined by python
"""
if name in builtins:
return True
if name in SPECIAL_BUILTINS:
return True
return False
def is_defined_before(var_node):
"""return True if the variable node is defined by a parent node (list,
set, dict, or generator comprehension, lambda) or in a previous sibling
node on the same line (statement_defining ; statement_using)
"""
varname = var_node.name
_node = var_node.parent
while _node:
if isinstance(_node, COMP_NODE_TYPES):
for ass_node in _node.nodes_of_class(astroid.AssName):
if ass_node.name == varname:
return True
elif isinstance(_node, astroid.For):
for ass_node in _node.target.nodes_of_class(astroid.AssName):
if ass_node.name == varname:
return True
elif isinstance(_node, astroid.With):
for expr, ids in _node.items:
if expr.parent_of(var_node):
break
if (ids and
isinstance(ids, astroid.AssName) and
ids.name == varname):
return True
elif isinstance(_node, (astroid.Lambda, astroid.Function)):
if _node.args.is_argument(varname):
return True
if getattr(_node, 'name', None) == varname:
return True
break
elif isinstance(_node, astroid.ExceptHandler):
if isinstance(_node.name, astroid.AssName):
ass_node = _node.name
if ass_node.name == varname:
return True
_node = _node.parent
# possibly multiple statements on the same line using semi colon separator
stmt = var_node.statement()
_node = stmt.previous_sibling()
lineno = stmt.fromlineno
while _node and _node.fromlineno == lineno:
for ass_node in _node.nodes_of_class(astroid.AssName):
if ass_node.name == varname:
return True
for imp_node in _node.nodes_of_class((astroid.From, astroid.Import)):
if varname in [name[1] or name[0] for name in imp_node.names]:
return True
_node = _node.previous_sibling()
return False
def is_func_default(node):
"""return true if the given Name node is used in function default argument's
value
"""
parent = node.scope()
if isinstance(parent, astroid.Function):
for default_node in parent.args.defaults:
for default_name_node in default_node.nodes_of_class(astroid.Name):
if default_name_node is node:
return True
return False
def is_func_decorator(node):
"""return true if the name is used in function decorator"""
parent = node.parent
while parent is not None:
if isinstance(parent, astroid.Decorators):
return True
if (parent.is_statement or
isinstance(parent, astroid.Lambda) or
isinstance(parent, (scoped_nodes.ComprehensionScope,
scoped_nodes.ListComp))):
break
parent = parent.parent
return False
def is_ancestor_name(frame, node):
"""return True if `frame` is a astroid.Class node with `node` in the
subtree of its bases attribute
"""
try:
bases = frame.bases
except AttributeError:
return False
for base in bases:
if node in base.nodes_of_class(astroid.Name):
return True
return False
def assign_parent(node):
"""return the higher parent which is not an AssName, Tuple or List node
"""
while node and isinstance(node, (astroid.AssName,
astroid.Tuple,
astroid.List)):
node = node.parent
return node
def overrides_an_abstract_method(class_node, name):
"""return True if pnode is a parent of node"""
for ancestor in class_node.ancestors():
if name in ancestor and isinstance(ancestor[name], astroid.Function) and \
ancestor[name].is_abstract(pass_is_abstract=False):
return True
return False
def overrides_a_method(class_node, name):
"""return True if <name> is a method overridden from an ancestor"""
for ancestor in class_node.ancestors():
if name in ancestor and isinstance(ancestor[name], astroid.Function):
return True
return False
PYMETHODS = set(('__new__', '__init__', '__del__', '__hash__',
'__str__', '__repr__',
'__len__', '__iter__',
'__delete__', '__get__', '__set__',
'__getitem__', '__setitem__', '__delitem__', '__contains__',
'__getattribute__', '__getattr__', '__setattr__', '__delattr__',
'__call__',
'__enter__', '__exit__',
'__cmp__', '__ge__', '__gt__', '__le__', '__lt__', '__eq__',
'__nonzero__', '__neg__', '__invert__',
'__mul__', '__imul__', '__rmul__',
'__div__', '__idiv__', '__rdiv__',
'__add__', '__iadd__', '__radd__',
'__sub__', '__isub__', '__rsub__',
'__pow__', '__ipow__', '__rpow__',
'__mod__', '__imod__', '__rmod__',
'__and__', '__iand__', '__rand__',
'__or__', '__ior__', '__ror__',
'__xor__', '__ixor__', '__rxor__',
# XXX To be continued
))
def check_messages(*messages):
"""decorator to store messages that are handled by a checker method"""
def store_messages(func):
func.checks_msgs = messages
return func
return store_messages
class IncompleteFormatString(Exception):
"""A format string ended in the middle of a format specifier."""
pass
class UnsupportedFormatCharacter(Exception):
"""A format character in a format string is not one of the supported
format characters."""
def __init__(self, index):
Exception.__init__(self, index)
self.index = index
def parse_format_string(format_string):
"""Parses a format string, returning a tuple of (keys, num_args), where keys
is the set of mapping keys in the format string, and num_args is the number
of arguments required by the format string. Raises
IncompleteFormatString or UnsupportedFormatCharacter if a
parse error occurs."""
keys = set()
num_args = 0
def next_char(i):
i += 1
if i == len(format_string):
raise IncompleteFormatString
return (i, format_string[i])
i = 0
while i < len(format_string):
char = format_string[i]
if char == '%':
i, char = next_char(i)
# Parse the mapping key (optional).
key = None
if char == '(':
depth = 1
i, char = next_char(i)
key_start = i
while depth != 0:
if char == '(':
depth += 1
elif char == ')':
depth -= 1
i, char = next_char(i)
key_end = i - 1
key = format_string[key_start:key_end]
# Parse the conversion flags (optional).
while char in '#0- +':
i, char = next_char(i)
# Parse the minimum field width (optional).
if char == '*':
num_args += 1
i, char = next_char(i)
else:
while char in string.digits:
i, char = next_char(i)
# Parse the precision (optional).
if char == '.':
i, char = next_char(i)
if char == '*':
num_args += 1
i, char = next_char(i)
else:
while char in string.digits:
i, char = next_char(i)
# Parse the length modifier (optional).
if char in 'hlL':
i, char = next_char(i)
# Parse the conversion type (mandatory).
if PY3K:
flags = 'diouxXeEfFgGcrs%a'
else:
flags = 'diouxXeEfFgGcrs%'
if char not in flags:
raise UnsupportedFormatCharacter(i)
if key:
keys.add(key)
elif char != '%':
num_args += 1
i += 1
return keys, num_args
def is_attr_protected(attrname):
"""return True if attribute name is protected (start with _ and some other
details), False otherwise.
"""
return attrname[0] == '_' and not attrname == '_' and not (
attrname.startswith('__') and attrname.endswith('__'))
def node_frame_class(node):
"""return klass node for a method node (or a staticmethod or a
classmethod), return null otherwise
"""
klass = node.frame()
while klass is not None and not isinstance(klass, astroid.Class):
if klass.parent is None:
klass = None
else:
klass = klass.parent.frame()
return klass
def is_super_call(expr):
"""return True if expression node is a function call and if function name
is super. Check before that you're in a method.
"""
return (isinstance(expr, astroid.CallFunc) and
isinstance(expr.func, astroid.Name) and
expr.func.name == 'super')
def is_attr_private(attrname):
"""Check that attribute name is private (at least two leading underscores,
at most one trailing underscore)
"""
regex = re.compile('^_{2,}.*[^_]+_?$')
return regex.match(attrname)
def get_argument_from_call(callfunc_node, position=None, keyword=None):
"""Returns the specified argument from a function call.
:param callfunc_node: Node representing a function call to check.
:param int position: position of the argument.
:param str keyword: the keyword of the argument.
:returns: The node representing the argument, None if the argument is not found.
:raises ValueError: if both position and keyword are None.
:raises NoSuchArgumentError: if no argument at the provided position or with
the provided keyword.
"""
if position is None and keyword is None:
raise ValueError('Must specify at least one of: position or keyword.')
try:
if position is not None and not isinstance(callfunc_node.args[position], astroid.Keyword):
return callfunc_node.args[position]
except IndexError as error:
raise NoSuchArgumentError(error)
if keyword:
for arg in callfunc_node.args:
if isinstance(arg, astroid.Keyword) and arg.arg == keyword:
return arg.value
raise NoSuchArgumentError
def inherit_from_std_ex(node):
"""
Return true if the given class node is subclass of
exceptions.Exception.
"""
if node.name in ('Exception', 'BaseException') \
and node.root().name == EXCEPTIONS_MODULE:
return True
return any(inherit_from_std_ex(parent)
for parent in node.ancestors(recurs=False))
def is_import_error(handler):
"""
Check if the given exception handler catches
ImportError.
:param handler: A node, representing an ExceptHandler node.
:returns: True if the handler catches ImportError, False otherwise.
"""
names = None
if isinstance(handler.type, astroid.Tuple):
names = [name for name in handler.type.elts
if isinstance(name, astroid.Name)]
elif isinstance(handler.type, astroid.Name):
names = [handler.type]
else:
# Don't try to infer that.
return
for name in names:
try:
for infered in name.infer():
if (isinstance(infered, astroid.Class) and
inherit_from_std_ex(infered) and
infered.name == 'ImportError'):
return True
except astroid.InferenceError:
continue
def has_known_bases(klass):
"""Returns true if all base classes of a class could be inferred."""
try:
return klass._all_bases_known
except AttributeError:
pass
for base in klass.bases:
result = safe_infer(base)
# TODO: check for A->B->A->B pattern in class structure too?
if (not isinstance(result, astroid.Class) or
result is klass or
not has_known_bases(result)):
klass._all_bases_known = False
return False
klass._all_bases_known = True
return True
def decorated_with_property(node):
""" Detect if the given function node is decorated with a property. """
if not node.decorators:
return False
for decorator in node.decorators.nodes:
if not isinstance(decorator, astroid.Name):
continue
try:
for infered in decorator.infer():
if isinstance(infered, astroid.Class):
if (infered.root().name == BUILTINS_NAME and
infered.name == 'property'):
return True
for ancestor in infered.ancestors():
if (ancestor.name == 'property' and
ancestor.root().name == BUILTINS_NAME):
return True
except astroid.InferenceError:
pass
def decorated_with_abc(func):
"""Determine if the `func` node is decorated with `abc` decorators."""
if func.decorators:
for node in func.decorators.nodes:
try:
infered = next(node.infer())
except astroid.InferenceError:
continue
if infered and infered.qname() in ABC_METHODS:
return True
def unimplemented_abstract_methods(node, is_abstract_cb=decorated_with_abc):
"""
Get the unimplemented abstract methods for the given *node*.
A method can be considered abstract if the callback *is_abstract_cb*
returns a ``True`` value. The check defaults to verifying that
a method is decorated with abstract methods.
The function will work only for new-style classes. For old-style
classes, it will simply return an empty dictionary.
For the rest of them, it will return a dictionary of abstract method
names and their inferred objects.
"""
visited = {}
try:
mro = reversed(node.mro())
except NotImplementedError:
# Old style class, it will not have a mro.
return {}
for ancestor in mro:
for obj in ancestor.values():
infered = obj
if isinstance(obj, astroid.AssName):
infered = safe_infer(obj)
if not infered:
continue
if not isinstance(infered, astroid.Function):
if obj.name in visited:
del visited[obj.name]
if isinstance(infered, astroid.Function):
