code
stringlengths 22
1.05M
| apis
listlengths 1
3.31k
| extract_api
stringlengths 75
3.25M
|
|---|---|---|
import os
from discord.ext.commands import Bot
from Flare import Flare
bot = Bot("~~")
bot.add_cog(Flare(bot))
@bot.command("ping")
async def ping_pong(ctx):
ctx.send("pong")
bot.run(os.environ.get("BOT_TOKEN"))
|
[
"discord.ext.commands.Bot",
"os.environ.get",
"Flare.Flare"
] |
[((79, 88), 'discord.ext.commands.Bot', 'Bot', (['"""~~"""'], {}), "('~~')\n", (82, 88), False, 'from discord.ext.commands import Bot\n'), ((102, 112), 'Flare.Flare', 'Flare', (['bot'], {}), '(bot)\n', (107, 112), False, 'from Flare import Flare\n'), ((194, 221), 'os.environ.get', 'os.environ.get', (['"""BOT_TOKEN"""'], {}), "('BOT_TOKEN')\n", (208, 221), False, 'import os\n')]
|
import setuptools
ver = {}
with open('OpenControl/_version.py') as fd:
exec(fd.read(), ver)
version = ver.get('__version__')
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="OpenControl",
version=version,
author="VNOpenAI",
author_email="<EMAIL>",
description="A python control systems package",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://opencontrol.readthedocs.io/en/latest/",
project_urls={
"Bug Tracker": "https://github.com/VNOpenAI/OpenControl/issues",
},
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
packages=setuptools.find_packages(),
python_requires=">=3.7",
)
|
[
"setuptools.find_packages"
] |
[((815, 841), 'setuptools.find_packages', 'setuptools.find_packages', ([], {}), '()\n', (839, 841), False, 'import setuptools\n')]
|
'''See the shared Google Drive documentation for an inheritance diagram that
shows the relationships between the classes defined in this file.
'''
import numpy as np
import socket
import time
from riglib import source
from ismore import settings, udp_feedback_client
import ismore_bmi_lib
from utils.constants import *
#import armassist
#import rehand
from riglib.filter import Filter
from riglib.plants import Plant
import os
class BasePlantUDP(Plant):
'''
Common UDP interface for the ArmAssist/ReHand
'''
debug = 0
sensor_data_timeout = 1 # seconds. if this number of seconds has passed since sensor data was received, velocity commands will not be sent
lpf_vel = 0
# define in subclasses!
ssm_cls = None
addr = None
feedback_data_cls = None
data_source_name = None
n_dof = None
blocking_joints = None
safety_grid = None
feedback_str = ''
def __init__(self, *args, **kwargs):
self.source = source.DataSource(self.feedback_data_cls, bufferlen=5, name=self.data_source_name)
self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # used only for sending
ssm = self.ssm_cls()
self.pos_state_names = [s.name for s in ssm.states if s.order == 0]
self.vel_state_names = [s.name for s in ssm.states if s.order == 1]
self.aa_xy_ix = [i for i, j in enumerate(ssm.states) if j.name in ['aa_px', 'aa_py']]
self.aa_psi_ix = [i for i, j in enumerate(ssm.states) if j.name == 'aa_ppsi']
self.rh_pron_ix = [i for i, j in enumerate(ssm.states) if j.name == 'rh_pprono']
self.rh_pfings = [(i, j.name) for i, j in enumerate(ssm.states) if j.name in ['rh_pthumb', 'rh_pindex', 'rh_pfing3']]
self.drive_velocity_raw = np.zeros((len(self.vel_state_names),))
self.drive_velocity_raw_fb_gain = np.zeros((len(self.vel_state_names),))
self.drive_velocity_sent = np.zeros((len(self.vel_state_names),))
self.drive_velocity_sent_pre_safety = np.zeros((len(self.vel_state_names),))
self.pre_drive_state = np.zeros((len(self.vel_state_names), ))
# low-pass filters to smooth out command velocities
# from scipy.signal import butter
# b, a = butter(5, 0.1) # fifth order, 2 Hz bandpass (assuming 10 Hz update rate)
#omega, H = signal.freqz(b, a)
#plt.figure()
#plt.plot(omega/np.pi, np.abs(H))
# self.vel_command_lpfs = [None] * self.n_dof
# for k in range(self.n_dof):
# self.vel_command_lpfs[k] = Filter(b=b, a=a)
# self.last_sent_vel = np.ones(self.n_dof) * np.nan
# calculate coefficients for a 4th-order Butterworth LPF at 1.5 Hz for kinematic data received from the exo
# fs_synch = 20 #Frequency at which emg and kin data are synchronized
# nyq = 0.5 * fs_synch
# cuttoff_freq = 1.5 / nyq
# bpf_kin_coeffs = butter(4, cuttoff_freq, btype='low')
# self.pos_filt = [None] * self.n_dof
# for k in range(self.n_dof):
# self.pos_filt[k] = Filter(bpf_kin_coeffs[0], bpf_kin_coeffs[1])
def init(self):
from riglib import sink
sink.sinks.register(self.source)
def start(self):
# only start this DataSource after it has been registered with
# the SinkManager singleton (sink.sinks) in the call to init()
self.source.start()
self.ts_start_data = time.time()
def stop(self):
# send a zero-velocity command
self._send_command('SetSpeed %s %s\r' % (self.plant_type, self.pack_vel(np.zeros(self.n_dof))))
self.source.stop()
self.feedback_file.close()
def last_data_ts_arrival(self):
return self.source.read(n_pts=1)['ts_arrival'][0]
def _send_command(self, command):
self.sock.sendto(command, self.addr)
def pack_vel(self, vel):
format_str = "%f " * self.n_dof
return format_str % tuple(vel)
def send_vel(self, vel):
assert len(vel) == self.n_dof
vel = vel.copy()
vel *= self.vel_gain # change the units of the velocity, if necessary
self.last_sent_vel = vel
#command_vel is already fitlered at the task level, no need to filter it again.
#self.last_sent_vel = filt_vel = np.array([self.vel_command_lpfs[k](vel[k]) for k in range(self.n_dof)]).ravel()
if all(v <= 0.00000001 for v in abs(self.last_sent_vel)):
print ('last sent vel')
print (self.last_sent_vel)
if (self.last_data_ts_arrival() == 0) or ((self.last_data_ts_arrival() - time.time()) > self.sensor_data_timeout):
print ("sensor data not received for %s recently enough, not sending velocity command!" % self.plant_type)
return
# squash any velocities which would take joints outside of the rectangular bounding box
current_pos = self.get_pos() * self.vel_gain
projected_pos = current_pos + vel * 0.1
max_reached, = np.nonzero((projected_pos > self.max_pos_vals) * (vel > 0))
min_reached, = np.nonzero((projected_pos < self.min_pos_vals) * (vel < 0))
vel[max_reached] = 0
vel[min_reached] = 0
self._send_command('SetSpeed %s %s\r' % (self.plant_type, self.pack_vel(vel)))
# set max speed limts
faster_than_max_speed, = np.nonzero(np.abs(vel) > self.max_speed)
vel[faster_than_max_speed] = self.max_speed[faster_than_max_speed] * np.sign(vel[faster_than_max_speed])
#if we wanna define some limit values for the rehand use the filt_vel. Otherwise use vel
#self._send_command('SetSpeed %s %s\r' % (self.plant_type, self.pack_vel(filt_vel)))
self._send_command('SetSpeed %s %s\r' % (self.plant_type, self.pack_vel(vel)))
# set max speed limts
faster_than_max_speed, = np.nonzero(np.abs(vel) > self.max_speed)
vel[faster_than_max_speed] = self.max_speed[faster_than_max_speed] * np.sign(vel[faster_than_max_speed])
if self.debug:
print ("input vel")
print (vel)
print ("vel sent to %s" % self.plant_type)
print (vel)
print ("current_pos")
print (current_pos)
print ("projected_pos")
print (projected_pos)
print ("actual velocity")
print (self.get_vel())
if self.lpf_vel:
# squash any velocities which would take joints outside of the rectangular bounding box
current_pos = self.get_pos() * self.vel_gain
projected_pos = current_pos + vel * (1.0/20)
max_reached, = np.nonzero((projected_pos > self.max_pos_vals) * (vel > 0))
min_reached, = np.nonzero((projected_pos < self.min_pos_vals) * (vel < 0))
vel[max_reached] = 0
vel[min_reached] = 0
# set max speed limts
faster_than_max_speed, = np.nonzero(np.abs(vel) > self.max_speed)
vel[faster_than_max_speed] = self.max_speed[faster_than_max_speed] * np.sign(vel[faster_than_max_speed])
if faster_than_max_speed > 0:
print ('faster_than_max_speed')
print (faster_than_max_speed)
if self.debug:
print ("input vel")
print (vel)
print ("vel sent to %s" % self.plant_type)
print (vel)
#print "current_pos"
#print current_pos
#print "projected_pos"
#print projected_pos
#print "actual velocity"
#print self.get_vel()
self._send_command('SetSpeed %s %s\r' % (self.plant_type, self.pack_vel(vel)))
else:
self._send_command('SetSpeed %s %s\r' % (self.plant_type, self.pack_vel(vel)))
# def get_pos(self):
# # udp_feedback_client takes care of converting sensor data to cm or rad, as appropriate for the DOF
# return np.array(tuple(self.source.read(n_pts=1)['data'][self.pos_state_names][0]))
def drive(self, decoder):
vel = decoder['qdot']
vel_bl = vel.copy()
feedback_str = ''
if self.blocking_joints is not None:
vel_bl[self.blocking_joints] = 0
if self.safety_grid is not None:
#If the next position is outside of safety then damp velocity to only go to limit:
pos_pred = decoder['q'] + 0.1*vel_bl
#Make sure predicted AA PX, AA PY within bounds:
xy_change = True
if len(self.aa_xy_ix) > 0:
if self.safety_grid.is_valid_pos(pos_pred[self.aa_xy_ix]) is False:
#If not, make their velocity zero:
vel_bl[self.aa_xy_ix] = 0
xy_change = False
feedback_str = feedback_str+ ' stopping xy from moving'
else:
xy_change = False
# Make sure AA Psi within bounds:
if len(self.aa_psi_ix) > 0:
# If X/Y ok
if xy_change:
mn, mx = self.safety_grid.get_minmax_psi(pos_pred[self.aa_xy_ix])
# If x/y not ok:
else:
mn, mx = self.safety_grid.get_minmax_psi(decoder['q'][self.aa_xy_ix])
# Set psi velocity :
if np.logical_and(pos_pred[self.aa_psi_ix] >= mn, pos_pred[self.aa_psi_ix] <= mx):
pass
else:
vel_bl[self.aa_psi_ix] = 0
feedback_str = feedback_str+ 'stopping psi'
# Make sure RH Prono within bounds (if SSM is only RH, use settings.starting_pos for AAPX, AAPY)
if len(self.rh_pron_ix) > 0:
# If X/Y ok
if xy_change:
mn, mx = self.safety_grid.get_minmax_prono(pos_pred[self.aa_xy_ix])
# If x/y not ok or not moving bc not part of state pace :
else:
if len(self.aa_xy_ix) > 0:
mn, mx = self.safety_grid.get_minmax_prono(decoder['q'][self.aa_xy_ix])
else:
mn, mx = self.safety_grid.get_minmax_prono(settings.starting_pos['aa_px'], settings.starting_pos['aa_py'])
# Set prono velocity :
if np.logical_and(pos_pred[self.rh_pron_ix] >= mn, pos_pred[self.rh_pron_ix] <= mx):
pass
else:
vel_bl[self.rh_pron_ix] = 0
feedback_str = feedback_str+ 'stopping prono'
# Assure RH fingers are within range:
if len(self.rh_pfings) > 0:
for i, (ix, nm) in enumerate(self.rh_pfings):
mn, mx = self.safety_grid.get_rh_minmax(nm)
if np.logical_and(pos_pred[ix] >= mn, pos_pred[ix] <= mx):
pass
else:
vel_bl[ix] = 0
feedback_str = feedback_str+ 'stopping rh fings'
self.feedback_str = feedback_str
self.drive_velocity = vel_bl
self.send_vel(vel_bl)
decoder['q'] = self.get_pos()
def write_feedback(self):
pos_vel = [str(i) for i in np.hstack(( self.get_pos(), self.get_vel() )) ]
#self.feedback_file.write(','.join(pos_vel)+'\n')
if self.feedback_str != '':
self.feedback_file.write(self.feedback_str+ time.ctime() + '\n')
class ArmAssistPlantUDP(BasePlantUDP):
'''Sends velocity commands and receives feedback over UDP. Can be used
with either the real or simulated ArmAssist.
'''
ssm_cls = ismore_bmi_lib.StateSpaceArmAssist
addr = settings.ARMASSIST_UDP_SERVER_ADDR
feedback_data_cls = udp_feedback_client.ArmAssistData
data_source_name = 'armassist'
n_dof = 3
plant_type = 'ArmAssist'
vel_gain = np.array([cm_to_mm, cm_to_mm, rad_to_deg]) # convert units to: [mm/s, mm/s, deg/s]
max_pos_vals = np.array([np.inf, np.inf, np.inf])
min_pos_vals = np.array([-np.inf, -np.inf, -np.inf])
max_speed = np.array([np.inf, np.inf, np.inf])
feedback_file = open(os.path.expandvars('$HOME/code/bmi3d/log/armassist.txt'), 'w')
#max_speed = np.array([40, 60, 20]) # in mm/s and deg/s
#max_speed = np.array([60, 80, 50]) # in mm/s and deg/s
#parameters for kinematics low-pass filtering
from scipy.signal import butter, lfilter
from ismore.filter import Filter
fs_synch = 25 #Frequency at which emg and kin data are synchronized
nyq = 0.5 * fs_synch
cuttoff_freq = 1.5 / nyq
bpf_kin_coeffs = butter(2, cuttoff_freq, btype='low')
n_dof = 3
vel_filter = [None] * n_dof
for k in range(n_dof):
vel_filter[k] = Filter(bpf_kin_coeffs[0], bpf_kin_coeffs[1])
n_getpos_iter= 0
def __init__(self, *args, **kwargs):
super(ArmAssistPlantUDP, self).__init__(*args, **kwargs)
def set_pos_control(self): # position control with global reference system
self._send_command('SetControlMode ArmAssist Position')
def set_global_control(self): #velocity control with global reference system
self._send_command('SetControlMode ArmAssist Global')
def set_trajectory_control(self): #trajectory control with global reference system
self._send_command('SetControlMode ArmAssist Trajectory')
def send_vel(self, vel):
vel = vel.copy()
# units of vel should be: [cm/s, cm/s, rad/s]
assert len(vel) == self.n_dof
# convert units to: [mm/s, mm/s, deg/s] to send them through UDP to the ArmAssist application
vel[0] *= cm_to_mm
vel[1] *= cm_to_mm
vel[2] *= rad_to_deg
# set max speed limts
faster_than_max_speed, = np.nonzero(np.abs(vel) > self.max_speed)
vel[faster_than_max_speed] = self.max_speed[faster_than_max_speed] * np.sign(vel[faster_than_max_speed])
self.debug = True
if self.debug:
# print "vel sent to armassist"
# print vel
if faster_than_max_speed.any() > 0:
print ('faster_than_max_speed')
print (faster_than_max_speed)
print ("speed set to: ")
print (vel)
self._send_command('SetSpeed ArmAssist %f %f %f\r' % tuple(vel))
# get raw position
def get_pos_raw(self):
# udp_feedback_client takes care of converting sensor data to cm or rad, as appropriate for the DOF
#get the last poitns of data of the armassist and low-pass filter
return np.array(tuple(self.source.read(n_pts=1)['data'][self.pos_state_names][0]))
# get filtered position
def get_pos(self):
return np.array(tuple(self.source.read(n_pts=1)['data_filt'][self.pos_state_names][0]))
# calculate vel from raw position
def get_vel_raw(self):
recent_pos_data = self.source.read(n_pts=2)
pos = recent_pos_data['data'][self.pos_state_names]
ts = recent_pos_data['ts']
delta_pos = np.array(tuple(pos[1])) - np.array(tuple(pos[0]))
delta_ts = ts[1] - ts[0]
vel = delta_pos / delta_ts
#filt_vel = np.array([self.vel_command_lpfs[k](vel[k]) for k in range(self.n_dof)]).ravel() #nerea --> to test!
if ts[0] != 0 and any(np.isnan(v) for v in vel):
print ("WARNING -- delta_ts = 0 in AA vel calculation:", vel)
for i in range(3):
if np.isnan(vel[i]):
vel[i] = 0
return vel
#calculate vel from raw position and filter
def get_vel(self):
recent_pos_data = self.source.read(n_pts=2)
pos = recent_pos_data['data'][self.pos_state_names]
ts = recent_pos_data['ts']
delta_pos = np.array(tuple(pos[1])) - np.array(tuple(pos[0]))
delta_ts = ts[1] - ts[0]
vel = delta_pos / delta_ts
if ts[0] != 0 and any(np.isnan(v) for v in vel):
print ("WARNING -- delta_ts = 0 in AA vel calculation:", vel)
for i in range(3):
if np.isnan(vel[i]):
vel[i] = 0
# the first value of the pos because it is always NaN and if a NaN is introduced in the filter, all the following filtered values will be also NaNs
if np.any(np.isnan(vel)):
self.n_getpos_iter = self.n_getpos_iter +1
vel_filt = vel
else:
vel_filt = np.array([self.vel_filter[k](vel[k]) for k in range(self.n_dof)]).ravel()
return vel_filt
def send_pos(self, pos, time):
pos = pos.copy()
# units of vel should be: [cm/s, cm/s, rad/s]
assert len(pos) == 3
# convert units to: [mm/s, mm/s, deg/s]
pos[0] *= cm_to_mm
pos[1] *= cm_to_mm
pos[2] *= rad_to_deg
# mode 1: the forearm angle (psi) stays the same as it is. mode 2: psi will move according to the determined value
mode = 2
pos_command = np.zeros(5)
pos_command[0] = pos[0]
pos_command[1] = pos[1]
pos_command[2] = pos[2]
pos_command[3] = time
pos_command[4] = mode
print ("pos")
print (pos)
print ("time")
print (time)
self._send_command('SetPosition ArmAssist %f %f %f %f %f\r' % tuple(pos_command))
def enable(self):
self._send_command('SetControlMode ArmAssist Global\r')
def disable(self):
self._send_command('SetControlMode ArmAssist Disable\r')
def enable_watchdog(self, timeout_ms):
print ('ArmAssist watchdog not enabled, doing nothing')
def send_traj(self, pos_vel):
pos_vel = pos_vel.copy()
# units of vel should be: [cm/s, cm/s, rad/s]
assert len(pos_vel) == 6
# units to are alread in [mm/s, mm/s, rad/s]
# convert values to integers to reduce noise
#pos_vel_int = np.rint(pos_vel)
pos_vel_int = pos_vel
print ("trajectory sent to AA")
print ("x y psi vx vy vpsi")
print (pos_vel_int)
traj_command = np.zeros(6)
traj_command[0] = pos_vel_int[0]
traj_command[1] = pos_vel_int[1]
traj_command[2] = pos_vel_int[2]
traj_command[3] = pos_vel_int[3]
traj_command[4] = pos_vel_int[4]
traj_command[5] = pos_vel_int[5]
self._send_command('SetTrajectory ArmAssist %d %d %d %d %d %d\r' % tuple(traj_command))
class DummyPlantUDP(object):
drive_velocity_raw = np.array([0,0,0])
drive_velocity_sent = np.array([0,0,0])
drive_velocity_sent_pre_safety = np.array([0,0,0])
pre_drive_state = np.array([0, 0, 0])
def init(self):
pass
def enable(self):
pass
def start(self):
pass
def stop(self):
pass
def write_feedback(self):
pass
def get_pos_raw(self):
return np.array([0,0,0])
def get_pos(self):
return np.array([0,0,0])
def get_vel_raw(self):
return np.array([0,0,0])
def get_vel(self):
return np.array([0,0,0])
class ReHandPlantUDP(BasePlantUDP):
'''Sends velocity commands and receives feedback over UDP. Can be used
with either the real or simulated ReHand.
'''
ssm_cls = ismore_bmi_lib.StateSpaceReHand
addr = settings.REHAND_UDP_SERVER_ADDR
feedback_data_cls = udp_feedback_client.ReHandData
data_source_name = 'rehand'
n_dof = 4
plant_type = 'ReHand'
vel_gain = np.array([rad_to_deg, rad_to_deg, rad_to_deg, rad_to_deg])
max_pos_vals = np.array([60, 60, 60, 90], dtype=np.float64) # degrees
min_pos_vals = np.array([25, 25, 25, 25], dtype=np.float64) # degrees
max_speed = np.array([np.inf, np.inf, np.inf, np.inf], dtype=np.float64) # degrees/sec
#max_speed = np.array([15., 15., 15., 15.], dtype=np.float64) # degrees/sec
feedback_file = open(os.path.expandvars('$HOME/code/bmi3d/log/rehand.txt'), 'w')
def send_vel(self, vel):
vel = vel.copy()
# units of vel should be: [rad/s, rad/s, rad/s, rad/s]
assert len(vel) == self.n_dof
# convert units to: [deg/s, deg/s, deg/s, deg/s]
vel *= rad_to_deg
#filt_vel = np.array([self.vel_command_lpfs[k](vel[k]) for k in range(self.n_dof)]).ravel()
# set max speed limts
faster_than_max_speed, = np.nonzero(np.abs(vel) > self.max_speed)
vel[faster_than_max_speed] = self.max_speed[faster_than_max_speed] * np.sign(vel[faster_than_max_speed])
self.debug = True
if self.debug:
# print 'filt_vel in plants in degrees'
# print filt_vel #*np.array([deg_to_rad, deg_to_rad, deg_to_rad, deg_to_rad])
if faster_than_max_speed.any() > 0:
print ('faster_than_max_speed')
print (faster_than_max_speed)
print ("speed set to: ")
print (vel)
# self.plant.enable() #when we send vel commands always enable the rehand motors
# self._send_command('SystemEnable ReHand\r')
self._send_command('SetSpeed ReHand %f %f %f %f\r' % tuple(vel))
def get_vel_raw(self):
return np.array(tuple(self.source.read(n_pts=1)['data'][self.vel_state_names][0]))
def get_vel(self):
return np.array(tuple(self.source.read(n_pts=1)['data_filt'][self.vel_state_names][0]))
def enable(self):
self._send_command('SystemEnable ReHand\r')
def disable(self):
self._send_command('SystemDisable ReHand\r')
def diff_enable(self,DoFs):
self._send_command('DiffEnable ReHand %i %i %i %i\r' % tuple(DoFs))
def get_enable_state(self):
self._send_command('GetEnableState ReHand\r')
def enable_watchdog(self, timeout_ms):
self._send_command('WatchDogEnable ReHand %d\r' % timeout_ms)
def get_pos_raw(self):
# udp_feedback_client takes care of converting sensor data to cm or rad, as appropriate for the DOF
return np.array(tuple(self.source.read(n_pts=1)['data'][self.pos_state_names][0]))
#get pos filtered
def get_pos(self):
return np.array(tuple(self.source.read(n_pts=1)['data_filt'][self.pos_state_names][0]))
################################################
class BasePlantIsMore(Plant):
# define in subclasses!
aa_plant_cls = None
rh_plant_cls = None
safety_grid = None
both_feedback_str = ''
def __init__(self, *args, **kwargs):
self.aa_plant = self.aa_plant_cls()
self.rh_plant = self.rh_plant_cls()
self.drive_velocity_raw = np.zeros((7,))
self.drive_velocity_sent= np.zeros((7,))
self.drive_velocity_sent_pre_safety = np.zeros((7, ))
self.pre_drive_state = np.zeros((7, ))
self.prev_vel_bl_aa = np.zeros((3, ))*np.NaN
self.prev_vel_bl_rh = np.zeros((4, ))*np.NaN
self.accel_lim_armassist = np.inf #0.8
self.accel_lim_psi = np.inf #0.16
self.accel_lim_rehand = np.inf #0.16
def init(self):
self.aa_plant.init()
self.rh_plant.init()
def start(self):
self.aa_plant.start()
self.rh_plant.start()
self.ts_start_data = time.time()
def stop(self):
self.aa_plant.stop()
self.rh_plant.stop()
def last_data_ts_arrival(self):
return {
'ArmAssist': self.aa_plant.last_data_ts_arrival(),
'ReHand': self.rh_plant.last_data_ts_arrival(),
}
def send_vel(self, vel):
self.aa_plant.send_vel(vel[0:3])
self.rh_plant.send_vel(vel[3:7])
def get_pos_raw(self):
aa_pos = self.aa_plant.get_pos_raw()
rh_pos = self.rh_plant.get_pos_raw()
return np.hstack([aa_pos, rh_pos])
def get_pos(self):
aa_pos = self.aa_plant.get_pos()
rh_pos = self.rh_plant.get_pos()
return np.hstack([aa_pos, rh_pos])
def get_vel_raw(self):
aa_vel = self.aa_plant.get_vel_raw()
rh_vel = self.rh_plant.get_vel_raw()
return np.hstack([aa_vel, rh_vel])
def get_vel(self):
aa_vel = self.aa_plant.get_vel()
rh_vel = self.rh_plant.get_vel()
return np.hstack([aa_vel, rh_vel])
def enable(self):
self.aa_plant.enable()
self.rh_plant.enable()
def disable(self):
self.aa_plant.disable()
self.rh_plant.disable()
def drive(self, decoder):
# print self.aa_plant.aa_xy_ix: [0, 1]
# print self.aa_plant.aa_psi_ix: [2]
# print self.rh_plant.rh_pfings: [0, 1, 2]
# print self.rh_plant.rh_pron_ix: [3]
vel = decoder['qdot']
vel_bl = vel.copy()
current_state = self.get_pos()
self.pre_drive_state = current_state.copy()
self.drive_velocity_raw = vel_bl.copy()
if self.blocking_joints is not None:
vel_bl[self.blocking_joints] = 0
vel_bl_aa0 = vel_bl[0:3].copy()
vel_bl_rh0 = vel_bl[3:7].copy()
### Accel Limit Velocitites ###
# if not np.all(np.isnan(np.hstack((self.prev_vel_bl_aa, self.prev_vel_bl_rh)))):
# aa_output_accel = vel_bl_aa - self.prev_vel_bl_aa
# rh_output_accel = vel_bl_rh - self.prev_vel_bl_rh
# ### AA XY ###
# for i in np.arange(2):
# if aa_output_accel[i] > self.accel_lim_armassist:
# vel_bl_aa[i] = self.prev_vel_bl_aa[i] + self.accel_lim_armassist
# elif aa_output_accel[i] < -1*self.accel_lim_armassist:
# vel_bl_aa[i] = self.prev_vel_bl_aa[i] - self.accel_lim_armassist
# ### AA PSI ###
# if aa_output_accel[2] > self.accel_lim_psi:
# vel_bl_aa[2] = self.prev_vel_bl_aa[2] + self.accel_lim_psi
# elif aa_output_accel[2] < -1*self.accel_lim_psi:
# vel_bl_aa[2] = self.prev_vel_bl_aa[2] - self.accel_lim_psi
# ### RH All ###
# for i in np.arange(4):
# if rh_output_accel[i] > self.accel_lim_rehand:
# vel_bl_rh[i] = self.prev_vel_bl_rh[i] + self.accel_lim_rehand
# elif rh_output_accel[i] < -1*self.accel_lim_rehand:
# vel_bl_rh[i] = self.prev_vel_bl_rh[i] - self.accel_lim_rehand
### Add Attractor ###
if self.safety_grid is not None:
attractor_point_aa = self.safety_grid.attractor_point[:3]
attractor_point_rh = self.safety_grid.attractor_point[3:]
vel_bl_aa_pull = self.attractor_speed_const*(attractor_point_aa - current_state[:3])/0.05
vel_bl_rh_pull = self.attractor_speed_const*(attractor_point_rh - current_state[3:])/0.05
vel_bl_aa = vel_bl_aa0 + vel_bl_aa_pull.copy()
vel_bl_rh = vel_bl_rh0 + vel_bl_rh_pull.copy()
else:
vel_bl_aa = vel_bl_aa0
vel_bl_rh = vel_bl_rh0
### LPF Filter Velocities ###
for s, state in enumerate(['aa_vx', 'aa_vy', 'aa_vpsi']):
vel_bl_aa[s] = self.command_lpfs[state](vel_bl_aa[s])
if np.isnan(vel_bl_aa[s]):
vel_bl_aa[s] = 0
for s, state in enumerate(['rh_vthumb', 'rh_vindex', 'rh_vfing3', 'rh_vprono']):
vel_bl_rh[s] = self.command_lpfs[state](vel_bl_rh[s])
if np.isnan(vel_bl_rh[s]):
vel_bl_rh[s] = 0
self.drive_velocity_sent_pre_safety = np.hstack(( vel_bl_aa.copy(), vel_bl_rh.copy()))
#If the next position is outside of safety then damp velocity to only go to limit:
pos_pred = current_state + 0.05*np.hstack((vel_bl_aa, vel_bl_rh))
pos_pred_aa = pos_pred[0:3]
pos_pred_rh = pos_pred[3:7]
both_feedback_str = ''
if self.safety_grid is not None:
if len(self.aa_plant.aa_xy_ix) > 0:
x_tmp = self.safety_grid.is_valid_pos(pos_pred_aa[self.aa_plant.aa_xy_ix])
if x_tmp == False:
current_pos = current_state[self.aa_plant.aa_xy_ix]
pos_valid = attractor_point_aa[self.aa_plant.aa_xy_ix]
#d_to_valid, pos_valid = self.safety_grid.dist_to_valid_point(current_pos)
vel_bl_aa[self.aa_plant.aa_xy_ix] = self.attractor_speed*(pos_valid - current_pos)/0.05
pos_pred_aa[self.aa_plant.aa_xy_ix] = current_pos + 0.05*vel_bl_aa[self.aa_plant.aa_xy_ix]
#print 'plant adjust: ', vel_bl_aa[self.aa_plant.aa_xy_ix], pos_pred_aa[self.aa_plant.aa_xy_ix]
xy_change = True
# Make sure AA Psi within bounds:
if len(self.aa_plant.aa_psi_ix) > 0:
mn, mx = self.safety_grid.get_minmax_psi(pos_pred_aa[self.aa_plant.aa_xy_ix])
predx, predy= pos_pred_aa[[0, 1]]
# Set psi velocity :
psi_ok = False
if np.logical_and(pos_pred_aa[self.aa_plant.aa_psi_ix] >= mn, pos_pred_aa[self.aa_plant.aa_psi_ix] <= mx):
# Test if globally ok:
global_ok = self.safety_grid.global_hull.hull3d.find_simplex(np.array([predx, predy, pos_pred_aa[2]])) >=0
if global_ok:
psi_ok = True
if psi_ok == False:
# Move psi back to attractor pos:
psi_neutral = attractor_point_aa[self.aa_plant.aa_psi_ix]
vel_bl_aa[self.aa_plant.aa_psi_ix] = self.attractor_speed*(psi_neutral-current_state[self.aa_plant.aa_psi_ix])/0.05
# Make sure RH Prono within bounds (if SSM is only RH, use settings.starting_pos for AAPX, AAPY)
if len(self.rh_plant.rh_pron_ix) > 0:
mn, mx = self.safety_grid.get_minmax_prono(pos_pred_aa[self.aa_plant.aa_xy_ix])
# Set prono velocity :
if np.logical_and(pos_pred_rh[self.rh_plant.rh_pron_ix] >= mn, pos_pred_rh[self.rh_plant.rh_pron_ix] <= mx):
pass
else:
tmp_pos = pos_pred_rh[self.rh_plant.rh_pron_ix]
prono_neutral = attractor_point_rh[self.rh_plant.rh_pron_ix]
vel_bl_rh[self.rh_plant.rh_pron_ix] = self.attractor_speed*(prono_neutral-tmp_pos)/0.05
# Assure RH fingers are within range:
if len(self.rh_plant.rh_pfings) > 0:
for i, (ix, nm) in enumerate(self.rh_plant.rh_pfings):
mn, mx = self.safety_grid.get_rh_minmax(nm)
if np.logical_and(pos_pred_rh[ix] >= mn, pos_pred_rh[ix] <= mx):
pass
else:
tmp_ = pos_pred_rh[ix]
neutral = attractor_point_rh[ix]
vel_bl_rh[ix] = self.attractor_speed*(neutral - tmp_)/0.05
# If in the rest state -- block the arm:
if self.task_state in ['rest', 'prep', 'baseline_check']:
vel_bl_aa[:] = 0
vel_bl_rh[:] = 0
elif self.task_state == 'emg_rest':
scaling = self.rest_emg_output
if scaling <= 0.5:
scaling = 0
else:
scaling = 0.5*scaling
vel_bl_aa = scaling*vel_bl_aa
vel_bl_rh = scaling*vel_bl_rh
max_vel_xy = 10.
vel_bl_aa[vel_bl_aa>max_vel_xy] = max_vel_xy
vel_bl_aa[vel_bl_aa<-1*max_vel_xy] = -1*max_vel_xy
max_vel_ang = 2.
if vel_bl_aa[2] > max_vel_ang:
vel_bl_aa[2] = max_vel_ang
elif vel_bl_aa[2] < -1*max_vel_ang:
vel_bl_aa[2] = -1*max_vel_ang
vel_bl_rh[vel_bl_rh>max_vel_ang] = max_vel_ang
vel_bl_rh[vel_bl_rh<-1*max_vel_ang] = -1*max_vel_ang
if self.blocking_joints is not None:
for j in [0, 1, 2]:
if j in self.blocking_joints:
vel_bl_aa[j] = 0
#print 'blocking vel_bl_aa: ', j
for j in [3, 4, 5, 6]:
if j in self.blocking_joints:
vel_bl_rh[j-3] = 0
#print 'blocking vel_bl_rh: ', j-3
self.both_feedback_str = both_feedback_str
self.aa_plant.send_vel(vel_bl_aa)
self.rh_plant.send_vel(vel_bl_rh)
self.prev_vel_bl_aa = vel_bl_aa.copy()
self.prev_vel_bl_rh = vel_bl_rh.copy()
self.drive_velocity_sent = np.hstack(( vel_bl_aa.copy(), vel_bl_rh.copy()))
decoder['q'] = self.get_pos()
class IsMorePlantUDP(BasePlantIsMore):
'''Sends velocity commands and receives feedback over UDP. Can be used
with either the real or simulated ArmAssist+ReHand.
'''
aa_plant_cls = ArmAssistPlantUDP
rh_plant_cls = ReHandPlantUDP
def write_feedback(self):
self.aa_plant.feedback_str = self.both_feedback_str
self.aa_plant.write_feedback()
#self.rh_plant.write_feedback()
class IsMorePlantEMGControl(IsMorePlantUDP): # Plant used for the pure EMG control task
def drive(self):
vel_bl = self.drive_velocity_raw
current_state = self.get_pos()
self.pre_drive_state = current_state.copy()
if self.blocking_joints is not None:
vel_bl[self.blocking_joints] = 0
vel_bl_aa0 = vel_bl[0:3].copy()
vel_bl_rh0 = vel_bl[3:7].copy()
### Accel Limit Velocitites ###
# if not np.all(np.isnan(np.hstack((self.prev_vel_bl_aa, self.prev_vel_bl_rh)))):
# aa_output_accel = vel_bl_aa - self.prev_vel_bl_aa
# rh_output_accel = vel_bl_rh - self.prev_vel_bl_rh
# ### AA XY ###
# for i in np.arange(2):
# if aa_output_accel[i] > self.accel_lim_armassist:
# vel_bl_aa[i] = self.prev_vel_bl_aa[i] + self.accel_lim_armassist
# elif aa_output_accel[i] < -1*self.accel_lim_armassist:
# vel_bl_aa[i] = self.prev_vel_bl_aa[i] - self.accel_lim_armassist
# ### AA PSI ###
# if aa_output_accel[2] > self.accel_lim_psi:
# vel_bl_aa[2] = self.prev_vel_bl_aa[2] + self.accel_lim_psi
# elif aa_output_accel[2] < -1*self.accel_lim_psi:
# vel_bl_aa[2] = self.prev_vel_bl_aa[2] - self.accel_lim_psi
# ### RH All ###
# for i in np.arange(4):
# if rh_output_accel[i] > self.accel_lim_rehand:
# vel_bl_rh[i] = self.prev_vel_bl_rh[i] + self.accel_lim_rehand
# elif rh_output_accel[i] < -1*self.accel_lim_rehand:
# vel_bl_rh[i] = self.prev_vel_bl_rh[i] - self.accel_lim_rehand
### Add Attractor ###
attractor_point_aa = self.safety_grid.attractor_point[:3]
attractor_point_rh = self.safety_grid.attractor_point[3:]
vel_bl_aa_pull = self.attractor_speed_const*(attractor_point_aa - current_state[:3])/0.05
vel_bl_rh_pull = self.attractor_speed_const*(attractor_point_rh - current_state[3:])/0.05
vel_bl_aa = vel_bl_aa0 + vel_bl_aa_pull.copy()
vel_bl_rh = vel_bl_rh0 + vel_bl_rh_pull.copy()
### LPF Filter Velocities ###
for s, state in enumerate(['aa_vx', 'aa_vy', 'aa_vpsi']):
vel_bl_aa[s] = self.command_lpfs[state](vel_bl_aa[s])
if np.isnan(vel_bl_aa[s]):
vel_bl_aa[s] = 0
for s, state in enumerate(['rh_vthumb', 'rh_vindex', 'rh_vfing3', 'rh_vprono']):
vel_bl_rh[s] = self.command_lpfs[state](vel_bl_rh[s])
if np.isnan(vel_bl_rh[s]):
vel_bl_rh[s] = 0
self.drive_velocity_sent_pre_safety = np.hstack(( vel_bl_aa.copy(), vel_bl_rh.copy()))
#If the next position is outside of safety then damp velocity to only go to limit:
pos_pred = current_state + 0.05*np.hstack((vel_bl_aa, vel_bl_rh))
pos_pred_aa = pos_pred[0:3]
pos_pred_rh = pos_pred[3:7]
both_feedback_str = ''
if self.safety_grid is not None:
if len(self.aa_plant.aa_xy_ix) > 0:
x_tmp = self.safety_grid.is_valid_pos(pos_pred_aa[self.aa_plant.aa_xy_ix])
if x_tmp == False:
current_pos = current_state[self.aa_plant.aa_xy_ix]
pos_valid = attractor_point_aa[self.aa_plant.aa_xy_ix]
#d_to_valid, pos_valid = self.safety_grid.dist_to_valid_point(current_pos)
vel_bl_aa[self.aa_plant.aa_xy_ix] = self.attractor_speed*(pos_valid - current_pos)/0.05
pos_pred_aa[self.aa_plant.aa_xy_ix] = current_pos + 0.05*vel_bl_aa[self.aa_plant.aa_xy_ix]
#print 'plant adjust: ', vel_bl_aa[self.aa_plant.aa_xy_ix], pos_pred_aa[self.aa_plant.aa_xy_ix]
xy_change = True
# Make sure AA Psi within bounds:
if len(self.aa_plant.aa_psi_ix) > 0:
mn, mx = self.safety_grid.get_minmax_psi(pos_pred_aa[self.aa_plant.aa_xy_ix])
predx, predy= pos_pred_aa[[0, 1]]
# Set psi velocity :
psi_ok = False
if np.logical_and(pos_pred_aa[self.aa_plant.aa_psi_ix] >= mn, pos_pred_aa[self.aa_plant.aa_psi_ix] <= mx):
# Test if globally ok:
global_ok = self.safety_grid.global_hull.hull3d.find_simplex(np.array([predx, predy, pos_pred_aa[2]])) >=0
if global_ok:
psi_ok = True
if psi_ok == False:
# Move psi back to attractor pos:
psi_neutral = attractor_point_aa[self.aa_plant.aa_psi_ix]
vel_bl_aa[self.aa_plant.aa_psi_ix] = self.attractor_speed*(psi_neutral-current_state[self.aa_plant.aa_psi_ix])/0.05
# Make sure RH Prono within bounds (if SSM is only RH, use settings.starting_pos for AAPX, AAPY)
if len(self.rh_plant.rh_pron_ix) > 0:
mn, mx = self.safety_grid.get_minmax_prono(pos_pred_aa[self.aa_plant.aa_xy_ix])
# Set prono velocity :
if np.logical_and(pos_pred_rh[self.rh_plant.rh_pron_ix] >= mn, pos_pred_rh[self.rh_plant.rh_pron_ix] <= mx):
pass
else:
tmp_pos = pos_pred_rh[self.rh_plant.rh_pron_ix]
prono_neutral = attractor_point_rh[self.rh_plant.rh_pron_ix]
vel_bl_rh[self.rh_plant.rh_pron_ix] = self.attractor_speed*(prono_neutral-tmp_pos)/0.05
# Assure RH fingers are within range:
if len(self.rh_plant.rh_pfings) > 0:
for i, (ix, nm) in enumerate(self.rh_plant.rh_pfings):
mn, mx = self.safety_grid.get_rh_minmax(nm)
if np.logical_and(pos_pred_rh[ix] >= mn, pos_pred_rh[ix] <= mx):
pass
else:
tmp_ = pos_pred_rh[ix]
neutral = attractor_point_rh[ix]
vel_bl_rh[ix] = self.attractor_speed*(neutral - tmp_)/0.05
# If in the rest state -- block the arm:
if self.task_state in ['rest', 'prep']:
vel_bl_aa[:] = 0
vel_bl_rh[:] = 0
elif self.task_state == 'emg_rest':
scaling = self.rest_emg_output
if scaling <= 0.5:
scaling = 0
else:
scaling = 0.5*scaling
vel_bl_aa = scaling*vel_bl_aa
vel_bl_rh = scaling*vel_bl_rh
max_vel_xy = 10.
vel_bl_aa[vel_bl_aa>max_vel_xy] = max_vel_xy
vel_bl_aa[vel_bl_aa<-1*max_vel_xy] = -1*max_vel_xy
max_vel_ang = 2.
if vel_bl_aa[2] > max_vel_ang:
vel_bl_aa[2] = max_vel_ang
elif vel_bl_aa[2] < -1*max_vel_ang:
vel_bl_aa[2] = -1*max_vel_ang
vel_bl_rh[vel_bl_rh>max_vel_ang] = max_vel_ang
vel_bl_rh[vel_bl_rh<-1*max_vel_ang] = -1*max_vel_ang
if self.blocking_joints is not None:
for j in [0, 1, 2]:
if j in self.blocking_joints:
vel_bl_aa[j] = 0
#print 'blocking vel_bl_aa: ', j
for j in [3, 4, 5, 6]:
if j in self.blocking_joints:
vel_bl_rh[j-3] = 0
#print 'blocking vel_bl_rh: ', j-3
self.both_feedback_str = both_feedback_str
self.aa_plant.send_vel(vel_bl_aa)
self.rh_plant.send_vel(vel_bl_rh)
self.prev_vel_bl_aa = vel_bl_aa.copy()
self.prev_vel_bl_rh = vel_bl_rh.copy()
self.drive_velocity_sent = np.hstack(( vel_bl_aa.copy(), vel_bl_rh.copy()))
class IsMorePlantHybridBMI(IsMorePlantUDP): # Plant used for the hybrid (EMG + brain) BMI task.
def __init__(self, *args, **kwargs):
self.drive_velocity_raw_brain = np.zeros((7,))
self.emg_vel_raw_scaled = np.zeros((7,))
super(IsMorePlantHybridBMI, self).__init__(*args, **kwargs)
def drive(self, decoder):
vel = decoder['qdot']
vel_brain = vel.copy()
vel_brain_aa = vel_brain[[0, 1, 2]]
vel_brain_fingers = vel_brain[[3, 4, 5]]
vel_brain_prono = vel_brain[[6]]
self.drive_velocity_raw_brain = vel_brain.copy()
# Use EMG scaled array to scale the output:
vel_emg = self.emg_vel.copy()
vel_emg_scaled = []
for i in range(7):
vel_emg_scaled.append(vel_emg[i]*self.scale_emg_pred_arr[i])
vel_emg_scaled = np.hstack((vel_emg_scaled))
self.emg_vel_raw_scaled = vel_emg_scaled.copy()
vel_emg_aa = vel_emg_scaled[[0, 1, 2]]
vel_emg_fingers = vel_emg_scaled[[3, 4, 5]]
vel_emg_prono = vel_emg_scaled[[6]]
vel_bl_aa = vel_emg_aa*self.emg_weight_aa + vel_brain_aa*(1-self.emg_weight_aa)
vel_bl_fingers = vel_emg_fingers*self.emg_weight_fingers + vel_brain_fingers*(1-self.emg_weight_fingers)
vel_bl_prono = vel_emg_prono*self.emg_weight_prono + vel_brain_prono*(1-self.emg_weight_prono)
vel_bl = np.hstack((vel_bl_aa, vel_bl_fingers, vel_bl_prono))
# Fuse velocities from EMG and neural decoders
#vel_bl = vel_emg*self.emg_weight + vel_brain*(1-self.emg_weight)
self.drive_velocity_raw = vel_bl.copy()
vel_bl_fb_gain = []
for i in range(7):
vel_bl_fb_gain.append(vel_bl[i]*self.fb_vel_gain[i])
vel_bl_fb_gain = np.hstack((vel_bl_fb_gain))
self.drive_velocity_raw_fb_gain = vel_bl_fb_gain.copy()
current_state = self.get_pos()
self.pre_drive_state = current_state.copy()
if self.blocking_joints is not None:
print ('self.blocking_joints --> ', self.blocking_joints)
vel_bl_fb_gain[self.blocking_joints] = 0
vel_bl_aa0 = vel_bl_fb_gain[0:3].copy()
vel_bl_rh0 = vel_bl_fb_gain[3:7].copy()
### Accel Limit Velocitites ###
# if not np.all(np.isnan(np.hstack((self.prev_vel_bl_aa, self.prev_vel_bl_rh)))):
# aa_output_accel = vel_bl_aa - self.prev_vel_bl_aa
# rh_output_accel = vel_bl_rh - self.prev_vel_bl_rh
# ### AA XY ###
# for i in np.arange(2):
# if aa_output_accel[i] > self.accel_lim_armassist:
# vel_bl_aa[i] = self.prev_vel_bl_aa[i] + self.accel_lim_armassist
# elif aa_output_accel[i] < -1*self.accel_lim_armassist:
# vel_bl_aa[i] = self.prev_vel_bl_aa[i] - self.accel_lim_armassist
# ### AA PSI ###
# if aa_output_accel[2] > self.accel_lim_psi:
# vel_bl_aa[2] = self.prev_vel_bl_aa[2] + self.accel_lim_psi
# elif aa_output_accel[2] < -1*self.accel_lim_psi:
# vel_bl_aa[2] = self.prev_vel_bl_aa[2] - self.accel_lim_psi
# ### RH All ###
# for i in np.arange(4):
# if rh_output_accel[i] > self.accel_lim_rehand:
# vel_bl_rh[i] = self.prev_vel_bl_rh[i] + self.accel_lim_rehand
# elif rh_output_accel[i] < -1*self.accel_lim_rehand:
# vel_bl_rh[i] = self.prev_vel_bl_rh[i] - self.accel_lim_rehand
### Add Attractor ###
attractor_point_aa = self.safety_grid.attractor_point[:3]
attractor_point_rh = self.safety_grid.attractor_point[3:]
vel_bl_aa_pull = self.attractor_speed_const*(attractor_point_aa - current_state[:3])/0.05
vel_bl_rh_pull = self.attractor_speed_const*(attractor_point_rh - current_state[3:])/0.05
vel_bl_aa = vel_bl_aa0 + vel_bl_aa_pull.copy()
vel_bl_rh = vel_bl_rh0 + vel_bl_rh_pull.copy()
### LPF Filter Velocities ###
for s, state in enumerate(['aa_vx', 'aa_vy', 'aa_vpsi']):
vel_bl_aa[s] = self.command_lpfs[state](vel_bl_aa[s])
if np.isnan(vel_bl_aa[s]):
vel_bl_aa[s] = 0
for s, state in enumerate(['rh_vthumb', 'rh_vindex', 'rh_vfing3', 'rh_vprono']):
vel_bl_rh[s] = self.command_lpfs[state](vel_bl_rh[s])
if np.isnan(vel_bl_rh[s]):
vel_bl_rh[s] = 0
self.drive_velocity_sent_pre_safety = np.hstack(( vel_bl_aa.copy(), vel_bl_rh.copy()))
#If the next position is outside of safety then damp velocity to only go to limit:
pos_pred = current_state + 0.05*np.hstack((vel_bl_aa, vel_bl_rh))
pos_pred_aa = pos_pred[0:3]
pos_pred_rh = pos_pred[3:7]
both_feedback_str = ''
if self.safety_grid is not None:
if len(self.aa_plant.aa_xy_ix) > 0:
x_tmp = self.safety_grid.is_valid_pos(pos_pred_aa[self.aa_plant.aa_xy_ix])
if x_tmp == False:
print ('false position')
current_pos = current_state[self.aa_plant.aa_xy_ix]
pos_valid = attractor_point_aa[self.aa_plant.aa_xy_ix]
#d_to_valid, pos_valid = self.safety_grid.dist_to_valid_point(current_pos)
vel_bl_aa[self.aa_plant.aa_xy_ix] = self.attractor_speed*(pos_valid - current_pos)/0.05
pos_pred_aa[self.aa_plant.aa_xy_ix] = current_pos + 0.05*vel_bl_aa[self.aa_plant.aa_xy_ix]
#print 'plant adjust: ', vel_bl_aa[self.aa_plant.aa_xy_ix], pos_pred_aa[self.aa_plant.aa_xy_ix]
xy_change = True
# Make sure AA Psi within bounds:
if len(self.aa_plant.aa_psi_ix) > 0:
mn, mx = self.safety_grid.get_minmax_psi(pos_pred_aa[self.aa_plant.aa_xy_ix])
predx, predy= pos_pred_aa[[0, 1]]
# Set psi velocity :
psi_ok = False
if np.logical_and(pos_pred_aa[self.aa_plant.aa_psi_ix] >= mn, pos_pred_aa[self.aa_plant.aa_psi_ix] <= mx):
# Test if globally ok:
#global_ok = self.safety_grid.global_hull.hull3d.find_simplex(np.array([predx, predy, pos_pred_aa[2]])) >=0
global_ok = True
if global_ok:
psi_ok = True
if psi_ok == False:
# Move psi back to attractor pos:
psi_neutral = attractor_point_aa[self.aa_plant.aa_psi_ix]
vel_bl_aa[self.aa_plant.aa_psi_ix] = self.attractor_speed*(psi_neutral-current_state[self.aa_plant.aa_psi_ix])/0.05
# Make sure RH Prono within bounds (if SSM is only RH, use settings.starting_pos for AAPX, AAPY)
if len(self.rh_plant.rh_pron_ix) > 0:
mn, mx = self.safety_grid.get_minmax_prono(pos_pred_aa[self.aa_plant.aa_xy_ix])
# Set prono velocity :
if np.logical_and(pos_pred_rh[self.rh_plant.rh_pron_ix] >= mn, pos_pred_rh[self.rh_plant.rh_pron_ix] <= mx):
pass
else:
tmp_pos = pos_pred_rh[self.rh_plant.rh_pron_ix]
prono_neutral = attractor_point_rh[self.rh_plant.rh_pron_ix]
vel_bl_rh[self.rh_plant.rh_pron_ix] = self.attractor_speed*(prono_neutral-tmp_pos)/0.05
# Assure RH fingers are within range:
if len(self.rh_plant.rh_pfings) > 0:
for i, (ix, nm) in enumerate(self.rh_plant.rh_pfings):
mn, mx = self.safety_grid.get_rh_minmax(nm)
if np.logical_and(pos_pred_rh[ix] >= mn, pos_pred_rh[ix] <= mx):
pass
else:
tmp_ = pos_pred_rh[ix]
neutral = attractor_point_rh[ix]
vel_bl_rh[ix] = self.attractor_speed*(neutral - tmp_)/0.05
# print 'safely adjusting fingers! ', nm, 'min: ', mn, ' max: ', mx, ' pred: ', pos_pred_rh[ix]
# If in the rest state -- block the arm:
if self.task_state in ['rest', 'prep', 'baseline_check']:
vel_bl_aa[:] = 0
vel_bl_rh[:] = 0
elif self.task_state == 'emg_rest':
scaling = self.rest_emg_output
if scaling <= 0.5:
scaling = 0
else:
scaling = 0.5*scaling
vel_bl_aa = scaling*vel_bl_aa
vel_bl_rh = scaling*vel_bl_rh
elif self.task_state == 'rest_back':
vel_bl_aa = vel_bl_aa_pull/self.attractor_speed_const*self.rest_back_attractor_speed
vel_bl_rh = vel_bl_rh_pull/self.attractor_speed_const*self.rest_back_attractor_speed
elif self.task_state in ['drive_to_start', 'drive_to_rest']:
vel_bl_aa = self.back_to_target_speed*(self.drive_to_start_target[:3] - current_state[:3])/0.05
vel_bl_rh = self.back_to_target_speed*(self.drive_to_start_target[3:] - current_state[3:])/0.05
max_vel_xy = 10.
vel_bl_aa[vel_bl_aa>max_vel_xy] = max_vel_xy
vel_bl_aa[vel_bl_aa<-1*max_vel_xy] = -1*max_vel_xy
max_vel_ang = 2.
if vel_bl_aa[2] > max_vel_ang:
vel_bl_aa[2] = max_vel_ang
elif vel_bl_aa[2] < -1*max_vel_ang:
vel_bl_aa[2] = -1*max_vel_ang
vel_bl_rh[vel_bl_rh>max_vel_ang] = max_vel_ang
vel_bl_rh[vel_bl_rh<-1*max_vel_ang] = -1*max_vel_ang
if self.blocking_joints is not None:
for j in [0, 1, 2]:
if j in self.blocking_joints:
vel_bl_aa[j] = 0
#print 'blocking vel_bl_aa: ', j
for j in [3, 4, 5, 6]:
if j in self.blocking_joints:
vel_bl_rh[j-3] = 0
#print 'blocking vel_bl_rh: ', j-3
self.both_feedback_str = both_feedback_str
self.aa_plant.send_vel(vel_bl_aa)
self.rh_plant.send_vel(vel_bl_rh)
self.prev_vel_bl_aa = vel_bl_aa.copy()
self.prev_vel_bl_rh = vel_bl_rh.copy()
self.drive_velocity_sent = np.hstack(( vel_bl_aa.copy(), vel_bl_rh.copy()))
decoder['q'] = self.get_pos()
class IsMorePlantHybridBMISoftSafety(IsMorePlantHybridBMI):
def drive(self, decoder):
vel = decoder['qdot']
vel_brain = vel.copy()
vel_brain_aa = vel_brain[[0, 1, 2]]
vel_brain_fingers = vel_brain[[3, 4, 5]]
vel_brain_prono = vel_brain[[6]]
self.drive_velocity_raw_brain = vel_brain.copy()
# Use EMG scaled array to scale the output:
vel_emg = self.emg_vel.copy()
vel_emg_scaled = []
for i in range(7):
vel_emg_scaled.append(vel_emg[i]*self.scale_emg_pred_arr[i])
vel_emg_scaled = np.hstack((vel_emg_scaled))
self.emg_vel_raw_scaled = vel_emg_scaled.copy()
vel_emg_aa = vel_emg_scaled[[0, 1, 2]]
vel_emg_fingers = vel_emg_scaled[[3, 4, 5]]
vel_emg_prono = vel_emg_scaled[[6]]
vel_bl_aa = vel_emg_aa*self.emg_weight_aa + vel_brain_aa*(1-self.emg_weight_aa)
vel_bl_fingers = vel_emg_fingers*self.emg_weight_fingers + vel_brain_fingers*(1-self.emg_weight_fingers)
vel_bl_prono = vel_emg_prono*self.emg_weight_prono + vel_brain_prono*(1-self.emg_weight_prono)
vel_bl = np.hstack((vel_bl_aa, vel_bl_fingers, vel_bl_prono))
# Fuse velocities from EMG and neural decoders
#vel_bl = vel_emg*self.emg_weight + vel_brain*(1-self.emg_weight)
self.drive_velocity_raw = vel_bl.copy()
vel_bl_fb_gain = []
for i in range(7):
vel_bl_fb_gain.append(vel_bl[i]*self.fb_vel_gain[i])
vel_bl_fb_gain = np.hstack((vel_bl_fb_gain))
self.drive_velocity_raw_fb_gain = vel_bl_fb_gain.copy()
current_state = self.get_pos()
self.pre_drive_state = current_state.copy()
if self.blocking_joints is not None:
vel_bl_fb_gain[self.blocking_joints] = 0
vel_bl_aa0 = vel_bl_fb_gain[0:3].copy()
vel_bl_rh0 = vel_bl_fb_gain[3:7].copy()
### Accel Limit Velocitites ###
# if not np.all(np.isnan(np.hstack((self.prev_vel_bl_aa, self.prev_vel_bl_rh)))):
# aa_output_accel = vel_bl_aa - self.prev_vel_bl_aa
# rh_output_accel = vel_bl_rh - self.prev_vel_bl_rh
# ### AA XY ###
# for i in np.arange(2):
# if aa_output_accel[i] > self.accel_lim_armassist:
# vel_bl_aa[i] = self.prev_vel_bl_aa[i] + self.accel_lim_armassist
# elif aa_output_accel[i] < -1*self.accel_lim_armassist:
# vel_bl_aa[i] = self.prev_vel_bl_aa[i] - self.accel_lim_armassist
# ### AA PSI ###
# if aa_output_accel[2] > self.accel_lim_psi:
# vel_bl_aa[2] = self.prev_vel_bl_aa[2] + self.accel_lim_psi
# elif aa_output_accel[2] < -1*self.accel_lim_psi:
# vel_bl_aa[2] = self.prev_vel_bl_aa[2] - self.accel_lim_psi
# ### RH All ###
# for i in np.arange(4):
# if rh_output_accel[i] > self.accel_lim_rehand:
# vel_bl_rh[i] = self.prev_vel_bl_rh[i] + self.accel_lim_rehand
# elif rh_output_accel[i] < -1*self.accel_lim_rehand:
# vel_bl_rh[i] = self.prev_vel_bl_rh[i] - self.accel_lim_rehand
### Add Attractor ###
attractor_point_aa = self.safety_grid.attractor_point[:3]
attractor_point_rh = self.safety_grid.attractor_point[3:]
vel_bl_aa_pull = self.attractor_speed_const*(attractor_point_aa - current_state[:3])/0.05
vel_bl_rh_pull = self.attractor_speed_const*(attractor_point_rh - current_state[3:])/0.05
vel_bl_aa = vel_bl_aa0 + vel_bl_aa_pull.copy()
vel_bl_rh = vel_bl_rh0 + vel_bl_rh_pull.copy()
### LPF Filter Velocities ###
for s, state in enumerate(['aa_vx', 'aa_vy', 'aa_vpsi']):
vel_bl_aa[s] = self.command_lpfs[state](vel_bl_aa[s])
if np.isnan(vel_bl_aa[s]):
vel_bl_aa[s] = 0
for s, state in enumerate(['rh_vthumb', 'rh_vindex', 'rh_vfing3', 'rh_vprono']):
vel_bl_rh[s] = self.command_lpfs[state](vel_bl_rh[s])
if np.isnan(vel_bl_rh[s]):
vel_bl_rh[s] = 0
self.drive_velocity_sent_pre_safety = np.hstack(( vel_bl_aa.copy(), vel_bl_rh.copy()))
#If the next position is outside of safety then damp velocity to only go to limit:
pos_pred = current_state + 0.05*np.hstack((vel_bl_aa, vel_bl_rh))
pos_pred_aa = pos_pred[0:3]
pos_pred_rh = pos_pred[3:7]
both_feedback_str = ''
if self.safety_grid is not None:
if len(self.aa_plant.aa_xy_ix) > 0:
x_tmp = self.safety_grid.is_valid_pos(pos_pred_aa[self.aa_plant.aa_xy_ix])
if x_tmp == False:
# Find the closest point on the boundary of the safety grid and set velocity in same
# direction, but at 90% of way to get to the edge of the safety grid:
current_pos = current_state[self.aa_plant.aa_xy_ix]
### loop through percentages of velocity and check validity of point:
valid_scale = False
scale = 1.0
while valid_scale is False:
scale -= 0.05
pos_pred_xy = current_pos + 0.05*(vel_bl_aa[self.aa_plant.aa_xy_ix]*scale)
valid_scale = self.safety_grid.is_valid_pos(pos_pred_xy)
if scale < -1.0:
scale = 0.0
break
#d_to_valid, pos_valid = self.safety_grid.dist_to_valid_point(current_pos)
vel_bl_aa[self.aa_plant.aa_xy_ix] = vel_bl_aa[self.aa_plant.aa_xy_ix]*scale
pos_pred_aa[self.aa_plant.aa_xy_ix] = current_pos + 0.05*vel_bl_aa[self.aa_plant.aa_xy_ix]
#print 'plant adjust: ', vel_bl_aa[self.aa_plant.aa_xy_ix], pos_pred_aa[self.aa_plant.aa_xy_ix]
xy_change = True
# Make sure AA Psi within bounds:
if len(self.aa_plant.aa_psi_ix) > 0:
mn, mx = self.safety_grid.get_minmax_psi(pos_pred_aa[self.aa_plant.aa_xy_ix])
predx, predy= pos_pred_aa[[0, 1]]
# Set psi velocity :
psi_ok = False
if np.logical_and(pos_pred_aa[self.aa_plant.aa_psi_ix] >= mn, pos_pred_aa[self.aa_plant.aa_psi_ix] <= mx):
# Test if globally ok:
#global_ok = self.safety_grid.global_hull.hull3d.find_simplex(np.array([predx, predy, pos_pred_aa[2]])) >=0
global_ok = True
if global_ok:
psi_ok = True
if psi_ok == False:
valid_scale_psi = False
scale = 1.0
while valid_scale_psi is False:
scale -= 0.05
psi_pred = current_state[self.aa_plant.aa_psi_ix] + 0.05*(scale*vel_bl_aa[self.aa_plant.aa_psi_ix])
if np.logical_and(psi_pred >= mn, psi_pred <= mx):
valid_scale_psi = True
if scale < -1.0:
scale = 0.0
break
vel_bl_aa[self.aa_plant.aa_psi_ix] = scale*vel_bl_aa[self.aa_plant.aa_psi_ix]
# Make sure RH Prono within bounds (if SSM is only RH, use settings.starting_pos for AAPX, AAPY)
if len(self.rh_plant.rh_pron_ix) > 0:
mn, mx = self.safety_grid.get_minmax_prono(pos_pred_aa[self.aa_plant.aa_xy_ix])
# Set prono velocity :
if np.logical_and(pos_pred_rh[self.rh_plant.rh_pron_ix] >= mn, pos_pred_rh[self.rh_plant.rh_pron_ix] <= mx):
pass
else:
valid_scale_prono = False
scale = 1.0
while valid_scale_prono is False:
scale -= 0.05
pron_pred = pos_pred_rh[self.rh_plant.rh_pron_ix] + 0.05*(scale*vel_bl_rh[self.rh_plant.rh_pron_ix])
if np.logical_and(pron_pred >= mn, pron_pred <= mx):
valid_scale_prono = True
if scale < -1.0:
scale = 0.
break
vel_bl_rh[self.rh_plant.rh_pron_ix] = scale*vel_bl_rh[self.rh_plant.rh_pron_ix]
# Assure RH fingers are within range:
if len(self.rh_plant.rh_pfings) > 0:
for i, (ix, nm) in enumerate(self.rh_plant.rh_pfings):
mn, mx = self.safety_grid.get_rh_minmax(nm)
if np.logical_and(pos_pred_rh[ix] >= mn, pos_pred_rh[ix] <= mx):
pass
else:
finger_scale = False
scale = 1.0
while finger_scale is False:
scale -= 0.05
fing_pred = pos_pred_rh[ix] + 0.05*(scale*vel_bl_rh[ix])
if np.logical_and(fing_pred >= mn, fing_pred<= mx):
finger_scale = True
if scale < -1.0:
scale = 0.0
break
vel_bl_rh[ix] = scale*vel_bl_rh[ix]
# If in the rest state -- block the arm:
if self.task_state in ['rest', 'prep', 'baseline_check', 'wait']:
vel_bl_aa[:] = 0
vel_bl_rh[:] = 0
elif self.task_state == 'emg_rest':
scaling = self.rest_emg_output
if scaling <= 0.5:
scaling = 0
else:
scaling = 0.5*scaling
vel_bl_aa = scaling*vel_bl_aa
vel_bl_rh = scaling*vel_bl_rh
elif self.task_state == 'rest_back':
vel_bl_aa = vel_bl_aa_pull/self.attractor_speed_const*self.rest_back_attractor_speed
vel_bl_rh = vel_bl_rh_pull/self.attractor_speed_const*self.rest_back_attractor_speed
elif self.task_state in ['drive_to_start', 'drive_to_rest']:
vel_bl_aa = self.back_to_target_speed*(self.drive_to_start_target[:3] - current_state[:3])/0.05
vel_bl_rh = self.back_to_target_speed*(self.drive_to_start_target[3:] - current_state[3:])/0.05
max_vel_xy = 10.
vel_bl_aa[vel_bl_aa>max_vel_xy] = max_vel_xy
vel_bl_aa[vel_bl_aa<-1*max_vel_xy] = -1*max_vel_xy
max_vel_ang = 2.
if vel_bl_aa[2] > max_vel_ang:
vel_bl_aa[2] = max_vel_ang
elif vel_bl_aa[2] < -1*max_vel_ang:
vel_bl_aa[2] = -1*max_vel_ang
vel_bl_rh[vel_bl_rh>max_vel_ang] = max_vel_ang
vel_bl_rh[vel_bl_rh<-1*max_vel_ang] = -1*max_vel_ang
if self.blocking_joints is not None:
for j in [0, 1, 2]:
if j in self.blocking_joints:
vel_bl_aa[j] = 0
#print 'blocking vel_bl_aa: ', j
for j in [3, 4, 5, 6]:
if j in self.blocking_joints:
vel_bl_rh[j-3] = 0
#print 'blocking vel_bl_rh: ', j-3
self.both_feedback_str = both_feedback_str
self.aa_plant.send_vel(vel_bl_aa)
self.rh_plant.send_vel(vel_bl_rh)
self.prev_vel_bl_aa = vel_bl_aa.copy()
self.prev_vel_bl_rh = vel_bl_rh.copy()
self.drive_velocity_sent = np.hstack(( vel_bl_aa.copy(), vel_bl_rh.copy()))
decoder['q'] = self.get_pos()
UDP_PLANT_CLS_DICT = {
'ArmAssist': ArmAssistPlantUDP,
'ReHand': ReHandPlantUDP,
'IsMore': IsMorePlantUDP,
'IsMoreEMGControl': IsMorePlantEMGControl,
'IsMoreHybridControl': IsMorePlantHybridBMI,
'IsMorePlantHybridBMISoftSafety': IsMorePlantHybridBMISoftSafety,
'DummyPlant': DummyPlantUDP,
}
###########################
##### Deprecated code #####
###########################
class BasePlant(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError('Implement in subclasses!')
def init(self):
raise NotImplementedError('Implement in subclasses!')
def start(self):
raise NotImplementedError('Implement in subclasses!')
def stop(self):
raise NotImplementedError('Implement in subclasses!')
def last_data_ts_arrival(self):
raise NotImplementedError('Implement in subclasses!')
def send_vel(self, vel):
raise NotImplementedError('Implement in subclasses!')
def get_pos(self):
raise NotImplementedError('Implement in subclasses!')
def get_vel(self):
raise NotImplementedError('Implement in subclasses!')
def enable(self):
'''Disable the device's motor drivers.'''
raise NotImplementedError('Implement in subclasses!')
def disable(self):
'''Disable the device's motor drivers.'''
raise NotImplementedError('Implement in subclasses!')
def enable_watchdog(self, timeout_ms):
raise NotImplementedError('Implement in subclasses!')
def get_intrinsic_coordinates(self):
return self.get_pos()
|
[
"numpy.abs",
"time.ctime",
"socket.socket",
"numpy.hstack",
"os.path.expandvars",
"ismore.filter.Filter",
"numpy.logical_and",
"scipy.signal.butter",
"riglib.sink.sinks.register",
"numpy.array",
"numpy.zeros",
"numpy.isnan",
"numpy.sign",
"numpy.nonzero",
"time.time",
"riglib.source.DataSource"
] |
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|
import logging
import pytest
from ocs_ci.framework.testlib import tier1, skipif_ui_not_support, ui
from ocs_ci.ocs.ui.pvc_ui import PvcUI
from ocs_ci.framework.testlib import skipif_ocs_version
from ocs_ci.framework.pytest_customization.marks import green_squad
from ocs_ci.ocs.resources.pvc import get_all_pvc_objs, get_pvc_objs
from ocs_ci.ocs import constants
from ocs_ci.helpers import helpers
from ocs_ci.helpers.helpers import wait_for_resource_state, create_unique_resource_name
from ocs_ci.utility.utils import get_ocp_version
from ocs_ci.ocs.ui.views import locators
from ocs_ci.ocs.resources.pod import get_fio_rw_iops
logger = logging.getLogger(__name__)
@ui
@skipif_ocs_version("<4.6")
@skipif_ui_not_support("pvc")
@green_squad
class TestPvcUserInterface(object):
"""
Test PVC User Interface
"""
@tier1
@pytest.mark.parametrize(
argnames=["sc_name", "access_mode", "pvc_size", "vol_mode"],
argvalues=[
pytest.param(
"ocs-storagecluster-cephfs",
"ReadWriteMany",
"2",
"Filesystem",
),
pytest.param(
"ocs-storagecluster-ceph-rbd",
"ReadWriteMany",
"3",
"Block",
),
pytest.param(
"ocs-storagecluster-cephfs",
"ReadWriteOnce",
"10",
"Filesystem",
),
pytest.param(
"ocs-storagecluster-ceph-rbd",
"ReadWriteOnce",
"11",
"Block",
),
pytest.param(
"ocs-storagecluster-ceph-rbd",
"ReadWriteOnce",
"13",
"Filesystem",
),
],
)
def test_create_resize_delete_pvc(
self,
project_factory,
teardown_factory,
setup_ui,
sc_name,
access_mode,
pvc_size,
vol_mode,
):
"""
Test create, resize and delete pvc via UI
"""
# Creating a test project via CLI
pro_obj = project_factory()
project_name = pro_obj.namespace
pvc_ui_obj = PvcUI(setup_ui)
# Creating PVC via UI
pvc_name = create_unique_resource_name("test", "pvc")
pvc_ui_obj.create_pvc_ui(
project_name, sc_name, pvc_name, access_mode, pvc_size, vol_mode
)
pvc_objs = get_all_pvc_objs(namespace=project_name)
pvc = [pvc_obj for pvc_obj in pvc_objs if pvc_obj.name == pvc_name]
assert pvc[0].size == int(pvc_size), (
f"size error| expected size:{pvc_size} \n "
f"actual size:{str(pvc[0].size)}"
)
assert pvc[0].get_pvc_access_mode == access_mode, (
f"access mode error| expected access mode:{access_mode} "
f"\n actual access mode:{pvc[0].get_pvc_access_mode}"
)
assert pvc[0].backed_sc == sc_name, (
f"storage class error| expected storage class:{sc_name} "
f"\n actual storage class:{pvc[0].backed_sc}"
)
assert pvc[0].get_pvc_vol_mode == vol_mode, (
f"volume mode error| expected volume mode:{vol_mode} "
f"\n actual volume mode:{pvc[0].get_pvc_vol_mode}"
)
# Verifying PVC via UI
logger.info("Verifying PVC Details via UI")
pvc_ui_obj.verify_pvc_ui(
pvc_size=pvc_size,
access_mode=access_mode,
vol_mode=vol_mode,
sc_name=sc_name,
pvc_name=pvc_name,
project_name=project_name,
)
logger.info("PVC Details Verified via UI..!!")
# Creating Pod via CLI
logger.info("Creating Pod")
if sc_name in (constants.DEFAULT_STORAGECLASS_RBD,):
interface_type = constants.CEPHBLOCKPOOL
else:
interface_type = constants.CEPHFILESYSTEM
new_pod = helpers.create_pod(
interface_type=interface_type,
pvc_name=pvc_name,
namespace=project_name,
raw_block_pv=vol_mode == constants.VOLUME_MODE_BLOCK,
)
logger.info(f"Waiting for Pod: state= {constants.STATUS_RUNNING}")
wait_for_resource_state(resource=new_pod, state=constants.STATUS_RUNNING)
# Calling the Teardown Factory Method to make sure Pod is deleted
teardown_factory(new_pod)
# Expanding the PVC
logger.info("Pvc Resizing")
new_size = int(pvc_size) + 3
pvc_ui_obj.pvc_resize_ui(
pvc_name=pvc_name, new_size=new_size, project_name=project_name
)
assert new_size > int(
pvc_size
), f"New size of the PVC cannot be less than existing size: new size is {new_size})"
ocp_version = get_ocp_version()
self.pvc_loc = locators[ocp_version]["pvc"]
# Verifying PVC expansion
logger.info("Verifying PVC resize")
expected_capacity = f"{new_size} GiB"
pvc_resize = pvc_ui_obj.verify_pvc_resize_ui(
project_name=project_name,
pvc_name=pvc_name,
expected_capacity=expected_capacity,
)
assert pvc_resize, "PVC resize failed"
logger.info(
"Pvc resize verified..!!"
f"New Capacity after PVC resize is {expected_capacity}"
)
# Running FIO
logger.info("Execute FIO on a Pod")
if vol_mode == constants.VOLUME_MODE_BLOCK:
storage_type = constants.WORKLOAD_STORAGE_TYPE_BLOCK
else:
storage_type = constants.WORKLOAD_STORAGE_TYPE_FS
new_pod.run_io(storage_type, size=(new_size - 1), invalidate=0, rate="1000m")
get_fio_rw_iops(new_pod)
logger.info("FIO execution on Pod successfully completed..!!")
# Checking if the Pod is deleted or not
new_pod.delete(wait=True)
new_pod.ocp.wait_for_delete(resource_name=new_pod.name)
# Deleting the PVC via UI
logger.info(f"Delete {pvc_name} pvc")
pvc_ui_obj.delete_pvc_ui(pvc_name, project_name)
pvc[0].ocp.wait_for_delete(pvc_name, timeout=120)
pvc_objs = get_all_pvc_objs(namespace=project_name)
pvcs = [pvc_obj for pvc_obj in pvc_objs if pvc_obj.name == pvc_name]
if len(pvcs) > 0:
assert f"PVC {pvcs[0].name} does not deleted"
@tier1
@pytest.mark.parametrize(
argnames=["sc_name", "access_mode", "clone_access_mode"],
argvalues=[
pytest.param(
"ocs-storagecluster-ceph-rbd",
constants.ACCESS_MODE_RWO,
constants.ACCESS_MODE_RWO,
),
pytest.param(
"ocs-storagecluster-cephfs",
constants.ACCESS_MODE_RWX,
constants.ACCESS_MODE_RWO,
),
],
)
def test_clone_pvc(
self,
project_factory,
teardown_factory,
setup_ui,
sc_name,
access_mode,
clone_access_mode,
):
"""
Test to verify PVC clone from UI
"""
pvc_size = "1"
vol_mode = constants.VOLUME_MODE_FILESYSTEM
# Creating a project from CLI
pro_obj = project_factory()
project_name = pro_obj.namespace
pvc_ui_obj = PvcUI(setup_ui)
# Creating PVC from UI
pvc_name = create_unique_resource_name("test", "pvc")
pvc_ui_obj.create_pvc_ui(
project_name, sc_name, pvc_name, access_mode, pvc_size, vol_mode
)
teardown_factory(get_pvc_objs(pvc_names=[pvc_name], namespace=project_name)[0])
# Verifying PVC details in UI
logger.info("Verifying PVC details in UI")
pvc_ui_obj.verify_pvc_ui(
pvc_size=pvc_size,
access_mode=access_mode,
vol_mode=vol_mode,
sc_name=sc_name,
pvc_name=pvc_name,
project_name=project_name,
)
logger.info("Verified PVC details in UI")
# Clone PVC from UI
clone_pvc_name = f"{pvc_name}-clone"
pvc_ui_obj.pvc_clone_ui(
project_name=project_name,
pvc_name=pvc_name,
cloned_pvc_access_mode=clone_access_mode,
cloned_pvc_name=clone_pvc_name,
)
teardown_factory(
get_pvc_objs(pvc_names=[clone_pvc_name], namespace=project_name)[0]
)
# Verifying cloned PVC details in UI
logger.info("Verifying cloned PVC details in UI")
pvc_ui_obj.verify_pvc_ui(
pvc_size=pvc_size,
access_mode=clone_access_mode,
vol_mode=vol_mode,
sc_name=sc_name,
pvc_name=clone_pvc_name,
project_name=project_name,
)
logger.info("Verified cloned PVC details in UI")
|
[
"logging.getLogger",
"ocs_ci.helpers.helpers.create_pod",
"ocs_ci.ocs.resources.pod.get_fio_rw_iops",
"ocs_ci.ocs.ui.pvc_ui.PvcUI",
"ocs_ci.ocs.resources.pvc.get_all_pvc_objs",
"pytest.param",
"ocs_ci.framework.testlib.skipif_ui_not_support",
"ocs_ci.framework.testlib.skipif_ocs_version",
"ocs_ci.helpers.helpers.create_unique_resource_name",
"ocs_ci.ocs.resources.pvc.get_pvc_objs",
"ocs_ci.helpers.helpers.wait_for_resource_state",
"ocs_ci.utility.utils.get_ocp_version"
] |
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|
from sys import exit
from colour import *
from rs.reaction_system import ReactionSystem
class ReactionSystemWithConcentrations(ReactionSystem):
def __init__(self):
self.reactions = []
self.meta_reactions = dict()
self.permanent_entities = dict()
self.background_set = []
self.context_entities = [] # legacy. to be removed
self.reactions_by_prod = None
self.max_concentration = 0
self.max_conc_per_ent = dict()
def add_bg_set_entity(self, e):
name = ""
def_max_conc = -1
if type(e) is tuple and len(e) == 2:
name, def_max_conc = e
elif type(e) is str:
name = e
print("\nWARNING: no maximal concentration level specified for:", e, "\n")
else:
raise RuntimeError(
"Bad entity type when adding background set element")
self.assume_not_in_bgset(name)
self.background_set.append(name)
if def_max_conc != -1:
ent_id = self.get_entity_id(name)
self.max_conc_per_ent.setdefault(ent_id, 0)
if self.max_conc_per_ent[ent_id] < def_max_conc:
self.max_conc_per_ent[ent_id] = def_max_conc
if self.max_concentration < def_max_conc:
self.max_concentration = def_max_conc
def get_max_concentration_level(self, e):
if e in self.max_conc_per_ent:
return self.max_conc_per_ent[e]
else:
return self.max_concentration
def is_valid_entity_with_concentration(self, e):
"""Sanity check for entities with concentration"""
if type(e) is tuple:
if len(e) == 2 and type(e[1]) is int:
return True
if type(e) is list:
if len(e) == 2 and type(e[1]) is int:
return True
print("FATAL. Invalid entity+concentration: {:s}".format(e))
exit(1)
return False
def get_state_ids(self, state):
"""Returns entities of the given state without levels"""
return [e for e, c in state]
def has_non_zero_concentration(self, elem):
if elem[1] < 1:
raise RuntimeError(
"Unexpected concentration level in state: " + str(elem))
def process_rip(self, R, I, P, ignore_empty_R=False):
"""Chcecks concentration levels and converts entities names into their ids"""
if R == [] and not ignore_empty_R:
raise RuntimeError("No reactants defined")
reactants = []
for r in R:
self.is_valid_entity_with_concentration(r)
self.has_non_zero_concentration(r)
entity, level = r
reactants.append((self.get_entity_id(entity), level))
if self.max_concentration < level:
self.max_concentration = level
inhibitors = []
for i in I:
self.is_valid_entity_with_concentration(i)
self.has_non_zero_concentration(i)
entity, level = i
inhibitors.append((self.get_entity_id(entity), level))
if self.max_concentration < level:
self.max_concentration = level
products = []
for p in P:
self.is_valid_entity_with_concentration(p)
self.has_non_zero_concentration(p)
entity, level = p
products.append((self.get_entity_id(entity), level))
return reactants, inhibitors, products
def add_reaction(self, R, I, P):
"""Adds a reaction"""
if P == []:
raise RuntimeError("No products defined")
reaction = self.process_rip(R, I, P)
self.reactions.append(reaction)
def add_reaction_without_reactants(self, R, I, P):
"""Adds a reaction"""
if P == []:
raise RuntimeError("No products defined")
reaction = self.process_rip(R, I, P, ignore_empty_R=True)
self.reactions.append(reaction)
def add_reaction_inc(self, incr_entity, incrementer, R, I):
"""Adds a macro/meta reaction for increasing the value of incr_entity"""
reactants, inhibitors, products = self.process_rip(
R, I, [], ignore_empty_R=True)
incr_entity_id = self.get_entity_id(incr_entity)
self.meta_reactions.setdefault(incr_entity_id, [])
self.meta_reactions[incr_entity_id].append(
("inc", self.get_entity_id(incrementer), reactants, inhibitors))
def add_reaction_dec(self, decr_entity, decrementer, R, I):
"""Adds a macro/meta reaction for decreasing the value of incr_entity"""
reactants, inhibitors, products = self.process_rip(
R, I, [], ignore_empty_R=True)
decr_entity_id = self.get_entity_id(decr_entity)
self.meta_reactions.setdefault(decr_entity_id, [])
self.meta_reactions[decr_entity_id].append(
("dec", self.get_entity_id(decrementer), reactants, inhibitors))
def add_permanency(self, ent, I):
"""Sets entity to be permanent unless it is inhibited"""
ent_id = self.get_entity_id(ent)
if ent_id in self.permanent_entities:
raise RuntimeError(
"Permanency for {0} already defined.".format(ent))
inhibitors = self.process_rip([], I, [], ignore_empty_R=True)[1]
self.permanent_entities[ent_id] = inhibitors
def set_context_entities(self, entities):
raise NotImplementedError
def entities_names_set_to_str(self, entities):
s = ""
for entity in entities:
s += entity + ", "
s = s[:-2]
return s
def entities_ids_set_to_str(self, entities):
s = ""
for entity in entities:
s += self.get_entity_name(entity) + ", "
s = s[:-2]
return s
def state_to_str(self, state):
s = ""
for ent, level in state:
s += self.get_entity_name(ent) + "=" + str(level) + ", "
s = s[:-2]
return s
def show_background_set(self):
print(
C_MARK_INFO + " Background set: {" + self.entities_names_set_to_str(self.background_set) + "}")
def show_meta_reactions(self):
print(C_MARK_INFO + " Meta reactions:")
for param_ent, reactions in self.meta_reactions.items():
for r_type, command, reactants, inhibitors in reactions:
if r_type == "inc" or r_type == "dec":
print(" - [ Type=" + repr(r_type) + " Operand=( " + self.get_entity_name(param_ent) + " ) Command=( " + self.get_entity_name(
command) + " ) ] -- ( R={" + self.state_to_str(reactants) + "}, I={" + self.state_to_str(inhibitors) + "} )")
else:
raise RuntimeError(
"Unknown meta-reaction type: " + repr(r_type))
def show_max_concentrations(self):
print(
C_MARK_INFO +
" Maximal allowed concentration levels (for optimized translation to RS):")
for e, max_conc in self.max_conc_per_ent.items():
print(" - {0:^20} = {1:<6}".format(self.get_entity_name(e), max_conc))
def show_permanent_entities(self):
print(C_MARK_INFO + " Permanent entities:")
for e, inhibitors in self.permanent_entities.items():
print(" - {0:^20}{1:<6}".format(self.get_entity_name(e) + ": ",
"I={" + self.state_to_str(inhibitors) + "}"))
def show(self, soft=False):
self.show_background_set()
self.show_reactions(soft)
self.show_permanent_entities()
self.show_meta_reactions()
self.show_max_concentrations()
def get_reactions_by_product(self):
"""Sorts reactions by their products and returns a dictionary of products"""
if self.reactions_by_prod != None:
return self.reactions_by_prod
producible_entities = set()
for reaction in self.reactions:
product_entities = [e for e, c in reaction[2]]
producible_entities = producible_entities.union(
set(product_entities))
reactions_by_prod = {}
for p_e in producible_entities:
reactions_by_prod[p_e] = []
rcts_for_p_e = reactions_by_prod[p_e]
for r in self.reactions:
product_entities = [e for e, c in r[2]]
if p_e in product_entities:
reactants = r[0]
inhibitors = r[1]
products = [(e, c) for e, c in r[2] if e == p_e]
prod_conc = products[0][1]
insert_place = None
# we need to order the reactions w.r.t. the concentration levels produced (increasing order)
for i in range(0, len(rcts_for_p_e)):
checked_conc = rcts_for_p_e[i][2][0][1]
if prod_conc <= checked_conc:
insert_place = i
break
if insert_place == None: # empty or the is only one element which is smaller than the element being added
# we append (to the end)
rcts_for_p_e.append((reactants, inhibitors, products))
else:
rcts_for_p_e.insert(
insert_place, (reactants, inhibitors, products))
# save in cache
self.reactions_by_prod = reactions_by_prod
return reactions_by_prod
def get_reaction_system(self):
rs = ReactionSystem()
for reactants, inhibitors, products in self.reactions:
new_reactants = []
new_inhibitors = []
new_products = []
for ent, conc in reactants:
n = self.get_entity_name(ent) + "#" + str(conc)
rs.ensure_bg_set_entity(n)
new_reactants.append(n)
for ent, conc in inhibitors:
n = self.get_entity_name(ent) + "#" + str(conc)
rs.ensure_bg_set_entity(n)
new_inhibitors.append(n)
for ent, conc in products:
for i in range(1, conc+1):
n = self.get_entity_name(ent) + "#" + str(i)
rs.ensure_bg_set_entity(n)
new_products.append(n)
rs.add_reaction(new_reactants, new_inhibitors, new_products)
for param_ent, reactions in self.meta_reactions.items():
for r_type, command, reactants, inhibitors in reactions:
param_ent_name = self.get_entity_name(param_ent)
new_reactants = []
new_inhibitors = []
for ent, conc in reactants:
n = self.get_entity_name(ent) + "#" + str(conc)
rs.ensure_bg_set_entity(n)
new_reactants.append(n)
for ent, conc in inhibitors:
n = self.get_entity_name(ent) + "#" + str(conc)
rs.ensure_bg_set_entity(n)
new_inhibitors.append(n)
max_cmd_c = self.max_concentration
if command in self.max_conc_per_ent:
max_cmd_c = self.max_conc_per_ent[command]
else:
print(
"WARNING:\n\tThere is no maximal concentration level defined for "
+ self.get_entity_name(command))
print("\tThis is a very bad idea -- expect degraded performance\n")
for l in range(1, max_cmd_c+1):
cmd_ent = self.get_entity_name(command) + "#" + str(l)
rs.ensure_bg_set_entity(cmd_ent)
if r_type == "inc":
# pre_conc -- predecessor concentration
# succ_conc -- successor concentration concentration
for i in range(1, self.max_concentration):
pre_conc = param_ent_name + "#" + str(i)
rs.ensure_bg_set_entity(pre_conc)
new_products = []
succ_value = i+l
for j in range(1, succ_value+1):
if j > self.max_concentration:
break
new_p = param_ent_name + "#" + str(j)
rs.ensure_bg_set_entity(new_p)
new_products.append(new_p)
if new_products != []:
rs.add_reaction(
set(new_reactants + [pre_conc, cmd_ent]),
set(new_inhibitors),
set(new_products))
elif r_type == "dec":
for i in range(1, self.max_concentration+1):
pre_conc = param_ent_name + "#" + str(i)
rs.ensure_bg_set_entity(pre_conc)
new_products = []
succ_value = i-l
for j in range(1, succ_value+1):
if j > self.max_concentration:
break
new_p = param_ent_name + "#" + str(j)
rs.ensure_bg_set_entity(new_p)
new_products.append(new_p)
if new_products != []:
rs.add_reaction(
set(new_reactants + [pre_conc, cmd_ent]),
set(new_inhibitors),
set(new_products))
else:
raise RuntimeError(
"Unknown meta-reaction type: " + repr(r_type))
for ent, inhibitors in self.permanent_entities.items():
max_c = self.max_concentration
if ent in self.max_conc_per_ent:
max_c = self.max_conc_per_ent[ent]
else:
print(
"WARNING:\n\tThere is no maximal concentration level defined for "
+ self.get_entity_name(ent))
print("\tThis is a very bad idea -- expect degraded performance\n")
def e_value(i):
return self.get_entity_name(ent) + "#" + str(i)
for value in range(1, max_c+1):
new_reactants = []
new_inhibitors = []
new_products = []
new_reactants = [e_value(value)]
for e_inh, conc in inhibitors:
n = self.get_entity_name(e_inh) + "#" + str(conc)
rs.ensure_bg_set_entity(n)
new_inhibitors.append(n)
for i in range(1, value+1):
new_products.append(e_value(i))
rs.add_reaction(new_reactants, new_inhibitors, new_products)
return rs
class ReactionSystemWithAutomaton(object):
def __init__(self, reaction_system, context_automaton):
self.rs = reaction_system
self.ca = context_automaton
def show(self, soft=False):
self.rs.show(soft)
self.ca.show()
def is_with_concentrations(self):
if not isinstance(self.rs, ReactionSystemWithConcentrations):
return False
if not isinstance(self.ca, ContextAutomatonWithConcentrations):
return False
return True
def sanity_check(self):
pass
def get_ordinary_reaction_system_with_automaton(self):
if not self.is_with_concentrations():
raise RuntimeError("Not RS/CA with concentrations")
ors = self.rs.get_reaction_system()
oca = self.ca.get_automaton_with_flat_contexts(ors)
return ReactionSystemWithAutomaton(ors, oca)
# EOF
|
[
"rs.reaction_system.ReactionSystem",
"sys.exit"
] |
[((1936, 1943), 'sys.exit', 'exit', (['(1)'], {}), '(1)\n', (1940, 1943), False, 'from sys import exit\n'), ((9620, 9636), 'rs.reaction_system.ReactionSystem', 'ReactionSystem', ([], {}), '()\n', (9634, 9636), False, 'from rs.reaction_system import ReactionSystem\n')]
|
# Copyright <NAME> 2004. Distributed under the Boost
# Software License, Version 1.0. (See accompanying
# file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
from __future__ import print_function
'''
>>> from iterator_ext import *
>>> from input_iterator import *
>>> x = list_int()
>>> x.push_back(1)
>>> x.back()
1
>>> x.push_back(3)
>>> x.push_back(5)
>>> for y in x:
... print(y)
1
3
5
>>> z = range(x)
>>> for y in z:
... print(y)
1
3
5
Range2 wraps a transform_iterator which doubles the elements it
traverses. This proves we can wrap input iterators
>>> z2 = range2(x)
>>> for y in z2:
... print(y)
2
6
10
>>> l2 = two_lists()
>>> for y in l2.primes:
... print(y)
2
3
5
7
11
13
>>> for y in l2.evens:
... print(y)
2
4
6
8
10
12
>>> ll = list_list()
>>> ll.push_back(x)
>>> x.push_back(7)
>>> ll.push_back(x)
>>> for a in ll: #doctest: +NORMALIZE_WHITESPACE
... for b in a:
... print(b, end='')
... print('')
...
