content stringlengths 5 1.05M |
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from django.shortcuts import render
from products.models import Product
# Create your views here.
def index(request):
context = {
'products' : Product.objects.all(),
}
return render(request,'landing/index.html',context=context)
def about(request):
return render(request,'landing/about.html')
def support(request):
return render(request,'landing/support.html')
|
# Evaluates and ranks UIs
import pybrain
from scipy import *
import numpy as np
import math
import sys
from copy import copy
import operator as op
from UIEnv import UI, UITask
from evaluation import evaluation
from episodic import EpisodicExperiment
from experiment import Experiment
from initialParams import initializeParams
from pybrain.rl.environments.environment import Environment
from pybrain.rl.agents import LearningAgent
from q import Q
from pybrain.rl.learners import SARSA
from interface import ActionValueTable
from pybrain.rl.learners.learner import EpisodicLearner
from egreedy import EpsilonGreedyExplorer
def rl_optimizer(UImatrix, num_of_actions, top_UI, params, batch_num, logging):
#global policies
#global top_UI
policies = [([])]*params.num_states
num_states = params.num_states
# Defining UI environment
actionmatrix = range(num_of_actions)
goal = False # Default goal
ui_env = UI(num_states, actionmatrix, num_of_actions, goal, params.sensor_errors, params.confusion_error, params.penalties, params.grid, params.dimensions, params.init_position, params.goal_position)
av_table = ActionValueTable(num_states, num_of_actions, ui_env)
av_table.initialize(1)
# Train agent for each goal
klm_tot = 0
klm_avg = 0
policies = []
best_actions = []# [0]*ui_env.num_of_states*(ui_env.num_of_states-1)
objective = -1
p_learned = 1
ii = 0
#########
# Define Q-learning agent
learner = Q(0.5, 0.99) #Q(0.6, 0.99) # 0.5, 0.99
learner.explorer.epsilon = 0.7 # 0.7 # 0.9
learner.explorer.decay = 0.999 # 0.99
learner.explorer.env = ui_env
agent = LearningAgent(av_table, learner)
# Define task and experiment
task = UITask(ui_env)
experiment = EpisodicExperiment(task, agent, av_table)
#######
#Removed bad actions, give action matric as input
av_table.initialize(-5., actionmatrix)
for j in range(8): # Learning iterations
initial_state = 0
runs = 50 # Episodes in one iteration
experiment.doEpisodes(runs)
agent.learn()
agent.reset()
##############################################
# Save policy
# For optimization
p = list(av_table.params) # Copies to new memory slot
policies.append(p)
##############################################
# Evaluation of UI and policy for current goal
# Loop to get average : use only if errors used
klm_tasks_tot = np.array([0.]*(params.num_states-1))
total_iterations = 1
klm_tot = 0
for i in range(total_iterations):
# KLM value
klm_g, best_path = evaluation(av_table, ui_env, task, False, params, batch_num, logging)
if klm_g == -1: # Not learned
klm_tot += 20*5
p_learned = 0
print "error"
break
# Save to total KLM
klm_tot += klm_g
klm_avg += klm_tot/total_iterations
return top_UI, objective, best_actions, best_path, klm_g
|
import json
from pprint import pprint
departureTime_hour = ""
departureTime_minute = ""
flightNumber = ""
aircraftType = ""
destination = ""
flightStatus = ""
baseCost = ""
durationInMinutes = ""
fl = "lo"
new_val = ""
with open('/Users/abinavc/AA-Mock-Engine/mock/flightData.json') as f:
data = json.load(f)
for i in data.values():
for x in i["flights"]:
flightNumber = str(json.dumps((x["flightNumber"])))
aircraftType = str(json.dumps((x["aircraftType"])))
destination = str(json.dumps((x["destination"])))
flightStatus = str(json.dumps((x["flightStatus"])))
baseCost = str(json.dumps((x["baseCost"])))
durationInMinutes = str(json.dumps((x["durationInMinutes"])))
departureTime_hour = str(json.dumps((x["departureTime"]["hour"])))
departureTime_minute = str(json.dumps((x["departureTime"]["minute"])))
#sql = "INSERT INTO flight_data (id, flight_number, aircraft_type, destination, flight_status, base_cost, duration_minutes, departuretime_hour, departuretime_minute, departuretime_minute) VALUES ('', flightNumber, aircraftType, destination, flightStatus, baseCost, durationInMinutes, departureTime_hour, departureTime_minute)"
#mycursor.execute(sql)
#mydb.commit()
#print(mycursor.rowcount, "record inserted.")
#print(x["destination"])
#departureTime_minute.append(y["minute"])
#print()
#pprint(flightNumber)
#pprint(aircraftType)
#pprint(destination)
#pprint(flightStatus)
#pprint(baseCost)
#pprint(durationInMinutes)
|
#!/usr/bin/env python
# TO DO: Revamp functions to
# -compare different Lennard-Jones grid transformations
# TO DO: Clean up output
# TO DO: Write docstrings
# TO DO: Set up checkpoints
# Ideas from Prodock by Trosset & Scheraga
# TO DO: Grid interpolation with Bezier splines, which provides second derivatives [Oberlin, D.,Jr.; Scheraga, H. A. J Comput Chem 1998, 19, 71:85.]. This could allow for layer sampling.
# TO DO: Scaled collective variable Monte Carlo for dihedral angle moves [Noguti, T.; Go, N. Biopolymers 1985, 24, 527:546.]
# Ideas from QXP by McMartin and Bohacek
# TO DO: Dihedral angle moves using cylindrical coordinates (makes energy evalulations faster?)
# Ideas from Ribodock by Morley and Afshar
# TO DO: Scale number of MC trials by number of rotatable bonds
# TO DO: Genetic Monte Carlo
import os
from os.path import join
from os.path import exists
from os.path import abspath
import pickle
import gzip
import copy
import time
import numpy as np
import MMTK
import MMTK.Units
from MMTK.ParticleProperties import Configuration
import Scientific
from Scientific_vector import Vector # @UnresolvedImport
import AlGDock as a
# Constants
R = 8.3144621*MMTK.Units.J/MMTK.Units.mol/MMTK.Units.K
T_HIGH = 800.0*MMTK.Units.K
T_TARGET = 300.0*MMTK.Units.K
RT_HIGH = R*T_HIGH
RT_TARGET = R*T_TARGET
term_map = {
'cosine dihedral angle':'MM',
'electrostatic/pair sum':'MM',
'harmonic bond':'MM',
'harmonic bond angle':'MM',
'Lennard-Jones':'MM',
'site':'site',
'sLJr':'sLJr',
'sLJa':'sLJa',
'LJr':'LJr',
'LJa':'LJa',
'ELE':'ELE',
'electrostatic':'misc'}
########################
# Auxilliary functions #
########################
def random_rotate():
"""
Return a random rotation matrix
"""
u = np.random.uniform(size=3)
# Random quaternion
q = np.array([np.sqrt(1-u[0])*np.sin(2*np.pi*u[1]),
np.sqrt(1-u[0])*np.cos(2*np.pi*u[1]),
np.sqrt(u[0])*np.sin(2*np.pi*u[2]),
np.sqrt(u[0])*np.cos(2*np.pi*u[2])])
# Convert the quaternion into a rotation matrix
rotMat = np.array([[q[0]*q[0] + q[1]*q[1] - q[2]*q[2] - q[3]*q[3],
2*q[1]*q[2] - 2*q[0]*q[3],
2*q[1]*q[3] + 2*q[0]*q[2]],
[2*q[1]*q[2] + 2*q[0]*q[3],
q[0]*q[0] - q[1]*q[1] + q[2]*q[2] - q[3]*q[3],
2*q[2]*q[3] - 2*q[0]*q[1]],
[2*q[1]*q[3] - 2*q[0]*q[2],
2*q[2]*q[3] + 2*q[0]*q[1],
q[0]*q[0] - q[1]*q[1] - q[2]*q[2] + q[3]*q[3]]])
return rotMat
def choice(N, weights=None):
"""
Weighted random number selection of N values
"""
weights /= np.sum(weights)
cumweights = np.cumsum(weights)
beginIndicies = np.zeros(N, dtype=int)
randVals = np.random.uniform(size=N)
for (bI_idx,randVal) in zip(range(N),randVals):
beginIndicies[bI_idx] = sum(cumweights < randVal)
beginIndicies.sort()
return beginIndicies
def merge_dictionaries(dicts, required_consistency=[], print_inconsistent=False):
"""
Merges a list of dictionaries, giving priority to items in descending order.
Items in the required_consistency list must be consistent with one another.
"""
merged = {}
for a in range(len(dicts)): # Loop over all dictionaries, giving priority to the first
if not isinstance(dicts[a],dict):
continue
for key in dicts[a].keys():
if dicts[a][key] is None:
if not key in merged.keys():
merged[key] = None
elif isinstance(dicts[a][key],dict):
merged[key] = merge_dictionaries(
[dicts[n][key] for n in range(len(dicts)) if key in dicts[n].keys()],
required_consistency=required_consistency)
else:
# Check for consistency with other dictionaries
for b in (range(a) + range(a+1,len(dicts))):
if isinstance(dicts[b],dict) and (key in dicts[b].keys()) and (dicts[b][key] is not None):
if (isinstance(dicts[b][key],np.ndarray)):
inconsistent_items = (dicts[b][key]!=dicts[a][key]).any()
else:
inconsistent_items = (dicts[b][key]!=dicts[a][key])
if inconsistent_items:
if print_inconsistent:
print 'Dictionary items are inconsistent:'
print dicts[a][key]
print dicts[b][key]
if key in required_consistency:
raise Exception('Items must be consistent!')
merged[key] = dicts[a][key]
return merged
def convert_dictionary_relpath(d, relpath_o=None, relpath_n=None):
"""
Converts all file names in a dictionary from one relative path to another.
If relpath_o is None, nothing is joined to the original path.
If relpath_n is None, an absolute path is used.
"""
converted = {}
for key in d.keys():
if d[key] is None:
pass
elif isinstance(d[key],dict):
converted[key] = convert_dictionary_relpath(d[key],
relpath_o = relpath_o, relpath_n = relpath_n)
elif isinstance(d[key],str):
if relpath_o is not None:
p = abspath(join(relpath_o,d[key]))
else:
p = abspath(d[key])
if relpath_n is not None:
converted[key] = os.path.relpath(p,relpath_n)
else:
converted[key] = p
return converted
class NullDevice():
"""
A device to suppress output
"""
def write(self, s):
pass
##############
# Main Class #
##############
class Docker:
def __init__(self,
# Arguments are either constant or run-dependent.
# Constant arguments are stored in the dir_cool and dir_dock directories when they are first used. In subsequent instances of Dock, the stored values will be used. Run-dependent arguments can vary from instance to instance.
# Directory and file names
# Run-dependent
dir_dock=None,
dir_cool=None,
namd=None,
vmd=None,
# Stored in both dir_cool and dir_dock
ligand_database=None,
forcefield=None, frcmodList=None,
ligand_prmtop=None, ligand_inpcrd=None,
# Stored in dir_dock
receptor_prmtop=None, receptor_inpcrd=None,
receptor_fixed_atoms=None,
complex_prmtop=None, complex_inpcrd=None,
complex_fixed_atoms=None,
dir_grid='', grid_LJr=None, grid_LJa=None, grid_ELE=None,
grid_force=None, # Optional, only for NAMD grids
# Arguments - simulation parameters and constants
# Run-dependent
cool_repX_cycles=None,
dock_repX_cycles=None,
run_type=None,
# Defaults for dir_cool and dir_dock
protocol=None, therm_speed=None,
sampler=None,
seeds_per_state=None, steps_per_seed=None,
repX_cycles=None,
sweeps_per_cycle=None, steps_per_sweep=None,
keep_intermediate=None,
# Stored in dir_cool
cool_protocol=None, cool_therm_speed=None,
cool_sampler=None,
cool_seeds_per_state=None, cool_steps_per_seed=None,
cool_sweeps_per_cycle=None, cool_steps_per_sweep=None,
cool_keep_intermediate=None,
# Stored in dir_dock
dock_protocol=None, dock_therm_speed=None,
dock_sampler=None,
dock_seeds_per_state=None, dock_steps_per_seed=None,
dock_sweeps_per_cycle=None, dock_steps_per_sweep=None,
dock_keep_intermediate=None,
legs=None,
site=None, site_center=None, site_direction=None, # Site parameters
site_max_X=None, site_max_R=None, # Site parameters
site_density=None,
do_calc_random_dock_stats=None,
rmsd_ref=None, # RMSD calculation parameters
receptor_GBSA=None, receptor_Gas=None): # Energy values
"""Parses the input arguments and runs the requested docking calculation"""
print """
###########
# ALGDock #
###########
version {0}
Molecular docking with adaptively scaled alchemical interaction grids
""".format(a.__version__)
print '*** Directories ***'
self.dir = {}
if dir_dock is not None:
self.dir['dock'] = abspath(dir_dock)
else:
self.dir['dock'] = abspath('.')
if dir_cool is not None:
self.dir['cool'] = abspath(dir_cool)
else:
self.dir['cool'] = self.dir['dock']
for p in ['cool','dock']:
if not os.path.isdir(self.dir[p]):
os.makedirs(self.dir[p])
print self.dir
# Load previously stored file names and arguments
FNs = {}
args = {}
for p in ['cool','dock']:
store_FN = join(self.dir[p],p+'_store.pkl.gz')
if exists(store_FN):
(fn_dict, arg_dict) = self._load_pkl_gz(store_FN)
FNs[p] = convert_dictionary_relpath(fn_dict,
relpath_o=self.dir[p], relpath_n=None)
args[p] = arg_dict
else:
FNs[p] = {}
args[p] = {}
# Set up file name dictionary
print '\n*** Files ***'
for p in ['cool','dock']:
if p in FNs.keys():
if FNs[p]!={}:
print 'previous stored in %s directory:'%p
print FNs[p]
FNs['new'] = {
'ligand_database':a.findPath([ligand_database]),
'forcefield':a.findPath([forcefield,'../Data/gaff.dat']),
'frcmodList':frcmodList,
'prmtop':{'L':a.findPath([ligand_prmtop]),
'R':a.findPath([receptor_prmtop]),
'RL':a.findPath([complex_prmtop])},
'inpcrd':{'L':a.findPath([ligand_inpcrd]),
'R':a.findPath([receptor_inpcrd]),
'RL':a.findPath([complex_inpcrd])},
'fixed_atoms':{'R':a.findPath([receptor_fixed_atoms]),
'RL':a.findPath([complex_fixed_atoms])},
'grids':{'LJr':a.findPath([grid_LJr,
join(dir_grid,'LJr.dx'),
join(dir_grid,'LJr.dx.gz')]),
'LJa':a.findPath([grid_LJa,
join(dir_grid,'LJa.dx'),
join(dir_grid,'LJa.dx.gz')]),
'ELE':a.findPath([grid_ELE,
join(dir_grid,'electrostatic.dx'),
join(dir_grid,'electrostatic.dx.gz'),
join(dir_grid,'pbsa.dx'),
join(dir_grid,'pbsa.dx.gz')])},
'grid_force':a.findPath([grid_force]),
'namd':a.findPath([namd] + a.search_paths['namd']),
'vmd':a.findPath([vmd] + a.search_paths['vmd'])}
if not (FNs['cool']=={} and FNs['dock']=={}):
print 'from arguments and defaults:'
print FNs['new']
print '\nto be used:'
self._FNs = merge_dictionaries(
[FNs[src] for src in ['new','cool','dock']],
required_consistency=['L','R','RL','ligand_database'],
print_inconsistent=True)
# Default: a force field modification is in the same directory as the ligand
if (self._FNs['frcmodList'] is None):
dir_ligand_db = os.path.dirname(self._FNs['ligand_database'])
frcmod = a.findPath([abspath(join(dir_ligand_db,'lig.frcmod')),
abspath(join(dir_ligand_db,'ligand.frcmod'))])
if frcmod is not None:
self._FNs['frcmodList'] = [frcmod]
print self._FNs
print '\n*** Simulation parameters and constants ***'
args['default_cool'] = {
'protocol':'Adaptive',
'therm_speed':2.0,
'sampler':'NUTS',
'seeds_per_state':50,
'steps_per_seed':20000,
'repX_cycles':20,
'sweeps_per_cycle':100,
'steps_per_sweep':2500,
'keep_intermediate':False}
args['default_dock'] = dict(args['default_cool'].items() + {
'legs':2,
'site':None, 'site_center':None, 'site_direction':None,
'site_max_X':None, 'site_max_R':None,
'site_density':500.,
'do_calc_random_dock_stats':False,
'rmsd_ref':{'vmdcrd':None,
'species':'RL',
'selection':'resname iga'},
'receptor_GBSA':None, 'receptor_Gas':None}.items())
# Store passed arguments in dictionary
if rmsd_ref is not None:
rmsd_ref_dict = {}
if isinstance(rmsd_ref,str):
rmsd_ref = [rmsd_ref]
rmsd_ref[0] = os.path.relpath(rmsd_ref[0],self.dir['dock'])
for ind in range(len(rmsd_ref)):
rmsd_ref_dict[['vmdcrd','species','selection'][ind]] = rmsd_ref[ind]
rmsd_ref = rmsd_ref_dict
namespace = locals()
for p in ['cool','dock']:
args['new_'+p] = {}
for key in args['default_'+p].keys():
specific_key = p + '_' + key
if (specific_key in namespace.keys()) and \
(namespace[specific_key] is not None):
# Use the specific key if it exists
args['new_'+p][key] = namespace[specific_key]
elif (key in ['site_center', 'site_direction',
'receptor_Gas','receptor_GBSA']) and \
(namespace[key] is not None):
# Convert these to arrays of floats
args['new_'+p][key] = np.array(namespace[key], dtype=float)
else:
# Use the general key
args['new_'+p][key] = namespace[key]
self.params = {}
for p in ['cool','dock']:
self.params[p] = merge_dictionaries(
[args[src] for src in ['default_'+p,'new_'+p,p]])
for p in ['cool','dock']:
print 'for %s:'%p
print self.params[p]
# Variables dependent on the parameters
self.original_Es = [[{}]]
for phase in ['Gas','GBSA']:
if self.params['dock']['receptor_'+phase] is not None:
self.original_Es[0][0]['R'+phase] = self.params['dock']['receptor_'+phase]
if self.params['dock']['legs']==4:
self._scalables = ['sLJr','sLJa','LJr','LJa','ELE']
else:
self._scalables = ['LJr','LJa','ELE']
self.lambda_full = {'T':T_HIGH,'MM':True,'site':True}
self.lambda_scalables = {'T':T_HIGH}
for scalable in self._scalables:
self.lambda_full[scalable] = 1
self.lambda_scalables[scalable] = 1
# Check for existence of required files
for FN in [self._FNs['ligand_database'],
self._FNs['forcefield'],
self._FNs['prmtop']['L'],
self._FNs['inpcrd']['L']]:
if (FN is None) or (not exists(FN)):
raise Exception('Required file %s is missing!'%FN)
for FN in [self._FNs['prmtop']['RL'],
self._FNs['inpcrd']['RL'],
self._FNs['fixed_atoms']['RL'],
self._FNs['grids']['LJr'],
self._FNs['grids']['LJa'],
self._FNs['grids']['ELE']]:
if (FN is None) or (not exists(FN)):
print 'Missing file %s is necessary for docking!'%FN
if ('run_type' is args) and \
(args.run_type is not None) and \
(args.run_type in ['Dock0_0','ToDock0_0','Dock','ToDock']):
raise Exception('File %s is required!'%FN)
if (self._FNs['inpcrd']['RL'] is None) and \
(self._FNs['inpcrd']['R'] is None):
raise Exception('Receptor coordinates needed')
self._setup_universe()
if run_type=='initial_cool':
self.initial_cool()
elif run_type=='cool':
self.cool()
elif run_type=='initial_dock':
self.initial_dock()
elif run_type=='dock':
self.dock()
elif run_type=='all':
self.cool()
self.calc_f_L()
self.dock()
self.calc_f_RL()
self.calc_RMSD()
def _setup_universe(self):
"""Creates an MMTK InfiniteUniverse and adds the ligand"""
MMTK.Database.molecule_types.directory = \
os.path.dirname(self._FNs['ligand_database'])
# Force fields
from MMTK.ForceFields import Amber12SBForceField
self._forceFields = {}
self._forceFields['gaff'] = Amber12SBForceField(
parameter_file=self._FNs['forcefield'],mod_files=self._FNs['frcmodList'])
if self.params['dock']['site']=='Sphere':
from AlGDock.ForceFields.Sphere.Sphere import SphereForceField
self._forceFields['site'] = SphereForceField(
center=self.params['dock']['site_center'],
max_R=self.params['dock']['site_max_R'], name='site')
elif self.params['dock']['site']=='Cylinder':
from AlGDock.ForceFields.Cylinder.Cylinder import CylinderForceField
self._forceFields['site'] = CylinderForceField(
origin=self.params['dock']['site_center'],
direction=self.params['dock']['site_direction'],
max_X=self.params['dock']['site_max_X'],
max_R=self.params['dock']['site_max_R'], name='site')
else:
raise Exception('Binding site type not recognized!')