# It's critical to use the original name,
# since after inferring, an object can be something
# else than expected, as in the case of the
# following assignment.
#
# class A:
# def keys(self): pass
# __iter__ = keys
abstract = is_abstract_cb(infered)
if abstract:
visited[obj.name] = infered
elif not abstract and obj.name in visited:
del visited[obj.name]
return visited
| 2.046875 | 2 |
chapter_4/problem_2.py | misterwilliam/cracking-coding-interview | 0 | 12767517 | import unittest
class Node(object):
def __init__(self, data, children=None):
self.data = data
if children is None:
self.children = []
else:
self.children = children
def is_connected(a, b):
todo = [a]
seen = set(todo)
while len(todo) > 0:
current = todo.pop() # DFS
if current is b:
return True
for child in current.children:
if child not in seen:
seen.add(child)
todo.append(child)
return False
class IsConnectedTests(unittest.TestCase):
def test_is_connected(self):
a = Node("a")
b = Node("b")
c = Node("c")
a.children.append(b)
b.children.append(c)
c.children.append(a)
self.assertTrue(is_connected(a, c))
def test_is_not_connected(self):
a = Node("a")
b = Node("b")
self.assertFalse(is_connected(a, b))
def test_does_not_get_stuck_in_loops(self):
# Create loop
a = Node("a")
b = Node("b")
c = Node("c")
a.children.append(b)
b.children.append(c)
c.children.append(a)
d = Node("d")
self.assertFalse(is_connected(a, d))
if __name__ == "__main__":
unittest.main() | 3.71875 | 4 |
uninas/modules/modules/abstract.py | cogsys-tuebingen/uninas | 18 | 12767518 | import types
from collections.abc import Iterator
import torch
import torch.nn as nn
from uninas.register import Register
from uninas.utils.shape import Shape, ShapeList, ShapeOrList
from uninas.utils.args import ArgsInterface
from uninas.utils.paths import make_base_dirs
from uninas.utils.torch.misc import randomize_parameters
from typing import Union, List
tensor_type = Union[torch.Tensor, List[torch.Tensor]]
class AbstractModule(nn.Module):
"""
the basis for all .config() saving + restoring
"""
def __init__(self, *_, **__):
nn.Module.__init__(self)
if len(_) > 0:
print('unknown args (%s):' % self.__class__.__name__, __)
if len(__) > 0:
print('unknown kwargs (%s):' % self.__class__.__name__, __)
# dicts that contain the keys of everything that goes into a config and can be restored
self._kwargs = [] # saved, printed
self._np_kwargs = [] # saved, not printed
self._p_kwargs = [] # not saved, printed
self._submodules = []
self._submodule_lists = []
self._submodule_dicts = []
self._add_to_print_kwargs(**__)
self.dropout_rate = None
self.cached = dict(built=False) # some info about shapes in/out
def set(self, **kwargs):
""" set new value to a parameter and kwargs / misc_kwargs """
for k, v in kwargs.items():
self.__dict__[k] = v
def get_cached(self, k: str, default=None):
return self.cached.get(k, default)
def is_built(self) -> bool:
return self.cached.get("built", False)
def get_shape_in(self, may_be_none=False) -> ShapeOrList:
s_in = self.get_cached('shape_in')
if not may_be_none:
assert isinstance(s_in, ShapeOrList.__args__)
return s_in
def get_shape_out(self, may_be_none=False) -> ShapeOrList:
s_out = self.get_cached('shape_out')
if not may_be_none:
assert isinstance(s_out, ShapeOrList.__args__)
return s_out
# listing modules/kwargs to save+restore via configs ---------------------------------------------------------------
def _add(self, lst: list, are_modules=False, **kwargs):
for k, v in kwargs.items():
lst.append(k)
if are_modules:
self.add_module(k, v)
else:
self.__dict__[k] = v
def _add_to_kwargs(self, **kwargs):
""" store named values (not Modules, which need to have config stored and be rebuilt) """
self._add(self._kwargs, are_modules=False, **kwargs)
def _add_to_kwargs_np(self, **kwargs):
""" store named values (not Modules, which need to have config stored and be rebuilt) """
self._add(self._np_kwargs, are_modules=False, **kwargs)
def _add_to_print_kwargs(self, **kwargs):
""" store named values for printing only """
self._add(self._p_kwargs, are_modules=False, **kwargs)
def _add_to_submodules(self, **kwargs):
""" store named modules """
self._add(self._submodules, are_modules=True, **kwargs)
def _add_to_submodule_lists(self, **kwargs):
""" store named lists of modules (nn.ModuleList) """
self._add(self._submodule_lists, are_modules=True, **kwargs)
def _add_to_submodule_dict(self, **kwargs):
""" store named dicts of modules (nn.ModuleDict) """
self._add(self._submodule_dicts, are_modules=True, **kwargs)
def kwargs(self):
return {k: self.__dict__[k] for k in self._kwargs+self._np_kwargs}
def config(self, **_) -> dict:
"""
get a dictionary describing this module, so that a builder can assemble it correctly again
subclasses may receive specific instructions via kwargs, e.g. whether to finalize a search architecture
"""
cfg_keys = ['kwargs', 'submodules', 'submodule_lists', 'submodule_dicts']
cfg = dict(name=self.__class__.__name__)
cfg.update({k: {} for k in cfg_keys})
for k in self._kwargs+self._np_kwargs:
cfg['kwargs'][k] = self.__dict__[k]
for k in self._submodules:
cfg['submodules'][k] = self._modules[k].config(**_)
for k in self._submodule_lists:
lst = self._modules[k]
cfg['submodule_lists'][k] = [v.config(**_) if v is not None else None for v in iter(lst)]
for k in self._submodule_dicts:
dct = self._modules[k]
cfg['submodule_dicts'][k] = {dk: dv.config(**_) if dv is not None else None for dk, dv in dct.items()}
# remove empty dicts
for k in list(cfg_keys):
if len(cfg[k]) == 0:
cfg.pop(k)
return cfg
@classmethod
def from_config(cls, **kwargs):
""" upon receiving a dictionary as created in self.config(), reassemble this module properly """
kwargs_ = kwargs.pop('kwargs', {})
submodules_ = {k: Register.builder.from_config(v) if v is not None else None
for k, v in kwargs.pop('submodules', {}).items()}
submodule_lists_ = {k: nn.ModuleList([Register.builder.from_config(v) if v is not None else None for v in lst])
for k, lst in kwargs.pop('submodule_lists', {}).items()}
submodule_dicts_ = {k: {dk: Register.builder.from_config(dv) if dv is not None else None
for dk, dv in dct.items()}
for k, dct in kwargs.pop('submodule_dicts', {}).items()}
return cls(**kwargs_, **submodules_, **submodule_lists_, **submodule_dicts_, **kwargs)
# presenting as string ---------------------------------------------------------------------------------------------
def _str_kwargs(self) -> str:
lst = []
for k in self._kwargs+self._p_kwargs:
lst.append('%s=%s' % (k, str(self.__dict__[k])))
return ', '.join(lst)
@staticmethod
def _str_tuple_submodule(obj, depth: int, max_depth: int, name='') -> [(int, str)]:
""" describe this module via indentation instructions and strings """
ss = []
if obj is not None and len(obj) > 0:
if depth < max_depth:
if isinstance(obj, (dict, nn.ModuleDict)):
for n, m in obj.items():
if isinstance(m, AbstractModule):
ss += m.str_tuples(depth=depth+1, max_depth=max_depth, name=n)
else:
ss += AbstractModule._str_tuple_submodule(m, depth + 1, max_depth, name=n)
elif isinstance(obj, (list, nn.ModuleList)):
for i, m in enumerate(obj):
n = '(%d)' % i
if isinstance(m, AbstractModule):
ss += m.str_tuples(depth=depth+1, max_depth=max_depth, name=n)
else:
ss += AbstractModule._str_tuple_submodule(m, depth + 1, max_depth, name=n)
else:
ss.append((depth, '<%d entries>' % (len(obj))))
if len(ss) == 0:
return []
s0, s1 = '%s = [' % name, ']'
if len(ss) == 1:
return [(depth, s0 + ss[0][1] + s1)]
return [(depth, s0)] + ss + [(depth, s1)]
def str_tuples(self, depth=0, max_depth=5, name='', add_s=None, add_sl=None, add_sd=None) -> [(int, str)]:
""" describe this module via indentation instructions and strings """
add_s = {} if add_s is None else add_s.copy()
add_sl = {} if add_sl is None else add_sl.copy()
add_sd = {} if add_sd is None else add_sd.copy()
add_s['Modules'] = {k: self._modules[k] for k in self._submodules}
add_sl['Module Lists'] = {k: self._modules[k] for k in self._submodule_lists}
add_sd['Module Dicts'] = {k: self._modules[k] for k in self._submodule_dicts}
s0 = '{n}{cls}({k}) ['.format(**{
'n': ('%s = ' % name) if len(name) > 0 else '',
'cls': self.__class__.__name__,
'k': self._str_kwargs(),
})
s1 = ']'
if depth >= max_depth:
ss = [(depth, '<%d modules, %d module lists, %d module dicts>' % (len(add_s), len(add_sl), len(add_sd)))]
else:
ss = []
for k, v in add_s.copy().items():
ss.extend(self._str_tuple_submodule(v, depth+1, max_depth, name=k))
for k, v in add_sl.copy().items():
ss.extend(self._str_tuple_submodule(v, depth+1, max_depth, name=k))
for k, v in add_sd.copy().items():
ss.extend(self._str_tuple_submodule(v, depth+1, max_depth, name=k))
ss = [s for s in ss if s is not None]
if len(ss) == 0:
return [(depth, s0 + s1)]
if len(ss) == 1:
return [(depth, s0 + ss[0][1] + s1)]
return [(depth, s0)] + ss + [(depth, s1)]
def str(self, depth=0, max_depth=5, name='', add_s=None, add_sl=None, add_sd=None) -> str:
strings = self.str_tuples(depth, max_depth, name, add_s, add_sl, add_sd)
return ''.join('\n%s%s' % ('. '*d, s) for d, s in strings)
# (recursive) utility ----------------------------------------------------------------------------------------------
@classmethod
def _get_base_modules(cls, m) -> list:
base_modules = []
if isinstance(m, AbstractModule):
base_modules.append(m)
elif isinstance(m, nn.ModuleList):
for m2 in iter(m):
base_modules.extend(cls._get_base_modules(m2))
elif isinstance(m, nn.ModuleDict):
for m2 in m.values():
base_modules.extend(cls._get_base_modules(m2))
return base_modules
def base_modules(self, recursive=True) -> Iterator:
""" yield all base modules, therefore all layers/modules of this project """
fun = self.modules if recursive else self.children
for m in fun():
for m2 in self._get_base_modules(m):
yield m2
def base_modules_by_condition(self, condition, recursive=True) -> Iterator:
""" get list of all base modules that pass a condition, condition is a function that returns a boolean """
for m in self.base_modules(recursive=recursive):
if condition(m):
yield m
def hierarchical_base_modules(self) -> (type, ShapeOrList, ShapeOrList, list):
""" get a hierarchical/recursive representation of (class, shapes_in, shapes_out, submodules) """
submodules = list(self.base_modules(recursive=False))
r0 = self.get_shape_in(may_be_none=True)
r1 = self.get_shape_out(may_be_none=True)
r2 = [m.hierarchical_base_modules() for m in submodules]
return self, r0, r1, r2
def set_dropout_rate(self, p=None):
""" set the dropout rate of every dropout layer to p, no change for p=None """
for m in self.base_modules(recursive=False):
m.set_dropout_rate(p)
def get_device(self) -> torch.device:
""" get the device of one of the weights """
for w in self.parameters():
return w.device
def is_layer(self, cls) -> bool:
return isinstance(self, cls)
# building and running ---------------------------------------------------------------------------------------------
def probe_outputs(self, s_in: ShapeOrList, module: nn.Module = None, multiple_outputs=False) -> ShapeOrList:
""" returning the output shape of one forward pass using zero tensors """
with torch.no_grad():
if module is None:
module = self
x = s_in.random_tensor(batch_size=2)
s = module(x)
if multiple_outputs:
return ShapeList([Shape(list(sx.shape)[1:]) for sx in s])
return Shape(list(s.shape)[1:])
def build(self, *args, **kwargs) -> ShapeOrList:
""" build/compile this module, save input/output shape(s), return output shape """
assert not self.is_built(), "The module is already built"
for arg in list(args) + list(kwargs.values()):
if isinstance(arg, (Shape, ShapeList)):
self.cached['shape_in'] = arg.copy(copy_id=True)
break
s_out = self._build(*args, **kwargs)
self.cached['shape_out'] = s_out.copy(copy_id=True)
self.cached['built'] = True
return s_out
def _build(self, *args, **kwargs) -> ShapeOrList:
""" build/compile this module, return output shape """
raise NotImplementedError
def forward(self, x: tensor_type) -> tensor_type:
raise NotImplementedError
def export_onnx(self, save_path: str, **kwargs):
save_path = make_base_dirs(save_path)
x = self.get_shape_in(may_be_none=False).random_tensor(batch_size=2).to(self.get_device())
torch.onnx.export(model=self, args=x, f=save_path, **kwargs)
# can disable state dict
def disable_state_dict(self):
"""
makes the state_dict irreversibly disfunctional for this module and all children
this is used to prevent specific modules to save/load
"""
def state_dict(self_, *args, **kwargs):
return None
def _load_from_state_dict(self_, *args, **kwargs):
pass
def _disable_state_dict(module: nn.Module):
for name, child in module._modules.items():
if child is not None:
_disable_state_dict(child)
module.state_dict = types.MethodType(state_dict, self)
module._load_from_state_dict = types.MethodType(_load_from_state_dict, self)
_disable_state_dict(self)
_disable_state_dict = None
# misc -------------------------------------------------------------------------------------------------------------
def randomize_parameters(self):
""" set all parameters to normally distributed values """
randomize_parameters(self)
class AbstractArgsModule(AbstractModule, ArgsInterface):
"""
an AbstractModule that can easily store+reuse the parsed argparse arguments of previous times
"""
def __init__(self, *_, **kwargs_to_store):
AbstractModule.__init__(self, *_)
ArgsInterface.__init__(self)
self._add_to_kwargs(**kwargs_to_store)
def _build(self, *args) -> ShapeOrList:
raise NotImplementedError
def forward(self, x: tensor_type) -> tensor_type:
raise NotImplementedError
| 2.015625 | 2 |
exp_mixture_model/__version__.py | naokimas/exp_mixture_model | 2 | 12767519 | # -*- coding: utf-8 -*-
__title__ = 'exp_mixture_model'
__version__ = '1.0.0'
__description__ = 'Maximum likelihood estimation and model selection of EMMs'
__copyright__ = 'Copyright (C) 2019 <NAME> and <NAME>'
__license__ = 'MIT License'
__author__ = '<NAME>, <NAME> and <NAME>'
__author_email__ = '<EMAIL>'
__url__ = 'https://github.com/naokimas/exp_mixture_model'
| 1.085938 | 1 |
scratch/get_fisher_new.py | AdriJD/cmb_sst_ksw | 0 | 12767520 | import os
import numpy as np
from sst import Fisher
from sst import camb_tools as ct
from sst import plot_tools
opj = os.path.join
def get_cls(cls_path, lmax, A_lens=1):
'''
returns
-------
cls : array-like
Lensed Cls (shape (4,lmax-1) with BB lensing power
reduced depending on A_lens.
order: TT, EE, BB, TE
'''
cls_nolens, _ = ct.get_spectra(cls_path, tag='r0',
lensed=False, prim_type='tot')
cls_lensed, _ = ct.get_spectra(cls_path, tag='r0',
lensed=True, prim_type='tot')
# truncate to lmax
cls_nolens = cls_nolens[:,:lmax-1]
cls_lensed = cls_lensed[:,:lmax-1]
BB_nolens = cls_nolens[2]
BB_lensed = cls_lensed[2]
# difference BB (lensed - unlensed = lens_contribution)
BB_lens_contr = BB_lensed - BB_nolens
# depending on A_lens, remove lensing contribution
cls_lensed[2] -= (1. - A_lens) * BB_lens_contr
return cls_lensed
def get_nls(lat_path, lmax, sac_path=None,
deproj_level=0):
'''
Arguments
-----------------
lat_path : str
Path to folder containing LAT noise cuves
lmax : int
Keyword Arguments
-----------------
sac_path : str, None
Path to folder containing SAC noise cuves
deproj_level : int
Foreground cleaning assumption, 0 - 4
0 is most optimistic
Returns
-------
nls : array-like
Shape (6, lmax - 1), order: TT, EE, BB, TE, TB, EB
Notes
-----
Looks like SAC noise curves are only for pol, so use
SAT TT for TT.
'''
# Add option to skip SAT.
# SO V3 (deproj0, S2(goal) 16000 deg2
# init noise curves (fill with 1K^2 noise)
# truncate later
nls = np.ones((6, 20000)) * 1e12
# load up LAT
# lat_tt_file = 'S4_2LAT_T_default_noisecurves_'\
# 'deproj{}_SENS0_mask_16000_ell_TT_yy.txt'.format(deproj_level) # NOTE
lat_tt_file = 'SOV3_T_default1-4-2_noisecurves_'\
'deproj{}_SENS2_mask_16000_ell_TT_yy.txt'.format(deproj_level)
lat_pol_file = lat_tt_file.replace('_T_', '_pol_')
lat_pol_file = lat_pol_file.replace('_TT_yy', '_EE_BB')
lat_tt_file = opj(lat_path, lat_tt_file)
lat_pol_file = opj(lat_path, lat_pol_file)
# load lat
ells_tt, nl_tt, ells_pol, nl_ee, nl_bb = ct.get_so_noise(
tt_file=lat_tt_file, pol_file=lat_pol_file, sat_file=None)
lmin_tt = int(ells_tt[0])
lmax_tt = int(ells_tt[-1])
#lmin_pol = int(ells_pol[0])
lmin_pol = 30 # as suggested on wiki
lmax_pol = int(ells_pol[-1])
if sac_path is not None:
sac_file = 'Db_noise_04.00_ilc_bin3_av.dat'
sac_file = opj(sac_path, sac_file)
# load sac, note these are Dell bandpowers
ell, sac_ee, sac_bb = np.loadtxt(sac_file).transpose()
dell = ell * (ell + 1) / 2. / np.pi
sac_ee /= dell
sac_bb /= dell
# interpolate
lmin_sac = int(ell[0])
lmax_sac = int(ell[-1])
ell_f = np.arange(lmin_sac, lmax_sac+1)
sac_ee = np.interp(ell_f, ell, sac_ee)
sac_bb = np.interp(ell_f, ell, sac_bb)
# combine, first lat then (if needed )sac because lat has lower lmin
nls[0,lmin_tt - 2:lmax_tt - 1] = nl_tt
nls[1,lmin_pol - 2:lmax_pol - 1] = nl_ee[ells_pol >= lmin_pol]
nls[2,lmin_pol - 2:lmax_pol - 1] = nl_bb[ells_pol >= lmin_pol]
nls[3] *= 0.
nls[4] *= 0.
nls[5] *= 0.
if sac_path is not None:
nls[1,lmin_sac - 2:lmax_sac - 1] = sac_ee
nls[2,lmin_sac - 2:lmax_sac - 1] = sac_bb
# trunacte to lmax
nls = nls[:,:lmax - 1]
return nls
def get_fiducial_nls(noise_amp_temp, noise_amp_pol, lmax):
'''
Create N_{\ell} = noise_amp^2 noise arrays.