1 3 5
1 3 5 7
'''
def run(args = None):
import sys
import doctest
if args is not None:
sys.argv = args
return doctest.testmod(sys.modules.get(__name__))
if __name__ == '__main__':
print("running...")
import sys
status = run()[0]
if (status == 0): print("Done.")
sys.exit(status)
|
[
"sys.modules.get",
"sys.exit"
] |
[((1297, 1313), 'sys.exit', 'sys.exit', (['status'], {}), '(status)\n', (1305, 1313), False, 'import sys\n'), ((1136, 1161), 'sys.modules.get', 'sys.modules.get', (['__name__'], {}), '(__name__)\n', (1151, 1161), False, 'import sys\n')]
|
# -*- Python -*-
# license
# license.
# ======================================================================
"""Looks name up in the [geonames database](http://www.geonames.org/).
[GeoNames Search Webservice API](http://www.geonames.org/export/geonames-search.html)
"""
import sys, os, urllib.request, json, time
from pathlib import Path
import logging; module_logger = logging.getLogger(__name__)
from .utilities import is_chinese
# ======================================================================
def geonames(name):
if not name:
return name
if is_chinese(name):
r = _lookup_chinese(name=name)
else:
r = _lookup("search", isNameRequired="true", name=name)
return r
# ----------------------------------------------------------------------
def _lookup(feature, **args):
def make(entry):
if entry.get("fcl") in ["A", "P"]:
return {
# "local_name": entry[],
"name": entry["toponymName"],
"province": entry["adminName1"],
"country": entry["countryName"],
"latitude": entry["lat"],
"longitude": entry["lng"],
}
else:
return None
return _get(feature, make, args)
# ----------------------------------------------------------------------
def _get(feature, result_maker, args):
args.update({"username": "acorg", "type": "json"})
url = "http://api.geonames.org/{}?{}".format(feature, urllib.parse.urlencode(args))
# module_logger.debug('_lookup {!r}'.format(url))
while True:
rj = json.loads(urllib.request.urlopen(url=url).read().decode("utf-8"))
try:
return [e2 for e2 in (result_maker(e1) for e1 in rj["geonames"]) if e2]
except KeyError:
if "the hourly limit of" in rj.get("status", {}).get("message"):
print(f"WARNING: {rj['status']['message']}", file=sys.stderr)
seconds_to_wait = 120
print(f"WARNING: about to wait {seconds_to_wait} seconds", file=sys.stderr)
time.sleep(seconds_to_wait)
else:
print(f"ERROR: {rj}", file=sys.stderr)
raise RuntimeError(str(rj))
except Exception as err:
print(f"ERROR: {rj}: {err}", file=sys.stderr)
raise RuntimeError(f"{rj}: {err}")
# ----------------------------------------------------------------------
def _lookup_chinese(name):
if len(name) > 3:
r = []
if provinces := _find_chinese_province(name):
province = provinces[0]
county = _find_chinese_county(name, province);
if county:
r = [{
"local_name": name,
"name": _make_chinese_name(province, county),
"province": _make_province_name(province),
"country": province["countryName"],
"latitude": county["lat"],
"longitude": county["lng"],
}]
else:
def make(entry):
province_name = _make_province_name(entry)
return {
"local_name": name,
"name": province_name,
"province": province_name,
"country": entry["countryName"],
"latitude": entry["lat"],
"longitude": entry["lng"],
}
r = [make(e) for e in _find_chinese_province(name)]
return r
# ----------------------------------------------------------------------
def _find_chinese_province(name):
r = _get("search", lambda e: e if e["name"] == name[:2] else None, {"isNameRequired": "true", "name_startsWith": name[:2], "fclass": "A", "fcode": "ADM1", "lang": "cn"})
# module_logger.debug('name: {!r} : {!r}'.format(name[:2], r))
if not r: # Inner Mongolia is written using 3 Hanzi
r = _get("search", lambda e: e if e["name"] == name[:3] else None, {"isNameRequired": "true", "name_startsWith": name[:3], "fclass": "A", "fcode": "ADM1", "lang": "cn"})
return r
# ----------------------------------------------------------------------
def _make_province_name(entry):
r = entry["toponymName"].upper()
space_pos = r.find(' ', 6 if r[:6] == "INNER " else 0)
if space_pos >= 0:
r = r[:space_pos]
return r;
# ----------------------------------------------------------------------
def _find_chinese_county(full_name, province):
name = full_name[len(province["name"]):]
r = _get("search", lambda e: e, {"isNameRequired": "true", "name_startsWith": name, "fclass": "A", "fcode": "ADM3", "adminCode1": province["adminCode1"], "lang": "cn"})
if not r:
r = _get("search", lambda e: e, {"isNameRequired": "true", "name_startsWith": name, "adminCode1": province["adminCode1"], "lang": "cn"})
# module_logger.debug('_find_chinese_county {}'.format(r))
return r[0] if r else None
# ----------------------------------------------------------------------
def _make_chinese_name(province, county):
return _make_province_name(province) + " " + _make_county_name(county)
# ----------------------------------------------------------------------
def _make_county_name(county):
def remove_suffix(source, suffix):
if source[-len(suffix):] == suffix:
source = source[:-len(suffix)]
return source
def remove_apostrophe(source):
return source.replace("’", "")
r = county["toponymName"].upper()
r1 = remove_suffix(r, " ZIZHIXIAN")
if r1 != r:
r = remove_suffix(r1, "ZU")
else:
for s in [" QU", " XIAN", " SHI"]:
r2 = remove_suffix(r, s)
if r2 != r:
r = r2
break
r = remove_apostrophe(r)
return r
# ======================================================================
### Local Variables:
### eval: (if (fboundp 'eu-rename-buffer) (eu-rename-buffer))
### End:
|
[
"logging.getLogger",
"time.sleep"
] |
[((374, 401), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (391, 401), False, 'import logging\n'), ((2104, 2131), 'time.sleep', 'time.sleep', (['seconds_to_wait'], {}), '(seconds_to_wait)\n', (2114, 2131), False, 'import sys, os, urllib.request, json, time\n')]
|
# Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from openvino.tools.mo.front.common.partial_infer.utils import mo_array
from openvino.tools.mo.ops.proposal import ProposalOp
from openvino.tools.mo.front.caffe.collect_attributes import merge_attrs
from openvino.tools.mo.front.extractor import FrontExtractorOp
class ProposalFrontExtractor(FrontExtractorOp):
op = 'Proposal'
enabled = True
@classmethod
def extract(cls, node):
proto_layer = node.pb
param = proto_layer.proposal_param
update_attrs = {
'feat_stride': param.feat_stride,
'base_size': param.base_size,
'min_size': param.min_size,
'ratio': mo_array(param.ratio),
'scale': mo_array(param.scale),
'pre_nms_topn': param.pre_nms_topn,
'post_nms_topn': param.post_nms_topn,
'nms_thresh': param.nms_thresh
}
mapping_rule = merge_attrs(param, update_attrs)
# update the attributes of the node
ProposalOp.update_node_stat(node, mapping_rule)
return cls.enabled
|
[
"openvino.tools.mo.ops.proposal.ProposalOp.update_node_stat",
"openvino.tools.mo.front.caffe.collect_attributes.merge_attrs",
"openvino.tools.mo.front.common.partial_infer.utils.mo_array"
] |
[((969, 1001), 'openvino.tools.mo.front.caffe.collect_attributes.merge_attrs', 'merge_attrs', (['param', 'update_attrs'], {}), '(param, update_attrs)\n', (980, 1001), False, 'from openvino.tools.mo.front.caffe.collect_attributes import merge_attrs\n'), ((1054, 1101), 'openvino.tools.mo.ops.proposal.ProposalOp.update_node_stat', 'ProposalOp.update_node_stat', (['node', 'mapping_rule'], {}), '(node, mapping_rule)\n', (1081, 1101), False, 'from openvino.tools.mo.ops.proposal import ProposalOp\n'), ((727, 748), 'openvino.tools.mo.front.common.partial_infer.utils.mo_array', 'mo_array', (['param.ratio'], {}), '(param.ratio)\n', (735, 748), False, 'from openvino.tools.mo.front.common.partial_infer.utils import mo_array\n'), ((771, 792), 'openvino.tools.mo.front.common.partial_infer.utils.mo_array', 'mo_array', (['param.scale'], {}), '(param.scale)\n', (779, 792), False, 'from openvino.tools.mo.front.common.partial_infer.utils import mo_array\n')]
|
# -*- coding: utf-8 -*-
# mk42
# mk42/apps/users/migrations/0003_auto_20170614_0038.py
# Generated by Django 1.11.2 on 2017-06-14 00:38
from __future__ import unicode_literals
from django.db import (
migrations,
models,
)
class Migration(migrations.Migration):
dependencies = [
("users", "0002_auto_20170613_2124"),
]
operations = [
migrations.AlterField(
model_name="user",
name="language",
field=models.CharField(choices=[("en", "English"), ("uk", "\u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0430")], default="en", max_length=5, verbose_name="language"),
),
]
|
[
"django.db.models.CharField"
] |
[((478, 603), 'django.db.models.CharField', 'models.CharField', ([], {'choices': "[('en', 'English'), ('uk', 'Українська')]", 'default': '"""en"""', 'max_length': '(5)', 'verbose_name': '"""language"""'}), "(choices=[('en', 'English'), ('uk', 'Українська')], default\n ='en', max_length=5, verbose_name='language')\n", (494, 603), False, 'from django.db import migrations, models\n')]
|
import pickle
import pandas as pd
import torch
import torch.nn as nn
import torchvision.transforms as T
from torch.utils import data
from torch.utils.data import random_split
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataset import Dataset
from datasets.celeba import CelebA1000
from datasets.facescrub import FaceScrub
from datasets.stanford_dogs import StanfordDogs
def get_normalization():
normalization = T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
return normalization
def get_train_val_split(data, split_ratio, seed=0):
validation_set_length = int(split_ratio * len(data))
training_set_length = len(data) - validation_set_length
torch.manual_seed(seed)
training_set, validation_set = random_split(
data, [training_set_length, validation_set_length])
return training_set, validation_set
def get_subsampled_dataset(dataset,
dataset_size=None,
proportion=None,
seed=0):
if dataset_size > len(dataset):
raise ValueError(
'Dataset size is smaller than specified subsample size')
if dataset_size is None:
if proportion is None:
raise ValueError('Neither dataset_size nor proportion specified')
else:
dataset_size = int(proportion * len(dataset))
torch.manual_seed(seed)
subsample, _ = random_split(
dataset, [dataset_size, len(dataset) - dataset_size])
return subsample
def get_facescrub_idx_to_class():
with open('utils/files/facescrub_idx_to_class.pkl', 'rb') as f:
idx_to_class = pickle.load(f)
return idx_to_class
def get_facescrub_class_to_idx():
with open('utils/files/facescrub_class_to_idx.pkl', 'rb') as f:
class_to_idx = pickle.load(f)
return class_to_idx
def get_celeba_idx_to_attr(list_attr_file='data/celeba/list_attr_celeba.txt'):
file = pd.read_csv(list_attr_file)
attributes = file.iloc[0].tolist()[0].split(' ')[:-1]
attr_dict = {idx: attributes[idx] for idx in range(len(attributes))}
return attr_dict
def get_celeba_attr_to_idx(list_attr_file='data/celeba/list_attr_celeba.txt'):
file = pd.read_csv(list_attr_file)
attributes = file.iloc[0].tolist()[0].split(' ')[:-1]
attr_dict = {attributes[idx]: idx for idx in range(len(attributes))}
return attr_dict
def get_stanford_dogs_idx_to_class():
with open('utils/files/stanford_dogs_idx_to_class.pkl', 'rb') as f:
idx_to_class = pickle.load(f)
return idx_to_class
def get_stanford_dogs_class_to_idx():
with open('utils/files/stanford_dogs_class_to_idx.pkl', 'rb') as f:
class_to_idx = pickle.load(f)
return class_to_idx
def create_target_dataset(dataset_name, transform):
if dataset_name.lower() == 'facescrub':
return FaceScrub(group='all',
train=True,
transform=transform)
elif dataset_name.lower() == 'celeba_identities':
return CelebA1000(train=True, transform=transform)
elif 'stanford_dogs' in dataset_name.lower():
return StanfordDogs(train=True, cropped=True, transform=transform)
else:
print(f'{dataset_name} is no valid dataset.')
|
[
"torch.manual_seed",
"pandas.read_csv",
"torch.utils.data.random_split",
"datasets.facescrub.FaceScrub",
"pickle.load",
"torchvision.transforms.Normalize",
"datasets.celeba.CelebA1000",
"datasets.stanford_dogs.StanfordDogs"
] |
[((448, 502), 'torchvision.transforms.Normalize', 'T.Normalize', ([], {'mean': '[0.5, 0.5, 0.5]', 'std': '[0.5, 0.5, 0.5]'}), '(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n', (459, 502), True, 'import torchvision.transforms as T\n'), ((703, 726), 'torch.manual_seed', 'torch.manual_seed', (['seed'], {}), '(seed)\n', (720, 726), False, 'import torch\n'), ((762, 826), 'torch.utils.data.random_split', 'random_split', (['data', '[training_set_length, validation_set_length]'], {}), '(data, [training_set_length, validation_set_length])\n', (774, 826), False, 'from torch.utils.data import random_split\n'), ((1386, 1409), 'torch.manual_seed', 'torch.manual_seed', (['seed'], {}), '(seed)\n', (1403, 1409), False, 'import torch\n'), ((1950, 1977), 'pandas.read_csv', 'pd.read_csv', (['list_attr_file'], {}), '(list_attr_file)\n', (1961, 1977), True, 'import pandas as pd\n'), ((2222, 2249), 'pandas.read_csv', 'pd.read_csv', (['list_attr_file'], {}), '(list_attr_file)\n', (2233, 2249), True, 'import pandas as pd\n'), ((1653, 1667), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (1664, 1667), False, 'import pickle\n'), ((1819, 1833), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (1830, 1833), False, 'import pickle\n'), ((2537, 2551), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (2548, 2551), False, 'import pickle\n'), ((2711, 2725), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (2722, 2725), False, 'import pickle\n'), ((2863, 2918), 'datasets.facescrub.FaceScrub', 'FaceScrub', ([], {'group': '"""all"""', 'train': '(True)', 'transform': 'transform'}), "(group='all', train=True, transform=transform)\n", (2872, 2918), False, 'from datasets.facescrub import FaceScrub\n'), ((3038, 3081), 'datasets.celeba.CelebA1000', 'CelebA1000', ([], {'train': '(True)', 'transform': 'transform'}), '(train=True, transform=transform)\n', (3048, 3081), False, 'from datasets.celeba import CelebA1000\n'), ((3147, 3206), 'datasets.stanford_dogs.StanfordDogs', 'StanfordDogs', ([], {'train': '(True)', 'cropped': '(True)', 'transform': 'transform'}), '(train=True, cropped=True, transform=transform)\n', (3159, 3206), False, 'from datasets.stanford_dogs import StanfordDogs\n')]
|
from __future__ import print_function, absolute_import, division #makes KratosMultiphysics backward compatible with python 2.6 and 2.7
from KratosMultiphysics import *
from KratosMultiphysics.IncompressibleFluidApplication import *
from KratosMultiphysics.FluidDynamicsApplication import *
from KratosMultiphysics.ExternalSolversApplication import *
from KratosMultiphysics.MeshingApplication import *
import KratosMultiphysics.MappingApplication as KratosMapping
# In this example two domains are solved, a coarse background mesh and a fine mesh around
# an obstacle. The fine domain receives the values from the coarse domain as input on it's boundary
######################################################################################
######################################################################################
######################################################################################
##PARSING THE PARAMETERS
#import define_output
parameter_file_background = open("ProjectParameters_Background.json",'r')
Projectparameters_BG = Parameters( parameter_file_background.read())
parameter_file_bodyfitted = open("ProjectParameters_BodyFitted.json",'r')
Projectparameters_BF = Parameters( parameter_file_bodyfitted.read())
## Fluid model part definition
main_model_part_bg = ModelPart(Projectparameters_BG["problem_data"]["model_part_name"].GetString())
main_model_part_bg.ProcessInfo.SetValue(DOMAIN_SIZE, Projectparameters_BG["problem_data"]["domain_size"].GetInt())
main_model_part_bf = ModelPart(Projectparameters_BF["problem_data"]["model_part_name"].GetString())
main_model_part_bf.ProcessInfo.SetValue(DOMAIN_SIZE, Projectparameters_BF["problem_data"]["domain_size"].GetInt())
###TODO replace this "model" for real one once available
Model_BG = {Projectparameters_BG["problem_data"]["model_part_name"].GetString() : main_model_part_bg}
Model_BF = {Projectparameters_BF["problem_data"]["model_part_name"].GetString() : main_model_part_bf}
## Solver construction
solver_module = __import__(Projectparameters_BG["solver_settings"]["solver_type"].GetString())
solver_bg = solver_module.CreateSolver(main_model_part_bg, Projectparameters_BG["solver_settings"])
solver_bg.AddVariables()
solver_module = __import__(Projectparameters_BF["solver_settings"]["solver_type"].GetString())
solver_bf = solver_module.CreateSolver(main_model_part_bf, Projectparameters_BF["solver_settings"])
solver_bf.AddVariables()
## Read the model - note that SetBufferSize is done here
solver_bg.ImportModelPart()
solver_bf.ImportModelPart()
## Add AddDofs
solver_bg.AddDofs()
solver_bf.AddDofs()
## Initialize GiD I/O
from gid_output_process import GiDOutputProcess
gid_output_bg = GiDOutputProcess(solver_bg.GetComputingModelPart(),
Projectparameters_BG["problem_data"]["problem_name"].GetString() ,
Projectparameters_BG["output_configuration"])
gid_output_bg.ExecuteInitialize()
gid_output_bf = GiDOutputProcess(solver_bf.GetComputingModelPart(),
Projectparameters_BF["problem_data"]["problem_name"].GetString() ,
Projectparameters_BF["output_configuration"])
gid_output_bf.ExecuteInitialize()
##here all of the allocation of the strategies etc is done
solver_bg.Initialize()
solver_bf.Initialize()
##TODO: replace MODEL for the Kratos one ASAP
## Get the list of the skin submodel parts in the object Model
for i in range(Projectparameters_BG["solver_settings"]["skin_parts"].size()):
skin_part_name = Projectparameters_BG["solver_settings"]["skin_parts"][i].GetString()
Model_BG.update({skin_part_name: main_model_part_bg.GetSubModelPart(skin_part_name)})
for i in range(Projectparameters_BF["solver_settings"]["skin_parts"].size()):
skin_part_name = Projectparameters_BF["solver_settings"]["skin_parts"][i].GetString()
Model_BF.update({skin_part_name: main_model_part_bf.GetSubModelPart(skin_part_name)})
## Get the list of the initial conditions submodel parts in the object Model
for i in range(Projectparameters_BF["initial_conditions_process_list"].size()):
initial_cond_part_name = Projectparameters_BF["initial_conditions_process_list"][i]["Parameters"]["model_part_name"].GetString()
Model_BF.update({initial_cond_part_name: main_model_part_bf.GetSubModelPart(initial_cond_part_name)})
## Processes construction
import process_factory
# "list_of_processes_bg" contains all the processes already constructed (boundary conditions, initial conditions and gravity)
# Note that the conditions are firstly constructed. Otherwise, they may overwrite the BCs information.
list_of_processes_bg = process_factory.KratosProcessFactory(Model_BG).ConstructListOfProcesses( Projectparameters_BG["initial_conditions_process_list"] )
list_of_processes_bg += process_factory.KratosProcessFactory(Model_BG).ConstructListOfProcesses( Projectparameters_BG["boundary_conditions_process_list"] )
list_of_processes_bf = process_factory.KratosProcessFactory(Model_BF).ConstructListOfProcesses( Projectparameters_BF["initial_conditions_process_list"] )
list_of_processes_bf += process_factory.KratosProcessFactory(Model_BF).ConstructListOfProcesses( Projectparameters_BF["boundary_conditions_process_list"] )
## Processes initialization
for process in list_of_processes_bg:
process.ExecuteInitialize()
for process in list_of_processes_bf:
process.ExecuteInitialize()
# Mapper initialization
mapper_settings_file = open("MapperSettings.json",'r')
Projectparameters_Mapper = Parameters( mapper_settings_file.read())["mapper_settings"]
inlet_mapper = KratosMapping.MapperFactory.CreateMapper(main_model_part_bg,
main_model_part_bf,
Projectparameters_Mapper[0])
sides_mapper = KratosMapping.MapperFactory.CreateMapper(main_model_part_bg,
main_model_part_bf,
Projectparameters_Mapper[1])
outlet_mapper = KratosMapping.MapperFactory.CreateMapper(main_model_part_bg,
main_model_part_bf,
Projectparameters_Mapper[2])
## Stepping and time settings
Dt = Projectparameters_BG["problem_data"]["time_step"].GetDouble()
end_time = Projectparameters_BG["problem_data"]["end_time"].GetDouble()
time = 0.0
step = 0
out = 0.0
gid_output_bg.ExecuteBeforeSolutionLoop()
gid_output_bf.ExecuteBeforeSolutionLoop()
for process in list_of_processes_bg:
process.ExecuteBeforeSolutionLoop()
for process in list_of_processes_bf:
process.ExecuteBeforeSolutionLoop()
while(time <= end_time):
time = time + Dt
step = step + 1
main_model_part_bg.CloneTimeStep(time)
main_model_part_bf.CloneTimeStep(time)
print("STEP = ", step)
print("TIME = ", time)
if(step >= 3):
for process in list_of_processes_bg:
process.ExecuteInitializeSolutionStep()
for process in list_of_processes_bf:
process.ExecuteInitializeSolutionStep()
gid_output_bg.ExecuteInitializeSolutionStep()
gid_output_bf.ExecuteInitializeSolutionStep()
solver_bg.Solve()
inlet_mapper.Map(VELOCITY, VELOCITY)
sides_mapper.Map(VELOCITY, VELOCITY)
outlet_mapper.Map(VELOCITY, VELOCITY)
solver_bf.Solve()
for process in list_of_processes_bg:
process.ExecuteFinalizeSolutionStep()
for process in list_of_processes_bf:
process.ExecuteFinalizeSolutionStep()
gid_output_bg.ExecuteFinalizeSolutionStep()
gid_output_bf.ExecuteFinalizeSolutionStep()
#TODO: decide if it shall be done only when output is processed or not
for process in list_of_processes_bg:
process.ExecuteBeforeOutputStep()
for process in list_of_processes_bf:
process.ExecuteBeforeOutputStep()
if gid_output_bg.IsOutputStep():
gid_output_bg.PrintOutput()
gid_output_bf.PrintOutput()
for process in list_of_processes_bg:
process.ExecuteAfterOutputStep()
for process in list_of_processes_bf:
process.ExecuteAfterOutputStep()
out = out + Dt
for process in list_of_processes_bg:
process.ExecuteFinalize()
for process in list_of_processes_bf:
process.ExecuteFinalize()
gid_output_bg.ExecuteFinalize()
gid_output_bf.ExecuteFinalize()
|
[
"KratosMultiphysics.MappingApplication.MapperFactory.CreateMapper",
"process_factory.KratosProcessFactory"
] |
[((5636, 5749), 'KratosMultiphysics.MappingApplication.MapperFactory.CreateMapper', 'KratosMapping.MapperFactory.CreateMapper', (['main_model_part_bg', 'main_model_part_bf', 'Projectparameters_Mapper[0]'], {}), '(main_model_part_bg,\n main_model_part_bf, Projectparameters_Mapper[0])\n', (5676, 5749), True, 'import KratosMultiphysics.MappingApplication as KratosMapping\n'), ((5850, 5963), 'KratosMultiphysics.MappingApplication.MapperFactory.CreateMapper', 'KratosMapping.MapperFactory.CreateMapper', (['main_model_part_bg', 'main_model_part_bf', 'Projectparameters_Mapper[1]'], {}), '(main_model_part_bg,\n main_model_part_bf, Projectparameters_Mapper[1])\n', (5890, 5963), True, 'import KratosMultiphysics.MappingApplication as KratosMapping\n'), ((6065, 6178), 'KratosMultiphysics.MappingApplication.MapperFactory.CreateMapper', 'KratosMapping.MapperFactory.CreateMapper', (['main_model_part_bg', 'main_model_part_bf', 'Projectparameters_Mapper[2]'], {}), '(main_model_part_bg,\n main_model_part_bf, Projectparameters_Mapper[2])\n', (6105, 6178), True, 'import KratosMultiphysics.MappingApplication as KratosMapping\n'), ((4683, 4729), 'process_factory.KratosProcessFactory', 'process_factory.KratosProcessFactory', (['Model_BG'], {}), '(Model_BG)\n', (4719, 4729), False, 'import process_factory\n'), ((4838, 4884), 'process_factory.KratosProcessFactory', 'process_factory.KratosProcessFactory', (['Model_BG'], {}), '(Model_BG)\n', (4874, 4884), False, 'import process_factory\n'), ((4994, 5040), 'process_factory.KratosProcessFactory', 'process_factory.KratosProcessFactory', (['Model_BF'], {}), '(Model_BF)\n', (5030, 5040), False, 'import process_factory\n'), ((5149, 5195), 'process_factory.KratosProcessFactory', 'process_factory.KratosProcessFactory', (['Model_BF'], {}), '(Model_BF)\n', (5185, 5195), False, 'import process_factory\n')]
|
"""
提供几种常用的控制器。
这些验证器通常需要提供一些参数进行一次调用，返回的结果才是真正的验证器，其中的技巧在于通过闭包使要控制的对象能够被内部函数访问。
版本: 1.3.0+
"""
import re
from nonebot import CommandSession
from nonebot.helpers import render_expression
def handle_cancellation(session: CommandSession):
"""
在用户发送 `算了`、`不用了`、`取消吧`、`停` 之类的话的时候，结束当前传入的命令会话（调用 `session.finish()`），并发送配置项 `SESSION_CANCEL_EXPRESSION` 所填的内容。
如果不是上述取消指令，则将输入原样输出。
参数:
session: 要控制的命令会话
"""
def control(value):
if _is_cancellation(value) is True:
session.finish(
render_expression(session.bot.config.SESSION_CANCEL_EXPRESSION))
return value
return control
def _is_cancellation(sentence: str) -> bool:
for kw in ('算', '别', '不', '停', '取消'):
if kw in sentence:
# a keyword matches
break
else:
# no keyword matches
return False
if re.match(r'^那?[算别不停]\w{0,3}了?吧?$', sentence) or \
re.match(r'^那?(?:[给帮]我)?取消了?吧?$', sentence):
return True
return False
__all__ = [
'handle_cancellation',
]
|
[
"re.match",
"nonebot.helpers.render_expression"
] |
[((888, 932), 're.match', 're.match', (['"""^那?[算别不停]\\\\w{0,3}了?吧?$"""', 'sentence'], {}), "('^那?[算别不停]\\\\w{0,3}了?吧?$', sentence)\n", (896, 932), False, 'import re\n'), ((950, 992), 're.match', 're.match', (['"""^那?(?:[给帮]我)?取消了?吧?$"""', 'sentence'], {}), "('^那?(?:[给帮]我)?取消了?吧?$', sentence)\n", (958, 992), False, 'import re\n'), ((548, 611), 'nonebot.helpers.render_expression', 'render_expression', (['session.bot.config.SESSION_CANCEL_EXPRESSION'], {}), '(session.bot.config.SESSION_CANCEL_EXPRESSION)\n', (565, 611), False, 'from nonebot.helpers import render_expression\n')]
|
import argparse
import numpy as np
import glob
import re
from log import print_to_file
from scipy.fftpack import fftn, ifftn
from skimage.feature import peak_local_max, canny
from skimage.transform import hough_circle
import pickle as pickle
from paths import TRAIN_DATA_PATH, LOGS_PATH, PKL_TRAIN_DATA_PATH, PKL_TEST_DATA_PATH
from paths import TEST_DATA_PATH
def orthogonal_projection_on_slice(percentual_coordinate, source_metadata, target_metadata):
point = np.array([[percentual_coordinate[0]],
[percentual_coordinate[1]],
[0],
[1]])
image_size = [source_metadata["Rows"], source_metadata["Columns"]]
point = np.dot(np.array( [[image_size[0],0,0,0],
[0,image_size[1],0,0],
[0,0,0,0],
[0,0,0,1]]), point)
pixel_spacing = source_metadata["PixelSpacing"]
point = np.dot(np.array( [[pixel_spacing[0],0,0,0],
[0,pixel_spacing[1],0,0],
[0,0,0,0],
[0,0,0,1]]), point)
Fa = np.array(source_metadata["ImageOrientationPatient"]).reshape( (2,3) )[::-1,:]
posa = source_metadata["ImagePositionPatient"]
point = np.dot(np.array( [[Fa[0,0],Fa[1,0],0,posa[0]],
[Fa[0,1],Fa[1,1],0,posa[1]],
[Fa[0,2],Fa[1,2],0,posa[2]],
[0,0,0,1]]), point)
posb = target_metadata["ImagePositionPatient"]
point = np.dot(np.array( [[1,0,0,-posb[0]],
[0,1,0,-posb[1]],
[0,0,1,-posb[2]],
[0,0,0,1]]), point)
Fb = np.array(target_metadata["ImageOrientationPatient"]).reshape( (2,3) )[::-1,:]
ff0 = np.sqrt(np.sum(Fb[0,:]*Fb[0,:]))
ff1 = np.sqrt(np.sum(Fb[1,:]*Fb[1,:]))
point = np.dot(np.array( [[Fb[0,0]/ff0,Fb[0,1]/ff0,Fb[0,2]/ff0,0],
[Fb[1,0]/ff1,Fb[1,1]/ff1,Fb[1,2]/ff1,0],
[0,0,0,0],
[0,0,0,1]]), point)
pixel_spacing = target_metadata["PixelSpacing"]
point = np.dot(np.array( [[1./pixel_spacing[0],0,0,0],
[0,1./pixel_spacing[1],0,0],
[0,0,0,0],
[0,0,0,1]]), point)
image_size = [target_metadata["Rows"], target_metadata["Columns"]]
point = np.dot(np.array( [[1./image_size[0],0,0,0],
[0,1./image_size[1],0,0],
[0,0,0,0],
[0,0,0,1]]), point)
return point[:2,0] # percentual coordinate as well
#joni
minradius = 15
maxradius = 65
kernel_width = 5
center_margin = 8
num_peaks = 10
num_circles = 10 # 20
radstep = 2
#ira
minradius_mm=25
maxradius_mm=45
kernel_width=5
center_margin=8
num_peaks=10
num_circles=20
radstep=2
def extract_roi(data, pixel_spacing, minradius_mm=15, maxradius_mm=65, kernel_width=5, center_margin=8, num_peaks=10,
num_circles=10, radstep=2):
"""
Returns center and radii of ROI region in (i,j) format
"""
# radius of the smallest and largest circles in mm estimated from the train set
# convert to pixel counts
minradius = int(minradius_mm / pixel_spacing)
maxradius = int(maxradius_mm / pixel_spacing)
ximagesize = data[0]['data'].shape[1]
yimagesize = data[0]['data'].shape[2]
xsurface = np.tile(list(range(ximagesize)), (yimagesize, 1)).T
ysurface = np.tile(list(range(yimagesize)), (ximagesize, 1))
lsurface = np.zeros((ximagesize, yimagesize))
allcenters = []
allaccums = []
allradii = []
for dslice in data:
ff1 = fftn(dslice['data'])
fh = np.absolute(ifftn(ff1[1, :, :]))
fh[fh < 0.1 * np.max(fh)] = 0.0
image = 1. * fh / np.max(fh)
# find hough circles and detect two radii
edges = canny(image, sigma=3)
hough_radii = np.arange(minradius, maxradius, radstep)
hough_res = hough_circle(edges, hough_radii)
if hough_res.any():
centers = []
accums = []
radii = []
for radius, h in zip(hough_radii, hough_res):
# For each radius, extract num_peaks circles
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
# Keep the most prominent num_circles circles
sorted_circles_idxs = np.argsort(accums)[::-1][:num_circles]
for idx in sorted_circles_idxs:
center_x, center_y = centers[idx]
allcenters.append(centers[idx])
allradii.append(radii[idx])
allaccums.append(accums[idx])
brightness = accums[idx]
lsurface = lsurface + brightness * np.exp(
-((xsurface - center_x) ** 2 + (ysurface - center_y) ** 2) / kernel_width ** 2)
lsurface = lsurface / lsurface.max()
# select most likely ROI center
roi_center = np.unravel_index(lsurface.argmax(), lsurface.shape)
# determine ROI radius
roi_x_radius = 0
roi_y_radius = 0
for idx in range(len(allcenters)):
xshift = np.abs(allcenters[idx][0] - roi_center[0])
yshift = np.abs(allcenters[idx][1] - roi_center[1])
if (xshift <= center_margin) & (yshift <= center_margin):
roi_x_radius = np.max((roi_x_radius, allradii[idx] + xshift))
roi_y_radius = np.max((roi_y_radius, allradii[idx] + yshift))
if roi_x_radius > 0 and roi_y_radius > 0:
roi_radii = roi_x_radius, roi_y_radius
else:
roi_radii = None
return roi_center, roi_radii
def read_slice(path):
return pickle.load(open(path))['data']
def read_metadata(path):
d = pickle.load(open(path))['metadata'][0]
metadata = {k: d[k] for k in ['PixelSpacing', 'ImageOrientationPatient', 'ImagePositionPatient', 'SliceLocation',
'PatientSex', 'PatientAge', 'Rows', 'Columns']}
metadata['PixelSpacing'] = np.float32(metadata['PixelSpacing'])
metadata['ImageOrientationPatient'] = np.float32(metadata['ImageOrientationPatient'])
metadata['SliceLocation'] = np.float32(metadata['SliceLocation'])
metadata['ImagePositionPatient'] = np.float32(metadata['ImagePositionPatient'])
metadata['PatientSex'] = 1 if metadata['PatientSex'] == 'F' else 0
metadata['PatientAge'] = int(metadata['PatientAge'][1:3])
metadata['Rows'] = int(metadata['Rows'])
metadata['Columns'] = int(metadata['Columns'])
return metadata
def get_patient_data(patient_data_path):
patient_data = []
spaths = sorted(glob.glob(patient_data_path + '/sax_*.pkl'),
key=lambda x: int(re.search(r'/\w*_(\d+)*\.pkl$', x).group(1)))
pid = re.search(r'/(\d+)/study$', patient_data_path).group(1)
for s in spaths:
slice_id = re.search(r'/(sax_\d+\.pkl)$', s).group(1)
metadata = read_metadata(s)
d = read_slice(s)
patient_data.append({'data': d, 'metadata': metadata,
'slice_id': slice_id, 'patient_id': pid})
return patient_data
def get_patient_ch_data(patient_data_path):
patient_data = []
spaths = sorted(glob.glob(patient_data_path + '/*ch_*.pkl'),
key=lambda x: int(re.search(r'/\w*_(\d+)*\.pkl$', x).group(1)))
pid = re.search(r'/(\d+)/study$', patient_data_path).group(1)
for s in spaths:
slice_id = re.search(r'/(\d+ch_\d+\.pkl)$', s).group(1)
metadata = read_metadata(s)
d = read_slice(s)
patient_data.append({'data': d, 'metadata': metadata,
'slice_id': slice_id, 'patient_id': pid})
return patient_data
def sort_slices(slices):
nslices = len(slices)
positions = np.zeros((nslices,))
for i in range(nslices):
positions[i] = slices[i]['metadata']['SliceLocation']
sorted_slices = [s for pos, s in sorted(zip(positions.tolist(), slices),
key=lambda x: x[0], reverse=True)]
return sorted_slices
def group_slices(slice_stack):
"""
Groups slices into stacks with the same image orientation
:param slice_stack:
:return: list of slice stacks
"""
img_orientations = []
for s in slice_stack:
img_orientations.append(tuple(s['metadata']['ImageOrientationPatient']))
img_orientations = list(set(img_orientations))
if len(img_orientations) == 1:
return [slice_stack]
else:
slice_groups = [[] for _ in range(len(img_orientations))]
for s in slice_stack:
group = img_orientations.index(tuple(s['metadata']['ImageOrientationPatient']))
slice_groups[group].append(s)
return slice_groups
def plot_roi(slice_group, roi_center, roi_radii):
x_roi_center, y_roi_center = roi_center[0], roi_center[1]
x_roi_radius, y_roi_radius = roi_radii[0], roi_radii[1]
print('nslices', len(slice_group))
for dslice in [slice_group[len(slice_group) / 2]]:
outdata = dslice['data']
# print dslice['slice_id']
# print dslice['metadata']['SliceLocation']
# print dslice['metadata']['ImageOrientationPatient']
# print dslice['metadata']['PixelSpacing']
# print dslice['data'].shape
# print '--------------------------------------'
roi_mask = np.zeros_like(outdata[0])
roi_mask[x_roi_center - x_roi_radius:x_roi_center + x_roi_radius,
y_roi_center - y_roi_radius:y_roi_center + y_roi_radius] = 1
outdata[:, roi_mask > 0.5] = 0.4 * outdata[:, roi_mask > 0.5]
outdata[:, roi_mask > 0.5] = 0.4 * outdata[:, roi_mask > 0.5]
fig = plt.figure(1)
fig.canvas.set_window_title(dslice['patient_id'] + dslice['slice_id'])
def init_out():
im.set_data(outdata[0])
def animate_out(i):
im.set_data(outdata[i])
return im
im = fig.gca().imshow(outdata[0], cmap='gist_gray_r', vmin=0, vmax=255)
anim = animation.FuncAnimation(fig, animate_out, init_func=init_out, frames=30, interval=50)
plt.show()
def get_slice2roi(data_path, plot=False):
patient_paths = sorted(glob.glob(data_path + '*/study'))
slice2roi = {}
for p in patient_paths:
patient_data = get_patient_data(p)
sorted_slices = sort_slices(patient_data)
grouped_slices = group_slices(sorted_slices)
ch_data = get_patient_ch_data(p)
ch4, ch2 = None,None
for data in ch_data:
if data['slice_id'].startswith("4"):
ch4 = data
elif data['slice_id'].startswith("2"):
ch2 = data
# init patient dict
pid = sorted_slices[0]['patient_id']
print("processing patient %s" % pid)
# print pid
slice2roi[pid] = {}
# pixel spacing doesn't change within one patient
pixel_spacing = sorted_slices[0]['metadata']['PixelSpacing'][0]
for slice_group in grouped_slices:
try:
roi_center, roi_radii = extract_roi(slice_group, pixel_spacing)
except:
print('Could not find ROI')
roi_center, roi_radii = None, None
print(roi_center, roi_radii)
if plot and roi_center and roi_radii:
pass
#plot_roi(slice_group, roi_center, roi_radii)
for s in slice_group:
sid = s['slice_id']
slice2roi[pid][sid] = {'roi_center': roi_center, 'roi_radii': roi_radii}
# project found roi_centers on the 4ch and 2ch slice
ch4_centers = []
ch2_centers = []
for slice in sorted_slices:
sid = slice['slice_id']
roi_center = slice2roi[pid][sid]['roi_center']
metadata_source = slice['metadata']
hough_roi_center = (float(roi_center[0]) / metadata_source['Rows'],
float(roi_center[1]) / metadata_source['Columns'])
if ch4 is not None:
metadata_target = ch4['metadata']
result = orthogonal_projection_on_slice(hough_roi_center, metadata_source, metadata_target)
ch_roi_center = [float(result[0]) * metadata_target['Rows'],
float(result[1]) * metadata_target['Columns']]
ch4_centers.append(ch_roi_center)
if ch2 is not None:
metadata_target = ch2['metadata']
result = orthogonal_projection_on_slice(hough_roi_center, metadata_source, metadata_target)
ch_roi_center = [float(result[0]) * metadata_target['Rows'],
float(result[1]) * metadata_target['Columns']]
ch2_centers.append(ch_roi_center)
if ch4 is not None:
centers = np.array(ch4_centers)
ch4_result_center = np.mean(centers, axis=0)
ch4_result_radius = np.max(np.sqrt((centers - ch4_result_center)**2))
sid = ch4['slice_id']
slice2roi[pid][sid] = {'roi_center': tuple(ch4_result_center), 'roi_radii': (ch4_result_radius, ch4_result_radius)}
if ch2 is not None:
centers = np.array(ch2_centers)
ch2_result_center = np.mean(centers, axis=0)
ch2_result_radius = np.max(np.sqrt((centers - ch2_result_center)**2))
sid = ch2['slice_id']
slice2roi[pid][sid] = {'roi_center': tuple(ch2_result_center), 'roi_radii': (ch2_result_radius, ch2_result_radius)}
filename = data_path.split('/')[-1] + '_slice2roi_joni.pkl'
with open(filename, 'w') as f:
pickle.dump(slice2roi, f)
print('saved to ', filename)
return slice2roi
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__)
required = parser.add_argument_group('required arguments')
#required.add_argument('-c', '--config',
# help='configuration to run',
# required=True)
args = parser.parse_args()
data_paths = [PKL_TRAIN_DATA_PATH, PKL_TEST_DATA_PATH]
log_path = LOGS_PATH + "generate_roi.log"
with print_to_file(log_path):
for d in data_paths:
get_slice2roi(d, plot=True)
print("log saved to '%s'" % log_path)
|
[
"numpy.sqrt",
"scipy.fftpack.fftn",
"numpy.array",
"numpy.argsort",
"numpy.arange",
"re.search",
"numpy.mean",
"argparse.ArgumentParser",
"numpy.max",
"numpy.exp",
"log.print_to_file",
"glob.glob",
"numpy.abs",
"numpy.float32",
"pickle.dump",
"numpy.sum",
"numpy.zeros",
"skimage.feature.canny",
"skimage.transform.hough_circle",
"scipy.fftpack.ifftn",
"numpy.zeros_like",
"skimage.feature.peak_local_max"
] |
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|
# -*- coding: utf-8 -*-
"""Main module."""
import os
from google.cloud import bigquery
from pbq.query import Query
from google.cloud import bigquery_storage_v1beta1
from google.cloud.exceptions import NotFound
from google.api_core.exceptions import BadRequest
import pandas as pd
import datetime
class PBQ(object):
"""
bigquery driver using the google official API
Attributes
------
query : str
the query
query_obj : Query
pbq.Query object
client : Client
the client object for bigquery
bqstorage_client : BigQueryStorageClient
the google storage client object
Methods
------
to_dataframe(save_query=False, **params)
return the query results as data frame
to_csv(filename, sep=',', save_query=False, **params)
save the query results to a csv file
save_to_table(table, dataset, project=None, replace=True, partition=None)
save query to table
run_query()
simply execute your query
table_details(table, dataset, project)
get the information about the table
Static Methods
------
save_file_to_table(filename, table, dataset, project, file_format=bigquery.SourceFormat.CSV, max_bad_records=0,
replace=True, partition=None)
save file to table, it can be partitioned and it can append to existing table.
the supported formats are CSV or PARQUET
save_dataframe_to_table(df: pd.DataFrame, table, dataset, project, max_bad_records=0, replace=True,
partition=None)
same as save file just with pandas dataframe
table_exists(client: bigquery.Client, table_ref: bigquery.table.TableReference)
check if table exists - if True - table exists else not exists
Examples
------
getting query to dataframe
>>> from pbq import Query, PBQ
>>> query = Query("select * from table")
>>> print("the query price:", query.price)
>>> if not query.validate():
>>> raise RuntimeError("table not valid")
>>> pbq = PBQ(query)
>>> pbq.to_dataframe()
saving query to csv
>>> from pbq import Query, PBQ
>>> query = Query("select * from table")
>>> pbq = PBQ(query)
>>> pbq.to_csv()
saving dataframe to table
>>> import pandas as pd
>>> from pbq import Query, PBQ
>>> df = pd.DataFrame()
>>> PBQ.save_dataframe_to_table(df, 'table', 'dataset', 'project_id', partition='20191013', replace=False)
"""
def __init__(self, query: Query, project=None):
"""
bigquery driver using the google official API
:param query: Query object
:param project: str
the BQ project
"""
self.query = query.query
self.query_obj = query
self.project = project
if project:
self.client = bigquery.Client(project=project)
else:
self.client = bigquery.Client()
self.bqstorage_client = bigquery_storage_v1beta1.BigQueryStorageClient()
def to_dataframe(self, save_query=False, **params):
"""
return the query results as data frame
in order to save the query to a table as well as getting the dataframe, send a dict as params with:
- table
- dataset
it will save to the same project
:param save_query: boolean
if to save the query to a table also
:param params: dict
when `save_query` flag is on you need to give the relevant params
:return: pd.DataFrame
the query results
"""
job_config = bigquery.QueryJobConfig()
if save_query:
table_ref = self.client.dataset(params['dataset']).table(params['table'])
job_config.destination = table_ref
query_job = self.client.query(query=self.query, job_config=job_config)
query_job_res = query_job.result()
df = query_job_res.to_dataframe(bqstorage_client=self.bqstorage_client)
return df
def to_csv(self, filename, sep=',', save_query=False, **params):
"""
save the query results to a csv file
in order to save the query to a table as well as getting the dataframe, send a dict as params with:
- table
- dataset
it will save to the same project
:param filename: str
with the path to save the file
:param sep: str
separator to the csv file
:param save_query: boolean
if to save the query to a table also
:param params: dict
when `save_query` flag is on you need to give the relevant params
"""
df = self.to_dataframe(save_query, **params)
df.to_csv(filename, sep=sep, index=False)
def run_query(self):
"""
execute your query
"""
# Set the destination table
client = self.client
query_job = client.query(self.query)
query_job.result()
print('Done running your amazing query')
def save_to_table(self, table, dataset, project=None, replace=True, partition=None):
"""
save query to table
:param table: str
table name
:param dataset: str
data set name
:param project: str
project name
:param replace: boolean
if set as true - it will replace the table, else append to table (default: True)
:param partition: str
partition format DDMMYYY (default: None)
"""
job_config = bigquery.QueryJobConfig()
# Set the destination table
client = self.client
if partition:
table = '{0}${1}'.format(table, partition)
table_ref = client.dataset(dataset).table(table.split('$')[0])
exists_ok = PBQ._writing_disposition(job_config, replace)
if project:
table_ref = client.dataset(dataset, project=project).table(table)
PBQ._create_table(client, exists_ok, partition, replace, table_ref)
job_config.destination = table_ref
query_job = client.query(self.query, job_config=job_config)
query_job.result()
print('Query results loaded to table {}'.format(table_ref.path))
@staticmethod
def _writing_disposition(job_config: bigquery.QueryJobConfig, replace):
exists_ok = False
if replace:
exists_ok = True
job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE
else:
job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND
return exists_ok
@staticmethod
def _create_table(client: bigquery.Client, exists_ok, partition, replace, table_ref):
if (partition and not PBQ.table_exists(client, table_ref)) or (not partition and replace):
bq_table = bigquery.Table(table_ref)
if partition:
time_partitioning = bigquery.TimePartitioning()
bq_table.time_partitioning = time_partitioning
client.create_table(bq_table, exists_ok=exists_ok)
@staticmethod
def save_file_to_table(filename, table, dataset, project, file_format=bigquery.SourceFormat.CSV, max_bad_records=0,
replace=True, partition=None):
"""
save file to table, it can be partitioned and it can append to existing table.
the supported formats are CSV or PARQUET
:param filename: str
with the path to save the file
:param table: str
table name
:param dataset: str
data set name
:param project: str
project name
:param file_format: str
possible file format (CSV, PARQUET) (default: CSV)
:param max_bad_records: int
number of bad records allowed in file (default: 0)
:param replace: boolean
if set as trueit will replace the table, else append to table (default: True)
:param partition: str
partition format DDMMYYY (default: None)
"""
client = bigquery.Client(project=project)
dataset_ref = client.dataset(dataset)
table_ref = dataset_ref.table(table)
job_config = bigquery.LoadJobConfig()
job_config.max_bad_records = max_bad_records
job_config.source_format = file_format
exists_ok = PBQ._writing_disposition(job_config, replace)
if file_format == bigquery.SourceFormat.CSV:
job_config.skip_leading_rows = 1
job_config.autodetect = True
PBQ._create_table(client, exists_ok, partition, replace, table_ref)
if not partition:
with open(filename, "rb") as source_file:
job = client.load_table_from_file(source_file, table_ref, job_config=job_config)
job.result() # Waits for table load to complete.
print("Loaded {} rows into {}:{}.".format(job.output_rows, dataset, table))
else:
print('fallback loading by CMD command due to missing api feature for partition')
table = '{0}${1}'.format(table, partition)
cmd = "bq load"
if replace:
cmd = "{} --replace".format(cmd)
cmd = "{cmd} --source_format={file_format} '{project}:{dataset}.{tbl_name}' {filename}". \
format(cmd=cmd, tbl_name=table, filename=filename, project=project, dataset=dataset,
file_format=file_format)
os.system(cmd)
@staticmethod
def save_dataframe_to_table(df: pd.DataFrame, table, dataset, project, max_bad_records=0, replace=True,
partition=None, validate_params=False):
"""
save pd.DataFrame object to table
:param df: pd.DataFrame
the dataframe you want to save
:param table: str
table name
:param dataset: str
data set name
:param project: str
project name
:param max_bad_records: int
number of bad records allowed in file (default: 0)
:param replace: boolean
if set as true - it will replace the table, else append to table (default: True)
:param partition: str
partition format DDMMYYY (default: None)
:param validate_params: boolean
validate the schema of the table to the dataframe object (default: False)
"""
now = datetime.datetime.now()
random_string = '{}'.format(now.strftime('%y%m%d%H%M%S'))
input_path = "/tmp/tmp-{}.parquet".format(random_string)
schema = None
if validate_params: # because of the fallback it need to change to be as the schema
table_details = PBQ.table_details(table, dataset, project)
if 'schema' in table_details:
schema = table_details['schema']
PBQ._save_df_to_parquet(df, input_path, schema=schema)
PBQ.save_file_to_table(input_path, table, dataset, project, file_format=bigquery.SourceFormat.PARQUET,
max_bad_records=max_bad_records, replace=replace, partition=partition)
@staticmethod
def _save_df_to_parquet(df, input_path, index=False, schema=None):
if schema:
for s in schema:
if s['field_type'] == 'STRING':
s['field_type'] = 'str'
if s['field_type'] == 'INTEGER':
s['field_type'] = 'int'
if s['field_type'] == 'TIMESTAMP':
df[s['column']] = pd.to_datetime(df[s['column']], errors='coerce')
continue
if s['field_type'] == 'DATE':
df[s['column']] = pd.to_datetime(df[s['column']], errors='coerce')
df[s['column']] = df[s['column']].dt.date
continue
df.columns = ["{}".format(col) for col in df.columns]
df.to_parquet(input_path, index=index)
@staticmethod
def table_details(table, dataset, project):
"""
return a dict object with some details about the table
:param table: str
table name
:param dataset: str
data set name
:param project: str
project name
:return: dict
with some table information like, last_modified_time, num_bytes, num_rows, and creation_time
"""
client = bigquery.Client(project=project)
dataset_ref = client.dataset(dataset, project=project)
table_ref = dataset_ref.table(table)
try:
table = client.get_table(table_ref)
except NotFound as error:
return {}
schema = []
for s in table.schema:
schema.append({'column': s.name, 'field_type': s.field_type})
res = {'last_modified_time': table.modified, 'num_bytes': table.num_bytes, 'num_rows': table.num_rows,
'creation_time': table.created, 'schema': schema}
return res
@staticmethod
def table_exists(client: bigquery.Client, table_ref: bigquery.table.TableReference):
"""
check if table exists - if True - table exists else not exists
:param client: bigquery.Client object
:param table_ref: bigquery.table.TableReference object
with the table name and dataset
:return: boolean
True if table exists
False if table not exists
"""
try:
table = client.get_table(table_ref)
if table:
return True
except NotFound as error:
return False
except BadRequest as error:
return True
|
[
"google.cloud.bigquery.QueryJobConfig",
"datetime.datetime.now",
"google.cloud.bigquery_storage_v1beta1.BigQueryStorageClient",
"google.cloud.bigquery.LoadJobConfig",
"google.cloud.bigquery.Table",
"google.cloud.bigquery.TimePartitioning",
"os.system",
"google.cloud.bigquery.Client",
"pandas.to_datetime"
] |
[((3002, 3050), 'google.cloud.bigquery_storage_v1beta1.BigQueryStorageClient', 'bigquery_storage_v1beta1.BigQueryStorageClient', ([], {}), '()\n', (3048, 3050), False, 'from google.cloud import bigquery_storage_v1beta1\n'), ((3634, 3659), 'google.cloud.bigquery.QueryJobConfig', 'bigquery.QueryJobConfig', ([], {}), '()\n', (3657, 3659), False, 'from google.cloud import bigquery\n'), ((5590, 5615), 'google.cloud.bigquery.QueryJobConfig', 'bigquery.QueryJobConfig', ([], {}), '()\n', (5613, 5615), False, 'from google.cloud import bigquery\n'), ((8141, 8173), 'google.cloud.bigquery.Client', 'bigquery.Client', ([], {'project': 'project'}), '(project=project)\n', (8156, 8173), False, 'from google.cloud import bigquery\n'), ((8287, 8311), 'google.cloud.bigquery.LoadJobConfig', 'bigquery.LoadJobConfig', ([], {}), '()\n', (8309, 8311), False, 'from google.cloud import bigquery\n'), ((10515, 10538), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (10536, 10538), False, 'import datetime\n'), ((12544, 12576), 'google.cloud.bigquery.Client', 'bigquery.Client', ([], {'project': 'project'}), '(project=project)\n', (12559, 12576), False, 'from google.cloud import bigquery\n'), ((2879, 2911), 'google.cloud.bigquery.Client', 'bigquery.Client', ([], {'project': 'project'}), '(project=project)\n', (2894, 2911), False, 'from google.cloud import bigquery\n'), ((2952, 2969), 'google.cloud.bigquery.Client', 'bigquery.Client', ([], {}), '()\n', (2967, 2969), False, 'from google.cloud import bigquery\n'), ((6890, 6915), 'google.cloud.bigquery.Table', 'bigquery.Table', (['table_ref'], {}), '(table_ref)\n', (6904, 6915), False, 'from google.cloud import bigquery\n'), ((9548, 9562), 'os.system', 'os.system', (['cmd'], {}), '(cmd)\n', (9557, 9562), False, 'import os\n'), ((6978, 7005), 'google.cloud.bigquery.TimePartitioning', 'bigquery.TimePartitioning', ([], {}), '()\n', (7003, 7005), False, 'from google.cloud import bigquery\n'), ((11641, 11689), 'pandas.to_datetime', 'pd.to_datetime', (["df[s['column']]"], {'errors': '"""coerce"""'}), "(df[s['column']], errors='coerce')\n", (11655, 11689), True, 'import pandas as pd\n'), ((11820, 11868), 'pandas.to_datetime', 'pd.to_datetime', (["df[s['column']]"], {'errors': '"""coerce"""'}), "(df[s['column']], errors='coerce')\n", (11834, 11868), True, 'import pandas as pd\n')]
|
import re
import types
from functools import partial
LITERAL_TYPE = types.StringTypes + (int, float, long, bool, )
class Spec(object):
"""
This object, when overridden with an object that implements a file format
specification, will perform validation on a given parsed version of the
format input.
SPEC Node Documentation:
========================
expected_type:
A type object whose type the object should match.
required_nodes:
A list of nodes that are required for the current node.
required_nodes_when:
A dict of node name/lambda pairs. If the lambda evaluates to True, a
node whose name corresponds to the node name is required.
The current node is passed as a parameter to the lambda as the only
argument.
disallowed_nodes:
A list of nodes that explicitly are disallowed in the current node.
allowed_once_nodes:
A list of nodes that are allowed only once.
allowed_nodes:
A list of nodes that are allowed multiple times.
unknown_node_level:
The message type to return when an unknown node is encountered.
child_nodes:
A dict of node definitions for nodes that can exist within this node.
max_length:
For sequence values only. An integer describing the maximum length of
the string.
not_empty:
A boolean value describing whether the string/list/dict can be empty.
values:
A list of possible values for the node. Only applies to lists and
literal nodes.
value_matches:
If `values` is not set, the value must match this regex. Only applies
to string nodes.
process:
A lambda function that returns a function to process the node. The
lambda accepts one parameter (self) and should return a function that
accepts two parameters (self, node).
child_process:
A lambda function (similar to `process` that returns a function to
process a child node. The lambda accepts one parameter (self) and
should return a function that accepts three parameters (self, node_name,
node).
If this is set, no further testing will take place on child nodes.
"""
SPEC_NAME = "Specification"
MORE_INFO = "You can find more info online."
SPEC = None
def __init__(self, data, err):
self.data = self.parse(data)
self.err = err
self.error = partial(self._err_message, self.err.error)
self.warning = partial(self._err_message, self.err.warning)
self.notice = partial(self._err_message, self.err.notice)
self.err_map = {"error": self.error,
"warning": self.warning,
"notice": self.notice}
self.path = []
def _err_message(self, func, *args, **kwargs):
if self.path:
nodepath = "Node: %s" % self._get_path()
if isinstance(kwargs["description"], list):
kwargs["description"].append(nodepath)
else:
kwargs["description"] = [
kwargs["description"], nodepath]
func(*args, **kwargs)
def _message(self, type_, *args, **kwargs):
kwargs[type_] = kwargs.pop("message")
self.err_map[type_](*args, **kwargs)
def validate(self):
# Validate the root node.
root_name, root_node = self.get_root_node(self.data)
root_val_result = self.validate_root_node(root_node)
if root_val_result == False:
return
# Iterate the tree and validate as we go.
self.iterate(root_name, root_node, self.SPEC)
def parse(self, data): pass
def validate_root_node(self, node): pass
def get_root_node(self, data):
"""
We expect this function to return a tuple:
("Root Node Name", root_node)
"""
def has_attribute(self, node, key): pass
def get_attribute(self, node, key): pass
def has_child(self, node, child_name): pass
def get_children(self, node):
"""
This function should return a list of (child_name, child)-form tuples.
"""
def iterate(self, branch_name, branch, spec_branch):
self.path.append(branch_name)
self._iterate(branch_name, branch, spec_branch)
self.path.pop()
def _get_path(self):
return ' > '.join(self.path)
def _iterate(self, branch_name, branch, spec_branch):
"""Iterate the tree of nodes and validate as we go."""
# Check that the node is of the proper type. If it isn't, then we need
# to stop iterating at this point.
exp_type = spec_branch.get("expected_type")
if (exp_type and
not isinstance(branch, exp_type) or
# Handle `isinstance(True, int) == True` :(
(isinstance(branch, bool) and
(exp_type == int if isinstance(exp_type, type) else
bool not in exp_type))):
self.error(
err_id=("spec", "iterate", "bad_type"),
error="%s's `%s` was of an unexpected type." %
(self.SPEC_NAME, branch_name),
description=["While validating a %s, a `%s` was encountered "
"which is of an improper type." %
(self.SPEC_NAME, branch_name),
"Found: %s" % repr(branch),
self.MORE_INFO])
return
# Handle any generic processing.
if "process" in spec_branch:
# Let the spec processor resolve the processor and then run the
# processor.
spec_branch["process"](self)(branch)
if "not_empty" in spec_branch and not branch:
self.error(
err_id=("spec", "iterate", "empty"),
error="`%s` is empty." % branch_name,
description=["A value was expected for `%s`, but one wasn't "
"found." % branch_name,
self.MORE_INFO])
# If the node isn't an object...
if not isinstance(branch, dict):
if "values" in spec_branch and branch not in spec_branch["values"]:
self.error(
err_id=("spec", "iterate", "bad_value"),
error="`%s` contains an invalid value in %s" %
(branch_name, self.SPEC_NAME),
description=["A `%s` was encountered while validating a "
"`%s` containing the value '%s'. This value "
"is not appropriate for this type of "
"element." %
(branch_name, self.SPEC_NAME, branch),
self.MORE_INFO])
elif ("value_matches" in spec_branch and
isinstance(branch, types.StringTypes)):
raw_pattern = spec_branch["value_matches"]
if not re.match(raw_pattern, branch):
self.error(
err_id=("spec", "iterate", "value_pattern_fail"),
error="`%s` contains an invalid value in %s" %
(branch_name, self.SPEC_NAME),
description=["A `%s` was encountered while validating "
"a `%s`. Its value does not match the "
"pattern required for `%s`s." %
(branch_name, self.SPEC_NAME,
branch_name),
"Found value: %s" % branch,
"Pattern: %s" % raw_pattern,
self.MORE_INFO])
if ("max_length" in spec_branch and
len(branch) > spec_branch["max_length"]):
self.error(
err_id=("spec", "iterate", "max_length"),
error="`%s` has exceeded its maximum length." % branch_name,
description=["`%s` has a maximum length (%d), which has "
"been exceeded (%d)." %
(branch_name, spec_branch["max_length"],
len(branch)),
self.MORE_INFO])
# The rest of the tests are for child items.
if not isinstance(branch, (list, tuple)):
return
if "child_nodes" in spec_branch:
for child in branch:
self.iterate(branch_name + " descendant", child,
spec_branch["child_nodes"])
# We've got nothing else to do with lists.
return
# If we need to process the child nodes individually, do that now.
if "child_process" in spec_branch:
processor = spec_branch["child_process"](self)
for child_name, child in self.get_children(branch):
processor(child_name, child)
# If there's nothing else to do, don't go down that path.
if ("required_nodes" not in spec_branch and
"required_nodes_when" not in spec_branch and
"disallowed_nodes" not in spec_branch):
return
considered_nodes = set()
# Check that all required node as present.
if "required_nodes" in spec_branch:
considered_nodes.update(spec_branch["required_nodes"])
for req_node in [n for n in spec_branch["required_nodes"] if
not self.has_child(branch, n)]:
self.error(
err_id=("spec", "iterate", "missing_req"),
error="%s expecting `%s`" % (self.SPEC_NAME, req_node),
description=["The '%s' node of the %s expects a `%s` "
"element, which was not found." %
(branch_name, self.SPEC_NAME, req_node),
self.MORE_INFO])
# Check that conditionally required nodes are present.
if "required_nodes_when" in spec_branch:
considered_nodes.update(spec_branch["required_nodes_when"].keys())
for req_node in [name for name, cond in
spec_branch["required_nodes_when"].items() if
cond(branch) and not self.has_child(branch, name)]:
self.error(
err_id=("spec", "iterate", "missing_req_cond"),
error="%s expecting `%s`" % (self.SPEC_NAME, req_node),
description=["The '%s' node, under the current "
"circumstances, is missing a `%s` element. "
"This is a required condition of a %s." %
(branch_name, req_node, self.SPEC_NAME),
self.MORE_INFO])
# Check that there are no disallowed nodes.
if "disallowed_nodes" in spec_branch:
disallowed_nodes = spec_branch["disallowed_nodes"]
considered_nodes.update(disallowed_nodes)
for dnode in [n for n in disallowed_nodes if
self.has_child(branch, n)]:
self.error(
err_id=("spec", "iterate", "disallowed"),
error="%s found `%s`, which is not allowed." %
(self.SPEC_NAME, dnode),
description=["The '%s' node contains `%s`, which is a "
"disallowed element. It should be removed." %
(branch_name, dnode),
self.MORE_INFO])
if ("allowed_nodes" not in spec_branch and
"allowed_once_nodes" not in spec_branch):
return
# Check that allowed nodes are obeyed.
allowed_nodes = set(spec_branch.setdefault("allowed_nodes", []))
allowed_once_nodes = spec_branch.setdefault("allowed_once_nodes", [])
allowed_nodes.update(allowed_once_nodes)
child_node_specs = spec_branch.setdefault("child_nodes", {})
seen_nodes = set()
warned_nodes = set()
for child_name, child in self.get_children(branch):
cspec_branch = None
# Process the node first.
if child_name in child_node_specs:
cspec_branch = child_node_specs[child_name]
elif "*" in child_node_specs:
cspec_branch = child_node_specs["*"]
if cspec_branch is not None:
# If it's a lazily evaluated branch, evaluate it now.
if isinstance(cspec_branch, types.LambdaType):
cspec_branch = cspec_branch(self)
# Iterate the node.
self.iterate(child_name, child, cspec_branch)
# If we've seen a node before that's only supposed to be seen a
# single time, warn about it.
if child_name in allowed_once_nodes and child_name in seen_nodes:
# Don't warn about the same node multiple times.
if child_name in warned_nodes:
continue
self.error(
err_id=("spec", "iterate", "allow_once_multiple"),
error="%s found `%s` more than once." %
(self.SPEC_NAME, child_name),
description=["%ss may only contain a single `%s` element, "
"however, it was encountered multiple times." %
(self.SPEC_NAME, child_name),
self.MORE_INFO])
continue
# Remember that we've seen this node.
seen_nodes.add(child_name)
if child_name in considered_nodes:
continue
# If the child isn't allowed, throw an error.
if child_name not in allowed_nodes and "*" not in allowed_nodes:
self._message(
spec_branch.get("unknown_node_level", "warning"),
err_id=("spec", "iterate", "not_allowed"),
message="`%s` is not a recognized element within a %s" %
(child_name, self.SPEC_NAME),
description=["While iterating a %s, a `%s` was found "
"within a %s, which is not valid." %
(self.SPEC_NAME, child_name, branch_name),
self.MORE_INFO])
|
[
"functools.partial",
"re.match"
] |
[((2456, 2498), 'functools.partial', 'partial', (['self._err_message', 'self.err.error'], {}), '(self._err_message, self.err.error)\n', (2463, 2498), False, 'from functools import partial\n'), ((2522, 2566), 'functools.partial', 'partial', (['self._err_message', 'self.err.warning'], {}), '(self._err_message, self.err.warning)\n', (2529, 2566), False, 'from functools import partial\n'), ((2589, 2632), 'functools.partial', 'partial', (['self._err_message', 'self.err.notice'], {}), '(self._err_message, self.err.notice)\n', (2596, 2632), False, 'from functools import partial\n'), ((7062, 7091), 're.match', 're.match', (['raw_pattern', 'branch'], {}), '(raw_pattern, branch)\n', (7070, 7091), False, 'import re\n')]
|
from setuptools import setup
setup(
name='lsearch',
version='1.0',
description='The Head First Python Search Tools', author='<NAME>', author_email='<EMAIL>',
url='headfirstlabs.com',
py_modules=['lsearch'],
)
|
[
"setuptools.setup"
] |
[((30, 216), 'setuptools.setup', 'setup', ([], {'name': '"""lsearch"""', 'version': '"""1.0"""', 'description': '"""The Head First Python Search Tools"""', 'author': '"""<NAME>"""', 'author_email': '"""<EMAIL>"""', 'url': '"""headfirstlabs.com"""', 'py_modules': "['lsearch']"}), "(name='lsearch', version='1.0', description=\n 'The Head First Python Search Tools', author='<NAME>', author_email=\n '<EMAIL>', url='headfirstlabs.com', py_modules=['lsearch'])\n", (35, 216), False, 'from setuptools import setup\n')]
|
import json
import logging
from http import HTTPStatus
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import werkzeug
from flask import Blueprint, Flask, Response, abort, jsonify
from flask.views import MethodView
from flask_cors import CORS
from gevent.pywsgi import WSGIServer
from geventwebsocket import Resource as WebsocketResource, WebSocketServer
from marshmallow import Schema
from marshmallow.exceptions import ValidationError
from webargs.flaskparser import parser
from werkzeug.exceptions import NotFound
from rotkehlchen.api.rest import RestAPI, api_response, wrap_in_fail_result
from rotkehlchen.api.v1.parser import ignore_kwarg_parser, resource_parser
from rotkehlchen.api.v1.resources import (
AaveBalancesResource,
AaveHistoryResource,
AccountingReportDataResource,
AccountingReportsResource,
AdexBalancesResource,
AdexHistoryResource,
AllAssetsResource,
AllBalancesResource,
AssetIconsResource,
AssetMovementsResource,
AssetsReplaceResource,
AssetsTypesResource,
AssetUpdatesResource,
AssociatedLocations,
AsyncTasksResource,
AvalancheTransactionsResource,
BalancerBalancesResource,
BalancerEventsHistoryResource,
BalancerTradesHistoryResource,
BinanceAvailableMarkets,
BinanceUserMarkets,
BlockchainBalancesResource,
BlockchainsAccountsResource,
BTCXpubResource,
CompoundBalancesResource,
CompoundHistoryResource,
CounterpartiesResource,
CurrentAssetsPriceResource,
DatabaseBackupsResource,
DatabaseInfoResource,
DataImportResource,
DBSnapshotDeletingResource,
DBSnapshotDownloadingResource,
DBSnapshotExportingResource,
DBSnapshotImportingResource,
DefiBalancesResource,
ERC20TokenInfo,
ERC20TokenInfoAVAX,
Eth2DailyStatsResource,
Eth2StakeDepositsResource,
Eth2StakeDetailsResource,
Eth2ValidatorsResource,
EthereumAirdropsResource,
EthereumAssetsResource,
EthereumModuleDataResource,
EthereumModuleResource,
EthereumTransactionsResource,
ExchangeBalancesResource,
ExchangeRatesResource,
ExchangesDataResource,
ExchangesResource,
ExternalServicesResource,
HistoricalAssetsPriceResource,
HistoryActionableItemsResource,
HistoryBaseEntryResource,
HistoryDownloadingResource,
HistoryExportingResource,
HistoryProcessingResource,
HistoryStatusResource,
IgnoredActionsResource,
IgnoredAssetsResource,
InfoResource,
LedgerActionsResource,
LiquityStakingHistoryResource,
LiquityStakingResource,
LiquityTrovesHistoryResource,
LiquityTrovesResource,
LoopringBalancesResource,
MakerdaoDSRBalanceResource,
MakerdaoDSRHistoryResource,
MakerdaoVaultDetailsResource,
MakerdaoVaultsResource,
ManuallyTrackedBalancesResource,
MessagesResource,
NamedEthereumModuleDataResource,
NamedOracleCacheResource,
NFTSBalanceResource,
NFTSResource,
OraclesResource,
OwnedAssetsResource,
PeriodicDataResource,
PickleDillResource,
PingResource,
QueriedAddressesResource,
ReverseEnsResource,
SettingsResource,
StakingResource,
StatisticsAssetBalanceResource,
StatisticsNetvalueResource,
StatisticsRendererResource,
StatisticsValueDistributionResource,
SushiswapBalancesResource,
SushiswapEventsHistoryResource,
SushiswapTradesHistoryResource,
TagsResource,
TradesResource,
UniswapBalancesResource,
UniswapEventsHistoryResource,
UniswapTradesHistoryResource,
UserAssetsResource,
UserPasswordChangeResource,
UserPremiumKeyResource,
UserPremiumSyncResource,
UsersByNameResource,
UsersResource,
WatchersResource,
YearnVaultsBalancesResource,
YearnVaultsHistoryResource,
YearnVaultsV2BalancesResource,
YearnVaultsV2HistoryResource,
create_blueprint,
)
from rotkehlchen.api.websockets.notifier import RotkiNotifier, RotkiWSApp
from rotkehlchen.logging import RotkehlchenLogsAdapter
URLS = List[
Union[
Tuple[str, Type[MethodView]],
Tuple[str, Type[MethodView], str],
]
]
URLS_V1: URLS = [
('/users', UsersResource),
('/watchers', WatchersResource),
('/users/<string:name>', UsersByNameResource),
('/users/<string:name>/password', UserPasswordChangeResource),
('/premium', UserPremiumKeyResource),
('/premium/sync', UserPremiumSyncResource),
('/settings', SettingsResource),
('/tasks/', AsyncTasksResource),
('/tasks/<int:task_id>', AsyncTasksResource, 'specific_async_tasks_resource'),
('/exchange_rates', ExchangeRatesResource),
('/external_services/', ExternalServicesResource),
('/oracles', OraclesResource),
('/oracles/<string:oracle>/cache', NamedOracleCacheResource),
('/exchanges', ExchangesResource),
('/exchanges/balances', ExchangeBalancesResource),
(
'/exchanges/balances/<string:location>',
ExchangeBalancesResource,
'named_exchanges_balances_resource',
),
('/assets/<string:asset>/icon', AssetIconsResource),
('/trades', TradesResource),
('/ledgeractions', LedgerActionsResource),
('/asset_movements', AssetMovementsResource),
('/tags', TagsResource),
('/exchanges/binance/pairs', BinanceAvailableMarkets),
('/exchanges/binance/pairs/<string:name>', BinanceUserMarkets),
('/exchanges/data/', ExchangesDataResource),
('/exchanges/data/<string:location>', ExchangesDataResource, 'named_exchanges_data_resource'),
('/balances/blockchains', BlockchainBalancesResource),
(
'/balances/blockchains/<string:blockchain>',
BlockchainBalancesResource,
'named_blockchain_balances_resource',
),
('/balances/', AllBalancesResource),
('/balances/manual', ManuallyTrackedBalancesResource),
('/statistics/netvalue', StatisticsNetvalueResource),
('/statistics/balance/<string:asset>', StatisticsAssetBalanceResource),
('/statistics/value_distribution', StatisticsValueDistributionResource),
('/statistics/renderer', StatisticsRendererResource),
('/messages/', MessagesResource),
('/periodic/', PeriodicDataResource),
('/history/', HistoryProcessingResource),
('/history/status', HistoryStatusResource),
('/history/export/', HistoryExportingResource),
('/history/download/', HistoryDownloadingResource),
('/history/events', HistoryBaseEntryResource),
('/history/actionable_items', HistoryActionableItemsResource),
('/reports/', AccountingReportsResource),
(
'/reports/<int:report_id>',
AccountingReportsResource,
'per_report_resource',
),
(
'/reports/<int:report_id>/data',
AccountingReportDataResource,
'per_report_data_resource',
),
('/queried_addresses', QueriedAddressesResource),
('/blockchains/ETH/transactions', EthereumTransactionsResource),
(
'/blockchains/ETH/transactions/<string:address>',
EthereumTransactionsResource,
'per_address_ethereum_transactions_resource',
),
('/blockchains/ETH2/validators', Eth2ValidatorsResource),
('/blockchains/ETH2/stake/deposits', Eth2StakeDepositsResource),
('/blockchains/ETH2/stake/details', Eth2StakeDetailsResource),
('/blockchains/ETH2/stake/dailystats', Eth2DailyStatsResource),
('/blockchains/ETH/defi', DefiBalancesResource),
('/blockchains/ETH/airdrops', EthereumAirdropsResource),
('/blockchains/ETH/erc20details/', ERC20TokenInfo),
('/blockchains/ETH/modules/<string:module_name>/data', NamedEthereumModuleDataResource),
('/blockchains/ETH/modules/data', EthereumModuleDataResource),
('/blockchains/ETH/modules/data/counterparties', CounterpartiesResource),
('/blockchains/ETH/modules/', EthereumModuleResource),
('/blockchains/ETH/modules/makerdao/dsrbalance', MakerdaoDSRBalanceResource),
('/blockchains/ETH/modules/makerdao/dsrhistory', MakerdaoDSRHistoryResource),
('/blockchains/ETH/modules/makerdao/vaults', MakerdaoVaultsResource),
('/blockchains/ETH/modules/makerdao/vaultdetails', MakerdaoVaultDetailsResource),
('/blockchains/ETH/modules/aave/balances', AaveBalancesResource),
('/blockchains/ETH/modules/aave/history', AaveHistoryResource),
('/blockchains/ETH/modules/adex/balances', AdexBalancesResource),
('/blockchains/ETH/modules/adex/history', AdexHistoryResource),
('/blockchains/ETH/modules/balancer/balances', BalancerBalancesResource),
('/blockchains/ETH/modules/balancer/history/trades', BalancerTradesHistoryResource),
('/blockchains/ETH/modules/balancer/history/events', BalancerEventsHistoryResource),
('/blockchains/ETH/modules/compound/balances', CompoundBalancesResource),
('/blockchains/ETH/modules/compound/history', CompoundHistoryResource),
('/blockchains/ETH/modules/uniswap/balances', UniswapBalancesResource),
('/blockchains/ETH/modules/uniswap/history/events', UniswapEventsHistoryResource),
('/blockchains/ETH/modules/uniswap/history/trades', UniswapTradesHistoryResource),
('/blockchains/ETH/modules/sushiswap/balances', SushiswapBalancesResource),
('/blockchains/ETH/modules/sushiswap/history/events', SushiswapEventsHistoryResource),
('/blockchains/ETH/modules/sushiswap/history/trades', SushiswapTradesHistoryResource),
('/blockchains/ETH/modules/yearn/vaults/balances', YearnVaultsBalancesResource),
('/blockchains/ETH/modules/yearn/vaults/history', YearnVaultsHistoryResource),
('/blockchains/ETH/modules/yearn/vaultsv2/balances', YearnVaultsV2BalancesResource),
('/blockchains/ETH/modules/yearn/vaultsv2/history', YearnVaultsV2HistoryResource),
('/blockchains/ETH/modules/liquity/balances', LiquityTrovesResource),
('/blockchains/ETH/modules/liquity/events/trove', LiquityTrovesHistoryResource),
('/blockchains/ETH/modules/liquity/events/staking', LiquityStakingHistoryResource),
('/blockchains/ETH/modules/liquity/staking', LiquityStakingResource),
('/blockchains/ETH/modules/pickle/dill', PickleDillResource),
('/blockchains/ETH/modules/loopring/balances', LoopringBalancesResource),
('/blockchains/<string:blockchain>', BlockchainsAccountsResource),
('/blockchains/BTC/xpub', BTCXpubResource),
('/blockchains/AVAX/transactions', AvalancheTransactionsResource),
(
'/blockchains/AVAX/transactions/<string:address>',
AvalancheTransactionsResource,
'per_address_avalanche_transactions_resource',
),
('/blockchains/AVAX/erc20details/', ERC20TokenInfoAVAX),
('/assets', OwnedAssetsResource),
('/assets/types', AssetsTypesResource),
('/assets/replace', AssetsReplaceResource),
('/assets/all', AllAssetsResource),
('/assets/ethereum', EthereumAssetsResource),
('/assets/prices/current', CurrentAssetsPriceResource),
('/assets/prices/historical', HistoricalAssetsPriceResource),
('/assets/ignored', IgnoredAssetsResource),
('/assets/updates', AssetUpdatesResource),
('/assets/user', UserAssetsResource),
('/actions/ignored', IgnoredActionsResource),
('/info', InfoResource),
('/ping', PingResource),
('/import', DataImportResource),
('/nfts', NFTSResource),
('/nfts/balances', NFTSBalanceResource),
('/database/info', DatabaseInfoResource),
('/database/backups', DatabaseBackupsResource),
('/locations/associated', AssociatedLocations),
('/staking/kraken', StakingResource),
('/snapshot/download', DBSnapshotDownloadingResource),
('/snapshot/export', DBSnapshotExportingResource),
('/snapshot/import', DBSnapshotImportingResource),
('/snapshot/delete', DBSnapshotDeletingResource),
('/ens/reverse', ReverseEnsResource),
]
logger = logging.getLogger(__name__)
log = RotkehlchenLogsAdapter(logger)
def setup_urls(
rest_api: RestAPI,
blueprint: Blueprint,
urls: URLS,
) -> None:
for url_tuple in urls:
if len(url_tuple) == 2:
route, resource_cls = url_tuple # type: ignore
endpoint = resource_cls.__name__.lower()
elif len(url_tuple) == 3:
route, resource_cls, endpoint = url_tuple # type: ignore
else:
raise ValueError(f"Invalid URL format: {url_tuple!r}")
blueprint.add_url_rule(
route,
view_func=resource_cls.as_view(endpoint, rest_api_object=rest_api),
)
def endpoint_not_found(e: NotFound) -> Response:
msg = 'invalid endpoint'
# The isinstance check is because I am not sure if `e` is always going to
# be a "NotFound" error here
if isinstance(e, NotFound):
msg = e.description
return api_response(wrap_in_fail_result(msg), HTTPStatus.NOT_FOUND)
@parser.error_handler # type: ignore
@resource_parser.error_handler
@ignore_kwarg_parser.error_handler
def handle_request_parsing_error(
err: ValidationError,
_request: werkzeug.local.LocalProxy,
_schema: Schema,
error_status_code: Optional[int], # pylint: disable=unused-argument
error_headers: Optional[Dict], # pylint: disable=unused-argument
) -> None:
""" This handles request parsing errors generated for example by schema
field validation failing."""