# Set up the system
import sys
original_stderr = sys.stderr
sys.stderr = NullDevice()
self.molecule = MMTK.Molecule(os.path.basename(self._FNs['ligand_database']))
sys.stderr = original_stderr
# Randomly rotate the molecule and translate it into the binding site
from Scientific.Geometry.Transformation import Rotation
self.molecule.applyTransformation(Rotation(random_rotate()))
self.molecule.translateTo(Vector(self._forceFields['site'].randomPoint()))
self.universe = MMTK.Universe.InfiniteUniverse()
self.universe.addObject(self.molecule)
self._set_universe_force_field({'MM':True, 'T':T_HIGH})
self._ligand_natoms = self.universe.numberOfAtoms()
from Integrators.HamiltonianMonteCarlo.HamiltonianMonteCarlo import HamiltonianMonteCarloIntegrator
from NUTS import NUTSIntegrator # @UnresolvedImport
self.sampler = {}
for p in ['cool', 'dock']:
if self.params[p]['sampler'] == 'NUTS':
self.sampler[p] = NUTSIntegrator(self.universe)
else:
raise Exception('Unrecognized sampler!')
prmtop_atom_order = np.array([atom.number \
for atom in self.molecule.prmtop_order], dtype=int)
self._inv_prmtop_atom_order = np.zeros(shape=len(prmtop_atom_order), dtype=int)
for i in range(len(prmtop_atom_order)):
self._inv_prmtop_atom_order[prmtop_atom_order[i]] = i
# Determine ligand atomic index
def load_prmtop(prmtopFN, varnames=['RESIDUE_LABEL','RESIDUE_POINTER']):
"""
Loads records from an AMBER prmtop file and stores data in a dictionary
"""
def load_record(record):
items = []
lines = record.split('\n')
lines.pop(0) # Name
FORMAT = lines.pop(0).strip()[8:-1] # Format
if FORMAT.find('a')>-1: # Text
w = int(FORMAT[FORMAT.find('a')+1:])
for line in lines:
items = items + [line[x:x+w] for x in range(0,len(line),w)]
return np.array(items)
elif FORMAT.find('I')>-1: # Integer
w = int(FORMAT[FORMAT.find('I')+1:])
for line in lines:
items = items + [int(line[x:x+w]) for x in range(0,len(line),w)]
return np.array(items, dtype=int)
elif FORMAT.find('E')>-1: # Scientific
w = int(FORMAT[FORMAT.find('E')+1:FORMAT.find('.')])
for line in lines:
items = items + [float(line[x:x+w]) for x in range(0,len(line),w)]
return np.array(items, dtype=float)
prmtopF = open(prmtopFN,'r')
prmtopData = prmtopF.read().split('%FLAG ')
prmtopF.close()
prmtop = {}
for record in prmtopData:
name = record[:record.find('\n')].strip()
if name in varnames:
prmtop[name] = load_record(record)
return prmtop
if (self._FNs['prmtop']['R'] is not None) and \
(self._FNs['prmtop']['RL'] is not None):
prmtop_R = load_prmtop(self._FNs['prmtop']['R'])
prmtop_RL = load_prmtop(self._FNs['prmtop']['RL'])
ligand_ind = [ind for (r,ind) in \
zip(prmtop_RL['RESIDUE_LABEL'], range(len(prmtop_RL['RESIDUE_LABEL']))) \
if r not in prmtop_R['RESIDUE_LABEL']]
if len(ligand_ind) > 1:
raise Exception('Ligand residue label is ambiguous')
self._ligand_first_atom = prmtop_RL['RESIDUE_POINTER'][ligand_ind[0]] - 1
else:
raise Exception('Missing AMBER prmtop files')
# Read the receptor coordinates
if self._FNs['inpcrd']['R'] is not None:
self._receptor_conf = self._read_crd(self._FNs['inpcrd']['R'], split=False)
else:
complex_crd = self._read_crd(self._FNs['inpcrd']['RL'], split=False)
self._receptor_conf = np.vstack(\
(complex_crd[:self._ligand_first_atom,:],\
complex_crd[self._ligand_first_atom + self._ligand_natoms:,:]))
# Load progress
self.confs = {}
self._load_progress('cool')
self._load_progress('dock')
self._postprocess(readOnly=True)
self.calc_f_L(readOnly=True)
self.calc_f_RL(readOnly=True)
###########
# Cooling #
###########
def initial_cool(self):
"""
Cools the ligand from T_HIGH to T_TARGET
Intermediate thermodynamic states are chosen such that
thermodynamic length intervals are approximately constant.
Configurations from each state are subsampled to seed the next simulation.
The following are saved:
(1) the cooling protocol (cool_protocol.pkl.gz)
(2) starting configurations for replica exchange (cool_replicas.crd.gz)
"""
if (len(self.cool_protocol)>0) and (self.cool_protocol[-1]['crossed']):
return # Initial cooling is already complete
self._set_lock('cool')
print "\n>>> Initial cooling of the ligand from %d K to %d K"%(T_HIGH,T_TARGET)
cool_start_time = time.time()
# Save the simulation parameters
self._write_pkl_gz(join(self.dir['cool'],'cool_store.pkl.gz'),
(convert_dictionary_relpath({
'ligand_database':self._FNs['ligand_database'],
'forcefield':self._FNs['forcefield'],
'frcmodList':self._FNs['frcmodList'],
'prmtop':{'L':self._FNs['prmtop']['L']},
'inpcrd':{'L':self._FNs['inpcrd']['L']}},
relpath_o=None, relpath_n=self.dir['cool']),
dict([tp for tp in self.params['cool'].items() if not tp[0] in ['repX_cycles']])))
# Set up the force field
self.cool_protocol = [{'MM':True, 'T':T_HIGH, \
'delta_t':1.0*MMTK.Units.fs, 'crossed':False}]
self._set_universe_force_field(self.cool_protocol[-1])
# Minimize and ramp the temperature from 0 to the desired high temperature
from MMTK.Minimization import ConjugateGradientMinimizer # @UnresolvedImport
minimizer = ConjugateGradientMinimizer(self.universe)
minimizer(steps = 500)
for T in np.linspace(0.,T_HIGH,33)[1:]:
self.sampler['cool'](steps = 500, T=T, delta_t=self.delta_t)
# Run at high temperature
state_start_time = time.time()
(confs, Es_MM, g) = self._initial_sim_state('cool',
[self.universe.configuration().array], normalize=True)
self.confs['cool_replicas'] = [confs[np.argmin(Es_MM)]]
self.cool_protocol[-1]['delta_t'] = self.delta_t
self.cool_protocol[-1]['g'] = g
print " generated %d configurations "%len(confs) + \
"at %d K "%self.cool_protocol[-1]['T'] + \
"in %f s "%(time.time()-state_start_time) + \
"(g=%f, "%self.cool_protocol[-1]['g'] + \
"Delta t=%f)"%self.cool_protocol[-1]['delta_t']
# Main loop for initial cooling: choose new temperature, randomly select seeds, simulate
T = T_HIGH
while (not self.cool_protocol[-1]['crossed']):
# Choose new temperature
To = self.cool_protocol[-1]['T']
dL = Es_MM.std()/(R*T*T)
if dL>0:
T = T - self.params['cool']['therm_speed']/dL
if T < T_TARGET:
lambda_n = {'T':T_TARGET, 'MM':True, 'crossed':True}
else:
lambda_n = {'T':T, 'MM':True, 'crossed':False}
else:
lambda_n = {'T':T_TARGET, 'MM':True, 'crossed':True}
self.cool_protocol.append(lambda_n)
self._set_universe_force_field(self.cool_protocol[-1])
# Randomly select seeds for new trajectory
seedIndicies = choice(self.params['cool']['seeds_per_state'], \
np.exp(-Es_MM/R*(1/T-1/To)))
# Simulate and store data
state_start_time = time.time()
(confs, Es_MM, g) = self._initial_sim_state('cool', \
[confs[ind] for ind in seedIndicies], normalize=True)
self.confs['cool_replicas'].append(confs[np.argmin(Es_MM)])
self.cool_protocol[-1]['delta_t'] = self.delta_t
self.cool_protocol[-1]['g'] = g
print " generated %d configurations "%len(confs) + \
"at %d K "%self.cool_protocol[-1]['T'] + \
"in %f s "%(time.time()-state_start_time) + \
"(g=%f, "%self.cool_protocol[-1]['g'] + \
"Delta t=%f)"%self.cool_protocol[-1]['delta_t']
self.cool_Es = [[] for s in range(len(self.cool_protocol))]
# Save data
self._write_crd(join(self.dir['cool'],'cool_replicas.crd.gz'),
self.confs['cool_replicas'], title='cool_replicas')
self._write_pkl_gz(join(self.dir['cool'],'cool_protocol.pkl.gz'), self.cool_protocol)
self._clear_lock('cool')
print "\nElapsed time for initial cooling: %f s"%(time.time()-cool_start_time)
def cool(self):
"""
Samples different ligand configurations
at thermodynamic states between T_HIGH and T_TARGET
"""
self._sim_process('cool')
def calc_f_L(self, readOnly=False):
"""
Calculates ligand-specific free energies:
1. solvation free energy of the ligand using single-step free energy perturbation
2. reduced free energy of cooling the ligand from T_HIGH to T_TARGET
"""
if self.cool_protocol==[]:
return # Initial cooling is incomplete
# Initialize variables by loading data or as empty lists
f_L_FN = join(self.dir['cool'],'f_L.pkl.gz')
if exists(f_L_FN):
(self.f_L_solvation, self.f_cool, self.cool_equilibrated_cycle, self.cool_mean_acc) = self._load_pkl_gz(f_L_FN)
else:
for var in ['f_L_solvation', 'f_cool', 'cool_equilibrated_cycle', 'cool_mean_acc']:
if not hasattr(self,var):
setattr(self,var,[])
if readOnly:
return
self._postprocess([('cool',-1,-1,'L')])
# Estimate cycle at which simulation has equilibrated
K = len(self.cool_protocol)
u_Ks = [self._u_kln([self.cool_Es[K-1][c]],[self.cool_protocol[K-1]]) for c in range(self._cool_cycle)]
mean_u_Ks = np.array([np.mean(u_K) for u_K in u_Ks])
std_u_Ks = np.array([np.std(u_K) for u_K in u_Ks])
for c in range(len(self.cool_equilibrated_cycle), self._cool_cycle):
nearMean = (mean_u_Ks - mean_u_Ks[c])<std_u_Ks[c]
for (item, ind) in zip(nearMean, range(len(nearMean))):
if item:
self.cool_equilibrated_cycle.append(ind)
break
# Calculate cooling free energies that have not already been calculated
updated = False
for c in range(len(self.f_L_solvation), self._cool_cycle):
fromCycle = self.cool_equilibrated_cycle[c]
toCycle = c + 1
du = \
(np.concatenate([self.cool_Es[K-1][c]['LGBSA'][:,-1] for c in range(fromCycle,toCycle)]) - \
np.concatenate([self.cool_Es[K-1][c]['LGas'][:,-1] for c in range(fromCycle,toCycle)]))/RT_TARGET
min_du = min(du)
self.f_L_solvation.append(-np.log(np.exp(-du+min_du).mean()) + min_du)
# Cooling free energy
cool_Es = []
for cool_Es_state in self.cool_Es:
cool_Es.append(cool_Es_state[fromCycle:toCycle])
(u_kln,N_k) = self._u_kln(cool_Es,self.cool_protocol)
self.f_cool.append(self._run_MBAR(u_kln,N_k))
# Average acceptance probabilities
cool_mean_acc = np.zeros(K-1)
for k in range(1, K-1):
(u_kln,N_k) = self._u_kln(cool_Es[k:k+2],self.cool_protocol[k:k+2])
N = min(N_k)
acc = np.exp(-u_kln[0,1,:N]-u_kln[1,0,:N]+u_kln[0,0,:N]+u_kln[1,1,:N])
cool_mean_acc[k] = np.mean(np.minimum(acc,np.ones(acc.shape)))
self.cool_mean_acc.append(cool_mean_acc)
print " calculated cooling free energy of %f kT using cycles %d to %d"%(self.f_cool[-1][-1], fromCycle, toCycle-1)
updated = True
if updated:
self._write_pkl_gz(f_L_FN, (self.f_L_solvation, self.f_cool, self.cool_equilibrated_cycle, self.cool_mean_acc))
###########
# Docking #
###########
def initial_dock(self):
"""
Docks the ligand into the receptor
The first state of docking is sampled by randomly placing configurations
from the high temperature ligand simulation into the binding site.
Intermediate thermodynamic states are chosen such that
thermodynamic length intervals are approximately constant.
Configurations from each state are subsampled to seed the next simulation.