Arguments
-----------------
noise_amp_temp : float
Noise ampltidue in uK arcmin.
noise_amp_pol : float
lmax : int
Returns
-------
nls : array-like
Shape (6, lmax - 1), order: TT, EE, BB, TE, TB, EB
'''
# init noise curves (fill with 1K^2 noise)
# truncate later
nls = np.ones((6, 20000)) * 1e12
# N_{\ell} = uK^2 radians^2
arcmin2radians = np.pi / 180. / 60.
noise_amp_temp *= arcmin2radians
noise_amp_pol *= arcmin2radians
# combine, first lat then sac because lat has lower lmin
nls[0,:] = noise_amp_temp ** 2
nls[1,:] = noise_amp_pol ** 2
nls[2,:] = noise_amp_pol ** 2
nls[3] *= 0.
nls[4] *= 0.
nls[5] *= 0.
# trunacte to lmax
nls = nls[:,:lmax - 1]
return nls
def get_prim_amp(prim_template='local', scalar_amp=2.1e-9):
common_amp = 16 * np.pi**4 * scalar_amp**2
if prim_template == 'local':
return 2 * common_amp
elif prim_template == 'equilateral':
return 6 * common_amp
elif prim_template == 'orthogonal':
return 6 * common_amp
def get_totcov(cls, nls, no_ee=False, no_tt=False):
totcov = nls.copy()
totcov[:4,:] += cls
if no_ee:
totcov[1,:] = 1e12
if no_tt:
totcov[0,:] = 1e12
return totcov
def run_fisher(template, ana_dir, camb_dir, totcov, ells, lmin=2, lmax=4999,fsky=0.03,
plot_tag='', tag=None):
F = Fisher(ana_dir)
camb_opts = dict(camb_out_dir=camb_dir,
tag='r0',
lensed=False,
high_ell=True)
F.get_camb_output(**camb_opts)
radii = F.get_updated_radii()
radii = radii[::2]
F.get_bins(lmin=lmin, lmax=lmax, load=True, verbose=False,
parity='odd', tag=tag)
# F.get_beta(func='equilateral', load=True, verbose=False, radii=radii, tag=tag)
F.get_beta(func='equilateral', load=True, verbose=True, radii=radii, tag=tag,
interp_factor=10)
# F.get_binned_bispec(template, load=True, tag=tag)
F.get_binned_bispec(template, load=True, tag=tag)
bin_invcov, bin_cov = F.get_binned_invcov(ells, totcov, return_bin_cov=True)
# Plot invcov, cov
plot_opts = dict(lmin=2)
bins = F.bins['bins']
plot_tools.cls_matrix(plot_tag, bins, bin_invcov, log=False, plot_dell=False,
inv=True, **plot_opts)
plot_tools.cls_matrix(plot_tag.replace('invcov', 'cov_dell'),
bins, bin_cov, log=False, plot_dell=True,
**plot_opts)
print(lmin, lmax)
fisher = F.naive_fisher(bin_invcov, lmin=lmin, lmax=lmax, fsky=fsky)
sigma = 1/np.sqrt(fisher)
return fisher, sigma
###### OLD
amp = get_prim_amp(template)
F.bispec['bispec'] *= amp
F.get_binned_invcov(nls=totcov)
bin_invcov = F.bin_invcov
bin_cov = F.bin_cov
bin_size = F.bins['bins'].size
bins = F.bins['bins']
num_pass = F.bins['num_pass_full']
bispec = F.bispec['bispec']
# Plot invcov, cov
plot_opts = dict(lmin=2)
plot_tools.cls_matrix(plot_tag, bins, bin_invcov, log=False, plot_dell=False,
inv=True, **plot_opts)
plot_tools.cls_matrix(plot_tag.replace('invcov', 'cov_dell'),
bins, bin_cov, log=False, plot_dell=True,
**plot_opts)
plot_tools.cls_matrix(plot_tag.replace('invcov', 'cov'),
bins, bin_cov, log=False, plot_dell=False,
**plot_opts)
# allocate bin-sized fisher matrix (same size as outer loop)
fisher_per_bin = np.ones(bin_size) * np.nan
# allocate 12 x 12 cov for use in inner loop
invcov = np.zeros((F.bispec['pol_trpl'].size, F.bispec['pol_trpl'].size))
# create (binned) inverse cov matrix for each ell
# i.e. use the fact that 12x12 pol invcov can be factored
# as (Cl-1)_l1^ip (Cl-1)_l2^jq (Cl-1)_l3^kr
invcov1 = np.ones((bin_size, 12, 12))
invcov2 = np.ones((bin_size, 12, 12))
invcov3 = np.ones((bin_size, 12, 12))
f_check = 0
for tidx_a, ptrp_a in enumerate(F.bispec['pol_trpl']):
# ptrp_a = ijk
for tidx_b, ptrp_b in enumerate(F.bispec['pol_trpl']):
# ptrp_a = pqr
# a is first bispectrum, b second one
# ptrp = pol triplet
ptrp_a1 = ptrp_a[0]
ptrp_a2 = ptrp_a[1]
ptrp_a3 = ptrp_a[2]
ptrp_b1 = ptrp_b[0]
ptrp_b2 = ptrp_b[1]
ptrp_b3 = ptrp_b[2]
invcov1[:,tidx_a,tidx_b] = bin_invcov[:,ptrp_a1,ptrp_b1]
invcov2[:,tidx_a,tidx_b] = bin_invcov[:,ptrp_a2,ptrp_b2]
invcov3[:,tidx_a,tidx_b] = bin_invcov[:,ptrp_a3,ptrp_b3]
# Depending on lmin, start outer loop not at first bin.
start_bidx = np.where(bins >= lmin)[0][0]
end_bidx = np.where(bins >= min(lmax, bins[-1]))[0][0] + 1
# loop same loop as in binned_bispectrum
for idx1, i1 in enumerate(bins[start_bidx:end_bidx]):
idx1 += start_bidx
cl1 = invcov1[idx1,:,:] # 12x12
# init
fisher_per_bin[idx1] = 0.
for idx2, i2 in enumerate(bins[idx1:end_bidx]):
idx2 += idx1
cl2 = invcov2[idx1,:,:] # 12x12
cl12 = cl1 * cl2
for idx3, i3 in enumerate(bins[idx2:end_bidx]):
idx3 += idx2
num = num_pass[idx1,idx2,idx3]
if num == 0:
continue
cl123 = cl12 * invcov3[idx3,:,:] #12x12
B = bispec[idx1,idx2,idx3,:]
f = np.einsum("i,ij,j", B, cl123, B)
# f0 = np.einsum("i,i", B, B)
# b0 = np.einsum("ij,ij", cl123, cl123)
# both B's have num
f /= float(num)
if i1 == i2 == i3:
f /= 6.
elif i1 != i2 != i3:
pass
else:
f /= 2.
fisher_per_bin[idx1] += f
f_check += f
fisher_per_bin *= fsky
f_check *= fsky
min_f = []
# print 'fisher_check:', f_check * (4*np.pi / np.sqrt(8))**2
# print 'sigma:', 1/np.sqrt(f_check) * (np.sqrt(8)/4./np.pi)
fisher_check = f_check * (4*np.pi / np.sqrt(8))**2
sigma = 1/np.sqrt(f_check) * (np.sqrt(8)/4./np.pi)
return fisher_check, sigma
# for lidx, lmin in enumerate(range(2, 40)):
# f = np.sum(fisher_per_bin[lmin-2:])
# min_f.append(np.sqrt(f))
if __name__ == '__main__':
# ana_dir = '/mn/stornext/d8/ITA/spider/adri/analysis/20181112_sst/' # S5
# ana_dir = '/mn/stornext/d8/ITA/spider/adri/analysis/20181123_sst/'
ana_dir = '/mn/stornext/d8/ITA/spider/adri/analysis/20181214_sst_debug/'
out_dir = opj(ana_dir, 'fisher')
camb_base = '/mn/stornext/d8/ITA/spider/adri/analysis/20171217_sst'
camb_dir = opj(camb_base, 'camb_output/high_acy/sparse_5000')
noise_base = '/mn/stornext/u3/adriaand/cmb_sst_ksw/ancillary/noise_curves'
# noise_base = '/mn/stornext/u3/adriaand/cmb_sst_ksw/ancillary/noise_curves/so/v3/so'
# lat_path = opj(noise_base, 's4/S4_2LAT_Tpol_default_noisecurves')
lat_path = opj(noise_base, 'so/v3/so')
# sac_path = noise_base
sac_path = None
# fixed
lmin = 2
# lmax = 4999
lmax = 4000 # has to match beta etc
lmax_f = 3000 # for fisher
lmin_f = 250
# A_lens = 0.13
A_lens = 1.
noise_amp_temp = 6.
noise_amp_pol = 6 * np.sqrt(2)
# NOTE
# noise_amp_temp = .0
# noise_amp_pol = .0 * np.sqrt(2)
opts = {}
# opts['nominal'] = dict(fsky=0.03, no_ee=False, no_tt=False, no_noise=False)
# opts['no_ee'] = dict(fsky=0.03, no_ee=True, no_tt=False, no_noise=False)
# opts['no_tt'] = dict(fsky=0.03, no_ee=False, no_tt=True, no_noise=False)
opts['nominal'] = dict(fsky=1., no_ee=False, no_tt=False, no_noise=False)
opts['no_ee'] = dict(fsky=1., no_ee=True, no_tt=False, no_noise=False)
opts['no_tt'] = dict(fsky=1., no_ee=False, no_tt=True, no_noise=False)
opts['cv_lim'] = dict(fsky=1., no_ee=False, no_tt=False, no_noise=True)
opts['no_ee_cv_lim'] = dict(fsky=1., no_ee=True, no_tt=False, no_noise=True)
opts['no_tt_cv_lim'] = dict(fsky=1., no_ee=False, no_tt=True, no_noise=True)
# for template in ['local', 'equilateral']:
for template in ['local']:
# with open(opj(out_dir, 'fisher_{}.txt'.format(template)), 'w') as text_file:
with open(opj(out_dir, 'fisher_so_{}.txt'.format(template)), 'w') as text_file:
for key in opts:
opt = opts[key]
no_noise = opt.get('no_noise')
fsky = opt.get('fsky')
no_ee = opt.get('no_ee')
no_tt = opt.get('no_tt')
cls = get_cls(camb_dir, lmax, A_lens=A_lens)
nls = get_nls(lat_path, lmax, sac_path=sac_path)
#nls = get_fiducial_nls(noise_amp_temp, noise_amp_pol, lmax)
if no_noise:
nls *= 0.
totcov = get_totcov(cls, nls, no_ee=no_ee, no_tt=no_tt)
ells = np.arange(2, lmax+1)
# plot_name = opj(out_dir, 'b_invcov_{}.png'.format(key))
plot_name = opj(out_dir, 'b_so_invcov_{}.png'.format(key))
# for template in ['local', 'equilateral', 'orthogonal']:
text_file.write('template: {}\n'.format(template))
text_file.write('option: {}\n'.format(key))
text_file.write('no_noise: {}\n'.format(no_noise))
text_file.write('fsky: {}\n'.format(fsky))
text_file.write('A_lens: {}\n'.format(A_lens))
text_file.write('no_ee: {}\n'.format(no_ee))
text_file.write('no_tt: {}\n'.format(no_tt))
fisher_check, sigma = run_fisher(template,
ana_dir, camb_dir, totcov, ells,
lmin=lmin_f, lmax=lmax_f, fsky=fsky,
plot_tag=plot_name, tag='r1_i10_b4')
text_file.write('fisher: {}\n'.format(fisher_check))
text_file.write('sigma: {}\n'.format(sigma))
text_file.write('\n')
| 1.976563 | 2 |
xdufacool/collect_local.py | fredqi/xdufacool | 7 | 12767521 | <gh_stars>1-10
# -*- encoding: utf-8 -*-
# collect_local.py ---
#
# Filename: collect_local.py
# Author: <NAME>
# Created: 2017-01-03 20:35:44(+0800)
#
# Last-Updated: 2017-01-08 19:24:52(+0800) [by <NAME>]
# Update #: 473
#
# Commentary:
#
#
#
# Change Log:
#
#
#
from __future__ import print_function
import re
import os
import sys
import glob
import shutil
from zipfile import ZipFile
import subprocess as subproc
import csv
def temporal_func():
classid = sys.argv[1]
folders = glob.glob(classid + '-*')
for fld in folders:
if not os.path.isdir(fld):
continue
print(fld)
subfolders = os.listdir(fld)
for stu in subfolders:
if os.path.isdir(stu):
stu_id = stu
print(stu_id)
def move_file():
"""Move files out of sub-directories in the current working directory."""
# print("\n".join(os.listdir(filepath)))
# folders = [os.path.join(filepath, fld) for fld in os.listdir(filepath)]
# print(filepath + ":\n " + "\n ".join(folders))
folders = filter(os.path.isdir, os.listdir(u"."))