msg = str(err)
if isinstance(err.messages, dict):
# first key is just the location. Ignore
key = list(err.messages.keys())[0]
msg = json.dumps(err.messages[key])
elif isinstance(err.messages, list):
msg = ','.join(err.messages)
err_response = jsonify(result=None, message=msg)
err_response.status_code = HTTPStatus.BAD_REQUEST
abort(err_response)
class APIServer():
_api_prefix = '/api/1'
def __init__(
self,
rest_api: RestAPI,
ws_notifier: RotkiNotifier,
cors_domain_list: List[str] = None,
) -> None:
flask_app = Flask(__name__)
if cors_domain_list:
CORS(flask_app, origins=cors_domain_list)
blueprint = create_blueprint(self._api_prefix)
setup_urls(
blueprint=blueprint,
rest_api=rest_api,
urls=URLS_V1,
)
self.rest_api = rest_api
self.rotki_notifier = ws_notifier
self.flask_app = flask_app
self.blueprint = blueprint
self.wsgiserver: Optional[WSGIServer] = None
self.flask_app.register_blueprint(self.blueprint)
self.ws_server: Optional[WebSocketServer] = None
self.flask_app.errorhandler(HTTPStatus.NOT_FOUND)(endpoint_not_found)
self.flask_app.register_error_handler(Exception, self.unhandled_exception)
@staticmethod
def unhandled_exception(exception: Exception) -> Response:
""" Flask.errorhandler when an exception wasn't correctly handled """
log.critical(
'Unhandled exception when processing endpoint request',
exc_info=True,
exception=str(exception),
)
return api_response(wrap_in_fail_result(str(exception)), HTTPStatus.INTERNAL_SERVER_ERROR)
def run(self, host: str = '127.0.0.1', port: int = 5042, **kwargs: Any) -> None:
"""This is only used for the data faker and not used in production"""
self.flask_app.run(host=host, port=port, **kwargs)
def start(
self,
host: str = '127.0.0.1',
rest_port: int = 5042,
websockets_port: int = 5043,
) -> None:
"""This is used to start the API server in production"""
wsgi_logger = logging.getLogger(__name__ + '.pywsgi')
self.wsgiserver = WSGIServer(
listener=(host, rest_port),
application=self.flask_app,
log=wsgi_logger,
error_log=wsgi_logger,
)
msg = f'rotki REST API server is running at: {host}:{rest_port}'
print(msg)
log.info(msg)
self.wsgiserver.start()
self.ws_server = WebSocketServer(
listener=(host, websockets_port),
application=WebsocketResource([
('^/', RotkiWSApp),
]),
debug=False,
environ={'rotki_notifier': self.rotki_notifier},
)
msg = f'rotki Websockets API server is running at: {host}:{websockets_port}'
print(msg)
log.info(msg)
self.ws_server.start()
def stop(self, timeout: int = 5) -> None:
"""Stops the API server. If handlers are running after timeout they are killed"""
if self.wsgiserver is not None:
self.wsgiserver.stop(timeout)
self.wsgiserver = None
if self.ws_server is not None:
self.ws_server.stop(timeout)
self.wsgiserver = None
self.rest_api.stop()
|
[
"logging.getLogger",
"rotkehlchen.api.v1.resources.create_blueprint",
"rotkehlchen.logging.RotkehlchenLogsAdapter",
"flask_cors.CORS",
"flask.Flask",
"json.dumps",
"rotkehlchen.api.rest.wrap_in_fail_result",
"gevent.pywsgi.WSGIServer",
"flask.abort",
"geventwebsocket.Resource",
"flask.jsonify"
] |
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(['err.messages[key]'], {}), '(err.messages[key])\n', (13408, 13427), False, 'import json\n'), ((13878, 13893), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (13883, 13893), False, 'from flask import Blueprint, Flask, Response, abort, jsonify\n'), ((13997, 14031), 'rotkehlchen.api.v1.resources.create_blueprint', 'create_blueprint', (['self._api_prefix'], {}), '(self._api_prefix)\n', (14013, 14031), False, 'from rotkehlchen.api.v1.resources import AaveBalancesResource, AaveHistoryResource, AccountingReportDataResource, AccountingReportsResource, AdexBalancesResource, AdexHistoryResource, AllAssetsResource, AllBalancesResource, AssetIconsResource, AssetMovementsResource, AssetsReplaceResource, AssetsTypesResource, AssetUpdatesResource, AssociatedLocations, AsyncTasksResource, AvalancheTransactionsResource, BalancerBalancesResource, BalancerEventsHistoryResource, BalancerTradesHistoryResource, BinanceAvailableMarkets, BinanceUserMarkets, BlockchainBalancesResource, BlockchainsAccountsResource, BTCXpubResource, CompoundBalancesResource, CompoundHistoryResource, CounterpartiesResource, CurrentAssetsPriceResource, DatabaseBackupsResource, DatabaseInfoResource, DataImportResource, DBSnapshotDeletingResource, DBSnapshotDownloadingResource, DBSnapshotExportingResource, DBSnapshotImportingResource, DefiBalancesResource, ERC20TokenInfo, ERC20TokenInfoAVAX, Eth2DailyStatsResource, Eth2StakeDepositsResource, Eth2StakeDetailsResource, Eth2ValidatorsResource, EthereumAirdropsResource, EthereumAssetsResource, EthereumModuleDataResource, EthereumModuleResource, EthereumTransactionsResource, ExchangeBalancesResource, ExchangeRatesResource, ExchangesDataResource, ExchangesResource, ExternalServicesResource, HistoricalAssetsPriceResource, HistoryActionableItemsResource, HistoryBaseEntryResource, HistoryDownloadingResource, HistoryExportingResource, HistoryProcessingResource, HistoryStatusResource, IgnoredActionsResource, IgnoredAssetsResource, InfoResource, LedgerActionsResource, LiquityStakingHistoryResource, LiquityStakingResource, LiquityTrovesHistoryResource, LiquityTrovesResource, LoopringBalancesResource, MakerdaoDSRBalanceResource, MakerdaoDSRHistoryResource, MakerdaoVaultDetailsResource, MakerdaoVaultsResource, ManuallyTrackedBalancesResource, MessagesResource, NamedEthereumModuleDataResource, NamedOracleCacheResource, NFTSBalanceResource, NFTSResource, OraclesResource, OwnedAssetsResource, PeriodicDataResource, PickleDillResource, PingResource, QueriedAddressesResource, ReverseEnsResource, SettingsResource, StakingResource, StatisticsAssetBalanceResource, StatisticsNetvalueResource, StatisticsRendererResource, StatisticsValueDistributionResource, SushiswapBalancesResource, SushiswapEventsHistoryResource, SushiswapTradesHistoryResource, TagsResource, TradesResource, UniswapBalancesResource, UniswapEventsHistoryResource, UniswapTradesHistoryResource, UserAssetsResource, UserPasswordChangeResource, UserPremiumKeyResource, UserPremiumSyncResource, UsersByNameResource, UsersResource, WatchersResource, YearnVaultsBalancesResource, YearnVaultsHistoryResource, YearnVaultsV2BalancesResource, YearnVaultsV2HistoryResource, create_blueprint\n'), ((15525, 15564), 'logging.getLogger', 'logging.getLogger', (["(__name__ + '.pywsgi')"], {}), "(__name__ + '.pywsgi')\n", (15542, 15564), False, 'import logging\n'), ((15591, 15702), 'gevent.pywsgi.WSGIServer', 'WSGIServer', ([], {'listener': '(host, rest_port)', 'application': 'self.flask_app', 'log': 'wsgi_logger', 'error_log': 'wsgi_logger'}), '(listener=(host, rest_port), application=self.flask_app, log=\n wsgi_logger, error_log=wsgi_logger)\n', (15601, 15702), False, 'from gevent.pywsgi import WSGIServer\n'), ((13935, 13976), 'flask_cors.CORS', 'CORS', (['flask_app'], {'origins': 'cors_domain_list'}), '(flask_app, origins=cors_domain_list)\n', (13939, 13976), False, 'from flask_cors import CORS\n'), ((16015, 16054), 'geventwebsocket.Resource', 'WebsocketResource', (["[('^/', RotkiWSApp)]"], {}), "([('^/', RotkiWSApp)])\n", (16032, 16054), True, 'from geventwebsocket import Resource as WebsocketResource, WebSocketServer\n')]
|
import numpy as np
pos = []
normals = []
p = [[-0.4722227, -0.24517583, -0.6370031]]
n = [[2.02215104e-04, -3.23903880e-05, 9.99999979e-01]]
pos.append(p)
normals.append(n)
p = [[-0.2549828, -0.24587737, -0.63704705]]
n = [[2.02215104e-04, -3.23903880e-05, 9.99999979e-01]]
pos.append(p)
normals.append(n)
p = [[-0.25787751, -0.38255749, -0.63705089]]
n = [[2.02215104e-04, -3.23903880e-05, 9.99999979e-01]]
pos.append(p)
normals.append(n)
p = [[-0.47206733, -0.38317576, -0.6370076]]
n = [[2.02215104e-04, -3.23903880e-05, 9.99999979e-01]]
pos.append(p)
normals.append(n)
#Contact lgripper/handrail
#Left
p = [[0.3651077, 0.33419711, 0.63609439]]
n = [[-3.39491173e-05, 9.99999875e-01, 4.99472000e-04]]
pos.append(p)
normals.append(n)
#Right
#p = [[0.36510907, 0.29419711, 0.63607441]]
#p = [[0.3651077, 0.33419711, 0.63609439]]
#n = [[3.44761855e-05, -9.99999874e-01, -5.00077386e-04]]
#pos.append(p)
#normals.append(n)
#Bottom
#p = [[0.34212609, 0.31418314, 0.66248165]]
#n = [[-6.56636734e-01, -3.99160434e-04, 7.54206895e-01]]
#pos.append(p)
#normals.append(n)
#
##Top
p = [[0.38480749, 0.31420908, 0.61345819]]
n = [[6.56636734e-01, 4.00439950e-04, -7.54206894e-01]]
pos.append(p)
normals.append(n)
pos = [np.array(px).T for px in pos]
#for p in pos:
# p[2, 0] = 0.0
normals = [np.array(nx).T for nx in normals]
|
[
"numpy.array"
] |
[((1222, 1234), 'numpy.array', 'np.array', (['px'], {}), '(px)\n', (1230, 1234), True, 'import numpy as np\n'), ((1295, 1307), 'numpy.array', 'np.array', (['nx'], {}), '(nx)\n', (1303, 1307), True, 'import numpy as np\n')]
|
import setuptools
setuptools.setup(
name="qualityforward",
version="1.1",
author="<NAME>",
author_email="<EMAIL>",
description="Python library for QualityForward API",
long_description="This is python library for QualityForward API. QualityForward is cloud based test management service.",
long_description_content_type="text/markdown",
url="https://cloud.veriserve.co.jp/",
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3.7",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
]
)
|
[
"setuptools.find_packages"
] |
[((422, 448), 'setuptools.find_packages', 'setuptools.find_packages', ([], {}), '()\n', (446, 448), False, 'import setuptools\n')]
|
from django.db import models
from django.contrib.auth.models import AbstractBaseUser, PermissionsMixin, AbstractUser
from django.utils import timezone
from django.utils.translation import gettext as _
from django import forms
from django.contrib.auth.hashers import make_password
from django.contrib.auth import get_user_model
from django.contrib.auth.models import User
from phonenumber_field.modelfields import PhoneNumberField
from datetime import datetime
class CarTrip(models.Model):
class Meta:
verbose_name = _('carTrip')
verbose_name_plural = _('cartrips')
def __str__(self):
return f'{self.driver_name} Car Trip'
driver_name = models.CharField(max_length=200)
destination = models.CharField(max_length=200)
number_of_seats = models.IntegerField('number of seats')
trip_date = models.CharField(max_length=200)
pub_date = models.DateTimeField('date published')
@classmethod
def create(cls , driver_name, destination, number_of_seats, trip_date):
trip = cls(driver_name= driver_name,
destination=destination,
number_of_seats=number_of_seats,
trip_date=trip_date,
pub_date=datetime.now()
)
return trip
def was_published_recently(self):
now = timezone.now()
return now - datetime.timedelta(days=1) <= self.pub_date <= now
class Relation(models.Model):
class Meta:
verbose_name = _('relation')
verbose_name_plural = _('relation')
trip_number = models.IntegerField('trip_number')
hiker_name = models.CharField(max_length=200)
def __str__(self ):
return f'{self.hiker_name} going on trip id = {self.trip_number}'
@classmethod
def create(cls , trip_number, hiker_name):
rel = cls(trip_number=trip_number,
hiker_name=hiker_name,
)
return rel
|
[
"django.utils.translation.gettext",
"django.db.models.IntegerField",
"django.utils.timezone.now",
"datetime.datetime.now",
"django.db.models.DateTimeField",
"datetime.datetime.timedelta",
"django.db.models.CharField"
] |
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|
import numpy
with open ("dic.txt", "w", encoding="utf-8") as dic:
for x in range(5, 790, 1):
if 92 < x <= 113:
dic.write('"'+str(x)+'"'+":"+ '"'+'1'+'",')
elif 113 < x <= 133:
dic.write('"'+str(x)+'"'+":"+ '"'+'2'+'",')
elif 133 < x <= 153:
dic.write('"'+str(x)+'"'+":"+ '"'+'3'+'",')
elif 153 < x <= 173:
dic.write('"'+str(x)+'"'+":"+ '"'+'4'+'",')
elif 173 < x <= 193:
dic.write('"'+str(x)+'"'+":"+ '"'+'5'+'",')
elif 193 < x <= 213:
dic.write('"'+str(x)+'"'+":"+ '"'+'6'+'",')
elif 213 < x <= 233:
dic.write('"'+str(x)+'"'+":"+ '"'+'7'+'",')
elif 233 < x <= 253:
dic.write('"'+str(x)+'"'+":"+ '"'+'8'+'",')
elif 253 < x <= 273:
dic.write('"'+str(x)+'"'+":"+ '"'+'9'+'",')
elif 273 < x <= 293:
dic.write('"'+str(x)+'"'+":"+ '"'+'10'+'",')
elif 293 < x <= 313:
dic.write('"'+str(x)+'"'+":"+ '"'+'11'+'",')
elif 313 < x <= 333:
dic.write('"'+str(x)+'"'+":"+ '"'+'12'+'",')
elif 333 < x <= 353:
dic.write('"'+str(x)+'"'+":"+ '"'+'13'+'",')
elif 353 < x <= 373:
dic.write('"'+str(x)+'"'+":"+ '"'+'14'+'",')
elif 373 < x <= 393:
dic.write('"'+str(x)+'"'+":"+ '"'+'15'+'",')
elif 393 < x <= 413:
dic.write('"'+str(x)+'"'+":"+ '"'+'16'+'",')
elif 413 < x <= 433:
dic.write('"'+str(x)+'"'+":"+ '"'+'17'+'",')
elif 433 < x <= 453:
dic.write('"'+str(x)+'"'+":"+ '"'+'18'+'",')
elif 453 < x <= 473:
dic.write('"'+str(x)+'"'+":"+ '"'+'19'+'",')
elif 473 < x <= 493:
dic.write('"'+str(x)+'"'+":"+ '"'+'20'+'",')
elif 493 < x <= 513:
dic.write('"'+str(x)+'"'+":"+ '"'+'21'+'",')
elif 513 < x <= 533:
dic.write('"'+str(x)+'"'+":"+ '"'+'22'+'",')
elif 533 < x <= 553:
dic.write('"'+str(x)+'"'+":"+ '"'+'23'+'",')
elif 553 < x <= 573:
dic.write('"'+str(x)+'"'+":"+ '"'+'24'+'",')
elif 573 < x <= 593:
dic.write('"'+str(x)+'"'+":"+ '"'+'25'+'",')
elif 593 < x <= 613:
dic.write('"'+str(x)+'"'+":"+ '"'+'26'+'",')
elif 613 < x <= 633:
dic.write('"'+str(x)+'"'+":"+ '"'+'27'+'",')
elif 633 < x <= 653:
dic.write('"'+str(x)+'"'+":"+ '"'+'28'+'",')
elif 653 < x <= 673:
dic.write('"'+str(x)+'"'+":"+ '"'+'29'+'",')
elif 673 < x <= 693:
dic.write('"'+str(x)+'"'+":"+ '"'+'30'+'",')
elif 693 < x <= 713:
dic.write('"'+str(x)+'"'+":"+ '"'+'31'+'",')
elif 713 < x <= 733:
dic.write('"'+str(x)+'"'+":"+ '"'+'32'+'",')
elif 733 < x <= 753:
dic.write('"'+str(x)+'"'+":"+ '"'+'33'+'",')
elif 753 < x <= 773:
dic.write('"'+str(x)+'"'+":"+ '"'+'34'+'",')
elif 773 < x <= 793:
dic.write('"'+str(x)+'"'+":"+ '"'+'35'+'",')
elif 4 < x <= 15:
dic.write('"'+str(x)+'"'+":"+ '"'+'36'+'",')
elif 15 < x <= 25:
dic.write('"'+str(x)+'"'+":"+ '"'+'37'+'",')
elif 25 < x <= 35:
dic.write('"'+str(x)+'"'+":"+ '"'+'38'+'",')
elif 35 < x <= 45:
dic.write('"'+str(x)+'"'+":"+ '"'+'39'+'",')
elif 45 < x <= 55:
dic.write('"'+str(x)+'"'+":"+ '"'+'40'+'",')
elif 55 < x <= 65:
dic.write('"'+str(x)+'"'+":"+ '"'+'41'+'",')
elif 65 < x <= 75:
dic.write('"'+str(x)+'"'+":"+ '"'+'42'+'",')
elif 75 < x <= 85:
dic.write('"'+str(x)+'"'+":"+ '"'+'43'+'",')
elif 85 < x <= 92:
dic.write('"'+str(x)+'"'+":"+ '"'+'44'+'",')
with open ("time.txt", "w", encoding="utf-8") as duree:
for y in numpy.arange(0, 1.7, 0.01):
if 0 < y <= 0.1:
duree.write('"'+str(y)+'"'+":"+ '"'+'80'+'",')
elif 0.1 < y <= 0.2:
duree.write('"'+str(y)+'"'+":"+ '"'+'81'+'",')
elif 0.2 < y <= 0.3:
duree.write('"'+str(y)+'"'+":"+ '"'+'82'+'",')
elif 0.3 < y <= 0.4:
duree.write('"'+str(y)+'"'+":"+ '"'+'83'+'",')
elif 0.4 < y <= 0.5:
duree.write('"'+str(y)+'"'+":"+ '"'+'84'+'",')
elif 0.5 < y <= 0.6:
duree.write('"'+str(y)+'"'+":"+ '"'+'85'+'",')
elif 0.6 < y <= 0.7:
duree.write('"'+str(y)+'"'+":"+ '"'+'86'+'",')
elif 0.7 < y <= 0.8:
duree.write('"'+str(y)+'"'+":"+ '"'+'87'+'",')
elif 0.8 < y <= 0.9:
duree.write('"'+str(y)+'"'+":"+ '"'+'88'+'",')
elif 0.9 < y <= 1:
duree.write('"'+str(y)+'"'+":"+ '"'+'89'+'",')
elif 1 < y <= 1.1:
duree.write('"'+str(y)+'"'+":"+ '"'+'90'+'",')
elif 1.1 < y <= 1.2:
duree.write('"'+str(y)+'"'+":"+ '"'+'91'+'",')
elif 1.2 < y <= 1.3:
duree.write('"'+str(y)+'"'+":"+ '"'+'92'+'",')
elif 1.3 < y <= 1.4:
duree.write('"'+str(y)+'"'+":"+ '"'+'93'+'",')
elif 1.4 < y <= 1.5:
duree.write('"'+str(y)+'"'+":"+ '"'+'94'+'",')
elif 1.5 < y <= 1.6:
duree.write('"'+str(y)+'"'+":"+ '"'+'95'+'",')
elif 1.6 < y <= 1.7:
duree.write('"'+str(y)+'"'+":"+ '"'+'96'+'",')
|
[
"numpy.arange"
] |
[((4023, 4049), 'numpy.arange', 'numpy.arange', (['(0)', '(1.7)', '(0.01)'], {}), '(0, 1.7, 0.01)\n', (4035, 4049), False, 'import numpy\n')]
|
# -*- coding: utf-8 -*-
'''
=============
scrim.globals
=============
Defines variables passed into the python script via Environment Variables by
scrim scripts. If SCRIM_SHELL is None, then the python script was not executed
by a scrim script.
SHELLS (list): list of available shells
SCRIM_SHELL (str): Parent shell, one of the above SHELLS
SCRIM_PATH (str): Path to output shell script
SCRIM_AUTO_WRITE (bool): Write to SCRIM_PATH when python exits?
SCRIM_SCRIPT (str): Path to the scrim script that invoked python
SCRIM_DEBUG (bool): Is scrim script running in debug mode?
'''
from __future__ import absolute_import
import os
__all__ = [
'SHELLS', 'SCRIM_SHELL', 'SCRIM_PATH', 'SCRIM_AUTO_WRITE',
'SCRIM_SCRIPT', 'SCRIM_DEBUG'
]
SHELLS = [
'powershell.exe',
'cmd.exe',
'bash'
]
SCRIM_SHELL = os.environ.get('SCRIM_SHELL', None)
SCRIM_PATH = os.environ.get('SCRIM_PATH', None)
SCRIM_AUTO_WRITE = bool(os.environ.get('SCRIM_AUTO_WRITE', False))
SCRIM_SCRIPT = os.environ.get('SCRIM_SCRIPT', None)
SCRIM_DEBUG = bool(os.environ.get('SCRIM_DEBUG', False))
|
[
"os.environ.get"
] |
[((841, 876), 'os.environ.get', 'os.environ.get', (['"""SCRIM_SHELL"""', 'None'], {}), "('SCRIM_SHELL', None)\n", (855, 876), False, 'import os\n'), ((890, 924), 'os.environ.get', 'os.environ.get', (['"""SCRIM_PATH"""', 'None'], {}), "('SCRIM_PATH', None)\n", (904, 924), False, 'import os\n'), ((1007, 1043), 'os.environ.get', 'os.environ.get', (['"""SCRIM_SCRIPT"""', 'None'], {}), "('SCRIM_SCRIPT', None)\n", (1021, 1043), False, 'import os\n'), ((949, 990), 'os.environ.get', 'os.environ.get', (['"""SCRIM_AUTO_WRITE"""', '(False)'], {}), "('SCRIM_AUTO_WRITE', False)\n", (963, 990), False, 'import os\n'), ((1063, 1099), 'os.environ.get', 'os.environ.get', (['"""SCRIM_DEBUG"""', '(False)'], {}), "('SCRIM_DEBUG', False)\n", (1077, 1099), False, 'import os\n')]
|
import csnd6
class Control:
def __init__(self, volume, frequency):
engine = csnd6.Csound()
engine.SetOption("-odac")
engine.Compile("osc.csd")
thread = csnd6.CsoundPerformanceThread(engine)
thread.Play()
self.engine = engine
self.thread = thread
self.set_volume(volume)
self.set_frequency(frequency)
def set_volume(self, volume):
self.engine.SetChannel("volume", volume)
def set_frequency(self, frequency):
self.engine.SetChannel("frequency", frequency)
def close(self):
self.thread.Stop()
self.thread.Join()
|
[
"csnd6.Csound",
"csnd6.CsoundPerformanceThread"
] |
[((89, 103), 'csnd6.Csound', 'csnd6.Csound', ([], {}), '()\n', (101, 103), False, 'import csnd6\n'), ((191, 228), 'csnd6.CsoundPerformanceThread', 'csnd6.CsoundPerformanceThread', (['engine'], {}), '(engine)\n', (220, 228), False, 'import csnd6\n')]
|
# [LICENSE]
# Copyright (c) 2020, Alliance for Sustainable Energy.
# All rights reserved.
#
# Redistribution and use in source and binary forms,
# with or without modification, are permitted provided
# that the following conditions are met:
#
# 1. Redistributions of source code must retain the above
# copyright notice, this list of conditions and the
# following disclaimer.
#
# 2. Redistributions in binary form must reproduce the
# above copyright notice, this list of conditions and the
# following disclaimer in the documentation and/or other
# materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the
# names of its contributors may be used to endorse or
# promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
# OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# [/LICENSE]
import pytest
def pytest_addoption(parser):
parser.addoption(
"--no-clean-up", action="store_true", default=False,
help="Pass this option to leave test outputs in place"
)
@pytest.fixture(scope="session",autouse=True)
def clean_up(request):
return (not request.config.getoption('--no-clean-up'))
|
[
"pytest.fixture"
] |
[((1818, 1863), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""session"""', 'autouse': '(True)'}), "(scope='session', autouse=True)\n", (1832, 1863), False, 'import pytest\n')]
|
from django.db import models
# Create your models here.
class Tipo(models.Model):
descrizione = models.CharField(blank=False, null=False, max_length=128)
def __unicode__(self):
return "{}".format(self.descrizione)
class Meta:
verbose_name_plural = 'Tipi'
class Marca(models.Model):
descrizione = models.CharField(blank=False, null=False, max_length=128)
def __unicode__(self):
return "{}".format(self.descrizione)
class Meta:
verbose_name_plural = 'Marche'
class Modello(models.Model):
descrizione = models.CharField(blank=False, null=False, max_length=128)
marca = models.ForeignKey(Marca, null=False, blank=False)
tipo = models.ForeignKey(Tipo, null=False, blank=False)
def __unicode__(self):
return "{}".format(self.descrizione)
class Meta:
verbose_name_plural = 'Modelli'
class Alimentazione(models.Model):
descrizione = models.CharField(blank=False, null=False, max_length=128)
def __unicode__(self):
return "{}".format(self.descrizione)
class Meta:
verbose_name_plural = 'Alimentazioni'
class Mezzo(models.Model):
telaio = models.CharField(blank=False, null=False, max_length=128)
colore = models.CharField(blank=False, null=False, max_length=128)
alimentazione = models.ForeignKey(Alimentazione, null=False, blank=False)
modello = models.ForeignKey(Modello, null=False, blank=False)
def __unicode__(self):
return "{} {}".format(self.telaio, self.modello)
class Meta:
verbose_name_plural = 'Mezzi'
class Targa(models.Model):
numero = models.CharField(null=False, blank=False, max_length=16)
dal = models.DateField()
al = models.DateField()
mezzo = models.ForeignKey(Mezzo, null=False, blank=False)
def __unicode__(self):
return "{}".format(self.numero)
class Meta:
verbose_name_plural = 'Targhe'
|
[
"django.db.models.DateField",
"django.db.models.CharField",
"django.db.models.ForeignKey"
] |
[((104, 161), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(False)', 'null': '(False)', 'max_length': '(128)'}), '(blank=False, null=False, max_length=128)\n', (120, 161), False, 'from django.db import models\n'), ((336, 393), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(False)', 'null': '(False)', 'max_length': '(128)'}), '(blank=False, null=False, max_length=128)\n', (352, 393), False, 'from django.db import models\n'), ((572, 629), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(False)', 'null': '(False)', 'max_length': '(128)'}), '(blank=False, null=False, max_length=128)\n', (588, 629), False, 'from django.db import models\n'), ((642, 691), 'django.db.models.ForeignKey', 'models.ForeignKey', (['Marca'], {'null': '(False)', 'blank': '(False)'}), '(Marca, null=False, blank=False)\n', (659, 691), False, 'from django.db import models\n'), ((703, 751), 'django.db.models.ForeignKey', 'models.ForeignKey', (['Tipo'], {'null': '(False)', 'blank': '(False)'}), '(Tipo, null=False, blank=False)\n', (720, 751), False, 'from django.db import models\n'), ((937, 994), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(False)', 'null': '(False)', 'max_length': '(128)'}), '(blank=False, null=False, max_length=128)\n', (953, 994), False, 'from django.db import models\n'), ((1173, 1230), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(False)', 'null': '(False)', 'max_length': '(128)'}), '(blank=False, null=False, max_length=128)\n', (1189, 1230), False, 'from django.db import models\n'), ((1244, 1301), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(False)', 'null': '(False)', 'max_length': '(128)'}), '(blank=False, null=False, max_length=128)\n', (1260, 1301), False, 'from django.db import models\n'), ((1322, 1379), 'django.db.models.ForeignKey', 'models.ForeignKey', (['Alimentazione'], {'null': '(False)', 'blank': '(False)'}), '(Alimentazione, null=False, blank=False)\n', (1339, 1379), False, 'from django.db import models\n'), ((1394, 1445), 'django.db.models.ForeignKey', 'models.ForeignKey', (['Modello'], {'null': '(False)', 'blank': '(False)'}), '(Modello, null=False, blank=False)\n', (1411, 1445), False, 'from django.db import models\n'), ((1628, 1684), 'django.db.models.CharField', 'models.CharField', ([], {'null': '(False)', 'blank': '(False)', 'max_length': '(16)'}), '(null=False, blank=False, max_length=16)\n', (1644, 1684), False, 'from django.db import models\n'), ((1695, 1713), 'django.db.models.DateField', 'models.DateField', ([], {}), '()\n', (1711, 1713), False, 'from django.db import models\n'), ((1723, 1741), 'django.db.models.DateField', 'models.DateField', ([], {}), '()\n', (1739, 1741), False, 'from django.db import models\n'), ((1754, 1803), 'django.db.models.ForeignKey', 'models.ForeignKey', (['Mezzo'], {'null': '(False)', 'blank': '(False)'}), '(Mezzo, null=False, blank=False)\n', (1771, 1803), False, 'from django.db import models\n')]
|
import pickle
from time import sleep
import googleapiclient.errors
from transliterate import translit
from logs.logging import get_logger
from api_google.google_api_sheets import get_sheets_service, get_multiple_ranges
from api_google.google_api_directory import get_directory_service, get_users_for_domain, \
get_groups_for_domain, create_group, add_user_to_group
from api_google.google_api_groupsettings import get_groupsettings_service, \
get_group_settings, update_group_settings
from config.config import sync_sheets_and_groups, path_data_directory
def main():
logger = get_logger('sync_sheets_and_groups', sync_sheets_and_groups['logging_level'])
data_path = path_data_directory / 'sync_sheets_and_groups'
data_path.mkdir(parents=True, exist_ok=True)
synced_users_path = data_path / 'synced_users.pickle'
while True:
# number_of_registered_users = 0
# synced_users_dictionary_creation = False
#
# # Getting a list of users who have already been synced
# if synced_users_path.exists():
# logger.debug('Reading synced users from: %s', synced_users_path)
# with open(synced_users_path, 'rb') as f:
# synced_users = pickle.load(f)
# else:
# logger.info('Creating synced users dictionary')
# synced_users = dict()
# synced_users_dictionary_creation = True
try:
service_directory = get_directory_service()
service_sheets = get_sheets_service()
# ranges = get_multiple_ranges(
# service_sheets,
# sync_sheets_and_groups['spreadsheet_id'],
# sync_sheets_and_groups['range_names']
# )
#
# with open(data_path / 'ranges.pickle', 'wb') as file:
# pickle.dump(ranges, file)
with open(data_path / 'ranges.pickle', 'rb') as file:
ranges = pickle.load(file)
#
# [logger.debug(x) for x in ranges]
# group_results = []
# for group in ranges[0]['values']:
# group_name = group[0].split(" ", 1)[0]
#
# email = (translit(group_name, "ru", reversed=True)).lower() \
# + "@" \
# + sync_sheets_and_groups['google_domain']
#
# try:
# group_results.append(create_group(service_directory, email, group_name, ""))
# except googleapiclient.errors.HttpError as exception:
# # If group already exists among other things
# logger.error(exception, exc_info=False)
#
# logger.debug(group_name, email)
#
# group_results.sort(key=lambda x: x['name'])
# with open(data_path / 'group_results.pickle', 'wb') as file:
# pickle.dump(group_results, file)
with open(data_path / 'group_results.pickle', 'rb') as file:
group_results = pickle.load(file)
#
# [logger.debug(x) for x in group_results]
created_group_names = [x['name'] for x in group_results]
[logger.debug(x) for x in created_group_names]
# # A client should wait 1 minute before adding users or sending messages to a new group
# sleep(60)
students = dict(zip(
[i[0] if i else "" for i in ranges[1]['values']],
[i[0] if i else "" for i in ranges[2]['values']]
))
logger.debug(students.items())
leaders = dict(zip(
[i[0] if i else "" for i in ranges[3]['values']],
[i[0] if i else "" for i in ranges[4]['values']]
))
logger.debug(leaders.items())
group_users = {}
for group in ranges[0]['values']:
id = group[0].split(" ", 1)[0]
if id not in created_group_names:
logger.debug("Skipping group: ", id)
continue
else:
logger.debug("Adding users to group: ", id)
group_users[id] = []
# Leader email
group_users[id].append(
[leaders[group[1]], 'MEMBER']
)
# Member emails
for i in range(2, len(group)):
group_users[id].append(
[students[group[i]], 'MEMBER']
)
# Mandatory user
group_users[id] += sync_sheets_and_groups['mandatory_members']
with open(data_path / 'group_users.pickle', 'wb') as file:
pickle.dump(group_users, file)
with open(data_path / 'group_users.pickle', 'rb') as file:
group_users = pickle.load(file)
[logger.debug(x) for x in group_users]
# # Add users to groups
# user_results = []
# for group in group_users:
# for group_user in group_users[group]:
# user_results.append(
# add_user_to_group(service, group, group_user[0], group_user[1])
# )
#
# with open(data_path / 'user_results.pickle', 'wb') as file:
# pickle.dump(user_results, file)
# with open(data_path / 'user_results.pickle', 'rb') as file:
# user_results = pickle.load(file)
#
# [logger.debug(x) for x in user_results]
# students = dict(zip(
# [i[0] if i else "" for i in ranges[1]['values']],
# [i[0] if i else "" for i in ranges[2]['values']]
# ))
#
# leaders = dict(zip(
# [i[0] if i else "" for i in ranges[3]['values']],
# [i[0] if i else "" for i in ranges[4]['values']]
# ))
# if id not in synced_users:
# synced_users[id] = set()
#
# member_emails = set()
#
# # Leader email
# member_emails.add(
# leaders[group[1]]
# )
#
# # Member emails
# for i in range(2, len(group)):
# member_emails.add(
# students[group[i]]
# )
#
# # Mandatory emails
# member_emails |= set(sync_sheets_and_groups['mandatory_members'])
#
# # Synced users
# member_emails -= synced_users[id]
# synced_users[id] |= member_emails
#
# member_emails = list(member_emails)
#
# logger.debug('Name: %s - Description: %s - Users: %s',
# name, description, member_emails)
#
# if not synced_users_dictionary_creation:
# # TODO
# number_of_registered_users += len(member_emails)
#
# logger.debug('Result: %s', result)
# # -----
# # Might need rework
# # -----
#
# service = get_groupsettings_service()
#
# group_emails = []
# for group_name in group_names:
# group_emails.append(
# (translit(group_name, "ru", reversed=True)).lower() \
# + "@" \
# + create_google_groups['google_domain']
# )
#
# with open(data_path / 'group_emails.pickle', 'wb') as file:
# pickle.dump(group_emails, file)
# with open(data_path / 'group_emails.pickle', 'rb') as file:
# group_emails = pickle.load(file)
#
# [logger.debug(x) for x in group_emails]
#
# settings_results = []
# for group_email in group_emails:
# settings_results.append(
# update_group_settings(
# service,
# group_email,
# {
# "whoCanJoin": "INVITED_CAN_JOIN",
# "whoCanViewMembership": "ALL_IN_DOMAIN_CAN_VIEW",
# "whoCanViewGroup": "ALL_IN_DOMAIN_CAN_VIEW",
# "whoCanPostMessage": "ALL_IN_DOMAIN_CAN_POST",
# "isArchived": "true"
# }
# )
# )
#
# with open(data_path / 'settings_results.pickle', 'wb') as file:
# pickle.dump(settings_results, file)
# with open(data_path / 'settings_results.pickle', 'rb') as file:
# settings_results = pickle.load(file)
#
# [logger.debug(x) for x in settings_results]
except Exception as exception:
logger.error(exception, exc_info=True)
# logger.debug('Writing synced users to: %s', synced_users_path)
# with open(synced_users_path, 'wb') as f:
# pickle.dump(synced_users, f)
#
# logger.info('Update finished. Registered %s users. Sleeping for %s seconds.',
# number_of_registered_users, sync_sheets_and_groups['sleep_time'])
sleep(sync_sheets_and_groups['sleep_time'])
if __name__ == '__main__':
main()
|
[
"pickle.dump",
"pickle.load",
"logs.logging.get_logger",
"time.sleep",
"api_google.google_api_sheets.get_sheets_service",
"api_google.google_api_directory.get_directory_service"
] |
[((591, 668), 'logs.logging.get_logger', 'get_logger', (['"""sync_sheets_and_groups"""', "sync_sheets_and_groups['logging_level']"], {}), "('sync_sheets_and_groups', sync_sheets_and_groups['logging_level'])\n", (601, 668), False, 'from logs.logging import get_logger\n'), ((9521, 9564), 'time.sleep', 'sleep', (["sync_sheets_and_groups['sleep_time']"], {}), "(sync_sheets_and_groups['sleep_time'])\n", (9526, 9564), False, 'from time import sleep\n'), ((1462, 1485), 'api_google.google_api_directory.get_directory_service', 'get_directory_service', ([], {}), '()\n', (1483, 1485), False, 'from api_google.google_api_directory import get_directory_service, get_users_for_domain, get_groups_for_domain, create_group, add_user_to_group\n'), ((1515, 1535), 'api_google.google_api_sheets.get_sheets_service', 'get_sheets_service', ([], {}), '()\n', (1533, 1535), False, 'from api_google.google_api_sheets import get_sheets_service, get_multiple_ranges\n'), ((1964, 1981), 'pickle.load', 'pickle.load', (['file'], {}), '(file)\n', (1975, 1981), False, 'import pickle\n'), ((3084, 3101), 'pickle.load', 'pickle.load', (['file'], {}), '(file)\n', (3095, 3101), False, 'import pickle\n'), ((4799, 4829), 'pickle.dump', 'pickle.dump', (['group_users', 'file'], {}), '(group_users, file)\n', (4810, 4829), False, 'import pickle\n'), ((4931, 4948), 'pickle.load', 'pickle.load', (['file'], {}), '(file)\n', (4942, 4948), False, 'import pickle\n')]
|
#!/usr/bin/env python
# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
Lidar/BB check for CARLA
This script obtains the LiDAR's point cloud corresponding to all the vehicles
of the scene and make sure that they are inside the bounding box of the
corresponding actor.
This is done in a predefined route in Town03 with a high speed and several agressive
turns.
In a nutshell, the script have a queue that is filled in each frame with a lidar point
cloud and an structure for storing the Bounding Boxes. This last one is emulated as a
sensor filling the queue in the on_tick callback of the carla.world. In this way, we make
sure that we are correctly syncronizing the lidar point cloud and BB/actor transformations.
Then, we select the points corresponding to each actor (car) in the scene and check they
are inside the bounding boxes of that actor, all in each vehicle frame of reference.
Important Data structure description:
+ Lidar data structure: four element tuple with:
- [0] Frame
- [1] Sensor name: 'semlidar'
- [2] Point cloud in the form of a numpy dictionary with all semantic lidar information
- [3] Global transformation of the sensor
+ Bounding box data structure: four element tuple with:
- [0] Frame
- [1] Sensor name: 'bb'
- [2] List of actor information: each a tuple with:
- [0] Actor id
- [1] Actor type (blueprint's name)
- [0] Actor's global transformation
- [0] Actor's bounding box
+ ActorTrace class: Takes the Lidar data structure and one actor information and
check if all the data points related with this actor are inside its BB.
This is done in the local coordinate frame of the actor and should be done like:
trace = ActorTrace(actor_info, lidar_data)
trace.process()
trace.check_lidar_data()
"""
import glob
import os
import sys
import numpy as np
from queue import Queue
from queue import Empty
try:
sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
sys.version_info.major,
sys.version_info.minor,
'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
pass
import carla
class ActorTrace(object):
"""Class that store and process information about an actor at certain moment."""
def __init__(self, actor, lidar):
self.set_lidar(lidar)
self.set_actor(actor)
self._lidar_pc_local = np.array([])
self._bb_vertices = np.array([])
self._bb_minlimits = [0, 0, 0]
self._bb_maxlimits = [0, 0, 0]
def set_lidar(self, lidar):
self._frame = lidar[0]
self._lidar_data = lidar[2]
self._lidar_transf = lidar[3]
def set_actor(self, actor):
self._actor_id = actor[0]
self._actor_type = actor[1]
self._actor_transf = actor[2]
self._actor_bb = actor[3]
def process(self):
# Filter lidar points that correspond to my actor id
data_actor = self._lidar_data[self._lidar_data['ObjIdx'] == self._actor_id]
# Take the xyz point cloud data and transform it to actor's frame
points = np.array([data_actor['x'], data_actor['y'], data_actor['z']]).T
points = np.append(points, np.ones((points.shape[0], 1)), axis=1)
points = np.dot(self._lidar_transf.get_matrix(), points.T).T # sensor -> world
points = np.dot(self._actor_transf.get_inverse_matrix(), points.T).T # world -> actor
points = points[:, :-1]
# Saving the points in 'local' coordinates
self._lidar_pc_local = points
# We compute the limits in the local frame of reference using the
# vertices of the bounding box
vertices = self._actor_bb.get_local_vertices()
ver_py = []
for v in vertices:
ver_py.append([v.x, v.y, v.z])
ver_np = np.array(ver_py)
self._bb_vertices = ver_np
self._bb_minlimits = ver_np.min(axis=0) - 0.001
self._bb_maxlimits = ver_np.max(axis=0) + 0.001
def print(self, print_if_empty = False):
if self._lidar_pc_local.shape[0] > 0 or print_if_empty:
np.savetxt("veh_data_%d_%s_%d.out" % (self._frame, self._actor_type, self._actor_id), self._lidar_pc_local)
np.savetxt("bb_data_%d_%s_%d.out" % (self._frame, self._actor_type, self._actor_id), self._bb_vertices)
def lidar_is_outside_bb(self, check_axis = [True, True, True]):
lidar_pc = self._lidar_pc_local
if check_axis[0]:
xmin = self._bb_minlimits[0]
xmax = self._bb_maxlimits[0]
out = np.any((lidar_pc[:,0] > xmax) | (lidar_pc[:,0] < xmin))
if out:
print("Problem with x axis")
return True
if check_axis[1]:
ymin = self._bb_minlimits[1]
ymax = self._bb_maxlimits[1]
out = np.any((lidar_pc[:, 1] > ymax) | (lidar_pc[:, 1] < ymin))
if out:
print("Problem with y axis")
return True
if check_axis[2]:
zmin = self._bb_minlimits[2]
zmax = self._bb_maxlimits[2]
out = np.any((lidar_pc[:, 2] > zmax) | (lidar_pc[:, 2] < zmin))
if out:
print("Problem with z axis")
return True
return False
def check_lidar_data(self):
if self.lidar_is_outside_bb():
print("Error!!! Points of lidar point cloud are outside its BB for car %d: %s " % (self._actor_id, self._actor_type))
self.print()
return False
else:
return True
def wait(world, frames=100, queue = None, slist = None):
for i in range(0, frames):
world.tick()
if queue != None and slist != None:
try:
for _i in range (0, len(slist)):
s_frame = queue.get(True, 1.0)
except Empty:
print(" Some of the sensor information is missed")
# Sensor callback.
# This is where you receive the sensor data and
# process it as you liked and the important part is that,
# at the end, it should include an element into the sensor queue.
def lidar_callback(sensor_data, sensor_queue, sensor_name):
sensor_pc_local = np.frombuffer(sensor_data.raw_data, dtype=np.dtype([
('x', np.float32), ('y', np.float32), ('z', np.float32),
('CosAngle', np.float32), ('ObjIdx', np.uint32), ('ObjTag', np.uint32)]))
sensor_transf = sensor_data.transform
sensor_queue.put((sensor_data.frame, sensor_name, sensor_pc_local, sensor_transf))
def bb_callback(snapshot, world, sensor_queue, sensor_name):
data_array = []
vehicles = world.get_actors().filter('vehicle.*')
for actor in vehicles:
data_array.append((actor.id, actor.type_id, actor.get_transform(), actor.bounding_box))
sensor_queue.put((snapshot.frame, sensor_name, data_array))
def move_spectator(world, actor):
actor_tr = actor.get_transform()
spectator_transform = carla.Transform(actor_tr.location, actor_tr.rotation)
spectator_transform.location -= actor_tr.get_forward_vector() * 5
spectator_transform.location -= actor_tr.get_up_vector() * 3
spectator = world.get_spectator()
spectator.set_transform(spectator_transform)
def world_callback(snapshot, world, sensor_queue, sensor_name, actor):
move_spectator(world, actor)
bb_callback(snapshot, world, sensor_queue, sensor_name)
def process_sensors(w_frame, sensor_queue, sensor_number):
if sensor_number != 2:
print("Error!!! Sensor number should be two")
sl_data = None
bb_data = None
try:
for i in range (0, sensor_number):
s_frame = sensor_queue.get(True, 1.0)
while s_frame[0] != w_frame:
print("Warning! Missmatch for sensor %s in the frame timestamp (w: %d, s: %d)" % (s_frame[1], w_frame, s_frame[0]))
print("This could be due to accumulated data for previous steps")
s_frame = sensor_queue.get(True, 1.0)
if s_frame[1] == "semlidar":
sl_data = s_frame
elif s_frame[1] == "bb":
bb_data = s_frame
#print(" Frame: %d Sensor: %s Len: %d " % (s_frame[0], s_frame[1], len(s_frame[2])))
except Empty:
print("Error!!! The needeinformation is not here!!!")
return
if sl_data == None or bb_data == None:
print("Error!!! Missmatch for sensor %s in the frame timestamp (w: %d, s: %d)" % (s_frame[1], w_frame, s_frame[0]))
for actor_data in bb_data[2]:
trace_vehicle = ActorTrace(actor_data, sl_data)
trace_vehicle.process()
trace_vehicle.check_lidar_data()
class SpawnCar(object):
def __init__(self, location, rotation, filter="vehicle.*", autopilot = False, velocity = None):
self._filter = filter
self._transform = carla.Transform(location, rotation)
self._autopilot = autopilot
self._velocity = velocity
self._actor = None
self._world = None
def spawn(self, world):
self._world = world
actor_BP = world.get_blueprint_library().filter(self._filter)[0]
self._actor = world.spawn_actor(actor_BP, self._transform)
self._actor.set_autopilot(True)
return self._actor
def destroy(self):
if self._actor != None:
self._actor.destroy()
CarPropList = [
SpawnCar(carla.Location(x=83, y= -40, z=5), carla.Rotation(yaw=-90), filter= "*lincoln*", autopilot=True),
SpawnCar(carla.Location(x=83, y= -30, z=3), carla.Rotation(yaw=-90), filter= "*ambulance*", autopilot=True),
SpawnCar(carla.Location(x=83, y= -20, z=3), carla.Rotation(yaw=-90), filter= "*etron*", autopilot=True),
SpawnCar(carla.Location(x=120, y= -3.5, z=2), carla.Rotation(yaw=+180), filter= "*isetta*", autopilot=True),
SpawnCar(carla.Location(x=100, y= -3.5, z=2), carla.Rotation(yaw=+180), filter= "*etron*", autopilot=True),
SpawnCar(carla.Location(x=140, y= -3.5, z=2), carla.Rotation(yaw=+180), filter= "*model3*", autopilot=True),
SpawnCar(carla.Location(x=160, y= -3.5, z=2), carla.Rotation(yaw=+180), filter= "*impala*", autopilot=False),
SpawnCar(carla.Location(x=180, y= -3.5, z=2), carla.Rotation(yaw=+180), filter= "*a2*", autopilot=True),
SpawnCar(carla.Location(x=60, y= +6, z=2), carla.Rotation(yaw=+00), filter= "*model3*", autopilot=True),
SpawnCar(carla.Location(x=80, y= +6, z=2), carla.Rotation(yaw=+00), filter= "*etron*", autopilot=True),
SpawnCar(carla.Location(x=100, y= +6, z=2), carla.Rotation(yaw=+00), filter= "*mustan*", autopilot=True),
SpawnCar(carla.Location(x=120, y= +6, z=2), carla.Rotation(yaw=+00), filter= "*isetta*", autopilot=True),
SpawnCar(carla.Location(x=140, y= +6, z=2), carla.Rotation(yaw=+00), filter= "*impala*", autopilot=True),
SpawnCar(carla.Location(x=160, y= +6, z=2), carla.Rotation(yaw=+00), filter= "*prius*", autopilot=True),
SpawnCar(carla.Location(x=234, y= +20,z=2), carla.Rotation(yaw=+90), filter= "*dodge*", autopilot=True),
SpawnCar(carla.Location(x=234, y= +40,z=2), carla.Rotation(yaw=+90), filter= "*isetta*", autopilot=True),
SpawnCar(carla.Location(x=234, y= +80,z=2), carla.Rotation(yaw=+90), filter= "*tt*", autopilot=True),
SpawnCar(carla.Location(x=243, y= -40,z=2), carla.Rotation(yaw=-90), filter= "*etron*", autopilot=True),
SpawnCar(carla.Location(x=243, y= -20,z=2), carla.Rotation(yaw=-90), filter= "*mkz2017*", autopilot=True),
SpawnCar(carla.Location(x=243, y= +00,z=2), carla.Rotation(yaw=-90), filter= "*mustan*", autopilot=True),
SpawnCar(carla.Location(x=243, y= +20,z=2), carla.Rotation(yaw=-90), filter= "*dodge*", autopilot=True),
SpawnCar(carla.Location(x=243, y= +40,z=2), carla.Rotation(yaw=-90), filter= "*charger2020*", autopilot=True),
SpawnCar(carla.Location(x=243, y= +60,z=2), carla.Rotation(yaw=-90), filter= "*lincoln2020*", autopilot=True),
SpawnCar(carla.Location(x=243, y= +80,z=2), carla.Rotation(yaw=-90), filter= "*tt*", autopilot=True),
SpawnCar(carla.Location(x=243, y=+100,z=2), carla.Rotation(yaw=-90), filter= "*a2*", autopilot=True),
SpawnCar(carla.Location(x=243, y=+120,z=2), carla.Rotation(yaw=-90), filter= "*wrangler_rubicon*", autopilot=True),
SpawnCar(carla.Location(x=243, y=+140,z=2), carla.Rotation(yaw=-90), filter= "*c3*", autopilot=True)
]
def spawn_prop_vehicles(world):
for car in CarPropList:
car.spawn(world)
def destroy_prop_vehicles():
for car in CarPropList:
car.destroy()
def main():
# We start creating the client
client = carla.Client('localhost', 2000)
client.set_timeout(2.0)
world = client.get_world()
try:
# We need to save the settings to be able to recover them at the end
# of the script to leave the server in the same state that we found it.
original_settings = world.get_settings()
settings = world.get_settings()
# We set CARLA syncronous mode
settings.fixed_delta_seconds = 0.05
settings.synchronous_mode = True
world.apply_settings(settings)
traffic_manager = client.get_trafficmanager(8000)
traffic_manager.set_synchronous_mode(True)
# We create the sensor queue in which we keep track of the information
# already received. This structure is thread safe and can be
# accessed by all the sensors callback concurrently without problem.
sensor_queue = Queue()
# Spawning ego vehicle
actor_BP = world.get_blueprint_library().filter("vehicle.lincoln.mkz2017")[0]
car_tr = carla.Transform(carla.Location(x=239, y=125, z=0.9), carla.Rotation(yaw=-88.5))
actor = world.spawn_actor(actor_BP, car_tr)
world.tick()
move_spectator(world, actor)
spawn_prop_vehicles(world)
wait(world, 10)
# We create all the sensors and keep them in a list for convenience.
sensor_list = []
lidar_bp = world.get_blueprint_library().find('sensor.lidar.ray_cast_semantic')
lidar_bp.set_attribute('channels', '64')
lidar_bp.set_attribute('points_per_second', '500000')
lidar_bp.set_attribute('range', '300')
lidar_bp.set_attribute('upper_fov', '10.0')
lidar_bp.set_attribute('lower_fov', '-90.0')
lidar_tr = carla.Transform(carla.Location(z=3), carla.Rotation(yaw=0))
lidar = world.spawn_actor(lidar_bp, lidar_tr, attach_to=actor)
lidar.listen(lambda data: lidar_callback(data, sensor_queue, "semlidar"))
world.on_tick(lambda snapshot: world_callback(snapshot, world, sensor_queue, "bb", actor))
sensor_list.append(lidar)
sensor_list.append(actor) # actor acts as a 'sensor' to simplify bb-lidar data comparison
# Set autopilot for main vehicle
actor.enable_constant_velocity(carla.Vector3D(20, 0, 0))
for _i in range(0, 100):
# Tick the server
world.tick()
w_frame = world.get_snapshot().frame
process_sensors(w_frame, sensor_queue, len(sensor_list))
actor.disable_constant_velocity()
finally:
world.apply_settings(original_settings)
# Destroy all the actors
destroy_prop_vehicles()
for sensor in sensor_list:
sensor.destroy()
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
print(' - Exited by user.')
|
[
"numpy.dtype",
"numpy.ones",
"carla.Transform",
"carla.Vector3D",
"carla.Location",
"numpy.any",
"numpy.array",
"carla.Client",
"numpy.savetxt",
"queue.Queue",
"carla.Rotation",
"glob.glob"
] |
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'(yaw=-90)\n', (12316, 12325), False, 'import carla\n'), ((12374, 12408), 'carla.Location', 'carla.Location', ([], {'x': '(243)', 'y': '(+100)', 'z': '(2)'}), '(x=243, y=+100, z=2)\n', (12388, 12408), False, 'import carla\n'), ((12411, 12434), 'carla.Rotation', 'carla.Rotation', ([], {'yaw': '(-90)'}), '(yaw=-90)\n', (12425, 12434), False, 'import carla\n'), ((12483, 12517), 'carla.Location', 'carla.Location', ([], {'x': '(243)', 'y': '(+120)', 'z': '(2)'}), '(x=243, y=+120, z=2)\n', (12497, 12517), False, 'import carla\n'), ((12520, 12543), 'carla.Rotation', 'carla.Rotation', ([], {'yaw': '(-90)'}), '(yaw=-90)\n', (12534, 12543), False, 'import carla\n'), ((12606, 12640), 'carla.Location', 'carla.Location', ([], {'x': '(243)', 'y': '(+140)', 'z': '(2)'}), '(x=243, y=+140, z=2)\n', (12620, 12640), False, 'import carla\n'), ((12643, 12666), 'carla.Rotation', 'carla.Rotation', ([], {'yaw': '(-90)'}), '(yaw=-90)\n', (12657, 12666), False, 'import carla\n'), ((13801, 13808), 'queue.Queue', 'Queue', ([], {}), '()\n', (13806, 13808), False, 'from queue import Queue\n'), ((2166, 2324), 'glob.glob', 'glob.glob', (["('../carla/dist/carla-*%d.%d-%s.egg' % (sys.version_info.major, sys.\n version_info.minor, 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))"], {}), "('../carla/dist/carla-*%d.%d-%s.egg' % (sys.version_info.major,\n sys.version_info.minor, 'win-amd64' if os.name == 'nt' else 'linux-x86_64')\n )\n", (2175, 2324), False, 'import glob\n'), ((3335, 3396), 'numpy.array', 'np.array', (["[data_actor['x'], data_actor['y'], data_actor['z']]"], {}), "([data_actor['x'], data_actor['y'], data_actor['z']])\n", (3343, 3396), True, 'import numpy as np\n'), ((3434, 3463), 'numpy.ones', 'np.ones', (['(points.shape[0], 1)'], {}), '((points.shape[0], 1))\n', (3441, 3463), True, 'import numpy as np\n'), ((4348, 4460), 'numpy.savetxt', 'np.savetxt', (["('veh_data_%d_%s_%d.out' % (self._frame, self._actor_type, self._actor_id))", 'self._lidar_pc_local'], {}), "('veh_data_%d_%s_%d.out' % (self._frame, self._actor_type, self.\n _actor_id), self._lidar_pc_local)\n", (4358, 4460), True, 'import numpy as np\n'), ((4468, 4576), 'numpy.savetxt', 'np.savetxt', (["('bb_data_%d_%s_%d.out' % (self._frame, self._actor_type, self._actor_id))", 'self._bb_vertices'], {}), "('bb_data_%d_%s_%d.out' % (self._frame, self._actor_type, self.\n _actor_id), self._bb_vertices)\n", (4478, 4576), True, 'import numpy as np\n'), ((4809, 4866), 'numpy.any', 'np.any', (['((lidar_pc[:, 0] > xmax) | (lidar_pc[:, 0] < xmin))'], {}), '((lidar_pc[:, 0] > xmax) | (lidar_pc[:, 0] < xmin))\n', (4815, 4866), True, 'import numpy as np\n'), ((5085, 5142), 'numpy.any', 'np.any', (['((lidar_pc[:, 1] > ymax) | (lidar_pc[:, 1] < ymin))'], {}), '((lidar_pc[:, 1] > ymax) | (lidar_pc[:, 1] < ymin))\n', (5091, 5142), True, 'import numpy as np\n'), ((5363, 5420), 'numpy.any', 'np.any', (['((lidar_pc[:, 2] > zmax) | (lidar_pc[:, 2] < zmin))'], {}), '((lidar_pc[:, 2] > zmax) | (lidar_pc[:, 2] < zmin))\n', (5369, 5420), True, 'import numpy as np\n'), ((6515, 6659), 'numpy.dtype', 'np.dtype', (["[('x', np.float32), ('y', np.float32), ('z', np.float32), ('CosAngle', np.\n float32), ('ObjIdx', np.uint32), ('ObjTag', np.uint32)]"], {}), "([('x', np.float32), ('y', np.float32), ('z', np.float32), (\n 'CosAngle', np.float32), ('ObjIdx', np.uint32), ('ObjTag', np.uint32)])\n", (6523, 6659), True, 'import numpy as np\n'), ((13960, 13995), 'carla.Location', 'carla.Location', ([], {'x': '(239)', 'y': '(125)', 'z': '(0.9)'}), '(x=239, y=125, z=0.9)\n', (13974, 13995), False, 'import carla\n'), ((13997, 14022), 'carla.Rotation', 'carla.Rotation', ([], {'yaw': '(-88.5)'}), '(yaw=-88.5)\n', (14011, 14022), False, 'import carla\n'), ((14686, 14705), 'carla.Location', 'carla.Location', ([], {'z': '(3)'}), '(z=3)\n', (14700, 14705), False, 'import carla\n'), ((14707, 14728), 'carla.Rotation', 'carla.Rotation', ([], {'yaw': '(0)'}), '(yaw=0)\n', (14721, 14728), False, 'import carla\n'), ((15204, 15228), 'carla.Vector3D', 'carla.Vector3D', (['(20)', '(0)', '(0)'], {}), '(20, 0, 0)\n', (15218, 15228), False, 'import carla\n')]
|
# <NAME>
# Software Engineering 001
# jumble_solver.py
# 2/17/2021
# Assignment:
# Consider the game "Jumble"
# https://www.sandiegouniontribune.com/sd-jumble-daily-htmlstory.html
# Create a Python program to find the individual words in Jumble puzzles such
# that INJURE prints after entering the following: solve("JNUIER")
from PyDictionary import PyDictionary # Installation: pip install PyDictionary
from math import factorial
from random import shuffle
def solve(jumble):
combos = []
chars = list(jumble.upper())
dict = PyDictionary()
# Maximum possible unique combinations of chars
limit = factorial(len(chars))
while len(combos) < limit:
# Generates random string from chars
shuffle(chars)
tmp = "".join(chars)
# Appends tmp to combos list only if it is unique
if tmp not in combos:
combos.append(tmp)
# Prints tmp only if it returns an English meaning
if dict.meaning(tmp, disable_errors = True):
print(tmp)
break
|
[
"PyDictionary.PyDictionary",
"random.shuffle"
] |
[((540, 554), 'PyDictionary.PyDictionary', 'PyDictionary', ([], {}), '()\n', (552, 554), False, 'from PyDictionary import PyDictionary\n'), ((727, 741), 'random.shuffle', 'shuffle', (['chars'], {}), '(chars)\n', (734, 741), False, 'from random import shuffle\n')]
|
# Copyright (c) 2018 <NAME>, Inc.
#
# This file is part of Mycroft Skills Manager
# (see https://github.com/MycroftAI/mycroft-skills-manager).
#
# 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 sys
import logging
import os
import shutil
import subprocess
import yaml
from contextlib import contextmanager
from difflib import SequenceMatcher
from functools import wraps
from git import Repo, GitError
from git.exc import GitCommandError
from lazy import lazy
from os.path import exists, join, basename, dirname, isfile
from shutil import rmtree, move
from subprocess import PIPE, Popen
from tempfile import mktemp, gettempdir
from threading import Lock
from typing import Callable
from pako import PakoManager
from msm import SkillRequirementsException, git_to_msm_exceptions
from msm.exceptions import PipRequirementsException, \
SystemRequirementsException, AlreadyInstalled, SkillModified, \
AlreadyRemoved, RemoveException, CloneException, NotInstalled, GitException
from msm.util import cached_property, Git
LOG = logging.getLogger(__name__)
# Branches which can be switched from when updating
# TODO Make this configurable
SWITCHABLE_BRANCHES = ['master']
# default constraints to use if no are given
DEFAULT_CONSTRAINTS = '/etc/mycroft/constraints.txt'
FIVE_MINUTES = 300
@contextmanager
def work_dir(directory):
old_dir = os.getcwd()
os.chdir(directory)
try:
yield
finally:
os.chdir(old_dir)
def _backup_previous_version(func: Callable = None):
"""Private decorator to back up previous skill folder"""
@wraps(func)
def wrapper(self, *args, **kwargs):
self.old_path = None
if self.is_local:
self.old_path = join(gettempdir(), self.name)
if exists(self.old_path):
rmtree(self.old_path)
shutil.copytree(self.path, self.old_path)
try:
func(self, *args, **kwargs)
# Modified skill or GitError should not restore working copy
except (SkillModified, GitError, GitException):
raise
except Exception:
LOG.info('Problem performing action. Restoring skill to '
'previous state...')
if exists(self.path):
rmtree(self.path)
if self.old_path and exists(self.old_path):
shutil.copytree(self.old_path, self.path)
self.is_local = exists(self.path)
raise
finally:
# Remove temporary path if needed
if self.old_path and exists(self.old_path):
rmtree(self.old_path)
return wrapper
class SkillEntry(object):
pip_lock = Lock()
manifest_yml_format = {
'dependencies': {
'system': {},
'exes': [],
'skill': [],
'python': []
}
}
def __init__(self, name, path, url='', sha='', msm=None):
url = url.rstrip('/')
url = url[:-len('.git')] if url.endswith('.git') else url
self.path = path
self.url = url
self.sha = sha
self.msm = msm
if msm:
u = url.lower()
self.meta_info = msm.repo.skills_meta_info.get(u, {})
else:
self.meta_info = {}
if name is not None:
self.name = name
elif 'name' in self.meta_info:
self.name = self.meta_info['name']
else:
self.name = basename(path)
# TODO: Handle git:// urls as well
from_github = False
if url.startswith('https://'):
url_tokens = url.rstrip("/").split("/")
from_github = url_tokens[-3] == 'github.com' if url else False
self.author = self.extract_author(url) if from_github else ''
self.id = self.extract_repo_id(url) if from_github else self.name
self.is_local = exists(path)
self.old_path = None # Path of previous version while upgrading
@property
def is_beta(self):
return not self.sha or self.sha == 'HEAD'
@property
def is_dirty(self):
"""True if different from the version in the mycroft-skills repo.
Considers a skill dirty if
- the checkout sha doesn't match the mycroft-skills repo
- the skill doesn't exist in the mycroft-skills repo
- the skill is not a git repo
- has local modifications
"""
if not exists(self.path):
return False
try:
checkout = Git(self.path)
mod = checkout.status(porcelain=True, untracked_files='no') != ''
current_sha = checkout.rev_parse('HEAD')
except GitCommandError: # Not a git checkout
return True
skill_shas = {d[0]: d[3] for d in self.msm.repo.get_skill_data()}
return (self.name not in skill_shas or
current_sha != skill_shas[self.name] or
mod)
@cached_property(ttl=FIVE_MINUTES)
def skill_gid(self):
"""Format skill gid for the skill.
This property does some Git gymnastics to determine its return value.
When a device boots, each skill accesses this property several times.
To reduce the amount of boot time, cache the value returned by this
property. Cache expires five minutes after it is generated.
"""
LOG.debug('Generating skill_gid for ' + self.name)
gid = ''
if self.is_dirty:
gid += '@|'
if self.meta_info != {}:
gid += self.meta_info['skill_gid']
else:
name = self.name.split('.')[0]
gid += name
return gid
def __str__(self):
return self.name
def attach(self, remote_entry):
"""Attach a remote entry to a local entry"""
self.name = remote_entry.name
self.sha = remote_entry.sha
self.url = remote_entry.url
self.author = remote_entry.author
return self
@classmethod
def from_folder(cls, path, msm=None, use_cache=True):
"""Find or create skill entry from folder path.
Arguments:
path: path of skill folder
msm: msm instance to use for caching and extended information
retrieval.
use_cache: Enable/Disable cache usage. defaults to True
"""
if msm and use_cache:
skills = {skill.path: skill for skill in msm.local_skills.values()}
if path in skills:
return skills[path]
return cls(None, path, cls.find_git_url(path), msm=msm)
@classmethod
def create_path(cls, folder, url, name=''):
return join(folder, '{}.{}'.format(
name or cls.extract_repo_name(url), cls.extract_author(url)
).lower())
@staticmethod
def extract_repo_name(url):
s = url.rstrip('/').split("/")[-1]
a, b, c = s.rpartition('.git')
if not c:
return a
return s
@staticmethod
def extract_author(url):
return url.rstrip('/').split("/")[-2].split(':')[-1]
@classmethod
def extract_repo_id(cls, url):
return '{}:{}'.format(cls.extract_author(url).lower(),
cls.extract_repo_name(url)).lower()
@staticmethod
def _tokenize(x):
return x.replace('-', ' ').split()
@staticmethod
def _extract_tokens(s, tokens):
s = s.lower().replace('-', ' ')
extracted = []
for token in tokens:
extracted += [token] * s.count(token)
s = s.replace(token, '')
s = ' '.join(i for i in s.split(' ') if i)
tokens = [i for i in s.split(' ') if i]
return s, tokens, extracted
@classmethod
def _compare(cls, a, b):
return SequenceMatcher(a=a, b=b).ratio()
def match(self, query, author=None):
search, search_tokens, search_common = self._extract_tokens(
query, ['skill', 'fallback', 'mycroft']
)
name, name_tokens, name_common = self._extract_tokens(
self.name, ['skill', 'fallback', 'mycroft']
)
weights = [
(9, self._compare(name, search)),
(9, self._compare(name.split(' '), search_tokens)),
(2, self._compare(name_common, search_common)),
]
if author:
author_weight = self._compare(self.author, author)
weights.append((5, author_weight))
author_weight = author_weight
else:
author_weight = 1.0
return author_weight * (
sum(weight * val for weight, val in weights) /
sum(weight for weight, val in weights)
)
def run_pip(self, constraints=None):
if not self.dependent_python_packages:
return False
# Use constraints to limit the installed versions
if constraints and not exists(constraints):
LOG.error('Couldn\'t find the constraints file')
return False
elif exists(DEFAULT_CONSTRAINTS):
constraints = DEFAULT_CONSTRAINTS
LOG.info('Installing requirements.txt for ' + self.name)
can_pip = os.access(dirname(sys.executable), os.W_OK | os.X_OK)
pip_args = [sys.executable, '-m', 'pip', 'install']
if constraints:
pip_args += ['-c', constraints]
if not can_pip:
pip_args = ['sudo', '-n'] + pip_args
with self.pip_lock:
"""
Iterate over the individual Python packages and
install them one by one to enforce the order specified
in the manifest.
"""
for dependent_python_package in self.dependent_python_packages:
pip_command = pip_args + [dependent_python_package]
proc = Popen(pip_command, stdout=PIPE, stderr=PIPE)
pip_code = proc.wait()
if pip_code != 0:
stderr = proc.stderr.read().decode()
if pip_code == 1 and 'sudo:' in stderr and pip_args[0] == 'sudo':
raise PipRequirementsException(
2, '', 'Permission denied while installing pip '
'dependencies. Please run in virtualenv or use sudo'
)
raise PipRequirementsException(
pip_code, proc.stdout.read().decode(), stderr
)
return True
def install_system_deps(self):
self.run_requirements_sh()
system_packages = {
exe: (packages or '').split()
for exe, packages in self.dependent_system_packages.items()
}
LOG.info('Installing system requirements...')
all_deps = system_packages.pop('all', [])
try:
manager = PakoManager()
success = manager.install(all_deps, overrides=system_packages)
except RuntimeError as e:
LOG.warning('Failed to launch package manager: {}'.format(e))
success = False
missing_exes = [
exe for exe in self.dependencies.get('exes') or []
if not shutil.which(exe)
]
if missing_exes:
if not success:
LOG.warning('Failed to install dependencies.')
if all_deps:
LOG.warning('Please install manually: {}'.format(
' '.join(all_deps)
))
raise SkillRequirementsException('Could not find exes: {}'.format(
', '.join(missing_exes)
))
return success
def run_requirements_sh(self):
setup_script = join(self.path, "requirements.sh")
if not exists(setup_script):
return False
with work_dir(self.path):
rc = subprocess.call(["bash", setup_script])
if rc != 0:
LOG.error("Requirements.sh failed with error code: " + str(rc))
raise SystemRequirementsException(rc)
LOG.info("Successfully ran requirements.sh for " + self.name)
return True
def run_skill_requirements(self):
if not self.msm:
raise ValueError('Pass msm to SkillEntry to install skill deps')
try:
for skill_dep in self.dependent_skills:
LOG.info("Installing skill dependency: {}".format(skill_dep))
try:
self.msm.install(skill_dep)
except AlreadyInstalled:
pass
except Exception as e:
raise SkillRequirementsException(e)
def verify_info(self, info, fmt):
if not info:
return
if not isinstance(info, type(fmt)):
LOG.warning('Invalid value type manifest.yml for {}: {}'.format(
self.name, type(info)
))
return
if not isinstance(info, dict) or not fmt:
return
for key in info:
if key not in fmt:
LOG.warning('Unknown key in manifest.yml for {}: {}'.format(
self.name, key
))
continue
self.verify_info(info[key], fmt[key])
@lazy
def skill_info(self):
yml_path = join(self.path, 'manifest.yml')
if exists(yml_path):
LOG.info('Reading from manifest.yml')
with open(yml_path) as f:
info = yaml.safe_load(f)
self.verify_info(info, self.manifest_yml_format)
return info or {}
return {}
@lazy
def dependencies(self):
return self.skill_info.get('dependencies') or {}
@lazy
def dependent_skills(self):
skills = set()
reqs = join(self.path, "skill_requirements.txt")
if exists(reqs):
with open(reqs, "r") as f:
for i in f.readlines():
skill = i.strip()
if skill:
skills.add(skill)
for i in self.dependencies.get('skill') or []:
skills.add(i)
return list(skills)
@lazy
def dependent_python_packages(self):
reqs = join(self.path, "requirements.txt")
req_lines = []
if exists(reqs):
with open(reqs, "r") as f:
req_lines += f.readlines()
req_lines += self.dependencies.get('python') or []
# Strip comments
req_lines = [l.split('#')[0].strip() for l in req_lines]
return [i for i in req_lines if i] # Strip empty lines
@lazy
def dependent_system_packages(self):
return self.dependencies.get('system') or {}
def remove(self):
if not self.is_local:
raise AlreadyRemoved(self.name)
try:
rmtree(self.path)
self.is_local = False
except OSError as e:
raise RemoveException(str(e))
LOG.info('Successfully removed ' + self.name)
@_backup_previous_version
def install(self, constraints=None):
if self.is_local:
raise AlreadyInstalled(self.name)
LOG.info("Downloading skill: " + self.url)
try:
tmp_location = mktemp()
Repo.clone_from(self.url, tmp_location)
self.is_local = True
Git(tmp_location).reset(self.sha or 'HEAD', hard=True)
except GitCommandError as e:
raise CloneException(e.stderr)
if isfile(join(tmp_location, '__init__.py')):
move(join(tmp_location, '__init__.py'),
join(tmp_location, '__init__'))
try:
move(tmp_location, self.path)
if self.msm:
self.run_skill_requirements()
self.install_system_deps()
self.run_pip(constraints)
finally:
if isfile(join(self.path, '__init__')):
move(join(self.path, '__init__'),
join(self.path, '__init__.py'))
LOG.info('Successfully installed ' + self.name)
def update_deps(self, constraints=None):
if self.msm:
self.run_skill_requirements()
self.install_system_deps()
self.run_pip(constraints)
def _find_sha_branch(self):
git = Git(self.path)
sha_branches = git.branch(
contains=self.sha, all=True
).split('\n')
sha_branch = [b for b in sha_branches if ' -> ' not in b][0]
sha_branch = sha_branch.strip('* \n').replace('remotes/', '')
for remote in git.remote().split('\n'):
sha_branch = sha_branch.replace(remote + '/', '')
return sha_branch
@_backup_previous_version
def update(self):
if not self.is_local:
raise NotInstalled('{} is not installed'.format(self.name))
git = Git(self.path)
with git_to_msm_exceptions():
sha_before = git.rev_parse('HEAD')
modified_files = git.status(porcelain=True, untracked='no')
if modified_files != '':
raise SkillModified('Uncommitted changes:\n' + modified_files)
git.fetch()
current_branch = git.rev_parse('--abbrev-ref', 'HEAD').strip()
if self.sha and current_branch in SWITCHABLE_BRANCHES:
# Check out correct branch
git.checkout(self._find_sha_branch())
git.merge(self.sha or 'origin/HEAD', ff_only=True)
sha_after = git.rev_parse('HEAD')
if sha_before != sha_after:
self.update_deps()
LOG.info('Updated ' + self.name)
# Trigger reload by modifying the timestamp
os.utime(join(self.path, '__init__.py'))
return True
else:
LOG.info('Nothing new for ' + self.name)
return False
@staticmethod
def find_git_url(path):
"""Get the git url from a folder"""
try:
LOG.debug(
'Attempting to retrieve the remote origin URL config for '
'skill in path ' + path
)
return Git(path).config('remote.origin.url')
except GitError:
return ''
def __repr__(self):
return '<SkillEntry {}>'.format(' '.join(
'{}={}'.format(attr, self.__dict__[attr])
for attr in ['name', 'author', 'is_local']
))
|
[
"logging.getLogger",
"pako.PakoManager",
"git.Repo.clone_from",
"msm.exceptions.PipRequirementsException",
"msm.exceptions.AlreadyInstalled",
"os.path.exists",
"shutil.move",
"threading.Lock",
"msm.git_to_msm_exceptions",
"subprocess.Popen",
"functools.wraps",
"msm.util.Git",
"subprocess.call",
"msm.exceptions.SystemRequirementsException",
"msm.exceptions.CloneException",
"msm.exceptions.AlreadyRemoved",
"msm.SkillRequirementsException",
"shutil.which",
"os.path.dirname",
"msm.util.cached_property",
"difflib.SequenceMatcher",
"os.path.join",
"msm.exceptions.SkillModified",
"os.getcwd",
"os.chdir",
"shutil.copytree",
"tempfile.mktemp",
"yaml.safe_load",
"tempfile.gettempdir",
"os.path.basename",
"shutil.rmtree"
] |
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cached_property, Git\n'), ((10847, 10980), 'msm.exceptions.PipRequirementsException', 'PipRequirementsException', (['(2)', '""""""', '"""Permission denied while installing pip dependencies. Please run in virtualenv or use sudo"""'], {}), "(2, '',\n 'Permission denied while installing pip dependencies. Please run in virtualenv or use sudo'\n )\n", (10871, 10980), False, 'from msm.exceptions import PipRequirementsException, SystemRequirementsException, AlreadyInstalled, SkillModified, AlreadyRemoved, RemoveException, CloneException, NotInstalled, GitException\n')]
|
#tshark -r input.pcap -qz "follow,tcp,raw,0"
import struct
import sys
import binascii
import subprocess
result = subprocess.Popen( ["tshark", "-r", sys.argv[1], "-qz", "follow,tcp,raw,0"],
stdout=subprocess.PIPE)
sys.stdout.buffer.write(b"FPC\x80")
for i in range(4):
result.stdout.readline()
dp=result.stdout.readline().split(b":")[2]
sp=result.stdout.readline().split(b":")[2]
sys.stdout.buffer.write(struct.pack('>H', int(sp)))
sys.stdout.buffer.write(struct.pack('>H', int(dp)))
for l in result.stdout.readlines():
s2c = 0
if l[0] == 9:
l = l[1:]
s2c = 1
try:
r = binascii.unhexlify(l[:-1])
except:
continue
sys.stdout.buffer.write(struct.pack('>B', int(s2c)))
sys.stdout.buffer.write(r)
sys.stdout.buffer.write(b"FPC0")
|
[
"subprocess.Popen",
"sys.stdout.buffer.write",
"binascii.unhexlify"
] |
[((115, 217), 'subprocess.Popen', 'subprocess.Popen', (["['tshark', '-r', sys.argv[1], '-qz', 'follow,tcp,raw,0']"], {'stdout': 'subprocess.PIPE'}), "(['tshark', '-r', sys.argv[1], '-qz', 'follow,tcp,raw,0'],\n stdout=subprocess.PIPE)\n", (131, 217), False, 'import subprocess\n'), ((244, 279), 'sys.stdout.buffer.write', 'sys.stdout.buffer.write', (["b'FPC\\x80'"], {}), "(b'FPC\\x80')\n", (267, 279), False, 'import sys\n'), ((757, 783), 'sys.stdout.buffer.write', 'sys.stdout.buffer.write', (['r'], {}), '(r)\n', (780, 783), False, 'import sys\n'), ((788, 820), 'sys.stdout.buffer.write', 'sys.stdout.buffer.write', (["b'FPC0'"], {}), "(b'FPC0')\n", (811, 820), False, 'import sys\n'), ((640, 666), 'binascii.unhexlify', 'binascii.unhexlify', (['l[:-1]'], {}), '(l[:-1])\n', (658, 666), False, 'import binascii\n')]
|
"""
Created: November 11, 2020
Author: <NAME>
Python Version 3.9
This program is meant to make the process of collecting the different filters from AIJ excel spreadsheets faster.
The user enters however many nights they have and the program goes through and checks those text files for the
different columns for,HJD, Amag, and Amag error for the B and V filters.
The program will also calculate the R magnitude from the rel flux of T1.
There are error catching statements within the program so if the user mistypes, the program will not crash and
close on them.
"""
import pandas as pd
from os import path
def main(c):
# warning prompts for the user to read to make sure this program works correctly
if c == 0:
# warning prompts for the user to read to make sure this program works correctly
print()
print("Make sure you have turned the output xls files from AIJ into tab delimited text files. "
"Since these xls files are corrupt for reading directly from.")
print("You will also need to go into each night and filter and "
"make the HJD column 6 decimals instead of the output of 3 within Excel.")
print()
else:
print()
while True:
# checks to see whether you have entered a number and a correct filter letter
try:
num = int(input("Number of nights you have: "))
filter_name = input("Which filter are these nights in (B, V, R): ")
if filter_name.upper() == "B" or filter_name.upper() == "V" or filter_name.upper() == "R":
break
else:
print("Please enter B, V, or R for your filter.")
print()
continue
except ValueError:
print("You have entered an invalid number for your number of nights. Please enter a number.")
print("")
get_filters(num)
def get_filters(n):
"""
Takes a number of nights for a given filter and takes out the HJD, either A_Mag1 or T1_flux, and
error for mag or flux
:param n: Number of observation nights
:param f: The filter letter being used
:return: the output text files for each night in a given filter
"""
total_hjd = []
total_amag = []
total_error = []
# checks for either the b, v, r filter as either upper or lowercase will work
for i in range(n):
while True:
# makes sure the file pathway is real and points to some file
# (does not check if that file is the correct one though)
try:
# an example pathway for the files
# E:\Research\Data\NSVS_254037\2018.10.12-reduced\Check\V\2018.10.12.APASS.V_measurements.txt
file = input("Enter night %d file path: " % (i+1))
if path.exists(file):
break
else:
continue
except FileNotFoundError:
print("Please enter a correct file path")
# noinspection PyUnboundLocalVariable
df = pd.read_csv(file, delimiter="\t")
# set parameters to lists from the file by the column header
hjd = []
amag = []
amag_error = []
try:
hjd = list(df["HJD"])
amag = list(df["Source_AMag_T1"])
amag_error = list(df["Source_AMag_Err_T1"])
except KeyError:
print("The file you entered does not have the columns of HJD, Source_AMag_T1, or Source_AMag_Err_T1. "
"Please re-enter the file path and make sure its the correct file.")
c = 1
main(c)
total_hjd.append(hjd)
total_amag.append(amag)
total_error.append(amag_error)
# converts the Dataframe embedded lists into a normal flat list
new_hjd = [item for elem in total_hjd for item in elem]
new_amag = [item for elem in total_amag for item in elem]
new_error = [item for elem in total_error for item in elem]
# outputs the new file to dataframe and then into a text file for use in Peranso or PHOEBE
data = pd.DataFrame({
"HJD": new_hjd,
"AMag": new_amag,
"AMag Error": new_error
})
print("")
output = input("What is the file output name (with file extension .txt): ")
data.to_csv(output, index=False, header=False, sep='\t')
print("")
print("Fished saving the file to the same location as this program.")
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
count = 0
main(count)
|
[
"pandas.DataFrame",
"os.path.exists",
"pandas.read_csv"
] |
[((4117, 4190), 'pandas.DataFrame', 'pd.DataFrame', (["{'HJD': new_hjd, 'AMag': new_amag, 'AMag Error': new_error}"], {}), "({'HJD': new_hjd, 'AMag': new_amag, 'AMag Error': new_error})\n", (4129, 4190), True, 'import pandas as pd\n'), ((3072, 3105), 'pandas.read_csv', 'pd.read_csv', (['file'], {'delimiter': '"""\t"""'}), "(file, delimiter='\\t')\n", (3083, 3105), True, 'import pandas as pd\n'), ((2820, 2837), 'os.path.exists', 'path.exists', (['file'], {}), '(file)\n', (2831, 2837), False, 'from os import path\n')]
|
# -*- coding: utf-8 -*-
# Generated by Django 1.11.1 on 2017-09-02 05:23
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('songwriter', '0005_auto_20170824_1726'),
]
operations = [
migrations.AlterModelOptions(
name='author',
options={'ordering': ['lastname', 'firstname']},
),
migrations.AlterModelOptions(
name='chord',
options={'ordering': ['note']},
),
migrations.AlterModelOptions(
name='editor',
options={'ordering': ['name']},
),
migrations.AlterModelOptions(
name='theme',
options={'ordering': ['name']},
),
]
|
[
"django.db.migrations.AlterModelOptions"
] |
[((295, 392), 'django.db.migrations.AlterModelOptions', 'migrations.AlterModelOptions', ([], {'name': '"""author"""', 'options': "{'ordering': ['lastname', 'firstname']}"}), "(name='author', options={'ordering': [\n 'lastname', 'firstname']})\n", (323, 392), False, 'from django.db import migrations\n'), ((432, 506), 'django.db.migrations.AlterModelOptions', 'migrations.AlterModelOptions', ([], {'name': '"""chord"""', 'options': "{'ordering': ['note']}"}), "(name='chord', options={'ordering': ['note']})\n", (460, 506), False, 'from django.db import migrations\n'), ((551, 626), 'django.db.migrations.AlterModelOptions', 'migrations.AlterModelOptions', ([], {'name': '"""editor"""', 'options': "{'ordering': ['name']}"}), "(name='editor', options={'ordering': ['name']})\n", (579, 626), False, 'from django.db import migrations\n'), ((671, 745), 'django.db.migrations.AlterModelOptions', 'migrations.AlterModelOptions', ([], {'name': '"""theme"""', 'options': "{'ordering': ['name']}"}), "(name='theme', options={'ordering': ['name']})\n", (699, 745), False, 'from django.db import migrations\n')]
|
"""Loading MNIST dataset.
"""
import struct
import numpy as np
class MNIST:
"""
Loading MNIST dataset.
In the directory of MNIST dataset, there should be the following files:
- Training set:
- train-images-idx3-ubyte
- train-labels-idx1-ubyte
- Test set:
- t10k-images-idx3-ubyte
- t10k-labels-idx1-ubyte
Functions
---------
next_batch()
image_pair(index: int)
sample_batch(batch_index: int)
to_ndarray()
Attributes
----------
data_type: Can be either `"test"` or `"train"`.
path: Path for MNIST data.
data_size: Size of the dataset. Default value `None` means using all data in MNIST.
batch_size: Size of the mini-batch. Default value `None` means using the whole dataset as
a mini-batch.
binarize: Whether to binarize the images (using 0 and 1 values). Default value is True.
reshape: Whether to reshape the images into 2D arrays. Default value is False.
one_hot: whether to use one-hot encoding for labels (e.g. using vector
`[1, 0, 0, 0, 0, 0, 0, 0, 0, 0]` for 0). Default value is False.
"""
IMAGE_SIZE = 784
LABEL_SIZE = 1
_IMAGE_SIZE_FMT = ">784B"
_LABEL_SIZE_FMT = ">B"
IMAGE_SHAPE = (28, 28)
batch_index = 0
def __init__(self, data_type: str, path: str,
data_size: int = None,
batch_size: int = None,
binarize=True,
reshape=False,
one_hot=False):
self.data_type = data_type
self.path = path
# Options
self.binarize = binarize
self.reshape = reshape
self.one_hot = one_hot
# Data buffer
# `data_size` will be updated according to the actual data
image_buf, label_buf = self._read_file()
# Size
if data_size is None:
# `len(image_buf)` may not be exactly divided by 784
self.data_size = len(image_buf) // self.IMAGE_SIZE
else:
self.data_size = data_size
if batch_size is None:
self.batch_size = self.data_size
else:
if batch_size <= self.data_size:
self.batch_size = batch_size
else:
raise ValueError("batch size larger than data size")
self.batch_num = self.data_size // self.batch_size
# Data
self._images = self._get_image(image_buf)
self._labels = self._get_label(label_buf)
def _read_file(self):
if self.data_type == "test":
image_file_name = self.path + "t10k-images-idx3-ubyte"
label_file_name = self.path + "t10k-labels-idx1-ubyte"
elif self.data_type == "train":
image_file_name = self.path + "train-images-idx3-ubyte"
label_file_name = self.path + "train-labels-idx1-ubyte"
else:
raise ValueError("only type \"test\" and \"train\" are available")
# "rb" means reading + binary mode
with open(image_file_name, "rb") as image_file:
image_buf = image_file.read()
with open(label_file_name, "rb") as label_file:
label_buf = label_file.read()
return image_buf, label_buf
def _get_image(self, image_buf):
"""Get an image array from `image_buf`.
This is the structure of the image file (training set):
[offset] [type] [value] [description]
0000 32 bit integer 0x00000803(2051) magic number
0004 32 bit integer 60000 number of images
0008 32 bit integer 28 number of rows
0012 32 bit integer 28 number of columns
0016 unsigned byte ?? pixel
0017 unsigned byte ?? pixel
........
xxxx unsigned byte ?? pixel
"""
image_buf_len = self.data_size * self.IMAGE_SIZE + 16
image_offset = 16
image_arr = []
while image_offset < image_buf_len:
temp = struct.unpack_from(self._IMAGE_SIZE_FMT, image_buf, image_offset)
if self.binarize:
temp = np.vectorize(lambda x: 0 if x <= 127 else 1)(temp)
if self.reshape:
temp = np.reshape(temp, self.IMAGE_SHAPE)
image_arr.append(temp)
image_offset += self.IMAGE_SIZE
return image_arr
def _get_label(self, label_buf):
"""Get an label array from `label_buf`.
This is the structure of the label file (training set):
[offset] [type] [value] [description]
0000 32 bit integer 0x00000801(2049) magic number (MSB first)
0004 32 bit integer 60000 number of items
0008 unsigned byte ?? label
0009 unsigned byte ?? label
........
xxxx unsigned byte ?? label
"""
label_buf_len = self.data_size * self.LABEL_SIZE + 8
label_offset = 8
label_arr = []
while label_offset < label_buf_len:
temp = struct.unpack_from(self._LABEL_SIZE_FMT, label_buf, label_offset)[0]
if self.one_hot:
vec = np.zeros(10)
vec[temp] = 1
label_arr.append(vec)
else:
label_arr.append(temp)
label_offset += self.LABEL_SIZE
return label_arr
def next_batch(self):
"""Increase `batch_index` by 1, then return a mini-batch of (image, label) tuples."""
this_batch = self.batch(self.batch_index)
self.batch_index = (self.batch_index + 1) % self.batch_num
return this_batch
def image_pair(self, index: int):
"""Return a (image, label) tuple at `index`."""
if index < self.data_size:
return self._images[index], self._labels[index]
raise IndexError("image index out of range")
def batch(self, batch_index: int):
"""Return a mini-batch of (image, label) tuples at `batch_index`."""
if batch_index < self.batch_num:
begin = batch_index * self.batch_size
end = (batch_index + 1) * self.batch_size
return self._images[begin:end], self._labels[begin:end]
raise IndexError("batch index out of range")
def to_ndarray(self):
"""Return the raw data tuple `(images, labels)` as `np.ndarray`.