The following are saved:
(1) the docking protocol (dock_protocol.pkl.gz)
(2) starting configurations for replica exchange (dock_replicas.crd.gz)
"""
if self.cool_protocol==[]:
raise Exception('Cannot perform initial docking until cooling is complete')
if (len(self.dock_protocol)>0) and (self.dock_protocol[-1]['crossed']):
return # Initial docking already complete
# Load the first cooling state and make sure there are enough samples
print "\n>>> Loading the first state of cooling"
(cool0_confs, cool0_Es_MM) = self._load_cool0()
while len(cool0_Es_MM)<self.params['dock']['seeds_per_state']:
print "More samples from high temperature ligand simulation needed"
self._replica_exchange('cool')
(cool0_confs, cool0_Es_MM) = self._load_cool0()
print " using %d configurations from the first state of cooling"%len(cool0_Es_MM)
self._set_lock('dock')
print "\n>>> Initial docking"
dock_start_time = time.time()
# Store docking parameters
self._write_pkl_gz(join(self.dir['dock'],'dock_store.pkl.gz'),
(convert_dictionary_relpath(
dict([tp for tp in self._FNs.items() if not tp[0] in ['namd','vmd']]),
relpath_o=None, relpath_n=self.dir['dock']),
dict([tp for tp in self.params['dock'].items() \
if not tp[0] in ['repX_cycles']])))
# Set up the force field with full interaction grids
self._set_universe_force_field(self.lambda_scalables)
lambda_o = {'T':T_HIGH, 'MM':True, 'site':True, 'sLJr_crossed':False, 'crossed':False}
for scalable in self._scalables:
lambda_o[scalable] = 0
self.dock_protocol = [lambda_o]
state_start_time = time.time()
self._random_translations_and_rotations()
# Get interaction energies.
# Loop over configurations, random rotations, and random translations
E = {}
for term in (['MM','site']+self._scalables):
E[term] = np.zeros((self.params['dock']['seeds_per_state'], \
self._max_n_rot,self._max_n_trans))
converged = False
n_trans_o = 0
n_trans_n = self._n_trans
while not converged:
for c in range(self.params['dock']['seeds_per_state']):
E['MM'][c,:,:] = cool0_Es_MM[c]
for i_rot in range(self._n_rot):
conf_rot = Configuration(self.universe,\
np.dot(cool0_confs[c], self._random_rotT[i_rot,:,:]))
for i_trans in range(n_trans_o, n_trans_n):
self.universe.setConfiguration(conf_rot)
self.universe.translateBy(self._random_trans[i_trans])
eT = self.universe.energyTerms()
for (key,value) in eT.iteritems():
E[term_map[key]][c,i_rot,i_trans] += value
E_c = {}
for term in E.keys():
E_c[term] = np.ravel(E[term][:,:self._n_rot,:n_trans_n])
(u_kln,N_k) = self._u_kln([E_c],\
[lambda_o,self._next_dock_state(E=E_c, lambda_o=lambda_o)])
du = u_kln[0,1,:] - u_kln[0,0,:]
bootstrap_reps = 50
f_grid0 = np.zeros(bootstrap_reps)
for b in range(bootstrap_reps):
f_grid0[b] = -np.log(np.mean(np.exp(
-du[np.random.randint(0, len(du), len(du))])))
f_grid0_std = f_grid0.std()
converged = f_grid0_std<0.01
if not converged:
if n_trans_n == self._max_n_trans:
break
n_trans_o = n_trans_n
n_trans_n = min(n_trans_n + 25, self._max_n_trans)
if self._n_trans != n_trans_n:
# Save number translations used
self._n_trans = n_trans_n
self._write_pkl_gz(join(self.dir['dock'],'random_trans.pkl.gz'),
(self._n_trans, self._max_n_trans, self._random_trans))
print " %d ligand configurations were randomly docked "%len(cool0_Es_MM) + \
"into the binding site "+ \
"using %d translations and %d rotations "%(n_trans_n,self._n_rot) + \
"in %f s"%(time.time()-state_start_time)
print " the predicted free energy difference between the" + \
" first and second docking stages is %f (%f)"%(f_grid0.mean(),f_grid0_std)
for term in E.keys():
E[term] = np.ravel(E[term][:,:self._n_rot,:self._n_trans])
if self.params['dock']['do_calc_random_dock_stats']:
self.calc_random_dock_stats(E)
# Main loop for initial docking: choose new thermodynamic variables, randomly select seeds, simulate
while not self.dock_protocol[-1]['crossed']:
# Determine next value of the protocol
lambda_n = self._next_dock_state(E = E, lambda_o = lambda_o)
self.dock_protocol.append(lambda_n)
# Use free energy perturbation to estimate the free energy difference between
# the first and second docking stage
if len(self.dock_protocol)==2:
(u_kln,N_k) = self._u_kln([E], [lambda_o, lambda_n])
self.dock_protocol[0]['f_grid0'] = \
-np.log(np.mean(np.exp(-(u_kln[0,1,:] - u_kln[0,0,:]))))
# Randomly select seeds for new trajectory
u_o = self._u_kln([E],[lambda_o])
u_n = self._u_kln([E],[lambda_n])
seedIndicies = choice(self.params['dock']['seeds_per_state'], np.exp(u_o-u_n))
if len(self.dock_protocol)==2: # Cooling state 0 configurations, randomly oriented
# Use the lowest energy configuration in the first docking stage for replica exchange
ind = np.argmin(u_n)
(c,i_rot,i_trans) = np.unravel_index(ind, (self.params['dock']['seeds_per_state'], self._n_rot, self._n_trans))
repX_conf = np.add(np.dot(cool0_confs[c], self._random_rotT[i_rot,:,:]),\
self._random_trans[i_trans].array)
self.confs['dock_replicas'] = [repX_conf]
seeds = []
for ind in seedIndicies:
(c,i_rot,i_trans) = np.unravel_index(ind, (self.params['dock']['seeds_per_state'], self._n_rot, self._n_trans))
seeds.append(np.add(np.dot(cool0_confs[c], self._random_rotT[i_rot,:,:]), self._random_trans[i_trans].array))
else: # Seeds from last state
seeds = [confs[ind] for ind in seedIndicies]
# Simulate
state_start_time = time.time()
self._set_universe_force_field(lambda_n)
(confs, Es_tot, g) = self._initial_sim_state('dock', seeds, normalize=False)
# Store data
self.confs['dock_replicas'].append(confs[np.argmin(Es_tot)])
self.dock_protocol[-1]['delta_t'] = self.delta_t
print ' generated %d configurations in %f s using with the following parameters:'%(\
len(confs), time.time()-state_start_time)
print self.dock_protocol[-1]
# Get state energies
self._set_universe_force_field(self.lambda_full)
E = {}
for term in (['MM','site','misc'] + self._scalables):
E[term] = np.zeros(len(confs))
for c in range(len(confs)):
self.universe.setConfiguration(Configuration(self.universe,confs[c]))
eT = self.universe.energyTerms()
for (key,value) in eT.iteritems():
E[term_map[key]][c] += value
lambda_o = lambda_n
self.dock_Es = [[] for s in range(len(self.dock_protocol))]
print " there are %d stages in the docking process"%(len(self.dock_protocol))
# Save data
self._write_crd(join(self.dir['dock'], 'dock_replicas.crd.gz'), self.confs['dock_replicas'], title='dock_replicas')
self._write_pkl_gz(join(self.dir['dock'],'dock_protocol.pkl.gz'),self.dock_protocol)
self._clear_lock('dock')
print "\nElapsed time for initial docking: %f s"%(time.time()-dock_start_time)
def dock(self):
"""
Docks the ligand into the binding site
by simulating at thermodynamic states
between decoupled and fully interacting and
between T_HIGH and T_TARGET
"""
self._sim_process('dock')
def calc_f_RL(self, readOnly=False):
"""
Calculates the binding potential of mean force
"""
if self.dock_protocol==[]:
return # Initial docking is incomplete
# Initialize variables by loading data or as empty lists
f_RL_FN = join(self.dir['dock'],'f_RL.pkl.gz')
if exists(f_RL_FN):
dat = self._load_pkl_gz(f_RL_FN)
if dat is not None:
(self.f_grid, self.B, self.dock_equilibrated_cycle, self.dock_mean_acc) = dat
else:
for var in ['f_grid', 'B', 'dock_equilibrated_cycle', 'dock_mean_acc']:
setattr(self,var,[])
else:
for var in ['f_grid', 'B', 'dock_equilibrated_cycle', 'dock_mean_acc']:
setattr(self,var,[])
if readOnly:
return
self._postprocess()
self.calc_Psi()
# Estimate cycle at which simulation has equilibrated
K = len(self.dock_protocol)
u_Ks = [self._u_kln([self.dock_Es[K-1][c]],[self.dock_protocol[K-1]]) for c in range(self._dock_cycle)]
mean_u_Ks = np.array([np.mean(u_K) for u_K in u_Ks])
std_u_Ks = np.array([np.std(u_K) for u_K in u_Ks])
for c in range(len(self.dock_equilibrated_cycle), self._dock_cycle):
nearMean = (mean_u_Ks - mean_u_Ks[c])<std_u_Ks[c]
for (item, ind) in zip(nearMean, range(len(nearMean))):
if item:
self.dock_equilibrated_cycle.append(ind)
break
# Calculate docking free energies that have not already been calculated
updated = False
for c in range(len(self.f_grid), self._dock_cycle):
fromCycle = self.dock_equilibrated_cycle[c]
toCycle = c + 1
f_grid = [0, self.dock_protocol[0]['f_grid0']]
# Extract energies between fromCycle and toCycle
dock_Es = []
for dock_Es_state in self.dock_Es:
dock_Es.append(dock_Es_state[fromCycle:toCycle])
# Use MBAR for the remaining states.
(u_kln,N_k) = self._u_kln(\
dock_Es[1:K],
self.dock_protocol[1:K])
f_grid += list(self._run_MBAR(u_kln,N_k)[1:] + f_grid[-1])
self.f_grid.append(f_grid)
### Binding PMF
B = {}
u_K = self._u_kln([dock_Es[K-1]],[self.dock_protocol[K-1]])
for phase in ['Gas','GBSA']:
du = \
np.concatenate([self.dock_Es[K-1][c]['RL'+phase][:,-1] \
for c in range(fromCycle,toCycle)])/RT_TARGET - \
(u_K + self.params['dock']['receptor_'+phase]/RT_TARGET)
min_du = min(du)
B_RL = -np.log(np.exp(-du+min_du).mean()) + min_du
weight = np.exp(-du+min_du)
weight = weight/sum(weight)
Psi = np.concatenate([self.Psi[c][phase] for c in range(fromCycle,toCycle)])
min_Psi = min(Psi)
B[phase+'_MBAR_based'] = -self.f_cool[-1][-1] + f_grid[-1] + B_RL
B[phase+'_min_Psi'] = min_Psi
B[phase+'_inverse_FEP'] = \
np.log(sum(weight*np.exp(Psi-min_Psi))) + min_Psi
self.B.append(B)
# Average acceptance probabilities
dock_mean_acc = np.zeros(K-1)
for k in range(1, K-1):
(u_kln,N_k) = self._u_kln(dock_Es[k:k+2],self.dock_protocol[k:k+2])
N = min(N_k)
acc = np.exp(-u_kln[0,1,:N]-u_kln[1,0,:N]+u_kln[0,0,:N]+u_kln[1,1,:N])
dock_mean_acc[k] = np.mean(np.minimum(acc,np.ones(acc.shape)))
self.dock_mean_acc.append(dock_mean_acc)
print " calculated binding PMF of %f kT using cycles %d to %d"%(B['GBSA_MBAR_based'], fromCycle, toCycle-1)
updated = True
if updated:
self._write_pkl_gz(f_RL_FN, (self.f_grid, self.B, self.dock_equilibrated_cycle, self.dock_mean_acc))
##############################
# Analysis and Visualization #
##############################
def calc_random_dock_stats(self, E):
"""
Examines the convergence of the interaction energy
in the first docking state, looking at how the minimum,
mean, standard deviation, and other statistics vary as
a function of the number of rotations and translations.
"""
# Initiate variables
interval = 5
keys = ['min','mean','std','lambda_n','f_grid0','f_grid']
self._random_translations_and_rotations()
r_range = range(interval, self._n_rot, interval)
t_range = range(interval, self._n_trans, interval)
random_dock_stats = {}
for key in keys:
random_dock_stats[key] = np.zeros((len(r_range),len(t_range)))
Psi = np.reshape(E['ELE'],(-1,self._n_rot,self._n_trans)) + \
np.reshape(E['LJr'],(-1,self._n_rot,self._n_trans)) + \
np.reshape(E['LJa'],(-1,self._n_rot,self._n_trans))
w = np.exp((-1/RT_TARGET+1/RT_HIGH)*np.reshape(E['MM'] - min(E['MM']),(-1,self._n_rot,self._n_trans)))
for r in range(len(r_range)):
for t in range(len(t_range)):
Psi_c = np.ravel(Psi[:,:r_range[r],:t_range[t]])
Psi_c_std = Psi_c.std()
lambda_c = (self.params['dock']['therm_speed']*RT_HIGH)/Psi_c_std
random_dock_stats['min'][r][t] = Psi_c.min()
random_dock_stats['mean'][r][t] = Psi_c.mean()
random_dock_stats['std'][r][t] = Psi_c_std
random_dock_stats['lambda_n'][r][t] = lambda_c
du = lambda_c*Psi_c/RT_HIGH
min_du = min(du)
random_dock_stats['f_grid0'][r][t] = -RT_HIGH*(np.log(np.mean(np.exp(-du+min_du))) + min_du)
w_c = np.ravel(w[:,:r_range[r],:t_range[t]])
du = Psi_c/RT_TARGET
min_du = min(du)
random_dock_stats['f_grid'][r][t] = -RT_TARGET*(np.log(np.sum(w_c*np.exp(-du+min_du))/np.sum(w_c)) + min_du)
self._write_pkl_gz(join(self.dir['dock'],'random_dock_stats.pkl.gz'), random_dock_stats)
def calc_RMSD(self, rmsd_ref=None):
"""
Uses VMD to calculate the root mean square deviation between
the docked conformations and the reference structure in ref_FN.
"""
if ((self.dock_protocol==[]) or (not self.dock_protocol[-1]['crossed'])):
raise Exception('Initial docking not done!')
if (self._FNs['vmd'] is None) or (not exists(self._FNs['vmd'])):
print 'VMD not found! Cannot calculate RMSD.'
return
if rmsd_ref is None:
if self.params['dock']['rmsd_ref'] is None:
print 'Reference structure is needed for an RMSD calculation'
return
else:
rmsd_ref = self.params['dock']['rmsd_ref']
print "\n>>> RMSD calculation"
rmsd_ref['vmdcrd'] = a.findPath([rmsd_ref['vmdcrd'],
abspath(join(self.dir['dock'],rmsd_ref['vmdcrd']))])
if rmsd_ref['vmdcrd'] is None:
raise Exception('Reference structure is missing')
rmsd_FN = join(self.dir['dock'],'rmsd.pkl.gz')
if exists(rmsd_FN):
self.rmsd = self._load_pkl_gz(rmsd_FN)
else:
self.rmsd = []
ref_FN = rmsd_ref['vmdcrd']
state = len(self.dock_protocol)-1
# Write the DCD files
newDCD = False
for c in range(self._dock_cycle):
while len(self.rmsd)<=c:
self.rmsd.append({})
if not ref_FN in self.rmsd[c].keys():
crd = self._read_crd(join(self.dir['dock'],'dock%d_%d.crd.gz'%(state,c)), split=True)
dcdFN = join(self.dir['dock'],'dock%d_%d.dcd'%(state,c))
if not exists(dcdFN):
self._write_dcd(dcdFN, crd)
newDCD = True
if newDCD:
# Write the VMD script
import tempfile
scriptF = tempfile.NamedTemporaryFile(mode='w', dir=self.dir['dock'], delete=False)
scriptF.write('''
set id_ref [mol new '''+self._FNs['prmtop'][rmsd_ref['species']]+''']
set sel_ref [atomselect $id_ref "'''+rmsd_ref['selection']+''' and not mass 1.008"]
mol addfile '''+abspath(ref_FN)+''' type crd molid $id_ref
set id_lig [mol new '''+self._FNs['prmtop']['L']+''']
set sel_lig [atomselect $id_lig "'''+rmsd_ref['selection']+''' and not mass 1.008"]
''')
for c in range(self._dock_cycle):
if not ref_FN in self.rmsd[c].keys():
dcdFN = join(self.dir['dock'],'dock%d_%d.dcd'%(state,c))
scriptF.write('''
mol addfile '''+dcdFN+''' type dcd molid $id_lig waitfor all
set fo [open "'''+join(self.dir['dock'],'rmsd%d_%d.txt'%(state,c))+'''" "w"]
set nfr [molinfo $id_lig get numframes]
for {set n 0} {$n < $nfr} {incr n} {
$sel_lig frame $n
set rmsd [measure rmsd $sel_ref $sel_lig]
puts $fo "$rmsd"
}
close $fo
animate delete all
file delete '''+dcdFN+'\n')
scriptF.write('\nquit\n')
scriptF.close()
# Execute VMD
import subprocess
vmd_out = open(join(self.dir['dock'], 'vmd.out'), 'w')
subprocess.call([self._FNs['vmd'], '-nt', '-dispdev', 'text', '-e', scriptF.name],\
stdout=vmd_out)
vmd_out.close()
os.remove(scriptF.name)
# Read the rmsd data
for c in range(self._dock_cycle):
if not ref_FN in self.rmsd[c].keys():
temp_F = open(join(self.dir['dock'],'rmsd%d_%d.txt'%(state,c)),'r')
self.rmsd[c][ref_FN] = np.array(
[float(item) for item in temp_F.read().strip().split('\n')])
temp_F.close()
os.remove(join(self.dir['dock'],'rmsd%d_%d.txt'%(state,c)))
self._write_pkl_gz(rmsd_FN, self.rmsd)
def calc_Psi(self):
"""
Calculate the interaction energy between the ligand and receptor. Also stores the ligand internal energy.
"""
if ((self.dock_protocol==[]) or (not self.dock_protocol[-1]['crossed'])):
raise Exception('Initial docking not done!')