# print("Sub-folders: ", u"\n".join(folders))
for folder in folders:
files = [os.path.join(folder, fn) for fn in os.listdir(folder)]
files = filter(os.path.isfile, files)
for fn in files:
_, filename = os.path.split(fn)
shutil.move(fn, filename)
assert 0 == len(os.listdir(folder))
def extract_rar(filename):
"""Filename include path."""
cwd = os.getcwd()
filepath, filename = os.path.split(filename)
print(filepath, filename)
os.chdir(filepath)
subproc.call(["7z", "x", "-y", filename], stdout=subproc.PIPE)
move_file()
os.chdir(cwd)
def extract_zip(filename):
"""Filename include path."""
code_pages = [u"ascii", u"utf-8", u"GB18030", u"GBK", u"GB2312", u"hz"]
cwd = os.getcwdu()
filepath, filename = os.path.split(filename)
os.chdir(filepath)
zip_obj = ZipFile(filename, 'r')
names = zip_obj.namelist()
print(filepath, filename)
for name in names:
if name[-1] == "/" or os.path.isdir(name):
print(" Skipping: ", name)
continue
if name.find("__MACOSX") >= 0:
print(" Skipping: ", name)
continue
succeed = False
name_sys = name
for coding in code_pages:
try:
name_sys = name.decode(coding)
succeed = True
except:
succeed = False
if succeed:
break
_, name_sys = os.path.split(name_sys)
the_file = zip_obj.open(name, 'r')
contents = the_file.read()
the_file.close()
the_file = open(name_sys, 'w')
the_file.write(contents)
the_file.close()
zip_obj.close()
# move_file()
os.chdir(cwd)
def check_local_homeworks(folders, scores):
"""Check local homeworks."""
re_id = re.compile(r'(?P<stuid>[0-9]{10,11})')
for folder, sc in zip(folders, scores):
files = glob.glob(folder + "/*/*.py")
files += glob.glob(folder + "/*/*.ipynb")
homeworks = dict()
for filename in files:
m = re_id.search(filename)
if m is not None:
stu_id = m.group('stuid')
homeworks[stu_id] = [sc]
write_dict_to_csv(folder + ".csv", homeworks)
def find_duplication(homework):
"""Find duplications in submitted homework."""
re_id = re.compile(r'(?P<stuid>[0-9]{10,11})')
dup_check = dict()
with open(homework, 'r') as data:
lines = data.readlines()
for ln in lines:
dt = ln.split()
csum, right = dt[0], dt[1]
if csum not in dup_check:
dup_check[csum] = list()
m = re_id.search(right)
if m is not None:
stu_id = m.group('stuid')
dup_check[csum].append(stu_id)
dup_check = filter(lambda k, v: len(v) > 1, dup_check.items())
dup_check = [(key, sorted(val)) for key, val in dup_check]
return dup_check
def display_dup(dup_result):
"""Display the duplication check results."""
lines = [k + ": " + ", ".join(v) for k, v in dup_result]
return lines
def load_csv_to_dict(filename):
"""Load a CSV file into a dict with the first column as the key."""
row_len = list()
result = dict()
with open(filename, 'r') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
key = row[0].strip()
values = [v.strip() for v in row[1:]]
result[key] = values
row_len.append(len(values))
return result, max(row_len)
def write_dict_to_csv(filename, data):
"""Write a dictionary to a CSV file with the key as the first column."""
with open(filename, 'w') as csvfile:
writer = csv.writer(csvfile)
keys = sorted(data.keys())
for key in keys:
value = data[key]
row = [str(key)] + [str(v) for v in value]
writer.writerow(row)
def merge_csv(csv_files):
"""Merge CSV files based on keywords."""
results = dict()
data_all = list()
keys = set()
for filename in csv_files:
data, row_len = load_csv_to_dict(filename)
keys |= set(data.keys())
data_all.append((data, row_len))
for key in keys:
values = list()
for value, row_len in data_all:
fill = ["0"]*row_len
dt = value[key] if key in value else fill
values.extend(dt)
results[key] = values
return results
if __name__ == "__main__":
# rar_files = glob.glob(u"HW1603/*/*.rar")
# for fn in rar_files:
# extract_rar(fn)
# zip_files = glob.glob(u"HW1603/*/*.zip")
# for fn in zip_files:
# extract_zip(fn)
homeworks = ["HW1601", "HW1602", "HW1603"]
for hw in homeworks:
ret = find_duplication(hw + ".md5")
lines = display_dup(ret)
print(hw, "with", len(lines), "duplications:")
print("\n".join(lines))
check_local_homeworks(homeworks, [30, 30, 40])
csv_files = [u"EE5184-2016.csv",
u"HW1601.csv", u"HW1602.csv", u"HW1603.csv"]
merged = merge_csv(csv_files)
write_dict_to_csv("EE5184-2016-all.csv", merged)
#
# collect_local.py ends here
| 2.6875 | 3 |
code/HiddenLayer.py | PPeltola/tiralabra | 0 | 12767522 | from Vector import Vector
from Neuron import Neuron
class HiddenLayer:
def __init__(self, size, input_size, weights, activation, activation_d, loss, loss_d, bias=1.0):
self.size = size
#self.input_layer = input_layer
self.input_size = input_size
#self.output_layer = output_layer
self.loss = loss
self.loss_d = loss_d
self._neurons = []
for w in weights:
if len(w) != self.input_size:
raise ValueError("Mismatched weight and input size!")
self._neurons.append(Neuron(w, activation, activation_d, bias))
@property
def neurons(self):
return self._neurons
@neurons.setter
def neurons(self, n):
self._neurons = n
def __getitem__(self, key):
return self._neurons[key]
def __len__(self):
return self.size
def generate_output(self, input):
if len(input) != self.input_size:
raise ValueError("Input is not of the defined length!")
op = []
lin = []
for n in self._neurons:
output = n.output(input)
op.append(output['op'])
lin.append(output['lin'])
return {'op':Vector(op), 'lin':Vector(lin)} | 3.59375 | 4 |
data_aug/aug_utils.py | Animadversio/Foveated_Saccade_SimCLR | 14 | 12767523 | <filename>data_aug/aug_utils.py
import torch
from typing import Tuple, List, Optional
from packaging import version
torchver = version.parse(torch.__version__)
OLD_DIV = torchver < version.parse("1.8.0")
def unravel_indices(
indices: torch.LongTensor,
shape: Tuple[int, ...],
) -> torch.LongTensor:
r"""Converts flat indices into unraveled coordinates in a target shape.
Args:
indices: A tensor of (flat) indices, (*, N).
shape: The targeted shape, (D,).
Returns:
The unraveled coordinates, (*, N, D).
"""
coord = []
for dim in reversed(shape):
coord.append(indices % dim)
if OLD_DIV:
indices = indices // dim
else:
# use this version to suppress the torch warning about changed behavior in newer torch.
indices = torch.div(indices, dim, rounding_mode="floor")
coord = torch.stack(coord[::-1], dim=-1)
return coord
def send_to_clipboard(image):
"""https://stackoverflow.com/questions/34322132/copy-image-to-clipboard"""
from io import BytesIO
import win32clipboard # this is not in minimal env.
output = BytesIO()
image.convert('RGB').save(output, 'BMP')
data = output.getvalue()[14:]
output.close()
win32clipboard.OpenClipboard()
win32clipboard.EmptyClipboard()
win32clipboard.SetClipboardData(win32clipboard.CF_DIB, data)
win32clipboard.CloseClipboard()
| 2.59375 | 3 |
code/python/qppMeasures/sARE.py | Zendelo/QPP-EnhancedEval | 3 | 12767524 | <filename>code/python/qppMeasures/sARE.py
import numpy as np
def sARESingle(qppColRanks, msrDFRanks, mapper):
refMsr = mapper(qppColRanks.name)
return (np.abs(qppColRanks - msrDFRanks[refMsr])/len(qppColRanks))
def sARE(msrDF, qppDF, rankType, mapper):
#convert to ranks
msrDFRanks = msrDF.rank(method=rankType)
qppDFRanks = qppDF.rank(method=rankType)
return qppDFRanks.apply(lambda x: sARESingle(x, msrDFRanks, mapper))
| 2.640625 | 3 |
MNIST-lightning/model.py | dadi-vardhan/Assurance-cases-for-LEC | 0 | 12767525 | <reponame>dadi-vardhan/Assurance-cases-for-LEC<gh_stars>0
import torch
import numpy as np
import pytorch_lightning as pl
from torchvision.models import resnet18,mobilenet_v2,vgg16,alexnet
import torch.nn.functional as F
from torchmetrics.functional.classification.accuracy import accuracy
from torchvision.models.squeezenet import squeezenet1_1
from eval_metrics import eval_metrics
from neptune.new.types import File
import neptune.new as neptune
class MnistModel(pl.LightningModule):
def __init__(self, channels=1, width=28, height=28,hidden_size=32, learning_rate=0.008317637711026709):
super().__init__()
# We take in input dimensions as parameters and use those to dynamically build model.
#self.hparams = hparam
self.channels = channels
self.width = width
self.height = height
self.classes = ('Zero', 'One', 'Two', 'Three', 'Four',
'Five', 'Six', 'Seven', 'Eight', 'Nine')
self.num_classes = len(self.classes)
self.learning_rate = learning_rate
self.hidden_size = hidden_size
#self.layer_1 = torch.nn.Conv2d(1,28*28,kernel_size=(3,3),stride=(1,1),bias=False)
self.model = squeezenet1_1()
#self.model.conv1 = torch.nn.Conv2d(1,64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) # resnet
#self.model.fc = torch.nn.Linear(in_features=512,out_features=10,bias=True) #resnet
################################################################################################################
#self.model.features[0][0] = torch.nn.Conv2d(1,32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) #mobilenet
#self.model.classifier[1] = torch.nn.Linear(in_features=1280,out_features=10,bias=True) #mobilenet
################################################################################################################
self.model.features[0] = torch.nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(2, 2)) #sqeezenet
self.model.classifier[1] = torch.nn.Conv2d(512, 10, kernel_size=(1, 1), stride=(1, 1)) #sqeezenet
################################################################################################################
#self.model.features[0] = torch.nn.Conv2d(1,64,kernel_size=(3,3),stride=(1,1),padding=(1,1))#vgg16
#self.model.classifier[6] = torch.nn.Linear(in_features=4096,out_features=10,bias=True)#vgg16
#self.model.features[0] = torch.nn.Conv2d(1,64,kernel_size=(11,11),stride=(4,4),padding=(2,2))#alexnet
self.loss_func = torch.nn.CrossEntropyLoss()
self.save_hyperparameters()
def forward(self,x):
#x = self.layer_1(x)
x = self.model(x)
#x = torch.nn.Linear(in_features=1000, out_features=512, bias=True,device='cuda')(x)
# x = torch.nn.functional.relu(x)
# x = torch.nn.Linear(in_features=512, out_features=128, bias=True,device='cuda')(x)
# x = torch.nn.functional.relu(x)
# x = torch.nn.Linear(in_features=128, out_features=64, bias=True,device='cuda')(x)
# x = torch.nn.functional.relu(x)
# x = torch.nn.Linear(in_features=64, out_features=10, bias=True,device='cuda')(x)
#x = torch.nn.functional.log_softmax(x,dim=1)
return x
def training_step(self, batch, batch_idx):
x, y = batch
y= y.long()
logits = self(x)
loss = self.loss_func(logits, y)
self.log("train_loss", loss, prog_bar=True)
self.logger.experiment.log_metric('train/Train_loss',loss)
return {'loss': loss}
def training_epoch_end(self, outputs):
epoch_avg_loss = torch.stack([output['loss'] for output in outputs]).mean()
self.log("train_loss", epoch_avg_loss)
self.logger.experiment.log_metric('train/Train_avg_loss',epoch_avg_loss)
def validation_step(self, batch, batch_idx):
x, y = batch
y= y.long()
logits = self(x)
loss = self.loss_func(logits, y)
preds = torch.argmax(logits, dim=1)
acc = accuracy(preds, y)
self.log("val_loss", loss, prog_bar=True)
self.log("val_acc", acc, prog_bar=True)
self.logger.experiment.log_metric('val/Val_loss',loss)
self.logger.experiment.log_metric('val/Val_acc',acc)
return {'val_loss': loss}
def validation_epoch_end(self, outputs):
epoch_avg_loss = torch.stack([output['val_loss'] for output in outputs]).mean()
self.log(f"val_loss", epoch_avg_loss)
self.logger.experiment.log_metric('val/Avg_val_loss',epoch_avg_loss)
return epoch_avg_loss
def test_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = self.loss_func(logits, y)
preds = torch.argmax(logits, dim=1)
acc = accuracy(preds,y)
self.log("test_loss", loss,on_epoch=True, prog_bar=True)
self.log("test_acc", acc,on_epoch=True, prog_bar=True)
trgts = y
preds = preds
eval = eval_metrics(trgts,preds,self.classes)
cm = eval.plot_conf_matx()
cm_norm = eval.plot_conf_matx(normalized=True)
self.logger.experiment.log_image('metrics/Confusion Matrix',cm)
self.logger.experiment.log_image('metrics/Normalized Confusion Matrix',cm_norm)
cls_report = eval.classify_report()
self.logger.experiment.log_text("metrics/classification-report",cls_report)
f1 = eval.f1_score_weighted()
self.logger.experiment.log_metric('metrics/F1_score',f1)
recall = eval.recall_weighted()
self.logger.experiment.log_metric('metrics/Recall',recall)
prec = eval.precision_weighted()
self.logger.experiment.log_metric('metrics/Precision',prec)
output = {
'test_loss': loss,
'test_acc': acc}
return output
def test_end(self, outputs):
"""[Logging all metrics at the end of the test phase]
Args:
outputs ([tensors]): [model predictions]
Returns:
[tensors]: [avg test loss]
"""
avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
self.logger.experiment.log_metric('test/Avg_test_loss',avg_loss)
return avg_loss
# def predict_step(self, batch, batch_idx: int, dataloader_idx: int = None):
# return self(batch)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
return optimizer | 2.28125 | 2 |
batch_folder/batch_generator.py | Ouwen/registration-magi | 3 | 12767526 | <reponame>Ouwen/registration-magi
#!/usr/bin/python
from medpy.io import load
import csv
import sys
import copy
output = open('/output/batch_script.sh', 'w')
output.write('#!/bin/bash')
filename_dictionary = {
'pre-contrast filepath': 'pre',
'post-contrast filepath': 'post',
'FLAIR filepath': 'FLAIR'
}
def format_docker_string(row, reference_image_key, input_image_key):
# 0 is the input image filepath
# 1 is the reference image filepath
# 2 is the output path
# 3 is the output base name
# 4 is the input shorthand ['pre', 'post', 'FLAIR']
# 5 is the reference shorthand ['pre', 'post', 'FLAIR']
docker_string = """
docker run --rm -it \\
-v {0}:/mount{0} \\
-v {1}:/mount{1} \\
-v {2}:/output \\
-e FSLOUTPUTTYPE=NIFTI \\
ouwen/registration-magi flirt \\
-in /mount{0} \\
-ref /mount{1} \\
-out /output/{3}_{4}_to_{5}_r
"""
return docker_string.format(
row[input_image_key],
row[reference_image_key],
row['output filepath'],
row['output base filename'],
filename_dictionary[input_image_key],
filename_dictionary[reference_image_key]
)
def get_min_slices_image_type(row):
image_slices_dictionary = copy.copy(filename_dictionary)
for image_type in filename_dictionary.keys():
image_type_data, image_type_header = load('/mount' + row[image_type])
x, y, slices = image_type_data.shape
image_slices_dictionary[image_type] = slices
return min(image_slices_dictionary, key=image_slices_dictionary.get)
with open(sys.argv[1], 'rb') as csv_file:
image_reader = csv.DictReader(csv_file, delimiter=',')
for row in image_reader:
reference_image_key = get_min_slices_image_type(row)
for image_type in filename_dictionary.keys():
if(image_type != reference_image_key):
output.write(format_docker_string(row, reference_image_key, image_type))
| 2.390625 | 2 |
lib/bridgedb/parse/networkstatus.py | wfn/bridgedb | 1 | 12767527 | # -*- coding: utf-8 -*-
#
# This file is part of BridgeDB, a Tor bridge distribution system.
#
# :authors: <NAME> 0xA3ADB67A2CDB8B35 <<EMAIL>>
# please also see AUTHORS file
# :copyright: (c) 2013 Isis Lovecruft
# (c) 2007-2013, The Tor Project, Inc.
# (c) 2007-2013, all entities within the AUTHORS file
# :license: 3-clause BSD, see included LICENSE for information
"""Parsers for ``@type bridge-network-status 1.0`` descriptors.