"""
images = []
labels = []
for i in range(self.batch_num):
image, label = self.batch(i)
images.append(image)
labels.append(label)
return np.asarray(images), np.asarray(labels)
def _test():
data = MNIST("train", MNIST_PATH,
data_size=200, batch_size=8,
reshape=True, one_hot=False, binarize=False)
print("Meta-data:")
print("\tDataset size:", data.data_size)
print("\tBatch size:", data.batch_size)
col_num = 4
row_num = data.batch_size // col_num + 1
_test_random_images(data, col_num, row_num)
_test_random_batch(data, col_num, row_num)
_test_next_batch(data, col_num, row_num)
def _test_random_images(data, col_num, row_num):
images = []
labels = []
for _ in range(10):
index = random.randrange(data.data_size)
image, label = data.image_pair(index)
images.append(image)
labels.append(label)
_plot(images, labels, col_num=col_num, row_num=row_num)
def _test_random_batch(data, col_num, row_num):
index = random.randrange(data.batch_num)
images, labels = data.batch(index)
_plot(images, labels, col_num=col_num, row_num=row_num)
def _test_next_batch(data, col_num, row_num):
for _ in range(3):
images, labels = data.next_batch()
_plot(images, labels, col_num=col_num, row_num=row_num)
def _plot(images, labels, col_num, row_num):
for i, (image, label) in enumerate(zip(images, labels)):
plt.subplot(row_num, col_num, i + 1)
plt.imshow(image, cmap="gray")
plt.axis('off')
plt.title(str(label))
plt.show()
def _test_numpy():
images, labels = MNIST("train", MNIST_PATH,
data_size=200, batch_size=8,
reshape=False, one_hot=False, binarize=False).to_ndarray()
print(images.shape) # shape = (num_batches, batch_size, num_visible)
print(np.moveaxis(images, 0, -1).shape) # shape = (batch_size, num_visible, num_batches)
print(labels.shape) # shape = (num_batches, batch_size)
if __name__ == "__main__":
import random
import matplotlib.pyplot as plt
# Local MNIST data
MNIST_PATH = "../../machine-learning/data/mnist/"
_test()
_test_numpy()
|
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"matplotlib.pyplot.imshow",
"numpy.reshape",
"random.randrange",
"struct.unpack_from",
"numpy.asarray",
"numpy.zeros",
"numpy.vectorize",
"numpy.moveaxis",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show"
] |
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|
import threading, queue, time, os, pickle
# from queue import Queue
import numpy as np
import tensorflow as tf
import sarnet_td3.common.tf_util as U
from tensorflow.python.keras.backend import set_session
lock = threading.Lock()
class MultiTrainTD3(threading.Thread):
def __init__(self, input_queue, output_queue, args=(), kwargs=None):
threading.Thread.__init__(self, args=(), kwargs=None)
self.input_queue = input_queue
self.output_queue = output_queue
self.daemon = True
self.trainers = args[0]
self.args = args[1]
self.buffer_op = args[2]
self.num_env = args[3]
self.sess = args[4]
self.num_agents = args[5]
self.num_adversaries = args[6]
self.ep_rewards = [[0.0] for _ in range(self.num_env)]
self.ep_end_rewards = [[0.0] for _ in range(self.num_env)]
self.ep_success = [[0.0] for _ in range(self.num_env)]
self.agent_rewards = [[[0.0] for _ in range(self.num_agents)] for _ in range(self.num_env)]
self.agent_info = [[[[]] for i in range(self.num_agents)] for _ in range(self.num_env)]
# self.agent_info = [[[[]]] for _ in range(self.num_env)]
self.final_ep_rewards = [] # Shape: (batch, #) sum of rewards for training curve
self.final_ep_end_rewards = []
self.final_ep_ag_rewards = [] # agent rewards for training curve
self.save_rate = self.args.max_episode_len * 100
self.save_n_ep = self.num_env * 10
self.print_step = -int(self.save_n_ep / self.num_env)
self.q_h_init = np.zeros(shape=(self.num_env, self.args.critic_units))
self.mem_init = np.zeros(shape=(self.num_env, self.args.value_units))
self.time_prev = time.time()
def run(self):
# print(threading.currentThread().getName(), self.receive_messages)
with self.sess.as_default():
# Freeze graph to avoid memory leaks
# self.sess.graph.finalize()
while True:
try:
action, p_index, data = self.input_queue.get()
if action is "None": # If you send `None`, the thread will exit.
return
elif action is "get_action":
out = self.get_action(data, p_index)
self.output_queue.put(out)
elif action is "get_qdebug":
out = self.get_qdebug(data, p_index)
self.output_queue.put(out)
elif action is "get_loss":
out = self.get_loss(data, p_index)
self.output_queue.put(out)
elif action is "write_tboard":
self.write_tboard(data)
elif action is "add_to_buffer":
self.buffer_op.collect_exp(data)
elif action is "save_rew_info":
self.save_rew_info(data)
elif action is "save_benchmark":
out = self.save_benchmark(data)
self.output_queue.put(out)
elif action is "reset_rew_info":
self.reset_rew_info()
elif action is "save_model_rew":
if not (self.args.benchmark or self.args.display):
self.save_model(data)
self.plot_rewards(data)
except queue.Empty:
continue
def get_action(self, data, p_index):
with lock:
agent = self.trainers[p_index]
obs_n_t, h_n_t, c_n_t, mem_n_t, q1_h_t, is_train = data
obs_n_t = np.stack(obs_n_t, axis=-2) # This returns [agent, batch, dim]
obs_n_t = np.expand_dims(obs_n_t, axis=1) # This adds [agent, time, batch, dim]
p_input_j = agent.prep_input(obs_n_t, h_n_t, c_n_t, mem_n_t, q1_h_t[p_index], is_train)
# print(np.shape(obs_n_t))
act_j_t, state_j_t1, mem_j_t1, attn_j_t = agent.action(p_input_j, is_train)
if self.args.encoder_model == "LSTM" or self.args.encoder_model != "DDPG":
c_j_t1, h_j_t1 = state_j_t1
else:
h_j_t1 = state_j_t1
c_j_t1 = state_j_t1
if agent.comm_type in {"DDPG", "COMMNET", "IC3NET"}:
mem_j_t1 = np.zeros(shape=(self.num_env, self.args.value_units))
return act_j_t, h_j_t1, c_j_t1, mem_j_t1, attn_j_t
def get_qdebug(self, data, p_index):
with lock:
# with sess.as_default():
agent = self.trainers[p_index]
obs_n_t, action_n_t, q1_h_n_t, q2_h_n_t = data
obs_n_t = np.stack(obs_n_t, axis=-2) # This returns [agent, batch, dim]
obs_n_t = np.expand_dims(obs_n_t, axis=1) # This adds [agent, time, batch, dim]
q1_j_input = agent.prep_q_input(obs_n_t, action_n_t, q1_h_n_t[p_index])
_, q1_h_j_t1 = agent.q1_debug['q_values'](*(q1_j_input))
if self.args.td3:
q2_input = agent.prep_q_input(obs_n_t, action_n_t, q2_h_n_t[p_index])
_, q2_h_j_t1 = agent.q2_debug['q_values'](*(q2_input))
else:
q2_h_j_t1 = []
return q1_h_j_t1, q2_h_j_t1
def get_loss(self, data, p_index):
with lock:
# with sess.as_default():
agent = self.trainers[p_index]
train_step = data
loss = agent.update(self.trainers, self.buffer_op, train_step)
return loss
def write_tboard(self, data):
with lock:
loss, train_step, writer, summary_ops, summary_vars, num_agents = data
# Tensorboard
episode_b_rewards = []
for j in range(self.num_env):
if self.args.env_type == "mpe":
episode_b_rewards.append(np.mean(self.ep_rewards[j][self.print_step:]))
else:
episode_b_rewards.append(np.mean(self.ep_success[j][self.print_step:]))
episode_b_rewards = np.mean(np.array(episode_b_rewards))
num_steps = train_step * self.num_env
# Add to tensorboard only when actor agent is updated
if loss[0][1] is not None:
fd = {}
for i, key in enumerate(summary_vars):
if i == 0:
fd[key] = episode_b_rewards
else:
agnt_idx = int((i - 1) / 5)
if agnt_idx == num_agents: agnt_idx -= 1
if loss[agnt_idx] is not None:
fd[key] = loss[agnt_idx][int((i - 1) % 5)]
summary_str = U.get_session().run(summary_ops, feed_dict=fd)
writer.add_summary(summary_str, num_steps)
writer.flush()
def save_rew_info(self, data):
with lock:
rew_n, info_n, ep_step = data
# rew_n (num_env, num_agents)
if self.args.env_type == "mpe":
for j in range(self.num_env):
for i, rew in enumerate(rew_n[j]):
if ep_step >= self.args.max_episode_len - 10: # Compute only last 10 episode step rewards
self.ep_end_rewards[j][-1] += rew
self.ep_rewards[j][-1] += rew
self.agent_rewards[j][i][-1] += rew
elif self.args.env_type == "ic3net":
for j in range(self.num_env):
self.ep_success[j][-1] += info_n[j]
if self.args.benchmark and self.args.env_type == "mpe":
for j in range(self.num_env):
for i, info in enumerate(info_n[j]):
self.agent_info[j][i][-1].append(info)
def reset_rew_info(self):
with lock:
for j in range(self.num_env):
self.ep_rewards[j].append(0)
self.ep_success[j].append(0)
self.ep_end_rewards[j].append(0)
for i in range(self.num_agents):
self.agent_rewards[j][i].append(0)
if self.args.benchmark:
for j in range(self.num_env):
for i in range(self.num_agents):
self.agent_info[j][i].append([[]])
def save_benchmark(self, data):
with lock:
exp_name, exp_itr = data
benchmark_dir = os.path.join('./exp_data', exp_name, exp_itr, self.args.benchmark_dir)
if not os.path.exists(benchmark_dir):
os.mkdir(benchmark_dir)
file_name = './exp_data/' + exp_name + '/' + exp_itr + '/' + self.args.benchmark_dir + '/' + exp_name + '.pkl'
print('Finished benchmarking, now saving...')
# pickle_info = [self.agent_info[j] for j in range(self.num_env)]
with open(file_name, 'wb') as fp:
# Dump files as [num_env, [# agents, [#ep, [#stps, [dim]]]]
pickle.dump(self.agent_info, fp)
return "bench_saved"
def save_model(self, data):
with lock:
# train_step = t_step * num_env
train_step, num_episodes, time_taken, exp_name, exp_itr, data_file, saver = data
# Policy File
if num_episodes % (self.save_n_ep) == 0:
save_dir = './exp_data/' + exp_name + '/' + exp_itr + '/' + self.args.save_dir + str(train_step)
U.save_state(save_dir, self.sess, saver=saver)
# episode_rewards, agent_rewards, final_ep_rewards, final_ep_ag_rewards = rewards
if self.args.env_type == "mpe":
# print statement depends on whether or not there are adversaries
if self.num_adversaries == 0:
episode_b_rewards = []
ep_end_b_rewards = []
ep_ag_b_rewards = []
for j in range(self.num_env):
episode_b_rewards.append(np.mean(self.ep_rewards[j][self.print_step:]))
ep_end_b_rewards.append(np.mean(self.ep_end_rewards[j][self.print_step:]))
episode_b_rewards = np.mean(np.array(episode_b_rewards))
ep_end_b_rewards = np.mean(ep_end_b_rewards) / 10.
for i in range(self.num_agents):
temp_ag_reward = []
for j in range(self.num_env):
temp_ag_reward.append(np.mean(self.agent_rewards[j][i][self.print_step:]))
ep_ag_b_rewards.append(np.mean(np.array(temp_ag_reward)))
print("steps: {}, episodes: {}, mean episode reward: {}, mean end rewards: {}, time: {}".format(
train_step, num_episodes, episode_b_rewards, ep_end_b_rewards, round(time.time() - self.time_prev, 3)))
with open(data_file, "a+") as f:
f.write("\n" + "steps: {}, episodes: {}, mean episode reward: {}, mean end rewards: {}, time: {}".format(
train_step, num_episodes, episode_b_rewards, ep_end_b_rewards, round(time.time() - self.time_prev, 3)) + "\n")
else:
episode_b_rewards = []
ep_end_b_rewards = []
ep_ag_b_rewards = []
for j in range(self.num_env):
episode_b_rewards.append(np.mean(self.ep_rewards[j][self.print_step:]))
ep_end_b_rewards.append(np.mean(self.ep_end_rewards[j][self.print_step:]))
episode_b_rewards = np.mean(np.array(episode_b_rewards))
ep_end_b_rewards = np.mean(ep_end_b_rewards)
for i in range(self.num_agents):
temp_ag_reward = []
for j in range(self.num_env):
temp_ag_reward.append(np.mean(self.agent_rewards[j][i][self.print_step:]))
ep_ag_b_rewards.append(np.mean(np.array(temp_ag_reward)))
print("steps: {}, episodes: {}, mean episode reward: {}, mean end rewards: {}, agent episode reward: {}, time: {}".format(
train_step, num_episodes, episode_b_rewards, ep_end_b_rewards, [rew for rew in ep_ag_b_rewards],
round(time.time() - self.time_prev, 3)) + "\n")
with open(data_file, "a+") as f:
f.write("\n" + "steps: {}, episodes: {}, mean episode reward: {}, mean end rewards: {}, agent episode reward: {}, time: {}".format(
train_step, num_episodes, episode_b_rewards, ep_end_b_rewards, [rew for rew in ep_ag_b_rewards],
round(time.time() - self.time_prev, 3)) + "\n")
# Keep track of final episode reward
self.final_ep_rewards.append(episode_b_rewards)
self.final_ep_end_rewards.append(ep_end_b_rewards)
for rew in ep_ag_b_rewards:
self.final_ep_ag_rewards.append(rew)
self.time_prev = time.time()
def plot_rewards(self, data):
with lock:
train_step, num_episodes, t_start, exp_name, exp_itr, data_file, saver = data
plot_dir = os.path.join('./exp_data', exp_name, exp_itr, self.args.plots_dir)
if not os.path.exists(plot_dir):
os.mkdir(plot_dir)
rew_file_name = './exp_data/' + exp_name + '/' + exp_itr + '/' + self.args.plots_dir + '/' + exp_name + '_rewards.pkl'
with open(rew_file_name, 'wb') as fp:
pickle.dump(self.final_ep_rewards, fp)
rew_ep_end_file_name = './exp_data/' + exp_name + '/' + exp_itr + '/' + self.args.plots_dir + '/' + exp_name + '_rewards_ep_end.pkl'
with open(rew_ep_end_file_name, 'wb') as fp:
pickle.dump(self.final_ep_end_rewards, fp)
agrew_file_name = './exp_data/' + exp_name + '/' + exp_itr + '/' + self.args.plots_dir + '/' + exp_name + '_agrewards.pkl'
with open(agrew_file_name, 'wb') as fp:
pickle.dump(self.final_ep_ag_rewards, fp)
"""
REINFORCE Threads
"""
class MultiTrainVPG(threading.Thread):
def __init__(self, input_queue, output_queue, args=(), kwargs=None):
threading.Thread.__init__(self, args=(), kwargs=None)
self.input_queue = input_queue
self.output_queue = output_queue
self.daemon = True
self.trainers = args[0]
self.args = args[1]
self.buffer_op = args[2]
self.num_env = args[3]
self.sess = args[4]
self.num_agents = args[5]
self.num_adversaries = args[6]
self.ep_rewards = [[0.0] for _ in range(self.num_env)]
self.ep_success = [[0.0] for _ in range(self.num_env)]
self.agent_rewards = [[[0.0] for _ in range(self.num_agents)] for _ in range(self.num_env)]
self.agent_info = [[[[]]] for _ in range(self.num_env)]
self.final_ep_rewards = [] # Shape: (batch, #) sum of rewards for training curve
self.final_ep_ag_rewards = [] # agent rewards for training curve
self.save_rate = self.args.max_episode_len * 100
if self.args.env_type == "mpe":
self.print_step = -int(self.save_rate / self.num_env)
else: # print for episode end only (success rate)
self.print_step = -int(self.save_rate / (self.num_env * self.args.max_episode_len))
self.q_h_init = np.zeros(shape=(self.num_env, self.args.critic_units))
self.mem_init = np.zeros(shape=(self.num_env, self.args.value_units))
self.time_prev = time.time()
def run(self):
# print(threading.currentThread().getName(), self.receive_messages)
with self.sess.as_default():
# Freeze graph to avoid memory leaks
# self.sess.graph.finalize()
while True:
try:
action, p_index, data = self.input_queue.get()
if action is "None": # If you send `None`, the thread will exit.
return
elif action is "get_action":
out = self.get_action(data, p_index)
self.output_queue.put(out)
elif action is "get_loss":
out = self.get_loss(data, p_index)
self.output_queue.put(out)
elif action is "write_tboard":
self.write_tboard(data)
elif action is "add_to_buffer":
self.buffer_op.collect_exp(data)
elif action is "add_to_buffer_reinforce":
self.buffer_op.collect_exp(data)
elif action is "save_rew_info":
self.save_rew_info(data)
elif action is "save_benchmark":
out = self.save_benchmark(data)
self.output_queue.put(out)
elif action is "reset_rew_info":
self.reset_rew_info()
elif action is "save_model_rew":
if not (self.args.benchmark or self.args.display):
self.save_model(data)
self.plot_rewards(data)
except queue.Empty:
continue
def get_action(self, data, p_index):
with lock:
agent = self.trainers[p_index]
obs_n_t, h_n_t, c_n_t, mem_n_t, is_train = data
obs_n_t = np.stack(obs_n_t, axis=-2)
obs_n_t = np.expand_dims(obs_n_t, axis=1) # This adds [agent, time, batch, dim]
p_input_j = agent.prep_input(obs_n_t, h_n_t, c_n_t, mem_n_t, is_train)
act_j_t, act_soft_j_t, state_j_t1, mem_j_t1, attn_j_t, value_j_t = agent.action(p_input_j, is_train)
if self.args.encoder_model == "LSTM":
c_j_t1, h_j_t1 = state_j_t1
else:
h_j_t1 = state_j_t1
c_j_t1 = state_j_t1
if agent.comm_type in {"DDPG", "COMMNET", "IC3NET"}:
mem_j_t1 = np.zeros(shape=(self.num_env, self.args.value_units))
return act_j_t, act_soft_j_t, h_j_t1, c_j_t1, mem_j_t1, attn_j_t, value_j_t
def get_loss(self, data, p_index):
with lock:
# with sess.as_default():
train_step, buffer_data = data
agent = self.trainers[p_index]
loss = agent.update(self.trainers, buffer_data, train_step)
return loss
def write_tboard(self, data):
with lock:
loss, train_step, writer, summary_ops, summary_vars, num_agents = data
# Tensorboard
episode_b_rewards = []
for j in range(self.num_env):
if self.args.env_type == "mpe":
episode_b_rewards.append(np.mean(self.ep_rewards[j][self.print_step:]))
else:
episode_b_rewards.append(np.mean(self.ep_success[j][self.print_step:]))
episode_b_rewards = np.mean(np.array(episode_b_rewards))
num_steps = train_step * self.num_env
# Add to tensorboard only when actor agent is updated
if loss[0][1] is not None:
fd = {}
for i, key in enumerate(summary_vars):
if i == 0:
fd[key] = episode_b_rewards
else:
agnt_idx = int((i - 1) / 5)
if agnt_idx == num_agents: agnt_idx -= 1
if loss[agnt_idx] is not None:
fd[key] = loss[agnt_idx][int((i - 1) % 5)]
summary_str = U.get_session().run(summary_ops, feed_dict=fd)
writer.add_summary(summary_str, num_steps)
writer.flush()
def save_rew_info(self, data):
with lock:
rew_n, info_n, terminal = data
if self.args.env_type == "mpe":
for j in range(self.num_env):
for i, rew in enumerate(rew_n[j]):
self.ep_rewards[j][-1] += rew
self.agent_rewards[j][i][-1] += rew
elif self.args.env_type == "ic3net":
for j in range(self.num_env):
self.ep_success[j][-1] += info_n[j]
if self.args.benchmark and self.args.env_type == "mpe":
for j in range(self.num_env):
for i, info in enumerate(info_n[j]):
self.agent_info[-1][i].append(info_n[0]['n'])
def reset_rew_info(self):
with lock:
for j in range(self.num_env):
self.ep_rewards[j].append(0)
self.ep_success[j].append(0)
for i in range(self.num_agents):
self.agent_rewards[j][i].append(0)
if self.args.benchmark:
for j in range(self.num_env):
self.agent_info[j].append([[]])
def save_benchmark(self, data):
with lock:
exp_name, exp_itr = data
benchmark_dir = os.path.join('./exp_data', exp_name, exp_itr, self.args.benchmark_dir)
if not os.path.exists(benchmark_dir):
os.mkdir(benchmark_dir)
file_name = './exp_data/' + exp_name + '/' + exp_itr + '/' + self.args.benchmark_dir + '/' + exp_name + '.pkl'
print('Finished benchmarking, now saving...')
with open(file_name, 'wb') as fp:
pickle.dump(self.ep_success, fp)
return "bench_saved"
def save_model(self, data):
with lock:
# train_step = t_step * num_env
train_step, num_episodes, time_taken, exp_name, exp_itr, data_file, saver = data
# Policy File
save_dir = './exp_data/' + exp_name + '/' + exp_itr + '/' + self.args.save_dir + str(train_step)
U.save_state(save_dir, self.sess, saver=saver)
episode_b_success = []
for j in range(self.num_env):
episode_b_success.append(np.mean(self.ep_success[j][self.print_step:]))
episode_b_success = np.mean(np.array(episode_b_success)) / self.args.max_episode_len
print("steps: {}, episodes: {}, mean episode success: {}, time: {}".format(
train_step, num_episodes, episode_b_success, round(time.time() - self.time_prev, 3)) + "\n")
with open(data_file, "a+") as f:
f.write("\n" + "steps: {}, episodes: {}, mean episode success: {}, time: {}".format(
train_step, num_episodes, episode_b_success, round(time.time() - self.time_prev, 3)) + "\n")
self.final_ep_rewards.append(episode_b_success)
def plot_rewards(self, data):
with lock:
train_step, num_episodes, t_start, exp_name, exp_itr, data_file, saver = data
plot_dir = os.path.join('./exp_data', exp_name, exp_itr, self.args.plots_dir)
if not os.path.exists(plot_dir):
os.mkdir(plot_dir)
rew_file_name = './exp_data/' + exp_name + '/' + exp_itr + '/' + self.args.plots_dir + '/' + exp_name + '_rewards.pkl'
with open(rew_file_name, 'wb') as fp:
pickle.dump(self.final_ep_rewards, fp)
def get_gputhreads(trainers, args, buffer_op, num_env, num_agents, num_adv):
threads = []
sess = tf.compat.v1.get_default_session()
for t in range(args.num_gpu_threads):
input_q = queue.Queue()
output_q = queue.Queue()
if args.policy_grad == "maddpg":
threads.append(MultiTrainTD3(input_q, output_q, args=(trainers, args, buffer_op, num_env, sess, num_agents, num_adv)))
elif args.policy_grad == "reinforce":
threads.append(
MultiTrainVPG(input_q, output_q, args=(trainers, args, buffer_op, num_env, sess, num_agents, num_adv)))
threads[t].start()
time.sleep(1)
return threads
def close_gputhreads(threads):
for t in threads:
t.input_queue.put(("None", None, None))
for t in threads:
t.join()
print('GPU trainers cancelled')
return
|
[
"threading.Thread.__init__",
"os.path.exists",
"numpy.mean",
"pickle.dump",
"sarnet_td3.common.tf_util.save_state",
"threading.Lock",
"tensorflow.compat.v1.get_default_session",
"os.path.join",
"time.sleep",
"numpy.stack",
"numpy.zeros",
"numpy.array",
"os.mkdir",
"numpy.expand_dims",
"queue.Queue",
"time.time",
"sarnet_td3.common.tf_util.get_session"
] |
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|
from flask import Flask, request, render_template
from sklearn.externals import joblib
from feature import *
pipeline = joblib.load('pipeline.sav')
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/api',methods=['POST'])
def get_delay():
result=request.form
query_title = result['title']
query_author = result['author']
query_text = result['maintext']
print(query_text)
query = get_all_query(query_title, query_author, query_text)
##user_input = {'query':query}
pred = pipeline.predict(query)
print(pred)
dic = {1:'real',0:'fake'}
return f'<html><body><h1>{dic[pred[0]]}</h1> <form action="/"> <button type="submit">back </button> </form></body></html>'
if __name__ == '__main__':
app.run(port=8080, debug=True)
|
[
"flask.render_template",
"sklearn.externals.joblib.load",
"flask.Flask"
] |
[((122, 149), 'sklearn.externals.joblib.load', 'joblib.load', (['"""pipeline.sav"""'], {}), "('pipeline.sav')\n", (133, 149), False, 'from sklearn.externals import joblib\n'), ((157, 172), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (162, 172), False, 'from flask import Flask, request, render_template\n'), ((213, 242), 'flask.render_template', 'render_template', (['"""index.html"""'], {}), "('index.html')\n", (228, 242), False, 'from flask import Flask, request, render_template\n')]
|
from plaid import Client
from backend.link_token import LinkToken
from general_falcon_webserver import WebApp
client = Client(client_id='5e2e3527dd6924001167e8e8', secret='<KEY>', environment='sandbox')
app = WebApp()
app.add_route('link', LinkToken(client))
app.launch_webserver()
|
[
"plaid.Client",
"general_falcon_webserver.WebApp",
"backend.link_token.LinkToken"
] |
[((121, 209), 'plaid.Client', 'Client', ([], {'client_id': '"""5e2e3527dd6924001167e8e8"""', 'secret': '"""<KEY>"""', 'environment': '"""sandbox"""'}), "(client_id='5e2e3527dd6924001167e8e8', secret='<KEY>', environment=\n 'sandbox')\n", (127, 209), False, 'from plaid import Client\n'), ((212, 220), 'general_falcon_webserver.WebApp', 'WebApp', ([], {}), '()\n', (218, 220), False, 'from general_falcon_webserver import WebApp\n'), ((244, 261), 'backend.link_token.LinkToken', 'LinkToken', (['client'], {}), '(client)\n', (253, 261), False, 'from backend.link_token import LinkToken\n')]
|
#
# Copyright 2018, 2020 <NAME>
# 2019-2020 <NAME>
# 2015-2016 <NAME>
#
# ### MIT license
#
# 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.
#
"""
Tests to understand the difficulties in extracting hurst from noisy data
"""
import numpy as np
import scipy
import matplotlib.pyplot as plt
import PyCo.Tools as Tools
import SurfaceTopography as Surf
def plot_naive(surface, lam_max):
fig = plt.figure()
ax=fig.add_subplot(111)
ax.set_yscale('log')
ax.set_xscale('log')
surf = Tools.CharacterisePeriodicSurface(surface)
q = surf.q
C = surf.C
H, alpha = surf.estimate_hurst_naive(lambda_max=lam_max, full_output=True)
print("H = {}, alpha = {}".format(H, alpha))
ax.loglog(q, C, alpha=.1)
mean, err, q_g = surf.grouped_stats(100)
mask = np.isfinite(mean)
mean = mean[mask]
err = err[:, mask]
q_g = q_g[mask]
ax.errorbar(q_g, mean, yerr=err)
ax.set_title("Naive: H={:.2f}, h_rms={:.2e}".format(H, np.sqrt((surface.heights() ** 2).mean())))
a, b = np.polyfit(np.log(q), np.log(C), 1)
ax.plot(q, q**(-2-2*H)*alpha, label="{}, H={:.2f}".format('fit', H))
ax.legend(loc='best')
def plot_grad_C0(surface, H_in, lam_max):
surf = Tools.CharacterisePeriodicSurface(surface)
q_min = 2*np.pi/lam_max
sl = surf.q > q_min
q = surf.q[sl]
C = surf.C[sl]
dim = 2
def C0_of_H(H):
return ((q**(-3-2*H)).sum() /
(q**(-5-4*H)/C).sum())
def objective(H, C0):
return ((1 - C0*q**(-2*H-2)/C)**2 /
q**(dim-1)).sum()
C0 = C0_of_H(H_in)
O0 = objective(H_in, C0)
c_s = np.linspace(0, 2*C0, 51)
o_s = np.zeros_like(c_s)
for i, c in enumerate(c_s):
o_s[i] = objective(H_in, c)
fig = plt.figure()
ax=fig.add_subplot(111)
fig.suptitle('grad(C0)')
ax.plot(c_s, o_s, marker= '+')
ax.scatter(C0, O0, marker='x', label = 'root', c='r')
ax.grid(True)
print("C0 = {}, obj0 = {}".format(C0, O0))
return C0
def plot_grad_H(surface, lam_max):
surf = Tools.CharacterisePeriodicSurface(surface)
q_min = 2*np.pi/lam_max
sl = surf.q > q_min
q = surf.q[sl]# np.array(surf.q[sl][0], surf.q[sl][-1])
C = surf.C[sl]# np.array(surf.C[sl][0], surf.C[sl][-1])
dim = 2
def C0_of_H(H):
return ((C**2/q**(-5-dim-4*H)).sum() /
(C/q**(-3-dim-2*H)).sum())
def grad_h(H, C0):
return (4*C0/C*np.log(q)*q**(-1-2*H-dim)*(1 - C0*q**(-2-2*H)/C)).sum()
def objective(H, C0):
return ((c/q**(-2*H-2) - C0)**2 /
q**(dim-1)).sum()
def full_obj(H):
C0 = C0_of_H(H)
return ((1 - C0/C*q**(-2*H-2))**2 /
q**(dim-1)).sum()
h_s = np.linspace(.0, 2., 51)
o_s = np.zeros_like(h_s)
g_s = np.zeros_like(h_s)
for i, h in enumerate(h_s):
c = C0_of_H(h)
o_s[i] = objective(h, c)
g_s[i] = grad_h(h, c)
H_opt, obj_opt, err, nfeq = scipy.optimize.fminbound(full_obj, 0, 2, full_output=True)
if err != 0:
raise Exception()
fig = plt.figure()
ax=fig.add_subplot(211)
ax.set_xlim(h_s[0], h_s[-1])
fig.suptitle('grad(H)')
ax.plot(h_s, o_s, marker= '+')
ax.grid(True)
ax.scatter(H_opt, obj_opt, marker='x', label = 'root', c='r')
ax=fig.add_subplot(212)
ax.set_xlim(h_s[0], h_s[-1])
ax.plot(h_s, g_s, marker= '+')
grad_opt = grad_h(H_opt, C0_of_H(H_opt))
ax.scatter(H_opt, grad_opt, marker='x', label = 'root', c='r')
#res = scipy.optimize.fmin
#print("H_out = {}, obj0 = {}".format(C0, O0))
ax.grid(True)
return H_opt, C0_of_H(H_opt)
def compare_to_PyPy(surface, lam_max, H_ref, C0_ref):
fig = plt.figure()
ax=fig.add_subplot(111)
ax.set_yscale('log')
ax.set_xscale('log')
surf = Tools.CharacterisePeriodicSurface(surface)
q_min = 2*np.pi/lam_max
sl = surf.q > q_min
q = surf.q
C = surf.C
H, alpha, res = surf.estimate_hurst_alt(lambda_max=lam_max, full_output=True)
print("H = {}, alpha = {}".format(H, alpha))
ax.loglog(q, C, alpha=.1)
mean, err, q_g = surf.grouped_stats(100)
mask = np.isfinite(mean)
mean = mean[mask]
err = err[:, mask]
q_g = q_g[mask]
ax.errorbar(q_g, mean, yerr=err)
ax.set_title("New: H_pypy={:.2f}, H_ref = {:.2f}, h_rms={:.2e}".format(H, H_ref, np.sqrt((surface.heights() ** 2).mean())))
ax.plot(q[sl], q[sl]**(-2-2*H)*alpha, label="{}, H={:.4f}".format('fit', H), lw = 3)
ax.plot(q[sl], q[sl]**(-2-2*H_ref)*C0_ref, label="{}, H={:.4f}".format('ref_fit', H_ref), lw = 3)
ax.legend(loc='best')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.loglog(q[sl], C[sl]/(q[sl]**(-2-2*H_ref)*C0_ref), alpha=.1)
ax.errorbar(q_g, mean/(q_g**(-2-2*H_ref)*C0_ref), yerr=err/(q_g**(-2-2*H_ref)*C0_ref))
def main():
siz = 2000e-9
lam_max = .2*siz
size = (siz, siz)
hurst = .75
h_rms = 3.24e-8
res = 128
nb_grid_pts = (res, res)
seed = 2
surface = Tools.RandomSurfaceGaussian(
nb_grid_pts, size, hurst, h_rms, lambda_max=lam_max, seed=seed).get_surface()
plot_naive(surface, lam_max)
plot_grad_C0(surface, hurst, lam_max)
H, C0 = plot_grad_H(surface, lam_max)
print("H_ref = {}, C0_ref = {}".format(H, C0))
compare_to_PyPy(surface, lam_max, H, C0)
if __name__ == "__main__":
main()
plt.show()
|
[
"PyCo.Tools.CharacterisePeriodicSurface",
"scipy.optimize.fminbound",
"numpy.log",
"PyCo.Tools.RandomSurfaceGaussian",
"matplotlib.pyplot.figure",
"numpy.linspace",
"numpy.isfinite",
"numpy.zeros_like",
"matplotlib.pyplot.show"
] |
[((1421, 1433), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (1431, 1433), True, 'import matplotlib.pyplot as plt\n'), ((1524, 1566), 'PyCo.Tools.CharacterisePeriodicSurface', 'Tools.CharacterisePeriodicSurface', (['surface'], {}), '(surface)\n', (1557, 1566), True, 'import PyCo.Tools as Tools\n'), ((1811, 1828), 'numpy.isfinite', 'np.isfinite', (['mean'], {}), '(mean)\n', (1822, 1828), True, 'import numpy as np\n'), ((2234, 2276), 'PyCo.Tools.CharacterisePeriodicSurface', 'Tools.CharacterisePeriodicSurface', (['surface'], {}), '(surface)\n', (2267, 2276), True, 'import PyCo.Tools as Tools\n'), ((2641, 2667), 'numpy.linspace', 'np.linspace', (['(0)', '(2 * C0)', '(51)'], {}), '(0, 2 * C0, 51)\n', (2652, 2667), True, 'import numpy as np\n'), ((2676, 2694), 'numpy.zeros_like', 'np.zeros_like', (['c_s'], {}), '(c_s)\n', (2689, 2694), True, 'import numpy as np\n'), ((2775, 2787), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (2785, 2787), True, 'import matplotlib.pyplot as plt\n'), ((3064, 3106), 'PyCo.Tools.CharacterisePeriodicSurface', 'Tools.CharacterisePeriodicSurface', (['surface'], {}), '(surface)\n', (3097, 3106), True, 'import PyCo.Tools as Tools\n'), ((3739, 3764), 'numpy.linspace', 'np.linspace', (['(0.0)', '(2.0)', '(51)'], {}), '(0.0, 2.0, 51)\n', (3750, 3764), True, 'import numpy as np\n'), ((3773, 3791), 'numpy.zeros_like', 'np.zeros_like', (['h_s'], {}), '(h_s)\n', (3786, 3791), True, 'import numpy as np\n'), ((3802, 3820), 'numpy.zeros_like', 'np.zeros_like', (['h_s'], {}), '(h_s)\n', (3815, 3820), True, 'import numpy as np\n'), ((3973, 4031), 'scipy.optimize.fminbound', 'scipy.optimize.fminbound', (['full_obj', '(0)', '(2)'], {'full_output': '(True)'}), '(full_obj, 0, 2, full_output=True)\n', (3997, 4031), False, 'import scipy\n'), ((4086, 4098), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (4096, 4098), True, 'import matplotlib.pyplot as plt\n'), ((4715, 4727), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (4725, 4727), True, 'import matplotlib.pyplot as plt\n'), ((4818, 4860), 'PyCo.Tools.CharacterisePeriodicSurface', 'Tools.CharacterisePeriodicSurface', (['surface'], {}), '(surface)\n', (4851, 4860), True, 'import PyCo.Tools as Tools\n'), ((5160, 5177), 'numpy.isfinite', 'np.isfinite', (['mean'], {}), '(mean)\n', (5171, 5177), True, 'import numpy as np\n'), ((5642, 5654), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (5652, 5654), True, 'import matplotlib.pyplot as plt\n'), ((6401, 6411), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (6409, 6411), True, 'import matplotlib.pyplot as plt\n'), ((2056, 2065), 'numpy.log', 'np.log', (['q'], {}), '(q)\n', (2062, 2065), True, 'import numpy as np\n'), ((2067, 2076), 'numpy.log', 'np.log', (['C'], {}), '(C)\n', (2073, 2076), True, 'import numpy as np\n'), ((6026, 6122), 'PyCo.Tools.RandomSurfaceGaussian', 'Tools.RandomSurfaceGaussian', (['nb_grid_pts', 'size', 'hurst', 'h_rms'], {'lambda_max': 'lam_max', 'seed': 'seed'}), '(nb_grid_pts, size, hurst, h_rms, lambda_max=\n lam_max, seed=seed)\n', (6053, 6122), True, 'import PyCo.Tools as Tools\n'), ((3445, 3454), 'numpy.log', 'np.log', (['q'], {}), '(q)\n', (3451, 3454), True, 'import numpy as np\n')]
|
import threading
import requests
import json
import os
from nose.tools import *
from server import Httpd
app_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "app")
class TestServerlessMock(object):
def test_ok(self):
ok_(True)
def setUp(self):
self.httpd = Httpd(app_path, 0)
thread = threading.Thread(target=self.httpd.serve, args=())
thread.daemon = True
thread.start()
self.prefix = "http://localhost:%d" % self.httpd.port
def tearDown(self):
self.httpd.shutdown()
def test_return_hello_world(self):
response = requests.get(self.url(""))
eq_("Hello World", response.text)
def test_simple_get(self):
response = requests.get(self.url("/simple_get"))
eq_(200, response.status_code)
data = response.json()
eq_(200, data.get("statusCode"))
body = json.loads(data.get("body"))
eq_("Go Serverless v1.0! Your function executed successfully!", body.get("message"))
def test_simple_get_and_ignore_query_string(self):
response = requests.get(self.url("/simple_get?status=unknown"))
eq_(200, response.status_code)
data = response.json()
eq_(200, data.get("statusCode"))
body = json.loads(data.get("body"))
eq_("Go Serverless v1.0! Your function executed successfully!", body.get("message"))
def test_simple_post(self):
response = requests.post(self.url("/simple_post"))
eq_(200, response.status_code)
data = response.json()
eq_(201, data.get("statusCode"))
def test_post_with_payload(self):
response = requests.post(self.url("/post_with_payload"), data=json.dumps({"id" : 123}))
eq_(200, response.status_code)
data = response.json()
eq_(200, data.get("statusCode"))
eq_({"id" : 123}, data.get("body"))
def test_post_with_payload_and_template(self):
response = requests.post(self.url("/post_with_payload_and_template"), data=json.dumps({"id" : 123}))
eq_(200, response.status_code)
data = response.json()
eq_(200, data.get("statusCode"))
eq_({"body" : {"id" : 123}}, data.get("body"))
def test_post_with_payload_and_template_without_any_function(self):
response = requests.post(self.url("/post_with_payload_and_template_without_any_function"), data=json.dumps({"id" : 123}))
eq_(200, response.status_code)
data = response.json()
eq_(200, data.get("statusCode"))
eq_({"action" : "trigger"}, data.get("body"))
def url(self, path):
return "%s%s" % (self.prefix, path)
|
[
"os.path.realpath",
"threading.Thread",
"json.dumps",
"server.Httpd"
] |
[((146, 172), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (162, 172), False, 'import os\n'), ((302, 320), 'server.Httpd', 'Httpd', (['app_path', '(0)'], {}), '(app_path, 0)\n', (307, 320), False, 'from server import Httpd\n'), ((338, 388), 'threading.Thread', 'threading.Thread', ([], {'target': 'self.httpd.serve', 'args': '()'}), '(target=self.httpd.serve, args=())\n', (354, 388), False, 'import threading\n'), ((1719, 1742), 'json.dumps', 'json.dumps', (["{'id': 123}"], {}), "({'id': 123})\n", (1729, 1742), False, 'import json\n'), ((2036, 2059), 'json.dumps', 'json.dumps', (["{'id': 123}"], {}), "({'id': 123})\n", (2046, 2059), False, 'import json\n'), ((2406, 2429), 'json.dumps', 'json.dumps', (["{'id': 123}"], {}), "({'id': 123})\n", (2416, 2429), False, 'import json\n')]
|
# Generated by Django 3.1.12 on 2021-06-24 18:12
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('trip', '0006_remove_travelmodel_driver'),
]
operations = [
migrations.AddField(
model_name='tripmodel',
name='tickets_sold',
field=models.PositiveSmallIntegerField(default=0),
),
migrations.AlterField(
model_name='travelmodel',
name='trip',
field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='travel_trip_set', related_query_name='travel_trip_set', to='trip.tripmodel'),
),
]
|
[
"django.db.models.PositiveSmallIntegerField",
"django.db.models.ForeignKey"
] |
[((381, 424), 'django.db.models.PositiveSmallIntegerField', 'models.PositiveSmallIntegerField', ([], {'default': '(0)'}), '(default=0)\n', (413, 424), False, 'from django.db import migrations, models\n'), ((549, 712), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.PROTECT', 'related_name': '"""travel_trip_set"""', 'related_query_name': '"""travel_trip_set"""', 'to': '"""trip.tripmodel"""'}), "(on_delete=django.db.models.deletion.PROTECT, related_name\n ='travel_trip_set', related_query_name='travel_trip_set', to=\n 'trip.tripmodel')\n", (566, 712), False, 'from django.db import migrations, models\n')]
|
#!/usr/bin/python
import sys
import os
import shutil
from glob import glob
from PyQt5.QtCore import (Qt, qInstallMessageHandler, QtInfoMsg, QtCriticalMsg, QtDebugMsg,
QtWarningMsg, QtFatalMsg, QSettings, pyqtSlot, QStandardPaths, QUrl)
from PyQt5.QtGui import QIcon, QDesktopServices
from PyQt5.QtWidgets import (QApplication, QMessageBox, QMainWindow, QDockWidget, QAction,
QFileDialog, QProgressDialog)
from P13pt.spectrumfitter.dataloader import DataLoader
from P13pt.spectrumfitter.navigator import Navigator
from P13pt.spectrumfitter.fitter import Fitter
from P13pt.spectrumfitter.plotter import Plotter
from P13pt.spectrumfitter.load_fitresults import load_fitresults
from P13pt.params_from_filename import params_from_filename
class MainWindow(QMainWindow):
session_file = None
def __init__(self, parent=None):
super(MainWindow, self).__init__(parent)
self.settings = QSettings("Mercury", "SpectrumFitter")
# set up data loading area
self.dock_loader = QDockWidget('Data loading', self)
self.dock_loader.setObjectName('loader')
self.loader = DataLoader()
self.dock_loader.setWidget(self.loader)
# set up data navigator
self.dock_navigator = QDockWidget('Data navigation', self)
self.dock_navigator.setObjectName('navigator')
self.navigator = Navigator()
self.dock_navigator.setWidget(self.navigator)
# set up plotter
self.plotter = Plotter()
self.setCentralWidget(self.plotter)
# set up fitter
self.dock_fitter = QDockWidget('Fitting', self)
self.dock_fitter.setObjectName('fitter')
self.fitter = Fitter()
self.dock_fitter.setWidget(self.fitter)
# set up the dock positions
self.addDockWidget(Qt.TopDockWidgetArea, self.dock_loader)
self.addDockWidget(Qt.LeftDockWidgetArea, self.dock_navigator)
self.addDockWidget(Qt.RightDockWidgetArea, self.dock_fitter)
# set up menus
fileMenu = self.menuBar().addMenu('File')
self.act_new_session = QAction('New session', self)
self.act_load_session = QAction('Load session', self)
self.act_save_session = QAction('Save session', self)
self.act_save_session_as = QAction('Save session as...', self)
for a in [self.act_new_session, self.act_load_session, self.act_save_session, self.act_save_session_as]:
fileMenu.addAction(a)
self.recent_menu = fileMenu.addMenu('Recent sessions')
self.update_recent_list()
fileMenu.addSeparator()
self.act_save_image = QAction('Save spectrum as image', self)
self.act_save_allimages = QAction('Save all spectra as images', self)
for a in [self.act_save_image, self.act_save_allimages]:
fileMenu.addAction(a)
viewMenu = self.menuBar().addMenu('View')
for w in [self.dock_loader, self.dock_navigator, self.dock_fitter]:
viewMenu.addAction(w.toggleViewAction())
self.act_restore_default_view = QAction('Restore default', self)
viewMenu.addAction(self.act_restore_default_view)
self.act_toggle_display_style = QAction('Toggle display style', self)
self.act_toggle_display_style.setShortcut(Qt.Key_F8)
viewMenu.addAction(self.act_toggle_display_style)
toolsMenu = self.menuBar().addMenu('Tools')
self.act_install_builtin_models = QAction('Install built-in models', self)
toolsMenu.addAction(self.act_install_builtin_models)
self.act_open_model_folder = QAction('Open model folder', self)
toolsMenu.addAction(self.act_open_model_folder)
# make connections
self.loader.dataset_changed.connect(self.dataset_changed)
self.loader.new_file_in_dataset.connect(self.navigator.new_file_in_dataset)
self.loader.deembedding_changed.connect(self.deembedding_changed)
self.navigator.selection_changed.connect(self.selection_changed)
self.fitter.fit_changed.connect(lambda: self.plotter.plot_fit(self.fitter.model))
self.fitter.fitted_param_changed.connect(self.plotter.fitted_param_changed)
self.fitter.btn_fitall.clicked.connect(self.fit_all)
self.act_new_session.triggered.connect(self.new_session)
self.act_load_session.triggered.connect(self.load_session)
self.act_save_session.triggered.connect(self.save_session)
self.act_save_session_as.triggered.connect(self.save_session_as)
self.act_save_image.triggered.connect(self.save_image)
self.act_save_allimages.triggered.connect(self.save_all_images)
self.act_restore_default_view.triggered.connect(lambda: self.restoreState(self.default_state))
self.act_toggle_display_style.triggered.connect(self.toggle_display_style)
self.act_install_builtin_models.triggered.connect(self.install_builtin_models)
self.act_open_model_folder.triggered.connect(self.open_model_folder)
# set up fitted parameter (this has to be done after making connections, so that fitter and plotter sync)
self.fitter.fitted_param = '-Y12' # default value
# create new session
self.new_session()
# show window
self.show()
self.default_state = self.saveState()
# restore layout from config (this has to be done AFTER self.show())
if self.settings.contains('geometry'):
self.restoreGeometry(self.settings.value("geometry"))
if self.settings.contains('windowState'):
self.restoreState(self.settings.value("windowState"))
def closeEvent(self, event):
self.settings.setValue("geometry", self.saveGeometry())
self.settings.setValue("windowState", self.saveState())
super(MainWindow, self).closeEvent(event)
def dataset_changed(self):
self.fitter.empty_cache()
self.navigator.update_file_list(self.loader.dut_files)
for a in [self.act_save_session, self.act_save_session_as, self.act_save_image, self.act_save_allimages]:
a.setEnabled(True)
def toggle_display_style(self):
if self.plotter.display_style == 'MP':
self.plotter.display_style = 'RI'
else:
self.plotter.display_style = 'MP'
self.deembedding_changed() # TODO: rename and/or remove redundancies, c.f. deembedding_changed()
def deembedding_changed(self):
# TODO: reduce redundancy with selection_changed()
i = self.navigator.file_list.currentRow()
spectrum = self.loader.get_spectrum(i)
if spectrum is not None:
#TODO: show parameters on plot
self.plotter.plot(spectrum, {})
else:
self.plotter.clear()
self.fitter.update_network(spectrum, self.loader.dut_files[i])
def selection_changed(self, i):
if i < 0: # when file_list is cleared:
return
QApplication.setOverrideCursor(Qt.WaitCursor)
# TODO: the argument here should be a filename, not the index
spectrum = self.loader.get_spectrum(i)
if spectrum is not None:
self.plotter.plot(spectrum, params_from_filename(self.loader.dut_files[i]))
else:
self.plotter.clear()
self.fitter.update_network(spectrum, self.loader.dut_files[i])
QApplication.restoreOverrideCursor()
def new_session(self):
self.session_file = None
self.setWindowTitle('Spectrum Fitter - New session')
self.fitter.unload_model()
self.loader.clear()
self.navigator.clear()
self.plotter.clear()
for a in [self.act_save_session, self.act_save_session_as, self.act_save_image, self.act_save_allimages]:
a.setEnabled(False)
@pyqtSlot()
def save_session_as(self, res_file=None):
if not res_file:
res_file, filter = QFileDialog.getSaveFileName(self, 'Fit results file', filter='*.txt')
if not res_file:
return
res_folder = os.path.dirname(res_file)
try:
with open(res_file, 'w') as f:
# write the header
f.write('# fitting results generated by P13pt spectrum fitter\n')
if len(self.loader.dut_files) == 1:
f.write('# dut: ' +
os.path.join(
os.path.relpath(self.loader.dut_folder, res_folder),
self.loader.dut_files[0]
).replace('\\', '/') + '\n')
else:
f.write('# dut: ' + os.path.relpath(self.loader.dut_folder, res_folder).replace('\\', '/') + '\n')
if self.loader.thru and self.loader.thru_toggle_status:
f.write('# thru: ' + os.path.relpath(self.loader.thru_file, res_folder).replace('\\', '/') + '\n')
if self.loader.dummy and self.loader.dummy_toggle_status:
f.write('# dummy: ' + os.path.relpath(self.loader.dummy_file, res_folder).replace('\\', '/') + '\n')
f.write('# fitted_param: ' + self.plotter.fitted_param + '\n')
try:
ra = float(self.loader.txt_ra.text())
except:
ra = 0.
if not ra == 0:
f.write('# ra: ' + str(ra) + '\n')
if self.fitter.model:
f.write('# model: ' + os.path.basename(self.fitter.model_file).replace('\\', '/') + '\n')
f.write('# model_func: ' + self.fitter.cmb_modelfunc.currentText() + '\n')
# TODO: this all could clearly be done in a more elegant way
if self.fitter.cmb_fitmethod.currentText() != 'No fit methods found':
f.write('# fit_method: ' + self.fitter.cmb_fitmethod.currentText() + '\n')
# determine columns
f.write('# filename\t')
for p in params_from_filename(self.loader.dut_files[0]):
f.write(p + '\t')
f.write('\t'.join([p for p in self.fitter.model.params]))
f.write('\n')
# write data
filelist = sorted([filename for filename in self.fitter.model_params])
for filename in filelist:
f.write(filename + '\t')
# TODO: what if some filenames do not contain all parameters? should catch exceptions
for p in params_from_filename(self.loader.dut_files[0]):
f.write(str(params_from_filename(filename)[p]) + '\t')
f.write('\t'.join([str(self.fitter.model_params[filename][p]) for p in self.fitter.model.params]))
f.write('\n')
except EnvironmentError as e:
QMessageBox.critical(self, 'Error', 'Could not save session: '+str(e))
return
self.update_recent_list(res_file)
self.setWindowTitle('Spectrum Fitter - '+res_file)
self.session_file = res_file
def save_session(self):
self.save_session_as(self.session_file)
@pyqtSlot()
def load_session(self, res_file=None):
if not res_file:
res_file, filter = QFileDialog.getOpenFileName(self, 'Fit results file', filter='*.txt')
if not res_file:
return
res_folder = os.path.dirname(res_file)
self.new_session()
# read the data
try:
data, dut, thru, dummy, ra, fitter_info = load_fitresults(res_file, readfilenameparams=False, extrainfo=True)
except IOError as e:
QMessageBox.warning(self, 'Error', 'Could not load data: '+str(e))
return
# using os.path.realpath to get rid of relative path remainders ("..")
self.loader.load_dataset(dut=os.path.realpath(os.path.join(res_folder, dut)) if dut else None,
thru=os.path.realpath(os.path.join(res_folder, thru)) if thru else None,
dummy=os.path.realpath(os.path.join(res_folder, dummy)) if dummy else None,
ra=ra if ra else None)
# if a fitted_param was provided in the session file, set it up
if 'fitted_param' in fitter_info:
self.fitter.fitted_param = fitter_info['fitted_param']
# if a model was provided in the session file, load this model and the provided data
if 'model' in fitter_info:
self.fitter.load_model(filename=fitter_info['model'],
info=fitter_info,
data=data if data else None)
# update the fitter with the first spectrum in the list
self.fitter.update_network(self.loader.get_spectrum(0), self.loader.dut_files[0])
self.update_recent_list(res_file)
self.setWindowTitle('Spectrum Fitter - '+res_file)
self.session_file = res_file
#TODO: this is not really in the right place
@pyqtSlot()
def fit_all(self):
totalnum = len(self.loader.dut_files)
progressdialog = QProgressDialog('Fitting all spectra...', 'Cancel', 0, totalnum-1, self)
progressdialog.setWindowTitle('Progress')
progressdialog.setModal(True)
progressdialog.setAutoClose(True)
progressdialog.show()
for i in range(totalnum):
QApplication.processEvents()
if progressdialog.wasCanceled():
break
self.navigator.file_list.setCurrentRow(i)
self.fitter.fit_model()
progressdialog.setValue(i)
def save_image(self):
basename, ext = os.path.splitext(self.loader.dut_files[self.navigator.file_list.currentRow()])
filename, filter = QFileDialog.getSaveFileName(self, 'Choose file',
os.path.join(self.loader.dut_folder, basename+'.png'),
filter='*.png;;*.jpg;;*.eps')
if filename:
self.plotter.save_fig(filename)
def save_all_images(self):
foldername = QFileDialog.getExistingDirectory(self, 'Choose folder',
self.loader.dut_folder)
totalnum = len(self.loader.dut_files)
progressdialog = QProgressDialog('Saving all images...', 'Cancel', 0, totalnum - 1, self)
progressdialog.setWindowTitle('Progress')
progressdialog.setModal(True)
progressdialog.setAutoClose(True)
progressdialog.show()
for i in range(totalnum):
QApplication.processEvents()
if progressdialog.wasCanceled():
break
self.navigator.file_list.setCurrentRow(i)
basename, ext = os.path.splitext(self.loader.dut_files[self.navigator.file_list.currentRow()])
self.plotter.save_fig(os.path.join(foldername, basename+'.png'))
progressdialog.setValue(i)
def load_recent(self):
action = self.sender()
self.load_session(action.text())
def update_recent_list(self, filename=None):
recentlist = list(self.settings.value('recentSessions')) if self.settings.contains('recentSessions') \
else []
if filename:
if filename in recentlist:
recentlist.remove(filename)
recentlist.insert(0, filename)
recentlist = recentlist[0:5]
self.settings.setValue('recentSessions', recentlist)
self.recent_menu.clear()
for r in recentlist:
a = QAction(r, self)
self.recent_menu.addAction(a)
a.triggered.connect(self.load_recent)
def install_builtin_models(self):
builtin_folder = os.path.join(os.path.dirname(__file__), 'models')
for filename in sorted(glob(os.path.join(builtin_folder, '*.py'))):
# check if the file already exists in the models folder
if os.path.exists(os.path.join(self.fitter.models_dir, os.path.basename(filename))):
answer = QMessageBox.question(self, 'File already exists', 'The file: '+os.path.basename(filename)+
'already exists in your models folder. Would you like to replace it?')
if answer != QMessageBox.Yes:
continue
# if file does not exist or user does not mind replacing it, let's copy:
shutil.copyfile(filename, os.path.join(self.fitter.models_dir, os.path.basename(filename)))
def open_model_folder(self):
QDesktopServices.openUrl(QUrl.fromLocalFile(self.fitter.models_dir))
def msghandler(type, context, message):
if type == QtInfoMsg:
QMessageBox.information(None, 'Info', message)
elif type == QtDebugMsg:
QMessageBox.information(None, 'Debug', message)
elif type == QtCriticalMsg:
QMessageBox.critical(None, 'Critical', message)
elif type == QtWarningMsg:
QMessageBox.warning(None, 'Warning', message)
elif type == QtFatalMsg:
QMessageBox.critical(None, 'Fatal error', message)
def main():
qInstallMessageHandler(msghandler)
# CD into directory where this script is saved
d = os.path.dirname(__file__)
if d != '': os.chdir(d)
app = QApplication(sys.argv)
app.setWindowIcon(QIcon('audacity.png'))
mainwindow = MainWindow()
# Start the main loop.
ret = app.exec_()
sys.exit(ret)
if __name__ == '__main__':
main()
|
[
"PyQt5.QtGui.QIcon",
"PyQt5.QtCore.QUrl.fromLocalFile",
"P13pt.spectrumfitter.plotter.Plotter",
"PyQt5.QtWidgets.QProgressDialog",
"PyQt5.QtWidgets.QApplication.processEvents",
"PyQt5.QtWidgets.QApplication",
"sys.exit",
"PyQt5.QtWidgets.QDockWidget",
"PyQt5.QtWidgets.QFileDialog.getOpenFileName",
"P13pt.params_from_filename.params_from_filename",
"PyQt5.QtCore.qInstallMessageHandler",
"P13pt.spectrumfitter.load_fitresults.load_fitresults",
"os.path.relpath",
"P13pt.spectrumfitter.navigator.Navigator",
"PyQt5.QtWidgets.QMessageBox.information",
"os.path.dirname",
"PyQt5.QtWidgets.QApplication.restoreOverrideCursor",
"PyQt5.QtWidgets.QApplication.setOverrideCursor",
"PyQt5.QtWidgets.QFileDialog.getExistingDirectory",
"P13pt.spectrumfitter.fitter.Fitter",
"os.path.join",
"PyQt5.QtWidgets.QMessageBox.critical",
"PyQt5.QtCore.pyqtSlot",
"PyQt5.QtWidgets.QAction",
"os.chdir",
"P13pt.spectrumfitter.dataloader.DataLoader",
"PyQt5.QtCore.QSettings",
"os.path.basename",
"PyQt5.QtWidgets.QMessageBox.warning",
"PyQt5.QtWidgets.QFileDialog.getSaveFileName"
] |
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'import os\n'), ((17109, 17156), 'PyQt5.QtWidgets.QMessageBox.critical', 'QMessageBox.critical', (['None', '"""Critical"""', 'message'], {}), "(None, 'Critical', message)\n", (17129, 17156), False, 'from PyQt5.QtWidgets import QApplication, QMessageBox, QMainWindow, QDockWidget, QAction, QFileDialog, QProgressDialog\n'), ((10071, 10117), 'P13pt.params_from_filename.params_from_filename', 'params_from_filename', (['self.loader.dut_files[0]'], {}), '(self.loader.dut_files[0])\n', (10091, 10117), False, 'from P13pt.params_from_filename import params_from_filename\n'), ((16223, 16249), 'os.path.basename', 'os.path.basename', (['filename'], {}), '(filename)\n', (16239, 16249), False, 'import os\n'), ((16722, 16748), 'os.path.basename', 'os.path.basename', (['filename'], {}), '(filename)\n', (16738, 16748), False, 'import os\n'), ((17196, 17241), 'PyQt5.QtWidgets.QMessageBox.warning', 'QMessageBox.warning', (['None', '"""Warning"""', 'message'], {}), "(None, 'Warning', message)\n", (17215, 17241), False, 'from PyQt5.QtWidgets import QApplication, QMessageBox, QMainWindow, QDockWidget, QAction, QFileDialog, QProgressDialog\n'), ((10636, 10682), 'P13pt.params_from_filename.params_from_filename', 'params_from_filename', (['self.loader.dut_files[0]'], {}), '(self.loader.dut_files[0])\n', (10656, 10682), False, 'from P13pt.params_from_filename import params_from_filename\n'), ((12010, 12039), 'os.path.join', 'os.path.join', (['res_folder', 'dut'], {}), '(res_folder, dut)\n', (12022, 12039), False, 'import os\n'), ((12114, 12144), 'os.path.join', 'os.path.join', (['res_folder', 'thru'], {}), '(res_folder, thru)\n', (12126, 12144), False, 'import os\n'), ((12221, 12252), 'os.path.join', 'os.path.join', (['res_folder', 'dummy'], {}), '(res_folder, dummy)\n', (12233, 12252), False, 'import os\n'), ((17279, 17329), 'PyQt5.QtWidgets.QMessageBox.critical', 'QMessageBox.critical', (['None', '"""Fatal error"""', 'message'], {}), "(None, 'Fatal error', message)\n", (17299, 17329), 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'os.path.relpath', (['self.loader.dut_folder', 'res_folder'], {}), '(self.loader.dut_folder, res_folder)\n', (8469, 8505), False, 'import os\n'), ((10724, 10754), 'P13pt.params_from_filename.params_from_filename', 'params_from_filename', (['filename'], {}), '(filename)\n', (10744, 10754), False, 'from P13pt.params_from_filename import params_from_filename\n')]
|
from pycket.error import SchemeException
from rpython.tool.pairtype import extendabletype
from rpython.rlib import jit, objectmodel
class W_ProtoObject(object):
""" abstract base class of both actual values (W_Objects) and multiple
return values (Values)"""
_attrs_ = []
_settled_ = True
def as_real_value(self):
raise NotImplementedError("not a real value!")
def num_values(val):
raise NotImplementedError("not a real value!")
def get_value(val, index):
raise NotImplementedError("not a real value!")
def get_all_values(self):
raise NotImplementedError("not a real value!")
class W_Object(W_ProtoObject):
__metaclass__ = extendabletype
_attrs_ = []
errorname = "%%%%unreachable%%%%"
def __init__(self):
raise NotImplementedError("abstract base class")
def num_values(self):
return 1
def get_value(self, index):
assert index == 0
return self
def get_all_values(self):
return [self]
def iscallable(self):
return False
def call(self, args, env, cont):
raise SchemeException("%s is not callable" % self.tostring())
def call_with_extra_info(self, args, env, cont, calling_app):
return self.call(args, env, cont)
def enable_jitting(self):
pass # need to override in callables that are based on an AST
# an arity is a pair of a list of numbers and either -1 or a non-negative integer
def get_arity(self):
from pycket.interpreter import Arity
if self.iscallable():
return Arity.unknown
else:
raise SchemeException("%s does not have arity" % self.tostring())
def is_proper_list(self):
return False
def is_impersonator(self):
return self.is_chaperone()
def is_chaperone(self):
return False
def is_proxy(self):
return self.is_chaperone() or self.is_impersonator()
def get_proxied(self):
return self
def get_properties(self):
return {}
def is_non_interposing_chaperone(self):
return False
def immutable(self):
return False
def equal(self, other):
return self is other # default implementation
def eqv(self, other):
return self is other # default implementation
def hash_equal(self):
return objectmodel.compute_hash(self) # default implementation
hash_eqv = hash_equal
def tostring(self):
return str(self)
# for expose
@classmethod
def make_unwrapper(cls):
if cls is W_Object:
return lambda x: x, ''
def unwrap(w_object):
if isinstance(w_object, cls):
return w_object
return None
return unwrap, cls.errorname
class SingletonMeta(type):
def __new__(cls, name, bases, dct):
result = type.__new__(cls, name, bases, dct)
result.singleton = result()
return result
|
[
"rpython.rlib.objectmodel.compute_hash"
] |
[((2382, 2412), 'rpython.rlib.objectmodel.compute_hash', 'objectmodel.compute_hash', (['self'], {}), '(self)\n', (2406, 2412), False, 'from rpython.rlib import jit, objectmodel\n')]
|
import numpy as np
from stable_baselines import PPO2
from stable_baselines.common.policies import CnnPolicy
from stable_baselines.a2c.utils import conv, linear, conv_to_fc
from src.envs import CMDP, FrozenLakeEnvCustomMap
from src.envs.frozen_lake.frozen_maps import MAPS
from src.students import LagrangianStudent, identity_transfer
from src.online_learning import ExponetiatedGradient
from src.teacher import FrozenLakeEvaluationLogger, TeacherFrozenEnv, \
create_intervention, SmallFrozenTeacherEnv
from src.teacher.frozen_lake_env import SmallFrozenTrainingObservation, SmallFrozenNonStationaryBandits
from src.envs.frozen_lake.utils import create_intervention_from_map, \
OptimalAgent, add_teacher
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
__all__ = ['create_teacher_env', 'small_base_cenv_fn']
def constraint(info=None, **kwargs):
return {'g': float(info['next_state_type'] in 'H')}
def small_base_env_fn():
# Base MDP
world_map = MAPS['small']
not_slipping_prob = 0.8
env_kwargs = dict(desc=world_map,
not_slipping_prob=not_slipping_prob,
base_r_mapping=None,
timeout=200)
return FrozenLakeEnvCustomMap(**env_kwargs)
# Base CMDP
def small_base_cenv_fn():
return CMDP(small_base_env_fn(), constraint,
constraints_values=[0],
n_constraints=1,
avg_constraint=True)
def make_base_small_cenvs():
# Base MDP
world_map = MAPS['small']
# # 2 interventions
# dist = [1, 1]
# tau = [0.1, 0]
# buff_size = [1, 0]
# avg_constraint = [True, True]
# 3 Interventions
dist = [2, 1, 1]
tau = [0.1, 0.1, 0]
buff_size = [1, 1, 0]
avg_constraint = [True, True, True]
interventions = []
for d, t, b, avg in zip(dist, tau, buff_size, avg_constraint):
interventions.append(
create_intervention(
small_base_cenv_fn,
create_intervention_from_map(add_teacher(world_map, d)),
[t], b, use_vec=True, avg_constraint=avg)
)
assert callable(interventions[0])
test_env = create_intervention(
small_base_cenv_fn(), create_intervention_from_map(add_teacher(
world_map)),
[0.0], 0, avg_constraint=True)
return interventions, test_env
############################## TEACHER ENV ###################################
def my_small_cnn(scaled_images, **kwargs):
activ = tf.nn.relu
layer_1 = activ(conv(scaled_images, 'c1', n_filters=32, filter_size=3,
stride=1, **kwargs))
layer_2 = activ(conv(layer_1, 'c2', n_filters=64, filter_size=3,
stride=1, **kwargs))
layer_3 = conv_to_fc(layer_2)
return activ(
linear(layer_3, 'fc1', n_hidden=32, init_scale=np.sqrt(2)))
def create_teacher_env(new_br_kwargs={}, new_online_kwargs={},
original=False, obs_from_training=False,
non_stationary_bandit=False):
# Student definition
br_kwargs = dict(policy=CnnPolicy, verbose=0, n_steps=128,
ent_coef=0.05, cliprange=0.2, learning_rate=1e-3,
noptepochs=9,
policy_kwargs={'cnn_extractor': my_small_cnn})
br_kwargs.update(new_br_kwargs)
# Define online kwargs
online_kwargs = dict(B=0.5, eta=1.0)
online_kwargs.update(new_online_kwargs)
student_cls = LagrangianStudent
n_envs = 4
use_sub_proc_env = False
student_default_kwargs = {'env': None,
'br_algo': PPO2,
'online_algo': ExponetiatedGradient,
'br_kwargs': br_kwargs,
'online_kwargs': online_kwargs,
'lagrangian_ronuds': 2,
'curriculum_transfer': identity_transfer,
'br_uses_vec_env': True,
'use_sub_proc_env': use_sub_proc_env,
'n_envs': n_envs,
}
student_ranges_dict = {}
# Teacher interventions
if original:
# To preserve the teacher env interface while training in the
# original environment, we introduce a dummy intervention
# condition that is always False.
def dummy_intervention(**kwargs):
return 0
_, test_env = make_base_small_cenvs()
intervention = create_intervention(
base_cenv=small_base_cenv_fn,
interventions=[dummy_intervention], taus=[0], buf_size=0,
use_vec=True, avg_constraint=True)
interventions = [intervention]
else:
interventions, test_env = make_base_small_cenvs()
learning_steps = 4800 * 2
time_steps_lim = learning_steps * 10
test_episode_timeout = 200
test_episode_number = 5
if obs_from_training:
env_cls = SmallFrozenTrainingObservation
elif non_stationary_bandit:
env_cls = SmallFrozenNonStationaryBandits
else:
env_cls = SmallFrozenTeacherEnv
return env_cls(student_cls=student_cls,
student_default_kwargs=student_default_kwargs,
interventions=interventions,
final_env=test_env,
logger_cls=FrozenLakeEvaluationLogger,
student_ranges_dict=student_ranges_dict,
learning_steps=learning_steps,
test_episode_number=test_episode_number,
test_episode_timeout=test_episode_timeout,
time_steps_lim=time_steps_lim,
normalize_obs=False)
|
[
"stable_baselines.a2c.utils.conv_to_fc",
"numpy.sqrt",
"src.envs.FrozenLakeEnvCustomMap",
"stable_baselines.a2c.utils.conv",
"src.teacher.create_intervention",
"tensorflow.compat.v1.logging.set_verbosity",
"src.envs.frozen_lake.utils.add_teacher"
] |
[((741, 803), 'tensorflow.compat.v1.logging.set_verbosity', 'tf.compat.v1.logging.set_verbosity', (['tf.compat.v1.logging.ERROR'], {}), '(tf.compat.v1.logging.ERROR)\n', (775, 803), True, 'import tensorflow as tf\n'), ((1242, 1278), 'src.envs.FrozenLakeEnvCustomMap', 'FrozenLakeEnvCustomMap', ([], {}), '(**env_kwargs)\n', (1264, 1278), False, 'from src.envs import CMDP, FrozenLakeEnvCustomMap\n'), ((2792, 2811), 'stable_baselines.a2c.utils.conv_to_fc', 'conv_to_fc', (['layer_2'], {}), '(layer_2)\n', (2802, 2811), False, 'from stable_baselines.a2c.utils import conv, linear, conv_to_fc\n'), ((2562, 2636), 'stable_baselines.a2c.utils.conv', 'conv', (['scaled_images', '"""c1"""'], {'n_filters': '(32)', 'filter_size': '(3)', 'stride': '(1)'}), "(scaled_images, 'c1', n_filters=32, filter_size=3, stride=1, **kwargs)\n", (2566, 2636), False, 'from stable_baselines.a2c.utils import conv, linear, conv_to_fc\n'), ((2683, 2751), 'stable_baselines.a2c.utils.conv', 'conv', (['layer_1', '"""c2"""'], {'n_filters': '(64)', 'filter_size': '(3)', 'stride': '(1)'}), "(layer_1, 'c2', n_filters=64, filter_size=3, stride=1, **kwargs)\n", (2687, 2751), False, 'from stable_baselines.a2c.utils import conv, linear, conv_to_fc\n'), ((4559, 4711), 'src.teacher.create_intervention', 'create_intervention', ([], {'base_cenv': 'small_base_cenv_fn', 'interventions': '[dummy_intervention]', 'taus': '[0]', 'buf_size': '(0)', 'use_vec': '(True)', 'avg_constraint': '(True)'}), '(base_cenv=small_base_cenv_fn, interventions=[\n dummy_intervention], taus=[0], buf_size=0, use_vec=True, avg_constraint\n =True)\n', (4578, 4711), False, 'from src.teacher import FrozenLakeEvaluationLogger, TeacherFrozenEnv, create_intervention, SmallFrozenTeacherEnv\n'), ((2280, 2302), 'src.envs.frozen_lake.utils.add_teacher', 'add_teacher', (['world_map'], {}), '(world_map)\n', (2291, 2302), False, 'from src.envs.frozen_lake.utils import create_intervention_from_map, OptimalAgent, add_teacher\n'), ((2885, 2895), 'numpy.sqrt', 'np.sqrt', (['(2)'], {}), '(2)\n', (2892, 2895), True, 'import numpy as np\n'), ((2050, 2075), 'src.envs.frozen_lake.utils.add_teacher', 'add_teacher', (['world_map', 'd'], {}), '(world_map, d)\n', (2061, 2075), False, 'from src.envs.frozen_lake.utils import create_intervention_from_map, OptimalAgent, add_teacher\n')]
|
name = "scanapi"
import click
import logging
from scanapi.tree.api_tree import APITree
from scanapi.reporter import Reporter
from scanapi.requests_maker import RequestsMaker
from scanapi.settings import SETTINGS
from scanapi.yaml_loader import load_yaml
@click.command()
@click.option(
"-s",
"--spec-path",
"spec_path",
type=click.Path(exists=True),
default=SETTINGS["spec_path"],
)
@click.option("-o", "--output-path", "output_path")
@click.option(
"-r",
"--reporter",
"reporter",
type=click.Choice(["console", "markdown", "html"]),
default=SETTINGS["reporter"],
)
@click.option("-t", "--template", "template")
@click.option(
"--log-level",
"log_level",
type=click.Choice(["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]),
default="INFO",
)
def scan(spec_path, output_path, reporter, template, log_level):
"""Automated Testing and Documentation for your REST API."""
logging.basicConfig(level=log_level)
logger = logging.getLogger(__name__)
SETTINGS.update({"spec_path": spec_path, "output_path": output_path})
# custom templates to be implemented later
if template is not None:
logger.warn("Custom templates are not supported yet. Soon to be. Hang tight.")
spec_path = SETTINGS["spec_path"]
try:
api_spec = load_yaml(spec_path)
except FileNotFoundError as e:
error_message = f"Could not find spec file: {spec_path}. {str(e)}"
logger.error(error_message)
return
try:
api_tree = APITree(api_spec)
except Exception as e:
error_message = "Error loading API spec."
error_message = "{} {}".format(error_message, str(e))
logger.error(error_message)
return
RequestsMaker(api_tree).make_all()
Reporter(output_path, reporter, template).write(api_tree.responses.values())
|
[
"logging.getLogger",
"logging.basicConfig",
"click.Choice",
"click.option",
"scanapi.tree.api_tree.APITree",
"scanapi.yaml_loader.load_yaml",
"scanapi.requests_maker.RequestsMaker",
"click.Path",
"scanapi.reporter.Reporter",
"click.command",
"scanapi.settings.SETTINGS.update"
] |
[((259, 274), 'click.command', 'click.command', ([], {}), '()\n', (272, 274), False, 'import click\n'), ((408, 458), 'click.option', 'click.option', (['"""-o"""', '"""--output-path"""', '"""output_path"""'], {}), "('-o', '--output-path', 'output_path')\n", (420, 458), False, 'import click\n'), ((611, 655), 'click.option', 'click.option', (['"""-t"""', '"""--template"""', '"""template"""'], {}), "('-t', '--template', 'template')\n", (623, 655), False, 'import click\n'), ((938, 974), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'log_level'}), '(level=log_level)\n', (957, 974), False, 'import logging\n'), ((988, 1015), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1005, 1015), False, 'import logging\n'), ((1020, 1089), 'scanapi.settings.SETTINGS.update', 'SETTINGS.update', (["{'spec_path': spec_path, 'output_path': output_path}"], {}), "({'spec_path': spec_path, 'output_path': output_path})\n", (1035, 1089), False, 'from scanapi.settings import SETTINGS\n'), ((1321, 1341), 'scanapi.yaml_loader.load_yaml', 'load_yaml', (['spec_path'], {}), '(spec_path)\n', (1330, 1341), False, 'from scanapi.yaml_loader import load_yaml\n'), ((1532, 1549), 'scanapi.tree.api_tree.APITree', 'APITree', (['api_spec'], {}), '(api_spec)\n', (1539, 1549), False, 'from scanapi.tree.api_tree import APITree\n'), ((345, 368), 'click.Path', 'click.Path', ([], {'exists': '(True)'}), '(exists=True)\n', (355, 368), False, 'import click\n'), ((527, 572), 'click.Choice', 'click.Choice', (["['console', 'markdown', 'html']"], {}), "(['console', 'markdown', 'html'])\n", (539, 572), False, 'import click\n'), ((716, 779), 'click.Choice', 'click.Choice', (["['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL']"], {}), "(['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'])\n", (728, 779), False, 'import click\n'), ((1745, 1768), 'scanapi.requests_maker.RequestsMaker', 'RequestsMaker', (['api_tree'], {}), '(api_tree)\n', (1758, 1768), False, 'from scanapi.requests_maker import RequestsMaker\n'), ((1784, 1825), 'scanapi.reporter.Reporter', 'Reporter', (['output_path', 'reporter', 'template'], {}), '(output_path, reporter, template)\n', (1792, 1825), False, 'from scanapi.reporter import Reporter\n')]
|
# 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 sys
import re
import logging
import subprocess
import errno, os, pty
import shlex
from subprocess import Popen, PIPE
from ConfigReader import configuration
import mysql.connector
from mysql.connector import errorcode
from common.Singleton import Singleton
from DBImportConfig import import_config
from DBImportOperation import common_operations
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
import time
class operation(object, metaclass=Singleton):
def __init__(self, Hive_DB=None, Hive_Table=None):
logging.debug("Executing etl_operations.__init__()")
self.Hive_DB = None
self.Hive_Table = None
self.mysql_conn = None
self.mysql_cursor = None
self.startDate = None
self.common_operations = common_operations.operation(Hive_DB, Hive_Table)
self.import_config = import_config.config(Hive_DB, Hive_Table)
if Hive_DB != None and Hive_Table != None:
self.setHiveTable(Hive_DB, Hive_Table)
else:
# If the class already is initialized, we just pull the parameters and set them here
self.Hive_DB = self.common_operations.Hive_DB
self.Hive_Table = self.common_operations.Hive_Table
self.startDate = self.import_config.startDate
logging.debug("Executing etl_operations.__init__() - Finished")
def setHiveTable(self, Hive_DB, Hive_Table):
""" Sets the parameters to work against a new Hive database and table """
self.Hive_DB = Hive_DB.lower()
self.Hive_Table = Hive_Table.lower()
self.common_operations.setHiveTable(self.Hive_DB, self.Hive_Table)
self.import_config.setHiveTable(self.Hive_DB, self.Hive_Table)
try:
self.import_config.getImportConfig()
self.startDate = self.import_config.startDate
self.import_config.lookupConnectionAlias()
except:
self.import_config.remove_temporary_files()
sys.exit(1)
def remove_temporary_files(self):
self.import_config.remove_temporary_files()
def connectToHive(self,):
logging.debug("Executing etl_operations.connectToHive()")
try:
self.common_operations.connectToHive()
except Exception as ex:
logging.error(ex)
self.import_config.remove_temporary_files()
sys.exit(1)
logging.debug("Executing etl_operations.connectToHive() - Finished")
def mergeHiveTables(self, sourceDB, sourceTable, targetDB, targetTable, historyDB = None, historyTable=None, targetDeleteDB = None, targetDeleteTable=None, createHistoryAudit=False, sourceIsIncremental=False, sourceIsImportTable=False, softDelete=False, mergeTime=datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'), datalakeSource=None, PKColumns=None, hiveMergeJavaHeap=None, oracleFlashbackSource=False, mssqlChangeTrackingSource=False, deleteNotUpdatedRows=False, oracleFlashbackImportTable=None, mssqlChangeTrackingImportTable=None):
""" Merge source table into Target table. Also populate a History Audit table if selected """
logging.debug("Executing etl_operations.mergeHiveTables()")
targetColumns = self.common_operations.getHiveColumns(hiveDB=targetDB, hiveTable=targetTable, includeType=False, includeComment=False)
columnMerge = self.common_operations.getHiveColumnNameDiff(sourceDB=sourceDB, sourceTable=sourceTable, targetDB=targetDB, targetTable=targetTable, importTool = self.import_config.importTool, sourceIsImportTable=True)
if PKColumns == None:
PKColumns = self.common_operations.getPKfromTable(hiveDB=targetDB, hiveTable=targetTable, quotedColumns=False)
datalakeIUDExists = False
datalakeInsertExists = False
datalakeUpdateExists = False
datalakeDeleteExists = False
datalakeSourceExists = False
for index, row in targetColumns.iterrows():
if row['name'] == "datalake_iud": datalakeIUDExists = True
if row['name'] == "datalake_insert": datalakeInsertExists = True
if row['name'] == "datalake_update": datalakeUpdateExists = True
if row['name'] == "datalake_delete": datalakeDeleteExists = True
if row['name'] == "datalake_source": datalakeSourceExists = True
if hiveMergeJavaHeap != None:
query = "set hive.tez.container.size=%s"%(hiveMergeJavaHeap)
self.common_operations.executeHiveQuery(query)
query = "merge into `%s`.`%s` as T \n"%(targetDB, targetTable)
query += "using `%s`.`%s` as S \n"%(sourceDB, sourceTable)
query += "on \n"
for i, targetColumn in enumerate(PKColumns.split(",")):
try:
sourceColumn = columnMerge.loc[columnMerge['Exist'] == 'both'].loc[columnMerge['targetName'] == targetColumn].iloc[0]['sourceName']
except IndexError:
logging.error("Primary Key cant be found in the source target table. Please check PK override")
self.import_config.remove_temporary_files()
sys.exit(1)
if sourceColumn == None:
logging.error("ERROR: Problem determine column name in source table for primary key column '%s'"%(targetColumn))
self.import_config.remove_temporary_files()
sys.exit(1)
if i == 0:
query += " T.`%s` = S.`%s` "%(targetColumn, sourceColumn)
else:
query += "and\n T.`%s` = S.`%s` "%(targetColumn, sourceColumn)
query += "\n"
query += "when matched "
if sourceIsIncremental == False:
# If the source is not incremental, it means that we need to check all the values in
# all columns as we dont know if the row have changed or not
query += "and (\n"
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
foundPKcolumn = False
for column in PKColumns.split(","):
if row['targetName'] == column:
foundPKcolumn = True
if foundPKcolumn == False:
if firstIteration == True:
query += " "
firstIteration = False
else:
query += " or "
query += "T.`%s` != S.`%s` "%(row['targetName'], row['sourceName'])
query += "or ( T.`%s` is null and S.`%s` is not null ) "%(row['targetName'], row['sourceName'])
query += "or ( T.`%s` is not null and S.`%s` is null ) "%(row['targetName'], row['sourceName'])
query += "\n"
if softDelete == True and datalakeIUDExists == True:
# If a row is deleted and then inserted again with the same values in all fields, this will still trigger an update
query += " or T.datalake_iud = 'D' \n"
query += ") \n"
if oracleFlashbackSource == True:
query += "and ( S.datalake_flashback_operation is null or S.datalake_flashback_operation != 'D' ) \n"
if mssqlChangeTrackingSource == True:
query += "and ( S.datalake_mssql_changetrack_operation is null or S.datalake_mssql_changetrack_operation != 'D' ) \n"
query += "then update set "
firstIteration = True
nonPKcolumnFound = False
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
foundPKcolumn = False
for column in PKColumns.split(","):
if row['targetName'] == column:
foundPKcolumn = True
if foundPKcolumn == False:
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " `%s` = S.`%s`"%(row['targetName'], row['sourceName'])
nonPKcolumnFound = True
if nonPKcolumnFound == False:
# This will happen if there are only columns that is part of the PK in the table. Impossible to merge it with full history
logging.error("This table only have columns that is part of the PrimaryKey. Merge operations cant be used")
self.import_config.remove_temporary_files()
sys.exit(1)
if datalakeIUDExists == True: query += ", \n `datalake_iud` = 'U'"
if datalakeUpdateExists == True: query += ", \n `datalake_update` = '%s'"%(mergeTime)
if datalakeSourceExists == True and datalakeSource != None: query += ", \n `datalake_source` = '%s'"%(datalakeSource)
query += " \n"
if oracleFlashbackSource == True:
query += "when matched and S.datalake_flashback_operation = 'D' then delete \n"
if mssqlChangeTrackingSource == True:
query += "when matched and S.datalake_mssql_changetrack_operation = 'D' then delete \n"
query += "when not matched "
if oracleFlashbackSource == True:
query += "and ( S.datalake_flashback_operation is null or S.datalake_flashback_operation != 'D' ) \n"
if mssqlChangeTrackingSource == True:
query += "and ( S.datalake_mssql_changetrack_operation is null or S.datalake_mssql_changetrack_operation != 'D' ) \n"
query += "then insert values ( "
firstIteration = True
for index, row in targetColumns.iterrows():
ColumnName = row['name']
sourceColumnName = columnMerge.loc[columnMerge['targetName'] == ColumnName]['sourceName'].fillna('').iloc[0]
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
if sourceColumnName != "":
query += " S.`%s`"%(sourceColumnName)
elif ColumnName == "datalake_iud":
query += " 'I'"
elif ColumnName == "datalake_insert":
query += " '%s'"%(mergeTime)
elif ColumnName == "datalake_update":
query += " '%s'"%(mergeTime)
elif ColumnName == "datalake_source":
query += " '%s'"%(datalakeSource)
else:
query += " NULL"
query += " \n) \n"
# print("==============================================================")
# print(query)
# self.import_config.remove_temporary_files()
# sys.exit(1)
## query = query.replace('\n', '')
self.common_operations.executeHiveQuery(query)
if deleteNotUpdatedRows == True:
# This is used by Oracle Flashback and MSSQL Change Tracking imports when doing a reinitialization of the data and we need to
# remove the rows that was not updated
query = "delete from `%s`.`%s` where datalake_update != '%s' "%(targetDB, targetTable, mergeTime)
self.common_operations.executeHiveQuery(query)