Psi_FN = join(self.dir['dock'],'Psi.pkl.gz')
if exists(Psi_FN):
self.Psi = self._load_pkl_gz(Psi_FN)
else:
self.Psi = []
newPsi = False
for c in range(self._dock_cycle):
while len(self.Psi) <= c:
self.Psi.append({})
if not 'MM' in self.Psi[c].keys():
self.Psi[c]['MM'] = self.dock_Es[-1][c]['MM']/RT_TARGET
newPsi = True
if not 'grid' in self.Psi[c].keys():
self.Psi[c]['grid'] = \
(self.dock_Es[-1][c]['LJr'] + \
self.dock_Es[-1][c]['LJa'] + \
self.dock_Es[-1][c]['ELE'])/RT_TARGET
newPsi = True
for phase in ['Gas','GBSA']:
if not phase in self.Psi[c].keys():
self.Psi[c][phase] = \
(self.dock_Es[-1][c]['RL'+phase][:,-1] - \
self.dock_Es[-1][c]['L'+phase][:,-1] - \
self.params['dock']['receptor_'+phase])/RT_TARGET
newPsi = True
if newPsi:
self._write_pkl_gz(Psi_FN, self.Psi)
def plot_energies(self, process='cool', firstCycle=0, toCycle=None):
"""
Plots timeseries and histograms of the energies for each state.
Requires matplotlib extension for python.
"""
try:
import matplotlib.pyplot as plt # @UnresolvedImport
except:
print 'plot_energies requires matplotlib'
return
K = len(getattr(self,process+'_protocol'))
if toCycle is None:
toCycle = getattr(self,'_%s_cycle'%process)
Es = [getattr(self,process+'_Es')[0][firstCycle:toCycle]]
for Es_state in getattr(self,process+'_Es'):
Es.append(Es_state[firstCycle:toCycle])
(u_kln,N_k) = self._u_kln(Es, getattr(self,process+'_protocol'))
plt.figure(0)
for k in range(K-1):
plt.plot(u_kln[k,k,:N_k[k]],'.-')
plt.figure(1)
for k in range(K-1):
plt.hist(u_kln[k,k,:N_k[k]])
def view_samples(self, process='dock', state=None,
includeReceptor=False, displayComplex=False,
overlap=False, showGrid=True, conf_indices=None,
saveImage=False, execute=True, colorID=None, clearDCD=True):
"""
Views the trajectory with VMD
state - the simulation state to view
includeReceptor - show the receptor as well as the ligand
overlap - show ligands overlapping each other, rather than a trajectory
showGrid - shows the Lennard-Jones repulsive grid
stride - steps between snapshots
"""
if (self._FNs['vmd'] is None) or (not exists(self._FNs['vmd'])):
print 'VMD not found! Cannot view trajectory'
return
if includeReceptor:
if (self._FNs['prmtop']['RL'] is None):
raise Exception('AMBER prmtop file for complex required!')
prmtopFN = self ._FNs['prmtop']['RL']
else:
if (self._FNs['prmtop']['L'] is None):
raise Exception('AMBER prmtop file for the ligand required!')
prmtopFN = self._FNs['prmtop']['L']
# Default is last state
if state is None:
state = len(getattr(self,process+'_protocol'))-1
if process=='dock':
firstCycle = self.dock_equilibrated_cycle[-1]
elif process=='cool':
firstCycle = self.cool_equilibrated_cycle[-1]
else:
raise Exception('Invalid Process')
# Convert from crd to dcd files
for c in range(firstCycle,getattr(self,'_%s_cycle'%process)):
if not exists(join(self.dir[process],process+'%d_%d.dcd'%(state,c))):
crd = self._read_crd(join(self.dir[process],process+'%d_%d.crd.gz'%(state,c)), split=True)
self._write_dcd(join(self.dir[process],process+'%d_%d.dcd'%(state,c)), crd)
script = '# View samples\n'
script += 'update off\n'
script += 'set ID [mol new {' + prmtopFN + '}]\n'
if colorID is not None:
script += 'mol modcolor 0 $ID ColorID %d\n'%colorID
for c in range(firstCycle,getattr(self,'_%s_cycle'%process)):
script += 'mol addfile {' + join(self.dir[process],process+'%d_%d.dcd'%(state,c)) + '} waitfor all molid $ID\n'
if overlap:
script += 'set nfr [molinfo $ID get numframes]\n'
script += 'mol drawframes $ID 0 0:[expr $nfr-1]\n'
else:
script += 'mol modstyle 0 0 Licorice 0.300000 10.000000 10.000000\n'
script += 'display projection Orthographic\n'
if showGrid:
if self._FNs['grids']['LJr'][-3:]=='.gz':
import shutil
shutil.copyfile(self._FNs['grids']['LJr'], 'temp_grid.LJr.dx.gz')
os.system('gunzip temp_grid.LJr.dx.gz')
grid_LJr = 'temp_grid.LJr.dx'
else:
grid_LJr = self._FNs['grids']['LJr']
script += 'mol addfile {'+grid_LJr+'} type dx first 0 last -1 step 1 waitfor 1 volsets {0 } 0\n'
# Lennard-Jones Repulsive
script += 'mol representation Isosurface 75.0 0 2 1 1 1\n'
script += 'mol addrep 0\n'
if displayComplex:
script += 'set complexID [mol new {%s}]\n'%self._FNs['prmtop']['RL']
script += 'mol addfile {%s} type crd\n'%(self.params['dock']['rmsd_ref']['vmdcrd'])
script += '''
mol modstyle 0 $complexID NewCartoon 0.300000 10.000000 4.100000 0
mol color ColorID 0
mol representation Licorice 0.300000 10.000000 10.000000
mol selection resname iga
mol material Opaque
mol addrep $complexID
mol color Name
mol representation Lines 2.0
mol selection same residue as within 10 of resname iga
mol material Opaque
mol addrep $complexID
'''
script += 'display resize 600 600\n'
script += 'axes location off\n'
script += 'rotate y by -90.000000\n'
script += 'scale by 5\n'
script += 'translate by 0 -0.5 0\n'
script += 'display nearclip set 0.200000\n'
script += 'draw color green\n'
script += 'draw text {20 6 32} "Grid scaling: %.2e" size 2 thickness 2\n'%self.dock_protocol[state]['LJr']
script += 'draw text {20 4 32} "T: %.2f K" size 2 thickness 2\n'%self.dock_protocol[state]['T']
script += 'update\n'
script += 'update on\n'
if clearDCD:
for c in range(getattr(self,'_%s_cycle'%process)):
script += 'file delete {' + join(self.dir[process],process+'%d_%d.dcd'%(state,c)) + '}\n'
if showGrid and self._FNs['grids']['LJr'][-3:]=='.gz':
script += 'file delete {' + grid_LJr + '}\n'
if saveImage:
script += 'render TachyonInternal ' + os.path.join(self.dir['dock'],'state%d.tga'%state) + ' /usr/bin/open %s\n'
script += 'quit\n'
if execute:
import tempfile
scriptFN = tempfile.mktemp()
scriptF = open(scriptFN,'w')
scriptF.write(script)
scriptF.close()
import subprocess
subprocess.Popen([self._FNs['vmd'], '-nt', '-e', scriptFN])
return script
######################
# Internal Functions #
######################
def _set_universe_force_field(self,lambda_n):
"""
Sets the force field parameters of the universe to values appropriate for the given lambda_n dictionary.
The elements in the dictionary lambda_n can be:
MM - True, to turn on Generalized AMBER force field
site - True, to turn on the binding site
sLJr - scaling of the soft Lennard-Jones repulsive grid
sLJa - scaling of the soft Lennard-Jones attractive grid
LJr - scaling of the Lennard-Jones repulsive grid
LJa - scaling of the Lennard-Jones attractive grid
ELE - scaling of the electrostatic grid
T - the temperature in K
"""
self.T = lambda_n['T']
self.RT = R*lambda_n['T']
if 'delta_t' in lambda_n.keys():
self.delta_t = lambda_n['delta_t']
else:
self.delta_t = 1.0*MMTK.Units.fs
fflist = []
if ('MM' in lambda_n.keys()) and lambda_n['MM']:
fflist.append(self._forceFields['gaff'])
if ('site' in lambda_n.keys()) and lambda_n['site']:
fflist.append(self._forceFields['site'])
for scalable in self._scalables:
if (scalable in lambda_n.keys()) and lambda_n[scalable]>0:
# Load the force field if it has not been loaded
if not scalable in self._forceFields.keys():
import time
loading_start_time = time.time()
grid_FN = self._FNs['grids'][{'sLJr':'LJr','sLJa':'LJa','LJr':'LJr','LJa':'LJa','ELE':'ELE'}[scalable]]
grid_scaling_factor = 'scaling_factor_' + \
{'sLJr':'LJr','sLJa':'LJa','LJr':'LJr','LJa':'LJa','ELE':'electrostatic'}[scalable]
if scalable=='LJr':
from ForceFields.TrilinearISqrtGrid.TrilinearISqrtGrid import TrilinearISqrtGridForceField
self._forceFields[scalable] = TrilinearISqrtGridForceField(grid_FN, lambda_n[scalable], grid_scaling_factor, grid_name=scalable, max_val=-1)
else:
from ForceFields.TrilinearGrid.TrilinearGrid import TrilinearGridForceField
self._forceFields[scalable] = TrilinearGridForceField(
grid_FN, lambda_n[scalable], grid_scaling_factor, grid_name=scalable,
max_val=10. if scalable[0]=='s' else 10000.)
print ' %s loaded in %f s'%(grid_FN, time.time()-loading_start_time)
# Set the force field strength to the desired value
self._forceFields[scalable].strength = lambda_n[scalable]
fflist.append(self._forceFields[scalable])
compoundFF = fflist[0]
for ff in fflist[1:]:
compoundFF += ff
self.universe.setForceField(compoundFF)
def _random_translations_and_rotations(self):
"""
Either loads or generates the random translations and rotations for the first state of docking.
"""
if not hasattr(self,'_random_trans'):
random_translation_FN = join(self.dir['dock'],'random_trans.pkl.gz')
if exists(random_translation_FN):
(self._n_trans, self._max_n_trans, self._random_trans) = \
self._load_pkl_gz(random_translation_FN)
else:
self._max_n_trans = 10000
# Default density of points is 500 per nm**3
self._n_trans = min(np.int(np.ceil(self._forceFields['site'].volume*self.params['dock']['site_density'])),self._max_n_trans)
self._random_trans = np.ndarray((self._max_n_trans), dtype=Vector)
for ind in range(self._max_n_trans):
self._random_trans[ind] = Vector(self._forceFields['site'].randomPoint())
self._write_pkl_gz(random_translation_FN,
(self._n_trans, self._max_n_trans, self._random_trans))
else:
self._max_n_trans = self._random_trans.shape[0]
if not hasattr(self,'_random_rotT'):
random_rotation_FN = join(self.dir['dock'],'random_rotT.pkl.gz')
if exists(random_rotation_FN):
(self._n_rot, self._max_n_rot, self._random_rotT) = \
self._load_pkl_gz(random_rotation_FN)
else:
self._max_n_rot = 100
self._n_rot = 100
self._random_rotT = np.ndarray((self._max_n_rot,3,3))
for ind in range(self._max_n_rot):
self._random_rotT[ind,:,:] = np.transpose(random_rotate())
self._write_pkl_gz(random_rotation_FN,
(self._n_rot, self._max_n_rot, self._random_rotT))
else:
self._n_rot = self._random_rotT.shape[0]
def _initial_sim_state(self, process, seeds, normalize=False):
"""
Initializes a state, returning the configurations and potential energy.
"""
all_confs = []
all_potEs = []
Hs = []
for seed in seeds:
self.universe.setConfiguration(Configuration(self.universe,seed))
(confs_s, potEs_s, Hs_s, self.delta_t) = self.sampler[process](
steps = self.params[process]['steps_per_seed'],
T=self.T, normalize=normalize,
adapt=True, delta_t=self.delta_t)
# Restrict the range of the time step
self.delta_t = min(max(self.delta_t, 0.3*MMTK.Units.fs), 3.*MMTK.Units.fs)
all_confs.append(confs_s)
all_potEs.append(np.array(potEs_s))
Hs.append(Hs_s)
Hs = np.mean(np.array(Hs))
import sys
original_stderr = sys.stderr
sys.stderr = NullDevice()
# Decorrelation
import timeseries
if len(seeds)>1:
g = timeseries.statisticalInefficiencyMultiple(all_potEs)
else:
g = timeseries.statisticalInefficiency(all_potEs)
stride = int(np.ceil(g))
sys.stderr = original_stderr
confs = []
potEs = []
for s in range(len(seeds)):
nsnaps = len(all_confs[s])
uncorr_indicies = range(min(stride-1, nsnaps-1), nsnaps, stride)
confs.extend([all_confs[s][i] for i in uncorr_indicies])
potEs.extend([all_potEs[s][i] for i in uncorr_indicies])
return (confs, np.array(potEs), g)
def _replica_exchange(self, process):
"""
Performs a cycle of replica exchange
"""
if not process in ['dock','cool']:
raise Exception('Process must be dock or cool')
cycle_start_time = time.time()
self._set_lock(process)
if process=='cool':
terms = ['MM']
else:
terms = ['MM','site','misc'] + self._scalables
if not process+'_replicas' in self.confs.keys():
self.confs[process+'_replicas'] = self._read_crd(join(self.dir[process],process+'_replicas.crd.gz'))
confs = self.confs[process+'_replicas']
lambdas = getattr(self,process+'_protocol')
# A list of pairs of replica indicies
K = len(lambdas)
pairs_to_swap = []
for interval in range(1,min(5,K)):
lower_inds = []
for lowest_index in range(interval):
lower_inds += range(lowest_index,K-interval,interval)
upper_inds = np.array(lower_inds) + interval
pairs_to_swap += zip(lower_inds,upper_inds)
storage = {}
for var in ['confs','state_inds','energies']:
storage[var] = []
state_inds = range(K)
E = {}
for term in terms:
E[term] = np.zeros(K, dtype=float)
Ht = np.zeros(K, dtype=int)
for sweep in range(self.params[process]['sweeps_per_cycle']):
# Sample within each state
for k in range(K):
self._set_universe_force_field(lambdas[state_inds[k]])
self.universe.setConfiguration(Configuration(self.universe, confs[k]))
# By far, this step takes the most time within the cycle
(confs_k, potEs_k, Hs_k, delta_t) = self.sampler[process](steps = self.params[process]['steps_per_sweep'],T=self.T, normalize=(process=='cool'),adapt=False, delta_t=self.delta_t)
confs[k] = np.copy(confs_k[-1])
if process=='cool':
E['MM'][k] = potEs_k[-1]
Ht[k] += Hs_k
if process=='dock':
# Get energies
self._set_universe_force_field(self.lambda_full)
for term in terms:
E[term] = np.zeros(K, dtype=float)
for k in range(K):
self.universe.setConfiguration(Configuration(self.universe, confs[k]))
eT = self.universe.energyTerms()
for (key,value) in eT.iteritems():
E[term_map[key]][k] += value
# Calculate u_ij (i is the replica, and j is the configuration),
# a list of arrays
(u_ij,N_k) = self._u_kln(E, [lambdas[state_inds[c]] for c in range(K)])
# Do the replica exchange
for attempt in range(100):
for (a,b) in pairs_to_swap:
ddu = -u_ij[a][b]-u_ij[b][a]+u_ij[a][a]+u_ij[b][b]
if (ddu>0) or (np.random.uniform()<np.exp(ddu)):
u_ij[a],u_ij[b] = u_ij[b],u_ij[a]
state_inds[a],state_inds[b] = state_inds[b],state_inds[a]
# Store data in local variables
storage['confs'].append(list(confs))
storage['state_inds'].append(list(state_inds))
storage['energies'].append(copy.deepcopy(E))
print " generated configurations for %d replicas"%(len(confs)) + \
" in replica exchange cycle %d"%getattr(self,'_%s_cycle'%process) + \
" in %f s"%(time.time()-cycle_start_time)
Ht = Ht/self.params[process]['sweeps_per_cycle']
storage['Ht'] = Ht
tau2 = 1.0
stride = 1
store_indicies = np.array(\
range(min(stride-1,self.params[process]['sweeps_per_cycle']-1), self.params[process]['sweeps_per_cycle'], stride), dtype=int)
nsaved = len(store_indicies)
# Get indicies for storing global variables
inv_state_inds = np.zeros((nsaved,K),dtype=int)
for snap in range(nsaved):
state_inds = storage['state_inds'][store_indicies[snap]]
for state in range(K):
inv_state_inds[snap][state_inds[state]] = state
# Store global variables and data
for state in range(K):
E_state = {}
if state==0:
E_state['repXpath'] = inv_state_inds
for term in terms:
E_state[term] = np.array([storage['energies'][store_indicies[snap]][term][inv_state_inds[snap][state]] for snap in range(nsaved)])
getattr(self,process+'_Es')[state].append(E_state)
self.confs[process+'_replicas'] = [storage['confs'][store_indicies[-1]][inv_state_inds[-1][state]] for state in range(K)]
self._write_crd(join(self.dir[process],process+'_replicas.crd.gz'), self.confs[process+'_replicas'], title=(process+'_replicas'))
for state in range(K):
if self.params[process]['keep_intermediate'] or ((process=='cool') and (state==0)) or (state==(K-1)):
confs = [storage['confs'][store_indicies[snap]][inv_state_inds[snap][state]] for snap in range(nsaved)]
self._write_crd(
join(self.dir[process],process+'%d_%d.crd.gz'%(state,getattr(self,'_%s_cycle'%process))),
confs,
title='State_%d_%d'%(state,getattr(self,'_%s_cycle'%process)))
postprocess_start_time = time.time()
setattr(self,'_%s_cycle'%process,getattr(self,'_%s_cycle'%process) + 1)
if process=='cool':
self._postprocess([('cool',-1,-1,'L')])
self.calc_f_L()
elif process=='dock':
self._postprocess()
self.calc_f_RL()
self._clear_lock(process)
print ' postprocessed data in %f s\n'%(time.time()-postprocess_start_time)
def _sim_process(self, process):
"""
Simulate and analyze a cooling or docking process.