.. _descriptors: https://metrics.torproject.org/formats.html#descriptortypes
**Module Overview:**
..
parse
\_networkstatus
|_ isValidRouterNickname - Determine if a nickname is according to spec
|_ parseRLine - Parse an 'r'-line from a networkstatus document
|_ parseALine - Parse an 'a'-line from a networkstatus document
\_ parseSLine - Parse an 's'-line from a networkstatus document
"""
import binascii
import logging
import string
import time
import warnings
from twisted.python.log import showwarning
from bridgedb.parse import addr
from bridgedb.parse import parseUnpaddedBase64
from bridgedb.parse import InvalidBase64
class NetworkstatusParsingError(Exception):
"""Unable to parse networkstatus document line."""
class InvalidNetworkstatusRouterIdentity(ValueError):
"""The ID field of a networkstatus document 'r'-line is invalid."""
class InvalidRouterNickname(ValueError):
"""Router nickname doesn't follow tor-spec."""
def isValidRouterNickname(nickname):
"""Determine if a router's given nickname meets the specification.
:param string nickname: An OR's nickname.
"""
ALPHANUMERIC = string.letters + string.digits
if not (1 <= len(nickname) <= 19):
raise InvalidRouterNickname(
"Nicknames must be between 1 and 19 characters: %r" % nickname)
for letter in nickname:
if not letter in ALPHANUMERIC:
raise InvalidRouterNickname(
"Nicknames must only use [A-Za-z0-9]: %r" % nickname)
return True
def parseRLine(line):
"""Parse an 'r'-line from a networkstatus document.
From torspec.git/dir-spec.txt, commit 36761c7d553d L1499-1512:
|
|"r" SP nickname SP identity SP digest SP publication SP IP SP ORPort
| SP DirPort NL
|
| [At start, exactly once.]
|
| "Nickname" is the OR's nickname. "Identity" is a hash of its
| identity key, encoded in base64, with trailing equals sign(s)
| removed. "Digest" is a hash of its most recent descriptor as
| signed (that is, not including the signature), encoded in base64.
| "Publication" is the
| publication time of its most recent descriptor, in the form
| YYYY-MM-DD HH:MM:SS, in UTC. "IP" is its current IP address;
| ORPort is its current OR port, "DirPort" is its current directory
| port, or "0" for "none".
|
:param string line: An 'r'-line from an bridge-network-status descriptor.
"""
(nickname, ID, descDigest, timestamp,
ORaddr, ORport, dirport) = (None for x in xrange(7))
try:
if not line.startswith('r '):
raise NetworkstatusParsingError(
"Networkstatus parser received non 'r'-line: %r" % line)
line = line[2:] # Chop off the 'r '
fields = line.split()
if len(fields) != 8:
raise NetworkstatusParsingError(
"Wrong number of fields in networkstatus 'r'-line: %r" % line)
nickname, ID = fields[:2]
try:
ID = parseUnpaddedBase64(ID)
except InvalidBase64 as error:
raise InvalidNetworkstatusRouterIdentity(error)
# Check the nickname validity after parsing the ID, otherwise, if the
# nickname is invalid, we end up with the nickname being ``None`` and
# the ID being unparsed, unpadded (meaning it is technically invalid)
# base64.
isValidRouterNickname(nickname)
except NetworkstatusParsingError as error:
logging.error(error)
nickname, ID = None, None
except InvalidRouterNickname as error:
logging.error(error)
# Assume that we mostly care about obtaining the OR's ID, then it
# should be okay to set the nickname to ``None``, if it was invalid.
nickname = None
except InvalidNetworkstatusRouterIdentity as error:
logging.error(error)
ID = None
else:
try:
descDigest = parseUnpaddedBase64(fields[2])
timestamp = time.mktime(time.strptime(" ".join(fields[3:5]),
"%Y-%m-%d %H:%M:%S"))
ORaddr = fields[5]
ORport = int(fields[6])
dirport = fields[7]
except InvalidBase64 as error:
logging.error(error)
descDigest = None
except (AttributeError, ValueError, IndexError) as error:
logging.error(error)
timestamp = None
finally:
return (nickname, ID, descDigest, timestamp, ORaddr, ORport, dirport)
def parseALine(line, fingerprint=None):
"""Parse an 'a'-line of a bridge networkstatus document.
From torspec.git/dir-spec.txt, commit 36761c7d553d L1499-1512:
|
| "a" SP address ":" port NL
|
| [Any number.]
|
| Present only if the OR has at least one IPv6 address.
|
| Address and portlist are as for "or-address" as specified in
| 2.1.
|
| (Only included when the vote or consensus is generated with
| consensus-method 14 or later.)
:param string line: An 'a'-line from an bridge-network-status descriptor.
:type fingerprint: string or None
:param fingerprint: A string which identifies which OR the descriptor
we're parsing came from (since the 'a'-line doesn't tell us, this can
help make the log messages clearer).
:raises: :exc:`NetworkstatusParsingError`
:rtype: tuple
:returns: A 2-tuple of a string respresenting the IP address and a
:class:`bridgedb.parse.addr.PortList`.
"""
ip = None
portlist = None
if line.startswith('a '):
line = line[2:] # Chop off the 'a '
else:
logging.warn("Networkstatus parser received non 'a'-line for %r:"\
" %r" % (fingerprint or 'Unknown', line))
try:
ip, portlist = line.rsplit(':', 1)
except ValueError as error:
logging.error("Bad separator in networkstatus 'a'-line: %r" % line)
return (None, None)
if ip.startswith('[') and ip.endswith(']'):
ip = ip.strip('[]')
try:
if not addr.isIPAddress(ip):
raise NetworkstatusParsingError(
"Got invalid IP Address in networkstatus 'a'-line for %r: %r"
% (fingerprint or 'Unknown', line))
if addr.isIPv4(ip):
warnings.warn(FutureWarning(
"Got IPv4 address in networkstatus 'a'-line! "\
"Networkstatus document format may have changed!"))
except NetworkstatusParsingError as error:
logging.error(error)
ip, portlist = None, None
try:
portlist = addr.PortList(portlist)
if not portlist:
raise NetworkstatusParsingError(
"Got invalid portlist in 'a'-line for %r!\n Line: %r"
% (fingerprint or 'Unknown', line))
except (addr.InvalidPort, NetworkstatusParsingError) as error:
logging.error(error)
portlist = None
else:
logging.debug("Parsed networkstatus ORAddress line for %r:"\
"\n Address: %s \tPorts: %s"
% (fingerprint or 'Unknown', ip, portlist))
finally:
return (ip, portlist)
def parseSLine(line):
"""Parse an 's'-line from a bridge networkstatus document.
The 's'-line contains all flags assigned to a bridge. The flags which may
be assigned to a bridge are as follows:
From torspec.git/dir-spec.txt, commit 36761c7d553d L1526-1554:
|
| "s" SP Flags NL
|
| [Exactly once.]
|
| A series of space-separated status flags, in lexical order (as ASCII
| byte strings). Currently documented flags are:
|
| "BadDirectory" if the router is believed to be useless as a
| directory cache (because its directory port isn't working,
| its bandwidth is always throttled, or for some similar
| reason).
| "Fast" if the router is suitable for high-bandwidth circuits.
| "Guard" if the router is suitable for use as an entry guard.
| "HSDir" if the router is considered a v2 hidden service directory.
| "Named" if the router's identity-nickname mapping is canonical,
| and this authority binds names.
| "Stable" if the router is suitable for long-lived circuits.
| "Running" if the router is currently usable.
| "Valid" if the router has been 'validated'.
| "V2Dir" if the router implements the v2 directory protocol.
:param string line: An 's'-line from an bridge-network-status descriptor.
:rtype: tuple
:returns: A 2-tuple of booleans, the first is True if the bridge has the
"Running" flag, and the second is True if it has the "Stable" flag.
"""
line = line[2:]
flags = [x.capitalize() for x in line.split()]
fast = 'Fast' in flags
running = 'Running' in flags
stable = 'Stable' in flags
guard = 'Guard' in flags
valid = 'Valid' in flags
if (fast or running or stable or guard or valid):
logging.debug("Parsed Flags: %s%s%s%s%s"
% ('Fast ' if fast else '',
'Running ' if running else '',
'Stable ' if stable else '',
'Guard ' if guard else '',
'Valid ' if valid else ''))
# Right now, we only care about 'Running' and 'Stable'
return running, stable
| 2.28125 | 2 |
pymoo/util/nds/tree_based_non_dominated_sort.py | AIasd/pymoo | 5 | 12767528 | import weakref
import numpy as np
class Tree:
'''
Implementation of Nary-tree.
The source code is modified based on https://github.com/lianemeth/forest/blob/master/forest/NaryTree.py
Parameters
----------
key: object
key of the node
num_branch: int
how many branches in each node
children: Iterable[Tree]
reference of the children
parent: Tree
reference of the parent node
Returns
-------
an N-ary tree.
'''
def __init__(self, key, num_branch, children=None, parent=None):
self.key = key
self.children = children or [None for _ in range(num_branch)]
self._parent = weakref.ref(parent) if parent else None
@property
def parent(self):
if self._parent:
return self._parent()
def __getstate__(self):
self._parent = None
def __setstate__(self, state):
self.__dict__ = state
for child in self.children:
child._parent = weakref.ref(self)
def traversal(self, visit=None, *args, **kwargs):
if visit is not None:
visit(self, *args, **kwargs)
l = [self]
for child in self.children:
if child is not None:
l += child.traversal(visit, *args, **kwargs)
return l
def tree_based_non_dominated_sort(F):
"""
Tree-based efficient non-dominated sorting (T-ENS).
This algorithm is very efficient in many-objective optimization problems (MaOPs).
Parameters
----------
F: np.array
objective values for each individual.
Returns
-------
indices of the individuals in each front.
References
----------
<NAME>, <NAME>, <NAME>, and <NAME>,
A decision variable clustering based evolutionary algorithm for large-scale many-objective optimization,
IEEE Transactions on Evolutionary Computation, 2018, 22(1): 97-112.
"""
N, M = F.shape
# sort the rows in F
indices = np.lexsort(F.T[::-1])
F = F[indices]
obj_seq = np.argsort(F[:, :0:-1], axis=1) + 1
k = 0
forest = []
left = np.full(N, True)
while np.any(left):
forest.append(None)
for p, flag in enumerate(left):
if flag:
update_tree(F, p, forest, k, left, obj_seq)
k += 1
# convert forest to fronts
fronts = [[] for _ in range(k)]
for k, tree in enumerate(forest):
fronts[k].extend([indices[node.key] for node in tree.traversal()])
return fronts
def update_tree(F, p, forest, k, left, obj_seq):
_, M = F.shape
if forest[k] is None:
forest[k] = Tree(key=p, num_branch=M - 1)
left[p] = False
elif check_tree(F, p, forest[k], obj_seq, True):
left[p] = False
def check_tree(F, p, tree, obj_seq, add_pos):
if tree is None:
return True
N, M = F.shape
# find the minimal index m satisfying that p[obj_seq[tree.root][m]] < tree.root[obj_seq[tree.root][m]]
m = 0
while m < M - 1 and F[p, obj_seq[tree.key, m]] >= F[tree.key, obj_seq[tree.key, m]]:
m += 1
# if m not found
if m == M - 1:
# p is dominated by the solution at the root
return False
else:
for i in range(m + 1):
# p is dominated by a solution in the branch of the tree
if not check_tree(F, p, tree.children[i], obj_seq, i == m and add_pos):
return False
if tree.children[m] is None and add_pos:
# add p to the branch of the tree
tree.children[m] = Tree(key=p, num_branch=M - 1)
return True
| 2.953125 | 3 |
bayesiancoresets/coreset/__init__.py | trevorcampbell/hilbert-coresets | 118 | 12767529 | from .hilbert import HilbertCoreset
from .sampling import UniformSamplingCoreset
from .sparsevi import SparseVICoreset
from .bpsvi import BatchPSVICoreset
| 0.992188 | 1 |
gunicorn_config.py | ons-eq-team/eq-questionnaire-runner | 0 | 12767530 | import gunicorn
import os
workers = os.getenv("GUNICORN_WORKERS")
worker_class = "gevent"
keepalive = os.getenv("GUNICORN_KEEP_ALIVE")
bind = "0.0.0.0:5000"
gunicorn.SERVER_SOFTWARE = "None"
| 1.8125 | 2 |
ingestion/src/metadata/ingestion/models/json_serializable.py | rongfengliang/OpenMetadata | 0 | 12767531 | # Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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 abc
import json
NODE_KEY = "KEY"
NODE_LABEL = "LABEL"
NODE_REQUIRED_HEADERS = {NODE_LABEL, NODE_KEY}
class JsonSerializable(object, metaclass=abc.ABCMeta):
def __init__(self) -> None:
pass
@staticmethod
def snake_to_camel(s):
a = s.split("_")
a[0] = a[0].lower()
if len(a) > 1:
a[1:] = [u.title() for u in a[1:]]
return "".join(a)
@staticmethod
def serialize(obj):
return {JsonSerializable.snake_to_camel(k): v for k, v in obj.__dict__.items()}
def to_json(self):
return json.dumps(
JsonSerializable.serialize(self),
indent=4,
default=JsonSerializable.serialize,
)
| 2.265625 | 2 |
tests/urls.py | ajbeach2/django-sqs-tasks | 0 | 12767532 | from django.conf.urls import include
from django.urls import path
urlpatterns = [
path("task", include("aws_pubsub.urls")),
]
| 1.492188 | 1 |
cities/urls.py | platypotomus/python-react-travel-app | 0 | 12767533 | <gh_stars>0
from django.conf.urls import url
from . import views
urlpatterns = [
url('api/cities/', views.CityListIndex.as_view(), name="cities_list"),
url('api/cities/(?P<pk>\d+)/$', views.CityElementShow.as_view(), name="cities_element")
# url(r'^city/$', views.CityListIndex.as_view(), name="cities_list"),
# url(r'^city/(?P<pk>\d+)/$', views.CityElementShow.as_view(), name="cities_element")
]
| 1.828125 | 2 |
HydrogenCpx/test.py | EFerriss/Hydrogen_Cpx | 0 | 12767534 | # -*- coding: utf-8 -*-
"""
Created on Thu Apr 07 11:41:07 2016
@author: Ferriss
"""
from pynams import experiments
reload(experiments)
experiments.convertH(110.,) | 1.507813 | 2 |
app/routes.py | hyattdrew11/Built-Budget-API | 0 | 12767535 | #!
'''
..%%%%...%%%%%%..%%%%%%..........%%%%%...%%..%%..%%%%%%..%%......%%%%%%.
.%%......%%........%%............%%..%%..%%..%%....%%....%%........%%...
.%%.%%%..%%%%......%%............%%%%%...%%..%%....%%....%%........%%...
.%%..%%..%%........%%............%%..%%..%%..%%....%%....%%........%%...
..%%%%...%%%%%%....%%............%%%%%....%%%%...%%%%%%..%%%%%%....%%...
........................................................................