# If a row was previously deleted and now inserted again and we are using Soft Delete,
# then the information in the datalake_iud, datalake_insert and datalake_delete is wrong.
if softDelete == True:
query = "update `%s`.`%s` set "%(targetDB, targetTable)
query += " datalake_iud = 'I', "
query += " datalake_insert = datalake_update, "
query += " datalake_delete = null "
query += "where "
query += " datalake_iud = 'U' and "
query += " datalake_delete is not null"
# print("==============================================================")
# print(query)
# query = query.replace('\n', '')
self.common_operations.executeHiveQuery(query)
# Statement to select all rows that was changed in the Target table and insert them to the History table
if createHistoryAudit == True and historyDB != None and historyTable != None and oracleFlashbackSource == False:
query = "insert into table `%s`.`%s` \n"%(historyDB, historyTable)
query += "( "
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " `%s`"%(row['targetName'])
if datalakeSourceExists == True:
query += ",\n `datalake_source`"
query += ",\n `datalake_iud`"
query += ",\n `datalake_timestamp`"
query += "\n) \n"
query += "select "
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " `%s`"%(row['targetName'])
if datalakeSourceExists == True:
query += ",\n '%s'"%(datalakeSource)
query += ",\n `datalake_iud`"
query += ",\n `datalake_update`"
query += "\nfrom `%s`.`%s` \n"%(targetDB, targetTable)
query += "where datalake_update = '%s'"%(mergeTime)
self.common_operations.executeHiveQuery(query)
if sourceIsIncremental == False and targetDeleteDB != None and targetDeleteTable != None:
# Start with truncating the History Delete table as we need to rebuild this one from scratch to determine what rows are deleted
query = "truncate table `%s`.`%s`"%(targetDeleteDB, targetDeleteTable)
self.common_operations.executeHiveQuery(query)
# Insert all rows (PK columns only) that exists in the Target Table but dont exists in the Import table (the ones that was deleted)
query = "insert into table `%s`.`%s` \n(`"%(targetDeleteDB, targetDeleteTable)
query += "`, `".join(PKColumns.split(","))
query += "`) \nselect T.`"
query += "`, T.`".join(PKColumns.split(","))
query += "` \nfrom `%s`.`%s` as T \n"%(targetDB, targetTable)
query += "left outer join `%s`.`%s` as S \n"%(sourceDB, sourceTable)
query += "on \n"
for i, targetColumn in enumerate(PKColumns.split(",")):
sourceColumn = columnMerge.loc[columnMerge['Exist'] == 'both'].loc[columnMerge['targetName'] == targetColumn].iloc[0]['sourceName']
if i == 0:
query += " T.`%s` = S.`%s` "%(targetColumn, sourceColumn)
else:
query += "and\n T.`%s` = S.`%s` "%(targetColumn, sourceColumn)
query += "\nwhere \n"
for i, targetColumn in enumerate(PKColumns.split(",")):
sourceColumn = columnMerge.loc[columnMerge['Exist'] == 'both'].loc[columnMerge['targetName'] == targetColumn].iloc[0]['sourceName']
if i == 0:
query += " S.`%s` is null "%(sourceColumn)
else:
query += "and\n S.`%s` is null "%(sourceColumn)
self.common_operations.executeHiveQuery(query)
if oracleFlashbackSource == True and createHistoryAudit == True:
# If it is a history merge with Oracle Flashback, we need to handle the deletes separatly
query = "insert into table `%s`.`%s` \n"%(historyDB, historyTable)
query += "( "
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " `%s`"%(row['targetName'])
if datalakeSourceExists == True:
query += ",\n `datalake_source`"
query += ",\n `datalake_iud`"
query += ",\n `datalake_timestamp`"
query += "\n) \n"
query += "select "
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " `%s`"%(row['targetName'])
if datalakeSourceExists == True:
query += ",\n '%s'"%(datalakeSource)
query += ",\n `datalake_flashback_operation` as `datalake_iud`"
query += ",\n timestamp('%s') as `datalake_timestamp`"%(mergeTime)
query += "\nfrom `%s`.`%s`"%(sourceDB, oracleFlashbackImportTable)
self.common_operations.executeHiveQuery(query)
if mssqlChangeTrackingSource == True and createHistoryAudit == True:
# If it is a history merge with MSSQL Change Traging, we need to handle the deletes separatly
query = "insert into table `%s`.`%s` \n"%(historyDB, historyTable)
query += "( "
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " `%s`"%(row['targetName'])
if datalakeSourceExists == True:
query += ",\n `datalake_source`"
query += ",\n `datalake_iud`"
query += ",\n `datalake_timestamp`"
query += "\n) \n"
query += "select "
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " `%s`"%(row['targetName'])
if datalakeSourceExists == True:
query += ",\n '%s'"%(datalakeSource)
query += ",\n `datalake_mssql_changetrack_operation` as `datalake_iud`"
query += ",\n timestamp('%s') as `datalake_timestamp`"%(mergeTime)
query += "\nfrom `%s`.`%s`"%(sourceDB, mssqlChangeTrackingImportTable)
self.common_operations.executeHiveQuery(query)
# Insert the deleted rows into the History table. Without this, it's impossible to see what values the column had before the delete
if sourceIsIncremental == False and createHistoryAudit == True and historyDB != None and historyTable != None and targetDeleteDB != None and targetDeleteTable != None:
query = "insert into table `%s`.`%s` \n"%(historyDB, historyTable)
query += "( "
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " `%s`"%(row['targetName'])
if datalakeSourceExists == True:
query += ",\n `datalake_source`"
query += ",\n `datalake_iud`"
query += ",\n `datalake_timestamp`"
query += "\n) \n"
query += "select "
firstIteration = True
for index, row in columnMerge.loc[columnMerge['Exist'] == 'both'].iterrows():
if firstIteration == True:
firstIteration = False
query += " \n"
else:
query += ", \n"
query += " T.`%s`"%(row['targetName'])
if datalakeSourceExists == True:
query += ",\n '%s' as `datalake_source`"%(datalakeSource)
query += ",\n 'D' as `datalake_iud`"
query += ",\n timestamp('%s') as `datalake_timestamp`"%(mergeTime)
query += "\nfrom `%s`.`%s` as D \n"%(targetDeleteDB, targetDeleteTable)
query += "left join `%s`.`%s` as T \n"%(targetDB, targetTable)
query += "on \n"
for i, column in enumerate(PKColumns.split(",")):
if i == 0:
query += " T.`%s` = D.`%s` "%(column, column)
else:
query += "and\n T.`%s` = D.`%s` "%(column, column)
# print("==============================================================")
# print(query)
# query = query.replace('\n', '')
self.common_operations.executeHiveQuery(query)
if sourceIsIncremental == False and targetDeleteDB != None and targetDeleteTable != None:
# Use the merge command to delete found rows between the Delete Table and the History Table
query = "merge into `%s`.`%s` as T \n"%(targetDB, targetTable)
query += "using `%s`.`%s` as D \n"%(targetDeleteDB, targetDeleteTable)
query += "on \n"
for i, column in enumerate(PKColumns.split(",")):
if i == 0:
query += " T.`%s` = D.`%s` "%(column, column)
else:
query += "and\n T.`%s` = D.`%s` "%(column, column)
if softDelete == True:
query += "and\n T.`datalake_delete` != 'D' "
query += "\n"
if softDelete == False:
query += "when matched then delete \n"
else:
query += "when matched then update set \n"
query += "datalake_iud = 'D', \n"
query += "datalake_update = timestamp('%s'), \n"%(mergeTime)
query += "datalake_delete = timestamp('%s') "%(mergeTime)
# print("==============================================================")
# print(query)
# query = query.replace('\n', '')
self.common_operations.executeHiveQuery(query)
logging.debug("Executing etl_operations.mergeHiveTables() - Finished")
|
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] |
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|
import numpy as np
import os
from scanorama import *
from scipy.sparse import vstack
from process import load_names
from experiments import *
from utils import *
NAMESPACE = 'zeisel'
METHOD = 'svd'
DIMRED = 100
data_names = [
'data/mouse_brain/zeisel/amygdala',
'data/mouse_brain/zeisel/cerebellum',
'data/mouse_brain/zeisel/cortex1',
'data/mouse_brain/zeisel/cortex2',
'data/mouse_brain/zeisel/cortex3',
'data/mouse_brain/zeisel/enteric',
'data/mouse_brain/zeisel/hippocampus',
'data/mouse_brain/zeisel/hypothalamus',
'data/mouse_brain/zeisel/medulla',
'data/mouse_brain/zeisel/midbraindorsal',
'data/mouse_brain/zeisel/midbrainventral',
'data/mouse_brain/zeisel/olfactory',
'data/mouse_brain/zeisel/pons',
'data/mouse_brain/zeisel/spinalcord',
'data/mouse_brain/zeisel/striatumdorsal',
'data/mouse_brain/zeisel/striatumventral',
'data/mouse_brain/zeisel/sympathetic',
'data/mouse_brain/zeisel/thalamus',
]
if __name__ == '__main__':
datasets, genes_list, n_cells = load_names(data_names, norm=False)
datasets, genes = merge_datasets(datasets, genes_list)
X = vstack(datasets)
if not os.path.isfile('data/dimred/{}_{}.txt'.format(METHOD, NAMESPACE)):
log('Dimension reduction with {}...'.format(METHOD))
X_dimred = reduce_dimensionality(
normalize(X), method=METHOD, dimred=DIMRED
)
log('Dimensionality = {}'.format(X_dimred.shape[1]))
np.savetxt('data/dimred/{}_{}.txt'.format(METHOD, NAMESPACE), X_dimred)
else:
X_dimred = np.loadtxt('data/dimred/{}_{}.txt'.format(METHOD, NAMESPACE))
from ample import gs, uniform, srs
#samp_idx = gs(X_dimred, 20000, replace=False)
#samp_idx = uniform(X_dimred, 20000, replace=False)
samp_idx = srs(X_dimred, 20000, replace=False)
#from anndata import AnnData
#import scanpy.api as sc
#adata = AnnData(X=X_dimred[samp_idx, :])
#sc.pp.neighbors(adata, use_rep='X')
#sc.tl.louvain(adata, resolution=1.5, key_added='louvain')
#
#louv_labels = np.array(adata.obs['louvain'].tolist())
#le = LabelEncoder().fit(louv_labels)
#cell_labels = le.transform(louv_labels)
#
#np.savetxt('data/cell_labels/zeisel_louvain.txt', cell_labels)
labels = (
open('data/cell_labels/zeisel_cluster.txt')
.read().rstrip().split('\n')
)
le = LabelEncoder().fit(labels)
cell_labels = le.transform(labels)
experiments(
X_dimred, NAMESPACE, n_seeds=2,
cell_labels=cell_labels,
kmeans_ami=True, louvain_ami=True,
rare=True,
rare_label=le.transform(['Ependymal'])[0],
)
exit()
embedding = visualize(
[ X_dimred[samp_idx, :] ], cell_labels[samp_idx],
NAMESPACE + '_srs{}'.format(len(samp_idx)),
[ str(ct) for ct in sorted(set(cell_labels)) ],
perplexity=100, n_iter=500, image_suffix='.png',
viz_cluster=True
)
exit()
cell_labels = (
open('data/cell_labels/zeisel_louvain.txt')
.read().rstrip().split('\n')
)
le = LabelEncoder().fit(cell_labels)
cell_labels = le.transform(cell_labels)
astro = set([ 32, 38, 40, ])
oligo = set([ 2, 5, 12, 20, 23, 33, 37, ])
focus = set([ 15, 36, 41 ])
labels = []
aob_labels = []
for cl in cell_labels:
if cl in focus:
labels.append(0)
aob_labels.append('both')
elif cl in astro or cl in oligo:
labels.append(1)
if cl in astro:
aob_labels.append('astro')
else:
aob_labels.append('oligo')
else:
labels.append(2)
aob_labels.append('none')
labels = np.array(labels)
aob_labels = np.array(aob_labels)
X = np.log1p(normalize(X[samp_idx, :]))
from mouse_brain_astrocyte import astro_oligo_joint, astro_oligo_violin
#astro_oligo_joint(X, genes, 'GJA1', 'MBP', aob_labels, 'astro', NAMESPACE)
#astro_oligo_joint(X, genes, 'GJA1', 'MBP', aob_labels, 'oligo', NAMESPACE)
#astro_oligo_joint(X, genes, 'GJA1', 'MBP', aob_labels, 'both', NAMESPACE)
#astro_oligo_joint(X, genes, 'GJA1', 'PLP1', aob_labels, 'astro', NAMESPACE)
#astro_oligo_joint(X, genes, 'GJA1', 'PLP1', aob_labels, 'oligo', NAMESPACE)
#astro_oligo_joint(X, genes, 'GJA1', 'PLP1', aob_labels, 'both', NAMESPACE)
astro_oligo_violin(X, genes, 'GJA1', aob_labels, NAMESPACE)
astro_oligo_violin(X, genes, 'MBP', aob_labels, NAMESPACE)
astro_oligo_violin(X, genes, 'PLP1', aob_labels, NAMESPACE)
viz_genes = [
#'GJA1', 'MBP', 'PLP1', 'TRF',
#'CST3', 'CPE', 'FTH1', 'APOE', 'MT1', 'NDRG2', 'TSPAN7',
#'PLP1', 'MAL', 'PTGDS', 'CLDN11', 'APOD', 'QDPR', 'MAG', 'ERMN',
#'PLP1', 'MAL', 'PTGDS', 'MAG', 'CLDN11', 'APOD', 'FTH1',
#'ERMN', 'MBP', 'ENPP2', 'QDPR', 'MOBP', 'TRF',
#'CST3', 'SPARCL1', 'PTN', 'CD81', 'APOE', 'ATP1A2', 'ITM2B'
]
cell_labels = (
open('data/cell_labels/zeisel_cluster.txt')
.read().rstrip().split('\n')
)
le = LabelEncoder().fit(cell_labels)
cell_labels = le.transform(cell_labels)
embedding = visualize(
[ X_dimred[samp_idx, :] ], cell_labels[samp_idx],
NAMESPACE + '_astro{}'.format(len(samp_idx)),
[ str(ct) for ct in sorted(set(cell_labels)) ],
gene_names=viz_genes, gene_expr=X, genes=genes,
perplexity=100, n_iter=500, image_suffix='.png',
viz_cluster=True
)
#visualize_dropout(X, embedding, image_suffix='.png',
# viz_prefix=NAMESPACE + '_dropout')
from differential_entropies import differential_entropies
differential_entropies(X_dimred, labels)
|
[
"scipy.sparse.vstack",
"ample.srs",
"process.load_names",
"numpy.array",
"differential_entropies.differential_entropies",
"mouse_brain_astrocyte.astro_oligo_violin"
] |
[((1047, 1081), 'process.load_names', 'load_names', (['data_names'], {'norm': '(False)'}), '(data_names, norm=False)\n', (1057, 1081), False, 'from process import load_names\n'), ((1149, 1165), 'scipy.sparse.vstack', 'vstack', (['datasets'], {}), '(datasets)\n', (1155, 1165), False, 'from scipy.sparse import vstack\n'), ((1811, 1846), 'ample.srs', 'srs', (['X_dimred', '(20000)'], {'replace': '(False)'}), '(X_dimred, 20000, replace=False)\n', (1814, 1846), False, 'from ample import gs, uniform, srs\n'), ((3771, 3787), 'numpy.array', 'np.array', (['labels'], {}), '(labels)\n', (3779, 3787), True, 'import numpy as np\n'), ((3805, 3825), 'numpy.array', 'np.array', (['aob_labels'], {}), '(aob_labels)\n', (3813, 3825), True, 'import numpy as np\n'), ((4438, 4497), 'mouse_brain_astrocyte.astro_oligo_violin', 'astro_oligo_violin', (['X', 'genes', '"""GJA1"""', 'aob_labels', 'NAMESPACE'], {}), "(X, genes, 'GJA1', aob_labels, NAMESPACE)\n", (4456, 4497), False, 'from mouse_brain_astrocyte import astro_oligo_joint, astro_oligo_violin\n'), ((4502, 4560), 'mouse_brain_astrocyte.astro_oligo_violin', 'astro_oligo_violin', (['X', 'genes', '"""MBP"""', 'aob_labels', 'NAMESPACE'], {}), "(X, genes, 'MBP', aob_labels, NAMESPACE)\n", (4520, 4560), False, 'from mouse_brain_astrocyte import astro_oligo_joint, astro_oligo_violin\n'), ((4565, 4624), 'mouse_brain_astrocyte.astro_oligo_violin', 'astro_oligo_violin', (['X', 'genes', '"""PLP1"""', 'aob_labels', 'NAMESPACE'], {}), "(X, genes, 'PLP1', aob_labels, NAMESPACE)\n", (4583, 4624), False, 'from mouse_brain_astrocyte import astro_oligo_joint, astro_oligo_violin\n'), ((5765, 5805), 'differential_entropies.differential_entropies', 'differential_entropies', (['X_dimred', 'labels'], {}), '(X_dimred, labels)\n', (5787, 5805), False, 'from differential_entropies import differential_entropies\n')]
|
import fcntl
import os
import socket
import struct
import warnings
import subprocess
import logging
import base64
logger = logging.getLogger(__name__)
# Dummy socket used for fcntl functions
_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
class AddrMeta(type):
@property
def maxvalue(cls):
return (0x1 << (cls.bytelen * 8)) - 1
class Addr(metaclass=AddrMeta):
bytelen = 0
def __init__(self, addr):
self._str = None
self._int = None
self._bytes = None
if isinstance(addr, type(self)):
self._str = addr._str
self._bytes = addr._bytes
self._int = addr._int
elif isinstance(addr, str):
self._str = addr
elif isinstance(addr, int):
self._int = addr
elif isinstance(addr, bytes):
if len(addr) == self.bytelen:
self._bytes = addr
else:
self._str = addr.decode('utf-8')
else:
raise ValueError('Cannot create {!s} from {!s}'.format(type(self), type(addr)))
# Operations
def __and__(self, other):
return type(self)(int(self) & int(other))
def __or__(self, other):
return type(self)(int(self) | int(other))
def __xor__(self, other):
return type(self)(int(self) ^ int(other))
def __invert__(self):
return type(self)(int(self) ^ self.maxvalue)
# Conversions
def __str__(self):
if self._str is None:
self._str = self.bytes_to_str(bytes(self))
return self._str
def __int__(self):
return int.from_bytes(bytes(self), byteorder='big')
def __bytes__(self):
if self._bytes is None:
if self._str is not None:
self._bytes = self.str_to_bytes(self._str)
elif self._int is not None:
self._bytes = self._int.to_bytes(self.bytelen, byteorder='big')
return self._bytes
def __repr__(self):
return '<{0}.{1} {2!s}>'.format(__name__, type(self).__name__, self)
class Ip(Addr):
bytelen = 4
@staticmethod
def bytes_to_str(b):
return socket.inet_ntoa(b)
@staticmethod
def str_to_bytes(s):
return socket.inet_aton(s)
def slash(self):
x, i = int(self), 0
while x & 0x1 == 0:
x >>= 1
i += 1
return 32 - i
class Mac(Addr):
bytelen = 6
@staticmethod
def bytes_to_str(b):
return ':'.join('%02x' % byte for byte in b)
@staticmethod
def str_to_bytes(s):
return bytes.fromhex(s.replace(':', ''))
def _ifctl(ifname, code):
if isinstance(ifname, str):
ifname = ifname.encode('utf-8')
return fcntl.ioctl(
_socket.fileno(),
code,
struct.pack('256s', ifname[:15])
)
def ifaddr(ifname):
return Ip(_ifctl(ifname, 0x8915)[20:24]) # SIOCGIFADDR
def ifmask(ifname):
return Ip(_ifctl(ifname, 0x891b)[20:24]) # SIOCGIFNETMASK
def ifhwaddr(ifname):
return Mac(_ifctl(ifname, 0x8927)[18:24]) # SIOCGIFHWADDR
def cidr(ip, mask):
return "{!s}/{:d}".format(ip, mask.slash())
def parsecidr(ipnet):
ipstr, maskstr = ipnet.split('/')
ip = Ip(ipstr)
mask = Ip(0xffffffff ^ ((0x00000001 << (32-int(maskstr)))-1))
return ip, mask
def ifcidr(ifname):
return cidr(ifaddr(ifname), ifmask(ifname))
class OpenVpnError(Exception):
def __init__(self, instance, msg):
self.instance = instance
super().__init__(msg)
class OpenVpn:
exe = 'openvpn'
initmsg = b'Initialization Sequence Completed'
def __init__(self, **kwargs):
if 'daemonize' in kwargs:
warnings.warn("This class will not be able to close a daemonized tunnel", warnings.Warning)
self.options = kwargs
self.initialized = False
self._process = None
def args(self):
result = []
for name, value in self.options.items():
result.append('--{!s}'.format(name))
# None is special to indicate the option have no value
if value is not None:
result.append(str(value))
return result
def check(self):
if self._process is not None:
self._process.poll()
code = self._process.returncode
if code is not None and code != 0:
raise OpenVpnError(self, "`openvpn {:s}` exited with error code: {:d}".format(" ".join(self.args()), code))
def running(self):
return self._process is not None and self._process.poll() is None
@staticmethod
def maketun():
os.makedirs('/dev/net', exist_ok=True)
subprocess.run(['mknod', '/dev/net/tun', 'c', '10', '200'], check=True)
def connect(self):
if not os.path.exists('/dev/net/tun'):
self.maketun()
if not self.running():
self.initialized = False
self._process = subprocess.Popen(
[self.exe] + self.args(),
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
self.check()
def disconnect(self):
if self.running():
self._process.terminate()
os.waitpid(self._process.pid, 0)
def waitforinit(self):
if not self.initialized:
for line in self._process.stdout:
logger.debug("openvpn: %s", line.decode('utf-8').strip())
if self.initmsg in line:
self.initialized = True
break
else:
self.check()
raise OpenVpnError(self, "OpenVPN exited with code 0, but did not display init msg")
def __enter__(self):
self.connect()
return self
def __exit__(self, *args, **kwargs):
self.disconnect()
|
[
"logging.getLogger",
"os.path.exists",
"socket.socket",
"os.makedirs",
"os.waitpid",
"subprocess.run",
"struct.pack",
"socket.inet_aton",
"warnings.warn",
"socket.inet_ntoa"
] |
[((124, 151), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (141, 151), False, 'import logging\n'), ((203, 251), 'socket.socket', 'socket.socket', (['socket.AF_INET', 'socket.SOCK_DGRAM'], {}), '(socket.AF_INET, socket.SOCK_DGRAM)\n', (216, 251), False, 'import socket\n'), ((2152, 2171), 'socket.inet_ntoa', 'socket.inet_ntoa', (['b'], {}), '(b)\n', (2168, 2171), False, 'import socket\n'), ((2231, 2250), 'socket.inet_aton', 'socket.inet_aton', (['s'], {}), '(s)\n', (2247, 2250), False, 'import socket\n'), ((2786, 2818), 'struct.pack', 'struct.pack', (['"""256s"""', 'ifname[:15]'], {}), "('256s', ifname[:15])\n", (2797, 2818), False, 'import struct\n'), ((4621, 4659), 'os.makedirs', 'os.makedirs', (['"""/dev/net"""'], {'exist_ok': '(True)'}), "('/dev/net', exist_ok=True)\n", (4632, 4659), False, 'import os\n'), ((4668, 4739), 'subprocess.run', 'subprocess.run', (["['mknod', '/dev/net/tun', 'c', '10', '200']"], {'check': '(True)'}), "(['mknod', '/dev/net/tun', 'c', '10', '200'], check=True)\n", (4682, 4739), False, 'import subprocess\n'), ((3679, 3774), 'warnings.warn', 'warnings.warn', (['"""This class will not be able to close a daemonized tunnel"""', 'warnings.Warning'], {}), "('This class will not be able to close a daemonized tunnel',\n warnings.Warning)\n", (3692, 3774), False, 'import warnings\n'), ((4779, 4809), 'os.path.exists', 'os.path.exists', (['"""/dev/net/tun"""'], {}), "('/dev/net/tun')\n", (4793, 4809), False, 'import os\n'), ((5256, 5288), 'os.waitpid', 'os.waitpid', (['self._process.pid', '(0)'], {}), '(self._process.pid, 0)\n', (5266, 5288), False, 'import os\n')]
|
from collections import defaultdict
def return_default():
return 0
REAL=open("18.txt").readlines()
SAMPLE=open("18.sample").readlines()
OPEN="."
TREE="|"
LUMBERYARD="#"
import copy
def safe_grid_get(grid, x, y, missing=None):
if x < 0 or y < 0:
return missing
if y >= len(grid):
return missing
if x >= len(grid[y]):
return missing
return grid[y][x]
def parse_lines(lines):
return list(map(lambda l: list(l.strip()), lines))
def next_sq(grid, x, y):
around = defaultdict(return_default)
for dy in [-1, 0, 1]:
for dx in [-1, 0, 1]:
if dx == 0 and dy == 0:
continue
a = safe_grid_get(grid, x + dx, y + dy)
if a is not None:
around[a] += 1
here = grid[y][x]
if here == OPEN:
if around[TREE] >= 3:
return TREE
else:
return OPEN
elif here == TREE:
if around[LUMBERYARD] >= 3:
return LUMBERYARD
else:
return TREE
else:
assert here == LUMBERYARD
if around[LUMBERYARD] >= 1 and around[TREE] >= 1:
return LUMBERYARD
else:
return OPEN
def resource_value(board):
lands = defaultdict(return_default)
for y in range(len(board)):
for x in range(len(board[0])):
lands[board[y][x]] += 1
return lands[TREE] * lands[LUMBERYARD]
def solve(lines, minutes):
cache = {}
old_board = parse_lines(lines)
for minute in range(minutes):
board = copy.deepcopy(old_board)
for y in range(len(board)):
for x in range(len(board[0])):
board[y][x] = next_sq(old_board, x, y)
old_board = board
key = "\n".join(map(lambda r: "".join(r), board))
# print(key)
if key in cache:
print(minute, cache[key])
else:
cache[key] = (minute, resource_value(board))
return resource_value(board)
sample = solve(SAMPLE, 10)
assert sample == 1147
print("*** SAMPLE PASSED ***")
# print(solve(REAL, 10000))
loop = """598 570 191420
599 571 189168
600 572 185082
601 573 185227
602 574 185320
603 575 185790
604 576 186120
605 577 189956
606 578 190068
607 579 191080
608 580 190405 # too low
609 581 193795
610 582 190950
611 583 193569
612 584 194350
613 585 196308
614 586 195364
615 587 197911
616 588 199755
617 589 201144
618 590 201607
619 591 203580
620 592 201260
621 593 201950
622 594 200675 # TOO HIGH
623 595 202208
624 596 200151
625 597 198948
626 570 191420
627 571 189168
628 572 185082
629 573 185227
630 574 185320
631 575 185790
632 576 186120
633 577 189956
634 578 190068
635 579 191080
636 580 190405
637 581 193795"""
num = 1000000000
nmod = 28
for num in range(570, 638):
print(num, (num - 570) % nmod + 570)
num = 1000000000 - 1
print(num, (num - 570) % nmod + 570 + nmod)
|
[
"collections.defaultdict",
"copy.deepcopy"
] |
[((519, 546), 'collections.defaultdict', 'defaultdict', (['return_default'], {}), '(return_default)\n', (530, 546), False, 'from collections import defaultdict\n'), ((1250, 1277), 'collections.defaultdict', 'defaultdict', (['return_default'], {}), '(return_default)\n', (1261, 1277), False, 'from collections import defaultdict\n'), ((1558, 1582), 'copy.deepcopy', 'copy.deepcopy', (['old_board'], {}), '(old_board)\n', (1571, 1582), False, 'import copy\n')]
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('activity_log', '0003_activitylog_extra_data'),
]
operations = [
migrations.AlterField(
model_name='activitylog',
name='datetime',
field=models.DateTimeField(default=django.utils.timezone.now, verbose_name='datetime', db_index=True),
),
migrations.AlterField(
model_name='activitylog',
name='ip_address',
field=models.GenericIPAddressField(blank=True, null=True, verbose_name='user IP', db_index=True),
),
migrations.AlterField(
model_name='activitylog',
name='request_url',
field=models.CharField(db_index=True, verbose_name='url', max_length=256),
),
]
|
[
"django.db.models.DateTimeField",
"django.db.models.GenericIPAddressField",
"django.db.models.CharField"
] |
[((397, 497), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'default': 'django.utils.timezone.now', 'verbose_name': '"""datetime"""', 'db_index': '(True)'}), "(default=django.utils.timezone.now, verbose_name=\n 'datetime', db_index=True)\n", (417, 497), False, 'from django.db import models, migrations\n'), ((623, 717), 'django.db.models.GenericIPAddressField', 'models.GenericIPAddressField', ([], {'blank': '(True)', 'null': '(True)', 'verbose_name': '"""user IP"""', 'db_index': '(True)'}), "(blank=True, null=True, verbose_name='user IP',\n db_index=True)\n", (651, 717), False, 'from django.db import models, migrations\n'), ((845, 912), 'django.db.models.CharField', 'models.CharField', ([], {'db_index': '(True)', 'verbose_name': '"""url"""', 'max_length': '(256)'}), "(db_index=True, verbose_name='url', max_length=256)\n", (861, 912), False, 'from django.db import models, migrations\n')]
|
import numpy
import pytest
import os
from shutil import rmtree
from numpy.testing import assert_allclose
import scipy.stats
import scipy.integrate
import scipy.special
from fgivenx.mass import PMF, compute_pmf
def gaussian_pmf(y, mu=0, sigma=1):
return scipy.special.erfc(numpy.abs(y-mu)/numpy.sqrt(2)/sigma)
def test_gaussian():
numpy.random.seed(0)
nsamp = 5000
samples = numpy.random.randn(nsamp)
y = numpy.random.uniform(-3, 3, 10)
m = PMF(samples, y)
m_ = gaussian_pmf(y)
assert_allclose(m, m_, rtol=3e-1)
def test_PMF():
# Compute samples
numpy.random.seed(0)
nsamp = 100
samples = numpy.concatenate((-5+numpy.random.randn(nsamp//2),
5+numpy.random.randn(nsamp//2)))
# Compute PMF
y = numpy.random.uniform(-10, 10, 10)
m = PMF(samples, y)
# Compute PMF via monte carlo
N = 100000
kernel = scipy.stats.gaussian_kde(samples)
s = kernel.resample(N)[0]
m_ = [sum(kernel(s) <= kernel(y_i))/float(N) for y_i in y]
assert_allclose(m, m_, atol=3*N**-0.5)
# Compute PMF via quadrature
m_ = [scipy.integrate.quad(lambda x: kernel(x)*(kernel(x) <= kernel(y_i)),
-numpy.inf, numpy.inf, limit=500)[0]
for y_i in y]
assert_allclose(m, m_, atol=1e-4)
assert_allclose([0, 0], PMF(samples, [-1e3, 1e3]))
samples = [0, 0]
m = PMF(samples, y)
assert_allclose(m, numpy.zeros_like(y))
def test_compute_pmf():
with pytest.raises(TypeError):
compute_pmf(None, None, wrong_argument=None)
cache = '.test_cache/test'
numpy.random.seed(0)
nsamp = 5000
a, b, e, f = 0, 1, 0, 1
m = numpy.random.normal(a, b, nsamp)
c = numpy.random.normal(e, f, nsamp)
nx = 100
x = numpy.linspace(-1, 1, nx)
fsamps = (numpy.outer(x, m) + c)
ny = 100
y = numpy.linspace(-3, 3, ny)
assert(not os.path.isfile(cache + '_masses.pkl'))
m = compute_pmf(fsamps, y, cache=cache)
assert(os.path.isfile(cache + '_masses.pkl'))
m_ = [gaussian_pmf(y, a*xi+e, numpy.sqrt(b**2*xi**2+f**2)) for xi in x]
assert_allclose(m.transpose(), m_, atol=3e-1)
m = compute_pmf(fsamps, y, cache=cache)
assert_allclose(m.transpose(), m_, atol=3e-1)
rmtree('.test_cache')
|
[
"numpy.random.normal",
"numpy.abs",
"fgivenx.mass.compute_pmf",
"fgivenx.mass.PMF",
"numpy.sqrt",
"numpy.testing.assert_allclose",
"numpy.zeros_like",
"shutil.rmtree",
"os.path.isfile",
"numpy.linspace",
"numpy.outer",
"pytest.raises",
"numpy.random.seed",
"numpy.random.uniform",
"numpy.random.randn"
] |
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|
"""Config flow for Vera."""
from __future__ import annotations
from collections.abc import Mapping
import logging
import re
from typing import Any
import pyvera as pv
from requests.exceptions import RequestException
import voluptuous as vol
from homeassistant import config_entries
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import CONF_EXCLUDE, CONF_LIGHTS, CONF_SOURCE
from homeassistant.core import callback
from homeassistant.helpers import entity_registry as er
from .const import CONF_CONTROLLER, CONF_LEGACY_UNIQUE_ID, DOMAIN
LIST_REGEX = re.compile("[^0-9]+")
_LOGGER = logging.getLogger(__name__)
def fix_device_id_list(data: list[Any]) -> list[int]:
"""Fix the id list by converting it to a supported int list."""
return str_to_int_list(list_to_str(data))
def str_to_int_list(data: str) -> list[int]:
"""Convert a string to an int list."""
return [int(s) for s in LIST_REGEX.split(data) if len(s) > 0]
def list_to_str(data: list[Any]) -> str:
"""Convert an int list to a string."""
return " ".join([str(i) for i in data])
def new_options(lights: list[int], exclude: list[int]) -> dict:
"""Create a standard options object."""
return {CONF_LIGHTS: lights, CONF_EXCLUDE: exclude}
def options_schema(options: Mapping[str, Any] = None) -> dict:
"""Return options schema."""
options = options or {}
return {
vol.Optional(
CONF_LIGHTS,
default=list_to_str(options.get(CONF_LIGHTS, [])),
): str,
vol.Optional(
CONF_EXCLUDE,
default=list_to_str(options.get(CONF_EXCLUDE, [])),
): str,
}
def options_data(user_input: dict) -> dict:
"""Return options dict."""
return new_options(
str_to_int_list(user_input.get(CONF_LIGHTS, "")),
str_to_int_list(user_input.get(CONF_EXCLUDE, "")),
)
class OptionsFlowHandler(config_entries.OptionsFlow):
"""Options for the component."""
def __init__(self, config_entry: ConfigEntry) -> None:
"""Init object."""
self.config_entry = config_entry
async def async_step_init(self, user_input: dict = None):
"""Manage the options."""
if user_input is not None:
return self.async_create_entry(
title="",
data=options_data(user_input),
)
return self.async_show_form(
step_id="init",
data_schema=vol.Schema(options_schema(self.config_entry.options)),
)
class VeraFlowHandler(config_entries.ConfigFlow, domain=DOMAIN):
"""Vera config flow."""
@staticmethod
@callback
def async_get_options_flow(config_entry: ConfigEntry) -> OptionsFlowHandler:
"""Get the options flow."""
return OptionsFlowHandler(config_entry)
async def async_step_user(self, user_input: dict = None):
"""Handle user initiated flow."""
if user_input is not None:
return await self.async_step_finish(
{
**user_input,
**options_data(user_input),
**{CONF_SOURCE: config_entries.SOURCE_USER},
**{CONF_LEGACY_UNIQUE_ID: False},
}
)
return self.async_show_form(
step_id="user",
data_schema=vol.Schema(
{**{vol.Required(CONF_CONTROLLER): str}, **options_schema()}
),
)
async def async_step_import(self, config: dict):
"""Handle a flow initialized by import."""
# If there are entities with the legacy unique_id, then this imported config
# should also use the legacy unique_id for entity creation.
entity_registry = er.async_get(self.hass)
use_legacy_unique_id = (
len(
[
entry
for entry in entity_registry.entities.values()
if entry.platform == DOMAIN and entry.unique_id.isdigit()
]
)
> 0
)
return await self.async_step_finish(
{
**config,
**{CONF_SOURCE: config_entries.SOURCE_IMPORT},
**{CONF_LEGACY_UNIQUE_ID: use_legacy_unique_id},
}
)
async def async_step_finish(self, config: dict):
"""Validate and create config entry."""
base_url = config[CONF_CONTROLLER] = config[CONF_CONTROLLER].rstrip("/")
controller = pv.VeraController(base_url)
# Verify the controller is online and get the serial number.
try:
await self.hass.async_add_executor_job(controller.refresh_data)
except RequestException:
_LOGGER.error("Failed to connect to vera controller %s", base_url)
return self.async_abort(
reason="cannot_connect", description_placeholders={"base_url": base_url}
)
await self.async_set_unique_id(controller.serial_number)
self._abort_if_unique_id_configured(config)
return self.async_create_entry(title=base_url, data=config)
|
[
"logging.getLogger",
"voluptuous.Required",
"pyvera.VeraController",
"re.compile",
"homeassistant.helpers.entity_registry.async_get"
] |
[((586, 607), 're.compile', 're.compile', (['"""[^0-9]+"""'], {}), "('[^0-9]+')\n", (596, 607), False, 'import re\n'), ((618, 645), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (635, 645), False, 'import logging\n'), ((3752, 3775), 'homeassistant.helpers.entity_registry.async_get', 'er.async_get', (['self.hass'], {}), '(self.hass)\n', (3764, 3775), True, 'from homeassistant.helpers import entity_registry as er\n'), ((4515, 4542), 'pyvera.VeraController', 'pv.VeraController', (['base_url'], {}), '(base_url)\n', (4532, 4542), True, 'import pyvera as pv\n'), ((3385, 3414), 'voluptuous.Required', 'vol.Required', (['CONF_CONTROLLER'], {}), '(CONF_CONTROLLER)\n', (3397, 3414), True, 'import voluptuous as vol\n')]
|
from pprint import pprint
import SCons.Builder
from SCons.Script import *
import json
import os
import copy
import collections
import xml.etree.ElementTree as ET
from mplabx import MPLABXProperties
MAKEFILE_TEXT = '''
MKDIR=mkdir
CP=cp
CCADMIN=CCadmin
RANLIB=ranlib
build: .build-post
.build-pre:
.build-post: .build-impl
clean: .clean-post
.clean-pre:
.clean-post: .clean-impl
clobber: .clobber-post
.clobber-pre:
.clobber-post: .clobber-impl
all: .all-post
.all-pre:
.all-post: .all-impl
help: .help-post
.help-pre:
.help-post: .help-impl
include nbproject/Makefile-impl.mk
include nbproject/Makefile-variables.mk
'''
PROJECT_XML_TEXT = '''
<project>
<type>com.microchip.mplab.nbide.embedded.makeproject</type>
<configuration>
<data>
<name />
<sourceRootList />
<confList />
</data>
</configuration>
</project>
'''
CONFIGURATIONS_XML_TEXT = '''
<configurationDescriptor version="65">
<logicalFolder name="root" displayName="root" projectFiles="true" />
<sourceRootList />
<projectmakefile>Makefile</projectmakefile>
<confs />
</configurationDescriptor>
'''
CONFIGURATION_ELEMENT_TEXT = '''
<conf type="2">
<toolsSet>
<targetDevice />
<languageToolchain />
<languageToolchainVersion />
</toolsSet>
<HI-TECH-COMP />
<HI-TECH-LINK />
<XC8-config-global />
</conf>
'''
def nested_dict():
return collections.defaultdict(nested_dict)
def merge(destination, source):
for key, value in source.items():
if isinstance(value, dict):
# get node or create one
node = destination.setdefault(key, {})
merge(node, value)
else:
destination[key] = value
return destination
def build_mplabx_nbproject_configuration(
env,
name: str,
properties: MPLABXProperties,
additional_compiler_properties: dict[str, str] = {},
additional_linker_properties: dict[str, str] = {},
additional_xc8_properties: dict[str, str] = {},
):
defines_str = ';'.join(env['CPPDEFINES'])
includes_str = ';'.join([env.Dir(path).abspath for path in env['CPPPATH']])
default_compiler_properties = {
'define-macros': f'{defines_str}',
'extra-include-directories': f'{includes_str}',
}
root = ET.fromstring(CONFIGURATION_ELEMENT_TEXT)
root.set('name', name)
root.find('./toolsSet/targetDevice').text = properties.device
root.find('./toolsSet/languageToolchain').text = properties.toolchain
root.find('./toolsSet/languageToolchainVersion').text = properties.toolchain_version
group_properties_mapping = {
'HI-TECH-COMP': default_compiler_properties | properties.compiler_properties | additional_compiler_properties,
'HI-TECH-LINK': properties.linker_properties | additional_linker_properties,
'XC8-config-global': properties.xc8_properties | additional_xc8_properties,
}
for group_name, group_properties in group_properties_mapping.items():
for key, value in group_properties.items():
root.find(group_name).append(ET.Element('property', key=key, value=value))
# ET.dump(root)
return env.Value(root)
def _create_file_hierarchy(source_relpaths: list[str]):
hierarchy = nested_dict()
# Put all entries into the hierarchy, keyed from dirname to basename
for source_relpath in sorted(source_relpaths):
dirname, basename = os.path.split(source_relpath)
hierarchy[dirname][basename] = source_relpath
# Split all directory keys further
while True:
found_nested = False
modified_hierarchy = nested_dict()
for parent_key, entries in hierarchy.items():
dirname, basename = os.path.split(parent_key)
if dirname:
merge(modified_hierarchy[dirname][basename], entries)
found_nested = True
else:
merge(modified_hierarchy[parent_key], entries)
hierarchy = modified_hierarchy
if not found_nested:
break
return hierarchy
def _build_xml_files(project_name: str, project_dir, confs: list, source_files: list[str]):
# Create the `configurations.xml` and `project.xml` file
configurations_xml_root = ET.fromstring(CONFIGURATIONS_XML_TEXT)
project_xml_root = ET.fromstring(PROJECT_XML_TEXT)
project_xml_root.set('xmlns', 'http://www.netbeans.org/ns/project/1')
project_xml_root.find('./configuration/data').set('xmlns', 'http://www.netbeans.org/ns/make-project/1')
project_xml_root.find('./configuration/data/name').text = project_name
# Add each configuration to the two XML files
for configuration_node in confs:
# Modify each configuration to make absolute paths relative to the project directory
modified_node = copy.deepcopy(configuration_node.read())
for includes_element in modified_node.findall('.//property[@key="extra-include-directories"]'):
includes_value = includes_element.get('value')
includes_relative = ';'.join([os.path.relpath(abspath, project_dir.abspath) for abspath in includes_value.split(';')])
includes_element.set('value', includes_relative)
configurations_xml_root.find('./confs').append(modified_node)
# Update the `project.xml` configuration list
project_conf_list_element = project_xml_root.find('./configuration/data/confList')
project_conf_elem_element = ET.SubElement(project_conf_list_element, 'confElem')
project_conf_name_element = ET.SubElement(project_conf_elem_element, 'name')
project_conf_name_element.text = configuration_node.read().get('name')
project_conf_text_element = ET.SubElement(project_conf_elem_element, 'text')
project_conf_text_element.text = '2'
# Generate the source root list, which will have a single root (common path for all sources)
common_root_path = os.path.commonpath([os.path.abspath(path) for path in source_files])
source_root_relpath = os.path.relpath(common_root_path, project_dir.abspath)
configurations_source_root_element = ET.Element('Elem')
configurations_source_root_element.text = source_root_relpath
configurations_xml_root.find('./sourceRootList').append(configurations_source_root_element)
project_source_root_element = ET.Element('sourceRootElem')
project_source_root_element.text = os.path.relpath(common_root_path, project_dir.abspath)
project_xml_root.find('./configuration/data/sourceRootList').append(project_source_root_element)
# Generate all logical folders and private files
root_logical_folder = configurations_xml_root.find('./logicalFolder[@name="root"]')
source_relpaths = [os.path.relpath(source_path, common_root_path) for source_path in source_files]
source_hierarchy = _create_file_hierarchy(source_relpaths)
def _walk_tree(parent_element: ET.Element, tree: dict):
for key, data in tree.items():
if isinstance(data, dict):
folder_element = ET.SubElement(parent_element, 'logicalFolder', name=key, displayName=key, projectFiles="true")
_walk_tree(folder_element, data)
elif isinstance(data, str):
item_element = ET.SubElement(parent_element, 'itemPath')
item_element.text = os.path.relpath(data, project_dir.abspath)
_walk_tree(root_logical_folder, source_hierarchy)
# Generate an item for the build Makefile
ET.SubElement(root_logical_folder, 'itemPath').text = 'Makefile'
return (configurations_xml_root, project_xml_root)
def build_mplabx_nbproject(target, source, env):
'''
target - (singleton list) - Directory node to the project folder
source - (list) - XML value nodes for each project configuration
'''
project_dir = target[0]
nbproject_dir = project_dir.Dir('nbproject')
configurations_xml_file = nbproject_dir.File('configurations.xml')
project_xml_file = nbproject_dir.File('project.xml')
makefile_file = project_dir.File('Makefile')
# Make the directories
env.Execute(Mkdir(project_dir))
env.Execute(Mkdir(nbproject_dir))
# Generate the XML files
confs = source
configurations_xml_root, project_xml_root = _build_xml_files(
project_name=os.path.basename(str(project_dir)),
project_dir=project_dir,
confs=confs,
source_files=env['source_files'])
with open(str(configurations_xml_file), 'w') as f:
ET.indent(configurations_xml_root, space=' ')
ET.ElementTree(configurations_xml_root).write(f, encoding='unicode')
with open(str(project_xml_file), 'w') as f:
ET.indent(project_xml_root, space=' ')
ET.ElementTree(project_xml_root).write(f, encoding='unicode')
with open(str(makefile_file), 'w') as f:
f.write(MAKEFILE_TEXT)
_mplabx_nbproject_builder = SCons.Builder.Builder(action=build_mplabx_nbproject)
def generate(env):
env.AddMethod(build_mplabx_nbproject_configuration, 'MplabxNbprojectConfiguration')
env['BUILDERS']['MplabxNbproject'] = _mplabx_nbproject_builder
def exists(env):
return 1
|
[
"xml.etree.ElementTree.indent",
"os.path.split",
"xml.etree.ElementTree.Element",
"xml.etree.ElementTree.SubElement",
"xml.etree.ElementTree.ElementTree",
"collections.defaultdict",
"os.path.abspath",
"xml.etree.ElementTree.fromstring",
"os.path.relpath"
] |
[((1424, 1460), 'collections.defaultdict', 'collections.defaultdict', (['nested_dict'], {}), '(nested_dict)\n', (1447, 1460), False, 'import collections\n'), ((2310, 2351), 'xml.etree.ElementTree.fromstring', 'ET.fromstring', (['CONFIGURATION_ELEMENT_TEXT'], {}), '(CONFIGURATION_ELEMENT_TEXT)\n', (2323, 2351), True, 'import xml.etree.ElementTree as ET\n'), ((4268, 4306), 'xml.etree.ElementTree.fromstring', 'ET.fromstring', (['CONFIGURATIONS_XML_TEXT'], {}), '(CONFIGURATIONS_XML_TEXT)\n', (4281, 4306), True, 'import xml.etree.ElementTree as ET\n'), ((4335, 4366), 'xml.etree.ElementTree.fromstring', 'ET.fromstring', (['PROJECT_XML_TEXT'], {}), '(PROJECT_XML_TEXT)\n', (4348, 4366), True, 'import xml.etree.ElementTree as ET\n'), ((6040, 6094), 'os.path.relpath', 'os.path.relpath', (['common_root_path', 'project_dir.abspath'], {}), '(common_root_path, project_dir.abspath)\n', (6055, 6094), False, 'import os\n'), ((6137, 6155), 'xml.etree.ElementTree.Element', 'ET.Element', (['"""Elem"""'], {}), "('Elem')\n", (6147, 6155), True, 'import xml.etree.ElementTree as ET\n'), ((6353, 6381), 'xml.etree.ElementTree.Element', 'ET.Element', (['"""sourceRootElem"""'], {}), "('sourceRootElem')\n", (6363, 6381), True, 'import xml.etree.ElementTree as ET\n'), ((6421, 6475), 'os.path.relpath', 'os.path.relpath', (['common_root_path', 'project_dir.abspath'], {}), '(common_root_path, project_dir.abspath)\n', (6436, 6475), False, 'import os\n'), ((3439, 3468), 'os.path.split', 'os.path.split', (['source_relpath'], {}), '(source_relpath)\n', (3452, 3468), False, 'import os\n'), ((5477, 5529), 'xml.etree.ElementTree.SubElement', 'ET.SubElement', (['project_conf_list_element', '"""confElem"""'], {}), "(project_conf_list_element, 'confElem')\n", (5490, 5529), True, 'import xml.etree.ElementTree as ET\n'), ((5566, 5614), 'xml.etree.ElementTree.SubElement', 'ET.SubElement', (['project_conf_elem_element', '"""name"""'], {}), "(project_conf_elem_element, 'name')\n", (5579, 5614), True, 'import xml.etree.ElementTree as ET\n'), ((5730, 5778), 'xml.etree.ElementTree.SubElement', 'ET.SubElement', (['project_conf_elem_element', '"""text"""'], {}), "(project_conf_elem_element, 'text')\n", (5743, 5778), True, 'import xml.etree.ElementTree as ET\n'), ((6742, 6788), 'os.path.relpath', 'os.path.relpath', (['source_path', 'common_root_path'], {}), '(source_path, common_root_path)\n', (6757, 6788), False, 'import os\n'), ((7503, 7549), 'xml.etree.ElementTree.SubElement', 'ET.SubElement', (['root_logical_folder', '"""itemPath"""'], {}), "(root_logical_folder, 'itemPath')\n", (7516, 7549), True, 'import xml.etree.ElementTree as ET\n'), ((8524, 8570), 'xml.etree.ElementTree.indent', 'ET.indent', (['configurations_xml_root'], {'space': '""" """'}), "(configurations_xml_root, space=' ')\n", (8533, 8570), True, 'import xml.etree.ElementTree as ET\n'), ((8705, 8744), 'xml.etree.ElementTree.indent', 'ET.indent', (['project_xml_root'], {'space': '""" """'}), "(project_xml_root, space=' ')\n", (8714, 8744), True, 'import xml.etree.ElementTree as ET\n'), ((3737, 3762), 'os.path.split', 'os.path.split', (['parent_key'], {}), '(parent_key)\n', (3750, 3762), False, 'import os\n'), ((5965, 5986), 'os.path.abspath', 'os.path.abspath', (['path'], {}), '(path)\n', (5980, 5986), False, 'import os\n'), ((3105, 3149), 'xml.etree.ElementTree.Element', 'ET.Element', (['"""property"""'], {'key': 'key', 'value': 'value'}), "('property', key=key, value=value)\n", (3115, 3149), True, 'import xml.etree.ElementTree as ET\n'), ((7057, 7155), 'xml.etree.ElementTree.SubElement', 'ET.SubElement', (['parent_element', '"""logicalFolder"""'], {'name': 'key', 'displayName': 'key', 'projectFiles': '"""true"""'}), "(parent_element, 'logicalFolder', name=key, displayName=key,\n projectFiles='true')\n", (7070, 7155), True, 'import xml.etree.ElementTree as ET\n'), ((8579, 8618), 'xml.etree.ElementTree.ElementTree', 'ET.ElementTree', (['configurations_xml_root'], {}), '(configurations_xml_root)\n', (8593, 8618), True, 'import xml.etree.ElementTree as ET\n'), ((8753, 8785), 'xml.etree.ElementTree.ElementTree', 'ET.ElementTree', (['project_xml_root'], {}), '(project_xml_root)\n', (8767, 8785), True, 'import xml.etree.ElementTree as ET\n'), ((5075, 5120), 'os.path.relpath', 'os.path.relpath', (['abspath', 'project_dir.abspath'], {}), '(abspath, project_dir.abspath)\n', (5090, 5120), False, 'import os\n'), ((7272, 7313), 'xml.etree.ElementTree.SubElement', 'ET.SubElement', (['parent_element', '"""itemPath"""'], {}), "(parent_element, 'itemPath')\n", (7285, 7313), True, 'import xml.etree.ElementTree as ET\n'), ((7350, 7392), 'os.path.relpath', 'os.path.relpath', (['data', 'project_dir.abspath'], {}), '(data, project_dir.abspath)\n', (7365, 7392), False, 'import os\n')]
|
#! /usr/bin/env python
# Author: <NAME>
# Contact: <EMAIL>
# Revision: $Revision: 3915 $
# Date: $Date: 2005-10-02 03:06:42 +0200 (Sun, 02 Oct 2005) $
# Copyright: This module has been placed in the public domain.
"""
Tests for docutils.transforms.peps.
"""
from __init__ import DocutilsTestSupport
from docutils.transforms.peps import TargetNotes
from docutils.parsers.rst import Parser
def suite():
parser = Parser()
s = DocutilsTestSupport.TransformTestSuite(parser)
s.generateTests(totest)
return s
totest = {}
totest['target_notes'] = ((TargetNotes,), [
["""\
No references or targets exist, therefore
no "References" section should be generated.
""",
"""\
<document source="test data">
<paragraph>
No references or targets exist, therefore
no "References" section should be generated.
"""],
["""\
A target exists, here's the reference_.
A "References" section should be generated.
.. _reference: http://www.example.org
""",
"""\
<document source="test data">
<paragraph>
A target exists, here's the \n\
<reference name="reference" refname="reference">
reference
\n\
<footnote_reference auto="1" ids="id3" refname="TARGET_NOTE: id2">
.
A "References" section should be generated.
<target ids="reference" names="reference" refuri="http://www.example.org">
<section ids="id1">
<title>
References
<footnote auto="1" ids="id2" names="TARGET_NOTE:\ id2">
<paragraph>
<reference refuri="http://www.example.org">
http://www.example.org
"""],
])
if __name__ == '__main__':
import unittest
unittest.main(defaultTest='suite')
|
[
"unittest.main",
"docutils.parsers.rst.Parser",
"__init__.DocutilsTestSupport.TransformTestSuite"
] |
[((419, 427), 'docutils.parsers.rst.Parser', 'Parser', ([], {}), '()\n', (425, 427), False, 'from docutils.parsers.rst import Parser\n'), ((436, 482), '__init__.DocutilsTestSupport.TransformTestSuite', 'DocutilsTestSupport.TransformTestSuite', (['parser'], {}), '(parser)\n', (474, 482), False, 'from __init__ import DocutilsTestSupport\n'), ((1690, 1724), 'unittest.main', 'unittest.main', ([], {'defaultTest': '"""suite"""'}), "(defaultTest='suite')\n", (1703, 1724), False, 'import unittest\n')]
|
# Generated by Django 4.0.2 on 2022-03-15 22:43
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('rental_property', '0010_alter_rentalunit_status'),
]
operations = [
migrations.AlterModelOptions(
name='rentalunit',
options={'verbose_name_plural': 'Rental Houses'},
),
]
|
[
"django.db.migrations.AlterModelOptions"
] |
[((240, 342), 'django.db.migrations.AlterModelOptions', 'migrations.AlterModelOptions', ([], {'name': '"""rentalunit"""', 'options': "{'verbose_name_plural': 'Rental Houses'}"}), "(name='rentalunit', options={\n 'verbose_name_plural': 'Rental Houses'})\n", (268, 342), False, 'from django.db import migrations\n')]
|
from datetime import datetime
import base64
import os
import re
import requests
import sys
import urllib.parse
import xmltodict
import xbmc
import xbmcgui
import xbmcplugin
import xbmcaddon
import xbmcvfs
__PLUGIN_ID__ = "plugin.audio.podcasts"
# see https://forum.kodi.tv/showthread.php?tid=112916
_MONTHS = ["Jan", "Feb", "Mar", "Apr", "May",
"Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
GPODDER_API = {
"login": "%s/api/2/auth/%s/login.json",
"subscriptions": "%s/subscriptions/%s.%s"
}
settings = xbmcaddon.Addon(id=__PLUGIN_ID__)
addon_dir = xbmcvfs.translatePath(settings.getAddonInfo('path'))
class HttpStatusError(Exception):
message = ""
def __init__(self, msg):
self.message = msg
class Mediathek:
_GROUPS = 10
_ENTRIES = 10
addon_handle = None
def __init__(self):
pass
def _parse_outlines_from_opml(self, outline):
if type(outline) is not list:
outline = [outline]
entries = []
for i, o in enumerate(outline):
name = o["@title"] if "@title" in o else o["@text"]
if not name and "@xmlUrl" in o:
m = re.match(
"^https?:\/\/([^\/]+).*\/?.*\/([^\/]+)\/?$", o["@xmlUrl"])
if m:
name = "%s %s...%s" % (settings.getLocalizedString(
32053), m.groups()[0][:20], m.groups()[1][-40:])
entry = {
"path": str(i),
"name": name,
"node": []
}
if "@type" in o and o["@type"] == "rss" and "@xmlUrl" in o:
entry["params"] = [{
"rss": o["@xmlUrl"]
}]
entries.append(entry)
elif "outline" in o:
entry["node"] = self._parse_outlines_from_opml(
o["outline"])
entries.append(entry)
return entries
def _play_latest(self, url):
try:
title, description, image, items = self._load_rss(url)
item = items[0]
li = self._create_list_item(item)
xbmcplugin.setResolvedUrl(self.addon_handle, True, li)
except HttpStatusError as error:
xbmcgui.Dialog().notification(settings.getLocalizedString(32090), error.message)
def _create_list_item(self, item):
li = xbmcgui.ListItem(label=item["name"])
if "description" in item:
li.setProperty("label2", item["description"])
if "stream_url" in item:
li.setPath(item["stream_url"])
if "type" in item:
if item["type"] == "video":
li.setInfo(item["type"], {
"title": item["name"],
"plot": item["description"] if "description" in item else ""
})
elif item["type"] == "music":
li.setInfo(item["type"], {
"title": item["name"]
})
if "icon" in item and item["icon"]:
li.setArt({"icon": item["icon"]})
else:
li.setArt({"icon": os.path.join(
addon_dir, "resources", "assets", "icon.png")}
)
if "date" in item and item["date"]:
if "setDateTime" in dir(li): # available since Kodi v20
li.setDateTime(item["date"].strftime("%Y-%m-%dT%H:%M:%SZ"))
else:
pass
if "specialsort" in item:
li.setProperty("SpecialSort", item["specialsort"])
if "duration" in item and item["duration"] >= 0:
li.setInfo("music", {"duration": item["duration"]})
li.setInfo("video", {"duration": item["duration"]})
return li
def _add_list_item(self, entry, path):
def _build_param_string(params, current=""):
if params == None:
return current
for obj in params:
for name in obj:
enc_value = base64.urlsafe_b64encode(
obj[name].encode("utf-8"))
current += "?" if len(current) == 0 else "&"
current += name + "=" + str(enc_value, "utf-8")
return current
if path == "/":
path = ""
item_path = path + "/" + entry["path"]
param_string = ""
if "params" in entry:
param_string = _build_param_string(entry["params"],
current=param_string)
li = self._create_list_item(entry)
if "stream_url" in entry:
url = entry["stream_url"]
else:
url = "".join(
["plugin://", __PLUGIN_ID__, item_path, param_string])
is_folder = "node" in entry
li.setProperty("IsPlayable", "false" if is_folder else "true")
xbmcplugin.addDirectoryItem(handle=self.addon_handle,
listitem=li,
url=url,
isFolder=is_folder)
def _http_request(self, url, headers={}, method="GET"):
useragent = f"{settings.getAddonInfo('id')}/{settings.getAddonInfo('version')} (Kodi/{xbmc.getInfoLabel('System.BuildVersionShort')})"
headers["User-Agent"] = useragent
if method == "GET":
req = requests.get
elif method == "POST":
req = requests.post
else:
raise HttpStatusError(settings.getLocalizedString(32091) % method)
try:
res = req(url, headers=headers)
except requests.exceptions.RequestException as error:
xbmc.log("Request Exception: %s" % str(error), xbmc.LOGERROR)
raise HttpStatusError(settings.getLocalizedString(32092))
if res.status_code == 200:
return res.text, res.cookies
else:
raise HttpStatusError(settings.getLocalizedString(
32093) % (res.status_code, url))
def _load_rss(self, url):
def _parse_item(_ci):
if "enclosure" in _ci and "@url" in _ci["enclosure"]:
stream_url = _ci["enclosure"]["@url"]
if _ci["enclosure"]["@type"].split("/")[0] == "video":
_type = "video"
else:
_type = "music"
elif "guid" in _ci and _ci["guid"]:
# not supported yet
return None
else:
return None
if "itunes:image" in _ci and "@href" in _ci["itunes:image"]:
item_image = _ci["itunes:image"]["@href"]
else:
item_image = image
if "pubDate" in _ci:
_f = re.findall(
"(\d{1,2}) (\w{3}) (\d{4}) (\d{2}):(\d{2}):(\d{2})", _ci["pubDate"])
if _f:
_m = _MONTHS.index(_f[0][1]) + 1
pubDate = datetime(year=int(_f[0][2]), month=_m, day=int(_f[0][0]), hour=int(
_f[0][3]), minute=int(_f[0][4]), second=int(_f[0][5]))
else:
pubDate = None
if "itunes:duration" in _ci:
try:
duration = int(_ci["itunes:duration"]) #if duration is already in seconds
except:
try: #try converting HH:MM:SS or MM:SS string to integer seconds
durationList = _ci["itunes:duration"].split(":")
if len(durationList) == 3: #HH:MM:SS
duration = int(durationList[0]) * 3600 + int(durationList[1]) * 60 + int(durationList[2])
elif len(durationList) == 2: #MM:SS
duration = int(durationList[0]) * 60 + int(durationList[1])
else:
duration = -1
except:
duration = -1
else:
duration = -1
return {
"name": _ci["title"],
"description": _ci["description"] if "description" in _ci else "",
"date": pubDate,
"icon": item_image,
"stream_url": stream_url,
"type": _type,
"duration": duration
}
res, cookies = self._http_request(url)
if not res.startswith("<?xml"):
raise HttpStatusError("%s %s" % (
settings.getLocalizedString(32094), url))
else:
rss_feed = xmltodict.parse(res)
channel = rss_feed["rss"]["channel"]
title = channel["title"] if "title" in channel else ""
description = channel["description"] if "description" in channel else ""
if "image" in channel and "url" in channel["image"]:
image = channel["image"]["url"]
elif "itunes:image" in channel:
image = channel["itunes:image"]["@href"]
else:
image = None
items = []
if type(channel["item"]) is list:
for _ci in channel["item"]:
item = _parse_item(_ci)
if item is not None:
items += [item]
else:
item = _parse_item(channel["item"])
if item is not None:
items += [item]
return title, description, image, items
def _render_rss(self, path, url):
def _update_Image(path, image):
if path.startswith("/pod-"):
_p = path[5:].split("/")
settings.setSetting("group_%i_rss_%i_icon" %
(int(_p[0]), int(_p[1])), image)
try:
title, description, image, items = self._load_rss(url)
if image:
_update_Image(path, image)
except HttpStatusError as error:
xbmc.log("HTTP Status Error: %s, path=%s" %
(error.message, path), xbmc.LOGERROR)
xbmcgui.Dialog().notification(settings.getLocalizedString(32090), error.message)
else:
if len(items) > 0 and settings.getSetting("anchor") == "true":
entry = {
"path": "latest",
"name": "%s (%s)" % (title, settings.getLocalizedString(32052)),
"description": description,
"icon": image,
"date": datetime.now(),
"specialsort": "top",
"type": items[0]["type"],
"params": [
{
"play_latest": url
}
]
}
self._add_list_item(entry, path)
for item in items:
li = self._create_list_item(item)
xbmcplugin.addDirectoryItem(handle=self.addon_handle,
listitem=li,
url=item["stream_url"],
isFolder=False)
if "setDateTime" in dir(li): # available since Kodi v20
xbmcplugin.addSortMethod(
self.addon_handle, xbmcplugin.SORT_METHOD_DATE)
xbmcplugin.endOfDirectory(self.addon_handle)
def _browse(self, dir_structure, path, updateListing=False):
def _get_node_by_path(path):
if path == "/":
return dir_structure[0]
tokens = path.split("/")[1:]
node = dir_structure[0]
while len(tokens) > 0:
path = tokens.pop(0)
for n in node["node"]:
if n["path"] == path:
node = n
break
return node
node = _get_node_by_path(path)
for entry in node["node"]:
self._add_list_item(entry, path)
xbmcplugin.addSortMethod(
self.addon_handle, xbmcplugin.SORT_METHOD_FULLPATH)
xbmcplugin.addSortMethod(
self.addon_handle, xbmcplugin.SORT_METHOD_LABEL)
xbmcplugin.endOfDirectory(
self.addon_handle, updateListing=updateListing)
def _parse_opml(self, data):
opml_data = xmltodict.parse(data)
entries = self._parse_outlines_from_opml(
opml_data["opml"]["body"]["outline"])
return opml_data["opml"]["head"]["title"], entries
def _open_opml_file(self, path):
with open(path) as _opml_file:
return _opml_file.read()
def _build_dir_structure(self):
groups = []
# opml files / podcasts lists
for g in range(self._GROUPS):
if settings.getSetting("opml_file_%i" % g) == "":
continue
path = os.path.join(
addon_dir, settings.getSetting("opml_file_%i" % g))
try:
name, nodes = self._parse_opml(self._open_opml_file(path))
groups.append({
"path": "opml-%i" % g,
"name": name,
"node": nodes
})
except:
xbmc.log("Cannot read opml file %s" % path, xbmc.LOGERROR)
# rss feeds from settings
for g in range(self._GROUPS):
if settings.getSetting("group_%i_enable" % g) == "false":
continue
entries = []
for e in range(self._ENTRIES):
if settings.getSetting("group_%i_rss_%i_enable" % (g, e)) == "false":
continue
icon = settings.getSetting("group_%i_rss_%i_icon"
% (g, e))
entries += [{
"path": "%i" % e,
"name": settings.getSetting("group_%i_rss_%i_name"
% (g, e)),
"params": [
{
"rss": settings.getSetting("group_%i_rss_%i_url" % (g, e))
}
],
"icon": icon,
"node": []
}]
groups += [{
"path": "pod-%i" % g,
"name": settings.getSetting("group_%i_name" % g),
"node": entries
}]
return [
{ # root
"path": "",
"node": groups
}
]
def handle(self, argv):
def decode_param(encoded_param):
return base64.urlsafe_b64decode(encoded_param).decode("utf-8")
self.addon_handle = int(argv[1])
path = urllib.parse.urlparse(argv[0]).path.replace("//", "/")
url_params = urllib.parse.parse_qs(argv[2][1:])
if "rss" in url_params:
url = decode_param(url_params["rss"][0])
self._render_rss(path, url)
elif "play_latest" in url_params:
url = decode_param(url_params["play_latest"][0])
self._play_latest(url)
else:
_dir_structure = self._build_dir_structure()
self._browse(dir_structure=_dir_structure, path=path)
def _login_at_gpodder(self):
auth_string = "%s:%s" % (settings.getSetting(
"gpodder_username"), settings.getSetting("gpodder_password"))
b64auth = {
"Authorization": "Basic %s" % base64.urlsafe_b64encode(auth_string.encode("utf-8")).decode("utf-8")
}
response, cookies = self._http_request(
GPODDER_API["login"] % (settings.getSetting("gpodder_hostname"),
settings.getSetting("gpodder_username")), b64auth, "POST")
if "sessionid" not in cookies:
raise HttpStatusError(settings.getLocalizedString(32095))
return cookies["sessionid"]
def _load_gpodder_subscriptions(self, sessionid):
session_cookie = {
"Cookie": "%s=%s" % ("sessionid", sessionid)
}
response, cookies = self._http_request(
GPODDER_API["subscriptions"] % (settings.getSetting("gpodder_hostname"),
settings.getSetting(
"gpodder_username"),
"opml"), session_cookie)
return response
def _select_opml_file(self):
path = xbmcgui.Dialog().browse(
type=1, heading=settings.getLocalizedString(32070), shares="", mask=".xml|.opml")
if path == "":
return None, None
try:
return self._parse_opml(self._open_opml_file(path))
except:
xbmc.log("Cannot read opml file %s" % path, xbmc.LOGERROR)
return None, None
def _select_feeds(self, name, entries, freeslots):
selection = [e["name"]
for e in entries if "params" in e and len(e["params"]) == 1 and "rss" in e["params"][0]]
ok = False
while not ok:
feeds = xbmcgui.Dialog().multiselect(
settings.getLocalizedString(32071), selection)
if feeds == None:
ok = True
elif len(feeds) == 0:
xbmcgui.Dialog().ok(settings.getLocalizedString(32072),
settings.getLocalizedString(32073))
elif len(feeds) > freeslots:
xbmcgui.Dialog().ok(settings.getLocalizedString(32074),
settings.getLocalizedString(32075) % freeslots)
else:
ok = True
return feeds
def _select_target_group(self):
names = list()
freeslots = list()
for g in range(self._GROUPS):
free = sum("false" == settings.getSetting(
"group_%i_rss_%i_enable" % (g, r)) for r in range(self._ENTRIES))
freeslots.append(free)
names.append("%s %i: %s (%i %s)" %
(
settings.getLocalizedString(32000),
g + 1,
settings.getSetting("group_%i_name" % g),
free,
settings.getLocalizedString(32077)
))
selected = xbmcgui.Dialog().select(settings.getLocalizedString(32076), names)
if selected > -1 and freeslots[selected] == 0:
xbmcgui.Dialog().ok(heading=settings.getLocalizedString(32078),
message=settings.getLocalizedString(32084))
return -1, 0
elif selected == -1:
return -1, 0
else:
return selected, freeslots[selected]
def _apply_to_group(self, entries, group, feeds):
settings.setSetting("group_%i_enable" % group, "True")
i, j = 0, 0
while(i < self._ENTRIES):
if j < len(feeds) and "false" == settings.getSetting("group_%i_rss_%i_enable" % (group, i)):
settings.setSetting("group_%i_rss_%i_enable" %
(group, i), "True")
settings.setSetting("group_%i_rss_%i_name" %
(group, i), entries[feeds[j]]["name"])
settings.setSetting("group_%i_rss_%i_url" % (
group, i), entries[feeds[j]]["params"][0]["rss"])
settings.setSetting("group_%i_rss_%i_icon" % (group, i), "")
j += 1
i += 1
def _save_opml_file(self, data):
opml = xmltodict.parse(data)
filename = "%s.opml" % re.sub(
"[^A-Za-z0-9']", " ", opml["opml"]["head"]["title"])
path = xbmcgui.Dialog().browse(
type=3, heading=settings.getLocalizedString(32080), shares="")
if not path:
return None, None
try:
fullpath = "%s%s" % (path, filename)
with open(fullpath, "w") as _file:
_file.write(data)
return fullpath, filename
except:
xbmcgui.Dialog().ok(heading=settings.getLocalizedString(
32081), message=settings.getLocalizedString(32082))
return None, None
def _select_target_opml_slot(self, heading, multi=False):
selection = list()
for g in range(self._GROUPS):
filename = settings.getSetting("opml_file_%i" % g)
selection.append("%s %i%s" % (settings.getLocalizedString(
32023), g + 1, ": %s" % filename if filename else ""))
dialog = xbmcgui.Dialog().multiselect if multi else xbmcgui.Dialog().select
return dialog(heading, selection)
def import_opml(self):
# Step 1: Select target group
group, freeslots = self._select_target_group()
if group == -1:
return
# Step 2: Select file
name, entries = self._select_opml_file()
if name == None:
return
# Step 3: Select feeds
feeds = self._select_feeds(name, entries, freeslots)
if feeds == None:
return
# Step 4: Confirm
self._apply_to_group(entries, group, feeds)
# Success
xbmcgui.Dialog().notification(settings.getLocalizedString(
32085), settings.getLocalizedString(32086))
def import_gpodder_subscriptions(self):
# Step 1: Select target group
group, freeslots = self._select_target_group()
if group == -1:
return
# Step 2: query subscriptions from gPodder
try:
sessionid = self._login_at_gpodder()
name, entries = self._parse_opml(
self._load_gpodder_subscriptions(sessionid))
except HttpStatusError as error:
xbmcgui.Dialog().ok(settings.getLocalizedString(32090), error.message)
return
# Step 3: Select feeds
feeds = self._select_feeds(name, entries, freeslots)
if feeds == None:
return
# Step 4: Apply to group
self._apply_to_group(entries, group, feeds)
# Success
xbmcgui.Dialog().notification(settings.getLocalizedString(
32085), settings.getLocalizedString(32086))
def download_gpodder_subscriptions(self):
# Step 1: download subscriptions from gPodder
try:
sessionid = self._login_at_gpodder()
opml_data = self._load_gpodder_subscriptions(sessionid)
except HttpStatusError as error:
xbmcgui.Dialog().ok(settings.getLocalizedString(32090), error.message)
return
# Step 2: Save file in folder
path, filename = self._save_opml_file(opml_data)
if not path:
return
# Success
xbmcgui.Dialog().notification(settings.getLocalizedString(
32085), "%s %s" % (settings.getLocalizedString(32083), filename))
# Step 3: Select target opml slot
slot = self._select_target_opml_slot(
settings.getLocalizedString(32079))
if slot == -1:
return
settings.setSetting("opml_file_%i" % slot, path)
# Success
xbmcgui.Dialog().notification(settings.getLocalizedString(
32085), settings.getLocalizedString(32086))
def unassign_opml(self):
# Step 1: Select slots
slots = self._select_target_opml_slot(
settings.getLocalizedString(32087), multi=True)
if slots == None or len(slots) == 0:
return
# Step 2: empty slots
for slot in slots:
settings.setSetting("opml_file_%i" % slot, " ")
# Success
xbmcgui.Dialog().notification(settings.getLocalizedString(
32085), settings.getLocalizedString(32086))
if __name__ == '__main__':
mediathek = Mediathek()
if sys.argv[1] == "import_gpodder_subscriptions":
mediathek.import_gpodder_subscriptions()
elif sys.argv[1] == "import_opml":
mediathek.import_opml()
elif sys.argv[1] == "download_gpodder_subscriptions":
mediathek.download_gpodder_subscriptions()
elif sys.argv[1] == "unassign_opml":
mediathek.unassign_opml()
else:
mediathek.handle(sys.argv)
|
[
"xmltodict.parse",
"base64.urlsafe_b64decode",
"xbmcplugin.setResolvedUrl",
"xbmc.log",
"re.match",
"xbmc.getInfoLabel",
"os.path.join",
"re.findall",
"datetime.datetime.now",
"xbmcaddon.Addon",
"xbmcgui.ListItem",
"xbmcgui.Dialog",
"re.sub",
"xbmcplugin.endOfDirectory",
"xbmcplugin.addSortMethod",
"xbmcplugin.addDirectoryItem"
] |
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|
import re
import time
from lemoncheesecake.events import TestSessionSetupEndEvent, TestSessionTeardownEndEvent, \
TestEndEvent, SuiteSetupEndEvent, SuiteTeardownEndEvent, SuiteEndEvent, SteppedEvent
from lemoncheesecake.reporting.report import ReportLocation
DEFAULT_REPORT_SAVING_STRATEGY = "at_each_failed_test"
def _is_end_of_result_event(event):
if isinstance(event, TestEndEvent):
return ReportLocation.in_test(event.test)
if isinstance(event, SuiteSetupEndEvent):
return ReportLocation.in_suite_setup(event.suite)
if isinstance(event, SuiteTeardownEndEvent):
return ReportLocation.in_suite_teardown(event.suite)
if isinstance(event, TestSessionSetupEndEvent):
return ReportLocation.in_test_session_setup()
if isinstance(event, TestSessionTeardownEndEvent):
return ReportLocation.in_test_session_teardown()
return None
def save_at_each_suite_strategy(event, _):
return isinstance(event, SuiteEndEvent)
def save_at_each_test_strategy(event, _):
return _is_end_of_result_event(event) is not None
def save_at_each_failed_test_strategy(event, report):
location = _is_end_of_result_event(event)
if location:
result = report.get(location)
return result and result.status == "failed"
else:
return False
def save_at_each_log_strategy(event, _):
return isinstance(event, SteppedEvent)
class SaveAtInterval(object):
def __init__(self, interval):
self.interval = interval
self.last_saving = None
def __call__(self, event, report):
now = time.time()
if self.last_saving:
must_be_saved = now > self.last_saving + self.interval
if must_be_saved:
self.last_saving = now
return must_be_saved
else:
self.last_saving = now # not a saving but an initialization
return False
def make_report_saving_strategy(expression):
# first, try with a static expression
static_expressions = {
"at_end_of_tests": None, # no need to an intermediate report saving in this case
"at_each_suite": save_at_each_suite_strategy,
"at_each_test": save_at_each_test_strategy,
"at_each_failed_test": save_at_each_failed_test_strategy,
"at_each_log": save_at_each_log_strategy,
"at_each_event": save_at_each_log_strategy # deprecated since 1.4.5, "at_each_log" must be used instead
}
try:
return static_expressions[expression]
except KeyError:
pass
# second, try with "every_Ns"
m = re.compile(r"^every[_ ](\d+)s$").match(expression)
if m:
return SaveAtInterval(int(m.group(1)))
# ok... nothing we know about
raise ValueError("Invalid expression '%s' for report saving strategy" % expression)
|
[
"lemoncheesecake.reporting.report.ReportLocation.in_suite_teardown",
"re.compile",
"lemoncheesecake.reporting.report.ReportLocation.in_test_session_setup",
"time.time",
"lemoncheesecake.reporting.report.ReportLocation.in_test",
"lemoncheesecake.reporting.report.ReportLocation.in_test_session_teardown",
"lemoncheesecake.reporting.report.ReportLocation.in_suite_setup"
] |
[((413, 447), 'lemoncheesecake.reporting.report.ReportLocation.in_test', 'ReportLocation.in_test', (['event.test'], {}), '(event.test)\n', (435, 447), False, 'from lemoncheesecake.reporting.report import ReportLocation\n'), ((510, 552), 'lemoncheesecake.reporting.report.ReportLocation.in_suite_setup', 'ReportLocation.in_suite_setup', (['event.suite'], {}), '(event.suite)\n', (539, 552), False, 'from lemoncheesecake.reporting.report import ReportLocation\n'), ((618, 663), 'lemoncheesecake.reporting.report.ReportLocation.in_suite_teardown', 'ReportLocation.in_suite_teardown', (['event.suite'], {}), '(event.suite)\n', (650, 663), False, 'from lemoncheesecake.reporting.report import ReportLocation\n'), ((732, 770), 'lemoncheesecake.reporting.report.ReportLocation.in_test_session_setup', 'ReportLocation.in_test_session_setup', ([], {}), '()\n', (768, 770), False, 'from lemoncheesecake.reporting.report import ReportLocation\n'), ((842, 883), 'lemoncheesecake.reporting.report.ReportLocation.in_test_session_teardown', 'ReportLocation.in_test_session_teardown', ([], {}), '()\n', (881, 883), False, 'from lemoncheesecake.reporting.report import ReportLocation\n'), ((1599, 1610), 'time.time', 'time.time', ([], {}), '()\n', (1608, 1610), False, 'import time\n'), ((2601, 2633), 're.compile', 're.compile', (['"""^every[_ ](\\\\d+)s$"""'], {}), "('^every[_ ](\\\\d+)s$')\n", (2611, 2633), False, 'import re\n')]
|
"""Genshin chronicle notes."""
import datetime
import typing
import pydantic
from genshin.models.genshin import character
from genshin.models.model import Aliased, APIModel
__all__ = ["Expedition", "ExpeditionCharacter", "Notes"]
def _process_timedelta(time: typing.Union[int, datetime.timedelta, datetime.datetime]) -> datetime.datetime:
if isinstance(time, int):
time = datetime.datetime.fromtimestamp(time).astimezone()
if isinstance(time, datetime.timedelta):
time = datetime.datetime.now().astimezone() + time
if time < datetime.datetime(2000, 1, 1).astimezone():
delta = datetime.timedelta(seconds=int(time.timestamp()))
time = datetime.datetime.now().astimezone() + delta
time = time.replace(second=0, microsecond=0)
return time
class ExpeditionCharacter(character.BaseCharacter):
"""Expedition character."""
class Expedition(APIModel):
"""Real-Time note expedition."""
character: ExpeditionCharacter = Aliased("avatar_side_icon")
status: typing.Literal["Ongoing", "Finished"]
remaining_time: datetime.timedelta = Aliased("remained_time")
@property
def finished(self) -> bool:
"""Whether the expedition has finished."""
return self.remaining_time <= datetime.timedelta(0)
@property
def completion_time(self) -> datetime.datetime:
return datetime.datetime.now().astimezone() + self.remaining_time
@pydantic.validator("character", pre=True)
def __complete_character(cls, v: typing.Any) -> ExpeditionCharacter:
if isinstance(v, str):
return ExpeditionCharacter(icon=v) # type: ignore
return v
class TransformerTimedelta(datetime.timedelta):
"""Transformer recovery time."""
@property
def timedata(self) -> typing.Tuple[int, int, int, int]:
seconds: int = super().seconds
days: int = super().days
hour, second = divmod(seconds, 3600)
minute, second = divmod(second, 60)
return days, hour, minute, second
@property
def hours(self) -> int:
return self.timedata[1]
@property
def minutes(self) -> int:
return self.timedata[2]
@property
def seconds(self) -> int:
return self.timedata[3]
class Notes(APIModel):
"""Real-Time notes."""
current_resin: int
max_resin: int
remaining_resin_recovery_time: datetime.timedelta = Aliased("resin_recovery_time")
current_realm_currency: int = Aliased("current_home_coin")
max_realm_currency: int = Aliased("max_home_coin")
remaining_realm_currency_recovery_time: datetime.timedelta = Aliased("home_coin_recovery_time")
completed_commissions: int = Aliased("finished_task_num")
max_commissions: int = Aliased("total_task_num")
claimed_commission_reward: bool = Aliased("is_extra_task_reward_received")
remaining_resin_discounts: int = Aliased("remain_resin_discount_num")
max_resin_discounts: int = Aliased("resin_discount_num_limit")
remaining_transformer_recovery_time: typing.Optional[TransformerTimedelta]
expeditions: typing.Sequence[Expedition]
max_expeditions: int = Aliased("max_expedition_num")
@property
def resin_recovery_time(self) -> datetime.datetime:
"""The remaining time until resin recovery in seconds."""
return datetime.datetime.now().astimezone() + self.remaining_resin_recovery_time
@property
def realm_currency_recovery_time(self) -> datetime.datetime:
"""The remaining time until realm currency recovery in seconds."""
return datetime.datetime.now().astimezone() + self.remaining_realm_currency_recovery_time
@property
def transformer_recovery_time(self) -> typing.Optional[datetime.datetime]:
"""The remaining time until realm currency recovery in seconds."""
if self.remaining_transformer_recovery_time is None:
return None
remaining = datetime.datetime.now().astimezone() + self.remaining_transformer_recovery_time
return remaining
@pydantic.root_validator(pre=True)
def __flatten_transformer(cls, values: typing.Dict[str, typing.Any]) -> typing.Dict[str, typing.Any]:
if "transformer_recovery_time" in values:
return values
if values.get("transformer") and values["transformer"]["obtained"]:
t = values["transformer"]["recovery_time"]
delta = TransformerTimedelta(days=t["Day"], hours=t["Hour"], minutes=t["Minute"], seconds=t["Second"])
values["remaining_transformer_recovery_time"] = delta
else:
values["remaining_transformer_recovery_time"] = None
return values
|
[
"datetime.datetime",
"genshin.models.model.Aliased",
"datetime.datetime.fromtimestamp",
"pydantic.root_validator",
"pydantic.validator",
"datetime.datetime.now",
"datetime.timedelta"
] |
[((988, 1015), 'genshin.models.model.Aliased', 'Aliased', (['"""avatar_side_icon"""'], {}), "('avatar_side_icon')\n", (995, 1015), False, 'from genshin.models.model import Aliased, APIModel\n'), ((1107, 1131), 'genshin.models.model.Aliased', 'Aliased', (['"""remained_time"""'], {}), "('remained_time')\n", (1114, 1131), False, 'from genshin.models.model import Aliased, APIModel\n'), ((1437, 1478), 'pydantic.validator', 'pydantic.validator', (['"""character"""'], {'pre': '(True)'}), "('character', pre=True)\n", (1455, 1478), False, 'import pydantic\n'), ((2410, 2440), 'genshin.models.model.Aliased', 'Aliased', (['"""resin_recovery_time"""'], {}), "('resin_recovery_time')\n", (2417, 2440), False, 'from genshin.models.model import Aliased, APIModel\n'), ((2476, 2504), 'genshin.models.model.Aliased', 'Aliased', (['"""current_home_coin"""'], {}), "('current_home_coin')\n", (2483, 2504), False, 'from genshin.models.model import Aliased, APIModel\n'), ((2535, 2559), 'genshin.models.model.Aliased', 'Aliased', (['"""max_home_coin"""'], {}), "('max_home_coin')\n", (2542, 2559), False, 'from genshin.models.model import Aliased, APIModel\n'), ((2625, 2659), 'genshin.models.model.Aliased', 'Aliased', (['"""home_coin_recovery_time"""'], {}), "('home_coin_recovery_time')\n", (2632, 2659), False, 'from genshin.models.model import Aliased, APIModel\n'), ((2694, 2722), 'genshin.models.model.Aliased', 'Aliased', (['"""finished_task_num"""'], {}), "('finished_task_num')\n", (2701, 2722), False, 'from genshin.models.model import Aliased, APIModel\n'), ((2750, 2775), 'genshin.models.model.Aliased', 'Aliased', (['"""total_task_num"""'], {}), "('total_task_num')\n", (2757, 2775), False, 'from genshin.models.model import Aliased, APIModel\n'), ((2814, 2854), 'genshin.models.model.Aliased', 'Aliased', (['"""is_extra_task_reward_received"""'], {}), "('is_extra_task_reward_received')\n", (2821, 2854), False, 'from genshin.models.model import Aliased, APIModel\n'), ((2893, 2929), 'genshin.models.model.Aliased', 'Aliased', (['"""remain_resin_discount_num"""'], {}), "('remain_resin_discount_num')\n", (2900, 2929), False, 'from genshin.models.model import Aliased, APIModel\n'), ((2961, 2996), 'genshin.models.model.Aliased', 'Aliased', (['"""resin_discount_num_limit"""'], {}), "('resin_discount_num_limit')\n", (2968, 2996), False, 'from genshin.models.model import Aliased, APIModel\n'), ((3150, 3179), 'genshin.models.model.Aliased', 'Aliased', (['"""max_expedition_num"""'], {}), "('max_expedition_num')\n", (3157, 3179), False, 'from genshin.models.model import Aliased, APIModel\n'), ((4045, 4078), 'pydantic.root_validator', 'pydantic.root_validator', ([], {'pre': '(True)'}), '(pre=True)\n', (4068, 4078), False, 'import pydantic\n'), ((1268, 1289), 'datetime.timedelta', 'datetime.timedelta', (['(0)'], {}), '(0)\n', (1286, 1289), False, 'import datetime\n'), ((389, 426), 'datetime.datetime.fromtimestamp', 'datetime.datetime.fromtimestamp', (['time'], {}), '(time)\n', (420, 426), False, 'import datetime\n'), ((560, 589), 'datetime.datetime', 'datetime.datetime', (['(2000)', '(1)', '(1)'], {}), '(2000, 1, 1)\n', (577, 589), False, 'import datetime\n'), ((501, 524), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (522, 524), False, 'import datetime\n'), ((685, 708), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (706, 708), False, 'import datetime\n'), ((1372, 1395), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (1393, 1395), False, 'import datetime\n'), ((3332, 3355), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (3353, 3355), False, 'import datetime\n'), ((3576, 3599), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (3597, 3599), False, 'import datetime\n'), ((3934, 3957), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (3955, 3957), False, 'import datetime\n')]
|
# 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.
# pylint: disable=missing-docstring
import tvm
from tvm import meta_schedule as ms
from tvm.ir import IRModule
from tvm.meta_schedule.testing.conv2d_winograd_cpu import conv2d_winograd_cpu
from tvm.target import Target
from tvm.tir.schedule import Schedule, Trace
def _get_mod():
# pylint: disable=invalid-name
def inline(sch: Schedule):
b1 = sch.get_block(name="A")
b2 = sch.get_block(name="B")
sch.compute_inline(block=b1)
sch.compute_inline(block=b2)
def input_tile_data_pad(sch: Schedule):
b78 = sch.get_block(name="input_tile")
l80 = sch.sample_compute_location(block=b78, decision=4)
sch.compute_at(block=b78, loop=l80, preserve_unit_loops=True)
b81 = sch.get_block(name="data_pad")
l83 = sch.sample_compute_location(block=b81, decision=-2)
sch.compute_at(block=b81, loop=l83, preserve_unit_loops=True)
def data_pack(sch: Schedule):
b18 = sch.get_block(name="data_pack")
l19, l20, l21, l22, l23, l24 = sch.get_loops(block=b18)
sch.unroll(loop=l19)
sch.unroll(loop=l20)
v25, v26 = sch.sample_perfect_tile(
n=2,
loop=l21,
max_innermost_factor=64,
decision=[9, 1],
)
l27, l28 = sch.split(loop=l21, factors=[v25, v26])
v29, v30 = sch.sample_perfect_tile(
n=2,
loop=l22,
max_innermost_factor=64,
decision=[32, 4],
)
l31, l32 = sch.split(loop=l22, factors=[v29, v30])
sch.unroll(loop=l23)
sch.unroll(loop=l24)
sch.reorder(l27, l31, l28, l32, l19, l20, l23, l24)
def bgemm(sch: Schedule):
bgemm = sch.get_block(name="bgemm")
write_cache = sch.cache_write(
block=bgemm,
write_buffer_index=0,
storage_scope="global",
)
sch.annotate(
block_or_loop=bgemm,
ann_key="meta_schedule.tiling_structure",
ann_val="SSRSRS",
)
# b33, b34 = b34, b33
l35, l36, l37, l38, l39 = sch.get_loops(block=bgemm)
v40, v41, v42, v43 = sch.sample_perfect_tile(
n=4,
loop=l35,
max_innermost_factor=64,
decision=[1, 2, 3, 1],
)
l44, l45, l46, l47 = sch.split(loop=l35, factors=[v40, v41, v42, v43])
v48, v49, v50, v51 = sch.sample_perfect_tile(
n=4,
loop=l36,
max_innermost_factor=64,
decision=[1, 1, 1, 6],
)
l52, l53, l54, l55 = sch.split(loop=l36, factors=[v48, v49, v50, v51])
v56, v57, v58, v59 = sch.sample_perfect_tile(
n=4,
loop=l37,
max_innermost_factor=64,
decision=[1, 1, 1, 9],
)
l60, l61, l62, l63 = sch.split(loop=l37, factors=[v56, v57, v58, v59])
v64, v65, v66, v67 = sch.sample_perfect_tile(
n=4,
loop=l38,
max_innermost_factor=64,
decision=[2, 1, 16, 4],
)
l68, l69, l70, l71 = sch.split(loop=l38, factors=[v64, v65, v66, v67])
v72, v73 = sch.sample_perfect_tile(
n=2,
loop=l39,
max_innermost_factor=64,
decision=[16, 8],
)
l74, l75 = sch.split(loop=l39, factors=[v72, v73])
sch.reorder(
# fmt: off
l44, l52, l60, l68,
l45, l53, l61, l69,
l74,
l46, l54, l62, l70,
l75,
l47, l55, l63, l71,
# fmt: on
)
sch.reverse_compute_at(block=write_cache, loop=l69, preserve_unit_loops=True)
def inverse(sch: Schedule):
b3 = sch.get_block(name="inverse")
l4, l5, l6, l7, l8, l9 = sch.get_loops(block=b3)
sch.unroll(loop=l4)
sch.unroll(loop=l5)
v10, v11 = sch.sample_perfect_tile(
n=2,
loop=l6,
max_innermost_factor=64,
decision=[1, 9],
)
l12, l13 = sch.split(loop=l6, factors=[v10, v11])
v14, v15 = sch.sample_perfect_tile(
n=2,
loop=l7,
max_innermost_factor=64,
decision=[2, 64],
)
l16, l17 = sch.split(loop=l7, factors=[v14, v15])
sch.unroll(loop=l8)
sch.unroll(loop=l9)
sch.reorder(l12, l16, l13, l17, l4, l5, l8, l9)
# pylint: enable=invalid-name
sch = Schedule(mod=conv2d_winograd_cpu)
inline(sch)
data_pack(sch)
input_tile_data_pad(sch)
bgemm(sch)
inverse(sch)
return sch.mod
def test_conv2d_winograd_cpu():
mod = conv2d_winograd_cpu
mod = IRModule({"main": mod})
target = Target("llvm --num-cores=16")
context = ms.TuneContext(
mod=mod,
target=target,
task_name="Custom Search Space Task",
space_generator=ms.space_generator.PostOrderApply(),
sch_rules=ms.default_config.schedule_rules(
None,
target,
),
)
context.initialize()
post_order_apply = context.space_generator
(sch,) = post_order_apply.generate_design_space(mod)
decisions = dict(
zip(
[i for i in sch.trace.insts[:-4] if i.kind.name.startswith("Sample")],
[
# data_pack
[9, 1],
[32, 4],
# input_tile
4,
# data_pad
-2,
# inverse
[1, 9],
[2, 64],
# bgemm
[1, 2, 3, 1],
[1, 1, 1, 6],
[1, 1, 1, 9],
[2, 1, 16, 4],
[16, 8],
],
)
)
trace = Trace(sch.trace.insts[:-4], decisions=decisions)
sch = Schedule(mod=mod)
trace.apply_to_schedule(sch, remove_postproc=False)
answer = sch.mod
expected = _get_mod()
tvm.ir.assert_structural_equal(answer, expected)
if __name__ == "__main__":
test_conv2d_winograd_cpu()
|
[
"tvm.tir.schedule.Schedule",
"tvm.meta_schedule.default_config.schedule_rules",
"tvm.tir.schedule.Trace",
"tvm.ir.assert_structural_equal",
"tvm.target.Target",
"tvm.ir.IRModule",
"tvm.meta_schedule.space_generator.PostOrderApply"
] |
[((5272, 5305), 'tvm.tir.schedule.Schedule', 'Schedule', ([], {'mod': 'conv2d_winograd_cpu'}), '(mod=conv2d_winograd_cpu)\n', (5280, 5305), False, 'from tvm.tir.schedule import Schedule, Trace\n'), ((5495, 5518), 'tvm.ir.IRModule', 'IRModule', (["{'main': mod}"], {}), "({'main': mod})\n", (5503, 5518), False, 'from tvm.ir import IRModule\n'), ((5532, 5561), 'tvm.target.Target', 'Target', (['"""llvm --num-cores=16"""'], {}), "('llvm --num-cores=16')\n", (5538, 5561), False, 'from tvm.target import Target\n'), ((6567, 6615), 'tvm.tir.schedule.Trace', 'Trace', (['sch.trace.insts[:-4]'], {'decisions': 'decisions'}), '(sch.trace.insts[:-4], decisions=decisions)\n', (6572, 6615), False, 'from tvm.tir.schedule import Schedule, Trace\n'), ((6626, 6643), 'tvm.tir.schedule.Schedule', 'Schedule', ([], {'mod': 'mod'}), '(mod=mod)\n', (6634, 6643), False, 'from tvm.tir.schedule import Schedule, Trace\n'), ((6751, 6799), 'tvm.ir.assert_structural_equal', 'tvm.ir.assert_structural_equal', (['answer', 'expected'], {}), '(answer, expected)\n', (6781, 6799), False, 'import tvm\n'), ((5702, 5737), 'tvm.meta_schedule.space_generator.PostOrderApply', 'ms.space_generator.PostOrderApply', ([], {}), '()\n', (5735, 5737), True, 'from tvm import meta_schedule as ms\n'), ((5757, 5803), 'tvm.meta_schedule.default_config.schedule_rules', 'ms.default_config.schedule_rules', (['None', 'target'], {}), '(None, target)\n', (5789, 5803), True, 'from tvm import meta_schedule as ms\n')]
|
#!/usr/bin/env python
import argparse
from igf_data.task_tracking.igf_slack import IGF_slack
from igf_data.process.data_transfer.sync_seqrun_data_on_remote import Sync_seqrun_data_from_remote
parser = argparse.ArgumentParser()
parser.add_argument('-r','--remote_server', required=True, help='Remote server address')
parser.add_argument('-p','--remote_base_path', required=True, help='Seqrun directory path in remote dir')
parser.add_argument('-d','--dbconfig', required=True, help='Database configuration file path')
parser.add_argument('-o','--output_dir', required=True, help='Local output directory path')
parser.add_argument('-n','--slack_config', required=True, help='Slack configuration file path')
args = parser.parse_args()
remote_server = args.remote_server
remote_base_path = args.remote_base_path
dbconfig = args.dbconfig
output_dir = args.output_dir
slack_config = args.slack_config
if __name__=='__main__':
try:
slack_obj=IGF_slack(slack_config=slack_config)
## FIX ME
except Exception as e:
message = 'Error while syncing sequencing run directory from remote server: {0}'.format(e)
slack_obj.post_message_to_channel(message,reaction='fail')
raise ValueError(message)
|
[
"argparse.ArgumentParser",
"igf_data.task_tracking.igf_slack.IGF_slack"
] |
[((202, 227), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (225, 227), False, 'import argparse\n'), ((944, 980), 'igf_data.task_tracking.igf_slack.IGF_slack', 'IGF_slack', ([], {'slack_config': 'slack_config'}), '(slack_config=slack_config)\n', (953, 980), False, 'from igf_data.task_tracking.igf_slack import IGF_slack\n')]
|
from typing import Callable, Iterable, TypeVar
T = TypeVar('T')
Num = TypeVar('Num', int, float)
def sumBy(array: Iterable[T], iteratee: Callable[[T], Num] = None, start: Num = 0) -> Num:
if iteratee is None:
return sum([y for y in array], start)
return sum([iteratee(y) for y in array], start)
|
[
"typing.TypeVar"
] |
[((52, 64), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (59, 64), False, 'from typing import Callable, Iterable, TypeVar\n'), ((71, 97), 'typing.TypeVar', 'TypeVar', (['"""Num"""', 'int', 'float'], {}), "('Num', int, float)\n", (78, 97), False, 'from typing import Callable, Iterable, TypeVar\n')]
|
import grpc
from consts import PORT, SERVER_CERT
from grpc_generated_files import api_pb2, api_pb2_grpc
def main(stub):
request = api_pb2.ApiRequest(
name="Shivam",
message="Hey there!"