As necessary, first conduct an initial docking
and then run a desired number of replica exchange cycles.
"""
if getattr(self,process+'_protocol')==[]:
getattr(self,'initial_'+process)()
if (self.params[process]['repX_cycles'] is not None) and \
(getattr(self,'_%s_cycle'%process) < self.params[process]['repX_cycles']):
print "\n>>> Replica exchange sampling for the {0}ing process".format(process)
import time
repEx_start_time = time.time()
start_cycle = getattr(self,'_%s_cycle'%process)
while getattr(self,'_%s_cycle'%process) < self.params[process]['repX_cycles']:
self._replica_exchange(process)
print "Elapsed time for %d cycles of replica exchange was %f s"%(\
(getattr(self,'_%s_cycle'%process)-start_cycle), \
(time.time()-repEx_start_time))
def _run_MBAR(self,u_kln,N_k):
"""
Estimates the free energy of a transition using MBAR
"""
import pymbar
K = len(N_k)
f_k_FEPF = np.zeros(K)
f_k_FEPR = np.zeros(K)
f_k_BAR = np.zeros(K)
for k in range(K-1):
w_F = u_kln[k,k+1,:N_k[k]] - u_kln[k,k,:N_k[k]]
min_w_F = min(w_F)
w_R = u_kln[k+1,k,:N_k[k+1]] - u_kln[k+1,k+1,:N_k[k+1]]
min_w_R = min(w_R)
f_k_FEPF[k+1] = -np.log(np.mean(np.exp(-w_F+min_w_F))) + min_w_F
f_k_FEPR[k+1] = np.log(np.mean(np.exp(-w_R+min_w_R))) - min_w_R
try:
f_k_BAR[k+1] = pymbar.computeBAR(w_F, w_R, relative_tolerance=0.000001, verbose=False, compute_uncertainty=False)
except:
f_k_BAR[k+1] = f_k_FEPF[k+1]
f_k_FEPF = np.cumsum(f_k_FEPF)
f_k_FEPR = np.cumsum(f_k_FEPR)
f_k_BAR = np.cumsum(f_k_BAR)
try:
f_k = pymbar.MBAR(u_kln, N_k,
verbose = False, method = 'adaptive',
initial_f_k = f_k_BAR,
maximum_iterations = 20).f_k
except:
f_k = f_k_BAR
return f_k
def _u_kln(self,eTs,lambdas,noBeta=False):
"""
Computes a reduced potential energy matrix. k is the sampled state. l is the state for which energies are evaluated.
Input:
eT is a
-dictionary (of mapped energy terms) of numpy arrays (over states)
-list (over states) of dictionaries (of mapped energy terms) of numpy arrays (over configurations), or a
-list (over states) of lists (over cycles) of dictionaries (of mapped energy terms) of numpy arrays (over configurations)
lambdas is a list of thermodynamic states
noBeta means that the energy will not be divided by RT
Output: u_kln or (u_kln, N_k)
u_kln is the matrix (as a numpy array)
N_k is an array of sample sizes
"""
L = len(lambdas)
addMM = ('MM' in lambdas[0].keys()) and (lambdas[0]['MM'])
addSite = ('site' in lambdas[0].keys()) and (lambdas[0]['site'])
probe_key = [key for key in lambdas[0].keys() if key in (['MM'] + self._scalables)][0]
if isinstance(eTs,dict):
# There is one configuration per state
K = len(eTs[probe_key])
N_k = np.ones(K, dtype=int)
u_kln = []
E_base = np.zeros(K)
if addMM:
E_base += eTs['MM']
if addSite:
E_base += eTs['site']
for l in range(L):
E = 1.*E_base
for scalable in self._scalables:
if scalable in lambdas[l].keys():
E += lambdas[l][scalable]*eTs[scalable]
if noBeta:
u_kln.append(E)
else:
u_kln.append(E/(R*lambdas[l]['T']))
elif isinstance(eTs[0],dict):
K = len(eTs)
N_k = np.array([len(eTs[k][probe_key]) for k in range(K)])
u_kln = np.zeros([K, L, N_k.max()], np.float)
for k in range(K):
E_base = 0.0
if addMM:
E_base += eTs[k]['MM']
if addSite:
E_base += eTs[k]['site']
for l in range(L):
E = 1.*E_base
for scalable in self._scalables:
if scalable in lambdas[l].keys():
E += lambdas[l][scalable]*eTs[k][scalable]
if noBeta:
u_kln[k,l,:N_k[k]] = E
else:
u_kln[k,l,:N_k[k]] = E/(R*lambdas[l]['T'])
elif isinstance(eTs[0],list):
K = len(eTs)
N_k = np.zeros(K, dtype=int)
for k in range(K):
for c in range(len(eTs[k])):
N_k[k] += len(eTs[k][c][probe_key])
u_kln = np.zeros([K, L, N_k.max()], np.float)
for k in range(K):
E_base = 0.0
C = len(eTs[k])
if addMM:
E_base += np.concatenate([eTs[k][c]['MM'] for c in range(C)])
if addSite:
E_base += np.concatenate([eTs[k][c]['site'] for c in range(C)])
for l in range(L):
E = 1.*E_base
for scalable in self._scalables:
if scalable in lambdas[l].keys():
E += lambdas[l][scalable]*np.concatenate([eTs[k][c][scalable] for c in range(C)])
if noBeta:
u_kln[k,l,:N_k[k]] = E
else:
u_kln[k,l,:N_k[k]] = E/(R*lambdas[l]['T'])
if (K==1) and (L==1):
return u_kln.ravel()
else:
return (u_kln,N_k)
def _next_dock_state(self, E=None, lambda_o=None):
"""
Determines the parameters for the next state
"""
if E is None:
E = self.dock_Es[-1]
if lambda_o is None:
lambda_o = self.dock_protocol[-1]
lambda_n = copy.deepcopy(lambda_o)
if self.params['dock']['protocol']=='Set':
raise Exception("Set protocol not currently supported")
elif self.params['dock']['protocol']=='Adaptive':
if self.params['dock']['legs']==2:
# First change grid scaling, then
# temperature
if lambda_o['LJr']<1:
dL = self._u_kln([E],
[{'LJr':1,'LJa':1,'ELE':1}], noBeta=True).std()/(R*lambda_o['T'])
if dL>0:
grid_scale = min(lambda_o['LJr'] + self.params['dock']['therm_speed']/dL, 1)
else:
grid_scale = 1
lambda_n['LJr'] = grid_scale
lambda_n['LJa'] = grid_scale
lambda_n['ELE'] = grid_scale
elif lambda_o['T']>T_TARGET:
dL = self._u_kln([E],
[{'MM':True,'site':True,'T':lambda_o['T'],
'LJr':1,'LJa':1,'ELE':1}], noBeta=True).std()/(R*lambda_o['T']*lambda_o['T'])
if dL>0:
lambda_n['T'] = lambda_o['T'] - self.params['dock']['therm_speed']/dL
if lambda_n['T'] < T_TARGET:
lambda_n['T'] = T_TARGET
lambda_n['crossed'] = True
else:
lambda_n['T'] = T_TARGET
lambda_n['crossed'] = True
elif self.params['dock']['legs']==3:
# First change Lennard-Jones grid scaling, then
# electrostatic grid scaling, then
# temperature
if lambda_o['LJr']<1:
dL = self._u_kln([E],
[{'LJr':1,'LJa':1}], noBeta=True).std()/(R*lambda_o['T'])
if dL>0:
grid_scale = min(lambda_o['LJr'] + self.params['dock']['therm_speed']/dL, 1)
else:
grid_scale = 1
lambda_n['LJr'] = grid_scale
lambda_n['LJa'] = grid_scale
elif lambda_o['ELE']<1:
dL = self._u_kln([E],
[{'ELE':1}], noBeta=True).std()/(R*lambda_o['T'])
if dL>0:
grid_scale = min(lambda_o['ELE'] + self.params['dock']['therm_speed']/dL, 1)
else:
grid_scale = 1
lambda_n['ELE'] = grid_scale
elif lambda_o['T']>T_TARGET:
dL = self._u_kln([E],
[{'MM':True,'site':True,'T':lambda_o['T'],
'LJr':1,'LJa':1,'ELE':1}], noBeta=True).std()/(R*lambda_o['T']*lambda_o['T'])
if dL>0:
lambda_n['T'] = lambda_o['T'] - self.params['dock']['therm_speed']/dL
if lambda_n['T'] < T_TARGET:
lambda_n['T'] = T_TARGET
lambda_n['crossed'] = True
else:
lambda_n['T'] = T_TARGET
lambda_n['crossed'] = True
elif self.params['dock']['legs']==4:
# First change soft Lennard-Jones repulsive grid scaling, then
# Lennard-Jones grid scaling, then
# electrostatic grid scaling, then
# temperature
if (lambda_o['sLJr']<1) and not lambda_o['sLJr_crossed']:
dL = self._u_kln([E],
[{'sLJr':1}], noBeta=True).std()/(R*lambda_o['T'])
if dL>0:
grid_scale = lambda_o['sLJr'] + self.params['dock']['therm_speed']/dL
if grid_scale > 1:
grid_scale = 1
lambda_n['sLJr_crossed'] = True
else:
grid_scale = 1
lambda_n['sLJr_crossed'] = True
lambda_n['sLJr'] = grid_scale
elif lambda_o['LJr']<1:
dL = self._u_kln([E],
[{'LJr':1,'LJa':1}], noBeta=True).std()/(R*lambda_o['T'])
if dL>0:
grid_scale = min(lambda_o['LJr'] + self.params['dock']['therm_speed']/dL, 1)
else:
grid_scale = 1
lambda_n['sLJr'] = 0
lambda_n['LJr'] = grid_scale
lambda_n['LJa'] = grid_scale
elif lambda_o['ELE']<1:
dL = self._u_kln([E],
[{'ELE':1}], noBeta=True).std()/(R*lambda_o['T'])
if dL>0:
grid_scale = min(lambda_o['ELE'] + self.params['dock']['therm_speed']/dL, 1)
else:
grid_scale = 1
lambda_n['ELE'] = grid_scale
elif lambda_o['T']>T_TARGET:
dL = self._u_kln([E],
[{'MM':True,'site':True,'T':lambda_o['T'],
'LJr':1,'LJa':1,'ELE':1}], noBeta=True).std()/(R*lambda_o['T']*lambda_o['T'])
if dL>0:
lambda_n['T'] = lambda_o['T'] - self.params['dock']['therm_speed']/dL
if lambda_n['T'] < T_TARGET:
lambda_n['T'] = T_TARGET
lambda_n['crossed'] = True
else:
lambda_n['T'] = T_TARGET
lambda_n['crossed'] = True
return lambda_n
def _postprocess(self,
conditions=[('original',0, 0,'R'), ('cool',-1,-1,'L'), \
('dock', -1,-1,'L'), ('dock',-1,-1,'RL')],
phases=['GBSA','Gas'],
readOnly=False, debug=False):
"""
Obtains the NAMD energies of all the conditions using all the phases.
Saves both MMTK and NAMD energies after NAMD energies are estimated.
"""
# NAMD ENERGY FIELDS:
# 0. TS 1. BOND 2. ANGLE 3. DIHED 4. IMPRP 5. ELECT 6. VDW 7. BOUNDARY
# 8. MISC 9. KINETIC 10. TOTAL 11. TEMP 12. POTENTIAL 13. TOTAL3 14. TEMPAVG
# The saved fields are energyFields=[1, 2, 3, 4, 5, 6, 8, 12],
# and thus the new indicies are
# 0. BOND 1. ANGLE 2. DIHED 3. IMPRP 4. ELECT 5. VDW 6. MISC 7. POTENTIAL
updated = []
crd_FN_o = ''
# state == -1 means the last state
# cycle == -1 means all cycles
for (p, state, cycle, moiety) in conditions:
# Check that the values are legitimate
if not p in ['cool','dock','original']:
raise Exception("Type should be in ['cool', 'dock', 'original']")
if not moiety in ['R','L', 'RL']:
raise Exception("Species should in ['R','L', 'RL']")
if state==-1:
state = len(getattr(self,p+'_protocol'))-1
if cycle==-1:
cycles = range(getattr(self,'_'+p+'_cycle'))
else:
cycles = [cycle]
for c in cycles:
if p=='original':
prefix = p
else:
prefix = '%s%d_%d'%(p, state, c)
for phase in phases:
p_dir = {'cool':self.dir['cool'],
'original':self.dir['dock'],
'dock':self.dir['dock']}[p]
fixed = {'R':self._FNs['fixed_atoms']['R'],
'L':None,
'RL':self._FNs['fixed_atoms']['RL']}[moiety]
label = moiety+phase
crd_FN = join(p_dir,'%s.crd.gz'%(prefix))
dcd_FN = join(p_dir,'%s.%s.dcd'%(prefix,moiety))
# Continue if the energies are already in memory
if readOnly or \
(p == 'original' and (label in getattr(self,p+'_Es')[state][c].keys())) \
or (('MM' in getattr(self,p+'_Es')[state][c].keys()) and \
(label in getattr(self,p+'_Es')[state][c].keys()) and \
(len(getattr(self,p+'_Es')[state][c]['MM'])==\
len(getattr(self,p+'_Es')[state][c][label]))):
pass
else:
# Do the calculation
# Obtain configurations
if (moiety=='R'):
confs = self._receptor_conf
else:
if crd_FN != crd_FN_o:
confs = self._read_crd(crd_FN)
crd_FN_o = crd_FN
# Write the configurations
if not exists(dcd_FN):
self._write_dcd(dcd_FN, confs,
includeReceptor=(moiety.find('R')>-1),
includeLigand=(moiety.find('L')>-1))
# Run NAMD
import AlGDock.NAMD
energyCalc = AlGDock.NAMD.NAMD(prmtop=self._FNs['prmtop'][moiety],
inpcrd=self._FNs['inpcrd'][moiety],
fixed=fixed,
solvent={'GBSA':'GBSA','Gas':'vacuum'}[phase],
useCutoff=(phase=='GBSA'),
namd_command=self._FNs['namd'])
E = energyCalc.energies_PE(\
join(p_dir,'%s.%s%s'%(prefix,moiety,phase)), dcd_FN,
energyFields=[1, 2, 3, 4, 5, 6, 8, 12],
keepScript=debug, writeEnergyDatGZ=False)
getattr(self,p+'_Es')[state][c][label] = \
np.array(E, dtype=float)*MMTK.Units.kcal/MMTK.Units.mol
# Updated
if not (p, c) in updated:
updated.append((p, c))
# Clean up
if (not debug) and exists(dcd_FN):
os.remove(dcd_FN)
# If the receptor energy is updated
if ((self.params['dock']['receptor_Gas'] is None) or \
(self.params['dock']['receptor_GBSA'] is None)) and \
((self.original_Es[0][0]['RGas'] is not None) or \
(self.original_Es[0][0]['RGBSA'] is not None)):
for phase in ['Gas','GBSA']:
self.params['dock']['receptor_'+phase] = self.original_Es[0][0]['R'+phase]
self._write_pkl_gz(join(self.dir['dock'],'dock_store.pkl.gz'),
(convert_dictionary_relpath(
dict([tp for tp in self._FNs.items() if not tp[0] in ['namd','vmd']]),
relpath_o=None, relpath_n=self.dir['dock']),
dict([tp for tp in self.params['dock'].items() if not tp[0] in ['repX_cycles']])))
# Save progress
for (p,c) in updated:
print " postprocessed {0}ing cycle {1} with NAMD".format(p,c)
if p=='cool' or p=='dock':
nstates = len(getattr(self,p+'_protocol'))
energyFN = join(self.dir[p], '%s_Es_%d.pkl.gz'%(p, c))
energies = [getattr(self, p+'_Es')[state][c] for state in range(nstates)]
self._write_pkl_gz(energyFN, energies)
def _load_cool0(self):
# Load configurations
confs = []
c = 0
while exists(join(self.dir['cool'],'cool0_%d.crd.gz'%c)):
confs += self._read_crd(join(self.dir['cool'],'cool0_%d.crd.gz'%c))
c += 1
# Load energies
E_MM = []
for k in range(len(self.cool_Es[0])):
E_MM += list(self.cool_Es[0][k]['MM'])
# Check for consistency
if len(E_MM)!=len(confs):
raise Exception('Error in loading the first cooling state')
# Indicies to be used in first docking state
random_dock_inds = np.array(np.linspace(0,len(E_MM), \
self.params['dock']['seeds_per_state'],endpoint=False),dtype=int)
return ([confs[ind] for ind in random_dock_inds], \
[E_MM[ind] for ind in random_dock_inds])
def _read_crd(self, crdFN, split=True):
"""
Reads an AMBER or VMD format coordinate file.