'''
from flask import render_template, flash, redirect, url_for, jsonify, request, abort
from app import app, db
from app.models import Customer, BudgetItem, CustomerSchema, BudgetItemSchema
from marshmallow import validate, ValidationError
import json
from boto import kinesis
from flask_cors import CORS
CORS(app)
customer_schema = CustomerSchema()
budget_schema = BudgetItemSchema(many=True)
# kinesis = kinesis.connect_to_region(app.config['AWS_REGION'])
# kinesis.describe_stream(app.config['KINESIS_DATA_STREAM'])
@app.route('/')
@app.route('/index')
def index():
customers = Customer.query.all()
return render_template('index.html', title='Get Built', customers=customers)
@app.route('/customers')
def get_customers():
# STARTING TO BUILD OUT VUE FRONT END CONNECTION
customers = Customer.query.all()
many_customers = CustomerSchema(many=True)
res = many_customers.dumps(customers)
return res, 202
# ADD A NEW CUSTOMER
@app.route("/customer/details", methods=['POST',])
def create_customer():
data = request.get_json()
# VALIDATE POST DATA
try:
validate = customer_schema.load(data)
existing_customer = Customer.query.filter_by(name=data['name']).first()
if existing_customer is not None:
response = { 'message': 'customer already exists' }
return jsonify(response), 403
else:
# VALIDATION COMPLETE ADD NEW CUSTOMER
new_customer = Customer(**data)
db.session.add(new_customer)
db.session.commit()
try:
nc = customer_schema.dump(new_customer)
# kinesis.put_record(app.config['KINESIS_DATA_STREAM'], json.dumps(nc), "partitionkey")
except Exception as e:
print(e)
# CALL TO CLOUD WATCH OR OTHER ALERTING SYSTEM
response = { 'message': 'new customer registered', 'data': customer_schema.dump(new_customer) }
return jsonify(response), 202
except ValidationError as err:
errors = err.messages
validate = False
return jsonify(errors), 403
# GET CUSTOMER DETAILS
@app.route("/customer/details/<customer_id>", methods=['GET', 'DELETE'])
def get_customer(customer_id):
customer = Customer.query.filter_by(id=customer_id).first()
if customer is None:
response = { 'message': 'customer does not exist' }
return jsonify(response), 404
else:
if request.method == 'GET':
result = customer_schema.dump(customer)
response= { 'data': result }
return jsonify(response), 202
elif request.method == 'DELETE':
db.session.delete(customer)
db.session.commit()
response = { 'message': 'customer' + customer_id + ' deleted' }
return jsonify(response), 202
else:
return jsonify(response), 404
# GET ALL BUDGET ITEMS FOR A CUSTOMER
@app.route("/budget/details/<customer_id>", methods=['GET'])
def get_budget_items(customer_id):
items = BudgetItem.query.filter_by(customer_id=customer_id).all()
if items is None:
response = { 'message': 'customer does not exist' }
return jsonify(response), 404
else:
result = budget_schema.dump(items)
response = {'data': result, 'status_code' : 202 }
return jsonify(response)
# ADD OR UPDATE A NEW BUDGET ITEM
@app.route("/budget/details", methods=['POST', 'PUT'])
def create_budget_item():
data = request.get_json()
print(data)
print("Update a budget item")
try:
# VALIDATE JSON DATA
# AS WE HAVE ONE TO MANY RELATIONSHIP FROM CUSTOMER TO BUDGET ITEM DATA MUST BE ARRAY NOT OBJECT
validate = budget_schema.loads(json.dumps(data))
except ValidationError as err:
errors = err.messages
return jsonify(errors), 403
if request.method == 'POST':
# THERE IS A MORE ELEGANT WAY TO DO THIS, BUT WANT THE ABILITY TO ADD MANY BUDGET ITEMS AT ONCE
items = []
for x in data:
new_item = BudgetItem(**x)
db.session.add(new_item)
db.session.commit()
items.append(new_item)
items = budget_schema.dump(items)
response = { 'message': 'new budget item created','data': items }
return response, 202
elif request.method == 'PUT':
print("PUT")
# THERE IS A MORE ELEGANT WAY TO DO THIS, BUT WANT THE ABILITY TO ADD MANY BUDGET ITEMS AT ONCE
items = []
for x in data:
item = BudgetItem.query.filter_by(id=x['id']).update(dict(**x))
db.session.commit()
items.append(x)
items = budget_schema.dump(items)
response = { 'message': 'new budget item created', 'data' : items }
return jsonify(response), 202
else:
print("ELSE")
return abort(400)
# DELETE A BUDGET ITEM
@app.route("/budget/details/<item_id>", methods=['DELETE'])
def delete_budget_item(item_id):
item = BudgetItem.query.filter_by(id=item_id).first()
if item is None:
response = { 'message': 'budget item does not exist' }
return jsonify(response), 404
else:
db.session.delete(item)
db.session.commit()
response = { 'message': 'budget item ' + item_id + ' deleted' }
return jsonify(response), 202
'''
..%%%%...%%%%%%..%%%%%%..........%%%%%...%%..%%..%%%%%%..%%......%%%%%%.
.%%......%%........%%............%%..%%..%%..%%....%%....%%........%%...
.%%.%%%..%%%%......%%............%%%%%...%%..%%....%%....%%........%%...
.%%..%%..%%........%%............%%..%%..%%..%%....%%....%%........%%...
..%%%%...%%%%%%....%%............%%%%%....%%%%...%%%%%%..%%%%%%....%%...
........................................................................
''' | 2.015625 | 2 |
models/v0/net_definitions_tf.py | isl-org/adaptive-surface-reconstruction | 29 | 12767536 | #
# Copyright 2022 Intel (Autonomous Agents Lab)
#
# 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 models.common_tf import SpecialSparseConv, window_poly6
import open3d.ml.tf as ml3d
import tensorflow as tf
from collections import namedtuple
NNSResult = namedtuple(
"NNSResult",
["neighbors_index", "neighbors_distance", "neighbors_row_splits"])
class CConvAggregationBlock(tf.keras.Model):
"""The aggregation block is a single Continuous Convolution.
In addition to the features the block can return the importance information.
"""
def __init__(self, name, output_channels, return_importance=False):
super().__init__(name=name)
self._convs = []
self._return_importance = return_importance
conv_params = {
'kernel_size': [4, 4, 4],
'coordinate_mapping': 'ball_to_cube_radial',
'normalize': True,
}
def Conv(name, filters, activation, **kwargs):
conv = ml3d.layers.ContinuousConv(name=name,
filters=output_channels,
activation=activation,
**kwargs)
self._convs.append((name, conv))
setattr(self, name, conv)
activation = tf.keras.activations.relu
Conv(name='conv1',
filters=output_channels,
activation=activation,
**conv_params)
def call(self, feats, inp_points, out_points, out_extents, scale_compat,
nns):
"""Computes the features and optionally the importance.
Args:
feats: The point featurs.
inp_points: The input point positions.
out_points: These are the positions of the voxel centers of the
finest grid.
out_extents: This is the voxel size.
scale_compat: The scale compatibility between the input point radii
and the voxel sizes.
nns: tuple with
- neighbors_index: The indices to neighbor points for each voxel.
- neighbor_row_splits: Defines the start and end of each voxels
neighbors.
- neighbors_distance: The distance to each neighbor normalized with
respect to the voxel size.
"""
neighbors_importance = scale_compat * window_poly6(
nns.neighbors_distance)
feats = self.conv1(
feats,
inp_points,
out_points,
extents=out_extents,
user_neighbors_index=nns.neighbors_index,
user_neighbors_row_splits=nns.neighbors_row_splits,
user_neighbors_importance=neighbors_importance,
)
if self._return_importance:
return feats, neighbors_importance
else:
return feats
class SparseConvBlock(tf.keras.Model):
"""The convolution block for the adaptive grid.
Args:
input_channels: Number of input channels.
output_channels: The number of output channels.
normalized_channels: The number of channels that will be normalized
with respect to the importance values.
"""
def __init__(self, name, output_channels, normalized_channels=0):
super().__init__(name=name)
self._convs = []
self.output_channels = output_channels
self.normalized_channels = normalized_channels
conv_params = {
'kernel_size': 55,
}
def Conv(name, filters, activation, **kwargs):
conv = SpecialSparseConv(name=name,
filters=filters,
activation=activation,
**kwargs)
self._convs.append((name, conv))
setattr(self, name, conv)
activation = tf.keras.activations.relu
if normalized_channels == 'all':
Conv(name='conv1',
filters=output_channels,
activation=activation,
normalize=True,
**conv_params)
Conv(name='conv2',
filters=output_channels,
activation=activation,
normalize=True,
**conv_params)
Conv(name='conv3',
filters=output_channels,
activation=activation,
normalize=True,
**conv_params)
Conv(name='conv4',
filters=output_channels,
activation=activation,
normalize=True,
**conv_params)
elif normalized_channels and normalized_channels >= output_channels:
Conv(name='conv1',
filters=output_channels,
activation=activation,
normalize=True,
**conv_params)
Conv(name='conv2',
filters=output_channels,
activation=activation,
normalize=False,
**conv_params)
Conv(name='conv3',
filters=output_channels,
activation=activation,
normalize=False,
**conv_params)
Conv(name='conv4',
filters=output_channels,
activation=activation,
normalize=False,
**conv_params)
elif normalized_channels and normalized_channels < output_channels:
Conv(name='conv1a',
filters=output_channels - normalized_channels,
activation=activation,
**conv_params)
Conv(name='conv1b',
filters=normalized_channels,
activation=activation,
normalize=True,
**conv_params)
Conv(name='conv2',
filters=output_channels,
activation=activation,
**conv_params)
Conv(name='conv3',
filters=output_channels,
activation=activation,
**conv_params)
Conv(name='conv4',
filters=output_channels,
activation=activation,
**conv_params)
else:
Conv(name='conv1',
filters=output_channels,
activation=activation,
**conv_params)
Conv(name='conv2',
filters=output_channels,
activation=activation,
**conv_params)
Conv(name='conv3',
filters=output_channels,
activation=activation,
**conv_params)
Conv(name='conv4',
filters=output_channels,
activation=activation,
**conv_params)
def call(self, feats, points, neighbors, importance=None):
"""Computes the features and optionally the importance if there are
normalized channels.
Args:
feats: Input features.
neighbors: dict with the neighbor information.
importance: The per voxel importance value
"""
if self.normalized_channels == 'all':
feats1, out_importance = self.conv1(feats,
inp_importance=importance,
**neighbors)
feats2, _ = self.conv2(feats1,
inp_importance=importance,
**neighbors)
feats3, _ = self.conv3(feats2,
inp_importance=importance,
**neighbors)
feats4, _ = self.conv4(feats3,
inp_importance=importance,
**neighbors)
return feats4, out_importance
elif self.normalized_channels and self.normalized_channels < self.output_channels:
feats1a = self.conv1a(feats, **neighbors)
feats1b, out_importance = self.conv1b(feats,
inp_importance=importance,
**neighbors)
feats1 = tf.concat([feats1a, feats1b], axis=-1)
feats2 = self.conv2(feats1, **neighbors)
feats3 = self.conv3(feats2, **neighbors)
feats4 = self.conv4(feats3, **neighbors)
return feats4, out_importance
elif self.normalized_channels:
feats1, out_importance = self.conv1(feats,
inp_importance=importance,
**neighbors)
feats2 = self.conv2(feats1, **neighbors)
feats3 = self.conv3(feats2, **neighbors)
feats4 = self.conv4(feats3, **neighbors)
return feats4, out_importance
else:
feats1 = self.conv1(feats, **neighbors)
feats2 = self.conv2(feats1, **neighbors)
feats3 = self.conv3(feats2, **neighbors)
feats4 = self.conv4(feats3, **neighbors)
return feats4
class SparseConvTransitionBlock(tf.keras.Model):
"""The convolution block for transitions between grids (up- and downconvolutions).
Args:
input_channels: Number of input channels.
output_channels: The number of output channels.
normalized_channels: The number of channels that will be normalized
with respect to the importance values.
"""
def __init__(self, name, output_channels, normalized_channels=0):
super().__init__(name=name)
self._convs = []
self.output_channels = output_channels
self.normalized_channels = normalized_channels
conv_params = {
'kernel_size': 9,
'activation': tf.keras.activations.relu
}
def Conv(name, filters, activation, **kwargs):
conv = SpecialSparseConv(name=name,
filters=filters,
activation=activation,
**kwargs)
self._convs.append((name, conv))
setattr(self, name, conv)
if normalized_channels == 'all' or normalized_channels >= output_channels:
Conv(name='conv1',
filters=output_channels,
normalize=True,
**conv_params)
elif normalized_channels and normalized_channels < output_channels:
Conv(name='conv1a',
filters=output_channels - normalized_channels,
**conv_params)
Conv(name='conv1b',
filters=normalized_channels,
normalize=True,
**conv_params)
else:
Conv(name='conv1', filters=output_channels, **conv_params)
def call(self, feats, inp_points, out_points, neighbors, importance=None):
"""Computes the features and optionally the importance if there are
normalized channels.
Args:
feats: Input features.
neighbors: dict with the neighbor information.
importance: The per voxel importance value
"""
if self.normalized_channels == 'all':
feats1, out_importance = self.conv1(feats,
inp_importance=importance,
**neighbors)
return feats1, out_importance
elif self.normalized_channels and self.normalized_channels < self.output_channels:
feats1a = self.conv1a(feats, **neighbors)
feats1b, out_importance = self.conv1b(feats,
inp_importance=importance,
**neighbors)
feats1 = tf.concat([feats1a, feats1b], axis=-1)
return feats1, out_importance
elif self.normalized_channels:
feats1, out_importance = self.conv1(feats,
inp_importance=importance,
**neighbors)
return feats1, out_importance
else:
feats1 = self.conv1(feats, **neighbors)
return feats1
class UNet5(tf.keras.Model):
"""Unet for adaptive grids predicting the signed and unsigned distance field.
Args:
channel_div: Reduces the number of channels for each layer.
with_importance: Adds channels normalized with the importance values.
normalized_channels: How many channels should be normalized with the importance.
residual_skip_connection: If True uses a residual connection for the last skip
connection. If 'all' uses residual connction for every skip connection.