)
response = stub.ApiEndpoint(request)
print(response)
if __name__ == "__main__":
with open(SERVER_CERT, 'rb') as f:
server_cert = f.read()
creds = grpc.ssl_channel_credentials(server_cert)
# the server IP should be in the common name of the certificate
channel = grpc.secure_channel(f'localhost:{PORT}', creds)
stub = api_pb2_grpc.ApiStub(channel)
main(stub)
|
[
"grpc.ssl_channel_credentials",
"grpc_generated_files.api_pb2.ApiRequest",
"grpc.secure_channel",
"grpc_generated_files.api_pb2_grpc.ApiStub"
] |
[((137, 192), 'grpc_generated_files.api_pb2.ApiRequest', 'api_pb2.ApiRequest', ([], {'name': '"""Shivam"""', 'message': '"""Hey there!"""'}), "(name='Shivam', message='Hey there!')\n", (155, 192), False, 'from grpc_generated_files import api_pb2, api_pb2_grpc\n'), ((387, 428), 'grpc.ssl_channel_credentials', 'grpc.ssl_channel_credentials', (['server_cert'], {}), '(server_cert)\n', (415, 428), False, 'import grpc\n'), ((511, 558), 'grpc.secure_channel', 'grpc.secure_channel', (['f"""localhost:{PORT}"""', 'creds'], {}), "(f'localhost:{PORT}', creds)\n", (530, 558), False, 'import grpc\n'), ((570, 599), 'grpc_generated_files.api_pb2_grpc.ApiStub', 'api_pb2_grpc.ApiStub', (['channel'], {}), '(channel)\n', (590, 599), False, 'from grpc_generated_files import api_pb2, api_pb2_grpc\n')]
|
import pyblish.api
import avalon.api
from openpype.api import version_up
from openpype.action import get_errored_plugins_from_data
class IncrementCurrentFile(pyblish.api.InstancePlugin):
"""Increment the current file.
Saves the current scene with an increased version number.
"""
label = "Increment current file"
order = pyblish.api.IntegratorOrder + 9.0
hosts = ["houdini"]
families = ["colorbleed.usdrender", "redshift_rop"]
targets = ["local"]
def process(self, instance):
# This should be a ContextPlugin, but this is a workaround
# for a bug in pyblish to run once for a family: issue #250
context = instance.context
key = "__hasRun{}".format(self.__class__.__name__)
if context.data.get(key, False):
return
else:
context.data[key] = True
context = instance.context
errored_plugins = get_errored_plugins_from_data(context)
if any(
plugin.__name__ == "HoudiniSubmitPublishDeadline"
for plugin in errored_plugins
):
raise RuntimeError(
"Skipping incrementing current file because "
"submission to deadline failed."
)
# Filename must not have changed since collecting
host = avalon.api.registered_host()
current_file = host.current_file()
assert (
context.data["currentFile"] == current_file
), "Collected filename from current scene name."
new_filepath = version_up(current_file)
host.save(new_filepath)
|
[
"openpype.action.get_errored_plugins_from_data",
"openpype.api.version_up"
] |
[((922, 960), 'openpype.action.get_errored_plugins_from_data', 'get_errored_plugins_from_data', (['context'], {}), '(context)\n', (951, 960), False, 'from openpype.action import get_errored_plugins_from_data\n'), ((1549, 1573), 'openpype.api.version_up', 'version_up', (['current_file'], {}), '(current_file)\n', (1559, 1573), False, 'from openpype.api import version_up\n')]
|
from builtins import str
from builtins import range
from robust.simulations.simulate import filter_gamma_result_dict
from SimPleAC_save import load_obj
import pickle as pickle
import numpy as np
import matplotlib.pyplot as plt
from SimPleAC_pof_simulate import pof_parameters
if __name__ == "__main__":
# Retrieving pof parameters
[model, methods, gammas, number_of_iterations,
min_num_of_linear_sections, max_num_of_linear_sections, verbosity, linearization_tolerance,
number_of_time_average_solves, uncertainty_sets, nominal_solution, directly_uncertain_vars_subs, parallel,
nominal_number_of_constraints, nominal_solve_time] = pof_parameters()
method = methods[0] # only care about Best Pairs
# Loading results
margin = {}
nGammas = nmargins = len(gammas)
margins = gammas
margin['solutions'] = {}
for i in range(nmargins):
margin['solutions'][margins[i]] = pickle.load(open("marginResults/" +
str(margins[i]), 'rb'))
margin['number_of_constraints'] = load_obj('marginnumber_of_constraints', 'marginResults')
margin['simulation_results'] = load_obj('marginsimulation_results', 'marginResults')
gamma = {}
gamma['solutions'] = {}
for i in range(nGammas):
for j in range(len(methods)):
for k in range((len(uncertainty_sets))):
gamma['solutions'][gammas[i], methods[j]['name'], uncertainty_sets[k]] = pickle.load(open(
"gammaResults\\" + str((gammas[i], methods[j]['name'], uncertainty_sets[k])), 'rb'))
gamma['solve_times'] = load_obj('gammasolve_times', 'gammaResults')
gamma['simulation_results'] = load_obj('gammasimulation_results', 'gammaResults')
gamma['number_of_constraints'] = load_obj('gammanumber_of_constraints', 'gammaResults')
# Plotting of cost and probability of failure
objective_name = 'Total fuel weight'
objective_units = 'N'
title = ''
filteredResults = [margin['solutions'],
filter_gamma_result_dict(gamma['solutions'], 1, method['name'], 2, 'box'),
filter_gamma_result_dict(gamma['solutions'], 1, method['name'], 2, 'ellipsoidal')]
filteredSimulations = [margin['simulation_results'],
filter_gamma_result_dict(gamma['simulation_results'], 1, method['name'], 2, 'box'),
filter_gamma_result_dict(gamma['simulation_results'], 1, method['name'], 2, 'ellipsoidal')]
objective_varkey = 'W_{f_m}'
legend_keys = ['margins', 'box', 'ellipsoidal']
edgecolors = ['#FFBF00', '#CC0000', '#008000']
facecolors = ['#FFE135','#FF2052', '#8DB600']
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
lines = []
mincost = 1e10
maxcost = 0
for i in range(len(legend_keys)):
sims = list(filteredSimulations[i].items())
pofs = []
objective_costs = []
objective_stddev = []
for j in sims:
pofs.append(j[1][0])
objective_costs.append(j[1][1])
objective_stddev.append(j[1][2])
mincost = np.min([mincost] + objective_costs)
maxcost = np.max([maxcost] + objective_costs)
lines.append(ax1.plot(gammas, objective_costs, color=edgecolors[i], label=legend_keys[i] + ', cost'))
inds = np.nonzero(np.ones(len(gammas)) - pofs)[0]
uppers = [objective_costs[ind] + objective_stddev[ind] for ind in inds]
lowers = [objective_costs[ind] - objective_stddev[ind] for ind in inds]
x = [gammas[ind] for ind in inds]
ax1.fill_between(x, lowers, uppers,
alpha=0.5, edgecolor = edgecolors[i], facecolor = facecolors[i])
lines.append(ax2.plot(gammas, pofs, color=edgecolors[i], label=legend_keys[i] + ', PoF'))
ax1.set_xlabel(r'Uncertainty Set Scaling Factor $\Gamma$', fontsize=12)
ax1.set_ylabel('Cost [' + objective_name + ' (' + objective_units.capitalize() + ')]', fontsize=12)
ax2.set_ylabel("Probability of Failure", fontsize=12)
ax1.set_ylim([mincost, maxcost])
ax2.set_ylim([0, 1])
plt.title(title, fontsize=12)
labs = [lines[l][0].get_label() for l in [1,3,5,0,2,4]]
ax1.legend(labs, loc="lower right", fontsize=9, numpoints=1)
# ax1.legend(loc="lower right", fontsize=10, numpoints=1)
# fig.legend(loc="lower right", fontsize=10, numpoints=1)
plt.show()
|
[
"matplotlib.pyplot.show",
"SimPleAC_pof_simulate.pof_parameters",
"builtins.str",
"numpy.max",
"builtins.range",
"numpy.min",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"robust.simulations.simulate.filter_gamma_result_dict",
"SimPleAC_save.load_obj"
] |
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|
#!/usr/bin/env python
from __future__ import absolute_import
import numpy as np
import os
import pytest
import tempfile
import training_data
class TestTrainingData():
def test_add(self):
td = training_data.training_data()
assert np.array_equal(td.get_x(), np.empty([0, 4, 4], dtype=np.int))
assert np.array_equal(td.get_y_digit(), np.empty([0, 1], dtype=np.int))
assert np.allclose(td.get_reward(), np.empty([0, 1], dtype=np.float))
assert np.array_equal(td.get_next_x(), np.empty([0, 4, 4], dtype=np.int))
assert np.array_equal(td.get_done(), np.empty([0, 1], dtype=np.bool))
td.add(np.ones([1, 4, 4]), 1, 4, np.zeros([1, 4, 4]), True)
assert np.array_equal(td.get_x(), np.ones([1, 4, 4], dtype=np.int))
assert np.array_equal(td.get_y_digit(), np.array([[1]], dtype=np.int))
assert np.allclose(td.get_reward(), np.array([[4]], dtype=np.float))
assert np.array_equal(td.get_next_x(), np.zeros([1, 4, 4], dtype=np.int))
assert np.array_equal(td.get_done(), np.array([[1]], dtype=np.bool))
def test_get_x_stacked(self):
td = training_data.training_data()
td.add(np.full([4, 4], 2), 0, 4, np.zeros([4, 4]))
td.add(np.full([4, 4], 8), 1, 8, np.ones([4, 4]))
td.add(np.full([4, 4], 2048), 1, 8, np.ones([4, 4]))
expected_x_stacked = np.array([
[
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
],
[
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
],
[
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]]
]
], dtype=np.int)
assert np.array_equal(td.get_x_stacked(), expected_x_stacked)
def test_get_y_one_hot(self):
td = training_data.training_data()
td.add(np.ones([4, 4]), 0, 4, np.zeros([4, 4]))
td.add(np.zeros([4, 4]), 1, 8, np.ones([4, 4]))
td.add(np.zeros([4, 4]), 3, 8, np.ones([4, 4]))
td.add(np.zeros([4, 4]), 2, 8, np.ones([4, 4]))
expected_y_one_hot = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]
], dtype=np.int)
assert np.array_equal(td.get_y_one_hot(), expected_y_one_hot)
def test_get_total_reward(self):
td = training_data.training_data()
td.add(np.ones([4, 4]), 0, 4, np.zeros([4, 4]))
td.add(np.zeros([4, 4]), 1, 8, np.ones([4, 4]))
td.add(np.zeros([4, 4]), 3, 16, np.ones([4, 4]))
td.add(np.zeros([4, 4]), 2, 32, np.ones([4, 4]))
assert td.get_total_reward() == 60
def test_get_highest_tile(self):
td = training_data.training_data()
td.add(np.full((4, 4), 1), 0, 4, np.full((4, 4), 2))
td.add(np.full((4, 4), 2), 0, 4, np.full((4, 4), 4))
assert td.get_highest_tile() == 4
def test_get_n(self):
td = training_data.training_data()
td.add(np.ones([4, 4]), 1, 4, np.zeros([4, 4]))
td.add(np.zeros([4, 4]), 2, 8, np.ones([4, 4]))
(state, action, reward, next_state, done) = td.get_n(1)
assert np.array_equal(state, np.zeros([4, 4], dtype=np.int))
assert action == 2
assert reward == pytest.approx(8.)
assert np.array_equal(next_state, np.ones([4, 4], dtype=np.int))
def test_hflip(self):
td = training_data.training_data()
board1 = np.array([[1, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
board2 = np.array([[0, 0, 0, 0],
[2, 4, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
td.add(board1, 1, 2, board2)
td.add(board2, 2, 0, board1)
td.hflip()
expected_x = np.array([
[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 4, 2], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=np.int)
expected_y_digit = np.array([
[3],
[2]
], dtype=np.int)
expected_reward = np.array([
[2],
[0],
], dtype=np.float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 4, 2], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=np.int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.allclose(td.get_next_x(), expected_next_x)
def test_rotate(self):
td = training_data.training_data()
board1 = np.array([[1, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
board2 = np.array([[0, 0, 0, 0],
[2, 4, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
td.add(board1, 1, 2, board2)
td.add(board2, 2, 0, board1)
td.rotate(3)
expected_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 4, 0, 0], [0, 2, 0, 0]]
], dtype=np.int)
expected_y_digit = np.array([
[0],
[1]
], dtype=np.int)
expected_reward = np.array([
[2],
[0],
], dtype=np.float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 4, 0, 0], [0, 2, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]]
], dtype=np.int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.array_equal(td.get_next_x(), expected_next_x)
def test_augment(self):
td = training_data.training_data()
initial_board = np.array([[1, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
next_board = np.array([[0, 0, 0, 2],
[0, 2, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
td.add(initial_board, 1, 4, next_board)
td.augment()
assert td.size() == 8
expected_x = np.array([
[[1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]],
[[1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=np.int)
expected_y_digit = np.array([
[1],
[3],
[2],
[0],
[3],
[1],
[0],
[2]
], dtype=np.int)
expected_reward = np.array([
[4],
[4],
[4],
[4],
[4],
[4],
[4],
[4]
], dtype=np.float)
expected_next_x = np.array([
[[0, 0, 0, 2], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], # Original
[[2, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0]], # Hflip'd
[[0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 2]], # Original, rotated 90 degrees
[[0, 0, 0, 2], [0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0]], # Hflip, rotated 90 degrees
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 2, 0], [2, 0, 0, 0]], # Original, rotated 180 degrees
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 2]], # Hflip, rotated 180 degrees
[[2, 0, 0, 0], [0, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 0]], # Original, rotate 270 degrees
[[0, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 0], [2, 0, 0, 0]] # Hflip, rotated 270 degrees
], dtype=np.int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.array_equal(td.get_next_x(), expected_next_x)
def test_merge(self):
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 1, 16, np.zeros([1, 4, 4]))
td2 = training_data.training_data()
td2.add(np.zeros([1, 4, 4]), 2, 0, np.ones([1, 4, 4]))
td.merge(td2)
expected_x = np.array([
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=np.int)
expected_y_digit = np.array([
[1],
[2]
], dtype=np.int)
expected_reward = np.array([
[16],
[0]
], dtype=np.float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
], dtype=np.int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.array_equal(td.get_next_x(), expected_next_x)
def test_split(self):
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 1, 16, np.zeros([1, 4, 4]))
td2 = training_data.training_data()
td2.add(np.zeros([1, 4, 4]), 2, 0, np.ones([1, 4, 4]))
td.merge(td2)
a, b = td.split()
assert np.array_equal(a.get_x(), np.ones([1, 4, 4]))
assert np.array_equal(a.get_y_digit(), [[1]])
assert np.array_equal(a.get_reward(), [[16]])
assert np.array_equal(a.get_next_x(), np.zeros([1, 4, 4]))
assert np.array_equal(b.get_x(), np.zeros([1, 4, 4]))
assert np.array_equal(b.get_y_digit(), [[2]])
assert np.array_equal(b.get_reward(), [[0]])
assert np.array_equal(b.get_next_x(), np.ones([1, 4, 4]))
def test_sample(self):
td = training_data.training_data()
td.add(np.zeros([1, 4, 4]), 0, 0, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 1, 1, np.ones([1, 4, 4]))
sample = td.sample([1])
assert sample.size() == 1
assert sample.get_y_digit() in [[[0]], [[1]]]
if sample.get_y_digit() == 0:
assert np.array_equal(sample.get_x(), np.zeros([1, 4, 4]))
if sample.get_y_digit() == 1:
assert np.array_equal(sample.get_x(), np.ones([1, 4, 4]))
def test_size(self):
td = training_data.training_data()
assert td.size() == 0
td.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]))
assert td.size() == 1
def test_log2_rewards(self):
# Set up training data
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 0, 0, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 1, 2, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 3, 16, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 0, 75, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 1, 2048, np.zeros([1, 4, 4]))
td.log2_rewards()
expected_reward = np.array([
[0], [1], [2], [4], [6.2288], [11]
], dtype=np.float)
assert np.allclose(td.get_reward(), expected_reward)
expected_action = np.array([
[0], [1], [2], [3], [0], [1]
], dtype=np.int)
assert np.allclose(td.get_y_digit(), expected_action)
def test_get_discounted_return(self):
# Set up training data
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 1, 2, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 16, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 3, 2, np.zeros([1, 4, 4]))
# Test using default gamma value of 0.9
td2 = td.copy()
discounted_return = td2.get_discounted_return()
expected_return = np.array([
[20.218], [18.02], [17.8], [2.0]
], dtype=np.float)
assert np.allclose(discounted_return, expected_return)
# Test using gamma value of 0, should have no effect on rewards
td2 = td.copy()
discounted_return = td2.get_discounted_return(gamma=0.0)
expected_return = np.array([
[4], [2], [16], [2]
], dtype=np.float)
assert np.allclose(discounted_return, expected_return)
# Test end of episode
td3 = training_data.training_data()
td3.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]), False)
td3.add(np.ones([1, 4, 4]), 1, 2, np.zeros([1, 4, 4]), True)
td3.add(np.ones([1, 4, 4]), 2, 16, np.zeros([1, 4, 4]), False)
td3.add(np.ones([1, 4, 4]), 3, 2, np.zeros([1, 4, 4]), True)
discounted_return = td3.get_discounted_return()
expected_return = np.array([
[5.8], [2.0], [17.8], [2.0]
], dtype=np.float)
assert np.allclose(discounted_return, expected_return)
def test_normalize_rewards(self):
# Test calculating mean and standard deviation
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 1, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 3, 8, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 0, 16, np.zeros([1, 4, 4]))
td.normalize_rewards()
expected_reward = np.array([
[-0.8165], [-0.8165], [0.], [1.633],
], dtype=np.float)
assert np.allclose(td.get_reward(), expected_reward)
# Test specifying mean and standard deviation
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 1, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 3, 8, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 0, 16, np.zeros([1, 4, 4]))
td.normalize_rewards(mean=8, sd=1)
expected_reward = np.array([
[-4.], [-4.], [0.], [8.],
], dtype=np.float)
assert np.allclose(td.get_reward(), expected_reward)
def test_normalize_boards(self):
# Test calculating mean and standard deviation
td = training_data.training_data()
td.add(np.full((1, 4, 4), 4), 1, 4, np.full((1, 4, 4), 8))
td.add(np.full((1, 4, 4), 8), 2, 4, np.full((1, 4, 4), 16))
td.add(np.full((1, 4, 4), 16), 3, 4, np.full((1, 4, 4), 32))
td.add(np.full((1, 4, 4), 32), 4, 4, np.full((1, 4, 4), 64))
td.normalize_boards()
mean = 15.
sd = 10.7238052947636
a = (4. - mean) / sd
b = (8. - mean) / sd
c = (16. - mean) / sd
d = (32. - mean) / sd
e = (64. - mean) / sd
expected_x = np.array([
[[a, a, a, a], [a, a, a, a], [a, a, a, a], [a, a, a, a]],
[[b, b, b, b], [b, b, b, b], [b, b, b, b], [b, b, b, b]],
[[c, c, c, c], [c, c, c, c], [c, c, c, c], [c, c, c, c]],
[[d, d, d, d], [d, d, d, d], [d, d, d, d], [d, d, d, d]]
], dtype=np.float)
assert np.allclose(td.get_x(), expected_x)
expected_next_x = np.array([
[[b, b, b, b], [b, b, b, b], [b, b, b, b], [b, b, b, b]],
[[c, c, c, c], [c, c, c, c], [c, c, c, c], [c, c, c, c]],
[[d, d, d, d], [d, d, d, d], [d, d, d, d], [d, d, d, d]],
[[e, e, e, e], [e, e, e, e], [e, e, e, e], [e, e, e, e]]
], dtype=np.float)
assert np.allclose(td.get_next_x(), expected_next_x)
def test_save_restore(self):
# Set up training data
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]))
td.add(np.zeros([1, 4, 4]), 1, 2, np.ones([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 16, np.zeros([1, 4, 4]))
td.add(np.zeros([1, 4, 4]), 3, 2, np.ones([1, 4, 4]))
temp_dir = tempfile.mkdtemp()
temp_filename = os.path.join(temp_dir, 'data.csv')
td.export_csv(temp_filename)
td2 = training_data.training_data()
td2.import_csv(temp_filename)
expected_x = np.array([
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=np.int)
expected_y_digit = np.array([
[0],
[1],
[2],
[3]
], dtype=np.int)
expected_reward = np.array([
[4],
[2],
[16],
[2]
], dtype=np.float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
], dtype=np.int)
assert np.array_equal(td2.get_x(), expected_x)
assert np.array_equal(td2.get_y_digit(), expected_y_digit)
assert np.allclose(td2.get_reward(), expected_reward)
assert np.array_equal(td2.get_next_x(), expected_next_x)
os.remove(temp_filename)
os.rmdir(temp_dir)
def test_shuffle(self):
td = training_data.training_data()
n = 5
for i in range(n):
# Use "is odd" for done
td.add(np.full((1, 4, 4), i), i, i, np.full((1, 4, 4), i), (i % 2) == 1)
td.shuffle()
for i in range(n):
# Find where this has been shuffled too
index_of_val = np.where(td.get_y_digit() == i)[0].item(0)
# Check that all parts of this equal i
arrays = td.get_n(index_of_val)
for a in arrays:
if a.dtype is np.dtype(np.bool):
assert((a == ((i % 2) == 1)).all())
else:
assert((a == i).all())
def test_make_boards_unique(self):
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]))
td.add(np.zeros([1, 4, 4]), 1, 2, np.ones([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 16, np.zeros([1, 4, 4]))
td.add(np.zeros([1, 4, 4]), 3, 2, np.ones([1, 4, 4]))
td.make_boards_unique()
expected_x = np.array([
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=np.int)
expected_y_digit = np.array([
[0],
[1]
], dtype=np.int)
expected_reward = np.array([
[4],
[2]
], dtype=np.float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
], dtype=np.int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.array_equal(td.get_next_x(), expected_next_x)
if __name__ == '__main__':
import pytest
pytest.main()
|
[
"pytest.approx",
"numpy.allclose",
"numpy.ones",
"os.path.join",
"pytest.main",
"training_data.training_data",
"numpy.array",
"os.rmdir",
"tempfile.mkdtemp",
"numpy.empty",
"numpy.zeros",
"numpy.full",
"numpy.dtype",
"os.remove"
] |
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'np.zeros', (['[1, 4, 4]'], {}), '([1, 4, 4])\n', (21859, 21870), True, 'import numpy as np\n'), ((21878, 21896), 'numpy.ones', 'np.ones', (['[1, 4, 4]'], {}), '([1, 4, 4])\n', (21885, 21896), True, 'import numpy as np\n'), ((21913, 21931), 'numpy.ones', 'np.ones', (['[1, 4, 4]'], {}), '([1, 4, 4])\n', (21920, 21931), True, 'import numpy as np\n'), ((21940, 21959), 'numpy.zeros', 'np.zeros', (['[1, 4, 4]'], {}), '([1, 4, 4])\n', (21948, 21959), True, 'import numpy as np\n'), ((21976, 21995), 'numpy.zeros', 'np.zeros', (['[1, 4, 4]'], {}), '([1, 4, 4])\n', (21984, 21995), True, 'import numpy as np\n'), ((22003, 22021), 'numpy.ones', 'np.ones', (['[1, 4, 4]'], {}), '([1, 4, 4])\n', (22010, 22021), True, 'import numpy as np\n'), ((13811, 13830), 'numpy.zeros', 'np.zeros', (['[1, 4, 4]'], {}), '([1, 4, 4])\n', (13819, 13830), True, 'import numpy as np\n'), ((13920, 13938), 'numpy.ones', 'np.ones', (['[1, 4, 4]'], {}), '([1, 4, 4])\n', (13927, 13938), True, 'import numpy as np\n'), ((21160, 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|
import json
import os
from os.path import join
from random import shuffle
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import MinMaxScaler, normalize
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, StratifiedKFold, train_test_split
from sklearn.metrics import accuracy_score
from transformers import BertTokenizer, BertConfig, BartTokenizer
def make_vector(text, tokenizer):
token_ids = tokenizer.encode(text)[1:-1]
count_vector = np.zeros(tokenizer.vocab_size, dtype=np.int16)
for ID in token_ids:
count_vector[ID] += 1
return count_vector
def dataloader(data_dir, batch_size=5000):
names = [x[:-6] for x in os.listdir(data_dir) if x[-5:] == '3.txt']
index = 0
while index < len(names):
cur_names = names[index:index+batch_size]
tuples = []
for name in cur_names:
hard = open(join(data_dir, f'{name}.0.txt')).read()
simple = open(join(data_dir, f'{name}.3.txt')).read()
tuples.append((hard, simple))
yield tuples
index += batch_size
def construct_dataset(tuples, tokenizer):
X = np.empty((2*len(tuples), tokenizer.vocab_size), dtype=np.int16)
y = np.empty(2*len(tuples), dtype=np.int16)
index = 0
for s,t in tuples:
X[index] = make_vector(s, tokenizer)
X[index+1] = make_vector(t, tokenizer)
y[index] = 0
y[index+1] = 1
index += 2
return X, y
def get_vocab(tokenizer):
tokens = [tokenizer.decode([i], clean_up_tokenization_spaces=False) for i in range(tokenizer.vocab_size)]
return tokens
def simple_term_counts(data_dir='data/newsela/articles'):
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-xsum')
model = LogisticRegression(max_iter=100)
for batch in dataloader(data_dir):
X, y = construct_dataset(batch, tokenizer)
#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
#apply feature scaling
#X_train = normalize(X_train)
#X_test = normalize(X_test)
#model.fit(X_train, y_train)
#predictions = model.predict(X_test)
#print(accuracy_score(y_test, predictions))
X = normalize(X)
model.fit(X, y)
vocab = get_vocab(tokenizer)
weights = np.squeeze(model.coef_, axis=0).tolist()
sorted_weights = filter(lambda x: len(x[1].strip()) > 0, zip(range(tokenizer.vocab_size), vocab, weights))
sorted_weights = list(sorted(sorted_weights, key=lambda x: x[2]))
with open('data/logr_weights/bart_freq_newsela_ids.txt', 'w') as f:
for ID, word, weight in sorted_weights:
f.write(f'{ID} {weight}\n')
with open('data/logr_weights/bart_freq_newsela_tokens.txt', 'w') as f:
for ID, word, weight in sorted_weights:
f.write(f'{word} {weight}\n')
print(simple_term_counts())
|
[
"os.listdir",
"os.path.join",
"sklearn.linear_model.LogisticRegression",
"numpy.squeeze",
"numpy.zeros",
"transformers.BartTokenizer.from_pretrained",
"sklearn.preprocessing.normalize"
] |
[((576, 622), 'numpy.zeros', 'np.zeros', (['tokenizer.vocab_size'], {'dtype': 'np.int16'}), '(tokenizer.vocab_size, dtype=np.int16)\n', (584, 622), True, 'import numpy as np\n'), ((1803, 1860), 'transformers.BartTokenizer.from_pretrained', 'BartTokenizer.from_pretrained', (['"""facebook/bart-large-xsum"""'], {}), "('facebook/bart-large-xsum')\n", (1832, 1860), False, 'from transformers import BertTokenizer, BertConfig, BartTokenizer\n'), ((1873, 1905), 'sklearn.linear_model.LogisticRegression', 'LogisticRegression', ([], {'max_iter': '(100)'}), '(max_iter=100)\n', (1891, 1905), False, 'from sklearn.linear_model import LogisticRegression\n'), ((2334, 2346), 'sklearn.preprocessing.normalize', 'normalize', (['X'], {}), '(X)\n', (2343, 2346), False, 'from sklearn.preprocessing import MinMaxScaler, normalize\n'), ((777, 797), 'os.listdir', 'os.listdir', (['data_dir'], {}), '(data_dir)\n', (787, 797), False, 'import os\n'), ((2419, 2450), 'numpy.squeeze', 'np.squeeze', (['model.coef_'], {'axis': '(0)'}), '(model.coef_, axis=0)\n', (2429, 2450), True, 'import numpy as np\n'), ((999, 1030), 'os.path.join', 'join', (['data_dir', 'f"""{name}.0.txt"""'], {}), "(data_dir, f'{name}.0.txt')\n", (1003, 1030), False, 'from os.path import join\n'), ((1065, 1096), 'os.path.join', 'join', (['data_dir', 'f"""{name}.3.txt"""'], {}), "(data_dir, f'{name}.3.txt')\n", (1069, 1096), False, 'from os.path import join\n')]
|
import unittest, tempfile
from pygromos.simulations.hpc_queuing.job_scheduling.schedulers import simulation_scheduler
from pygromos.data.simulation_parameters_templates import template_md
from pygromos.data.topology_templates import blank_topo_template
from pygromos.simulations.hpc_queuing.submission_systems import DUMMY
from pygromos.files.gromos_system.gromos_system import Gromos_System
from pygromos.tests.in_testfiles import in_test_file_path
from pygromos.tests.test_files import out_test_root_dir
class test_MD_scheduler(unittest.TestCase):
submissionSystem = DUMMY
def setUp(self) -> None:
self.tmp_test_dir = tempfile.mkdtemp(dir=out_test_root_dir, prefix="scheduling_Dummy_")
def test_do(self):
in_cnf = in_test_file_path+"/cnf/in_cnf1.cnf"
out_dir_path = self.tmp_test_dir
in_simSystem = Gromos_System(system_name="test_do", work_folder=out_dir_path,
in_top_path=blank_topo_template, in_cnf_path=in_cnf, in_imd_path=template_md,
in_gromosXX_bin_dir=None, in_gromosPP_bin_dir=None)
submission_system = self.submissionSystem()
simulation_scheduler.do(in_simSystem=in_simSystem, out_dir_path=out_dir_path,
submission_system=submission_system,
simulation_run_num=2, verbose= True)
|
[
"pygromos.simulations.hpc_queuing.job_scheduling.schedulers.simulation_scheduler.do",
"tempfile.mkdtemp",
"pygromos.files.gromos_system.gromos_system.Gromos_System"
] |
[((640, 707), 'tempfile.mkdtemp', 'tempfile.mkdtemp', ([], {'dir': 'out_test_root_dir', 'prefix': '"""scheduling_Dummy_"""'}), "(dir=out_test_root_dir, prefix='scheduling_Dummy_')\n", (656, 707), False, 'import unittest, tempfile\n'), ((848, 1049), 'pygromos.files.gromos_system.gromos_system.Gromos_System', 'Gromos_System', ([], {'system_name': '"""test_do"""', 'work_folder': 'out_dir_path', 'in_top_path': 'blank_topo_template', 'in_cnf_path': 'in_cnf', 'in_imd_path': 'template_md', 'in_gromosXX_bin_dir': 'None', 'in_gromosPP_bin_dir': 'None'}), "(system_name='test_do', work_folder=out_dir_path, in_top_path=\n blank_topo_template, in_cnf_path=in_cnf, in_imd_path=template_md,\n in_gromosXX_bin_dir=None, in_gromosPP_bin_dir=None)\n", (861, 1049), False, 'from pygromos.files.gromos_system.gromos_system import Gromos_System\n'), ((1173, 1332), 'pygromos.simulations.hpc_queuing.job_scheduling.schedulers.simulation_scheduler.do', 'simulation_scheduler.do', ([], {'in_simSystem': 'in_simSystem', 'out_dir_path': 'out_dir_path', 'submission_system': 'submission_system', 'simulation_run_num': '(2)', 'verbose': '(True)'}), '(in_simSystem=in_simSystem, out_dir_path=\n out_dir_path, submission_system=submission_system, simulation_run_num=2,\n verbose=True)\n', (1196, 1332), False, 'from pygromos.simulations.hpc_queuing.job_scheduling.schedulers import simulation_scheduler\n')]
|
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 30 17:03:01 2017
@author: misakawa
"""
from pattern_matching import Match, when, var, T, t, _, overwrite
from numpy.random import randint
@overwrite(var[(t == int) | (t == float)], var[(t == int) | (t == float)])
def add(a, b):
return a + b
@when(var[t == str], var[t == str])
def add(a, b):
return a + b
class Bound1:
pass
class Bound2:
pass
class Bound3(Bound1, Bound2):
def __repr__(self):
return "bound3"
class Bound4(Bound3):
pass
@when(_[(t != Bound3) & (t < Bound4)])
def add():
return 2
@when(_)
def add():
return 3
assert add(1, 1) == 2
assert add(Bound2()) == 2
assert add(Bound3()) == 3
@when(_[int], _[Bound1], var)
def add(u):
return u
assert add(1, Bound1(), 'last') == 'last'
def is_type(x):
return isinstance(x, type)
m = Match(1, 2, (3, int))
[a, b, c] = m.case(var[int], var, *var[tuple]).get
assert a == 1 and b == 2 and c == ((3, int), )
[c2] = m.case((_, _, (_, var.when(is_type)))).get
assert c2 == int
@overwrite(_ == None)
def summary():
return 0
@when([var[int], *(_ == [])], var)
def summary(head, res):
return head + res
@when([var[int], *var[list]], var)
def summary(head, tail, res):
return summary(tail, res + head)
@when(var[list])
def summary(lst):
return summary(lst, 0)
assert summary(list(range(100))) == 4950
@overwrite([var, *var])
def qsort(head, tail):
lowers = [i for i in tail if i < head]
highers = [i for i in tail if i >= head]
return qsort(lowers) + [head] + qsort(highers)
@when(var)
def qsort(lst):
return lst
qsort(randint(0, 500, size=(1200, )))
@when(_[t.when(lambda _: _ == int)])
def trait_test():
return 1
assert trait_test(1) == 1
class Population:
num: int = 1000
@when(var[t.when(lambda _: hasattr(_, 'num'))])
def trait_test(x):
return x.num
assert trait_test(Population()) == 1000
|
[
"pattern_matching.t.when",
"pattern_matching.overwrite",
"pattern_matching.when",
"numpy.random.randint",
"pattern_matching.var.when",
"pattern_matching.Match"
] |
[((190, 263), 'pattern_matching.overwrite', 'overwrite', (['var[(t == int) | (t == float)]', 'var[(t == int) | (t == float)]'], {}), '(var[(t == int) | (t == float)], var[(t == int) | (t == float)])\n', (199, 263), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((299, 333), 'pattern_matching.when', 'when', (['var[t == str]', 'var[t == str]'], {}), '(var[t == str], var[t == str])\n', (303, 333), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((533, 570), 'pattern_matching.when', 'when', (['_[(t != Bound3) & (t < Bound4)]'], {}), '(_[(t != Bound3) & (t < Bound4)])\n', (537, 570), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((598, 605), 'pattern_matching.when', 'when', (['_'], {}), '(_)\n', (602, 605), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((709, 737), 'pattern_matching.when', 'when', (['_[int]', '_[Bound1]', 'var'], {}), '(_[int], _[Bound1], var)\n', (713, 737), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((862, 883), 'pattern_matching.Match', 'Match', (['(1)', '(2)', '(3, int)'], {}), '(1, 2, (3, int))\n', (867, 883), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((1053, 1073), 'pattern_matching.overwrite', 'overwrite', (['(_ == None)'], {}), '(_ == None)\n', (1062, 1073), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((1105, 1138), 'pattern_matching.when', 'when', (['[var[int], *(_ == [])]', 'var'], {}), '([var[int], *(_ == [])], var)\n', (1109, 1138), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((1188, 1221), 'pattern_matching.when', 'when', (['[var[int], *var[list]]', 'var'], {}), '([var[int], *var[list]], var)\n', (1192, 1221), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((1292, 1307), 'pattern_matching.when', 'when', (['var[list]'], {}), '(var[list])\n', (1296, 1307), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((1399, 1421), 'pattern_matching.overwrite', 'overwrite', (['[var, *var]'], {}), '([var, *var])\n', (1408, 1421), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((1587, 1596), 'pattern_matching.when', 'when', (['var'], {}), '(var)\n', (1591, 1596), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((1636, 1665), 'numpy.random.randint', 'randint', (['(0)', '(500)'], {'size': '(1200,)'}), '(0, 500, size=(1200,))\n', (1643, 1665), False, 'from numpy.random import randint\n'), ((1678, 1704), 'pattern_matching.t.when', 't.when', (['(lambda _: _ == int)'], {}), '(lambda _: _ == int)\n', (1684, 1704), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n'), ((1008, 1025), 'pattern_matching.var.when', 'var.when', (['is_type'], {}), '(is_type)\n', (1016, 1025), False, 'from pattern_matching import Match, when, var, T, t, _, overwrite\n')]
|
# ----------------------------------------------------------------------
# |
# | EnvironmentDiffs.py
# |
# | <NAME> <<EMAIL>>
# | 2018-06-02 22:19:34
# |
# ----------------------------------------------------------------------
# |
# | Copyright <NAME> 2018-22.
# | Distributed under the Boost Software License, Version 1.0.
# | (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
# |
# ----------------------------------------------------------------------
"""Displays changes made by an environment during activation."""
import json
import os
import sys
import textwrap
import six
import CommonEnvironment
from CommonEnvironment import CommandLine
from CommonEnvironment.Shell.All import CurrentShell
from RepositoryBootstrap import Constants
# ----------------------------------------------------------------------
_script_fullpath = CommonEnvironment.ThisFullpath()
_script_dir, _script_name = os.path.split(_script_fullpath)
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
@CommandLine.EntryPoint
@CommandLine.Constraints( output_stream=None,
)
def Before( decorate=False,
output_stream=sys.stdout,
):
_Display(GetOriginalEnvironment(), output_stream, decorate)
return 0
# ----------------------------------------------------------------------
@CommandLine.EntryPoint
@CommandLine.Constraints( output_stream=None,
)
def After( decorate=False,
output_stream=sys.stdout,
):
original_env = GetOriginalEnvironment()
# Compare to the current environment
this_env = dict(os.environ)
differences = {}
for k, v in six.iteritems(this_env):
if ( k not in original_env or
original_env[k] != v
):
differences[k] = v
_Display(differences, output_stream, decorate)
return 0
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
def GetOriginalEnvironment():
# Get the original environment
generated_dir = os.getenv(Constants.DE_REPO_GENERATED_NAME)
assert os.path.isdir(generated_dir), generated_dir
original_environment_filename = os.path.join(generated_dir, Constants.GENERATED_ACTIVATION_ORIGINAL_ENVIRONMENT_FILENAME)
assert os.path.isfile(original_environment_filename), original_environment_filename
with open(original_environment_filename) as f:
return json.load(f)
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
def _Display(content, output_stream, decorate):
if not isinstance(content, six.string_types):
content = json.dumps(content)
if decorate:
output_stream.write(textwrap.dedent(
"""\
//--//--//--//--//--//--//--//--//--//--//--//--//--//--//--//
{}
//--//--//--//--//--//--//--//--//--//--//--//--//--//--//--//
""").format(content))
else:
output_stream.write(content)
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
if __name__ == "__main__":
try: sys.exit(CommandLine.Main())
except KeyboardInterrupt: pass
|
[
"CommonEnvironment.CommandLine.Main",
"textwrap.dedent",
"os.getenv",
"json.dumps",
"os.path.join",
"os.path.split",
"os.path.isfile",
"CommonEnvironment.CommandLine.Constraints",
"os.path.isdir",
"json.load",
"CommonEnvironment.ThisFullpath",
"six.iteritems"
] |
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|
from datetime import datetime
from os.path import join
from tests.base import TestCase, main, assets, copy_of_directory
from ocrd_utils import (
initLogging,
VERSION,
MIMETYPE_PAGE
)
from ocrd_models import OcrdMets
# pylint: disable=protected-access,deprecated-method,too-many-public-methods
class TestOcrdMets(TestCase):
def setUp(self):
self.mets = OcrdMets(filename=assets.url_of('SBB0000F29300010000/data/mets.xml'))
initLogging()
def test_unique_identifier(self):
self.assertEqual(self.mets.unique_identifier, 'http://resolver.staatsbibliothek-berlin.de/SBB0000F29300010000', 'Right identifier')
self.mets.unique_identifier = 'foo'
self.assertEqual(self.mets.unique_identifier, 'foo', 'Right identifier after change')
def test_unique_identifier_from_nothing(self):
mets = OcrdMets.empty_mets()
self.assertEqual(mets.unique_identifier, None, 'no identifier')
mets.unique_identifier = 'foo'
self.assertEqual(mets.unique_identifier, 'foo', 'Right identifier after change')
as_string = mets.to_xml().decode('utf-8')
self.assertIn('ocrd/core v%s' % VERSION, as_string)
self.assertIn('CREATEDATE="%d-%d-%02dT' % (
datetime.now().year,
datetime.now().month,
datetime.now().day,
), as_string)
def test_str(self):
mets = OcrdMets(content='<mets/>')
self.assertEqual(str(mets), 'OcrdMets[fileGrps=[],files=[]]')
def test_override_constructor_args(self):
id2file = {'foo': {}}
mets = OcrdMets(id2file, content='<mets/>')
self.assertEqual(mets._file_by_id, id2file)
def test_file_groups(self):
self.assertEqual(len(self.mets.file_groups), 17, '17 file groups')
def test_find_files(self):
self.assertEqual(len(self.mets.find_files()), 35, '35 files total')
self.assertEqual(len(self.mets.find_files(fileGrp='OCR-D-IMG')), 3, '3 files in "OCR-D-IMG"')
self.assertEqual(len(self.mets.find_files(pageId='PHYS_0001')), 17, '17 files for page "PHYS_0001"')
self.assertEqual(len(self.mets.find_files(pageId='PHYS_0001-NOTEXIST')), 0, '0 pages for "PHYS_0001-NOTEXIST"')
self.assertEqual(len(self.mets.find_files(mimetype='image/tiff')), 13, '13 image/tiff')
self.assertEqual(len(self.mets.find_files(mimetype=MIMETYPE_PAGE)), 20, '20 ' + MIMETYPE_PAGE)
self.assertEqual(len(self.mets.find_files(url='OCR-D-IMG/FILE_0005_IMAGE.tif')), 1, '1 xlink:href="OCR-D-IMG/FILE_0005_IMAGE.tif"')
def test_find_files_local_only(self):
self.assertEqual(len(self.mets.find_files(pageId='PHYS_0001', local_only=True)), 3, '3 local files for page "PHYS_0001"')
def test_physical_pages(self):
self.assertEqual(len(self.mets.physical_pages), 3, '3 physical pages')
def test_physical_pages_from_empty_mets(self):
mets = OcrdMets(content="<mets></mets>")
self.assertEqual(len(mets.physical_pages), 0, 'no physical page')
mets.add_file('OUTPUT', ID="foo123", pageId="foobar")
self.assertEqual(len(mets.physical_pages), 1, '1 physical page')
def test_add_group(self):
mets = OcrdMets.empty_mets()
self.assertEqual(len(mets.file_groups), 0, '0 file groups')
mets.add_file_group('TEST')
self.assertEqual(len(mets.file_groups), 1, '1 file groups')
mets.add_file_group('TEST')
self.assertEqual(len(mets.file_groups), 1, '1 file groups')
def test_add_file(self):
mets = OcrdMets.empty_mets()
self.assertEqual(len(mets.file_groups), 0, '0 file groups')
self.assertEqual(len(mets.find_files(fileGrp='OUTPUT')), 0, '0 files in "OUTPUT"')
f = mets.add_file('OUTPUT', ID="foo123", mimetype="bla/quux", pageId="foobar")
f2 = mets.add_file('OUTPUT', ID="foo1232", mimetype="bla/quux", pageId="foobar")
self.assertEqual(f.pageId, 'foobar', 'pageId set')
self.assertEqual(len(mets.file_groups), 1, '1 file groups')
self.assertEqual(len(mets.find_files(fileGrp='OUTPUT')), 2, '2 files in "OUTPUT"')
mets.set_physical_page_for_file('barfoo', f, order='300', orderlabel="page 300")
self.assertEqual(f.pageId, 'barfoo', 'pageId changed')
mets.set_physical_page_for_file('quux', f2, order='302', orderlabel="page 302")
self.assertEqual(f2.pageId, 'quux', 'pageId changed')
mets.set_physical_page_for_file('barfoo', f2, order='301', orderlabel="page 301")
self.assertEqual(f2.pageId, 'barfoo', 'pageId changed')
self.assertEqual(len(mets.file_groups), 1, '1 file group')
def test_add_file_ID_fail(self):
f = self.mets.add_file('OUTPUT', ID='best-id-ever', mimetype="beep/boop")
self.assertEqual(f.ID, 'best-id-ever', "ID kept")
with self.assertRaises(Exception) as cm:
self.mets.add_file('OUTPUT', ID='best-id-ever', mimetype="boop/beep")
self.assertEqual(str(cm.exception), "File with ID='best-id-ever' already exists")
f2 = self.mets.add_file('OUTPUT', ID='best-id-ever', mimetype="boop/beep", force=True)
self.assertEqual(f._el, f2._el)
def test_filegrp_from_file(self):
f = self.mets.find_files(fileGrp='OCR-D-IMG')[0]
self.assertEqual(f.fileGrp, 'OCR-D-IMG')
def test_add_file_no_id(self):
with self.assertRaisesRegex(Exception, "Must set ID of the mets:file"):
self.mets.add_file('FOO')
def test_add_file_no_pageid(self):
f = self.mets.add_file('OUTPUT', mimetype="bla/quux", ID="foo3")
self.assertEqual(f.pageId, None, 'No pageId')
def test_file_pageid(self):
f = self.mets.find_files()[0]
self.assertEqual(f.pageId, 'PHYS_0001')
f.pageId = 'foo'
self.assertEqual(f.pageId, 'foo')
def test_agent(self):
# Processor(workspace=self.workspace)
mets = self.mets
beforelen = len(mets.agents)
mets.add_agent('foo bar v0.0.1', 'OTHER', 'OTHER', 'YETOTHERSTILL')
# print(['%s'%x for x in mets.agents])
self.assertEqual(len(mets.agents), beforelen + 1)
def test_metshdr(self):
"""
Test whether metsHdr is created on-demand
"""
mets = OcrdMets(content="<mets></mets>")
self.assertFalse(mets._tree.getroot().getchildren())
mets.add_agent()
self.assertEqual(len(mets._tree.getroot().getchildren()), 1)
def test_nocontent_nofilename(self):
with self.assertRaisesRegex(Exception, "Must pass 'filename' or 'content' to"):
OcrdMets()
def test_encoding_entities(self):
mets = OcrdMets(content="""
<mets>
<metsHdr>
<agent>
<name>Őh śéé Áŕ</name>
<note>OCR-D</note>
</agent>
</metsHdr>
</mets>
""")
self.assertIn('Őh śéé Áŕ', mets.to_xml().decode('utf-8'))
def test_remove_file_group(self):
"""
Test removal of filegrp
"""
with copy_of_directory(assets.path_to('SBB0000F29300010000/data')) as tempdir:
mets = OcrdMets(filename=join(tempdir, 'mets.xml'))
self.assertEqual(len(mets.file_groups), 17)
self.assertEqual(len(mets.find_files()), 35)
# print()
# before = sorted([x.ID for x in mets.find_files()])
with self.assertRaisesRegex(Exception, "not empty"):
mets.remove_file_group('OCR-D-GT-ALTO')
mets.remove_file_group('OCR-D-GT-PAGE', recursive=True)
# print([x for x in before if x not in sorted([x.ID for x in mets.find_files()])])
self.assertEqual(len(mets.file_groups), 16)
self.assertEqual(len(mets.find_files()), 33)
if __name__ == '__main__':
main()
|
[
"tests.base.assets.path_to",
"tests.base.main",
"ocrd_utils.initLogging",
"os.path.join",
"ocrd_models.OcrdMets.empty_mets",
"datetime.datetime.now",
"ocrd_models.OcrdMets",
"tests.base.assets.url_of"
] |
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|
"""
Environment for basic obstacle avoidance controlling a robotic arm from UR.
In this environment the obstacle is only moving up and down in a vertical line in front of the robot.
The goal is for the robot to stay within a predefined minimum distance to the moving obstacle.
When feasible the robot should continue to the original configuration,
otherwise wait for the obstacle to move away before proceeding
"""
import numpy as np
from typing import Tuple
from robo_gym_server_modules.robot_server.grpc_msgs.python import robot_server_pb2
from robo_gym.envs.simulation_wrapper import Simulation
from robo_gym.envs.ur.ur_base_avoidance_env import URBaseAvoidanceEnv
# base, shoulder, elbow, wrist_1, wrist_2, wrist_3
JOINT_POSITIONS = [-1.57, -1.31, -1.31, -2.18, 1.57, 0.0]
DEBUG = True
MINIMUM_DISTANCE = 0.3 # the distance [cm] the robot should keep to the obstacle
class BasicAvoidanceUR(URBaseAvoidanceEnv):
"""Universal Robots UR basic obstacle avoidance environment.
Args:
rs_address (str): Robot Server address. Formatted as 'ip:port'. Defaults to None.
fix_base (bool): Wether or not the base joint stays fixed or is moveable. Defaults to False.
fix_shoulder (bool): Wether or not the shoulder joint stays fixed or is moveable. Defaults to False.
fix_elbow (bool): Wether or not the elbow joint stays fixed or is moveable. Defaults to False.
fix_wrist_1 (bool): Wether or not the wrist 1 joint stays fixed or is moveable. Defaults to False.
fix_wrist_2 (bool): Wether or not the wrist 2 joint stays fixed or is moveable. Defaults to False.
fix_wrist_3 (bool): Wether or not the wrist 3 joint stays fixed or is moveable. Defaults to True.
ur_model (str): determines which ur model will be used in the environment. Defaults to 'ur5'.
include_polar_to_elbow (bool): determines wether or not the polar coordinates to the elbow joint are included in the state. Defaults to False.
Attributes:
ur (:obj:): Robot utilities object.
client (:obj:str): Robot Server client.
real_robot (bool): True if the environment is controlling a real robot.
"""
max_episode_steps = 1000
def _set_initial_robot_server_state(self, rs_state, fixed_object_position = None) -> robot_server_pb2.State:
if fixed_object_position:
state_msg = super()._set_initial_robot_server_state(rs_state=rs_state, fixed_object_position=fixed_object_position)
return state_msg
z_amplitude = np.random.default_rng().uniform(low=0.09, high=0.35)
z_frequency = 0.125
z_offset = np.random.default_rng().uniform(low=0.2, high=0.6)
string_params = {"object_0_function": "triangle_wave"}
float_params = {"object_0_x": 0.12,
"object_0_y": 0.34,
"object_0_z_amplitude": z_amplitude,
"object_0_z_frequency": z_frequency,
"object_0_z_offset": z_offset}
state = {}
state_msg = robot_server_pb2.State(state = state, float_params = float_params,
string_params = string_params, state_dict = rs_state)
return state_msg
def reset(self, joint_positions = JOINT_POSITIONS, fixed_object_position = None) -> np.array:
"""Environment reset.
Args:
joint_positions (list[6] or np.array[6]): robot joint positions in radians.
fixed_object_position (list[3]): x,y,z fixed position of object
"""
self.prev_action = np.zeros(6)
state = super().reset(joint_positions = joint_positions, fixed_object_position = fixed_object_position)
return state
def reward(self, rs_state, action) -> Tuple[float, bool, dict]:
env_state = self._robot_server_state_to_env_state(rs_state)
reward = 0
done = False
info = {}
# Reward weights
close_distance_weight = -2
delta_joint_weight = 1
action_usage_weight = 1
rapid_action_weight = -0.2
# Difference in joint position current vs. starting position
delta_joint_pos = env_state[9:15]
# Calculate distance to the obstacle
obstacle_coord = np.array([rs_state['object_0_to_ref_translation_x'], rs_state['object_0_to_ref_translation_y'], rs_state['object_0_to_ref_translation_z']])
ee_coord = np.array([rs_state['ee_to_ref_translation_x'], rs_state['ee_to_ref_translation_y'], rs_state['ee_to_ref_translation_z']])
forearm_coord = np.array([rs_state['forearm_to_ref_translation_x'], rs_state['forearm_to_ref_translation_y'], rs_state['forearm_to_ref_translation_z']])
distance_to_ee = np.linalg.norm(obstacle_coord - ee_coord)
distance_to_forearm = np.linalg.norm(obstacle_coord - forearm_coord)
distance_to_target = np.min([distance_to_ee, distance_to_forearm])
# Reward staying close to the predefined joint position
if abs(env_state[-6:]).sum() < 0.1 * action.size:
reward += delta_joint_weight * (1 - (abs(delta_joint_pos).sum()/(0.1 * action.size))) * (1/1000)
# Reward for not acting
if abs(action).sum() <= action.size:
reward += action_usage_weight * (1 - (np.square(action).sum()/action.size)) * (1/1000)
# Negative reward if actions change to rapidly between steps
for i in range(len(action)):
if abs(action[i] - self.prev_action[i]) > 0.5:
reward += rapid_action_weight * (1/1000)
# Negative reward if the obstacle is close than the predefined minimum distance
if distance_to_target < MINIMUM_DISTANCE:
reward += close_distance_weight * (1/self.max_episode_steps)
# Check if there is a collision
collision = True if rs_state['in_collision'] == 1 else False
if collision:
done = True
info['final_status'] = 'collision'
info['target_coord'] = obstacle_coord
self.last_position_on_success = []
if self.elapsed_steps >= self.max_episode_steps:
done = True
info['final_status'] = 'success'
info['target_coord'] = obstacle_coord
self.last_position_on_success = []
return reward, done, info
def step(self, action) -> Tuple[np.array, float, bool, dict]:
if type(action) == list: action = np.array(action)
state, reward, done, info = super().step(action)
self.prev_action = self.add_fixed_joints(action)
return state, reward, done, info
class BasicAvoidanceURSim(BasicAvoidanceUR, Simulation):
cmd = "roslaunch ur_robot_server ur_robot_server.launch \
world_name:=tabletop_sphere50.world \
reference_frame:=base_link \
max_velocity_scale_factor:=0.2 \
action_cycle_rate:=20 \
rviz_gui:=false \
gazebo_gui:=true \
objects_controller:=true \
rs_mode:=1moving2points \
n_objects:=1.0 \
object_0_model_name:=sphere50 \
object_0_frame:=target"
def __init__(self, ip=None, lower_bound_port=None, upper_bound_port=None, gui=False, ur_model='ur5', **kwargs):
self.cmd = self.cmd + ' ' + 'ur_model:=' + ur_model
Simulation.__init__(self, self.cmd, ip, lower_bound_port, upper_bound_port, gui, **kwargs)
BasicAvoidanceUR.__init__(self, rs_address=self.robot_server_ip, ur_model=ur_model, **kwargs)
class BasicAvoidanceURRob(BasicAvoidanceUR):
real_robot = True
# roslaunch ur_robot_server ur_robot_server.launch ur_model:=ur5 real_robot:=true rviz_gui:=true gui:=true reference_frame:=base max_velocity_scale_factor:=0.2 action_cycle_rate:=20 rs_mode:=moving
|
[
"numpy.random.default_rng",
"numpy.min",
"robo_gym_server_modules.robot_server.grpc_msgs.python.robot_server_pb2.State",
"numpy.square",
"numpy.array",
"numpy.zeros",
"numpy.linalg.norm",
"robo_gym.envs.simulation_wrapper.Simulation.__init__"
] |
[((3080, 3196), 'robo_gym_server_modules.robot_server.grpc_msgs.python.robot_server_pb2.State', 'robot_server_pb2.State', ([], {'state': 'state', 'float_params': 'float_params', 'string_params': 'string_params', 'state_dict': 'rs_state'}), '(state=state, float_params=float_params,\n string_params=string_params, state_dict=rs_state)\n', (3102, 3196), False, 'from robo_gym_server_modules.robot_server.grpc_msgs.python import robot_server_pb2\n'), ((3619, 3630), 'numpy.zeros', 'np.zeros', (['(6)'], {}), '(6)\n', (3627, 3630), True, 'import numpy as np\n'), ((4315, 4464), 'numpy.array', 'np.array', (["[rs_state['object_0_to_ref_translation_x'], rs_state[\n 'object_0_to_ref_translation_y'], rs_state['object_0_to_ref_translation_z']\n ]"], {}), "([rs_state['object_0_to_ref_translation_x'], rs_state[\n 'object_0_to_ref_translation_y'], rs_state[\n 'object_0_to_ref_translation_z']])\n", (4323, 4464), True, 'import numpy as np\n'), ((4474, 4600), 'numpy.array', 'np.array', (["[rs_state['ee_to_ref_translation_x'], rs_state['ee_to_ref_translation_y'],\n rs_state['ee_to_ref_translation_z']]"], {}), "([rs_state['ee_to_ref_translation_x'], rs_state[\n 'ee_to_ref_translation_y'], rs_state['ee_to_ref_translation_z']])\n", (4482, 4600), True, 'import numpy as np\n'), ((4620, 4761), 'numpy.array', 'np.array', (["[rs_state['forearm_to_ref_translation_x'], rs_state[\n 'forearm_to_ref_translation_y'], rs_state['forearm_to_ref_translation_z']]"], {}), "([rs_state['forearm_to_ref_translation_x'], rs_state[\n 'forearm_to_ref_translation_y'], rs_state['forearm_to_ref_translation_z']])\n", (4628, 4761), True, 'import numpy as np\n'), ((4782, 4823), 'numpy.linalg.norm', 'np.linalg.norm', (['(obstacle_coord - ee_coord)'], {}), '(obstacle_coord - ee_coord)\n', (4796, 4823), True, 'import numpy as np\n'), ((4855, 4901), 'numpy.linalg.norm', 'np.linalg.norm', (['(obstacle_coord - forearm_coord)'], {}), '(obstacle_coord - forearm_coord)\n', (4869, 4901), True, 'import numpy as np\n'), ((4932, 4977), 'numpy.min', 'np.min', (['[distance_to_ee, distance_to_forearm]'], {}), '([distance_to_ee, distance_to_forearm])\n', (4938, 4977), True, 'import numpy as np\n'), ((7402, 7496), 'robo_gym.envs.simulation_wrapper.Simulation.__init__', 'Simulation.__init__', (['self', 'self.cmd', 'ip', 'lower_bound_port', 'upper_bound_port', 'gui'], {}), '(self, self.cmd, ip, lower_bound_port, upper_bound_port,\n gui, **kwargs)\n', (7421, 7496), False, 'from robo_gym.envs.simulation_wrapper import Simulation\n'), ((6536, 6552), 'numpy.array', 'np.array', (['action'], {}), '(action)\n', (6544, 6552), True, 'import numpy as np\n'), ((2549, 2572), 'numpy.random.default_rng', 'np.random.default_rng', ([], {}), '()\n', (2570, 2572), True, 'import numpy as np\n'), ((2649, 2672), 'numpy.random.default_rng', 'np.random.default_rng', ([], {}), '()\n', (2670, 2672), True, 'import numpy as np\n'), ((5362, 5379), 'numpy.square', 'np.square', (['action'], {}), '(action)\n', (5371, 5379), True, 'import numpy as np\n')]
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pyqtgraph as pg
import numpy as np
class CustomWidget(pg.GraphicsWindow):
pg.setConfigOption('background', 'w')
pg.setConfigOption('foreground', 'k')
def __init__(self, parent=None, **kargs):
pg.GraphicsWindow.__init__(self, **kargs)
self.setParent(parent)
self.setWindowTitle('pyqtgraph example: Scrolling Plots')
self.p = self.addPlot(labels = {'left':'Position', 'bottom':'Time'})
self.data = np.zeros(10)
self.curve = self.p.plot(self.data, pen='b')
if __name__ == '__main__':
w = CustomWidget()
w.show()
|
[
"pyqtgraph.setConfigOption",
"numpy.zeros",
"pyqtgraph.GraphicsWindow.__init__"
] |
[((133, 170), 'pyqtgraph.setConfigOption', 'pg.setConfigOption', (['"""background"""', '"""w"""'], {}), "('background', 'w')\n", (151, 170), True, 'import pyqtgraph as pg\n'), ((175, 212), 'pyqtgraph.setConfigOption', 'pg.setConfigOption', (['"""foreground"""', '"""k"""'], {}), "('foreground', 'k')\n", (193, 212), True, 'import pyqtgraph as pg\n'), ((267, 308), 'pyqtgraph.GraphicsWindow.__init__', 'pg.GraphicsWindow.__init__', (['self'], {}), '(self, **kargs)\n', (293, 308), True, 'import pyqtgraph as pg\n'), ((504, 516), 'numpy.zeros', 'np.zeros', (['(10)'], {}), '(10)\n', (512, 516), True, 'import numpy as np\n')]
|
"""Tests for collapsible definition lists.
When the option ``html_collapsible_definitions``
is ``True``, some HTML classes should be added
to some definition lists but not all of them.
"""
from pathlib import Path
import pytest
from sphinx.application import Sphinx
from .util import parse_html
@pytest.mark.sphinx(
"html",
testroot="collapsible",
confoverrides={"html_theme": "sphinxawesome_theme"},
freshenv=True,
)
def test_no_permalinks(app: Sphinx) -> None:
"""It tests that there are no permalinks."""
app.config.html_permalinks = False # type: ignore[attr-defined]
app.build()
tree = parse_html(Path(app.outdir) / "index.html")
dl = tree("dl")
assert len(dl) == 2
headerlinks = tree("a", class_="headerlink")
assert len(headerlinks) == 0
@pytest.mark.sphinx(
"html",
testroot="collapsible",
confoverrides={"html_theme": "sphinxawesome_theme"},
freshenv=True,
)
def test_no_collapsible_definitions(app: Sphinx) -> None:
"""By default, no classes should be added."""
app.build()
tree = parse_html(Path(app.outdir) / "index.html")
dl = tree("dl")
assert len(dl) == 2
assert str(dl[0]).replace("\n", "") == (
'<dl class="simple"><dt>term</dt><dd><p>definition</p></dd></dl>'
)
assert dl[1]["class"] == ["std", "option", "code-definition"]
dt, dd = (c for c in dl[1].children if c.strip is None)
assert dt.name == "dt"
assert "accordion" not in dt["class"]
assert dd.name == "dd"
assert "class" not in dd
expand_more_button = dt("button", class_="expand-more")
assert len(expand_more_button) == 0
@pytest.mark.sphinx(
"html",
testroot="collapsible",
confoverrides={"html_theme": "sphinxawesome_theme"},
freshenv=True,
)
def test_collapsible_definitions(app: Sphinx) -> None:
"""It tests the correct classes being added to the definition lists.
It should not add the classes to normal definition lists.
"""
# if specified in 'confoverrides', this returns a warning
app.config.html_collapsible_definitions = True # type: ignore[attr-defined]
app.build()
tree = parse_html(Path(app.outdir) / "index.html")
dl = tree("dl")
assert len(dl) == 2
assert str(dl[0]).replace("\n", "") == (
'<dl class="simple"><dt>term</dt><dd><p>definition</p></dd></dl>'
)
assert "code-definition" in dl[1]["class"]
dt, dd = (c for c in dl[1].children if c.strip is None)
assert dt.name == "dt"
assert dt["class"] == ["sig", "sig-object", "std", "accordion"]
assert dd.name == "dd"
assert dd["class"] == ["panel"]
expand_more_button = dt("button", class_="expand-more")
assert len(expand_more_button) == 1
|
[
"pytest.mark.sphinx",
"pathlib.Path"
] |
[((302, 425), 'pytest.mark.sphinx', 'pytest.mark.sphinx', (['"""html"""'], {'testroot': '"""collapsible"""', 'confoverrides': "{'html_theme': 'sphinxawesome_theme'}", 'freshenv': '(True)'}), "('html', testroot='collapsible', confoverrides={\n 'html_theme': 'sphinxawesome_theme'}, freshenv=True)\n", (320, 425), False, 'import pytest\n'), ((803, 926), 'pytest.mark.sphinx', 'pytest.mark.sphinx', (['"""html"""'], {'testroot': '"""collapsible"""', 'confoverrides': "{'html_theme': 'sphinxawesome_theme'}", 'freshenv': '(True)'}), "('html', testroot='collapsible', confoverrides={\n 'html_theme': 'sphinxawesome_theme'}, freshenv=True)\n", (821, 926), False, 'import pytest\n'), ((1646, 1769), 'pytest.mark.sphinx', 'pytest.mark.sphinx', (['"""html"""'], {'testroot': '"""collapsible"""', 'confoverrides': "{'html_theme': 'sphinxawesome_theme'}", 'freshenv': '(True)'}), "('html', testroot='collapsible', confoverrides={\n 'html_theme': 'sphinxawesome_theme'}, freshenv=True)\n", (1664, 1769), False, 'import pytest\n'), ((641, 657), 'pathlib.Path', 'Path', (['app.outdir'], {}), '(app.outdir)\n', (645, 657), False, 'from pathlib import Path\n'), ((1087, 1103), 'pathlib.Path', 'Path', (['app.outdir'], {}), '(app.outdir)\n', (1091, 1103), False, 'from pathlib import Path\n'), ((2164, 2180), 'pathlib.Path', 'Path', (['app.outdir'], {}), '(app.outdir)\n', (2168, 2180), False, 'from pathlib import Path\n')]
|
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from network import NN
from evaluate import accuracy
def read_data(fpath):
iris = pd.read_csv(fpath)
iris.loc[iris['species'] == 'virginica', 'species'] = 0
iris.loc[iris['species'] == 'versicolor', 'species'] = 1
iris.loc[iris['species'] == 'setosa', 'species'] = 2
iris = iris[iris['species'] != 2]
return iris[['petal_length', 'petal_width']].values, iris[['species']].values.astype('uint8')
def plot_data(X, y):
plt.scatter(X[:, 0], X[:, 1], c=y[:, 0], s=40, cmap=plt.cm.Spectral)
plt.title("IRIS DATA | Blue - Versicolor, Red - Virginica ")
plt.xlabel('Petal Length')
plt.ylabel('Petal Width')
plt.show()
def train_test_split(X, y, ratio=0.8):
indices = np.arange(X.shape[0])
np.random.shuffle(indices)
train_len = int(X.shape[0] * ratio)
return X[indices[:train_len]], y[indices[:train_len]], X[indices[train_len:]], y[indices[train_len:]]
if __name__ == '__main__':
X, y = read_data('iris.csv')
# comment the following line if you don't need the plot anymore
plot_data(X, y)
X_train, y_train, X_test, y_test = train_test_split(X, y, 0.7)
nn = NN(len(X[0]), 5, 1)
output = nn.feedforward(X_train)
print(output)
print(f'w1 before backward propagation: \n{nn.w1} \nw2 before backward propagation:\n{nn.w2}')
nn.backward(X_train, y_train, output)
print(f'w1 after backward propagation: \n{nn.w1} \nw2 after backward propagation:\n{nn.w2}')
nn.train(X_train, y_train)
print("Accuracy:")
print(accuracy(nn, X_test, y_test))
|
[
"numpy.random.shuffle",
"pandas.read_csv",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"evaluate.accuracy",
"numpy.arange",
"matplotlib.pyplot.show"
] |
[((159, 177), 'pandas.read_csv', 'pd.read_csv', (['fpath'], {}), '(fpath)\n', (170, 177), True, 'import pandas as pd\n'), ((519, 587), 'matplotlib.pyplot.scatter', 'plt.scatter', (['X[:, 0]', 'X[:, 1]'], {'c': 'y[:, 0]', 's': '(40)', 'cmap': 'plt.cm.Spectral'}), '(X[:, 0], X[:, 1], c=y[:, 0], s=40, cmap=plt.cm.Spectral)\n', (530, 587), True, 'import matplotlib.pyplot as plt\n'), ((592, 652), 'matplotlib.pyplot.title', 'plt.title', (['"""IRIS DATA | Blue - Versicolor, Red - Virginica """'], {}), "('IRIS DATA | Blue - Versicolor, Red - Virginica ')\n", (601, 652), True, 'import matplotlib.pyplot as plt\n'), ((657, 683), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['"""Petal Length"""'], {}), "('Petal Length')\n", (667, 683), True, 'import matplotlib.pyplot as plt\n'), ((688, 713), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['"""Petal Width"""'], {}), "('Petal Width')\n", (698, 713), True, 'import matplotlib.pyplot as plt\n'), ((718, 728), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (726, 728), True, 'import matplotlib.pyplot as plt\n'), ((784, 805), 'numpy.arange', 'np.arange', (['X.shape[0]'], {}), '(X.shape[0])\n', (793, 805), True, 'import numpy as np\n'), ((810, 836), 'numpy.random.shuffle', 'np.random.shuffle', (['indices'], {}), '(indices)\n', (827, 836), True, 'import numpy as np\n'), ((1586, 1614), 'evaluate.accuracy', 'accuracy', (['nn', 'X_test', 'y_test'], {}), '(nn, X_test, y_test)\n', (1594, 1614), False, 'from evaluate import accuracy\n')]
|
import unittest
import unittest.mock
from programy.storage.entities.nodes import NodesStore
class NodesStoreTest(unittest.TestCase):
def test_load(self):
store = NodesStore()
with self.assertRaises(NotImplementedError):
collector = unittest.mock.Mock()
store.load(collector)
|
[
"programy.storage.entities.nodes.NodesStore",
"unittest.mock.Mock"
] |
[((178, 190), 'programy.storage.entities.nodes.NodesStore', 'NodesStore', ([], {}), '()\n', (188, 190), False, 'from programy.storage.entities.nodes import NodesStore\n'), ((268, 288), 'unittest.mock.Mock', 'unittest.mock.Mock', ([], {}), '()\n', (286, 288), False, 'import unittest\n')]
|
import graphviz
def convert_to_visualize(graph_ir, vgraph):
for name, graph in graph_ir.items():
if name == '_training_config':
continue
with vgraph.subgraph(name='cluster'+name) as subgraph:
subgraph.attr(color='blue')
cell_node = {}
ioput = {'_inputs': '{}-{}'.format(name, '_'.join(graph['inputs'])),
'_outputs': '{}-{}'.format(name, '_'.join(graph['outputs']))}
subgraph.node(ioput['_inputs'])
subgraph.node(ioput['_outputs'])
for node_name, node_value in graph['nodes'].items():
value = node_value['operation']
if value['type'] == '_cell':
cell_input_name = '{}-{}'.format(value['cell_name'], '_'.join(graph_ir[value['cell_name']]['inputs']))
cell_output_name = '{}-{}'.format(value['cell_name'], '_'.join(graph_ir[value['cell_name']]['outputs']))
cell_node[node_name] = (cell_input_name, cell_output_name)
print('cell: ', node_name, cell_input_name, cell_output_name)
else:
subgraph.node(node_name)
for edge in graph['edges']:
src = edge['head'][0]
if src == '_inputs':
src = ioput['_inputs']
elif src in cell_node:
src = cell_node[src][1]
dst = edge['tail'][0]
if dst == '_outputs':
dst = ioput['_outputs']
elif dst in cell_node:
dst = cell_node[dst][0]
subgraph.edge(src, dst)
def visualize_model(graph_ir):
vgraph = graphviz.Digraph('G', filename='vgraph', format='jpg')
convert_to_visualize(graph_ir, vgraph)
vgraph.render()
|
[
"graphviz.Digraph"
] |
[((1710, 1764), 'graphviz.Digraph', 'graphviz.Digraph', (['"""G"""'], {'filename': '"""vgraph"""', 'format': '"""jpg"""'}), "('G', filename='vgraph', format='jpg')\n", (1726, 1764), False, 'import graphviz\n')]
|
import unittest
class PrefixNotIncluded(unittest.TestCase):
def test_not_included(self):
pass
if __name__ == '__main__':
unittest.main()
|
[
"unittest.main"
] |
[((139, 154), 'unittest.main', 'unittest.main', ([], {}), '()\n', (152, 154), False, 'import unittest\n')]
|
'''
This is the class to create a scrolling background.
Because the background was so large, it was made to be a .jpg.
'''
import pygame, os
class Background(pygame.sprite.Sprite):
# Initialize the sprite.
def __init__(self,disp):
pygame.sprite.Sprite.__init__(self)
self.image = pygame.image.load(os.path.join("images", "spacebackground.jpg"))
self.image = self.image.convert()
self.rect = self.image.get_rect()
self.dx = 10
self.reset()
# Constantly have the sprite move to the left.
# If the right side of the image moves beyond the right side of the screen, reset the image.
def update(self):
self.rect.left -= self.dx
if self.rect.right <= 800:
self.reset()
# Reset the image's left side to the left side of the screen.
def reset(self):
self.rect.left = 0
|
[
"pygame.sprite.Sprite.__init__",
"os.path.join"
] |
[((257, 292), 'pygame.sprite.Sprite.__init__', 'pygame.sprite.Sprite.__init__', (['self'], {}), '(self)\n', (286, 292), False, 'import pygame, os\n'), ((332, 377), 'os.path.join', 'os.path.join', (['"""images"""', '"""spacebackground.jpg"""'], {}), "('images', 'spacebackground.jpg')\n", (344, 377), False, 'import pygame, os\n')]
|
# -*- coding: utf-8 -*-
"""Linear module for dqn algorithms
- Author: <NAME>
- Contact: <EMAIL>
"""
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from rl_algorithms.common.helper_functions import numpy2floattensor
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class NoisyLinear(nn.Module):
"""Noisy linear module for NoisyNet.
References:
https://github.com/higgsfield/RL-Adventure/blob/master/5.noisy%20dqn.ipynb
https://github.com/Kaixhin/Rainbow/blob/master/model.py
Attributes:
in_features (int): input size of linear module
out_features (int): output size of linear module
std_init (float): initial std value
weight_mu (nn.Parameter): mean value weight parameter
weight_sigma (nn.Parameter): std value weight parameter
bias_mu (nn.Parameter): mean value bias parameter
bias_sigma (nn.Parameter): std value bias parameter
"""
def __init__(self, in_features: int, out_features: int, std_init: float = 0.5):
"""Initialize."""
super(NoisyLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.std_init = std_init
self.weight_mu = nn.Parameter(torch.Tensor(out_features, in_features))
self.weight_sigma = nn.Parameter(torch.Tensor(out_features, in_features))
self.register_buffer("weight_epsilon", torch.Tensor(out_features, in_features))
self.bias_mu = nn.Parameter(torch.Tensor(out_features))
self.bias_sigma = nn.Parameter(torch.Tensor(out_features))
self.register_buffer("bias_epsilon", torch.Tensor(out_features))
self.reset_parameters()
self.reset_noise()
def reset_parameters(self):
"""Reset trainable network parameters (factorized gaussian noise)."""
mu_range = 1 / math.sqrt(self.in_features)
self.weight_mu.data.uniform_(-mu_range, mu_range)
self.weight_sigma.data.fill_(self.std_init / math.sqrt(self.in_features))
self.bias_mu.data.uniform_(-mu_range, mu_range)
self.bias_sigma.data.fill_(self.std_init / math.sqrt(self.out_features))
@staticmethod
def scale_noise(size: int) -> torch.Tensor:
"""Set scale to make noise (factorized gaussian noise)."""
x = numpy2floattensor(np.random.normal(loc=0.0, scale=1.0, size=size), device)
return x.sign().mul(x.abs().sqrt())
def reset_noise(self):
"""Make new noise."""
epsilon_in = self.scale_noise(self.in_features)
epsilon_out = self.scale_noise(self.out_features)
# outer product
self.weight_epsilon.copy_(epsilon_out.ger(epsilon_in))
self.bias_epsilon.copy_(epsilon_out)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward method implementation.
We don't use separate statements on train / eval mode.
It doesn't show remarkable difference of performance.
"""
return F.linear(
x,
self.weight_mu + self.weight_sigma * self.weight_epsilon,
self.bias_mu + self.bias_sigma * self.bias_epsilon,
)
class NoisyLinearConstructor:
"""Constructor class for changing hyper parameters of NoisyLinear.
Attributes:
std_init (float): initial std value
"""
def __init__(self, std_init: float = 0.5):
"""Initialize."""
self.std_init = std_init
def __call__(self, in_features: int, out_features: int) -> NoisyLinear:
"""Return NoisyLinear instance set hyper parameters"""
return NoisyLinear(in_features, out_features, self.std_init)
class NoisyMLPHandler:
"""Includes methods to handle noisy linear."""
def reset_noise(self):
"""Re-sample noise"""
for _, module in self.named_children():
module.reset_noise()
|
[
"torch.nn.functional.linear",
"numpy.random.normal",
"math.sqrt",
"torch.Tensor",
"torch.cuda.is_available"
] |
[((305, 330), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (328, 330), False, 'import torch\n'), ((3051, 3177), 'torch.nn.functional.linear', 'F.linear', (['x', '(self.weight_mu + self.weight_sigma * self.weight_epsilon)', '(self.bias_mu + self.bias_sigma * self.bias_epsilon)'], {}), '(x, self.weight_mu + self.weight_sigma * self.weight_epsilon, self.\n bias_mu + self.bias_sigma * self.bias_epsilon)\n', (3059, 3177), True, 'import torch.nn.functional as F\n'), ((1313, 1352), 'torch.Tensor', 'torch.Tensor', (['out_features', 'in_features'], {}), '(out_features, in_features)\n', (1325, 1352), False, 'import torch\n'), ((1395, 1434), 'torch.Tensor', 'torch.Tensor', (['out_features', 'in_features'], {}), '(out_features, in_features)\n', (1407, 1434), False, 'import torch\n'), ((1483, 1522), 'torch.Tensor', 'torch.Tensor', (['out_features', 'in_features'], {}), '(out_features, in_features)\n', (1495, 1522), False, 'import torch\n'), ((1561, 1587), 'torch.Tensor', 'torch.Tensor', (['out_features'], {}), '(out_features)\n', (1573, 1587), False, 'import torch\n'), ((1628, 1654), 'torch.Tensor', 'torch.Tensor', (['out_features'], {}), '(out_features)\n', (1640, 1654), False, 'import torch\n'), ((1701, 1727), 'torch.Tensor', 'torch.Tensor', (['out_features'], {}), '(out_features)\n', (1713, 1727), False, 'import torch\n'), ((1923, 1950), 'math.sqrt', 'math.sqrt', (['self.in_features'], {}), '(self.in_features)\n', (1932, 1950), False, 'import math\n'), ((2392, 2439), 'numpy.random.normal', 'np.random.normal', ([], {'loc': '(0.0)', 'scale': '(1.0)', 'size': 'size'}), '(loc=0.0, scale=1.0, size=size)\n', (2408, 2439), True, 'import numpy as np\n'), ((2062, 2089), 'math.sqrt', 'math.sqrt', (['self.in_features'], {}), '(self.in_features)\n', (2071, 2089), False, 'import math\n'), ((2198, 2226), 'math.sqrt', 'math.sqrt', (['self.out_features'], {}), '(self.out_features)\n', (2207, 2226), False, 'import math\n')]
|
# Advent of Code - 2015 - Day 7
# --- Day 7: Some Assembly Required ---
# This year, Santa brought little Bobby Tables a set of wires and bitwise logic gates! Unfortunately, little Bobby is a little under the recommended age range, and he needs help assembling the circuit.
# Each wire has an identifier (some lowercase letters) and can carry a 16-bit signal (a number from 0 to 65535). A signal is provided to each wire by a gate, another wire, or some specific value. Each wire can only get a signal from one source, but can provide its signal to multiple destinations. A gate provides no signal until all of its inputs have a signal.
# The included instructions booklet describes how to connect the parts together: x AND y -> z means to connect wires x and y to an AND gate, and then connect its output to wire z.