"""
if crdFN[-3:] == '.gz':
crdF = gzip.open(crdFN,'r')
else:
crdF = open(crdFN,'r')
dat = crdF.read().strip().split('\n')
crdF.close()
title = dat.pop(0) # Title
if len(dat[0].split())>1:
# VMD format (does not specify number of atoms)
crd = []
for line in dat:
crd = crd + [float(x)/10.0 for x in line.split()]
crd = np.resize(crd,(len(crd)/3,3))
self._write_crd(crdFN, crd, title=title)
else:
# AMBER format
natoms = int(dat.pop(0)) # Number of atoms
if split and (natoms!=self.universe.numberOfAtoms()):
print 'Incorrect number of atoms in crd file'
return np.array([])
w = 12
crd = []
for line in dat:
crd = crd + [float(line[x:x+w])/10.0 for x in range(0,len(line),w)]
crd = np.resize(crd,(len(crd)/3,3))
if split:
crd = np.vsplit(crd,crd.shape[0]/self.universe.numberOfAtoms())
print " read %d configurations from %s"%(len(crd), crdFN)
return crd
def _write_crd(self, crdFN, crd, title='', appendIfExists=False, vmdStyle=False):
"""
Writes an AMBER format trajectory file
"""
if (appendIfExists and exists(crdFN)):
if crdFN[-3:] == '.gz':
crdF = gzip.open(crdFN,'a')
else:
crdF = open(crdFN,'a')
else:
if exists(crdFN):
os.rename(crdFN,crdFN+'.BAK')
if crdFN[-3:] == '.gz':
crdF = gzip.open(crdFN,'w')
else:
crdF = open(crdFN,'w')
# Write the header
crdF.write(title+'\n') # Title
if not vmdStyle:
crdF.write('%d\n'%self.universe.numberOfAtoms())
for atom in (np.vstack(crd)/MMTK.Units.Ang):
crdF.write('%12.7f%12.7f%12.7f\n'%(atom[0],atom[1],atom[2]))
crdF.close()
print " wrote %d configurations to %s"%(len(crd), crdFN)
def _write_dcd(self, dcd_file_name, trajectory_array,
includeLigand=True, includeReceptor=False,
factor=1.0/MMTK.Units.Ang,
delta_t=0.1):
"""
Writes a DCD file for a trajectory.
If includeReceptor==True, the receptor coordinates are included.
"""
import MMTK_DCD # @UnresolvedImport
from Scientific import N
prmtop_atom_order = [ref.number for ref in self.molecule.prmtop_order]
i_start = 0 # always start at frame 0
n_savc = 1 # save every frame
n_atoms = 0
if includeReceptor:
receptor_x0 = factor*self._receptor_conf[:self._ligand_first_atom,0]
receptor_y0 = factor*self._receptor_conf[:self._ligand_first_atom,1]
receptor_z0 = factor*self._receptor_conf[:self._ligand_first_atom,2]
receptor_x1 = factor*self._receptor_conf[self._ligand_first_atom:,0]
receptor_y1 = factor*self._receptor_conf[self._ligand_first_atom:,1]
receptor_z1 = factor*self._receptor_conf[self._ligand_first_atom:,2]
n_atoms += self._receptor_conf.shape[0]
if includeLigand:
n_atoms += len(self.molecule.atoms)
n_snaps = len(trajectory_array)
fd = MMTK_DCD.writeOpenDCD(dcd_file_name, n_atoms, n_snaps,
i_start, n_savc, delta_t)
if includeReceptor and includeLigand:
for array in trajectory_array:
array = factor*array
x = N.concatenate((receptor_x0,N.take(array[:,0],prmtop_atom_order),receptor_x1)).astype(N.Float16)
y = N.concatenate((receptor_y0,N.take(array[:,1],prmtop_atom_order),receptor_y1)).astype(N.Float16)
z = N.concatenate((receptor_z0,N.take(array[:,2],prmtop_atom_order),receptor_z1)).astype(N.Float16)
MMTK_DCD.writeDCDStep(fd, x, y, z)
MMTK_DCD.writeCloseDCD(fd)
elif includeLigand:
for array in trajectory_array:
array = factor*array
x = N.take(array[:,0], prmtop_atom_order).astype(N.Float16)
y = N.take(array[:,1], prmtop_atom_order).astype(N.Float16)
z = N.take(array[:,2], prmtop_atom_order).astype(N.Float16)
MMTK_DCD.writeDCDStep(fd, x, y, z)
MMTK_DCD.writeCloseDCD(fd)
else:
x = N.concatenate((receptor_x0,receptor_x1)).astype(N.Float16)
y = N.concatenate((receptor_y0,receptor_y1)).astype(N.Float16)
z = N.concatenate((receptor_z0,receptor_z1)).astype(N.Float16)
MMTK_DCD.writeDCDStep(fd, x, y, z)
MMTK_DCD.writeCloseDCD(fd)
def _load_pkl_gz(self, FN):
if exists(FN):
F = gzip.open(FN,'r')
try:
data = pickle.load(F)
except:
print 'Could not load '+FN
F.close()
return None
F.close()
return data
else:
return None
def _write_pkl_gz(self, FN, data):
F = gzip.open(FN,'w')
pickle.dump(data,F)
F.close()
print " wrote to "+FN
def _load_progress(self, p):
setattr(self, p+'_protocol',
self._load_pkl_gz(join(self.dir[p], p+'_protocol.pkl.gz')))
if getattr(self, p+'_protocol') is None:
setattr(self, p+'_protocol', [])
setattr(self, '_%s_cycle'%p, 0)
return
# Load energies in each cycle
if (not hasattr(self, p+'_Es')) or (getattr(self, p+'_Es')==[]):
nstates = len(getattr(self, p+'_protocol'))
setattr(self, p+'_Es', [[] for state in range(nstates)])
c = 0
while exists(join(self.dir[p], '%s_Es_%d.pkl.gz'%(p, c))):
cycle_Es = self._load_pkl_gz(join(self.dir[p], '%s_Es_%d.pkl.gz'%(p, c)))
for state in range(len(cycle_Es)):
getattr(self, p+'_Es')[state].append(cycle_Es[state])
c += 1
if (not getattr(self, p+'_protocol')==[]) and \
(getattr(self, p+'_protocol')[-1]['crossed']):
setattr(self,'_%s_cycle'%p, len(getattr(self, p+'_Es')[0]))
else:
setattr(self,'_%s_cycle'%p, 0)
def _set_lock(self, p):
lockFN = join(self.dir[p],'.lock')
if exists(lockFN):
raise Exception(p + ' is locked')
else:
lockF = open(lockFN,'w')
lockF.close()
def _clear_lock(self, p):
lockFN = join(self.dir[p],'.lock')
if exists(lockFN):
os.remove(lockFN)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(
description='Molecular docking with adaptively scaled alchemical interaction grids')
# Files and directories
# Run-dependent
parser.add_argument('--dir_dock',
help='Directory where docking results are stored')
parser.add_argument('--dir_cool',
help='Directory where cooling results are stored')
parser.add_argument('--namd',
help='Location of Not Another Molecular Dynamics (NAMD)')
parser.add_argument('--vmd',
help='Location of Visual Molecular Dynamics (VMD)')
# Stored in both dir_cool and dir_dock
parser.add_argument('--ligand_database',
help='MMTK molecule definition for ligand')
parser.add_argument('--forcefield',
help='AMBER force field file')
parser.add_argument('--frcmodList', nargs='+',
help='AMBER force field modifications file(s)')
parser.add_argument('--ligand_prmtop',
help='AMBER prmtop for the ligand')
parser.add_argument('--ligand_inpcrd',
help='AMBER coordinates for the ligand')
# Stored in dir_dock
parser.add_argument('--receptor_prmtop',
help='AMBER prmtop for the receptor')
parser.add_argument('--receptor_inpcrd',
help='AMBER coordinates for the receptor')
parser.add_argument('--receptor_fixed_atoms',
help='PDB file with fixed atoms labeled by 1 in the occupancy column')
parser.add_argument('--complex_prmtop',
help='AMBER prmtop file for the complex')
parser.add_argument('--complex_inpcrd',
help='AMBER coordinates for the complex')
parser.add_argument('--complex_fixed_atoms',
help='PDB file with fixed atoms labeled by 1 in the occupancy column')
parser.add_argument('--dir_grid',
help='Directory containing potential energy grids')
parser.add_argument('--grid_LJr',
help='DX file for Lennard-Jones repulsive grid')
parser.add_argument('--grid_LJa',
help='DX file for Lennard-Jones attractive grid')
parser.add_argument('--grid_ELE',
help='DX file for electrostatic grid')
parser.add_argument('--grid_force',
help='PDB file with grid force multipliers (for NAMD)')
# Simulation settings and constants
# Run-dependent
parser.add_argument('--cool_repX_cycles', type=int,
help='Number of replica exchange cycles for cooling')
parser.add_argument('--dock_repX_cycles', type=int,
help='Number of replica exchange cycles for docking')
parser.add_argument('--run_type',
choices=['initial_cool','cool','initial_dock','dock','all'],
help='Type of calculation to run')
# Defaults
parser.add_argument('--protocol', choices=['Adaptive','Set'],
help='Approach to determining series of thermodynamic states')
parser.add_argument('--therm_speed', type=float,
help='Thermodynamic speed during adaptive simulation')
parser.add_argument('--sampler',
choices=['NUTS'],
help='Sampling method')
# For initialization
parser.add_argument('--seeds_per_state', type=int,
help='Number of starting configurations in each state during initialization')
parser.add_argument('--steps_per_seed', type=int,
help='Number of MD steps per state during initialization')
# For replica exchange
parser.add_argument('--repX_cycles', type=int,
help='Number of replica exchange cycles for docking and cooling')
parser.add_argument('--sweeps_per_cycle', type=int,
help='Number of replica exchange sweeps per cycle')
parser.add_argument('--steps_per_sweep', type=int,
help='Number of MD steps per replica exchange sweep')
parser.add_argument('--keep_intermediate', action='store_true',
help='Keep configurations for intermediate states?')
# Stored in dir_cool
parser.add_argument('--cool_protocol', choices=['Adaptive','Set'],
help='Approach to determining series of thermodynamic states')
parser.add_argument('--cool_therm_speed', type=float,
help='Thermodynamic speed during adaptive simulation')
parser.add_argument('--cool_sampler',
choices=['NUTS'],
help='Sampling method')
# For initialization
parser.add_argument('--cool_seeds_per_state', type=int,
help='Number of starting configurations in each state during initialization')
parser.add_argument('--cool_steps_per_seed', type=int,
help='Number of MD steps per state during initialization')
# For replica exchange
parser.add_argument('--cool_sweeps_per_cycle', type=int,
help='Number of replica exchange sweeps per cycle')
parser.add_argument('--cool_steps_per_sweep', type=int,
help='Number of MD steps per replica exchange sweep')
parser.add_argument('--cool_keep_intermediate', action='store_true',
help='Keep configurations for intermediate states?')
# Stored in dir_dock
parser.add_argument('--dock_protocol', choices=['Adaptive','Set'],
help='Approach to determining series of thermodynamic states')
parser.add_argument('--dock_therm_speed', type=float,
help='Thermodynamic speed during adaptive simulation')
parser.add_argument('--dock_sampler',
choices=['NUTS'],
help='Sampling method')
# For initialization
parser.add_argument('--dock_seeds_per_state', type=int,
help='Number of starting configurations in each state during initialization')
parser.add_argument('--dock_steps_per_seed', type=int,
help='Number of MD steps per state during initialization')
parser.add_argument('--legs', type=int, choices=[2,3,4],
help='Number of legs in the docking thermodynamic cycle. 2: Scale LJ and ELE grids simultaneously, and then lower the temperature. 3: Scale the LJ grids, the ELE grids, and then lower the temperature. 4: Scale a soft LJ repulsive grid, scale the LJ grids, scale the ELE grids, and then lower the temperature.')
# For replica exchange
parser.add_argument('--dock_sweeps_per_cycle', type=int,
help='Number of replica exchange sweeps per cycle')
parser.add_argument('--dock_steps_per_sweep', type=int,
help='Number of MD steps per replica exchange sweep')
parser.add_argument('--dock_keep_intermediate', action='store_true',
help='Keep configurations for intermediate states?')
# Binding site
parser.add_argument('--site',
choices=['Sphere','Cylinder'], help='Type of binding site')
parser.add_argument('--site_center', nargs=3, type=float,
help='Position of binding site center')
parser.add_argument('--site_direction', nargs=3, type=float,
help='Principal axis of a cylindrical binding site')
parser.add_argument('--site_max_X', type=float,
help='Maximum position along principal axis in a cylindrical binding site')
parser.add_argument('--site_max_R', type=float,
help='Maximum radial position for a spherical or cylindrical binding site')
parser.add_argument('--site_density', type=float,
help='Density of center-of-mass points in the first docking stage')
# Additional calculations
parser.add_argument('--do_calc_random_dock_stats', action='store_true',
help='Calculate convergence statistics for random placement of the ligand into the active site')
parser.add_argument('--rmsd_ref',
help='Performs an RMSD calculation. : 1. the file name, 2. the species, and 3. a VMD selection string')
parser.add_argument('--receptor_GBSA', type=float, nargs='+',
help='Receptor potential energies in GBSA implicit solvent (in units of kJ/mol)')
parser.add_argument('--receptor_Gas', type=float, nargs='+',
help='Receptor potential energies in gas phase (in units of kJ/mol)')
args = parser.parse_args()
self = Docker(**vars(args))
|
# -*- coding: utf-8 -*-
""" Installments """
INSTALLMENTS = 'I'
""" Recurring """
RECURRING = 'R'
""" Reauthorization """
REAUTHORIZATION = 'H'
""" Resubmission """
RESUBMISSION = 'E'
""" Delayed """
DELAYED = 'D'
""" Incremental """
INCREMENTAL = 'M'
""" No Show """
NO_SHOW = 'N'
""" Others """
OTHERS = 'C'
COF_TYPES = [
INSTALLMENTS,
RECURRING,
REAUTHORIZATION,
RESUBMISSION,
DELAYED,
INCREMENTAL,
NO_SHOW,
OTHERS
]
""" First COF yes """
SI = 'S'
""" First COF no """
NO = 'N'
""" First Boolean """
COF_FIRST_BOOLEAN = [
SI,
NO
]
|
"""succint json documents.
this module uses `semi-indexing` to efficiently deserialize elements
of json documents. it does this by combining a succint tree that
enables efficient navigation of the document with the `jq` query
language for specifying nodes within the document to deserialize.
for more details, see `"semi-indexing semi-structured data in tiny
space" <http://www.di.unipi.it/~ottavian/files/semi_index_cikm.pdf>`_.
"""
from __future__ import absolute_import
import sys
import numbers
import itertools
import collections
import json as pyjson
import lark
from succinct import (
tree,
encoding,
)
class Index(collections.Sequence):
"""a read-only mapping from json nodes to their original positions.
succinct json documents encode the structure of json text as
succinct trees. this index provides a mapping from the tree
nodes back to their corresponding locations in the original text.
"""
def __init__(self, src, enc): # pylint: disable=W0231
"""instantiate a json semi-index.
:param str src: the original json text
:param encoding.EliasFano enc: a sequence giving the position
of json nodes in the original text
"""
assert isinstance(enc, encoding.EliasFano)
self.src = src
self.enc = enc
def __len__(self):
return len(self.enc) * 2
def __str__(self):
return str(self.enc)
def __getitem__(self, idx):
"""return the json text corresponding to the given tree node.
:param idx: the succint tree node index
:type idx: ``int`` or ``slice``
:returns: the json text
"""
if not isinstance(idx, (slice, numbers.Integral)):
raise ValueError('Index indices must be integers')
if isinstance(idx, slice):
if idx.step not in (None, 1):
raise IndexError('Index does not support variable stepping')
s, e = None, None
if idx.start is not None:
s = idx.start
if s < 0:
s += len(self)
s = self.lookup(s)
if idx.stop is not None:
e = idx.stop
if e >= len(self):
e = None
else:
e = self.lookup(e)
idx = slice(s, e)
else:
idx = self.lookup(idx)
return self.src[idx]
def lookup(self, pos):
"""return the position in the json text corresponding to the tree node.
:param int pos: the succinct tree node index
:returns: the corresponding position in the json text
"""
return self.enc[pos / 2] + (pos % 2)
class Document(object):
"""succinct representation of a json document.
the json document is loaded lazily, and its members are only
deserialized on access. this can save time when you only need to
access a few nodes in very large documents.
"""
def __init__(self, src):
self._src = src
self._nav = None
self._idx = None
@property
def nav(self):
"""the succinct tree encoding the document structure"""
if self._nav is None:
self._loads()
return self._nav
@property
def idx(self):
"""the index mapping tree nodes to their locations in the source"""
if self._idx is None:
self._loads()
return self._idx
def root(self):
"""return the root node of the document"""
return self.render(self.nav.root())
def render(self, node):
"""return a json node corresponding to the given tree node.