"""
octree_levels = 5
def __init__(self,
name=None,
channel_div=1,
with_importance=False,
normalized_channels=0,
deeper=0,
residual_skip_connection=False):
super().__init__(name=name, autocast=False)
if not with_importance in (False, True, 'all'):
raise Exception('invalid value for "with_importance" {}'.format(
with_importance))
self.with_importance = with_importance
self.residual_skip_connection = residual_skip_connection
def SparseConvTransition(name,
filters,
normalized_channels=0,
**kwargs):
return SparseConvTransitionBlock(name, filters, normalized_channels)
d = channel_div
self.cconv_block_in = CConvAggregationBlock(
name="cconv_block_in",
output_channels=32 // d,
return_importance=with_importance)
params = {}
if with_importance:
params.update({
'normalized_channels': normalized_channels,
})
self.sparseconv_encblock0 = SparseConvBlock(name="sparseconv_encblock0",
output_channels=64 // d,
**params)
self.sparseconv_down1 = SparseConvTransition(name="sparseconv_down1",
filters=128 // d,
**params)
self.sparseconv_encblock1 = SparseConvBlock(name="sparseconv_encblock1",
output_channels=128 // d,
**params)
self.sparseconv_down2 = SparseConvTransition(name="sparseconv_down2",
filters=256 // d,
**params)
self.sparseconv_encblock2 = SparseConvBlock(name="sparseconv_encblock2",
output_channels=256 // d,
**params)
self.sparseconv_down3 = SparseConvTransition(name="sparseconv_down3",
filters=256 // d,
**params)
self.sparseconv_encblock3 = SparseConvBlock(name="sparseconv_encblock3",
output_channels=256 // d,
**params)
self.sparseconv_down4 = SparseConvTransition(name="sparseconv_down4",
filters=256 // d,
**params)
self.sparseconv_encblock4 = SparseConvBlock(name="sparseconv_encblock4",
output_channels=256 // d,
**params)
params = {}
self.sparseconv_up3 = SparseConvTransition(name="sparseconv_up3",
filters=256 // d)
self.sparseconv_decblock3 = SparseConvBlock(name="sparseconv_decblock3",
output_channels=256 // d,
**params)
self.sparseconv_up2 = SparseConvTransition(name="sparseconv_up2",
filters=256 // d)
self.sparseconv_decblock2 = SparseConvBlock(name="sparseconv_decblock2",
output_channels=256 // d,
**params)
if self.residual_skip_connection == 'all':
self.sparseconv_up1 = SparseConvTransition(name="sparseconv_up1",
filters=128 // d)
else:
self.sparseconv_up1 = SparseConvTransition(name="sparseconv_up1",
filters=256 // d)
self.sparseconv_decblock1 = SparseConvBlock(name="sparseconv_decblock1",
output_channels=128 // d,
**params)
self.sparseconv_up0 = SparseConvTransition(name="sparseconv_up0",
filters=64 // d)
self.sparseconv_decblock0 = SparseConvBlock(name="sparseconv_decblock0",
output_channels=32 // d,
**params)
activation = tf.keras.activations.relu
self.dense_decoder1 = tf.keras.layers.Dense(32 // d,
name='dense_decoder1',
activation=activation,
use_bias=True)
self.dense_decoder2 = tf.keras.layers.Dense(32 // d,
name='dense_decoder2',
activation=activation,
use_bias=True)
self.dense_decoder3 = tf.keras.layers.Dense(2,
name='dense_decoder3',
activation=None,
use_bias=False)
self._all_layers = []
self._collect_layers(self.layers)
def _collect_layers(self, layers):
for x in layers:
if hasattr(x, 'layers'):
self._collect_layers(x.layers)
else:
self._all_layers.append(x)
@tf.function
def call(self, input_dict):
"""Does a forward pass with aggregation and decode suited for training.
"""
feats = input_dict['feats']
feats1 = self.aggregate(input_dict)
code = self.unet(feats1, input_dict)
value, shifts_grad = self.decode(input_dict['voxel_shifts0'], code)
result = {
'value': value,
'shifts_grad': shifts_grad,
# 'code': code,
}
debug_info = {}
return result, debug_info
@tf.function
def unet(self, feats1, input_dict, keep_threshold=1):
"""Forward pass through the unet. Excludes aggregation and decode."""
neighbors = []
for i in range(5):
neighbors.append({
'neighbors_index':
input_dict['neighbors_index{}'.format(i)],
'neighbors_kernel_index':
input_dict['neighbors_kernel_index{}'.format(i)],
'neighbors_row_splits':
input_dict['neighbors_row_splits{}'.format(i)],
})
neighbors_down = []
for i in range(4):
num_points = tf.shape(input_dict['voxel_centers{}'.format(i + 1)],
out_type=tf.int64)[0]
ans = ml3d.ops.invert_neighbors_list(
num_points, input_dict['up_neighbors_index{}'.format(i)],
input_dict['up_neighbors_row_splits{}'.format(i)],
tf.cast(input_dict['up_neighbors_kernel_index{}'.format(i)],
dtype=tf.int32))
neighbors_down.append({
'neighbors_index':
ans.neighbors_index,
'neighbors_kernel_index':
tf.cast(ans.neighbors_attributes, dtype=tf.int16),
'neighbors_row_splits':
ans.neighbors_row_splits,
})
neighbors_up = []
for i in range(4):
neighbors_up.append({
'neighbors_index':
input_dict['up_neighbors_index{}'.format(i)],
'neighbors_kernel_index':
input_dict['up_neighbors_kernel_index{}'.format(i)],
'neighbors_row_splits':
input_dict['up_neighbors_row_splits{}'.format(i)],
})
if self.with_importance and keep_threshold < 1:
feats1, importance = feats1
nonzero_count = tf.math.count_nonzero(importance > 1e-3)
threshold_idx = tf.cast(tf.cast(nonzero_count, dtype=tf.float32) *
keep_threshold,
dtype=tf.int32)
threshold_value = tf.sort(importance,
direction='DESCENDING')[threshold_idx]
feats1 = tf.where((importance < threshold_value)[:, None],
tf.zeros_like(feats1), feats1)
importance = tf.where(importance < threshold_value,
tf.zeros_like(importance), importance)
feats1 = (feats1, importance)
if self.with_importance:
feats1, importance = feats1
else:
importance = None
feats2 = self.sparseconv_encblock0(feats1,
input_dict['voxel_centers0'],
neighbors[0],
importance=importance)
if self.with_importance == 'all':
feats2, importance = feats2
feats3, importance = self.sparseconv_down1(
feats2,
input_dict['voxel_centers0'],
input_dict['voxel_centers1'],
neighbors_down[0],
importance=importance)
feats4, importance = self.sparseconv_encblock1(
feats3,
input_dict['voxel_centers1'],
neighbors[1],
importance=importance)
feats5, importance = self.sparseconv_down2(
feats4,
input_dict['voxel_centers1'],
input_dict['voxel_centers2'],
neighbors_down[1],
importance=importance)
feats6, importance = self.sparseconv_encblock2(
feats5,
input_dict['voxel_centers2'],
neighbors[2],
importance=importance)
feats7, importance = self.sparseconv_down3(
feats6,
input_dict['voxel_centers2'],
input_dict['voxel_centers3'],
neighbors_down[2],
importance=importance)
feats8, importance = self.sparseconv_encblock3(
feats7,
input_dict['voxel_centers3'],
neighbors[3],
importance=importance)
feats9, importance = self.sparseconv_down3(
feats8,
input_dict['voxel_centers3'],
input_dict['voxel_centers4'],
neighbors_down[3],
importance=importance)
feats10, importance = self.sparseconv_encblock4(
feats9,
input_dict['voxel_centers4'],
neighbors[4],
importance=importance)
else:
if self.with_importance:
feats2, _ = feats2
feats3 = self.sparseconv_down1(feats2, input_dict['voxel_centers0'],
input_dict['voxel_centers1'],
neighbors_down[0])
feats4 = self.sparseconv_encblock1(feats3,
input_dict['voxel_centers1'],
neighbors[1])
feats5 = self.sparseconv_down2(feats4, input_dict['voxel_centers1'],
input_dict['voxel_centers2'],
neighbors_down[1])
feats6 = self.sparseconv_encblock2(feats5,
input_dict['voxel_centers2'],
neighbors[2])
feats7 = self.sparseconv_down3(feats6, input_dict['voxel_centers2'],
input_dict['voxel_centers3'],
neighbors_down[2])
feats8 = self.sparseconv_encblock3(feats7,
input_dict['voxel_centers3'],
neighbors[3])
feats9 = self.sparseconv_down3(feats8, input_dict['voxel_centers3'],
input_dict['voxel_centers4'],
neighbors_down[3])
feats10 = self.sparseconv_encblock4(feats9,
input_dict['voxel_centers4'],
neighbors[4])
feats11 = self.sparseconv_up3(feats10, input_dict['voxel_centers4'],
input_dict['voxel_centers3'],
neighbors_up[3])
if self.residual_skip_connection == 'all':
feats12 = feats11 + feats8
else:
feats12 = tf.concat([feats11, feats8], axis=-1)
feats13 = self.sparseconv_decblock3(feats12,
input_dict['voxel_centers3'],
neighbors[3])
feats14 = self.sparseconv_up2(feats13, input_dict['voxel_centers3'],
input_dict['voxel_centers2'],
neighbors_up[2])
if self.residual_skip_connection == 'all':
feats15 = feats14 + feats6
else:
feats15 = tf.concat([feats14, feats6], axis=-1)
feats16 = self.sparseconv_decblock2(feats15,
input_dict['voxel_centers2'],
neighbors[2])
feats17 = self.sparseconv_up1(feats16, input_dict['voxel_centers2'],
input_dict['voxel_centers1'],
neighbors_up[1])
if self.residual_skip_connection == 'all':
feats18 = feats17 + feats4
else:
feats18 = tf.concat([feats17, feats4], axis=-1)
feats19 = self.sparseconv_decblock1(feats18,
input_dict['voxel_centers1'],
neighbors[1])
feats20 = self.sparseconv_up0(feats19, input_dict['voxel_centers1'],
input_dict['voxel_centers0'],
neighbors_up[0])
if self.residual_skip_connection:
feats21 = feats20 + feats2
else:
feats21 = tf.concat([feats20, feats2], axis=-1)
code = self.sparseconv_decblock0(feats21, input_dict['voxel_centers0'],
neighbors[0])
return code
@tf.function
def aggregate(self, input_dict):
"""Aggregation step."""
feats = input_dict['feats']
nns = NNSResult(input_dict['aggregation_neighbors_index'],
input_dict['aggregation_neighbors_dist'],
input_dict['aggregation_row_splits'])
feats1 = self.cconv_block_in(
feats,
input_dict['points'],
input_dict['voxel_centers0'],
input_dict['voxel_sizes0'],
scale_compat=input_dict['aggregation_scale_compat'],
nns=nns)
return feats1
@tf.function
def decode(self, shifts, code):
"""Decode step and returns the gradient with respect to the shift.
Args:
shifts: Positions inside the voxels.
code: Output features of the unet for each voxel.
"""
new_code_shape = tf.concat(
[tf.shape(shifts)[:-1], tf.shape(code)[-1:]], axis=0)
code = tf.broadcast_to(code, new_code_shape)
decoder_input = tf.concat([shifts, code], axis=-1)
feats1 = self.dense_decoder1(decoder_input)
feats2 = self.dense_decoder2(feats1)
value = self.dense_decoder3(feats2)
shifts_grad = tf.gradients(value[..., 0], shifts)[0]
return tf.dtypes.cast(value, dtype=tf.float32), tf.dtypes.cast(
shifts_grad, dtype=tf.float32)
| 2.015625 | 2 |
QCA4020_SDK/target/sectools/qdn/sectools/common/crypto/functions/utils/openssl.py | r8d8/lastlock | 0 | 12767537 | <reponame>r8d8/lastlock<filename>QCA4020_SDK/target/sectools/qdn/sectools/common/crypto/functions/utils/openssl.py