# For example:
# 123 -> x means that the signal 123 is provided to wire x.
# x AND y -> z means that the bitwise AND of wire x and wire y is provided to wire z.
# p LSHIFT 2 -> q means that the value from wire p is left-shifted by 2 and then provided to wire q.
# NOT e -> f means that the bitwise complement of the value from wire e is provided to wire f.
# Other possible gates include OR (bitwise OR) and RSHIFT (right-shift). If, for some reason, you'd like to emulate the circuit instead, almost all programming languages (for example, C, JavaScript, or Python) provide operators for these gates.
# For example, here is a simple circuit:
# 123 -> x
# 456 -> y
# x AND y -> d
# x OR y -> e
# x LSHIFT 2 -> f
# y RSHIFT 2 -> g
# NOT x -> h
# NOT y -> i
# After it is run, these are the signals on the wires:
# d: 72
# e: 507
# f: 492
# g: 114
# h: 65412
# i: 65079
# x: 123
# y: 456
# In little Bobby's kit's instructions booklet (provided as your puzzle input), what signal is ultimately provided to wire a?
import time, math
def createCircuitDict():
global circuitStrings
global circuitDict
# this function takes the string as input (circuitStrings) and converts them (parses them) into a dictionary (circuitDict)
for circuitLine in circuitStrings:
# the string "->" is the delimeter (sp?) between the left side (input) and the wire name (dictionary key)
leftSide = circuitLine[0 : circuitLine.find("->") - 1]
# if debug:
# print("leftSide:", leftSide)
rightSide = circuitLine[circuitLine.find("->") + 3 : ]
# if debug:
# print("rightSide:", rightSide)
# we set the outputValue to nan (not a number) as a way of checking if we have successfully evaluated the wires inputs or not: default = nan, not evaluated
outputValue = math.nan
# check for numeric input string -- this is easy, just make it the output
if leftSide.isnumeric():
leftSide = int(leftSide)
outputValue = leftSide # simple -- the input to this wire is also it's output
# check for duplicate wire names (dictionary keys) in the input string
if circuitDict.get(rightSide) != None:
print("Weird... dictionary key ", rightSide, "already exists. This shouldn't happen.")
circuitDict[rightSide] = {"input" : leftSide, "output" : outputValue}
def evaluateInput(circuit, operator):
global circuitDict
# if debug:
# print(circuit, operator)
# check left argument for circuit name or number
inputWire1 = circuitDict[circuit]["input"][: circuitDict[circuit]["input"].find(operator) - 1]
inputWire2 = circuitDict[circuit]["input"][circuitDict[circuit]["input"].find(operator) + len(operator) + 1 : ]
# if debug:
# print(circuit, "=", inputWire1, operator, inputWire2)
# look up the output of the inputWire
if inputWire1.isnumeric():
input1 = int(inputWire1)
else:
input1 = circuitDict[inputWire1]["output"]
if inputWire2.isnumeric():
input2 = int(inputWire2)
else:
input2 = circuitDict[inputWire2]["output"]
if math.isnan(input1):
# print("input wire 1 isn't calculated yet")
pass
elif math.isnan(input2):
# print("input wire 2 isn't calculated yet")
pass
else:
# do the bitwise complement on the input number and assign it to the output of this wire
if operator == "AND":
circuitDict[circuit]["output"] = input1 & input2
elif operator == "OR":
circuitDict[circuit]["output"] = input1 | input2
elif operator == "LSHIFT":
circuitDict[circuit]["output"] = input1 << input2
elif operator == "RSHIFT":
circuitDict[circuit]["output"] = input1 >> input2
else:
print("Unknown operator", operator)
# check for rollunder 0
# this occurs because we are using a signed integer for what should be an unsigned 16-bit integer
# TODO figure out if Python has an unsigned 16-bit integer type
if circuitDict[circuit]["output"] < 0:
# if debug:
# print("result under zero, fix it")
circuitDict[circuit]["output"] = 65535 + circuitDict[circuit]["output"]
def doConnection():
global circuitDict
unfinishedCount = len(circuitDict)
lowCount = unfinishedCount
while unfinishedCount:
unfinishedCount = len(circuitDict)
if debug:
print("lowCount", lowCount)
for circuit in circuitDict:
# if the output is not a number, evaluate the input
if math.isnan(circuitDict[circuit]["output"]):
# parse the left side
# we can have NOT, AND, OR, LSHIFT, and RSHIFT as possible commands
if "NOT" in circuitDict[circuit]["input"]:
# operation is logical NOT, invert the input line to be the output
inputWire1 = circuitDict[circuit]["input"][circuitDict[circuit]["input"].find("NOT")+4 : ]
# if debug:
# print(circuit, "= NOT", inputWire1)
# look up the output of the inputWire
if inputWire1.isnumeric():
input1 = int(inputWire1)
else:
input1 = circuitDict[inputWire1]["output"]
if math.isnan(input1):
# print("input wire isn't calculated yet")
pass
else:
# do the bitwise complement on the input number and assign it to the output of this wire
circuitDict[circuit]["output"] = ~input1
# check for rollunder 0
if circuitDict[circuit]["output"] < 0:
# if debug:
# print("result under zero, fix it")
circuitDict[circuit]["output"] = 65536 + circuitDict[circuit]["output"]
elif "AND" in circuitDict[circuit]["input"]:
evaluateInput(circuit, "AND")
elif "OR" in circuitDict[circuit]["input"]:
evaluateInput(circuit, "OR")
elif "LSHIFT" in circuitDict[circuit]["input"]:
evaluateInput(circuit, "LSHIFT")
elif "RSHIFT" in circuitDict[circuit]["input"]:
evaluateInput(circuit, "RSHIFT")
else:
# simplest case -- one input only!
# copy the input wire
# this could be improved by doing it only if the inputWire is resolved
inputWire1 = circuitDict[circuit]["input"]
if debug:
print("simplest case circuit", circuit, " inputWire", inputWire1)
circuitDict[circuit]["output"] = circuitDict[inputWire1]["output"]
else:
# this circuit is done, move on
# if debug:
# print("circuit",circuit,"is done with output ", circuitDict[circuit]["output"], "Break.")
pass
if math.isnan(circuitDict[circuit]["output"]) is False:
# this output is calculated, decrement the unfinished counter
unfinishedCount -= 1
if unfinishedCount < lowCount:
lowCount = unfinishedCount
# if debug:
# print("unfinishedCount", unfinishedCount)
startTime = time.perf_counter() # time in seconds (float)
debug = False
timing = True
unitTesting = False
# maybe a dictionary again?
# circuitStrings = {"a" : {"input" : 1, "output" : NaN}}
# parse the input text file to set up the circuitStrings inputs, then just roll through the dictionary to calculate the outputs
# how will I be sure that the output has been calculated to be the input for the next circuitStrings?
# can I assume the input file is "in order"? Probably not.
# does this mean some sort of recursion algorithm?
# maybe if I populate the outputs with 'NaN' (or Python equivalent) then check that it's not that before using it's output
# I can make it recurse through the inputs, calculating any that have fully realized inputs?
circuitStrings = []
circuitDict = {}
# unit tests, kind of
if unitTesting:
print("Unit Testing")
circuitStrings = ["123 -> x","456 -> y", "x AND y -> d", "x OR y -> e", "x LSHIFT 2 -> f", "y RSHIFT 2 -> g", "NOT x -> h", "NOT y -> i"]
else:
# read the input text file into a variable called presents
with open("2015/day7/input-part2.txt","r") as inputString:
circuitStrings = inputString.readlines()
# remove newlines
for i in range(0, len(circuitStrings)):
circuitStrings[i] = circuitStrings[i].rstrip()
# parse the input to create the dictionary
createCircuitDict()
doConnection()
# show the circuits
if debug:
for circuit in circuitDict:
print(circuit,":",circuitDict[circuit])
if unitTesting:
testPass = False
testPassOutput = {"d": {"output" : 72}, "e": {"output" : 507}, "f": {"output" : 492}, "g": {"output" : 114}, "h": {"output" : 65412}, "i": {"output" : 65079}, "x": {"output" : 123}, "y": {"output" : 456}}
for wire in testPassOutput:
testPassWire = testPassOutput[wire]["output"]
circuitWire = circuitDict[wire]["output"]
if debug:
print("wire", wire, "test:", testPassWire, "calc:", circuitWire)
testPass = testPassWire == circuitWire
if testPass is False:
break
print("testPass:", testPass)
else:
print(circuitDict["a"]["output"])
# this answer for my input is 46065 (part 1), 14134 (part 2)
endTime = time.perf_counter() # time in seconds (float)
if timing:
print("Execution took ", endTime - startTime, " seconds.")
|
[
"time.perf_counter",
"math.isnan"
] |
[((8497, 8516), 'time.perf_counter', 'time.perf_counter', ([], {}), '()\n', (8514, 8516), False, 'import time, math\n'), ((10704, 10723), 'time.perf_counter', 'time.perf_counter', ([], {}), '()\n', (10721, 10723), False, 'import time, math\n'), ((4004, 4022), 'math.isnan', 'math.isnan', (['input1'], {}), '(input1)\n', (4014, 4022), False, 'import time, math\n'), ((4099, 4117), 'math.isnan', 'math.isnan', (['input2'], {}), '(input2)\n', (4109, 4117), False, 'import time, math\n'), ((5513, 5555), 'math.isnan', 'math.isnan', (["circuitDict[circuit]['output']"], {}), "(circuitDict[circuit]['output'])\n", (5523, 5555), False, 'import time, math\n'), ((8130, 8172), 'math.isnan', 'math.isnan', (["circuitDict[circuit]['output']"], {}), "(circuitDict[circuit]['output'])\n", (8140, 8172), False, 'import time, math\n'), ((6313, 6331), 'math.isnan', 'math.isnan', (['input1'], {}), '(input1)\n', (6323, 6331), False, 'import time, math\n')]
|
import logging
import pytest
from . import auth
from hydroengine_service import dgds_functions
logger = logging.getLogger(__name__)
class TestDGDSFunctions:
@pytest.mark.parametrize('source, start_date, end_date, limit',
[
('projects/dgds-gee/bathymetry/gebco/2019', None, None, 10),
('projects/dgds-gee/glossis/currents', None, None, None),
('projects/dgds-gee/glossis/waterlevel', '2020-11-01', '2020-12-01', None),
('projects/dgds-gee/glossis/wind', '2020-11-01', '2020-11-10', 10),
('projects/dgds-gee/glossis/waveheight', None, None, None),
('projects/dgds-gee/gloffis/weather', None, None, 5),
('projects/dgds-gee/gloffis/hydro', None, None, 5),
('projects/dgds-gee/metocean/waves/percentiles', None, None, 5),
('projects/dgds-gee/chasm/waves', None, None, None),
('projects/dgds-gee/chasm/wind', None, None, None),
('projects/dgds-gee/crucial/evaporation_deficit', None, None, None),
('projects/dgds-gee/crucial/groundwater_declining_trend', None, None, None),
('projects/dgds-gee/msfd/chlorophyll', None, None, None)
])
def test_get_image_collection_info(self, source, start_date, end_date, limit):
image_date_list = dgds_functions.get_image_collection_info(source, start_date, end_date, limit)
assert len(image_date_list) >= 1
assert "imageId" in image_date_list[0]
assert "date" in image_date_list[0]
|
[
"logging.getLogger",
"pytest.mark.parametrize",
"hydroengine_service.dgds_functions.get_image_collection_info"
] |
[((107, 134), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (124, 134), False, 'import logging\n'), ((167, 1097), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""source, start_date, end_date, limit"""', "[('projects/dgds-gee/bathymetry/gebco/2019', None, None, 10), (\n 'projects/dgds-gee/glossis/currents', None, None, None), (\n 'projects/dgds-gee/glossis/waterlevel', '2020-11-01', '2020-12-01',\n None), ('projects/dgds-gee/glossis/wind', '2020-11-01', '2020-11-10', \n 10), ('projects/dgds-gee/glossis/waveheight', None, None, None), (\n 'projects/dgds-gee/gloffis/weather', None, None, 5), (\n 'projects/dgds-gee/gloffis/hydro', None, None, 5), (\n 'projects/dgds-gee/metocean/waves/percentiles', None, None, 5), (\n 'projects/dgds-gee/chasm/waves', None, None, None), (\n 'projects/dgds-gee/chasm/wind', None, None, None), (\n 'projects/dgds-gee/crucial/evaporation_deficit', None, None, None), (\n 'projects/dgds-gee/crucial/groundwater_declining_trend', None, None,\n None), ('projects/dgds-gee/msfd/chlorophyll', None, None, None)]"], {}), "('source, start_date, end_date, limit', [(\n 'projects/dgds-gee/bathymetry/gebco/2019', None, None, 10), (\n 'projects/dgds-gee/glossis/currents', None, None, None), (\n 'projects/dgds-gee/glossis/waterlevel', '2020-11-01', '2020-12-01',\n None), ('projects/dgds-gee/glossis/wind', '2020-11-01', '2020-11-10', \n 10), ('projects/dgds-gee/glossis/waveheight', None, None, None), (\n 'projects/dgds-gee/gloffis/weather', None, None, 5), (\n 'projects/dgds-gee/gloffis/hydro', None, None, 5), (\n 'projects/dgds-gee/metocean/waves/percentiles', None, None, 5), (\n 'projects/dgds-gee/chasm/waves', None, None, None), (\n 'projects/dgds-gee/chasm/wind', None, None, None), (\n 'projects/dgds-gee/crucial/evaporation_deficit', None, None, None), (\n 'projects/dgds-gee/crucial/groundwater_declining_trend', None, None,\n None), ('projects/dgds-gee/msfd/chlorophyll', None, None, None)])\n", (190, 1097), False, 'import pytest\n'), ((1633, 1710), 'hydroengine_service.dgds_functions.get_image_collection_info', 'dgds_functions.get_image_collection_info', (['source', 'start_date', 'end_date', 'limit'], {}), '(source, start_date, end_date, limit)\n', (1673, 1710), False, 'from hydroengine_service import dgds_functions\n')]
|
from tweepy import StreamListener, OAuthHandler, Stream
from configs import Configs
import sys
class StdOutListener(StreamListener):
def __init__(self, kafka_producer, topic):
super().__init__()
self.kafka_producer = kafka_producer
self.topic = topic
""" A listener handles tweets that are received from the stream.
"""
def on_data(self, data):
self.kafka_producer.produce(topic=self.topic, value=data)
print(data)
return True
def on_error(self, status):
print(status)
def exit_gracefully(kafka_producer):
if kafka_producer is not None:
kafka_producer.flush(30)
print('kafka producer flushed')
sys.exit(0)
def create_twitter_client(kafka_producer, configs):
listener = StdOutListener(kafka_producer, configs.kafka_topic)
auth = OAuthHandler(configs.consumer_key, configs.consumer_secret)
auth.set_access_token(configs.access_token_key, configs.access_token_secret)
return Stream(auth, listener)
def create_kafka_producer():
# https://www.confluent.io/blog/introduction-to-apache-kafka-for-python-programmers/
from confluent_kafka import Producer
p = Producer({'bootstrap.servers': 'localhost:9092',
'acks': 'all',
'enable.idempotence': 'true',
'compression.type': 'snappy'})
return p
configs = Configs()
producer = None
try:
producer = create_kafka_producer()
client = create_twitter_client(producer, configs)
client.filter(track=configs.twitter_topics)
finally:
exit_gracefully(producer)
|
[
"tweepy.Stream",
"configs.Configs",
"confluent_kafka.Producer",
"sys.exit",
"tweepy.OAuthHandler"
] |
[((1395, 1404), 'configs.Configs', 'Configs', ([], {}), '()\n', (1402, 1404), False, 'from configs import Configs\n'), ((701, 712), 'sys.exit', 'sys.exit', (['(0)'], {}), '(0)\n', (709, 712), False, 'import sys\n'), ((845, 904), 'tweepy.OAuthHandler', 'OAuthHandler', (['configs.consumer_key', 'configs.consumer_secret'], {}), '(configs.consumer_key, configs.consumer_secret)\n', (857, 904), False, 'from tweepy import StreamListener, OAuthHandler, Stream\n'), ((998, 1020), 'tweepy.Stream', 'Stream', (['auth', 'listener'], {}), '(auth, listener)\n', (1004, 1020), False, 'from tweepy import StreamListener, OAuthHandler, Stream\n'), ((1191, 1319), 'confluent_kafka.Producer', 'Producer', (["{'bootstrap.servers': 'localhost:9092', 'acks': 'all', 'enable.idempotence':\n 'true', 'compression.type': 'snappy'}"], {}), "({'bootstrap.servers': 'localhost:9092', 'acks': 'all',\n 'enable.idempotence': 'true', 'compression.type': 'snappy'})\n", (1199, 1319), False, 'from confluent_kafka import Producer\n')]
|
"""
Stability analysis of the
D2Q4 solver for the advection equation
d_t(u) + c_x d_x(u) + c_y d_y(u) = 0
"""
import sympy as sp
import pylbm
# pylint: disable=invalid-name
# symbolic variables
U, X, Y = sp.symbols('U, X, Y')
# symbolic parameters
LA, CX, CY = sp.symbols('lambda, cx, cy', constants=True)
S_1, S_2 = sp.symbols('s1, s2', constants=True)
# numerical parameters
la = 1. # velocity of the scheme
s_1, s_2 = 2., 1. # relaxation parameters
c_x, c_y = 0.5, 0.25 # velocity of the advection equation
dico = {
'dim': 2,
'scheme_velocity': LA,
'schemes': [
{
'velocities': [1, 2, 3, 4],
'conserved_moments': U,
'polynomials': [1, X, Y, X**2-Y**2],
'relaxation_parameters': [0, S_1, S_1, S_2],
'equilibrium': [
U,
CX*U, CY*U,
(CX**2-CY**2)*U
],
},
],
'parameters': {
LA: la,
S_1: s_1,
S_2: s_2,
CX: c_x,
CY: c_y,
},
'relative_velocity': [CX, CY],
}
scheme = pylbm.Scheme(dico)
stab = pylbm.Stability(scheme)
stab.visualize({
'parameters': {
CX: {
'range': [0, 1],
'init': c_x,
'step': 0.01,
},
CY: {
'range': [0, 1],
'init': c_y,
'step': 0.01,
},
S_1: {
'name': r"$s_1$",
'range': [0, 2],
'init': s_1,
'step': 0.01,
},
S_2: {
'name': r"$s_2$",
'range': [0, 2],
'init': s_2,
'step': 0.01,
},
},
'number_of_wave_vectors': 4096,
})
|
[
"sympy.symbols",
"pylbm.Stability",
"pylbm.Scheme"
] |
[((215, 236), 'sympy.symbols', 'sp.symbols', (['"""U, X, Y"""'], {}), "('U, X, Y')\n", (225, 236), True, 'import sympy as sp\n'), ((273, 317), 'sympy.symbols', 'sp.symbols', (['"""lambda, cx, cy"""'], {'constants': '(True)'}), "('lambda, cx, cy', constants=True)\n", (283, 317), True, 'import sympy as sp\n'), ((329, 365), 'sympy.symbols', 'sp.symbols', (['"""s1, s2"""'], {'constants': '(True)'}), "('s1, s2', constants=True)\n", (339, 365), True, 'import sympy as sp\n'), ((1102, 1120), 'pylbm.Scheme', 'pylbm.Scheme', (['dico'], {}), '(dico)\n', (1114, 1120), False, 'import pylbm\n'), ((1128, 1151), 'pylbm.Stability', 'pylbm.Stability', (['scheme'], {}), '(scheme)\n', (1143, 1151), False, 'import pylbm\n')]
|
from numpy import reshape
def vec(x):
return reshape(x, (-1,) + x.shape[2:], order="F")
def unvec(x, shape):
return reshape(x, shape, order="F")
|
[
"numpy.reshape"
] |
[((51, 93), 'numpy.reshape', 'reshape', (['x', '((-1,) + x.shape[2:])'], {'order': '"""F"""'}), "(x, (-1,) + x.shape[2:], order='F')\n", (58, 93), False, 'from numpy import reshape\n'), ((128, 156), 'numpy.reshape', 'reshape', (['x', 'shape'], {'order': '"""F"""'}), "(x, shape, order='F')\n", (135, 156), False, 'from numpy import reshape\n')]
|
# -*- coding: utf-8 -*-
# *******************************************************
# Copyright (c) VMware, Inc. 2020-2021. All Rights Reserved.
# SPDX-License-Identifier: MIT
# *******************************************************
# *
# * DISCLAIMER. THIS PROGRAM IS PROVIDED TO YOU "AS IS" WITHOUT
# * WARRANTIES OR CONDITIONS OF ANY KIND, WHETHER ORAL OR WRITTEN,
# * EXPRESS OR IMPLIED. THE AUTHOR SPECIFICALLY DISCLAIMS ANY IMPLIED
# * WARRANTIES OR CONDITIONS OF MERCHANTABILITY, SATISFACTORY QUALITY,
# * NON-INFRINGEMENT AND FITNESS FOR A PARTICULAR PURPOSE.
"""Unit tests for the analysis engine"""
import pytest
from cbc_binary_toolkit import InitializationError
from cbc_binary_toolkit.config import Config
from cbc_binary_toolkit.engine import LocalEngineManager
from cbc_binary_toolkit.schemas import EngineResponseSchema
from tests.component.engine_fixtures.mock_engine import MockLocalEngine
from tests.component.schema_fixtures.mock_data import VALID_BINARY_METADATA, MISSING_FIELDS_BINARY_METADATA
ENGINE_NAME = "MockEngine"
@pytest.fixture(scope="session")
def config():
"""Configuration for all the test cases in this module."""
return Config.load(f"""
id: cbc_binary_toolkit
version: 0.0.1
engine:
name: {ENGINE_NAME}
type: local
_provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory
Test: TestPassed
""")
# ==================================== Unit TESTS BELOW ====================================
def test_create_engine(config):
"""Test successful creation of MockLocalEngine"""
manager = LocalEngineManager(config)
assert isinstance(manager.create_engine(), MockLocalEngine)
def test_analyze(config):
"""Test analyze pass through"""
manager = LocalEngineManager(config)
assert EngineResponseSchema.validate(manager.analyze(VALID_BINARY_METADATA))
@pytest.mark.parametrize("input", [
MISSING_FIELDS_BINARY_METADATA,
{}
])
def test_analyze_invalid_schema(config, input):
"""Test analyze pass through"""
manager = LocalEngineManager(config)
result = manager.analyze(input)
if result["binary_hash"] is not None:
result = EngineResponseSchema.validate(result)
assert not result["success"]
@pytest.mark.parametrize("engine_config, exception", [
["""
id: cbc_binary_toolkit
engine:
name: {ENGINE_NAME}
type: unknown
num_threads: 1
_provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory
""", InitializationError],
["""
id: cbc_binary_toolkit
engine:
name: {ENGINE_NAME}
type: local
_provider: INVALID.INVALID
""", ImportError],
["""
id: cbc_binary_toolkit
engine:
name: {ENGINE_NAME}
type: local
_provider: cbc_binary_toolkit.engine.LocalEngineFactory
""", NotImplementedError],
[f"""
id: cbc_binary_toolkit
version: 0.0.1
engine:
name: {ENGINE_NAME}
type: local
_provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory
""", AssertionError]
])
def test_failed_init(engine_config, exception):
"""Test raised exceptions on init of LocalEngineManager"""
config = Config.load(engine_config)
with pytest.raises(exception):
LocalEngineManager(config)
|
[
"cbc_binary_toolkit.schemas.EngineResponseSchema.validate",
"pytest.mark.parametrize",
"pytest.raises",
"cbc_binary_toolkit.engine.LocalEngineManager",
"pytest.fixture",
"cbc_binary_toolkit.config.Config.load"
] |
[((1051, 1082), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""session"""'}), "(scope='session')\n", (1065, 1082), False, 'import pytest\n'), ((1884, 1954), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""input"""', '[MISSING_FIELDS_BINARY_METADATA, {}]'], {}), "('input', [MISSING_FIELDS_BINARY_METADATA, {}])\n", (1907, 1954), False, 'import pytest\n'), ((2260, 3186), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""engine_config, exception"""', '[[\n """\n id: cbc_binary_toolkit\n engine:\n name: {ENGINE_NAME}\n type: unknown\n num_threads: 1\n _provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory\n """\n , InitializationError], [\n """\n id: cbc_binary_toolkit\n engine:\n name: {ENGINE_NAME}\n type: local\n _provider: INVALID.INVALID\n """\n , ImportError], [\n """\n id: cbc_binary_toolkit\n engine:\n name: {ENGINE_NAME}\n type: local\n _provider: cbc_binary_toolkit.engine.LocalEngineFactory\n """\n , NotImplementedError], [\n f"""\n id: cbc_binary_toolkit\n version: 0.0.1\n engine:\n name: {ENGINE_NAME}\n type: local\n _provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory\n """\n , AssertionError]]'], {}), '(\'engine_config, exception\', [[\n """\n id: cbc_binary_toolkit\n engine:\n name: {ENGINE_NAME}\n type: unknown\n num_threads: 1\n _provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory\n """\n , InitializationError], [\n """\n id: cbc_binary_toolkit\n engine:\n name: {ENGINE_NAME}\n type: local\n _provider: INVALID.INVALID\n """\n , ImportError], [\n """\n id: cbc_binary_toolkit\n engine:\n name: {ENGINE_NAME}\n type: local\n _provider: cbc_binary_toolkit.engine.LocalEngineFactory\n """\n , NotImplementedError], [\n f"""\n id: cbc_binary_toolkit\n version: 0.0.1\n engine:\n name: {ENGINE_NAME}\n type: local\n _provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory\n """\n , AssertionError]])\n', (2283, 3186), False, 'import pytest\n'), ((1171, 1415), 'cbc_binary_toolkit.config.Config.load', 'Config.load', (['f"""\n id: cbc_binary_toolkit\n version: 0.0.1\n engine:\n name: {ENGINE_NAME}\n type: local\n _provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory\n Test: TestPassed\n """'], {}), '(\n f"""\n id: cbc_binary_toolkit\n version: 0.0.1\n engine:\n name: {ENGINE_NAME}\n type: local\n _provider: tests.component.engine_fixtures.mock_engine.MockLocalEngineFactory\n Test: TestPassed\n """\n )\n', (1182, 1415), False, 'from cbc_binary_toolkit.config import Config\n'), ((1602, 1628), 'cbc_binary_toolkit.engine.LocalEngineManager', 'LocalEngineManager', (['config'], {}), '(config)\n', (1620, 1628), False, 'from cbc_binary_toolkit.engine import LocalEngineManager\n'), ((1772, 1798), 'cbc_binary_toolkit.engine.LocalEngineManager', 'LocalEngineManager', (['config'], {}), '(config)\n', (1790, 1798), False, 'from cbc_binary_toolkit.engine import LocalEngineManager\n'), ((2063, 2089), 'cbc_binary_toolkit.engine.LocalEngineManager', 'LocalEngineManager', (['config'], {}), '(config)\n', (2081, 2089), False, 'from cbc_binary_toolkit.engine import LocalEngineManager\n'), ((3289, 3315), 'cbc_binary_toolkit.config.Config.load', 'Config.load', (['engine_config'], {}), '(engine_config)\n', (3300, 3315), False, 'from cbc_binary_toolkit.config import Config\n'), ((2186, 2223), 'cbc_binary_toolkit.schemas.EngineResponseSchema.validate', 'EngineResponseSchema.validate', (['result'], {}), '(result)\n', (2215, 2223), False, 'from cbc_binary_toolkit.schemas import EngineResponseSchema\n'), ((3325, 3349), 'pytest.raises', 'pytest.raises', (['exception'], {}), '(exception)\n', (3338, 3349), False, 'import pytest\n'), ((3359, 3385), 'cbc_binary_toolkit.engine.LocalEngineManager', 'LocalEngineManager', (['config'], {}), '(config)\n', (3377, 3385), False, 'from cbc_binary_toolkit.engine import LocalEngineManager\n')]
|
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route("/")
def hello():
return render_template("index.html", name="WORLD!")
@app.route("/about")
def about():
return render_template("about.html")
|
[
"flask.render_template",
"flask.Flask"
] |
[((57, 72), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (62, 72), False, 'from flask import Flask, render_template, request\n'), ((112, 156), 'flask.render_template', 'render_template', (['"""index.html"""'], {'name': '"""WORLD!"""'}), "('index.html', name='WORLD!')\n", (127, 156), False, 'from flask import Flask, render_template, request\n'), ((201, 230), 'flask.render_template', 'render_template', (['"""about.html"""'], {}), "('about.html')\n", (216, 230), False, 'from flask import Flask, render_template, request\n')]
|
import os
import cv2
import albumentations as A
from albumentations.pytorch import ToTensorV2
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
__all__ = ['CatDogDataset', 'fetch_dataloader']
class CatDogDataset(Dataset):
def __init__(self, file_paths, labels, transform=None):
self.file_paths = file_paths
self.labels = labels
self.transform = transform
def __len__(self):
return len(self.file_paths)
def __getitem__(self, idx):
label = self.labels[idx]
file_path = self.file_paths[idx]
image = cv2.imread(file_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if self.transform:
transformed = self.transform(image=image)
image = transformed["image"]
return image, label
def fetch_dataloader(types, data_dir, batch_size, num_workers):
dataloaders = {}
train_dir = os.path.join(data_dir, "train")
train_files = sorted(os.listdir(train_dir))
train_labels = []
for file in train_files:
if "cat" in file:
train_labels.append(0)
else:
train_labels.append(1)
train_file_paths = [os.path.join(train_dir, path) for path in train_files]
train_file_paths, val_file_paths, train_labels, val_labels = train_test_split(
train_file_paths, train_labels, stratify=train_labels, random_state=42
)
train_transform = A.Compose([
A.SmallestMaxSize(max_size=256),
A.HorizontalFlip(p=0.5),
A.RandomCrop(224, 224),
A.Normalize(),
ToTensorV2()
])
eval_transform = A.Compose([
A.SmallestMaxSize(max_size=256),
A.CenterCrop(224, 224),
A.Normalize(),
ToTensorV2()
])
for split in ['train', 'val', 'test']:
if split in types:
if split == 'train':
dl = DataLoader(CatDogDataset(train_file_paths,
train_labels,
train_transform),
batch_size, shuffle=True, num_workers=num_workers)
elif split == "val":
dl = DataLoader(CatDogDataset(val_file_paths,
val_labels,
eval_transform),
batch_size, shuffle=False, num_workers=num_workers)
dataloaders[split] = dl
return dataloaders
|
[
"albumentations.pytorch.ToTensorV2",
"os.listdir",
"sklearn.model_selection.train_test_split",
"os.path.join",
"albumentations.RandomCrop",
"albumentations.Normalize",
"cv2.cvtColor",
"albumentations.CenterCrop",
"albumentations.HorizontalFlip",
"cv2.imread",
"albumentations.SmallestMaxSize"
] |
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|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 17 16:12:56 2020
@author: dylanroyston
"""
# import/configure packages
import numpy as np
import pandas as pd
#import pyarrow as pa
import librosa
import librosa.display
from pathlib import Path
#import Ipython.display as ipd
#import matplotlib.pyplot as plt
from pyspark.sql import *
import pyspark.sql.functions as f
from pyspark import SparkConf, SparkContext, SQLContext
import boto3
from tinytag import TinyTag as tt
import soundfile as sf
import audioread
from pydub import AudioSegment
from io import BytesIO
#from io import BytesIO
import os
import sys
import time
import struct
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)) + "/lib")
#import config
time_seq = []
#####
# create local Spark instance (for non-cluster dev)
sc = SparkContext('local')
spark = SparkSession (sc)
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
# define Spark config
def spark_conf():
conf = SparkConf().setAppName("decompress_audio_files")
sc = SparkContext(conf=conf)
spark = SparkSession.builder.getOrCreate()
return spark
spark = spark_conf()
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
#####
# Function to write spark-dataframe to mySQL
def write_df_to_psql(df, tablename):
psql_user = os.environ.get('PSQL_USR')
psql_pwd = os.environ.get('PSQL_PWD')
df.write.format('jdbc').options(
url='jdbc:postgresql://10.0.0.6:5432/spectralize',
dbtable=tablename,
user=psql_user,
#password=<PASSWORD>).mode('append').save()
password=psql_pwd).save()
#####
# function to read audio files from S3 bucket and extract tags
def read_audio_files():
# basic initialization
time_seq.append(['start-read-audio', time.time()])
# DataFrame schema
File_Tags = Row("s3_key", "song_id", "album", "albumartist", "artist",
"audio_offset", "bitrate", "channels", "comment", "composer",
"disc", "disc_total", "duration", "filesize", "genre",
"samplerate", "title", "track", "track_total", "year")
spec_labels = []
for sn in range(0,128):
spec_labels.append('spec' + str(sn+1))
spec_df_labels = ['song_id','timeseries'] + spec_labels
Spec_Tags = Row(spec_df_labels)
# configure S3 access
s3_bucket = 'mdp-spectralize-pal'
number_of_files = 0
s3 = boto3.resource('s3')
boto_client = boto3.client('s3')
bucket = s3.Bucket(s3_bucket)
number_of_files=0
file_limit=100
#local_path = './local_file.'
known_ext = [".mp3", ".wav", ".m4a"]
#read each file from S3 bucket
for obj in bucket.objects.all():
s3_key = obj.key
audio_obj_stream = boto_client.get_object(Bucket=s3_bucket, Key=s3_key)
audio_obj = BytesIO(audio_obj_stream['Body'].read())
song = bytes(audio_obj)
song = sf.SoundFile(audio_obj)
song = open(audio_obj, 'rb').read()
song = audioread.audio_open(audio_obj)
# extract tags from mp3 files
#if "mp3" in s3_key:
#if any(ext in s3_key for ext in known_ext):
#print(number_of_files)
#ext = s3_key[-4:]
#local_path = './localfile' + ext
number_of_files+=1
#bucket.download_file(s3_key, local_path)
local_path = '/home/dylanroyston/Music/spectralize_data/01 Konoha Densetsu.mp3'
song = open(local_path, 'rb').read()
##### tags
tags = tt.get(local_path)
tags = tt.get(audio_obj)
# extract tags from tinytag object
indiv_tags = (s3_key, number_of_files, tags.album, tags.albumartist, tags.artist,
tags.audio_offset, tags.bitrate, tags.channels,
tags.comment, tags.composer, tags.disc,
tags.disc_total, tags.duration, tags.filesize,
tags.genre, tags.samplerate, tags.title, tags.track,
tags.track_total, tags.year)
# convert tuple object to list
indiv_tag_list = list(indiv_tags)
indiv_tag_list = [str(i) for i in indiv_tag_list]
tag_seq=[]
tag_seq.append(indiv_tag_list)
tags_pdf = pd.DataFrame(data=tag_seq)
tag_df = spark.createDataFrame(tags_pdf, schema=File_Tags)
##### audio
# load audio file with Librosa
#y, sr = librosa.load(str(Path(local_path)), sr=None)
y, sr = librosa.load(local_path, sr=None)
# create indexing variables (song_id, timestamp)
# song_id defined as "repeat(number_of_files)"
song_num = pd.Series([number_of_files])
num_points = len(y)
song_id = song_num.repeat(num_points)
song_id = song_id.to_numpy()
# timeseries defined as "1 : length(audio_data)"
timeseries = np.arange(num_points)
timeseries = timeseries.transpose()
full_audio = {'song_id': song_id, 'timeseries': timeseries,
'intensity': y}
# create combined dataframe
audio_pdf = pd.DataFrame(data = full_audio)
audio_df = spark.createDataFrame(audio_pdf)
##### spectral
S = librosa.feature.melspectrogram(y, sr=sr, n_mels=128, fmax=10000)
log_S = librosa.power_to_db(S, ref=np.max)
log_S = log_S.transpose()
# song_id defined as "repeat(number_of_files)"
song_num = pd.Series([number_of_files])
num_points = len(S.transpose())
song_id = song_num.repeat(num_points)
song_id = song_id.to_numpy()
# timeseries defined as "1 : length(audio_data)"
timeseries = np.arange(num_points)
timeseries = timeseries.transpose()
full_index = {'song_id': song_id, 'timeseries': timeseries}
index_pdf = pd.DataFrame(full_index)
spec_pdf = pd.DataFrame(data=log_S, columns=spec_labels)
full_spec = pd.concat([index_pdf, spec_pdf], axis=1)
spec_df = spark.createDataFrame(full_spec)
##### write dataframes to psql
write_df_to_psql(tag_df, 'clean_metadata')
write_df_to_psql(audio_df, 'clean_audio')
write_df_to_psql(spec_df, 'clean_spec')
# stop process when file_limit is crossed (small batches)
if (number_of_files >= file_limit):
break
#####
time_seq.append(['end read-file', time.time()])
#df_tags = spark.createDataFrame(tag_seq, schema=File_Tags)
#df_audio = spark.createDataFrame(audio_seq)
#df_spec = spark.createDataFrame(audio_seq, schema=Spec_Tags)
# Additional run to
#df_audio_data = spark.createDataFrame(file_audio_data)
#process_df(df_audio_data)
#####
if __name__ == '__main__':
time_seq.append(['start', time.time()])
read_audio_files()
|
[
"pandas.Series",
"librosa.feature.melspectrogram",
"boto3.client",
"audioread.audio_open",
"os.environ.get",
"os.path.abspath",
"pyspark.SparkConf",
"librosa.power_to_db",
"boto3.resource",
"tinytag.TinyTag.get",
"pandas.concat",
"time.time",
"pandas.DataFrame",
"pyspark.SparkContext",
"soundfile.SoundFile",
"numpy.arange",
"librosa.load"
] |
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|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'ipetrash'
"""
Скрипт выводит список ip государственных организаций.
"""
import ipaddress
import sys
import requests
rs = requests.get('https://jarib.github.io/anon-history/RuGovEdits/ru/latest/ranges.json')
# Проверка удачного запроса и полученных данных
if not rs or not rs.json() or 'ranges' not in rs.json():
print('Не получилось получить список ip государственных организаций')
sys.exit()
# Получение и сортировка элементов по названию организации
items = sorted(rs.json()['ranges'].items(), key=lambda x: x[0])
ip_counter = 0
for i, (name, ip_network_list) in enumerate(items, 1):
print(f'{i}. {name}')
# Получение ip с маской подсети
for ip_network in ip_network_list:
print(f' {ip_network}:')
# Получение ip подсети
net4 = ipaddress.ip_network(ip_network)
# Перебор ip адресов указанной организации
for ip in net4.hosts():
print(f' {ip}')
ip_counter += 1
print()
print('Всего ip:', ip_counter)
|
[
"ipaddress.ip_network",
"requests.get",
"sys.exit"
] |
[((191, 281), 'requests.get', 'requests.get', (['"""https://jarib.github.io/anon-history/RuGovEdits/ru/latest/ranges.json"""'], {}), "(\n 'https://jarib.github.io/anon-history/RuGovEdits/ru/latest/ranges.json')\n", (203, 281), False, 'import requests\n'), ((461, 471), 'sys.exit', 'sys.exit', ([], {}), '()\n', (469, 471), False, 'import sys\n'), ((853, 885), 'ipaddress.ip_network', 'ipaddress.ip_network', (['ip_network'], {}), '(ip_network)\n', (873, 885), False, 'import ipaddress\n')]
|
# Copyright (c) 2015-2020, Swiss Federal Institute of Technology (ETH Zurich)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
"""Misc helpers"""
import math
import random
import re
import signal
import typing as t
from datetime import datetime
from enum import Enum
from functools import reduce
from inspect import isabstract
from string import ascii_letters
from subprocess import list2cmdline as _list2cmdline
from typing import Mapping as Map
import numpy as np
from exot.exceptions import *
__all__ = (
"call_with_leaves",
"dict_depth",
"dict_diff",
"find_attributes",
"flatten_dict",
"get_concrete_subclasses",
"get_subclasses",
"get_valid_access_paths",
"getitem",
"has_method",
"has_property",
"has_type",
"has_variable",
"is_abstract",
"is_scalar_numeric",
"leaves",
"list2cmdline",
"map_to_leaves",
"mro_getattr",
"mro_hasattr",
"random_string",
"safe_eval",
"sanitise_ansi",
"setgetattr",
"setitem",
"stub_recursively",
"unpack__all__",
"validate_helper",
"get_cores_and_schedules",
)
"""
Signatures
----------
call_with_leaves :: (function: Callable[[Any], Any], obj: ~T, _seq: bool = True) -> None
dict_depth :: (obj: Any, level: int = 0) -> int
dict_diff :: (left: Mapping, right: Mapping) -> List[Dict]
find_attributes :: (attr: str, klass: Any) -> List
flatten_dict :: (obj: Mapping, sep: str = '.') -> Mapping
get_concrete_subclasses :: (klass, recursive=True, derived=True) -> List
get_subclasses :: (klass, recursive=True, derived=True) -> List
get_valid_access_paths :: (obj: Mapping, _limit: int = 8192, _leaf_only: bool = False, _use_lists: bool = True, _fallthrough_empty: bool = True) -> Generator
getitem :: (obj: Mapping, query: Union[str, Tuple], *args: Any, sep: str = '/') -> Any
has_method :: (klass: Union[type, object], name: str) -> bool
has_property :: (klass: Union[type, object], name: str) -> bool
has_type :: (klass: Union[type, object]) -> bool
has_variable :: (klass: Union[type, object], name: str) -> bool
is_abstract :: (klass: Union[type, object]) -> bool
is_scalar_numeric :: (value: t.Any) -> bool
map_to_leaves :: (function: Callable[[Any], Any], obj: ~T, _seq: bool = True) -> Any
mro_getattr :: (cls: type, attr: str, *args: Any) -> Any
mro_hasattr :: (cls: type, attr: str) -> bool
random_string :: (length: int) -> str
safe_eval :: (to_eval: str, expect: Tuple[type], timeout: int) -> object
sanitise_ansi :: (value Union[List[str], str]) -> Union[List[str], str]
setgetattr :: (klass: Union[type, object], attr: str, default: Any) -> None
setitem :: (obj: MutableMapping, query: Tuple, value: Any) -> None
stub_recursively :: (obj: ~T, stub: Any = None, _stub_list_elements: bool = True) -> Optional[~T]
unpack__all__ :: (*imports: Collection[str]) -> Tuple[str]
validate_helper :: (what: Mapping, key: Any, *types: type, msg: str = '') -> NoReturn
"""
def call_with_leaves(function: t.Callable[[t.Any], t.Any], obj: t.T, _seq: bool = True) -> None:
"""Calls a function on leaves of an object
A leaf is considered to be an object that is not a Mapping (or, when _seq is set,
also not a Sequence except a string, which is also a Sequence).
Args:
function (t.Callable[[t.Any], t.Any]): The callable
obj (t.T): The tree-like or sequence-like object
_seq (bool, optional): Should sequences be considered?. Defaults to True.
"""
def inner(obj: t.T) -> t.Any:
if isinstance(obj, Map):
for v in obj.values():
inner(v)
elif _seq and isinstance(obj, (t.List, t.Set)):
for v in obj:
inner(v)
else:
return function(obj)
inner(obj)
def dict_depth(obj: t.Any, level: int = 0) -> int:
"""Get maximum depth of a dict-like object
Args:
obj (t.Any): The dict-like object
level (int): For internal use only. Defaults to 0.
.. note::
The depth of a non-dict-like object is considered to be 0.
An empty dict increases the depth if `_empty_increments` is True.
Examples:
>>> dict_depth(1) # returns 0
>>> dict_depth([1,2,3]) # returns 0
>>> dict_depth({1: 1, 2: 2}) # returns 1
>>> dict_depth({1: {2: {3: 3}}}) # returns 3
>>> dict_depth({1: {2: {3: {}}}}) # returns 4
"""
if not isinstance(obj, Map) or not obj:
return level
return max(dict_depth(v, level + 1) for k, v in obj.items())
def dict_diff(left: Map, right: Map) -> t.List[t.Dict]:
"""Get the difference between 2 dict-like objects
Args:
left (Map): The left dict-like object
right (Map): The right dict-like object
The value returned is a list of dictionaries with keys ["path", "left", "right"]
which contain the query path and the differences between the left and right mapping.
If a key is missing in either mapping, it will be indicated as a "None".
`math.nan` (not-a-number) is used for default values in the comparison because of
the property: `math.nan != math.nan`. Simple None cannot be used, since it would
not handle keys that both have a value of None. In general, this function might
report false-positives for keys that contain the math.nan (or np.nan) value simply
due to this property. There is no workaround available.
"""
left_paths = set(get_valid_access_paths(left, _leaf_only=True, _use_lists=False))
right_paths = set(get_valid_access_paths(right, _leaf_only=True, _use_lists=False))
return list(
{
"path": path,
"left": getitem(left, path, math.nan),
"right": getitem(right, path, math.nan),
}
for path in left_paths.union(right_paths)
if getitem(left, path, math.nan) != getitem(right, path, math.nan)
)
def find_attributes(klass: t.Any, attr: str) -> t.List:
"""Find attributes in any of a class'es bases
Args:
klass (t.Any): The type object
attr (str): The attribute
Returns:
t.List: List of found instances of the attribute in the class hierarchy
"""
if not isinstance(attr, str):
raise TypeError(attr)
mro = klass.__mro__ if hasattr(klass, "__mro__") else type(klass).mro()
return [attr for base in mro if hasattr(base, attr)]
def flatten_dict(obj: Map, sep: str = ".") -> Map:
"""Flatten a dict to a 1-level dict combining keys with a separator
Args:
obj (Map): The dict-like object
sep (str): The separator used when combining keys. Defaults to ".".
Returns:
Map: A flattened object of same type as 'obj'.
.. warning::
Flattening will enforce all keys to be string-types!
`reducer` is a function accepted by the functools.reduce function, which is of
form: f(a, b) where _a_ is the accumulated value, and _b_ is the updated value
from the iterable.
The .items() function produces key-value tuple-pairs. These can be expanded
with *, e.g. `*("a", "b")` will expand to `"a", "b"`. This property is used
to expand the `kv_pair` below.
Example walkthrough on `flatten_dict({'a': 1, 'b': {'c': {'d': 2}}})`: ::
`outer` <- obj: {'a': 1, 'b': {'c': {'d': 2}}}, prefix: ''
`reducer` <- key: 'a', value: 1
`inner` <- acc: {}, key: 'a', value: 1, prefix: ''
`inner` -> {'a': 1}
`reducer` -> {'a': 1}
`reducer` <- key: 'b', value: {'c': {'d': 2}}
`inner` <- acc: {'a': 1}, key: 'b', value: {'c': {'d': 2}}, prefix: ''
`outer` <- obj: {'c': {'d': 2}}, prefix: 'b.'
`reducer` <- key: 'c', value: {'d': 2}
`inner` <- acc: {}, key: 'c', value: {'d': 2}, prefix: 'b.'
`outer` <- obj: {'d': 2}, prefix: 'b.c.'
`reducer` <- key: 'd', value: 2
`inner` <- acc: {}, key: 'd', value: 2, prefix: 'b.c.'
`inner` -> {'b.c.d': 2}
`reducer` -> {'b.c.d': 2}
`outer` -> {'b.c.d': 2}
`inner` -> {'b.c.d': 2}
`reducer` -> {'b.c.d': 2}
`outer` -> {'b.c.d': 2}
`inner` -> {'a': 1, 'b.c.d': 2}
`reducer` -> {'a': 1, 'b.c.d': 2}
`outer` -> {'a': 1, 'b.c.d': 2}
"""
if not isinstance(obj, Map):
raise TypeError("flatten_dict works only on dict-like types", type(obj))
_t = type(obj)
def outer(obj: Map, prefix: str) -> Map:
def reducer(accumulator: Map, kv_pair: t.Tuple):
return inner(accumulator, *kv_pair, prefix)
return reduce(reducer, obj.items(), _t())
def inner(accumulator: Map, key: str, value: t.Any, prefix: str) -> Map:
if isinstance(value, Map):
return _t(**accumulator, **outer(value, prefix + key + sep))
else:
return _t(**accumulator, **_t({prefix + key: value}))
return outer(obj, "")
def expand_dict(obj: Map, sep: str = ".") -> Map:
"""Expands a flattened mapping by splitting keys with the given separator
Args:
obj (Map): The flattened dict-like object to unflatten
sep (str, optional): The key separator
Raises:
TypeError: If wrong type is supplied
ValueError: If a non-flat dict is supplied
Returns:
Map: The expanded mapping object of same type as 'obj'.
Example:
>>> d = {'a': 1, 'b': 2, 'c.ca': 1, 'c.cb': 2}
>>> expand_dict(d)
{'a': 1, 'b': 2, 'c': {'ca': 1, 'cb': 2}}
"""
if not isinstance(obj, Map):
raise TypeError("expand_dict works only on dict-like types", type(obj))
if dict_depth(obj) != 1:
raise ValueError(
"expand_dict works only on flat dict-like types, "
"got a mapping of depth: {}".format(dict_depth(obj))
)
def inner(obj):
accumulator = type(obj)()
for k, v in obj.items():
*head, last = k.split(sep)
_ = accumulator
# Create missing paths
for part in head:
if part not in _:
_[part] = type(obj)()
_ = _[part]
_[last] = v
return accumulator
return inner(obj)
def get_concrete_subclasses(klass, recursive: bool = True, derived: bool = True) -> t.List:
"""Get a list of non-abstract subclasses of a type
Args:
klass (t.Type): The type object
recursive (bool): Should the classes be extracted recursively? Defaults to True.
derived (bool): Use the 'derived' property of SubclassTracker-enhanced types? [True]
Returns:
t.List: A list of concrete subclasses of the type
"""
from exot.util.mixins import _SubclassTracker as __
if derived and hasattr(klass, __.concrete):
return list(getattr(klass, __.concrete))
subclasses = get_subclasses(klass, recursive=recursive)
return [k for k in subclasses if not isabstract(k)]
def get_subclasses(klass, recursive: bool = True, derived: bool = True) -> t.List:
"""Get a list of subclasses of a type
Args:
klass (t.Type): The type object
recursive (bool): Should the classes be extracted recursively? Defaults to True.
derived (bool): Use the 'derived' property of SubclassTracker-enhanced types? [True]
Returns:
t.List: A list of concrete subclasses of the type
"""
from exot.util.mixins import _SubclassTracker as __
if not (hasattr(klass, "__subclasses__") or hasattr(klass, __.derived)):
raise TypeError(f"__subclasses__ or {__.derived} attribute missing", klass)
if derived:
return list(getattr(klass, __.derived))
subclasses = klass.__subclasses__()
def walker(k):
first, *rest = k
if len(rest):
walker(rest)
if first not in subclasses:
subclasses.append(first)
if hasattr(first, "__subclasses__"):
_ = first.__subclasses__()
if len(_):
walker(_)
if recursive:
walker(subclasses)
return subclasses
def get_valid_access_paths(
obj: Map,
_limit: int = 8192,
_leaf_only: bool = False,
_use_lists: bool = True,
_fallthrough_empty: bool = True,
) -> t.Generator[t.Tuple, None, None]:
"""Generate valid key sequences in a dict, optionally including lists
Args:
obj (Map): The dict-like object
_limit (int): Maximum number of paths that can be created with list-like elements.
_leaf_only (bool): Provide paths for only the leaves of the mapping. Defaults to True.
_use_lists (bool): Provide paths for list-like elements in the mapping. Defaults to True.
_fallthrough_empty (bool): Discard empty list- or dict-like elements? Defaults to True.
Details:
If `_leaf_only` is set, only paths to leaves will be produced, a leaf being a value
that is not a mapping (or list).
If `_use_lists` is set, lists will also be *recursively* checked for valid paths.
if `_fallthrough_empty` is set, an empty dict or list will yield an empty tuple,
rendering a parent path.
Returns:
t.Generator[t.Tuple,None,None]: A generator that yields the access paths (tuples).
Examples:
>>> # Only leaves:
>>> d = {'a1': {'a2': None}, 'b2': None}
>>> list(get_valid_access_paths(d, _leaf_only=True))
[('a1', 'a2'), ('b2',)]
>>> # All paths:
>>> list(get_valid_access_paths(d, _leaf_only=False))
[('a1',), ('a1', 'a2'), ('b2',)]
"""
def thrower(o: object, t: type, n: str) -> t.NoReturn:
if not isinstance(o, t):
raise TypeError(
f"get_valid_access_paths expected {t!r} for {n!r}, got: {type(o)!r}"
)
thrower(obj, Map, "obj")
thrower(_limit, int, "_limit")
thrower(_leaf_only, bool, "_leaf_only")
thrower(_use_lists, bool, "_use_lists")
thrower(_fallthrough_empty, bool, "_fallthrough_empty")
def inner(obj: t.Union[Map, t.List, t.Set]) -> t.Generator:
if _fallthrough_empty and not obj:
yield tuple()
# if obj is a mapping
if isinstance(obj, Map):
for k, v in obj.items():
# if the value in obj is also a mapping...
if isinstance(v, Map):
if not _leaf_only:
yield (k,)
# ... make a recursive call
for vv in inner(v):
yield (k,) + vv
# if the value in obj is a list...
elif _use_lists and isinstance(v, (t.List, t.Set)):
# ... first yield the valid path to the key containing the list
if v and not _leaf_only:
yield (k,)
elif not v and _fallthrough_empty:
yield (k,)
# ... loop through elements, and keep track of indexes
for idx, vv in enumerate(v):
# if an element is also a mapping or list...
if isinstance(vv, (Map, (t.List, t.Set))):
# ... make a recursive call
for vvv in inner(vv):
yield (k,) + (idx,) + vvv
else:
# ... otherwise yield keypath + idx
yield (k,) + (idx,)
# if the value is neither a mapping nor a list, yield the key
else:
yield (k,)
# if obj is a list-like sequence
if _use_lists and isinstance(obj, (t.List, t.Set)):
# might be tricky to generate valid sequences for large lists!
if _limit and len(obj) >= _limit:
raise ValueError(
f"get_key_sequences list limit of {_limit} exceeded: {len(obj)}"
)
for idx, v in enumerate(obj):
if isinstance(v, (Map, (t.List, t.Set))):
for vv in inner(v):
yield (idx,) + vv
else:
yield (idx,)
return inner(obj)
def getitem(obj: Map, query: t.Union[str, t.Tuple], *args: t.Any, sep: str = "/") -> t.Any:
"""Get a value from a dict-like object using an XPath-like query, or a tuple-path
Accesses an object that provides a dict-like interface using a query: either a
tuple representing the path, or a string where consecutive keys are separated with
a separator, e.g. "key1/key2".
Returns the value of the object at the given key-sequence. Returns a default value
if provided, or throws a LookupError.
Args:
obj (Map): a mapping
query (t.Union[str, t.Tuple]): a query path using a separated string or a tuple
*args (t.Any): an optional default value, similar to `getattr`
sep (str, optional): a separator string used to split a string query path
Returns:
t.Any: the value stored in obj for the given query, or the default value
Raises:
LookupError: if query not found and no default value is provided
TypeError: if obj is not a mapping, or query is not a str or tuple
"""
if not isinstance(obj, Map):
raise TypeError("'obj' must be an instance of Mapping, e.g. dict", type(obj))
if not isinstance(query, (str, t.Tuple)):
raise TypeError("'query' must be a str or a tuple", type(query))
if len(args) > 1:
raise TypeError(f"getitem accepts at most 3 positional args, got {len(args)}")
_obj = obj
# handler for tuple queries
if isinstance(query, t.Tuple):
_valid = get_valid_access_paths(obj)
if query not in _valid:
if args:
return args[0]
else:
raise LookupError(f"query {query!r} not found")
else:
for node in query:
_obj = _obj[node]
return _obj
# handler for string queries
else:
try:
# loop through components in the query, consecutively accessing the mapping
for node in query.split(sep):
# handle empty nodes in the query, e.g. when query="a///b" -> "a/b"
if not node:
continue
if isinstance(_obj, Map):
for k in _obj.keys():
node = type(k)(node) if str(k) == node else node
elif isinstance(_obj, (t.List, t.Set)):
try:
node = int(node)
except TypeError:
raise LookupError(
f"{node} not convertible to int when attempting to access "
f"a list {_obj!r}"
)
_obj = _obj[node]
return _obj
except LookupError as Error:
if args:
return args[0]
else:
Error.args += (query,)
raise
def has_method(klass: t.Union[type, object], name: str) -> bool:
"""Check if a method exists in any of a klass'es bases
Args:
klass (t.Union[type, object]): The type or object
name (str): The name of the method
Returns:
bool: True if has a method with the given name.
"""
candidates = find_attributes(klass, name)
if not candidates:
return False
def is_callable(c):
return isinstance(getattr(klass, str(c), None), t.Callable)
return all(is_callable(f) for f in candidates)
def has_property(klass: t.Union[type, object], name: str) -> bool:
"""Check if a variable exists in any of a klass'es bases
Args:
klass (t.Union[type, object]): The type or object
name (str): The name of the property
Returns:
bool: True if has a property with the given name.
"""
candidates = find_attributes(klass, name)
if not candidates:
return False
def is_property(c):
return not isinstance(getattr(klass, str(c), None), property)
return all(is_property(f) for f in candidates)
def has_type(klass: t.Union[type, object]) -> bool:
"""Check if a type or instance has a Type member type that derives from Enum
Args:
klass (t.Union[type, object]): The type or object
Returns:
bool: True if has the "Type" attribute.
"""
if not isinstance(klass, (type, object)):
raise TypeError(klass)
return issubclass(getattr(klass, "Type", type(None)), Enum)
def has_variable(klass: t.Union[type, object], name: str) -> bool:
"""Check if a variable exists in any of a klass'es bases
Args:
klass (t.Union[type, object]): The type or object
name (str): The name of the variable
Returns:
bool: True if has a variable with the given name.
"""
candidates = find_attributes(klass, name)
if not candidates:
return False
def is_not_callable(c):
return not isinstance(getattr(klass, str(c), None), t.Callable)
return all(is_not_callable(f) for f in candidates)
def is_abstract(klass: t.Union[type, object]) -> bool:
"""Check if a type or instance is abstract
Args:
klass (t.Union[type, object]): The type or object
Returns:
bool: True if the type/instance is abstract.
"""
if not isinstance(klass, (type, object)):
raise TypeError(klass)
if hasattr(klass, "__abstractmethods__"):
return 0 != len(getattr(klass, "__abstractmethods__"))
else:
from inspect import isabstract
return isabstract(klass)
def is_scalar_numeric(value: t.Any) -> bool:
"""Check if is an int, a float, or a NumPy variant thereof
Args:
value (t.Any): The value to inspect
Returns:
bool: True if scalar and numeric.
"""
return isinstance(value, (float, int, np.integer, np.floating))
def leaves(obj: Map) -> t.Generator:
"""Get leaves of a mapping
Args:
obj (Map): The dict-like object
Returns:
t.Generator: A generator that yields the leaf elements of the mapping.
"""
paths = get_valid_access_paths(obj, _leaf_only=True, _use_lists=False)
return (getitem(obj, path) for path in paths)
def list2cmdline(seq: t.Iterable) -> str:
"""Translates a sequence of arguments into a command line string with "None" removal
Args:
seq (t.Iterable): The sequence of arguments
Returns:
str: The command-line string
"""
seq = [_ for _ in seq if _ is not None]
return _list2cmdline(seq)
def map_to_leaves(function: t.Callable[[t.Any], t.Any], obj: t.T, _seq: bool = True) -> t.Any:
"""Map a function to leaves of an object
A leaf is considered to be an object that is not a Mapping (or, when _seq is set,
also not a Sequence except a string, which is also a Sequence).
Args:
function (t.Callable[[t.Any], t.Any]): a function or signatude "a -> a"
obj (t.T): a dict-like, list-like, or plain object
_seq (bool, optional): map on elements of lists?
Returns:
t.T: the obj with transformed elements
"""
def inner(obj: t.T) -> t.Any:
if isinstance(obj, Map):
return type(obj)({k: inner(v) for k, v in obj.items()})
elif _seq and isinstance(obj, (t.List, t.Set)):
return type(obj)(inner(v) for v in obj)
else:
return function(obj)
return inner(obj)
def mro_getattr(cls: type, attr: str, *args: t.Any) -> t.Any:
"""Get an attribute from a type's class hierarchy
Args:
cls (type): The type
attr (str): The attribute
*args (t.Any): The default value (like in Python's default getattr)
Returns:
t.Any: The attribute, or when not found the default value (if provided)
Raises:
TypeError: Not called on a type
TypeError: Wrong number of arguments
AttributeError: Attribute not found and no default value provided
"""
if not isinstance(cls, type):
raise TypeError(f"mro_getattr can only be used on types, got {type(cls)}")
if len(args) > 1:
raise TypeError(f"mro_getattr expected at most 3 arguments, got {2 + len(args)}")
for klass in cls.mro()[1:]:
if hasattr(klass, attr):
# return first matching attribute
return getattr(klass, attr)
if args:
# if provided, return args[0], i.e. the a default value
return args[0]
else:
raise AttributeError(f"type object {cls.__name__!r} has not attribute {attr!r}")
def mro_hasattr(cls: type, attr: str) -> bool:
"""Check if an attribute exists in a type's class hierarchy
Args:
cls (type): The type
attr (str): The attribute
Returns:
bool: True if has the attribute.
Raises:
TypeError: Not called on a type
"""
if not isinstance(cls, type):
raise TypeError(f"mro_getattr can only be used on types, got {type(cls)}")
for klass in cls.mro()[1:]:
if hasattr(klass, attr):
return True
return False
def random_string(length: int) -> str:
"""Make a random string of specified length
Args:
length (int): The desired random string length
Returns:
str: The random string
"""
assert isinstance(length, int), f"'length' must be an int, got: {type(length)}"
return "".join(random.choices(ascii_letters, k=length))
def timestamp() -> str:
"""Make a timestamp with current time
Returns:
str: The timestamp in ISO format
"""
return datetime.now().isoformat("_", timespec="seconds").replace(":", "-")
def safe_eval(
to_eval: str, *, expect: t.Tuple[type] = (list, np.ndarray), timeout: int = 10
) -> object:
"""Evaluate a restricted subset of Python (and numpy) from a string
Args:
to_eval (str): The string to evaluate
expect (t.Tuple[type]): The list of expected resulting types. Defaults to list, ndarray.
timeout (int): The timeout after which the call fails in seconds. Defaults to 10.
The `safe_eval` function allows using a subset of commands, listed in `_globals` and
`_locals`, which includes a few numpy functions: linspace, arange, array, rand, and
randint. Examples:
>>> safe_eval("linspace(1, 10, 10, dtype=int).tolist()")
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> safe_eval("__import__('os').getcwd()")
NameError Traceback (most recent call last)
...
NameError: name '__import__' is not defined
>>> safe_eval("range(5)")
TypeError Traceback (most recent call last)
...
TypeError: eval produced a <class 'range'>, expected: (<class 'list'>, <class 'numpy.ndarray'>)
>>> safe_eval("list(round(rand(), 2) for _ in range(5))")
[0.96, 0.41, 0.9, 0.98, 0.02]
"""
assert isinstance(to_eval, str), "'to_eval' must be a str"
assert isinstance(expect, tuple), "'expect' must be a tuple"
assert all(isinstance(_, type) for _ in expect), "'expect' must contain only types"
_locals = {}
_globals = {
"__builtins__": {},
"list": list,
"range": range,
"len": len,
"int": int,
"float": float,
"min": min,
"max": max,
"round": round,
"linspace": np.linspace,
"geomspace": np.geomspace,
"logspace": np.logspace,
"hstack": np.hstack,
"vstack": np.vstack,
"split": np.split,
"arange": np.arange,
"array": np.array,
"rand": np.random.rand,
"randint": np.random.randint,
}
class AlarmException(Exception):
pass
def signal_handler(number: int, frame):
assert number == signal.SIGALRM.value
raise AlarmException()
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(timeout)
try:
_ = eval(to_eval, _globals, _locals)
except AlarmException:
raise TimeoutError(f"safe_eval took longer than {timeout} seconds")
else:
signal.signal(signal.SIGALRM, signal.SIG_IGN)
signal.alarm(0)
if not isinstance(_, expect):
raise EvalTypeError(f"eval produced a {type(_)}, expected: {expect}")
return _
def sanitise_ansi(value: t.Union[t.List[str], str]) -> t.Union[t.List[str], str]:
"""Remove all ANSI escape characters from a str or a list of str
Args:
value (t.Union[t.List[str], str]): The string or list of strings
Returns:
t.Union[t.List[str], str]: The sanitised string or a list of sanitised strings
"""
_ansi_escape = re.compile(r"(\x9B|\x1B\[)[0-?]*[ -\/]*[@-~]")
if isinstance(value, str):
return _ansi_escape.sub("", value)
elif isinstance(value, t.List):
return list(map(lambda x: _ansi_escape.sub("", x).strip(), value))
else:
raise TypeError("sanitise_ansi accepts only str or lists of str")
def setgetattr(klass: t.Union[type, object], attr: str, default: t.Any) -> None:
"""Combines `setattr` and `getattr` to set attributes
Args:
klass (t.Union[type, object]): The type or object
attr (str): The attribute
default (t.Any): The default value
"""
if not any([isinstance(klass, type), isinstance(klass, object)]):
raise TypeError("'klass' should be a type or an object", klass)
if not isinstance(attr, str):
raise TypeError("'attr' should be a str")
if not attr:
raise ValueError("'attr' should not be empty")
setattr(klass, attr, getattr(klass, attr, default))
def setitem(obj: t.MutableMapping, query: t.Tuple, value: t.Any, force: bool = False) -> None:
"""Set a value in a dict-like object using a tuple-path query
Args:
obj (t.MutableMapping): a mutable mapping
query (t.Tuple): a query path as a tuple
value (t.Any): value to set
Raises:
TypeError: if obj is not a mutable mapping
"""
if not isinstance(obj, t.MutableMapping):
raise TypeError("'obj' needs to be a mutable mapping", type(obj))
_obj = obj
_valid = get_valid_access_paths(obj)
if query not in _valid:
if not force:
raise KeyError(f"query-path {query!r} not found")
else:
for node in query[:-1]:
if node not in _obj:
_obj = dict()
_obj = _obj[node]
else:
for node in query[:-1]:
_obj = _obj[node]
_obj[query[-1]] = value
def stub_recursively(
obj: t.T, stub: t.Any = None, _stub_list_elements: bool = True
) -> t.Optional[t.T]:
"""Produce a copy with all leaf values recursively set to a 'stub' value
Args:
obj (t.T): the object to stub
stub (t.Any, optional): the value to set the leaf elements to
_stub_list_elements (bool, optional): stub individual elements in collections?
Returns:
(t.T, optional): the stubbed object
"""
def inner(obj):
if isinstance(obj, Map):
return type(obj)((k, inner(v)) for k, v in obj.items())
elif _stub_list_elements and isinstance(obj, (t.List, t.Set)):
return type(obj)(inner(v) for v in obj)
else:
return stub
return inner(obj)
def unpack__all__(*imports: t.Collection[str]) -> t.Tuple[str]:
"""Upacks a list of lists/tuples into a 1-dimensional list
Args:
*imports (t.Collection[str]): The collections of strings in "__all__"
Returns:
t.Tuple[str]: The flattened imports as a tuple of strings.
"""
from itertools import chain
_name = f"{__name__}.unpack__all__"
if not all(isinstance(e, (t.List, t.Tuple)) for e in imports):
raise TypeError(f"{_name}: arguments should be lists or tuples")
_ = chain(*imports)
assert all(
issubclass(type(e), str) for e in _
), f"{_name}: values in unpacked containers were not scalar or 'str'"
return tuple(_)
def validate_helper(what: t.Mapping, key: t.Any, *types: type, msg: str = "") -> t.NoReturn:
"""Validate types of key in a mapping using key-paths
Args:
what (t.Mapping): The mapping
key (t.Any): The key
*types (type): The valid types
msg (str): An additional error message. Defaults to "".
"""
if not isinstance(what, t.Mapping):
raise TypeError(f"validate_helper works only on mappings, got {type(what)}")
if not types:
raise TypeError(f"validate helper expects at least 1 'types' argument")
if isinstance(key, str) or not isinstance(key, t.Iterable):
key = tuple([key])
elif not isinstance(key, tuple):
key = tuple(key)
# The `config` property setter guarantees that `config` is a fully
# mutated AttributeDict, therefore :meth:`getattr` can be used.
if not isinstance(getitem(what, key, None), types):
raise MisconfiguredError(
"{0}config key: '{1!s}' should be of type {2!r}, got {3!s}".format(
f"{msg} " if msg else "", key, types, type(getitem(what, key, None))
)
)
def get_cores_and_schedules(environments_apps_zones: t.Mapping) -> set:
e_a_z = environments_apps_zones
_cores_and_schedules = set()
for env in e_a_z:
for app in e_a_z[env]:
if app != "src":
continue
_path_to_cores = ("app_config", "generator", "cores")
_path_to_schedule_tag = ("zone_config", "schedule_tag")
access_paths = list(get_valid_access_paths(e_a_z[env][app]))
if _path_to_cores not in access_paths:
raise LayerMisconfigured(
f"{env!r}->{app!r} must have a 'generator.cores' config key"
)
if _path_to_schedule_tag not in access_paths:
_ = e_a_z[env][app]["zone"]
raise LayerMisconfigured(
f"{env!r}.{_!r} of app {app!r} must have a schedule_tag"
)
_cores_and_schedules.add(
(
len(getitem(e_a_z[env][app], _path_to_cores)),
getitem(e_a_z[env][app], _path_to_schedule_tag),
)
)
return _cores_and_schedules
|
[
"itertools.chain",
"signal.signal",
"inspect.isabstract",
"subprocess.list2cmdline",
"re.compile",
"datetime.datetime.now",
"random.choices",
"signal.alarm"
] |
[((24516, 24534), 'subprocess.list2cmdline', '_list2cmdline', (['seq'], {}), '(seq)\n', (24529, 24534), True, 'from subprocess import list2cmdline as _list2cmdline\n'), ((29757, 29802), 'signal.signal', 'signal.signal', (['signal.SIGALRM', 'signal_handler'], {}), '(signal.SIGALRM, signal_handler)\n', (29770, 29802), False, 'import signal\n'), ((29807, 29828), 'signal.alarm', 'signal.alarm', (['timeout'], {}), '(timeout)\n', (29819, 29828), False, 'import signal\n'), ((30567, 30616), 're.compile', 're.compile', (['"""(\\\\x9B|\\\\x1B\\\\[)[0-?]*[ -\\\\/]*[@-~]"""'], {}), "('(\\\\x9B|\\\\x1B\\\\[)[0-?]*[ -\\\\/]*[@-~]')\n", (30577, 30616), False, 'import re\n'), ((33760, 33775), 'itertools.chain', 'chain', (['*imports'], {}), '(*imports)\n', (33765, 33775), False, 'from itertools import chain\n'), ((23543, 23560), 'inspect.isabstract', 'isabstract', (['klass'], {}), '(klass)\n', (23553, 23560), False, 'from inspect import isabstract\n'), ((27386, 27425), 'random.choices', 'random.choices', (['ascii_letters'], {'k': 'length'}), '(ascii_letters, k=length)\n', (27400, 27425), False, 'import random\n'), ((30005, 30050), 'signal.signal', 'signal.signal', (['signal.SIGALRM', 'signal.SIG_IGN'], {}), '(signal.SIGALRM, signal.SIG_IGN)\n', (30018, 30050), False, 'import signal\n'), ((30059, 30074), 'signal.alarm', 'signal.alarm', (['(0)'], {}), '(0)\n', (30071, 30074), False, 'import signal\n'), ((12778, 12791), 'inspect.isabstract', 'isabstract', (['k'], {}), '(k)\n', (12788, 12791), False, 'from inspect import isabstract\n'), ((27569, 27583), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (27581, 27583), False, 'from datetime import datetime\n')]
|
from django.contrib.auth import get_user_model
from rest_auth.registration.serializers import (
RegisterSerializer as BaseRegisterSerializer,
)
from rest_auth.registration.serializers import (
SocialLoginSerializer as BaseSocialLoginSerializer,
)
from rest_auth.serializers import LoginSerializer as BaseLoginSerializer
from rest_auth.serializers import (
PasswordResetConfirmSerializer as BasePasswordResetConfirmSerializer,
)
from rest_auth.serializers import UserDetailsSerializer as BaseUserDetailsSerializer
from rest_framework import serializers
from rest_framework.exceptions import ValidationError
from core.models import Profile
# noinspection PyAbstractClass
class LoginSerializer(BaseLoginSerializer):
"""
Extends the default LoginSerializer in order to return
custom error messages
"""
def validate(self, attrs):
try:
return super().validate(attrs)
except serializers.ValidationError as ex:
ex.detail = "The email or password you entered is incorrect!"
raise ex
# noinspection PyAbstractClass
class PasswordResetConfirmSerializer(BasePasswordResetConfirmSerializer):
"""
Extends the default PasswordResetConfirmSerializer in order to return
custom error messages
"""
def validate(self, attrs):
try:
return super().validate(attrs)
except serializers.ValidationError as ex:
if "new_password2" in ex.detail:
ex.detail = ex.detail["new_password2"][0]
else:
ex.detail = "Could not reset password. Reset token expired or invalid."
raise ex
# noinspection PyAbstractClass
class CustomSocialLoginSerializer(BaseSocialLoginSerializer):
"""
Extends default SocialLoginSerializer to add additional details to some
failed login attempts
"""
def validate(self, attrs):
try:
res = super().validate(attrs)
return res
except ValidationError as ex:
if "User is already registered with this e-mail address." in ex.detail:
ex.detail[0] = (
"User is already registered with this e-mail address. "
"Please login using the form above."
)
raise ex
# noinspection PyAbstractClass
class RegisterSerializer(BaseRegisterSerializer):
email = serializers.EmailField(required=True)
password = serializers.CharField(write_only=True)
first_name = serializers.CharField(write_only=True)
last_name = serializers.CharField(write_only=True)
# legacy compat
zip = serializers.CharField(write_only=True, required=False)
zipcode = serializers.CharField(write_only=True, required=False)
# Overrides the default required password fields
password1 = None
password2 = None
def get_cleaned_data(self):
return {
"username": self.validated_data.get("email", ""),
"email": self.validated_data.get("email", ""),
# allauth uses password1 internally for creation
"password1": self.validated_data.get("password", ""),
"first_name": self.validated_data.get("first_name", ""),
"last_name": self.validated_data.get("last_name", ""),
"zipcode": self.validated_data.get("zipcode", ""),
}
def validate(self, data):
return data
UserModel = get_user_model()
class ProfileSerializer(serializers.ModelSerializer):
class Meta:
model = Profile
fields = "__all__"
class UserDetailsSerializer(BaseUserDetailsSerializer):
profile = ProfileSerializer()
class Meta:
model = UserModel
fields = ("username", "email", "first_name", "last_name", "profile")
read_only_fields = ("email",)
def to_representation(self, instance: UserModel) -> dict:
"""Move fields from Profile to user representation."""
representation = super().to_representation(instance)
profile = representation.pop("profile")
representation["zipcode"] = profile["zipcode"]
representation["is_mentor"] = profile["is_mentor"]
return representation
class UserSerializer(BaseUserDetailsSerializer):
profile = ProfileSerializer()
class Meta:
model = UserModel
fields = ("username", "email", "first_name", "last_name", "profile")
read_only_fields = ("email",)
def to_representation(self, instance: UserModel) -> dict:
"""Move fields from Profile to user representation."""
representation = super().to_representation(instance)
profile = representation.pop("profile")
profile.pop("user")
for key, val in profile.items():
representation[key] = val
return representation
|
[
"django.contrib.auth.get_user_model",
"rest_framework.serializers.EmailField",
"rest_framework.serializers.CharField"
] |
[((3424, 3440), 'django.contrib.auth.get_user_model', 'get_user_model', ([], {}), '()\n', (3438, 3440), False, 'from django.contrib.auth import get_user_model\n'), ((2399, 2436), 'rest_framework.serializers.EmailField', 'serializers.EmailField', ([], {'required': '(True)'}), '(required=True)\n', (2421, 2436), False, 'from rest_framework import serializers\n'), ((2452, 2490), 'rest_framework.serializers.CharField', 'serializers.CharField', ([], {'write_only': '(True)'}), '(write_only=True)\n', (2473, 2490), False, 'from rest_framework import serializers\n'), ((2508, 2546), 'rest_framework.serializers.CharField', 'serializers.CharField', ([], {'write_only': '(True)'}), '(write_only=True)\n', (2529, 2546), False, 'from rest_framework import serializers\n'), ((2563, 2601), 'rest_framework.serializers.CharField', 'serializers.CharField', ([], {'write_only': '(True)'}), '(write_only=True)\n', (2584, 2601), False, 'from rest_framework import serializers\n'), ((2632, 2686), 'rest_framework.serializers.CharField', 'serializers.CharField', ([], {'write_only': '(True)', 'required': '(False)'}), '(write_only=True, required=False)\n', (2653, 2686), False, 'from rest_framework import serializers\n'), ((2701, 2755), 'rest_framework.serializers.CharField', 'serializers.CharField', ([], {'write_only': '(True)', 'required': '(False)'}), '(write_only=True, required=False)\n', (2722, 2755), False, 'from rest_framework import serializers\n')]
|
import os
import shutil
import sys
import tarfile
def include_package(envoy_api_protos, rst_file_path, prefix):
# `envoy_api_rst_files` is a list of file paths for .proto.rst files
# generated by protodoc
#
# we are only interested in the proto files generated for envoy protos,
# not for non-envoy dependencies
if ("pkg/" + prefix) not in rst_file_path:
return None
# derive the "canonical" path from the filepath
canonical = f"{rst_file_path.split('pkg/' + prefix)[1]}"
# we are only interested in the actual v3 protos, not their dependencies
if (prefix + canonical) not in envoy_api_protos:
return None
return canonical
def main():
proto_srcs = sys.argv[1]
envoy_api_rst_files = sys.argv[1:-1]
output_filename = sys.argv[-1]
with open(proto_srcs) as f:
# the contents of `proto_srcs` are the result of a bazel genquery,
# containing bazel target rules, eg:
#
# @envoy_api//envoy/watchdog/v3:abort_action.proto
#
# this transforms them to a list with a "canonical" form of:
#
# envoy/watchdog/v3/abort_action.proto.rst
#
envoy_api_protos = [
f"{src.split('//')[1].replace(':', '/')}.rst" for src in f.read().split("\n") if src
]
for rst_file_path in envoy_api_rst_files:
canonical = include_package(envoy_api_protos, rst_file_path, "envoy/")
if canonical is None:
canonical = include_package(envoy_api_protos, rst_file_path, "contrib/envoy/")
if canonical is None:
continue
target = os.path.join("rst-out/api-v3", canonical)
if not os.path.exists(os.path.dirname(target)):
os.makedirs(os.path.dirname(target))
shutil.copy(rst_file_path, target)
# output the generated rst files to a tarfile for consumption
# by other bazel rules
with tarfile.open(output_filename, "w") as tar:
tar.add("rst-out", arcname=".")
if __name__ == "__main__":
main()
|
[
"os.path.dirname",
"tarfile.open",
"os.path.join",
"shutil.copy"
] |
[((1634, 1675), 'os.path.join', 'os.path.join', (['"""rst-out/api-v3"""', 'canonical'], {}), "('rst-out/api-v3', canonical)\n", (1646, 1675), False, 'import os\n'), ((1789, 1823), 'shutil.copy', 'shutil.copy', (['rst_file_path', 'target'], {}), '(rst_file_path, target)\n', (1800, 1823), False, 'import shutil\n'), ((1927, 1961), 'tarfile.open', 'tarfile.open', (['output_filename', '"""w"""'], {}), "(output_filename, 'w')\n", (1939, 1961), False, 'import tarfile\n'), ((1706, 1729), 'os.path.dirname', 'os.path.dirname', (['target'], {}), '(target)\n', (1721, 1729), False, 'import os\n'), ((1756, 1779), 'os.path.dirname', 'os.path.dirname', (['target'], {}), '(target)\n', (1771, 1779), False, 'import os\n')]
|
import os
from collections import OrderedDict, defaultdict
from conans.model.ref import PackageReference
from conans.util.files import save
class RowResult(object):
def __init__(self, remote, reference, data):
self.remote = remote
self.reference = reference
self._data = data
@property
def recipe(self):
return self.reference
@property
def package_id(self):
return self._data['id']
@property
def outdated(self):
return self._data['outdated']
def row(self, headers):
""" Returns package data according to headers """
assert isinstance(headers, Headers), "Wrong type: {}".format(type(headers))
for it in headers.keys:
try:
yield getattr(self, it)
except AttributeError:
yield self._data[it]
for it in headers.settings:
yield self._data['settings'].get(it, None)
for it in headers.options:
yield self._data['options'].get(it, None)
if headers.requires:
prefs = [PackageReference.loads(it) for it in self._data['requires']]
yield ', '.join(map(str, [it.ref for it in prefs]))
class Headers(object):
_preferred_ordering = ['os', 'arch', 'compiler', 'build_type']
def __init__(self, settings, options, requires, keys):
# Keys: columns to classify
self.keys = keys
self.options = options
self.requires = requires
# - Order settings
_settings = defaultdict(list)
for it in settings:
try:
category, _ = it.split('.', 1)
except ValueError:
_settings[it].append(it)
else:
_settings[category].append(it)
self.settings = []
for it in self._preferred_ordering:
if it in _settings:
self.settings.extend(sorted(_settings[it]))
for it, values in _settings.items():
if it not in self._preferred_ordering:
self.settings.extend(sorted(values))
def row(self, n_rows=2):
"""
Retrieve list of headers as a single list (1-row) or as a list of tuples with
settings organized by categories (2-row).