:param tree.Node node: the tree node to render
:return: the corresponding json node
:rtype: Node
"""
assert isinstance(node, tree.Node)
idx = self.idx.lookup(node.pos)
while idx < len(self._src) and self._src[idx].isspace():
idx += 1
c = self._src[idx]
return (
List(self, node)
if c == '[' else
Object(self, node)
if c == '{' else
Primitive(self, node)
)
def _loads(self):
"""construct the succinct tree and index"""
from test.bitvector import BitVector
from test.encoding import BalancedParentheses
seq = iter(self._src)
bv, pos = BitVector(''), BitVector('')
while True:
c = next(seq, None)
if c is None:
break
if c in '[{':
pos.append('1')
bv.extend('11')
elif c in '}]':
pos.append('1')
bv.extend('00')
elif c in ':,':
pos.append('1')
bv.extend('01')
elif c == '"':
escaped = True
while escaped or c != '"':
pos.append('0')
c = next(seq, None)
if c is None:
raise ValueError('malformed json')
escaped = c == '\\'
pos.append('0')
else:
pos.append('0')
if bv and (len(bv) < 2 or bv[-2:] != '00'):
raise ValueError('malformed json')
self._nav = tree.Navigator(BalancedParentheses(bv))
self._idx = Index(self._src, encoding.EliasFano(pos))
class Null(object):
"""null json node"""
def __str__(self):
return 'null'
__repr__ = __str__
class Node(collections.Sized):
"""node in a json document"""
def __init__(self, doc, node=None): # pylint: disable=W0231
node = node or doc.nav.root()
assert isinstance(doc, Document)
assert isinstance(node, tree.Node)
self.doc = doc
self.node = node
def __len__(self):
return self.node.degree()
def __str__(self):
return self.doc.idx[
self.node.pos:self.doc.nav.enc.close(self.node.pos)
].strip()
__repr__ = __str__
class Primitive(Node):
"""json primitive node (i.e., string, number, or boolean)"""
pass
class Container(Node):
"""json container node"""
def __init__(self, doc, node):
# odd indices are commas -- skip them
node.pos += node.pos % 2
super(Container, self).__init__(doc, node)
class List(collections.Sequence, Container):
"""json list node"""
def __getitem__(self, item):
if isinstance(item, slice):
return [self[i] for i in range(*item.indices(len(self)))]
if not isinstance(item, numbers.Integral):
raise TypeError(
'list indices must be integers, not {}'.format(
type(item)
)
)
if item < 0:
item += len(self)
return self.doc.render(self.node.child(item))
class Object(collections.Mapping, Container):
"""json object node"""
def _items(self):
itr = iter(self.node.children())
while True:
key = next(itr, None)
if not key:
break
yield key, next(itr)
def _key(self, node):
return self.doc.idx[
node.pos:self.doc.nav.enc.close(node.pos)
].strip()[1:-1]
def _val(self, node):
return self.doc.render(node)
def __iter__(self):
for key, _ in self._items():
yield self._key(key)
def __getitem__(self, item):
if not isinstance(item, basestring):
raise TypeError('key must be a string')
for key, val in self._items():
if self._key(key) == item:
return self._val(val)
raise KeyError(item)
def loads(src):
"""deserialize a string to a succint json document.
:param str src: the json text
:returns: the json document root
:rtype: Node
"""
return Document(src).root()
class Query(object):
"""query engine for succinct json documents.
supports all basic `jq filters
<https://stedolan.github.io/jq/manual/#Basicfilters>`_:
* **Identity**: `.`
* **Object Identifier Index**: `.foo`, `.foo.bar`
* **Optional Object Identifier Index**: `.foo?`
* **Generic Object Index**: `.[<string>]`
* **Array Index**: `.[2]`
* **Array/String Slice**: `.[10:15]`
* **Array/Object Value Iterator**: `.[]`
* **Optional Array/Object Value Iterator**: `.[]?`
* **Comma**: `,`
* **Pipe**: `|`
"""
PARSER = lark.Lark(
r"""
query: pipeline
pipeline: expression ("|" expression)*
expression: identity
| primitive
| properties
| indexer
| iterator
| concatenator
identity: "."
primitive: null | number | boolean | string
properties: ("." property)+
property: (cname | string) [optional]
indexer: ".[" (string|number|cname|slice|expression) "]" [optional]
iterator: ".[" "]" [optional]
concatenator: expression ("," expression)+
slice: [start] ":" [end]
start: number
end: number
optional: "?"
null: "null"
?number: integer | float
boolean: "true" | "false"
cname: CNAME
float: SIGNED_FLOAT
integer: SIGNED_INT
string: ESCAPED_STRING
COMMENT: "#"/[^\n]/*
%import common.ESCAPED_STRING
%import common.SIGNED_FLOAT
%import common.SIGNED_INT
%import common.CNAME
%import common.WS
%ignore COMMENT
%ignore WS
""",
start='query',
)
def __init__(self, jq):
"""compile a new jq query.
:param str jq: the query to compile
"""
self.jq = jq
self.tree = self.PARSER.parse(self.jq)
def __str__(self):
return self.jq
def execute(self, root):
"""execute the query over a succint json tree.
:param Node root: the succint json tree root.
:returns: a sequence of query results.
"""
assert isinstance(root, Node)
null = Null()
def optional(expression):
"""return True iff expression is optional"""
return any(e.data == 'optional' for e in expression.children)
def concatenate(expression, stream):
"""evaluate query expressions and concatenate results"""
# fork the stream for each subexpression
streams = itertools.tee(stream, len(expression.children))
return itertools.chain.from_iterable(
evaluate(expression, stream)
for expression, stream in zip(expression.children, streams)
)
def iterate(expression, stream):
"""iterate over json stream"""
for node in stream:
itr = (
iter(node)
if isinstance(node, List) else
iter(node.values())
if isinstance(node, Object) else
iter([])
if optional(expression) else
None
)
if not itr:
raise TypeError(
'cannot iterate over {}'.format(
node.__class__.__name__
)
)
for child in itr:
yield child
def indexer(expression, stream):
"""extract elements from json containers"""
def throw(node, item):
raise TypeError(
'cannot index {} with {}'.format(
node.__class__.__name__,
item.__class__.__name__,
)
)
def mkint(expression):
if expression.data == 'integer':
return int(expression.children[0])
elif expression.data == 'float':
idx = float(expression.children[0])
if not idx.is_integer():
idx = int(idx) + 1
return idx
else:
assert False, 'bad number expression {}'.format(
expression
)
def mkslice(expression):
s, e = None, None
for idx in expression.children:
if idx.data == 'start':
s = mkint(idx.children[0])
elif idx.data == 'end':
e = mkint(idx.children[0])
yield slice(s, e)
def mkindex(expression):
if expression.data == 'expression':
return evaluate(expression, stream)
elif expression.data == 'slice':
return mkslice(expression)
elif expression.data == 'cname':
return expression.children
elif expression.data == 'string':
return [expression.children[0][1:-1]]
elif expression.data in ('integer', 'float'):
return [mkint(expression)]
else:
assert False, 'bad index expression {}'.format(expression)
for item in mkindex(expression.children[0]):
for node in stream:
if isinstance(node, Object):
if isinstance(item, Primitive):
item = str(item)[1:-1]
if isinstance(item, basestring):
yield node.get(item, null)
continue
if isinstance(node, List):
if isinstance(item, Primitive):
item = int(str(item))
if isinstance(item, (int, slice)):
try:
yield node[item]
except IndexError:
yield null
continue
if not optional(expression):
throw(node, item)
def properties(expression, stream):
"""extract values from json objects"""
def index(expression, stream):
item = expression.children[0].children[0]
for node in stream:
if isinstance(node, Object):
yield node.get(item, null)
elif not optional(expression):
itype = expression.children[0].data
if itype == 'cname':
itype = 'string'
raise TypeError(
'cannot index {} with {}'.format(
node.__class__.__name__, itype
)
)
for expression in expression.children:
stream = index(expression, stream)
for node in stream:
yield node
def primitive(expression):
"""return a primitive type"""
expression = expression.children[0]
if expression.data == 'null':
return null
elif expression.data == 'boolean':
return expression.children[0] == 'true'
elif expression.data == 'string':
return expression.children[0][1:-1]
elif expression.data == 'integer':
return int(expression.children[0])
elif expression.data == 'float':
return float(expression.children[0])
assert False, 'bad primitive {}'.format(expression)
def evaluate(expression, stream):
"""evaluate query expression over json stream"""
assert expression.data == 'expression', expression
assert len(expression.children) == 1
expression = expression.children[0]
if expression.data == 'identity':
for node in stream:
yield node
elif expression.data == 'primitive':
yield primitive(expression)
elif expression.data == 'properties':
for node in properties(expression, stream):
yield node
elif expression.data == 'indexer':
for node in indexer(expression, stream):
yield node
elif expression.data == 'iterator':
for node in iterate(expression, stream):
yield node
elif expression.data == 'concatenator':
for node in concatenate(expression, stream):
yield node
else:
assert False, 'bad expression {}'.format(expression)
stream, pipeline = [root], self.tree.children[0]
for expression in pipeline.children:
stream = evaluate(expression, stream)
for result in stream:
yield result
def query(src, jq):
"""render python objects from json text.
>>> query('{"foo": [0, 1, 2], "bar": [3, 4, 5]}', '.bar | .[:-1]')
[3, 4]
>>> query('[{"foo": True}, {"bar": False}]', '.[] | .[]')
[True, False]
:param str src: the json text
:param str jq: a jq query describing the objects to render
:returns: a sequence of python objects
"""
for res in Query(jq).execute(loads(src)):
if isinstance(res, (Null, Node)):
yield pyjson.loads(str(res))
else:
yield [pyjson.loads(str(item)) for item in res]
def main():
import argparse
import textwrap
p = argparse.ArgumentParser(
description='json query engine',
epilog=textwrap.dedent(
"""
this utility supports the basic filters of the jq query
langage. filters accept json as input and produce query
results as output. the supported filters are:
`.`: produce the input as the output.
`.foo`: produce the value of a json object at the key
"foo", or null if the key is not present. if the key
contains special characters, it must be enclosed in double
quotes: `."foo$"`.
`.foo|.bar`: equivalent to `.foo | .bar`.
`.foo?`: like `.foo`, but does not produce an error, even
when `.` is not an object.
`.["foo"]`: produce the value of a json object at the key
"foo", or null if the key is not present. `.foo` is a
shorthand version of `.["foo"]`, but only for
identifier-like strings.
`.[2]`: produce the list element at the given index,
starting from zero (so `.[2]` produces the third element).
`.[10:15]`: return the list slice starting at index 10
(inclusive) and ending at index 15 (exclusive).
`.[]`: return all the elements of a list, or all the
values of an object.
`.[]?`: like `.[]`, but does not produce an error, even
when `.` is not a list or object.
`,`: concatenate the output of two filters, in order. for
instance, `.foo, .bar` produces the "foo" field followed
by the "bar" field.
"""
),
formatter_class=argparse.RawDescriptionHelpFormatter,
)
p.add_argument(
'query',
help='jq query',
)
p.add_argument(
'files',
nargs='*',
type=argparse.FileType('r'),
help='files to query (defaults to stdin)',
)
def dumps(node):
return pyjson.dumps(pyjson.loads(str(node)), indent=2)
args = p.parse_args()
jq = Query(args.query)
for fp in args.files or [sys.stdin]:
for res in jq.execute(loads(fp.read())):
if isinstance(res, (Null, Node)):
print dumps(res)
else:
for item in res:
print dumps(item)
if __name__ == '__main__':
sys.exit(main())
|
import os
from pathlib import Path
from dotenv import load_dotenv
from celery import schedules
basedir = Path(__file__).resolve().parent
load_dotenv(os.path.join(basedir, '.env'))
class Config:
"""
Set base Flask configuration vars.
"""
# General Config
DEBUG = False
TESTING = False
SECRET_KEY = os.environ.get('SECRET_KEY')
# Default number of documents on page (api results pagination)
DEFAULT_PAGINATION_LIMIT = 6
MAX_PAGINATION_LIMIT = 30
# Flasgger Config
SWAGGER = {
'title': 'Monitoring API',
'description': '### The Monitoring API is a simple REST API based on Flask-RESTful library. '
'The main purpose of the application is to monitor the availability of selected services. '
'The application checks at regular intervals the availability of services on the '
'specified ip address (or hostname) and port',
'version': '1.0.0',
'uiversion': 3,
'termsOfService': '',
# 'specs_route': '/apidocs/'
}
# Celery Config
WATCHDOG_CHECK_INTERVAL = 30
BACKGROUND_CHECK_INTERVAL = 20
CELERY = {
'broker_url': os.environ.get("CELERY_BROKER_URL"),
'result_backend': os.environ.get("CELERY_RESULT_BACKEND_URL"),
'redbeat_redis_url': os.environ.get('CELERY_REDBEAT_REDIS_URL'),
'beat_scheduler': 'redbeat.RedBeatScheduler',
'redbeat_key_prefix': 'redbeat:',
'beat_max_loop_interval': 5,
'beat_schedule': {
# Execute every 30sec (by default) after enabled by user
'watchdog-task': {
'task': 'watchdog_task',
'schedule': schedules.schedule(run_every=WATCHDOG_CHECK_INTERVAL),
'relative': True,
'enabled': False
},
# Execute every 20sec (by default) since the application is launched
'background-task': {
'task': 'service_unknown_status_task',
'schedule': BACKGROUND_CHECK_INTERVAL,
'relative': True,
'enabled': True
}
}
}
class ProdConfig(Config):
"""
Set Flask configuration vars for production.
"""
MONGODB_SETTINGS = {
'host': os.environ.get('MONGODB_URI_PROD'),
}
class TestConfig(Config):
"""
Set Flask configuration vars for testing.
"""
DEBUG = True
TESTING = True
MONGODB_SETTINGS = {
'host': os.environ.get('MONGODB_URI_TEST', 'mongodb://localhost:27017/testdb'),
}
class DevConfig(Config):
"""
Set Flask configuration vars for testing.