# ===============================================================================
#
# Copyright (c) 2013-2017 Qualcomm Technologies, Inc.
# All Rights Reserved.
# Confidential and Proprietary - Qualcomm Technologies, Inc.
#
# ===============================================================================
"""
Created on Oct 26, 2014
@author: hraghav
"""
import hashlib
from sectools.common.crypto.functions.utils import UtilsBase
class UtilsOpenSSLImpl(UtilsBase):
hash_algos_map = {
UtilsBase.HASH_ALGO_SHA1: hashlib.sha1,
UtilsBase.HASH_ALGO_SHA256: hashlib.sha256,
UtilsBase.HASH_ALGO_SHA384: hashlib.sha384
}
def __init__(self, module):
super(UtilsOpenSSLImpl, self).__init__(module)
self.openssl = module
def hash(self, message, hash_algo):
if hash_algo in self.hash_algos_map.keys():
return self.hash_algos_map[hash_algo](message).hexdigest()
else:
return hashlib.sha256(message).hexdigest()
| 1.898438 | 2 |
src/modeling/__init__.py | clazaro97chosen/American-Community-Survey-Project | 2 | 12767538 | <filename>src/modeling/__init__.py
from .model_tryout import *
from .prep_or_featureselection import *
from .train_and_predict import * | 1.085938 | 1 |
tests/conftest.py | amagge/flair | 3,957 | 12767539 | import pytest
from pathlib import Path
@pytest.fixture(scope="module")
def resources_path():
return Path(__file__).parent / "resources"
@pytest.fixture(scope="module")
def tasks_base_path(resources_path):
return resources_path / "tasks"
@pytest.fixture(scope="module")
def results_base_path(resources_path):
return resources_path / "results"
def pytest_addoption(parser):
parser.addoption(
"--runslow", action="store_true", default=False, help="run slow tests"
)
parser.addoption(
"--runintegration",
action="store_true",
default=False,
help="run integration tests",
)
def pytest_collection_modifyitems(config, items):
if config.getoption("--runslow") and config.getoption("--runintegration"):
return
if not config.getoption("--runslow"):
skip_slow = pytest.mark.skip(reason="need --runslow option to run")
for item in items:
if "slow" in item.keywords:
item.add_marker(skip_slow)
if not config.getoption("--runintegration"):
skip_integration = pytest.mark.skip(
reason="need --runintegration option to run"
)
for item in items:
if "integration" in item.keywords:
item.add_marker(skip_integration)
| 2.09375 | 2 |
users/migrations/0005_auto_20210609_0406.py | JoyMbugua/arifa | 1 | 12767540 | <filename>users/migrations/0005_auto_20210609_0406.py<gh_stars>1-10
# Generated by Django 3.2.4 on 2021-06-09 04:06
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('users', '0004_rename_experince_customuser_experience'),
]
operations = [
migrations.RemoveField(
model_name='customuser',
name='employer',
),
migrations.RemoveField(
model_name='customuser',
name='experience',
),
migrations.RemoveField(
model_name='customuser',
name='expertise',
),
]
| 1.484375 | 1 |
command_line/fqm_mopac.py | qrefine/qrefine | 17 | 12767541 | <reponame>qrefine/qrefine
from __future__ import division
# LIBTBX_SET_DISPATCHER_NAME qr.fqm_mopac
import sys
import time
import os.path
import libtbx
import iotbx.pdb
import cPickle as pickle
from qrefine.fragment import fragments
from qrefine.fragment import fragment_extracts
from qrefine.fragment import get_qm_file_name_and_pdb_hierarchy
from qrefine.fragment import charge
from qrefine.fragment import write_mm_charge_file
from qrefine.plugin.ase.mopac_qr import Mopac
from qrefine.plugin.ase.orca_qr import Orca
from qrefine.restraints import ase_atoms_from_pdb_hierarchy
qrefine_path = libtbx.env.find_in_repositories("qrefine")
qr_yoink_path =os.path.join(qrefine_path, "plugin","yoink","yoink")
log = sys.stdout
legend = """\
Cluster a system into many small pieces
"""
def print_time(s):
outl = ''
secs=None
if s>60:
secs = s%60
s = s//60
if secs:
return '%02dm:%0.1fs' % (int(s), secs)
return '%0.1fs' % s
def get_qm_energy(qm_engine, fragments_extracted, index):
qm_pdb_file, ph = get_qm_file_name_and_pdb_hierarchy(
fragment_extracts=fragments_extracted,
index=index)
qm_charge = charge(fragment_extracts=fragments_extracted,
index=index)
atoms = ase_atoms_from_pdb_hierarchy(ph)
qm_engine.label = qm_pdb_file[:-4]
print 'LABEL',qm_engine.label
print 'qm_charge',qm_charge
qm_engine.run_qr(atoms,
charge=qm_charge,
pointcharges=None,
coordinates=qm_pdb_file[:-4]+".xyz",
define_str=None)
energy = qm_engine.energy_free #*unit_convert
return energy
class qm_energy_manager (object) :
def __init__ (self, qm_engine, fragments_extracted) :
self.qm_engine = qm_engine
self.fragments_extracted = fragments_extracted
def __call__ (self, index) :
return get_qm_energy(self.qm_engine,
self.fragments_extracted,
index,
)
def run_all (qm_engine,
fragments_extracted,
indices,
method='multiprocessing',
processes=1,
qsub_command=None,
callback=None) :
qm_engine_object = qm_energy_manager(qm_engine, fragments_extracted)
from libtbx.easy_mp import parallel_map
return parallel_map(
func=qm_engine_object,
iterable=indices,
method=method,
processes=processes,
callback=callback,
qsub_command=qsub_command)
def run(pdb_file, log):
pdb_inp = iotbx.pdb.input(pdb_file)
ph = pdb_inp.construct_hierarchy()
cs = pdb_inp.crystal_symmetry()
print >> log, '\n\tfragmenting "%s"' % pdb_file
t0=time.time()
#
# use a pickle file of fragments so testing of parallel is snappy
#
fq_fn = '%s_fq.pickle' % pdb_file.replace('.pdb','')
if not os.path.exists(fq_fn):
fq = fragments(
pdb_hierarchy=ph,
crystal_symmetry=cs,
charge_embedding=True,
debug=False,
qm_engine_name="orca")
print >> log, '\n\tfragmenting took %0.1f\n' % (time.time()-t0)
print >> log, "Residue indices for each cluster:\n", fq.clusters
fq_ext = fragment_extracts(fq)
f=file(fq_fn, 'wb')
pickle.dump(fq_ext, f)
f.close()
else:
f=file(fq_fn, 'rb')
fq_ext = pickle.load(f)
f.close()
#
# get QM engine
#
qm_engine = Orca()
if 0:
#
# use parallel_map
#
#indices = range(len(fq.clusters))
indices = range(len(fq_ext.fragment_selections))
rc = run_all(qm_engine,
fq_ext,
indices,
processes=6,
)
print rc
elif 0:
#
# use multi_core_run
#
from libtbx import easy_mp
nproc=6
argss = []
results = []
#for i in xrange(len(fq.clusters)):
for i in range(len(fq_ext.fragment_selections)):
argss.append([qm_engine, fq_ext, i])
for args, res, err_str in easy_mp.multi_core_run(get_qm_energy,
argss,
nproc,
):
print '%sTotal time: %6.2f (s)' % (' '*7, time.time()-t0)
print args[-1], res
if err_str:
print 'Error output from %s' % args
print err_str
print '_'*80
results.append([args, res, err_str])
print '-'*80
for args, res, err_str in results:
print args[-1], res
else:
#
# serial
#
#for i in xrange(len(fq.clusters)):
for i in range(len(fq_ext.fragment_selections)):
# add capping for the cluster and buffer
print >> log, "capping frag:", i
energy = get_qm_energy(qm_engine, fq_ext, i)
print >> log, " frag:", i, " energy:", energy, ' time:', print_time(time.time()-t0)
time.sleep(10)
if (__name__ == "__main__"):
t0 = time.time()
args = sys.argv[1:]
del sys.argv[1:]
run(args[0], log)
print >> log, "Time: %s" % print_time(time.time() - t0)
| 1.90625 | 2 |
mojito/event/EventGenerator.py | PPACI/mojito | 2 | 12767542 | <reponame>PPACI/mojito<filename>mojito/event/EventGenerator.py
from abc import ABC, abstractmethod
from typing import Any, Dict, List
class EventGenerator(ABC):
@abstractmethod
def generate(self) -> Dict[str, Any]:
"""
Generate a random event
:return: a dict like {property_name: value}
"""
raise NotImplementedError
@abstractmethod
def keys(self) -> List[str]:
"""
Return the list of properties name contained in this event generator
:return: a list of properties name
"""
raise NotImplementedError
| 3.078125 | 3 |
Network/WireShark.py | Penmast/Chameleon | 16 | 12767543 | #from __future__ import print_function
import winreg as reg
from scapy.all import *
import logging
logging.getLogger("scapy.runtime").setLevel(logging.ERROR)
ADAPTER_KEY = r'SYSTEM\CurrentControlSet\Control\Class\{4D36E972-E325-11CE-BFC1-08002BE10318}'
OpenVpnPath = "C:\\Program Files\\OpenVPN\\bin\\openvpn.exe"
ConfigPath = os.environ['USERPROFILE']+"\\OpenVPN\\config"
ConfTcp= "C:\\Users\\quent\\Downloads\\ovpn\\ovpn_tcp\\uk298.nordvpn.com.tcp.ovpn"
ConfUdp= "C:\\Users\\quent\\Downloads\\ovpn\\ovpn_udp\\uk298.nordvpn.com.udp.ovpn"
ConnectionKey = "SYSTEM\\CurrentControlSet\\Control\\Network\\{4D36E972-E325-11CE-BFC1-08002BE10318}"
interfaces = []
with reg.OpenKey(reg.HKEY_LOCAL_MACHINE, ADAPTER_KEY) as adapters:
try:
for i in range(10000):
key_name = reg.EnumKey(adapters, i)
with reg.OpenKey(adapters, key_name) as adapter:
try:
interfaces.append(reg.QueryValueEx(adapter, 'DriverDesc')[0])
except :
pass
except:
pass
print(interfaces[6])
#conf.color_theme=RastaTheme
#Description du nom de la carte wifi
conf.iface=interfaces[6]
"""
def packet_callback(packet):
if packet[TCP].payload:
pkt = str(packet[TCP].payload)
if packet[IP].dport == 80:
print("\n{} ----HTTP----> {}:{}:\n{}".format(packet[IP].src, packet[IP].dst, packet[IP].dport, str(bytes(packet[TCP].payload))))
sniff(filter="tcp", prn=packet_callback, store=0) """
pkt = []
#pkt = sniff(prn=lambda x: x.summary())
#print(pkt.summary())
packet = {}
cpt_pkt =0
def packet_callback(pkt):
global cpt_pkt
if pkt.haslayer(TCP):
packet[cpt_pkt]= {}
packet[cpt_pkt]["source_Port"] = pkt[TCP].sport
packet[cpt_pkt]["destination_Port"] = pkt[TCP].dport
print("Port Src:", packet[cpt_pkt]["source_Port"], "Port Dst:", packet[cpt_pkt]["destination_Port"])
if pkt.haslayer(IP):
packet[cpt_pkt]= {}
packet[cpt_pkt]["source_IP"] = pkt[IP].src
packet[cpt_pkt]["destination_IP"] = pkt[IP].dst
packet[cpt_pkt]["ttl"] = pkt.ttl
print("IP Src:", packet[cpt_pkt]["source_IP"], "Ip Dst:", packet[cpt_pkt]["destination_IP"])
packet[cpt_pkt] = {}
packet[cpt_pkt]["source_MAC"] = pkt.src
packet[cpt_pkt]["destination_MAC"] = pkt.dst
print("Mac Src:", packet[cpt_pkt]["source_MAC"], "Mac Dst:", packet[cpt_pkt]["destination_MAC"])
cpt_pkt += 1
#pkt.show()
pkt = sniff(count=10, prn=packet_callback, filter="tcp")
wrpcap('packets.pcap', pkt)
print(cpt_pkt)
i=0
print("boucle debut\n")
for cle, valeur in packet.items():
for key, value in packet[i].items():
print(key, value)
i =+1
print("boucle fin\n")
print("Packet 2\n")
print(packet[6])
print("Info packet 2 (IP:Port)\n")
print(packet[4]["source_IP"]+":"+packet[4]["source_Port"]+ "------>"+ packet[4]["destination_IP"]+":"+packet[4]["destination_Port"])
| 2.015625 | 2 |
PyFlow/Packages/CFRP/FunctionLibraries/ActionLibrary.py | yiwc/CFRP | 0 | 12767544 | from PyFlow.Core.Common import *
from PyFlow.Core import FunctionLibraryBase
from PyFlow.Core import IMPLEMENT_NODE
PIN_ALLOWS_ANYTHING = {PinSpecifires.ENABLED_OPTIONS: PinOptions.AllowAny | PinOptions.ArraySupported | PinOptions.DictSupported}
class ActionLibrary(FunctionLibraryBase):
'''doc string for DemoLib'''
def __init__(self, packageName):
super(ActionLibrary, self).__init__(packageName)
@staticmethod
@IMPLEMENT_NODE(returns=None, nodeType=NodeTypes.Callable, meta={NodeMeta.CATEGORY: 'ActionLibrary-L0', NodeMeta.KEYWORDS: []})
def Hi(robot=('AnyPin', "Robot", PIN_ALLOWS_ANYTHING.copy())):
print("Robot say: Hi!")
# @staticmethod
# @IMPLEMENT_NODE(returns=None, nodeType=NodeTypes.Callable, meta={NodeMeta.CATEGORY: 'ActionLibrary', NodeMeta.KEYWORDS: []})
# def set_grippers():
# print("Robot say: Hi!")
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L0', NodeMeta.KEYWORDS: []})
def set_grippers(robot=('AnyPin', "Robot", PIN_ALLOWS_ANYTHING.copy()),
value=('FloatPin', 'Value/0~1')
):
'''Returns attribute from object using "getattr(name)"'''
print("{} Gripper set to->{}".format(robot.name,value))
# return getattr(obj, name)
return True
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L0', NodeMeta.KEYWORDS: []})
def arm_cart_move(robot=("AnyPin","Robot", PIN_ALLOWS_ANYTHING.copy()),
arm=('StringPin', "Arm (l/r)"),
pos=('AnyPin',"pos=[x,y,z]"),
orn=('AnyPin',"orn=[a,b,c,g]"),
maxforce=('AnyPin',"maxforce=[Fx,Fy,Fz,Fr,Fp,Fy]"),
wait=('BoolPin',"wait=True/False")
):
'''Returns attribute from object using "getattr(name)"'''
print("Robot {} execute arm_cart_move to pos={} orn ={} with maxforce ={}".format(
robot.name,arm,pos,orn,maxforce
))
return True
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L0', NodeMeta.KEYWORDS: []})
def base_move(robot=("AnyPin","Robot", PIN_ALLOWS_ANYTHING.copy())
):
'''Returns attribute from object using "getattr(name)"'''
return True
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L1', NodeMeta.KEYWORDS: []})
def Pick(robot=("AnyPin","Robot", PIN_ALLOWS_ANYTHING.copy()),
execute=('ExecPin', "Execute")
):
'''Returns attribute from object using "getattr(name)"'''
print("Do Pick")
return True
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L1', NodeMeta.KEYWORDS: []})
def Place(robot=("AnyPin","Robot", PIN_ALLOWS_ANYTHING.copy()),
execute=('ExecPin', "Execute")
):
print("Do Place")
'''Returns attribute from object using "getattr(name)"'''
return True
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L1', NodeMeta.KEYWORDS: []})
def Insert(robot=("AnyPin","Robot", PIN_ALLOWS_ANYTHING.copy())
):
'''Returns attribute from object using "getattr(name)"'''
return True
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L1', NodeMeta.KEYWORDS: []})
def Screw(robot=("AnyPin","Robot", PIN_ALLOWS_ANYTHING.copy())
):
'''Returns attribute from object using "getattr(name)"'''
return True
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L2', NodeMeta.KEYWORDS: []})
def PickInsertScrew(robot=("AnyPin","Robot", PIN_ALLOWS_ANYTHING.copy())
):
'''Returns attribute from object using "getattr(name)"'''
return True
@staticmethod
@IMPLEMENT_NODE(returns=('AnyPin', None, PIN_ALLOWS_ANYTHING.copy()), meta={NodeMeta.CATEGORY: 'ActionLibrary-L2', NodeMeta.KEYWORDS: []})
def PickInsert(robot=("AnyPin","Robot", PIN_ALLOWS_ANYTHING.copy())
):
'''Returns attribute from object using "getattr(name)"'''
return True
| 2.40625 | 2 |
geo_college/geocode_tests.py | codelucas/antchatter | 1 | 12767545 | import os
import pickle
import codecs
import json
PARENT_DIR = os.path.dirname(os.path.abspath(__file__))
GEO_FILE = os.path.join(PARENT_DIR, 'college_geocode.txt')
geo_dat_prev = str(codecs.open(GEO_FILE, 'r', 'utf8').read())
geo_json = pickle.loads(geo_dat_prev)
for geo in geo_json[:10]:
print 'loc_range', geo['loc_range']
print'univ_name', geo['univ_name']
print 'loc_exact', geo['loc_exact']
print len(geo_json) | 2.671875 | 3 |
data/test.py | thapashani6/ML-Digit-Detection- | 0 | 12767546 | <gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 26 19:12:37 2019
@author: Shani
"""
#!/usr/bin/python
#!/usr/bin/python
from PIL import Image
import os, sys
path = "D:/thapa/Documents/Rowan/eighth_Semester/ML/Yolo-digit-detector-master/data"
dirs = os.listdir( path )
def resize():
for item in dirs:
if os.path.isfile(path+item):
im = Image.open(path+item)
f, e = os.path.splitext(path+item)
imResize = im.resize((200,200), Image.ANTIALIAS)
imResize.save(f + ' resized.jpg', 'JPEG', quality=90)
resize() | 2.765625 | 3 |
networks/__init__.py | xinxindefeiyu/S2VD-master_RESID | 48 | 12767547 | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Power by <NAME> 2020-09-15 13:00:15
| 1.203125 | 1 |
Part_2_intermediate/mod_2/lesson_5/homework_3/shop/product.py | Mikma03/InfoShareacademy_Python_Courses | 0 | 12767548 | class Product:
def __init__(self, name, category_name, unit_price):
self.name = name
self.category_name = category_name
self.unit_price = unit_price
def __str__(self):
return f"Nazwa: {self.name} | Kategoria: {self.category_name} | Cena: {self.unit_price} PLN/szt"
| 3.21875 | 3 |
build.py | ledocc/conan-turtle | 1 | 12767549 | from cpt.packager import ConanMultiPackager, tools
if __name__ == "__main__":
builder = ConanMultiPackager(
reference="turtle/{}".format( tools.load("version.txt") )
)
builder.add_common_builds()
builder.run()
| 1.554688 | 2 |
torchcde/__init__.py | jb-c/torchcde | 247 | 12767550 | <reponame>jb-c/torchcde
from .interpolation_base import InterpolationBase
from .interpolation_cubic import natural_cubic_spline_coeffs, natural_cubic_coeffs, CubicSpline
from .interpolation_linear import linear_interpolation_coeffs, LinearInterpolation
from .interpolation_hermite_cubic_bdiff import hermite_cubic_coefficients_with_backward_differences
from .log_ode import logsignature_windows, logsig_windows
from .misc import TupleControl
from .solver import cdeint
__version__ = "0.2.5"
| 0.898438 | 1 |