Example output:
1-row: ['os', 'arch', 'compiler', 'compiler.version', 'compiler.libcxx', 'build_type']
2-row: [('os', ['']), ('arch', ['']), ('compiler', ['', 'version', 'libcxx']),]
"""
headers = list(self.keys)
if n_rows == 1:
headers.extend(self.settings + self.options)
if self.requires:
headers.append('requires')
return headers
elif n_rows == 2:
headers = [(it, ['']) for it in headers]
settings = self._group_settings(self.settings)
headers.extend(settings)
headers.append(('options', self.options))
if self.requires:
headers.append(('requires', ['']))
return headers
else:
raise NotImplementedError("not yet")
@staticmethod
def _group_settings(settings):
"""
From one row to two-rows using '.' as separator
"""
ret = OrderedDict()
for setting in settings:
try:
category, value = setting.split(".", 1)
except ValueError:
ret.setdefault(setting, []).append('')
else:
ret.setdefault(category, []).append(value)
return [(key, values) for key, values in ret.items()]
class Results(object):
def __init__(self, results):
self._results = results
# Collect data inspecting the packages
_settings = set()
_options = set()
_remotes = set()
self.requires = False
for it in results:
_remotes.add(it['remote'])
for p in it['items'][0]['packages']:
_settings = _settings.union(list(p['settings'].keys()))
_options = _options.union(list(p['options'].keys()))
if len(p['requires']):
self.requires = True
self.settings = list(_settings)
self.options = list(_options)
self.remotes = list(_remotes)
def get_headers(self, keys=('remote', 'reference', 'outdated', 'package_id')):
return Headers(self.settings, self.options, self.requires, keys=keys)
def packages(self):
for it in self._results:
remote = it['remote']
reference = it['items'][0]['recipe']['id']
for p in it['items'][0]['packages']:
r = RowResult(remote, reference, p)
yield r
def html_binary_graph(search_info, reference, table_filename, template):
# Adapt data to the template (think twice about the format before documenting)
search = {'reference': str(reference)}
results = Results(search_info)
# Render and save
template_folder = os.path.dirname(template.filename)
content = template.render(search=search, results=results, base_template_path=template_folder)
save(table_filename, content)
|
[
"conans.model.ref.PackageReference.loads",
"collections.OrderedDict",
"os.path.dirname",
"collections.defaultdict",
"conans.util.files.save"
] |
[((5024, 5058), 'os.path.dirname', 'os.path.dirname', (['template.filename'], {}), '(template.filename)\n', (5039, 5058), False, 'import os\n'), ((5161, 5190), 'conans.util.files.save', 'save', (['table_filename', 'content'], {}), '(table_filename, content)\n', (5165, 5190), False, 'from conans.util.files import save\n'), ((1537, 1554), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (1548, 1554), False, 'from collections import OrderedDict, defaultdict\n'), ((3266, 3279), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (3277, 3279), False, 'from collections import OrderedDict, defaultdict\n'), ((1087, 1113), 'conans.model.ref.PackageReference.loads', 'PackageReference.loads', (['it'], {}), '(it)\n', (1109, 1113), False, 'from conans.model.ref import PackageReference\n')]
|
from typing import Dict
import numpy as np
import tensorflow as tf
import verres as V
class ConstantSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, learning_rate: float):
super().__init__()
self.learning_rate = float(learning_rate)
def __call__(self, step):
return self.learning_rate
def get_config(self):
return dict(learning_rate=self.learning_rate)
class LinearLRSchedule(tf.keras.callbacks.Callback):
def __init__(self,
cycle_length: int,
steps_per_epoch: int,
lr_map: Dict[int, float],
initial_lr: float = None):
super().__init__()
self.schedule = None
self.pointer = 0
self.cycle_length = None
self.make_schedule(cycle_length, steps_per_epoch, lr_map, initial_lr)
def make_schedule(self,
cycle_length: int,
steps_per_epoch: int,
lr_map: Dict[int, float],
initial_lr: float = None):
self.cycle_length = cycle_length
schedule = np.empty(self.cycle_length * steps_per_epoch, dtype="float32")
if 0 not in lr_map:
if initial_lr is None:
raise RuntimeError("Either pass the initial learning rate in the lr_map or as a dedicated parameter!")
else:
lr_map = lr_map.copy()
initial_lr = lr_map.pop(0)
start_step = 0
current_lr = initial_lr
for end_epoch, next_lr in sorted(lr_map.items(), key=lambda it: it[0]):
steps = end_epoch * steps_per_epoch - start_step
schedule[start_step:start_step+steps] = np.linspace(
current_lr, next_lr, num=steps, endpoint=False, dtype="float32")
start_step += steps
current_lr = next_lr
schedule[start_step:] = current_lr
self.schedule = schedule
def on_batch_end(self, batch, logs=None):
self.model.optimizer.lr = self.schedule[self.pointer]
self.pointer += 1
self.pointer %= self.cycle_length
def on_epoch_end(self, epoch, logs=None):
logs["lr"] = self.schedule[self.pointer]
def factory(spec: dict) -> tf.optimizers.schedules.LearningRateSchedule:
name = spec.pop("name", "default")
if name.lower() in {"default", "constant"}:
scheduler = ConstantSchedule(float(spec["learning_rate"]))
else:
scheduler_type = getattr(tf.optimizers.schedules, name, None)
if scheduler_type is None:
raise KeyError(f"No such scheduler: {name}")
scheduler = scheduler_type(**spec)
print(f" [Verres.schedule] - Factory built: {name}")
return scheduler
|
[
"numpy.linspace",
"numpy.empty"
] |
[((1143, 1205), 'numpy.empty', 'np.empty', (['(self.cycle_length * steps_per_epoch)'], {'dtype': '"""float32"""'}), "(self.cycle_length * steps_per_epoch, dtype='float32')\n", (1151, 1205), True, 'import numpy as np\n'), ((1725, 1801), 'numpy.linspace', 'np.linspace', (['current_lr', 'next_lr'], {'num': 'steps', 'endpoint': '(False)', 'dtype': '"""float32"""'}), "(current_lr, next_lr, num=steps, endpoint=False, dtype='float32')\n", (1736, 1801), True, 'import numpy as np\n')]
|
import json
import string
from datetime import datetime
import deap
import numpy as np
import hmm
from discriminator import Discriminator
from ea import EA
import random_search
DEFAULT_PARAMS = {
# Discriminator CNN model
"model": "CNNModel3",
# Algorithm Parameters
"states": 5,
"symbols": 5,
"epochs": 10,
"epoch_size": 500,
"batch_size": 200,
"seq_len": 20,
"pop_size": 25,
"gens": 50,
"offspring_prop": 1.0,
"cx_prob": 0.0,
"mut_fn": "uniform",
"mut_prob": 1.0,
"mut_rate": None, # None - default to 1/N where N is number of genes
# Implementation Parameters
"_pool_size": 4,
"_random_search": True, # Also run an elitist random search over #gens to compare performance
}
def param_assert(params):
assert params["states"] > 0
assert 0 < params["symbols"] <= 26
assert 0.0 <= params["offspring_prop"] <= 1.0
assert 0.0 <= params["cx_prob"] <= 1.0
assert 0.0 <= params["mut_prob"] <= 1.0
assert (params["mut_rate"] is None) or (0.0 <= params["mut_rate"] <= 1.0)
def run(param_subset):
# Overwrite the default values of the provided parameters
params = {**DEFAULT_PARAMS, **param_subset}
print(params)
param_assert(params)
x = params["states"]
y = string.ascii_lowercase[: params["symbols"]]
s = [1.0] + [0.0] * (x - 1)
# Random HMM that will act as the 'true' underlying distribution
real_hmm = hmm.random_hmm(x, y, s)
# Different random HMM that will be used to benchmark the best solution we find
rand_hmm = hmm.random_hmm(x, y, s)
d = Discriminator(
real_hmm,
params["epoch_size"],
params["batch_size"],
params["seq_len"],
model=params["model"],
pool_size=params["_pool_size"],
)
print("Pre-training discriminator...")
accs, losses = d.initial_train(params["epochs"])
acc = accs[-1]
loss = losses[-1]
print(f"Pre-trained discriminiator accuracy: {acc}, loss: {loss}")
g = EA(
discriminator=d,
pop_size=params["pop_size"],
states=x,
symbols=len(y),
offpr=params["offspring_prop"],
cxpb=params["cx_prob"],
mut_fn=params["mut_fn"],
mutpb=params["mut_prob"],
mut_rate=params["mut_rate"],
)
print("Running generator...")
final_pop, _, logbook = g.run(params["gens"])
best_ind = deap.tools.selBest(final_pop, 1)[0]
best_hmm = hmm.HMM(x, np.array(list(y)), best_ind[0], best_ind[1], np.array(s))
if params["_random_search"]:
print("Running random search benchmark...")
rs_best_hmm, rs_best_acc = random_search.run(
d, params["states"], params["symbols"], params["gens"]
)
else:
rs_best_hmm, rs_best_acc = None, None
return real_hmm, best_hmm, rand_hmm, rs_best_hmm, logbook
def experiment(params, runs):
all_params = {**DEFAULT_PARAMS, **params}
do_rand_search = all_params["_random_search"]
mean_fitnesses = []
best_l2s = []
rand_l2s = []
if do_rand_search:
rs_l2s = []
for i in range(runs):
print(f"Run {i+1}")
real_hmm, best_hmm, rand_hmm, rs_best_hmm, logbook = run(params)
best_l2 = hmm.total_l2_diff(real_hmm, best_hmm)
rand_l2 = hmm.total_l2_diff(real_hmm, rand_hmm)
if do_rand_search:
rs_l2 = hmm.total_l2_diff(real_hmm, rs_best_hmm)
mean_fitnesses.append(logbook.select("mean"))
best_l2s.append(best_l2)
rand_l2s.append(rand_l2)
extra_msg = ""
if do_rand_search:
rs_l2s.append(rs_l2)
extra_msg = f", RandSearch L2: {rs_l2}"
print(f"Best L2: {best_l2}, Rand L2: {rand_l2}{extra_msg}")
exp_data = {
"params": all_params,
"mean_fitnesses": mean_fitnesses,
"best_l2s": best_l2s,
"rand_l2s": rand_l2s,
}
if do_rand_search:
exp_data["rs_l2s"] = rs_l2s
exp_file = f'experiments/exp_{datetime.now().strftime("%y%m%d-%H%M%S%f")}.json'
with open(exp_file, "w") as f:
json.dump(exp_data, f, indent=4)
return exp_data
def main():
real_hmm, best_hmm, best_l2 = run(DEFAULT_PARAMS)
print(
f"""
Real HMM: {real_hmm}
Best HMM: {best_hmm}
Best L2: {best_l2}
"""
)
if __name__ == "__main__":
main()
|
[
"discriminator.Discriminator",
"random_search.run",
"hmm.total_l2_diff",
"numpy.array",
"datetime.datetime.now",
"deap.tools.selBest",
"hmm.random_hmm",
"json.dump"
] |
[((1444, 1467), 'hmm.random_hmm', 'hmm.random_hmm', (['x', 'y', 's'], {}), '(x, y, s)\n', (1458, 1467), False, 'import hmm\n'), ((1567, 1590), 'hmm.random_hmm', 'hmm.random_hmm', (['x', 'y', 's'], {}), '(x, y, s)\n', (1581, 1590), False, 'import hmm\n'), ((1600, 1746), 'discriminator.Discriminator', 'Discriminator', (['real_hmm', "params['epoch_size']", "params['batch_size']", "params['seq_len']"], {'model': "params['model']", 'pool_size': "params['_pool_size']"}), "(real_hmm, params['epoch_size'], params['batch_size'], params[\n 'seq_len'], model=params['model'], pool_size=params['_pool_size'])\n", (1613, 1746), False, 'from discriminator import Discriminator\n'), ((2406, 2438), 'deap.tools.selBest', 'deap.tools.selBest', (['final_pop', '(1)'], {}), '(final_pop, 1)\n', (2424, 2438), False, 'import deap\n'), ((2513, 2524), 'numpy.array', 'np.array', (['s'], {}), '(s)\n', (2521, 2524), True, 'import numpy as np\n'), ((2647, 2720), 'random_search.run', 'random_search.run', (['d', "params['states']", "params['symbols']", "params['gens']"], {}), "(d, params['states'], params['symbols'], params['gens'])\n", (2664, 2720), False, 'import random_search\n'), ((3241, 3278), 'hmm.total_l2_diff', 'hmm.total_l2_diff', (['real_hmm', 'best_hmm'], {}), '(real_hmm, best_hmm)\n', (3258, 3278), False, 'import hmm\n'), ((3297, 3334), 'hmm.total_l2_diff', 'hmm.total_l2_diff', (['real_hmm', 'rand_hmm'], {}), '(real_hmm, rand_hmm)\n', (3314, 3334), False, 'import hmm\n'), ((4091, 4123), 'json.dump', 'json.dump', (['exp_data', 'f'], {'indent': '(4)'}), '(exp_data, f, indent=4)\n', (4100, 4123), False, 'import json\n'), ((3382, 3422), 'hmm.total_l2_diff', 'hmm.total_l2_diff', (['real_hmm', 'rs_best_hmm'], {}), '(real_hmm, rs_best_hmm)\n', (3399, 3422), False, 'import hmm\n'), ((3998, 4012), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (4010, 4012), False, 'from datetime import datetime\n')]
|
#!/usr/bin/env python3
'''
This code traverses a directories of evaluation log files and
record evaluation scores as well as plotting the results.
'''
import os
import argparse
import json
import copy
from shutil import copyfile
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from utils import *
MAX_ALIGN_STEPS = 75000 - 1 # This depends on the evaluation code used to generate the logs
def generate_csv(log_dir, csv_file):
'''
Traverse and read log files, and then output csv file from the eval data.
- file to be generated: 'eval_scores.csv'
- columns: state_machine_id, timesteps, rot_error
'''
df = pd.DataFrame(columns=['state_machine_id', 'state_machine_name', 'timesteps', 'rot_error'])
model_names = extract_model_names(log_dir)
# Traverse all episodes and add each entry to data frame
for state_machine_id, episode_idx, episode_dir in traverse_all_episodes(log_dir):
json_util = JsonUtil(os.path.join(episode_dir, 'goal.json'))
entry = {
'state_machine_id': state_machine_id,
'state_machine_name': model_names[state_machine_id],
**json_util.load()
}
# Handling the timesteps==-1 case
if entry['reachfinish'] == -1:
entry['reachfinish'] = MAX_ALIGN_STEPS
if entry['reachstart'] == -1:
raise ValueError('\'reachstart\' in {episode_dir}/goal.json does not contain a valid value.')
# Rename dict keys
entry['timesteps'] = entry.pop('reachfinish') - entry.pop('reachstart')
entry['rot_error'] = entry.pop('align_obj_error')
entry['init_rot_error'] = entry.pop('init_align_obj_error', None)
# Add a new entry
entry['rot_error_diff'] = entry['init_rot_error'] - entry['rot_error']
df = df.append(entry, ignore_index=True) # df.append works differently from python since it is stupid
df.to_csv(csv_file, index=False)
def generate_plot(input_csv_file, plot_file):
data = pd.read_csv(input_csv_file)
sns.scatterplot(data=data, x="timesteps", y="rot_error", hue="state_machine_name", alpha=0.8)
plt.savefig(plot_file)
|
[
"matplotlib.pyplot.savefig",
"pandas.read_csv",
"os.path.join",
"seaborn.scatterplot",
"pandas.DataFrame"
] |
[((659, 753), 'pandas.DataFrame', 'pd.DataFrame', ([], {'columns': "['state_machine_id', 'state_machine_name', 'timesteps', 'rot_error']"}), "(columns=['state_machine_id', 'state_machine_name', 'timesteps',\n 'rot_error'])\n", (671, 753), True, 'import pandas as pd\n'), ((2020, 2047), 'pandas.read_csv', 'pd.read_csv', (['input_csv_file'], {}), '(input_csv_file)\n', (2031, 2047), True, 'import pandas as pd\n'), ((2052, 2150), 'seaborn.scatterplot', 'sns.scatterplot', ([], {'data': 'data', 'x': '"""timesteps"""', 'y': '"""rot_error"""', 'hue': '"""state_machine_name"""', 'alpha': '(0.8)'}), "(data=data, x='timesteps', y='rot_error', hue=\n 'state_machine_name', alpha=0.8)\n", (2067, 2150), True, 'import seaborn as sns\n'), ((2150, 2172), 'matplotlib.pyplot.savefig', 'plt.savefig', (['plot_file'], {}), '(plot_file)\n', (2161, 2172), True, 'import matplotlib.pyplot as plt\n'), ((974, 1012), 'os.path.join', 'os.path.join', (['episode_dir', '"""goal.json"""'], {}), "(episode_dir, 'goal.json')\n", (986, 1012), False, 'import os\n')]
|
from os import path
from typing import Union
from datetime import datetime
from flask import Flask, request, redirect, render_template
from flask_wtf import CSRFProtect
from werkzeug.utils import secure_filename
from data import db_session
from data.posts import Posts
from forms.edit_post_form import EditPostForm
app = Flask(__name__)
app.config['SECRET_KEY'] = 'SECRET_KEY'
csrf_protect = CSRFProtect(app)
UPLOAD_FOLDER = 'static/posts_img/'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
DATA_BASE = 'db/blog.sqlite'
app.config['DATA_BASE'] = DATA_BASE
def edit_post_in_data_base(form: EditPostForm, post: Union[Posts, None]):
db_sess = db_session.create_session()
post_title = form.title.data
post_text = form.text.data
post_author = form.author.data
post_image = form.image.data
# --- Фотография ---
if not post_image:
post_image_name = '' # Картинки нет
else:
current_id = db_sess.query(Posts).order_by(Posts.id.desc()).first()
current_id = current_id.id + 1 if current_id else 1
real_image_name = secure_filename(post_image.filename)
post_image_name = f'{current_id}{real_image_name[real_image_name.rfind("."):]}'
post_image.save(path.join(app.config['UPLOAD_FOLDER'], post_image_name))
# --- Фотография ---
if not post: # Добавление поста
post = Posts()
post.title = post_title
post.image_name = post_image_name
post.text = post_text
post.author = post_author
post.date = datetime.now()
db_sess.add(post)
else: # редактирование
post.title = post_title
post.image_name = post_image_name
post.text = post_text
post.author = post_author
post.date = datetime.now()
db_sess.merge(post)
db_sess.commit()
db_sess.close()
return redirect('/')
@app.route('/')
def index():
params = {'title': 'Blog', 'UPLOAD_FOLDER': app.config['UPLOAD_FOLDER']}
db_sess = db_session.create_session()
posts = db_sess.query(Posts).order_by(Posts.id.desc()).all()
view = render_template('blog.html', **params, posts=posts)
db_sess.close()
return view
@app.route('/add_post', methods=['GET', 'POST'])
def add_post():
params = {'title': 'Добавление поста', 'action_type': 'Добавление поста', 'submit_text': 'Добавить'}
form = EditPostForm()
params['form'] = form
if form.validate_on_submit():
return edit_post_in_data_base(form, None)
return render_template('edit_post.html', **params)
@app.route('/edit_post/<int:post_id>', methods=['GET', 'POST'])
def edit_post(post_id: int):
params = {'title': 'Редактирование поста', 'action_type': 'Редактирование поста', 'submit_text': 'Редактировать'}
form = EditPostForm()
params['form'] = form
db_sess = db_session.create_session()
post: Posts = db_sess.query(Posts).filter(Posts.id == post_id).first()
db_sess.close()
if not post:
return redirect('/')
if request.method == 'GET':
form.title.data = post.title
form.text.data = post.text
form.author.data = post.author
elif form.validate_on_submit():
return edit_post_in_data_base(form, post)
return render_template('edit_post.html', **params)
@app.route('/delete_post/<int:post_id>')
def delete_post(post_id: int):
db_sess = db_session.create_session()
post = db_sess.query(Posts).filter(Posts.id == post_id).first()
if post:
db_sess.delete(post)
db_sess.commit()
db_sess.close()
return redirect('/')
def main():
db_session.global_init(app.config['DATA_BASE'])
app.run('127.0.0.1', 8080)
if __name__ == '__main__':
main()
|
[
"flask.render_template",
"flask.Flask",
"forms.edit_post_form.EditPostForm",
"data.posts.Posts",
"os.path.join",
"flask.redirect",
"data.db_session.create_session",
"datetime.datetime.now",
"data.posts.Posts.id.desc",
"werkzeug.utils.secure_filename",
"flask_wtf.CSRFProtect",
"data.db_session.global_init"
] |
[((327, 342), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (332, 342), False, 'from flask import Flask, request, redirect, render_template\n'), ((399, 415), 'flask_wtf.CSRFProtect', 'CSRFProtect', (['app'], {}), '(app)\n', (410, 415), False, 'from flask_wtf import CSRFProtect\n'), ((653, 680), 'data.db_session.create_session', 'db_session.create_session', ([], {}), '()\n', (678, 680), False, 'from data import db_session\n'), ((1857, 1870), 'flask.redirect', 'redirect', (['"""/"""'], {}), "('/')\n", (1865, 1870), False, 'from flask import Flask, request, redirect, render_template\n'), ((1994, 2021), 'data.db_session.create_session', 'db_session.create_session', ([], {}), '()\n', (2019, 2021), False, 'from data import db_session\n'), ((2099, 2150), 'flask.render_template', 'render_template', (['"""blog.html"""'], {'posts': 'posts'}), "('blog.html', **params, posts=posts)\n", (2114, 2150), False, 'from flask import Flask, request, redirect, render_template\n'), ((2373, 2387), 'forms.edit_post_form.EditPostForm', 'EditPostForm', ([], {}), '()\n', (2385, 2387), False, 'from forms.edit_post_form import EditPostForm\n'), ((2511, 2554), 'flask.render_template', 'render_template', (['"""edit_post.html"""'], {}), "('edit_post.html', **params)\n", (2526, 2554), False, 'from flask import Flask, request, redirect, render_template\n'), ((2780, 2794), 'forms.edit_post_form.EditPostForm', 'EditPostForm', ([], {}), '()\n', (2792, 2794), False, 'from forms.edit_post_form import EditPostForm\n'), ((2836, 2863), 'data.db_session.create_session', 'db_session.create_session', ([], {}), '()\n', (2861, 2863), False, 'from data import db_session\n'), ((3248, 3291), 'flask.render_template', 'render_template', (['"""edit_post.html"""'], {}), "('edit_post.html', **params)\n", (3263, 3291), False, 'from flask import Flask, request, redirect, render_template\n'), ((3380, 3407), 'data.db_session.create_session', 'db_session.create_session', ([], {}), '()\n', (3405, 3407), False, 'from data import db_session\n'), ((3577, 3590), 'flask.redirect', 'redirect', (['"""/"""'], {}), "('/')\n", (3585, 3590), False, 'from flask import Flask, request, redirect, render_template\n'), ((3609, 3656), 'data.db_session.global_init', 'db_session.global_init', (["app.config['DATA_BASE']"], {}), "(app.config['DATA_BASE'])\n", (3631, 3656), False, 'from data import db_session\n'), ((1081, 1117), 'werkzeug.utils.secure_filename', 'secure_filename', (['post_image.filename'], {}), '(post_image.filename)\n', (1096, 1117), False, 'from werkzeug.utils import secure_filename\n'), ((1365, 1372), 'data.posts.Posts', 'Posts', ([], {}), '()\n', (1370, 1372), False, 'from data.posts import Posts\n'), ((1532, 1546), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (1544, 1546), False, 'from datetime import datetime\n'), ((1760, 1774), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (1772, 1774), False, 'from datetime import datetime\n'), ((2992, 3005), 'flask.redirect', 'redirect', (['"""/"""'], {}), "('/')\n", (3000, 3005), False, 'from flask import Flask, request, redirect, render_template\n'), ((1230, 1285), 'os.path.join', 'path.join', (["app.config['UPLOAD_FOLDER']", 'post_image_name'], {}), "(app.config['UPLOAD_FOLDER'], post_image_name)\n", (1239, 1285), False, 'from os import path\n'), ((2065, 2080), 'data.posts.Posts.id.desc', 'Posts.id.desc', ([], {}), '()\n', (2078, 2080), False, 'from data.posts import Posts\n'), ((969, 984), 'data.posts.Posts.id.desc', 'Posts.id.desc', ([], {}), '()\n', (982, 984), False, 'from data.posts import Posts\n')]
|
# ----------------------------------------------------------------------------
# Copyright 2014 Nervana Systems Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ----------------------------------------------------------------------------
"""
Neon backend wrapper for the NervanaGPU library. Most functions are thin
wrappers around functions from the NervanaGPU class, the GPUTensor is taken
directly from NervanaGPU as well.
NervanaGPU is available at `<https://github.com/NervanaSystems/nervanagpu>`
"""
import logging
from neon.backends.backend import Backend
from nervanagpu import NervanaGPU
from neon.diagnostics.timing_decorators import FlopsDecorator
import pycuda.driver as drv
import numpy as np
logger = logging.getLogger(__name__)
class GPU(Backend):
"""
Sets up a NervanaGPU based backend for matrix operations.
Note that some functions defined in the generic Backend class such as
cross-map pooling and normalization and are not implemented for
this backend.
"""
default_dtype = np.float32
def __init__(self, rng_seed, stochastic_round=False, device_id=0):
import pycuda.driver as drv
drv.init()
global ctx
ctx = drv.Device(device_id).make_context()
import atexit
atexit.register(ctx.pop)
self.ng = NervanaGPU(stochastic_round=stochastic_round)
logger.info("Initialized NervanaGPU with stochastic_round=%s",
stochastic_round)
self.rng_seed = rng_seed
self.rng_init()
self.device_id = device_id if device_id is not None else 0
def __getstate__(self):
"""
Defines what and how we go about serializing an instance of this class.
Returns:
self.__dict__: The full contents of the backend class instance,
except for the mem_pool which is on device and
cannot be serialized.
"""
if hasattr(self, 'mem_pool') and self.mem_pool is not None:
self.mem_pool_pickle = {'shape': self.mem_pool.shape,
'dtype': np.float32}
self.mem_pool = None
return self.__dict__
def __setstate__(self, state):
"""
Defines how we go about deserializing into an instance of this class.
Arguments:
self.__dict__: The full contents of the backend class instance,
except for the mem_pool which is on device and
cannot be serialized.
"""
self.__dict__.update(state)
self.mem_pool = self.ng.empty(self.mem_pool_pickle['shape'],
dtype=self.mem_pool_pickle['dtype'])
def init_mempool(self, shape, dtype=default_dtype):
"""
Allocates a memory pool for temporary storage
"""
self.mem_pool = self.ng.empty(shape, dtype=dtype)
def alloc_host_mem(self, shape, dtype=default_dtype):
return drv.pagelocked_empty(shape, dtype, order="C", mem_flags=0)
def create_stream(self):
return drv.Stream()
def synchronize(self):
pass
def async_copy(self, dest, src, stream=None):
drv.memcpy_htod_async(dest.gpudata, src, stream)
def rng_init(self):
"""
Initialize and seed the pseudo random number genrator. Random numbers
are generated on the host using numpy, then transfered to device.
"""
seed = None
if 'rng_seed' in self.__dict__:
seed = self.rng_seed
logger.info("Seeding random number generator with: %s", str(seed))
np.random.seed(seed)
def flop_timing_init(self, decorate_fc, decorate_conv, decorate_ew):
"""
Initialize FLOP timing. Wraps the specified MOP calls via a decorator
to record elapsed time and number of operations.
Arguments:
decorate_fc (list): string giving the function names of fully
connected layer forward/backward/update calls
to time.
decorate_conv (list): string giving the function names of
convolutional layer forward/backward/update
calls to time.
decorate_ew (list): string giving the function names of element-wise
calls to time.
Notes:
Must be called prior to first flop_timing_start call
"""
self.start = drv.Event()
self.end = drv.Event()
self.flop_timer = FlopsDecorator(self)
self.flop_timer.decorate(decorate_fc=decorate_fc,
decorate_conv=decorate_conv,
decorate_ew=decorate_ew)
def flop_timinig_start(self):
"""
Start a new FLOP timer.
Returns:
None: dummy value (not used)
"""
return self.start.record()
def flop_timing_finish(self, start_time):
"""
Complete current FLOP timing.
Arguments:
start_time (unused): ignored.
Returns:
float: elapsed time in seconds since prior flop_timing_start call.
"""
self.end.record()
self.end.synchronize()
return self.end.time_since(self.start)
def uniform(self, low=0.0, high=1.0, size=1, dtype=default_dtype,
persist_values=True, name=None):
"""
generate numpy random number and convert to a GPUTensor.
If called with dype=None it will probably explode
"""
ary = np.random.uniform(low, high, size)
return self.ng.array(ary, dtype=dtype, name=name)
def normal(self, loc=0.0, scale=1.0, size=1, dtype=default_dtype,
persist_values=True, name=None):
"""
Gaussian/Normal random number sample generation
"""
ary = np.random.normal(loc, scale, size)
return self.ng.array(ary, dtype=dtype, name=name)
def fprop_fc(self, out, inputs, weights, layer=None):
"""
Forward propagate the inputs of a fully connected network layer to
produce output pre-activations (ready for transformation by an
activation function).
Arguments:
out (GPUTensor): Where to store the forward propagated results.
inputs (GPUTensor): Will be either the dataset input values (first
layer), or the outputs from the previous layer.
weights (GPUTensor): The weight coefficient values for this layer.
layer (Layer): The layer object.
"""
self.ng.dot(weights, inputs, out)
def bprop_fc(self, out, weights, deltas, layer=None):
"""
Backward propagate the error through a fully connected network layer.
Arguments:
out (GPUTensor): Where to store the backward propagated errors.
weights (GPUTensor): The weight coefficient values for this layer.
deltas (GPUTensor): The error values for this layer
layer (Layer): The layer object.
"""
self.ng.dot(weights.T, deltas, out)
def update_fc(self, out, inputs, deltas, layer=None):
"""
Compute the updated gradient for a fully connected network layer.
Arguments:
out (GPUTensor): Where to store the updated gradient value.
inputs (GPUTensor): Will be either the dataset input values (first
layer), or the outputs from the previous layer.
deltas (GPUTensor): The error values for this layer
layer (Layer): The layer object.
"""
self.ng.dot(deltas, inputs.T, out)
def update_fc_bias(self, err, out):
"""
Compute the updated bias gradient for a fully connected network layer.
Arguments:
out (GPUTensor): Where to store the updated gradient value.
err (GPUTensor): backpropagated error
"""
self.ng.sum(err, axis=1, out=out)
def add_fc_bias(self, inputs, bias):
"""
Add the bias for a fully connected network layer.
Arguments:
inputs (GPUTensor): the input to update.
bias (GPUTensor): the amount to increment
"""
self.ng.add(inputs, bias, out=inputs)
def fprop_conv(self, out, inputs, weights, ofmshape, ofmsize, ofmlocs,
ifmshape, links, nifm, padding, stride, ngroups, fpropbuf,
local=False):
"""
Forward propagate the inputs of a convolutional network layer to
produce output pre-activations (ready for transformation by an
activation function).
Arguments:
out (GPUTensor): Where to store the forward propagated results.
inputs (GPUTensor): Will be either the dataset input values (first
layer), or the outputs from the previous layer.
weights (GPUTensor): The weight coefficient values for this layer.
ofmshape (tuple): Dimensions of each output feature map (typically
number of height and width neurons).
ofmsize (int): Total size of each output feature map.
ofmlocs (GPUTensor): Indices giving the location of each element
in each output feature map stored in out.
ifmshape (tuple): Dimensions of each input feature map (typically
number of height and width neurons). For this
backend we expect these values to be square.
links (GPUTensor): Input receptive field indices.
nifm (int): Total number of input feature maps.
padding (int): Number of additional elements to include along each
dimension of each local receptive field during the
convolution operation.
stride (int): Number of neurons to shift the filter at each step.
ngroups (int): Number of groups.
fpropbuf (GPUTensor): Temporary storage buffer used to hold the
convolved outputs for a single receptive
field. Not used for this backend.
local (bool, optional): Whether to do local filtering (True) or
convolution (False, the default)
"""
'''
N: Number of images in mini-batch
C: Number of input feature maps
K: Number of output feature maps
D: Depth of input image
H: Height of input image
W: Width of input image
T: Depth of filter kernel
R: Height of filter kernel
S: Width of filter kernel
'''
self.ng.fprop_conv(layer=fpropbuf, I=inputs, F=weights, O=out,
alpha=1.0, repeat=1)
def bprop_conv(self, out, weights, deltas, ofmshape, ofmsize, ofmlocs,
ifmshape, links, padding, stride, nifm, ngroups, bpropbuf,
local=False):
"""
Backward propagate the error through a convolutional network layer.
Arguments:
out (GPUTensor): Where to store the backward propagated errors.
weights (GPUTensor): The weight coefficient values for this layer.
deltas (GPUTensor): The error values for this layer
ofmshape (tuple): Dimensions of each output feature map (typically
height and width).
ofmsize (int): Total size of each output feature map.
ofmlocs (GPUTensor): Indices giving the location of each element in
each output feature map stored in out.
ifmshape (tuple): Dimensions of each input feature map (typically
height and width).
links (GPUTensor): Input receptive field indices.
nifm (int): Total number of input feature maps.
padding (int): Number of additional elements to include along each
dimension of each local receptive field during the
convolution operation.
stride (int): Number of neurons to shift the filter at each step.
ngroups (int): Number of groups.
bpropbuf (GPUTensor): Temporary storage buffer used to hold the
backpropagated error for a single receptive
field
local (bool, optional): Whether to do local filtering (True) or
convolution (False, the default)
"""
self.ng.bprop_conv(layer=bpropbuf, F=weights, E=deltas, grad_I=out,
alpha=1.0, repeat=1)
def update_conv(self, out, inputs, weights, deltas, ofmshape, ofmsize,
ofmlocs, ifmshape, links, nifm, padding, stride, ngroups,
fwidth, updatebuf, local=False, layer=None):
"""
Compute the updated gradient for a convolutional network layer.
Arguments:
out (GPUTensor): Where to store the updated gradient value.
inputs (GPUTensor): Will be either the dataset input values (first
layer), or the outputs from the previous layer.
weights (GPUTensor): The weight coefficient values for this layer.
deltas (GPUTensor): The error values for this layer
ofmshape (tuple): Dimensions of each output feature map (typically
height and width).
ofmsize (int): Total size of each output feature map.
ofmlocs (GPUTensor): Indices giving the location of each element in
each output feature map stored in out.
ifmshape (tuple): Dimensions of each input feature map (typically
height and width).
links (GPUTensor): Input receptive field indices.
nifm (int): Total number of input feature maps.
padding (int): Number of additional elements to include along each
dimension of each local receptive field during the
convolution operation.
stride (int): Number of neurons to shift the filter at each step.
ngroups (int): Number of groups.
fwidth (int): Filter width.
updatebuf (GPUTensor): Temporary storage buffer used to hold the
updated gradient for a single receptive
field
local (bool, optional): Whether to do local filtering (True) or
convolution (False, the default)
layer (Layer): The layer object.
"""
self.ng.update_conv(layer=updatebuf, I=inputs, E=deltas, grad_F=out,
alpha=1.0, repeat=1)
def fprop_pool(self, out, inputs, op, ofmshape, ofmsize, ofmlocs, fshape,
ifmshape, links, nifm, padding, stride, fpropbuf):
"""
Forward propagate the inputs of a Pooling network layer to
produce output pre-activations (ready for transformation by an
activation function).
Arguments:
out (GPUTensor): Where to store the forward propagated results.
inputs (GPUTensor): Will be either the dataset input values (first
layer), or the outputs from the previous layer.
op (string): The type of pooling operation to apply. We support
"max", "avg", "l2" currently.
ofmshape (tuple): Dimensions of each output feature map (typically
number of height and width neurons).
ofmsize (int): Total size of each output feature map.
ofmlocs (GPUTensor): Indices giving the location of each element in
each output feature map stored in out.
fshape (tuple): Dimensions of each filter (typically height and
width).
ifmshape (tuple): Dimensions of each input feature map (typically
number of height and width neurons).
links (GPUTensor): Input receptive field indices.
nifm (int): Total number of input feature maps.
padding (int): Number of additional elements to include along each
dimension of each local receptive field during the
pooling operation.
stride (int): Number of neurons to shift the filter at each step.
fpropbuf (GPUTensor): Temporary storage buffer used to hold the
pooled outputs for a single receptive field.
"""
op = op.lower()
if op == "max":
self.ng.fprop_pool(layer=fpropbuf, I=inputs, O=out, repeat=1)
else:
raise AttributeError("unexpected pooling op type: %s", op)
def bprop_pool(self, out, fouts, inputs, deltas, op, ofmshape, ofmsize,
ofmlocs, fshape, fpsize, ifmshape, links, nifm, padding,
stride, bpropbuf):
"""
Backward propagate the error through a pooling network layer.
Arguments:
out (GPUTensor): Where to store the backward propagated errors.
fouts (GPUTensor): Forward propagated outputs from the previous
layer.
inputs (GPUTensor): Will be either the dataset input values (first
layer), or the outputs from the previous layer.
deltas (GPUTensor): The error values for this layer
op (string): The type of pooling operation to apply. We support
"max", "avg", "l2" currently.
ofmshape (tuple): Dimensions of each output feature map (typically
height and width).
ofmsize (int): Total size of each output feature map.
ofmlocs (GPUTensor): Indices giving the location of each element in
each output feature map stored in out.
fshape (tuple): Dimensions of each filter (typically height and
width).
fpsize (int): The size of each filter.
ifmshape (tuple): Dimensions of each input feature map (typically
height and width).
links (GPUTensor): Input receptive field indices.
nifm (int): Total number of input feature maps.
padding (int): Number of additional elements to include along each
dimension of each local receptive field during the
pooling operation.
stride (int): Number of neurons to shift the filter at each step.
bpropbuf (GPUTensor): Temporary storage buffer used to hold the
backpropagated error for a single receptive
field
"""
op = op.lower()
if op == "max":
self.ng.bprop_pool(layer=bpropbuf, I=inputs, E=deltas, grad_I=out,
repeat=1)
else:
raise AttributeError("unexpected pooling op type: %s", op)
def logistic(self, x, out):
"""
Logistic sigmoid nonlinearity, 1/(1+exp(-x))
Arguments:
x (GPUTensor): Input tensor
out (GPUTensor): Output tensor
"""
self.ng.sig(x, out=out)
return out
def transpose(self, untransposed, transposed):
transposed[:] = untransposed.T
def crossent(self, y, t, partial, out, epsilon, doscale, ismulti=False):
"""
Computes cross entropy cost.
Arguments:
y (GPUTensor): Model outputs
t (GPUTensor): Targets
partial (GPUTensor): temporary buffer used for 2D reduction
out (GPUTensor): Storage for the cross entropy output
epsilon (float): constant for numerical stability
doscale (boolean): If True, cross_entropy is scaled by batch size
ismulti (boolean): If True, compute multi class cross_entropy
"""
sumbuf = partial.reshape((partial.size, 1))[:partial.shape[0]]
if ismulti:
self.ng.sum(-t * self.ng.log(y + epsilon),
axis=None, partial=sumbuf, out=out)
else:
self.ng.sum((t - 1) * self.ng.log(1 - y + epsilon) -
t * self.ng.log(y + epsilon),
axis=None, partial=sumbuf, out=out)
if doscale:
out[:] = out / y.shape[1]
return out
def logistic_compound(self, inputs, outputs):
"""
Applies logistic function and its derivative to the dataset passed.
Arguments:
inputs (GPUTensor): Input data to be transformed. This also
acts as storage for the output of the
derivative function.
outputs (GPUTensor): Storage for the transformed output.
"""
# Apply the logistic function.
outputs[:] = self.ng.sig(inputs)
inputs[:] = (1.0 - outputs) * inputs
def rectlin(self, x, out):
"""
Rectified Linear nonlinearity
Arguments:
x (GPUTensor): Input tensor
out (GPUTensor): Output tensor
"""
self.ng.maximum(x, 0., out=out)
return out
def rectlin_derivative(self, x, out):
"""
Rectified linear nonlinearity derivative
Arguments:
x (GPUTensor): Input tensor
out (GPUTensor): Output tensor
"""
self.ng.greater(x, 0, out=out)
return out
def rectleaky(self, x, slope, out):
"""
Leaky rectified linear nonlinearity
Arguments:
x (GPUTensor): Input tensor
slope (float): amount of gradient to apply when unit is not active
out (GPUTensor): Output tensor
"""
out[:] = self.ng.maximum(x, x*slope)
def rectleaky_derivative(self, x, slope, out):
"""
Leaky rectified linear nonlinearity derivative
Arguments:
x (GPUTensor): Input tensor
slope (float): amount of gradient to apply when unit is not active
out (GPUTensor): Output tensor
"""
out[:] = self.ng.greater(x, 0) * (1.0 - slope) + slope
def sum(self, tsr, axes, out):
"""
Sum
Arguments:
tsr (GPUTensor): Input tensor
axes (int): Axis along which the reduction is performed. If axes
is None, the tensor is flattened and reduced over
both dimensions.
out (GPUTensor): Output tensor
"""
if axes is None:
sze = tsr.shape[0]*tsr.shape[1]
self.ng.sum(tsr.reshape(sze, 1), axis=0, out=out)
else:
self.ng.sum(tsr, axis=axes, out=out)
return out
def norm(self, tsr, order=None, axis=None, out=None):
"""
Calculates and returns the vector p-norms of the GPUTensor along the
specified axis. The p-norm is defined on a vector A as
:math:`||A||_p = \sum_i(|A_i|^p)^{1/p}`.
Arguments:
tsr (GPUTensor): the GPUTensor on which to find the norms
order (int): The order or p upon which the norm is calculated.
Valid values include:
None, inf, -inf, 0, 1, -1, 2, -2, ...
axis (int): The axis along which to compute vector norms.
out (GPUTensor): where to write the results to. Must be
of the expected result shape.
Returns:
GPUTensor: p-norm of tsr along the specified axis.
Raises:
IndexError if invalid axis specified
AttributeError if invalid order specified
See Also:
`numpy.linalg.norm`
"""
if not isinstance(axis, int) or axis < 0 or axis >= len(tsr.shape):
raise IndexError("invalid axis value: %s", axis)
if not isinstance(order, (int, float)):
raise AttributeError("invalid order value: %s", order)
if out is None:
raise AttributeError("No output tensor speficied", order)
if order == float('Inf'):
self.ng.max(self.fabs(tsr), axis, out)
elif order == float('-Inf'):
self.ng.min(self.fabs(tsr), axis, out)
elif order == 0:
tmp = self.zeros(tsr.shape)
self.ng.not_equal(tsr, tmp, tmp)
self.ng.sum(tmp, axis, out)
else:
tmp = self.empty(tsr.shape)
self.ng.power(self.fabs(tsr), order, tmp)
self.ng.sum(tmp, axis, out)
self.ng.power(out, (1.0 / order), out)
return out
def mean(self, tsr, axes, out):
"""
Calculates the arithmetic mean of the elements along the specified
axes.
Arguments:
tsr (GPUTensor): Input tensor
axes (int): Axis along which the reduction is performed. If axes
is None, the tensor is flattened and reduced over
both dimensions.
out (GPUTensor): Output tensor
"""
if axes is None:
sze = tsr.shape[0]*tsr.shape[1]
self.ng.mean(tsr.reshape(sze, 1), axis=0, out=out)
else:
self.ng.mean(tsr, axis=axes, out=out)
return out
def min(self, tsr, axes, out):
"""
Calculates the minimum of the elements along the specified
axes.
Arguments:
tsr (GPUTensor): Input tensor
axes (int): Axis along which the reduction is performed. If axes
is None, the tensor is flattened and reduced over
both dimensions.
out (GPUTensor): Output tensor
"""
if axes is None:
sze = tsr.shape[0]*tsr.shape[1]
self.ng.min(tsr.reshape(sze, 1), axis=0, out=out)
else:
self.ng.min(tsr, axis=axes, out=out)
return out
def max(self, tsr, axes, out):
"""
Calculates the maximum of the elements along the specified
axes.
Arguments:
tsr (GPUTensor): Input tensor
axes (int): Axis along which the reduction is performed. If axes
is None, the tensor is flattened and reduced over
both dimensions.
out (GPUTensor): Output tensor
"""
if axes is None:
sze = tsr.shape[0]*tsr.shape[1]
self.ng.max(tsr.reshape(sze, 1), axis=0, out=out)
else:
self.ng.max(tsr, axis=axes, out=out)
return out
def variance(self, tsr, axes, out, mean=None):
"""
Calculates the variance of the elements along the specified
axes.
Arguments:
tsr (GPUTensor): the tensor on which to compute the variance
axes (int, list, optional): the dimension(s) along which to
variance. If set to None, we will
variance over all dimensions.
out (GPUTensor): where the result will be stored.
mean (GPUTensor): the tensor containing mean of tsr
Returns:
GPUTensor: reference to out
"""
if mean is None:
logger.error("GPUTensor requires mean to be specified.")
raise ValueError("mean not specified")
self.ng.mean(self.ng.square(tsr-mean), axis=axes, out=out)
return out
def fabs(self, x, out):
"""
Calculates absolute value of the elements in a tensor
Arguments:
x (GPUTensor): Input tensor
out (GPUTensor): Output tensor
Returns:
GPUTensor: reference to out
"""
self.ng.fabs(x, out=out)
return out
def sqrt(self, x, out):
"""
Calculates square root of the elements in a tensor
Arguments:
x (GPUTensor): Input tensor
out (GPUTensor): Output tensor
Returns:
GPUTensor: reference to out
"""
self.ng.sqrt(x, out=out)
return out
def zeros(self, shape, dtype=default_dtype, persist_values=True):
"""
Allocate a new GPUTensor and fill it with zeros.
Arguments:
shape (tupel): Shape of the desired GPUTensor
dtype (dtype): Optional datatype
persist_values (bool, optional): If set to True (the default), the
values assigned to this Tensor
will persist across multiple begin
and end calls. Setting to False
may provide a performance increase
if values do not need to be
maintained across such calls
Returns:
GPUTensor: output
"""
return self.ng.zeros(shape, dtype=dtype)
def ones(self, shape, dtype=default_dtype, persist_values=True):
"""
Allocate a new GPUTensor and fill it with ones.
Arguments:
shape (tupel): Shape of the desired GPUTensor
dtype (dtype): Optional datatype
persist_values (bool, optional): If set to True (the default), the
values assigned to this Tensor
will persist across multiple begin
and end calls. Setting to False
may provide a performance increase
if values do not need to be
maintained across such calls
Returns:
GPUTensor: output
"""
return self.ng.ones(shape, dtype=dtype)
def zeros_like(self, ary, dtype=default_dtype, persist_values=True,
name=None):
"""
Instantiate a new instance of this backend's Tensor class, with the
shape taken from ary and populating each element with a value of 0.
Arguments:
ary (tensor object): Tensor to inherit the dimensions of.
dtype (data-type, optional): If present, specifies the underlying
type to employ for each element.
persist_values (bool, optional): If set to True (the default), the
values assigned to this Tensor
will persist across multiple begin
and end calls. Setting to False
may provide a performance increase
if values do not need to be
maintained across such calls
Returns:
Tensor: array object
Raises:
NotImplementedError: Can't be instantiated directly.
See Also:
:py:func:`~neon.backends.backend.Backend.empty`,
:py:func:`~neon.backends.backend.Backend.ones`,
:py:func:`~neon.backends.backend.Backend.array`
"""
return self.zeros(ary.shape, dtype=dtype,
persist_values=persist_values)
def empty_like(self, ary, dtype=default_dtype, persist_values=True,
name=None):
"""
Instantiate a new instance of this backend's Tensor class, with the
shape taken from ary.
Arguments:
ary (tensor object): Tensor to inherit the dimensions of.
dtype (data-type, optional): If present, specifies the underlying
type to employ for each element.
persist_values (bool, optional): If set to True (the default), the
values assigned to this Tensor
will persist across multiple begin
and end calls. Setting to False
may provide a performance increase
if values do not need to be
maintained across such calls
Returns:
Tensor: array object
Raises:
NotImplementedError: Can't be instantiated directly.
See Also:
:py:func:`~neon.backends.backend.Backend.empty`,
:py:func:`~neon.backends.backend.Backend.ones`,
:py:func:`~neon.backends.backend.Backend.array`
"""
return self.empty(ary.shape, dtype=dtype,
persist_values=persist_values, name=name)
def empty(self, shape, dtype=default_dtype, persist_values=True,
name=None):
"""
Allocate a new GPUTensor.
Arguments:
shape (tupel): Shape of the desired GPUTensor
dtype (dtype): Optional datatype
persist_values (bool, optional): If set to True (the default), the
values assigned to this Tensor
will persist across multiple begin
and end calls. Setting to False
may provide a performance increase
if values do not need to be
maintained across such calls
Returns:
GPUTensor: output
"""
return self.ng.empty(shape, dtype=dtype)
def copy(self, ary):
"""
returns a copy of ary
"""
res = self.empty_like(ary)
res.copy(ary)
return res
def array(self, ary, dtype=default_dtype, persist_values=True, name=None,
allocator=drv.mem_alloc):
"""
Allocate a new GPUTensor and fill it with supplied numpy array.
Arguments:
ary (ndarray): Numpy array with source data
dtype (dtype, optional): Optional datatype
persist_values (bool, optional): If set to True (the default), the
values assigned to this Tensor
will persist across multiple begin
and end calls. Setting to False
may provide a performance increase
if values do not need to be
maintained across such calls
name (string): Name for the GPUTensor
allocator (pycuda): Pycuda memory allocator
Returns:
GPUTensor: output
"""
return self.ng.array(ary, dtype=dtype, name=name)
def add(self, left, right, out):
"""
Elementwise addition
Arguments:
left (GPUTensor, numeric): left-hand side operand.
right (GPUTensor, numeric): right-hand side operand.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.add(left, right, out=out)
return out
def subtract(self, left, right, out):
"""
Elementwise subtraction
Arguments:
left (GPUTensor, numeric): left-hand side operand.
right (GPUTensor, numeric): right-hand side operand.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.subtract(left, right, out=out)
return out
def multiply(self, left, right, out):
"""
Elementwise multiplication
Arguments:
left (GPUTensor, numeric): left-hand side operand.
right (GPUTensor, numeric): right-hand side operand.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.multiply(left, right, out=out)
return out
def divide(self, left, right, out):
"""
Elementwise division
Arguments:
left (GPUTensor, numeric): left-hand side operand.
right (GPUTensor, numeric): right-hand side operand.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.divide(left, right, out=out)
return out
def greater(self, left, right, out):
"""
Elementwise greater than testing
Arguments:
left (GPUTensor, numeric): left-hand side operand.
right (GPUTensor, numeric): right-hand side operand.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.greater(left, right, out=out)
return out
def equal(self, left, right, out):
"""
Performs element-wise equality testing on each element of left and
right, storing the result in out. Each operand is assumed to be the
same shape (or broadcastable as such).
Arguments:
left (GPUTensor, numeric): left-hand side operand.
right (GPUTensor, numeric): right-hand side operand.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.equal(left, right, out=out)
return out
def not_equal(self, left, right, out):
"""
Elementwise not equal testing
Arguments:
left (GPUTensor, numeric): left-hand side operand.
right (GPUTensor, numeric): right-hand side operand.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.not_equal(left, right, out=out)
return out
def clip(self, a, a_min, a_max, out):
"""
Elementwise clipping between a range of specified values
Arguments:
a (GPUTensor): input tensor.
a_min (float): floor value.
a_max (float): ceiling value.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.clip(a, a_min, a_max, out=out)
return out
def log(self, a, out):
"""
Elementwise base-e logarithm
Arguments:
a (GPUTensor): input tensor.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.log(a, out=out)
return out
def tanh(self, a, out):
"""
Elementwise tanh
Arguments:
a (GPUTensor): input tensor.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
self.ng.tanh(a, out=out)
return out
def argmax(self, a, out, axis=0):
"""
Calculates the indices of the maximal element value along the specified
axis. If multiple elements contain the maximum, only the elements of
the first are returned.
Arguments:
tsr (GPUTensor): The GPUTensor on which to find the maximum indices
axis (int): The dimension along which to find the maximum. If set
to None, find the overall maximum index of a flattened
representation of tsr.
out (GPUTensor): Where to store the result. Should be of the
appropriate type and expected shape
Returns:
GPUTensor: reference to out
"""
self.ng.argmax(a, out=out, axis=axis)
return out
def softmax(self, x, out):
"""
Softmax nonlinearity. Computes exp(x-max(x)) / sum_i exp(x_i-max(x_i))
Arguments:
x (GPUTensor): input tensor.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
out[:] = (self.ng.reciprocal(self.ng.sum(
self.ng.exp(x - self.ng.max(x, axis=0)), axis=0)) *
self.ng.exp(x - self.ng.max(x, axis=0)))
return out
def softmax_gradient(self, y, err, out):
"""
Gradient of the softmax nonlinearity.
Arguments:
y (GPUTensor): input tensor.
err (GPUTensor): backpropagated error.
out (GPUTensor): where the result will be stored.
Returns:
GPUTensor: reference to out
"""
raise NotImplementedError("Softmax gradient should use shortcut")
return out
def make_binary_mask(self, tsr, keepthresh=0.5, dtype=default_dtype):
"""
Create a binary mask for dropout layers.
Arguments:
tsr (GPUTensor): Output tensor
keepthresh (float): fraction of ones
"""
self.ng.dropout(keep=keepthresh, out=tsr)
def gdm_compound(self, ps_item, us_item, vs_item, momentum_coef,
learning_rate, epoch):
"""
Perform gradient descent update with momentum.
Arguments:
ps_item (GPUTensor): parameter tensor (e.g. a weight matrix)
us_item (GPUTensor): update tensor, contains gradient wrt. weights
vs_item (GPUTensor): velocity tensor.
momentum_coef (float): momentum coefficient.
learning_rate (float): learning rate.
epoch (int): epoch (used in conjunction with diagnostics).
Outputs are written to vs_item (updated velocity)
and ps_item (updated weights)
"""
vs_item[:] = vs_item * momentum_coef - us_item * learning_rate
ps_item[:] = ps_item + vs_item
def gdmwd_compound(self, ps_item, us_item, vs_item, momentum_coef,
learning_rate, wd, epoch):
"""
Perform gradient descent update with momentum and weight decay.
Arguments:
ps_item (GPUTensor): parameter tensor (e.g. a weight matrix)
us_item (GPUTensor): update tensor, contains gradient wrt. weights
vs_item (GPUTensor): velocity tensor.
momentum_coef (float): momentum coefficient.
learning_rate (float): learning rate.
wd (float): weight decay parameter.
epoch (int): epoch (used in conjunction with diagnostics).
Outputs:
ps_item, the updated weights.
vs_item, the updated velocity.
us_item, used as a temp buffer.
"""
vs_item[:] = (vs_item * momentum_coef -
us_item * learning_rate -
ps_item * learning_rate * wd)
ps_item[:] = ps_item + vs_item
def exp_mavg(self, mavg, newval, rho):
"""
Calculate the exponential moving average
Arguments:
mavg: The running value of the moving average
newval: New sample to be added to the moving average
rho: Interpolation value
"""
mavg[:] = rho * mavg + (1.0 - rho) * newval
def ada_update(self, ps_item, us_item, gs_item, ds_item, ls_item, ss_item,
rho, epsilon):
"""
Update rule for AdaDelta (Zeiler, http://arxiv.org/abs/1212.5701)
Arguments:
ps_item: weight / parameter (will be updated)
us_item: update
gs_item: expected value of Gradient Squared (will be updated)
ds_item: expected value of Delta Squared (will be updated)
ls_item: learning rate (will be updated)
ss_item: Scratch Space
rho: decay constant (determines window size)
epsilon: small positive constant for numerical stability
"""
# Accumulate E[Grad^2]
gs_item[:] = gs_item * rho + (1.0 - rho) * us_item * us_item
# Calculate Updates
ls_item[:] = self.ng.sqrt((ds_item + epsilon) /
(gs_item + epsilon)) * (-1.0) * us_item
# Accumulate E[Delt^2]
ds_item[:] = ds_item * rho + (1.0 - rho) * ls_item * ls_item
# Final update to the params
ps_item[:] = ps_item + ls_item
def rms_update(self, params, updates, run_squares, velocity, scratch_space,
gamma, epsilon, learning_rate, momentum_coef):
# Update running squares
run_squares[:] = gamma * run_squares + (1. - gamma) * updates * updates
# Now scale the gradient by lr / rms(grad) (with a epsilon term for
# stability) and use it to update the params
if momentum_coef == 0:
params[:] = params - learning_rate * updates * self.ng.reciprocal(
self.ng.sqrt(run_squares) + epsilon)
else:
velocity[:] = velocity * momentum_coef - \
learning_rate * updates * \
self.ng.reciprocal(self.ng.sqrt(run_squares) + epsilon)
params[:] = params + velocity
def fprop_bn_compound(self, inputs, beta, gamma, eps, xhat,
xmean, xvar, gmean, gvar, rho, out):
"""
Batch normalization forward pass, compounded to run in 3 kernel calls.
Arguments:
inputs: input data to be normalized
beta: location parameter
gamma: scale parameter
eps: small constant for numerical stability
xvar: variance (updated)
xhat: normalized input (updated)
out: normalized and rescaled input (updated)
"""
xvar[:] = self.ng.var(inputs, axis=1)
xmean[:] = self.ng.mean(inputs, axis=1)
gmean[:] = gmean * rho + (1.0 - rho) * xmean
gvar[:] = gvar * rho + (1.0 - rho) * xvar
xvar[:] = self.ng.reciprocal(self.ng.sqrt(xvar + eps))
xhat[:] = xvar * (inputs - xmean)
out[:] = xhat * gamma + beta
return out
def bprop_bn_compound(self, xhat, error, xvar, gamma,
beta_updates, gamma_updates):
"""
Batch normalization backward pass, compounded to run with 4 kernel
calls.
Arguments:
xhat: normalized input data (updated)
error: backpropagated deltas (updated)
xvar: precomputed variance
gamma: scale parameter
beta_updates: gradient update for beta (updated)
gamma_updates: gradient update for gamma (updated)
"""
gamma_updates[:] = self.ng.sum(xhat * error, axis=1)
beta_updates[:] = self.ng.sum(error, axis=1)
xhat[:] = (xhat * gamma_updates + beta_updates) / float(xhat.shape[1])
error[:] = xvar * gamma * (error - xhat)
|
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"pycuda.driver.init",
"neon.diagnostics.timing_decorators.FlopsDecorator",
"numpy.random.seed",
"numpy.random.uniform",
"pycuda.driver.Event",
"atexit.register"
] |
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|
# -*- encoding: utf-8 -*-
import os
import pickle
import sys
import time
import glob
import unittest
import unittest.mock
import numpy as np
import pandas as pd
import sklearn.datasets
from smac.scenario.scenario import Scenario
from smac.facade.roar_facade import ROAR
from autosklearn.util.backend import Backend
from autosklearn.automl import AutoML
import autosklearn.automl
from autosklearn.data.xy_data_manager import XYDataManager
from autosklearn.metrics import accuracy, log_loss, balanced_accuracy
import autosklearn.pipeline.util as putil
from autosklearn.util.logging_ import setup_logger, get_logger
from autosklearn.constants import MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION, REGRESSION
from smac.tae.execute_ta_run import StatusType
sys.path.append(os.path.dirname(__file__))
from base import Base # noqa (E402: module level import not at top of file)
class AutoMLStub(AutoML):
def __init__(self):
self.__class__ = AutoML
self._task = None
class AutoMLTest(Base, unittest.TestCase):
_multiprocess_can_split_ = True
def setUp(self):
super().setUp()
self.automl = AutoMLStub()
self.automl._shared_mode = False
self.automl._seed = 42
self.automl._backend = unittest.mock.Mock(spec=Backend)
self.automl._delete_output_directories = lambda: 0
def test_refit_shuffle_on_fail(self):
backend_api = self._create_backend('test_refit_shuffle_on_fail')
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
failing_model.fit_transformer.side_effect = [
ValueError(), ValueError(), (None, {})]
failing_model.get_max_iter.return_value = 100
auto = AutoML(backend_api, 20, 5)
ensemble_mock = unittest.mock.Mock()
ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)]
auto.ensemble_ = ensemble_mock
for budget_type in [None, 'iterations']:
auto._budget_type = budget_type
auto.models_ = {(1, 1, 50.0): failing_model}
# Make sure a valid 2D array is given to automl
X = np.array([1, 2, 3]).reshape(-1, 1)
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
self.assertEqual(failing_model.fit_transformer.call_count, 3)
del auto
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_only_loads_ensemble_models(self):
def side_effect(ids, *args, **kwargs):
return models if ids is identifiers else {}
# Add a resampling strategy as this is required by load_models
self.automl._resampling_strategy = 'holdout'
identifiers = [(1, 2), (3, 4)]
models = [42]
load_ensemble_mock = unittest.mock.Mock()
load_ensemble_mock.get_selected_model_identifiers.return_value = identifiers
self.automl._backend.load_ensemble.return_value = load_ensemble_mock
self.automl._backend.load_models_by_identifiers.side_effect = side_effect
self.automl._load_models()
self.assertEqual(models, self.automl.models_)
self.assertIsNone(self.automl.cv_models_)
self.automl._resampling_strategy = 'cv'
models = [42]
self.automl._backend.load_cv_models_by_identifiers.side_effect = side_effect
self.automl._load_models()
self.assertEqual(models, self.automl.cv_models_)
def test_check_for_models_if_no_ensemble(self):
models = [42]
self.automl._backend.load_ensemble.return_value = None
self.automl._backend.list_all_models.return_value = models
self.automl._disable_evaluator_output = False
self.automl._load_models()
def test_raises_if_no_models(self):
self.automl._backend.load_ensemble.return_value = None
self.automl._backend.list_all_models.return_value = []
self.automl._resampling_strategy = 'holdout'
self.automl._disable_evaluator_output = False
self.assertRaises(ValueError, self.automl._load_models)
self.automl._disable_evaluator_output = True
self.automl._load_models()
def test_fit(self):
backend_api = self._create_backend('test_fit')
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
automl = autosklearn.automl.AutoML(
backend=backend_api,
time_left_for_this_task=20,
per_run_time_limit=5,
metric=accuracy,
)
automl.fit(
X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
)
score = automl.score(X_test, Y_test)
self.assertGreaterEqual(score, 0.8)
self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION)
del automl
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_delete_non_candidate_models(self):
backend_api = self._create_backend(
'test_delete', delete_tmp_folder_after_terminate=False)
seed = 555
X, Y, _, _ = putil.get_dataset('iris')
automl = autosklearn.automl.AutoML(
backend_api,
time_left_for_this_task=30,
per_run_time_limit=5,
ensemble_nbest=3,
seed=seed,
initial_configurations_via_metalearning=0,
resampling_strategy='holdout',
include_estimators=['sgd'],
include_preprocessors=['no_preprocessing'],
metric=accuracy,
)
automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION,
X_test=X, y_test=Y)
# Assert at least one model file has been deleted and that there were no
# deletion errors
log_file_path = glob.glob(os.path.join(
backend_api.temporary_directory, 'AutoML(' + str(seed) + '):*.log'))
with open(log_file_path[0]) as log_file:
log_content = log_file.read()
self.assertIn('Deleted files of non-candidate model', log_content)
self.assertNotIn('Failed to delete files of non-candidate model', log_content)
self.assertNotIn('Failed to lock model', log_content)
# Assert that the files of the models used by the ensemble weren't deleted
model_files = backend_api.list_all_models(seed=seed)
model_files_idx = set()
for m_file in model_files:
# Extract the model identifiers from the filename
m_file = os.path.split(m_file)[1].replace('.model', '').split('.', 2)
model_files_idx.add((int(m_file[0]), int(m_file[1]), float(m_file[2])))
ensemble_members_idx = set(automl.ensemble_.identifiers_)
self.assertTrue(ensemble_members_idx.issubset(model_files_idx))
del automl
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_fit_roar(self):
def get_roar_object_callback(
scenario_dict,
seed,
ta,
ta_kwargs,
**kwargs
):
"""Random online adaptive racing.
http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf"""
scenario = Scenario(scenario_dict)
return ROAR(
scenario=scenario,
rng=seed,
tae_runner=ta,
tae_runner_kwargs=ta_kwargs,
)
backend_api = self._create_backend('test_fit_roar')
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
automl = autosklearn.automl.AutoML(
backend=backend_api,
time_left_for_this_task=20,
per_run_time_limit=5,
initial_configurations_via_metalearning=0,
get_smac_object_callback=get_roar_object_callback,
metric=accuracy,
)
setup_logger()
automl._logger = get_logger('test_fit_roar')
automl.fit(
X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
)
score = automl.score(X_test, Y_test)
self.assertGreaterEqual(score, 0.8)
self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION)
del automl
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_binary_score_and_include(self):
"""
Test fix for binary classification prediction
taking the index 1 of second dimension in prediction matrix
"""
backend_api = self._create_backend('test_binary_score_and_include')
data = sklearn.datasets.make_classification(
n_samples=400, n_features=10, n_redundant=1, n_informative=3,
n_repeated=1, n_clusters_per_class=2, random_state=1)
X_train = data[0][:200]
Y_train = data[1][:200]
X_test = data[0][200:]
Y_test = data[1][200:]
automl = autosklearn.automl.AutoML(
backend_api, 20, 5,
include_estimators=['sgd'],
include_preprocessors=['no_preprocessing'],
metric=accuracy,
)
automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION)
self.assertEqual(automl._task, BINARY_CLASSIFICATION)
# TODO, the assumption from above is not really tested here
# Also, the score method should be removed, it only makes little sense
score = automl.score(X_test, Y_test)
self.assertGreaterEqual(score, 0.4)
del automl
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_automl_outputs(self):
backend_api = self._create_backend('test_automl_outputs')
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
name = 'iris'
data_manager_file = os.path.join(
backend_api.temporary_directory,
'.auto-sklearn',
'datamanager.pkl'
)
auto = autosklearn.automl.AutoML(
backend_api, 20, 5,
initial_configurations_via_metalearning=0,
seed=100,
metric=accuracy,
)
setup_logger()
auto._logger = get_logger('test_automl_outputs')
auto.fit(
X=X_train,
y=Y_train,
X_test=X_test,
y_test=Y_test,
dataset_name=name,
task=MULTICLASS_CLASSIFICATION,
)
# pickled data manager (without one hot encoding!)
with open(data_manager_file, 'rb') as fh:
D = pickle.load(fh)
self.assertTrue(np.allclose(D.data['X_train'], X_train))
# Check that all directories are there
fixture = ['cv_models', 'true_targets_ensemble.npy',
'start_time_100', 'datamanager.pkl',
'predictions_ensemble',
'ensembles', 'predictions_test', 'models']
self.assertEqual(sorted(os.listdir(os.path.join(backend_api.temporary_directory,
'.auto-sklearn'))),
sorted(fixture))
# At least one ensemble, one validation, one test prediction and one
# model and one ensemble
fixture = os.listdir(os.path.join(backend_api.temporary_directory,
'.auto-sklearn', 'predictions_ensemble'))
self.assertGreater(len(fixture), 0)
fixture = glob.glob(os.path.join(backend_api.temporary_directory, '.auto-sklearn',
'models', '100.*.model'))
self.assertGreater(len(fixture), 0)
fixture = os.listdir(os.path.join(backend_api.temporary_directory,
'.auto-sklearn', 'ensembles'))
self.assertIn('100.0000000001.ensemble', fixture)
# Start time
start_time_file_path = os.path.join(backend_api.temporary_directory,
'.auto-sklearn', "start_time_100")
with open(start_time_file_path, 'r') as fh:
start_time = float(fh.read())
self.assertGreaterEqual(time.time() - start_time, 10)
del auto
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_do_dummy_prediction(self):
datasets = {
'breast_cancer': BINARY_CLASSIFICATION,
'wine': MULTICLASS_CLASSIFICATION,
'diabetes': REGRESSION,
}
for name, task in datasets.items():
backend_api = self._create_backend('test_do_dummy_prediction')
X_train, Y_train, X_test, Y_test = putil.get_dataset(name)
datamanager = XYDataManager(
X_train, Y_train,
X_test, Y_test,
task=task,
dataset_name=name,
feat_type=None,
)
auto = autosklearn.automl.AutoML(
backend_api, 20, 5,
initial_configurations_via_metalearning=25,
metric=accuracy,
)
setup_logger()
auto._logger = get_logger('test_do_dummy_predictions')
auto._backend.save_datamanager(datamanager)
D = backend_api.load_datamanager()
# Check if data manager is correcly loaded
self.assertEqual(D.info['task'], datamanager.info['task'])
auto._do_dummy_prediction(D, 1)
# Ensure that the dummy predictions are not in the current working
# directory, but in the temporary directory.
self.assertFalse(os.path.exists(os.path.join(os.getcwd(),
'.auto-sklearn')))
self.assertTrue(os.path.exists(os.path.join(
backend_api.temporary_directory, '.auto-sklearn', 'predictions_ensemble',
'predictions_ensemble_1_1_0.0.npy')))
del auto
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
@unittest.mock.patch('autosklearn.evaluation.ExecuteTaFuncWithQueue.run')
def test_fail_if_dummy_prediction_fails(self, ta_run_mock):
backend_api = self._create_backend('test_fail_if_dummy_prediction_fails')
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
datamanager = XYDataManager(
X_train, Y_train,
X_test, Y_test,
task=2,
feat_type=['Numerical' for i in range(X_train.shape[1])],
dataset_name='iris',
)
time_for_this_task = 30
per_run_time = 10
auto = autosklearn.automl.AutoML(backend_api,
time_for_this_task,
per_run_time,
initial_configurations_via_metalearning=25,
metric=accuracy,
)
setup_logger()
auto._logger = get_logger('test_fail_if_dummy_prediction_fails')
auto._backend._make_internals_directory()
auto._backend.save_datamanager(datamanager)
# First of all, check that ta.run() is actually called.
ta_run_mock.return_value = StatusType.SUCCESS, None, None, "test"
auto._do_dummy_prediction(datamanager, 1)
ta_run_mock.assert_called_once_with(1, cutoff=time_for_this_task)
# Case 1. Check that function raises no error when statustype == success.
# ta.run() returns status, cost, runtime, and additional info.
ta_run_mock.return_value = StatusType.SUCCESS, None, None, "test"
raised = False
try:
auto._do_dummy_prediction(datamanager, 1)
except ValueError:
raised = True
self.assertFalse(raised, 'Exception raised')
# Case 2. Check that if statustype returned by ta.run() != success,
# the function raises error.
ta_run_mock.return_value = StatusType.CRASHED, None, None, "test"
self.assertRaisesRegex(ValueError,
'Dummy prediction failed with run state StatusType.CRASHED '
'and additional output: test.',
auto._do_dummy_prediction,
datamanager, 1,
)
ta_run_mock.return_value = StatusType.ABORT, None, None, "test"
self.assertRaisesRegex(ValueError,
'Dummy prediction failed with run state StatusType.ABORT '
'and additional output: test.',
auto._do_dummy_prediction,
datamanager, 1,
)
ta_run_mock.return_value = StatusType.TIMEOUT, None, None, "test"
self.assertRaisesRegex(ValueError,
'Dummy prediction failed with run state StatusType.TIMEOUT '
'and additional output: test.',
auto._do_dummy_prediction,
datamanager, 1,
)
ta_run_mock.return_value = StatusType.MEMOUT, None, None, "test"
self.assertRaisesRegex(ValueError,
'Dummy prediction failed with run state StatusType.MEMOUT '
'and additional output: test.',
auto._do_dummy_prediction,
datamanager, 1,
)
ta_run_mock.return_value = StatusType.CAPPED, None, None, "test"
self.assertRaisesRegex(ValueError,
'Dummy prediction failed with run state StatusType.CAPPED '
'and additional output: test.',
auto._do_dummy_prediction,
datamanager, 1,
)
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
@unittest.mock.patch('autosklearn.smbo.AutoMLSMBO.run_smbo')
def test_exceptions_inside_log_in_smbo(self, smbo_run_mock):
# Make sure that any exception during the AutoML fit due to
# SMAC are properly captured in a log file
backend_api = self._create_backend('test_exceptions_inside_log')
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
automl = autosklearn.automl.AutoML(
backend_api,
20,
5,
metric=accuracy,
)
output_file = 'test_exceptions_inside_log.log'
setup_logger(output_file=output_file)
logger = get_logger('test_exceptions_inside_log')
# Create a custom exception to prevent other errors to slip in
class MyException(Exception):
pass
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
# The first call is on dummy predictor failure
message = str(np.random.randint(100)) + '_run_smbo'
smbo_run_mock.side_effect = MyException(message)
with unittest.mock.patch('autosklearn.automl.AutoML._get_logger') as mock:
mock.return_value = logger
with self.assertRaises(MyException):
automl.fit(
X_train,
Y_train,
task=MULTICLASS_CLASSIFICATION,
)
with open(output_file) as f:
self.assertTrue(message in f.read())
# Cleanup
os.unlink(output_file)
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_load_best_individual_model(self):
backend_api = self._create_backend('test_fit')
for metric in [log_loss, balanced_accuracy]:
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
automl = autosklearn.automl.AutoML(
backend=backend_api,
time_left_for_this_task=20,
per_run_time_limit=5,
metric=metric,
)
with unittest.mock.patch(
'autosklearn.ensemble_builder.EnsembleBuilder.run'
) as mock_ensemble_run:
mock_ensemble_run.side_effect = MemoryError
automl.fit(
X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
)
# A memory error occurs in the ensemble construction
self.assertIsNone(automl._backend.load_ensemble(automl._seed))
# The load model is robust to this and loads the best model
automl._load_models()
self.assertIsNotNone(automl.ensemble_)
# Just 1 model is there for ensemble and all weight must be on it
get_models_with_weights = automl.get_models_with_weights()
self.assertEqual(len(get_models_with_weights), 1)
self.assertEqual(get_models_with_weights[0][0], 1.0)
# Match a toy dataset
if metric._sign < 0:
self.assertLessEqual(automl.score(X_test, Y_test), 0.2)
else:
self.assertGreaterEqual(automl.score(X_test, Y_test), 0.8)
del automl
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_fail_if_feat_type_on_pandas_input(self):
"""We do not support feat type when pandas
is provided as an input
"""
backend_api = self._create_backend('test_fail_feat_pandas')
automl = autosklearn.automl.AutoML(
backend=backend_api,
time_left_for_this_task=20,
per_run_time_limit=5,
metric=accuracy,
)
X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]})
y_train = [1, 0]
with self.assertRaisesRegex(ValueError,
"feat_type cannot be provided when using pandas"):
automl.fit(
X_train, y_train,
task=BINARY_CLASSIFICATION,
feat_type=['Categorical', 'Numerical'],
)
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_fail_if_dtype_changes_automl(self):
"""We do not support changes in the input type.
Once a estimator is fitted, it should not change data type
"""
backend_api = self._create_backend('test_fail_feat_typechange')
automl = autosklearn.automl.AutoML(
backend=backend_api,
time_left_for_this_task=20,
per_run_time_limit=5,
metric=accuracy,
)
X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]})
y_train = [1, 0]
automl.InputValidator.validate(X_train, y_train, is_classification=True)
with self.assertRaisesRegex(ValueError,
"Auto-sklearn previously received features of type"):
automl.fit(
X_train.to_numpy(), y_train,
task=BINARY_CLASSIFICATION,
)
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
if __name__ == "__main__":
unittest.main()
|
[
"numpy.array",
"autosklearn.util.logging_.get_logger",
"unittest.main",
"unittest.mock.patch",
"autosklearn.pipeline.util.get_dataset",
"os.path.split",
"smac.facade.roar_facade.ROAR",
"os.unlink",
"pandas.DataFrame",
"autosklearn.data.xy_data_manager.XYDataManager",
"numpy.allclose",
"unittest.mock.Mock",
"pickle.load",
"os.path.dirname",
"time.time",
"os.path.join",
"os.getcwd",
"autosklearn.automl.AutoML",
"numpy.random.randint",
"smac.scenario.scenario.Scenario",
"autosklearn.util.logging_.setup_logger"
] |
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|
"""
This implements an abstrace base class Ring .
Rationale:
Goal is to separate the datatype specification from the algorithms and containers for the following reasons:
1) It allows to directly use the algorithms *without* overhead. E.g. calling mul(z.data, x.data, y.data)
has much less overhead than z = x.__mul__(y). data is to be kept as close as possible to
machine primitives. E.g. data is array or tuple of arrays.
2) Potential reuse of an algorithm in several datatypes.
3) Relatively easy to connect high performance algorithms with a very highlevel abstract description.
For instance, most programming languages allow calling C-functions. Therefore, the algorithms
should be given as void fcn(int A, double B, ...)
For instance, the datatype is a truncated Taylor polynomial R[t]/<t^D> of the class Foo.
The underlying container is a simple array of doubles.
"""
import numpy
class Ring(object):
"""
An abstract base class in an attempt to follow the DRY principle.
It implements the algebraic class of a ring as defined on
http://en.wikipedia.org/wiki/Ring_%28mathematics%29
The idea is that the set is described in data and the operations +,* etc.
are implemented as functions that operate on the data.
E.g. the factor ring of natural numbers modulo 4, x.data = 3 y.data = 2
then z = add(x,y) is implemented as
def add(x,y):
return self.__class__((x.data*y.data)%4)
and one obtains z.data = 1
Warning:
Since this class is only of little value it may be deprecated in the future.
"""
data = NotImplementedError()
def totype(self, x):
"""
tries to convert x to an object of the class
works for : scalar x, numpy.ndarray x
Remark:
at the moment, scalar x expanded as Ring with the same degree as self though.
The reason is a missing implementation that works for graded rings of different degree.
Once such implementations exist, this function should be adapted.
"""
if numpy.isscalar(x):
xdata = self.__class__.__zeros_like__(self.data)
self.__class__.__scalar_to_data__(xdata, x)
return self.__class__(xdata)
elif isinstance(x, numpy.ndarray):
raise NotImplementedError('sorry, not implemented just yet')
elif not isinstance(x, self.__class__):
raise NotImplementedError('Cannot convert x\n type(x) = %s but expected type(x) = %s'%(str(type(x))))
else:
return x
def __add__(self, rhs):
rhs = self.totype(rhs)
retval = self.__class__(self.__class__.__zeros_like__(self.data))
self.__class__.add(retval.data, self.data, rhs.data)
return retval
def __sub__(self, rhs):
rhs = self.totype(rhs)
retval = self.__class__(self.__class__.__zeros_like__(self.data))
self.__class__.sub(retval.data, self.data, rhs.data)
return retval
def __mul__(self,rhs):
rhs = self.totype(rhs)
retval = self.__class__(self.__class__.__zeros_like__(self.data))
self.__class__.mul(retval.data, self.data, rhs.data)
return retval
def __truediv__(self,rhs):
rhs = self.totype(rhs)
retval = self.__class__(self.__class__.__zeros_like__(self.data))
self.__class__.div(retval.data, self.data, rhs.data)
return retval
def __radd__(self, lhs):
return self + lhs
def __rmul__(self, lhs):
return self * lhs
def zeros_like(self):
return self.__class__(self.__class__.__zeros_like__(self.data))
def __str__(self):
return str(self.data)
|
[
"numpy.isscalar"
] |
[((2205, 2222), 'numpy.isscalar', 'numpy.isscalar', (['x'], {}), '(x)\n', (2219, 2222), False, 'import numpy\n')]
|
####################################
# author: <NAME>
# course: 2020 Complete Python Bootcamps: From Zero to Hero in Python
# purpose: lecture notes
# description: Section 15 - Web Scraping
# other: N/A
####################################
# RULES
# 1. always try to get permission before scraping, otherwise I might be blocked
# 2. check the laws of whatever country we are operating in (for legal issues)
# LIMITATIONS
# each website is unique -> so for each website there must exist a Python script
# an update to a website might brake my script
import requests
import bs4
# Grabbing a title
result = requests.get("http://example.com")
type(result)
result.text
# bs with lxml tranforms the previous raw html into the following
soup = bs4.BeautifulSoup(result.text,'lxml')
soup
# returns the tag we specified as a list (i.e., there might be more than one)
soup.select('title')
soup.select('title')[0].getText()
soup.select('p')
site_paragraphs = soup.select('p')
type(site_paragraphs[0]) # not a string, instead is a specialized bs object,
# which is why we can do something like call .getText()
# Grabbing a class (from CSS) using soup.select()
# 'div' : all elements with 'div' tag
# '#some_id' : elements containing id='some_id'
# '.some_class' : elements containing class='some_class'
# 'div span' : any element named span within a div element
# 'div > span' : any element named span directly within a div element, with
# nothing in between
res = requests.get("https://en.wikipedia.org/wiki/Jonas_Salk")
soup = bs4.BeautifulSoup(res.text,'lxml')
soup.select('.toctext')[0].text
soup.select('.toctext')[0].getText()
for item in soup.select('.toctext'):
print(item.text)
# Grabbing an image
#soup.select('img') # can return more than what is needeed (it will depend on
# the website)
soup.select('.thumbimage')
jonas_salk = soup.select('.thumbimage')[0]
jonas_salk['src'] # we can treat it as a dictionary
image_link = requests.get('http://upload.wikimedia.org/wikipedia/commons/thumb/3/3c/Roosevelt_OConnor.jpg/220px-Roosevelt_OConnor.jpg')
#image_link.content # raw content of the image which is a binary file
#make sure to use the same format that the image has
f = open('my_image_image.jpg','wb') # wb means write binary
f.write(image_link.content)
f.close()
# Multiple elements across multiple pages
# GOAL: get title of every book with a 2 star rating
#Check that this also work with page 1
#http://books.toscrape.com/catalogue/page-2.html
base_url = 'http://books.toscrape.com/catalogue/page-{}.html'
req = requests.get(base_url.format(1))
soup = bs4.BeautifulSoup(req.text,'lxml')
products = soup.select(".product_pod") # always check the length, in this case should be 20
example = products[0]
# one way (not useful everytime)
'star-rating Two' in str(example)
# another way (checking for the presence of a class)
example.select('.star-rating.Three') # if there is a space in a class we should add a dot
example.select('.star-rating.Two') # nothing
example.select('a')[1]['title']
two_star_titles = []
for n in range(1,51):
scrape_url = base_url.format(n)
req = requests.get(base_url.format(1))
soup = bs4.BeautifulSoup(req.text,'lxml')
books = soup.select(".product_pod")
for book in books:
if len(book.select('.star-rating.Two')) != 0:
two_star_titles.append(book.select('a')[1]['title'])
two_star_titles
|
[
"bs4.BeautifulSoup",
"requests.get"
] |
[((608, 642), 'requests.get', 'requests.get', (['"""http://example.com"""'], {}), "('http://example.com')\n", (620, 642), False, 'import requests\n'), ((743, 781), 'bs4.BeautifulSoup', 'bs4.BeautifulSoup', (['result.text', '"""lxml"""'], {}), "(result.text, 'lxml')\n", (760, 781), False, 'import bs4\n'), ((1520, 1576), 'requests.get', 'requests.get', (['"""https://en.wikipedia.org/wiki/Jonas_Salk"""'], {}), "('https://en.wikipedia.org/wiki/Jonas_Salk')\n", (1532, 1576), False, 'import requests\n'), ((1584, 1619), 'bs4.BeautifulSoup', 'bs4.BeautifulSoup', (['res.text', '"""lxml"""'], {}), "(res.text, 'lxml')\n", (1601, 1619), False, 'import bs4\n'), ((2021, 2153), 'requests.get', 'requests.get', (['"""http://upload.wikimedia.org/wikipedia/commons/thumb/3/3c/Roosevelt_OConnor.jpg/220px-Roosevelt_OConnor.jpg"""'], {}), "(\n 'http://upload.wikimedia.org/wikipedia/commons/thumb/3/3c/Roosevelt_OConnor.jpg/220px-Roosevelt_OConnor.jpg'\n )\n", (2033, 2153), False, 'import requests\n'), ((2664, 2699), 'bs4.BeautifulSoup', 'bs4.BeautifulSoup', (['req.text', '"""lxml"""'], {}), "(req.text, 'lxml')\n", (2681, 2699), False, 'import bs4\n'), ((3243, 3278), 'bs4.BeautifulSoup', 'bs4.BeautifulSoup', (['req.text', '"""lxml"""'], {}), "(req.text, 'lxml')\n", (3260, 3278), False, 'import bs4\n')]
|
#!/usr/bin/env python3
import sys
import os
import re
import json
import traceback
import pkg_resources
import tarfile
from collections import OrderedDict
import anchore_engine.analyzers.utils, anchore_engine.utils
def get_python_evidence(tfl, member, memberhash, evidence):
global binary_package_el
fullpath = "/{}".format(member.name)
filename = os.path.basename(fullpath)
el = {}
el.update(binary_package_el)
patt_bin = re.match("^python([0-9]+\.[0-9]+)$", filename)
patt_lib = re.match("^libpython([0-9]+\.[0-9]+).so.*$", filename)
if (patt_bin or patt_lib) and member.isreg():
f_vers = ""
if patt_bin:
f_vers = patt_bin.group(1)
elif patt_lib:
f_vers = patt_lib.group(1)
with tfl.extractfile(member) as FH:
for line in FH.readlines():
subline = line
try:
the_re = ".*{}\.([0-9]+[-_a-zA-Z0-9]*).*".format(f_vers)
patt = re.match(anchore_engine.utils.ensure_bytes(the_re), subline)
if patt and f_vers:
b_vers = "{}.{}".format(f_vers, anchore_engine.utils.ensure_str(patt.group(1)))
if b_vers.startswith(f_vers):
el['name'] = 'python'
el['version'] = b_vers
el['location'] = fullpath
evidence['python']['binary'].append( el )
break
except Exception as err:
raise err
elif filename == "patchlevel.h" and member.isreg():
with tfl.extractfile(member) as FH:
for line in FH.readlines():
line = line.strip()
patt = re.match(b".*#define +PY_VERSION +\"*([0-9\.\-_a-zA-Z]+)\"*", line)
if patt:
h_vers = anchore_engine.utils.ensure_str(patt.group(1))
el['name'] = 'python'
el['version'] = h_vers
el['location'] = fullpath
evidence['python']['devel'].append(el)
break
def get_golang_evidence(tfl, member, memberhash, evidence):
global binary_package_el
fullpath = "/{}".format(member.name)
filename = os.path.basename(fullpath)
el = {}
el.update(binary_package_el)
if filename in ['go'] and member.isreg():
with tfl.extractfile(member) as FH:
for line in FH.readlines():
subline = line
try:
the_re = ".*go([0-9]+\.[0-9]+(\.[0-9]+|beta[0-9]+|alpha[0-9]+|rc[0-9]+)*).*"
patt = re.match(anchore_engine.utils.ensure_bytes(the_re), subline)
if patt:
vers = anchore_engine.utils.ensure_str(patt.group(1))
el['name'] = 'go'
el['version'] = vers
el['location'] = fullpath
evidence['go']['binary'].append( el )
break
except Exception as err:
raise err
elif filename == "VERSION" and member.isreg():
with tfl.extractfile(member) as FH:
for line in FH.readlines():
line = line.strip()
patt = re.match(b".*go([0-9]+\.[0-9]+(\.[0-9]+|beta[0-9]+|alpha[0-9]+|rc[0-9]+)*).*", line)
if patt:
vers = anchore_engine.utils.ensure_str(patt.group(1))
final_loc = fullpath
if memberhash.get(os.path.join(os.path.dirname(member.name), 'bin', 'go'), None):
final_loc = os.path.join("/", os.path.dirname(member.name), 'bin', 'go')
el['name'] = 'go'
el['version'] = vers
el['location'] = final_loc
evidence['go']['devel'].append( el )
break
def get_busybox_evidence(tfl, member, memberhash, distrodict, evidence):
global binary_package_el
fullpath = "/{}".format(member.name)
filename = os.path.basename(fullpath)
if filename == "busybox" and (member.isreg() or member.islnk()):
# Perform any specific checks using prior metadata
if distrodict.get('flavor', "") == 'BUSYB':
patt = re.match(".*([0-9]+\.[0-9]+\.[0-9]+).*", distrodict.get('fullversion', ""))
if patt:
version = anchore_engine.utils.ensure_str(patt.group(1))
el = {}
el.update(binary_package_el)
el['name'] = 'busybox'
el['version'] = version
el['location'] = fullpath
evidence['busybox']['binary'].append(el)
analyzer_name = "package_list"
try:
config = anchore_engine.analyzers.utils.init_analyzer_cmdline(sys.argv, analyzer_name)
except Exception as err:
print(str(err))
sys.exit(1)
imgname = config['imgid']
imgid = config['imgid_full']
outputdir = config['dirs']['outputdir']
unpackdir = config['dirs']['unpackdir']
squashtar = os.path.join(unpackdir, "squashed.tar")
resultlist = {}
version_found_map = {}
binary_package_el = {
'name': None,
'version': None,
'location': None,
'type': 'binary',
'files': [],
'license': 'N/A',
'origin': 'N/A',
'metadata': json.dumps({})
}
try:
allfiles = {}
if os.path.exists(unpackdir + "/anchore_allfiles.json"):
with open(unpackdir + "/anchore_allfiles.json", 'r') as FH:
allfiles = json.loads(FH.read())
else:
fmap, allfiles = anchore_engine.analyzers.utils.get_files_from_squashtar(os.path.join(unpackdir, "squashed.tar"))
with open(unpackdir + "/anchore_allfiles.json", 'w') as OFH:
OFH.write(json.dumps(allfiles))
# read in previous analyzer output for helping to increase accuracy of findings
fname = os.path.join(outputdir, 'pkgfiles.all')
pkgfilesall = anchore_engine.analyzers.utils.read_kvfile_todict(fname)
meta = anchore_engine.analyzers.utils.get_distro_from_squashtar(os.path.join(unpackdir, "squashed.tar"), unpackdir=unpackdir)
distrodict = anchore_engine.analyzers.utils.get_distro_flavor(meta['DISTRO'], meta['DISTROVERS'], likedistro=meta['LIKEDISTRO'])
# set up ordered dictionary structure for the runtimes and evidence types
evidence = OrderedDict()
for runtime in ['python', 'go', 'busybox']:
evidence[runtime] = OrderedDict()
for etype in ['binary', 'devel']:
evidence[runtime][etype] = []
# Perform a per file routine to evaluate files for gathering binary package version evidence
with tarfile.open(os.path.join(unpackdir, "squashed.tar"), mode='r', format=tarfile.PAX_FORMAT) as tfl:
alltnames = tfl.getnames()
alltfiles = {}
for name in alltnames:
alltfiles[name] = True
memberhash = anchore_engine.analyzers.utils.get_memberhash(tfl)
for member in list(memberhash.values()):
try:
get_python_evidence(tfl, member, memberhash, evidence)
except Exception as err:
print ("WARN: caught exception evaluating file ({}) for python runtime evidence: {}".format(member.name, str(err)))
try:
get_golang_evidence(tfl, member, memberhash, evidence)
except Exception as err:
print ("WARN: caught exception evaluating file ({}) for golang runtime evidence: {}".format(member.name, str(err)))
try:
get_busybox_evidence(tfl, member, memberhash, distrodict, evidence)
except Exception as err:
print ("WARN: caught exception evaluating file ({}) for busybox runtime evidence: {}".format(member.name, str(err)))
resultlist = {}
for runtime in evidence.keys(): #['python', 'go']:
for e in evidence[runtime].keys(): #['binary', 'devel']:
for t in evidence[runtime][e]:
version = t.get('version')
location = t.get('location')
if location in pkgfilesall:
print ("INFO: Skipping evidence {} - file is owned by OS package".format(location))
else:
key = "{}-{}".format(runtime, version)
if key not in version_found_map:
result = {}
result.update(binary_package_el)
result.update(t)
result['metadata'] = json.dumps({"evidence_type": e})
resultlist[location] = json.dumps(result)
version_found_map[key] = True
try:
squashtar = os.path.join(unpackdir, "squashed.tar")
hints = anchore_engine.analyzers.utils.get_hintsfile(unpackdir, squashtar)
for pkg in hints.get('packages', []):
pkg_type = pkg.get('type', "").lower()
if pkg_type == 'binary':
try:
pkg_key, el = anchore_engine.analyzers.utils._hints_to_binary(pkg)
try:
resultlist[pkg_key] = json.dumps(el)
except Exception as err:
print ("WARN: unable to add binary package ({}) from hints - excpetion: {}".format(pkg_key, err))
except Exception as err:
print ("WARN: bad hints record encountered - exception: {}".format(err))
except Exception as err:
print ("WARN: problem honoring hints file - exception: {}".format(err))
except Exception as err:
import traceback
traceback.print_exc()
print("WARN: analyzer unable to complete - exception: " + str(err))
if resultlist:
ofile = os.path.join(outputdir, 'pkgs.binary')
anchore_engine.analyzers.utils.write_kvfile_fromdict(ofile, resultlist)
#print ("RESULT: {}".format(resultlist))
sys.exit(0)
|
[
"os.path.exists",
"collections.OrderedDict",
"json.dumps",
"os.path.join",
"re.match",
"os.path.dirname",
"os.path.basename",
"sys.exit",
"traceback.print_exc"
] |
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|
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