"""
DEBUG = True
MONGODB_SETTINGS = {
'host': os.environ.get('MONGODB_URI_DEV', 'mongodb://localhost:27017/devdb'),
}
app_config = {
'development': DevConfig,
'testing': TestConfig,
'production': ProdConfig,
} |
config = dict(
experiment = 'qgen', # Experiment - either qgen or dialogue
lstm_hidden_units = 100, # Number of hidden units for the LSTM
embedding_size = 300, # Word embedding dimension
num_layers = 1, # Number of LSTM layers
encoder_vocab = 40000, # Vocabulary size on the encoder side # 30000 for dialogue
decoder_vocab = 40000, # Vocabulary size on the decoder side # 30000 for dialogue
encoder_num_tokens = 30, # Number of words/tokens in the input sequence # 20 for dialogue
decoder_num_tokens = 20, # Number of words/tokens in the generated sequence
dropout_keep_prob = 0.8, # Dropout keep probability
initial_learning_rate = 0.005, # Initial learning rate
learning_rate_decay = 0.75, # Learning rate decay
min_learning_rate = 0.00001, # Minimum learning rate
latent_dim = 100, # Dimension of z-latent space
word_dropout_keep_probability = 0.75, # 1.0 - Word dropout rate for the decoder
z_temp = 1.0, # Sampling temperature to be multiplied with the standard deviation
attention_temp = 1.0, # Sampling temperature for the attention/context vector
use_hmean = True, # Whether to use N(h_mean, I) or N(0, I) as the prior
gamma_val = 0.1, # Coefficient of the attention KL loss
batch_size = 100, # Batch size # 128 for dialogue
n_epochs = 3, # Number of epochs
logs_dir = 'summary_logs/var-seq2seq-var-attn', # Path to save summary information for Tensorboard
model_checkpoint_dir = 'models/var-seq2seq-var-attn-', # Path to save model checkpoints
bleu_path = 'bleu/det-seq2seq-var-attn', # Path to save model checkpoints
w2v_dir = '../w2v_models/', # Word2Vec model directory
data_dir = '../data/', # Directory to store data csv files
load_checkpoint = 0, # Specify the trained model epoch/checkpoint number to be loaded for evaluation on test set, 0 means last saved checkpoint
) |
# coding: utf-8
# Author: Leo BRUNEL
# Contact: contact@leobrunel.com
# Python modules
import traceback
import os
import logging
logger = logging.getLogger(__name__)
# Wizard modules
from maya_wizard import wizard_tools
# Maya modules
import pymel.core as pm
# Hook modules
try:
import maya_hook
except:
maya_hook = None
logger.error(str(traceback.format_exc()))
logger.warning("Can't import maya_hook")
def reference_modeling(namespace, files_list):
old_objects = pm.ls()
if not pm.namespace(exists=namespace):
create_reference(files_list[0], namespace, 'MODELING')
trigger_after_reference_hook('modeling',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def update_modeling(namespace, files_list):
old_objects = pm.ls()
if pm.namespace(exists=namespace):
update_reference(namespace, files_list)
trigger_after_reference_hook('modeling',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def reference_rigging(namespace, files_list):
old_objects = pm.ls()
if not pm.namespace(exists=namespace):
create_reference(files_list[0], namespace, 'RIGGING')
trigger_after_reference_hook('rigging',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def update_rigging(namespace, files_list):
old_objects = pm.ls()
if pm.namespace(exists=namespace):
update_reference(namespace, files_list)
trigger_after_reference_hook('rigging',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def reference_custom(namespace, files_list):
old_objects = pm.ls()
if not pm.namespace(exists=namespace):
create_reference(files_list[0], namespace, 'CUSTOM')
trigger_after_reference_hook('custom',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def update_custom(namespace, files_list):
old_objects = pm.ls()
if pm.namespace(exists=namespace):
update_reference(namespace, files_list)
trigger_after_reference_hook('custom',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def reference_camrig(namespace, files_list):
old_objects = pm.ls()
if not pm.namespace(exists=namespace):
create_reference(files_list[0], namespace, 'CAMRIG')
trigger_after_reference_hook('camrig',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def update_camrig(namespace, files_list):
old_objects = pm.ls()
if pm.namespace(exists=namespace):
update_reference(namespace, files_list)
trigger_after_reference_hook('camrig',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def reference_layout(namespace, files_list):
old_objects = pm.ls()
if not pm.namespace(exists=namespace):
create_reference(files_list[0], namespace, 'LAYOUT')
trigger_after_reference_hook('layout',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def update_layout(namespace, files_list):
old_objects = pm.ls()
if pm.namespace(exists=namespace):
update_reference(namespace, files_list)
trigger_after_reference_hook('layout',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def reference_animation(namespace, files_list):
old_objects = pm.ls()
if not pm.namespace(exists=namespace):
create_reference(files_list[0], namespace, 'ANIMATION')
trigger_after_reference_hook('animation',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def update_animation(namespace, files_list):
old_objects = pm.ls()
if pm.namespace(exists=namespace):
update_reference(namespace, files_list)
trigger_after_reference_hook('animation',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def reference_cfx(namespace, files_list):
old_objects = pm.ls()
if not pm.namespace(exists=namespace):
create_reference(files_list[0], namespace, 'CFX')
trigger_after_reference_hook('cfx',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def update_cfx(namespace, files_list):
old_objects = pm.ls()
if pm.namespace(exists=namespace):
update_reference(namespace, files_list)
trigger_after_reference_hook('cfx',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def reference_camera(namespace, files_list):
old_objects = pm.ls()
if not pm.namespace(exists=namespace):
create_reference(files_list[0], namespace, 'CAMERA')
trigger_after_reference_hook('camera',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def update_camera(namespace, files_list):
old_objects = pm.ls()
if pm.namespace(exists=namespace):
update_reference(namespace, files_list)
trigger_after_reference_hook('camera',
files_list,
namespace,
wizard_tools.get_new_objects(old_objects))
def create_reference(file, namespace, group):
if not pm.objExists(group):
pm.group( em=True, name=group )
pm.createReference(file,
namespace=namespace,
groupName='wizard_temp_reference_node',
groupReference=True)
for object in pm.listRelatives('wizard_temp_reference_node'):
pm.parent(object, group)
pm.delete('wizard_temp_reference_node')
def update_reference(namespace, files_list):
references = pm.listReferences(namespaces=True)
for reference in references:
if reference[0] == namespace:
if os.path.normpath(files_list[0]) != os.path.normpath(reference[1].path):
reference[1].load(files_list[0])
def trigger_after_reference_hook(referenced_stage_name,
files_list,
namespace,
new_objects):
stage_name = os.environ['wizard_stage_name']
referenced_files_dir = wizard_tools.get_file_dir(files_list[0])
# Trigger the after export hook
if maya_hook:
try:
logger.info("Trigger after reference hook")
maya_hook.after_reference(stage_name,
referenced_stage_name,
referenced_files_dir,
namespace,
new_objects)
except:
logger.info("Can't trigger after reference hook")
logger.error(str(traceback.format_exc()))
|
from masonite.providers import (
RouteProvider,
FrameworkProvider,
ViewProvider,
ExceptionProvider,
SessionProvider,
QueueProvider,
StorageProvider,
AuthenticationProvider,
AuthorizationProvider,
ORMProvider,
EventProvider
)
PROVIDERS = [
FrameworkProvider,
RouteProvider,
ViewProvider,
EventProvider,
ExceptionProvider,
SessionProvider,
QueueProvider,
StorageProvider,
AuthenticationProvider,
AuthorizationProvider,
ORMProvider,
]
|
import re
import requests
import numpy as np
import json
import os
from collections import OrderedDict
import pandas as pd
import json
#将数组写入json文件方便pandas的读取
def write_list_to_json(list, json_file_name, json_file_save_path):
os.chdir(json_file_save_path)
with open(json_file_name, 'w') as f:
json.dump(list, f)
url = 'https://coronavirus.1point3acres.com/_next/static/chunks/eb866cddf7095f20f497cfebeab34fbb581cc2fd.e2b6d415563226f3802d.js'
headers = {
'User-Agent':'Opera/9.80(WindowsNT6.1;U;en)Presto/2.8.131Version/11.11',
}
res = requests.get(url,headers = headers)
res.encoding = "UTF-8"
pattern = re.compile('state_name":(.*?),"county":(.*?),"entries":(.*?)}',re.S)
end = list(re.findall(pattern,res.text))
state_name='state_name'
county='county'
entries='entries'
a=[]
#过滤出州和区的名字组成字典
for i in range(len(end)):
data={
"state_name:":[],
"county:":[],
}
data['state_name:'].append(end[i][0])
data["county:"].append(end[i][1])
data['state_name:']=','.join(data['state_name:'])
data["county:"]=','.join(data["county:"])
data['state_name:']=list(data['state_name:'])
data["county:"]=list(data["county:"])
#过滤出日期和确诊数，通过zip拉取起来
date=re.findall(pattern,res.text)
date_new=date[i][2].replace('[[',',[').replace(']]','],')
re_compile = re.compile('"(.*?)",(.*?)]')
date_care=re.findall(re_compile,date_new)
time=[]
care=[]
for i in range(len(date_care)):
time.append(date_care[i][0])
care.append(date_care[i][1])
time_care=OrderedDict(zip(time,care))
#数据清洗
for i in range(len(data['state_name:'])):
data['state_name:'][i]=data['state_name:'][i].replace('\"','').replace('[','').replace(']','')
for i in range(len(data["county:"])):
data["county:"][i]=data["county:"][i].replace('\"','').replace('[','').replace(']','')
data['state_name:']=''.join(data['state_name:'])
data["county:"]=''.join(data["county:"])
b={
"state_name:":'',
"county:":'',
"entries:":''
}
b["state_name:"]=data['state_name:']
b["county:"]=data["county:"]
b=OrderedDict(b,**time_care)
time.append('state_name:')
time.append("county:")
a.append(b)
write_list_to_json(a,'20200502-data.json','E:/python_code/world_cov19')
data_csv=pd.DataFrame(json.loads(open('20200502-data.json','r+').read()))
cols=time
data_csv=data_csv.ix[:,cols]
data_csv.to_csv('20200502-data.json.csv')
|
from part1 import (
gamma_board,
gamma_busy_fields,
gamma_delete,
gamma_free_fields,
gamma_golden_move,
gamma_golden_possible,
gamma_move,
gamma_new,
)
"""
scenario: test_random_actions
uuid: 138749984
"""
"""
random actions, total chaos
"""
board = gamma_new(5, 2, 4, 2)
assert board is not None
assert gamma_busy_fields(board, 1) == 0
assert gamma_move(board, 2, 2, 1) == 1
assert gamma_move(board, 2, 2, 0) == 1
assert gamma_busy_fields(board, 2) == 2
assert gamma_free_fields(board, 2) == 8
assert gamma_move(board, 3, 1, 0) == 1
board580516127 = gamma_board(board)
assert board580516127 is not None
assert board580516127 == ("..2..\n" ".32..\n")
del board580516127
board580516127 = None
assert gamma_move(board, 4, 1, 0) == 0
assert gamma_golden_possible(board, 4) == 1
assert gamma_move(board, 1, 0, 0) == 1
assert gamma_busy_fields(board, 1) == 1
assert gamma_free_fields(board, 1) == 6
assert gamma_move(board, 2, 0, 1) == 1
board373235482 = gamma_board(board)
assert board373235482 is not None
assert board373235482 == ("2.2..\n" "132..\n")
del board373235482
board373235482 = None
assert gamma_move(board, 3, 0, 4) == 0
assert gamma_move(board, 3, 3, 1) == 1
assert gamma_golden_possible(board, 3) == 1
assert gamma_move(board, 4, 1, 1) == 1
assert gamma_move(board, 1, 3, 0) == 1
assert gamma_busy_fields(board, 1) == 2
assert gamma_golden_possible(board, 1) == 1
assert gamma_move(board, 2, 1, 4) == 0
assert gamma_move(board, 3, 1, 4) == 0
assert gamma_move(board, 4, 0, 4) == 0
assert gamma_free_fields(board, 4) == 2
assert gamma_move(board, 1, 1, 4) == 0
assert gamma_move(board, 2, 1, 4) == 0
assert gamma_move(board, 2, 1, 0) == 0
assert gamma_golden_possible(board, 2) == 1
board730927527 = gamma_board(board)
assert board730927527 is not None
assert board730927527 == ("2423.\n" "1321.\n")
del board730927527
board730927527 = None
assert gamma_move(board, 3, 1, 4) == 0
assert gamma_move(board, 3, 2, 0) == 0
assert gamma_move(board, 4, 1, 4) == 0
assert gamma_move(board, 1, 0, 0) == 0
assert gamma_move(board, 1, 3, 0) == 0
assert gamma_golden_possible(board, 1) == 1
assert gamma_move(board, 2, 1, 4) == 0
assert gamma_busy_fields(board, 2) == 3
assert gamma_move(board, 3, 0, 4) == 0
assert gamma_free_fields(board, 3) == 1
board831262932 = gamma_board(board)
assert board831262932 is not None
assert board831262932 == ("2423.\n" "1321.\n")
del board831262932
board831262932 = None
gamma_delete(board)
|
# coding=utf-8
import logging
from nose.plugins.attrib import attr
from modelscript.interfaces.environment import Environment
from modelscript.test.framework import TEST_CASES_DIRECTORY
import os
import modelscript.scripts.usecases.parser
from modelscript.scripts.usecases.graphviz import (
UsecaseGraphvizPrinter
)
logging.basicConfig(level=logging.DEBUG)
log = logging.getLogger('test.'+__name__)
@attr('slow')
def testGenerator_UssGraphviz():
test_dir=os.path.join(
TEST_CASES_DIRECTORY,'uss')
#--- test all files ----------------------
files = [
os.path.join(test_dir, f)
for f in os.listdir(test_dir)
if f.endswith('.uss')]
for filename in files:
yield doBuildDiagram, filename
def doBuildDiagram(filename):
#--- parser: .uss -> system -------------------
source = modelscript.scripts.usecases.parser.UsecaseModelSource(
usecaseFileName=filename,
)
if not source.isValid:
print(('#'*10+' ignore invalid file %s' % filename ))
else:
usm = source.usecaseModel
#--- diag generation: system -> .gv --------------
# graphviz_file_path = os.path.join(
# USD_DIR,
# os.path.splitext(
# os.path.basename(filename)) [0]+'.usd.gv'
# )
graphviz_file_path=Environment.getWorkerFileName(
filename,
extension='.usd.gv')
print(('TST: '+'='*80))
print(('TST: result in %s' % graphviz_file_path))
print(('TST: '+'='*80))
gen = UsecaseGraphvizPrinter(usm)
# print(gen.do(outputFile=puml_file_path))
# #--- plantuml: .puml -> .svg ----------------------
# puml_engine.generate(puml_file_path)
gen.generate(graphviz_file_path, format='png')
# gen.generate(puml_file_path, format='svg' )
print('TST: generated')
print(('TST: '+'='*80))
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
# Third Party Stuff
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('conferences', '0012_historicalconferenceproposalreviewer'),
]
operations = [
migrations.AddField(
model_name='conference',
name='hashtags',
field=models.CharField(max_length=100, default='', null=True, help_text="Used in social sharing, use commas to separate to tags, no '#' required.", blank=True),
preserve_default=True,
),
migrations.AddField(
model_name='conference',
name='twitter_id',
field=models.CharField(max_length=100, default='', null=True, help_text='Used in social share widgets.', blank=True),
preserve_default=True,
),
migrations.AlterField(
model_name='conference',
name='venue',
field=models.ForeignKey(blank=True, null=True, to='conferences.ConferenceVenue'),
preserve_default=True,
),
]
|
from functools import partial
from subprocess import PIPE, run
from textwrap import dedent
import pytest
from flake8_annotations import error_codes
from testing.helpers import check_source
ERR = partial(error_codes.ANN401, lineno=3)
TEST_CASES = (
# Type annotations
(
dedent(
"""\
from typing import Any
def foo(a: Any) -> None:
...
"""
),
),
(
dedent(
"""\
from typing import Any
def foo(a: int) -> Any:
...
"""
),
),
(
dedent(
"""\
import typing as t
def foo(a: t.Any) -> None:
...
"""
),
),
(
dedent(
"""\
import typing as t
def foo(a: int) -> t.Any:
...
"""
),
),
# Type comments
(
dedent(
"""\
def foo(
a # type: Any
):
# type: (...) -> int
...
"""
),
),
(
dedent(
"""\
def foo(a):
# type: (int) -> Any
...
"""
),
),
)
@pytest.mark.parametrize(("src",), TEST_CASES)
def test_dynamic_typing_errors(src: str) -> None:
found_errors = list(check_source(src))
assert len(found_errors) == 1
_, _, err_msg, _ = found_errors[0]
assert "ANN401" in err_msg
INP = dedent(
"""\
import typing
def foo(a: typing.Any) -> None:
...
"""
)
def test_ANN401_ignored_default() -> None:
p = run(["flake8", "--select=ANN", "-"], stdout=PIPE, input=INP, encoding="ascii")
assert len(p.stdout) == 0
def test_ANN401_fire_when_selected() -> None:
p = run(
["flake8", "--select=ANN", "--ignore=''", "-"], stdout=PIPE, input=INP, encoding="ascii"
)
assert "ANN401" in p.stdout
STARG_CASES = (
(
dedent(
"""\
from typing import Any
def foo(*a: Any) -> None:
...
"""
),
),
(
dedent(
"""\
from typing import Any
def foo(**a: Any) -> None:
...
"""
),
),
(
dedent(
"""\
from typing import Any
def foo(a: int, *b: Any, c: int) -> None:
...
"""
),
),
(
dedent(
"""\
from typing import Any
def foo(a: int, *, b: int, **c: Any) -> None:
...
"""
),
),
)
@pytest.mark.parametrize(("src",), STARG_CASES)
def test_ignore_stargs(src: str) -> None:
found_errors = list(check_source(src, allow_star_arg_any=True))
assert len(found_errors) == 0
|
# Generated by Django 2.2.16 on 2020-10-23 07:00
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('sites', '0002_alter_domain_unique'),
('cms', '0022_auto_20180620_1551'),
('djangocms_charts', '0004_auto_20200521_2007'),
]
operations = [
migrations.CreateModel(
name='AxisOptionsGroupModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(help_text='Save and Reuse Options groups', max_length=100, verbose_name='Options Group Name')),
('type', models.CharField(blank=True, choices=[('linear', 'linear'), ('logarithmic', 'logarithmic'), ('category', 'category'), ('time', 'time'), ('radial', 'radial')], max_length=10, null=True, verbose_name='Axis Type')),
('slug', models.SlugField(blank=True, null=True, unique=True)),
('display', models.CharField(blank=True, choices=[('true', 'true'), ('false', 'false'), ('auto', 'auto')], help_text='Controls the axis global visibility (visible when true, hidden when false). When display: auto, the axis is visible only if at least one associated dataset is visible.', max_length=100, null=True, verbose_name='display')),
('weight', models.IntegerField(blank=True, help_text='The weight used to sort the axis. Higher weights are further away from the chart area.', null=True, verbose_name='weight')),
],
options={
'ordering': ['name'],
'abstract': False,
},
),
migrations.CreateModel(
name='AxisOptionsModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('label', models.CharField(help_text='Include the namespaces for options below root, e.g. hover.mode', max_length=150, verbose_name='Namespace Label')),
('type', models.CharField(default='text', help_text='Select the input data type', max_length=10, verbose_name='Data Type')),
('value', models.TextField(help_text='Value of the option JSON allowed', verbose_name='Option Value')),
('options_group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='options', to='djangocms_charts.AxisOptionsGroupModel')),
],
options={
'ordering': ['label'],
'abstract': False,
},
),
migrations.CreateModel(
name='ChartModel',
fields=[
('cmsplugin_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, related_name='djangocms_charts_chartmodel', serialize=False, to='cms.CMSPlugin')),
('label', models.CharField(blank=True, max_length=256, null=True, verbose_name='Name')),
('type', models.CharField(choices=[('line', 'line'), ('line_xy', 'line_xy'), ('bar', 'bar'), ('horizontalBar', 'horizontalBar'), ('radar', 'radar'), ('polarArea', 'polarArea'), ('pie', 'pie'), ('doughnut', 'doughnut'), ('bubble', 'bubble'), ('scatter', 'scatter')], max_length=50, verbose_name='Chart Type')),
('table_data', models.TextField(blank=True, verbose_name='Chart Table data')),
('labels_top', models.BooleanField(default=True, verbose_name='Labels top row')),
('labels_left', models.BooleanField(default=True, verbose_name='Labels left column')),
('data_series_format', models.CharField(choices=[('rows', 'rows'), ('cols', 'cols')], default='rows', max_length=10, verbose_name='Multiple Datasets in Rows or Columns')),
('color_by_dataset', models.BooleanField(blank=True, default=False, help_text='True to color each Dataset, False to color each element in a Series', null=True, verbose_name='Color by Dataset')),
('caption', models.TextField(blank=True, null=True, verbose_name='Caption text below chart')),
('display_title', models.BooleanField(default=True, verbose_name='Display Title')),
('display_legend', models.BooleanField(default=True, verbose_name='Display Legend')),
('legend_position', models.CharField(blank=True, choices=[('top', 'top'), ('left', 'left'), ('bottom', 'bottom'), ('right', 'right')], default='top', max_length=100, null=True, verbose_name='Legend Position')),
('chart_width', models.CharField(blank=True, max_length=50, null=True, verbose_name='Chart Width')),
('chart_height', models.CharField(blank=True, max_length=50, null=True, verbose_name='Chart Height')),
('chart_container_classes', models.TextField(blank=True, verbose_name='Additional classes for Chart Container')),
('chart_classes', models.TextField(blank=True, verbose_name='Additional classes for Chart')),
],
options={
'abstract': False,
},
bases=('cms.cmsplugin', models.Model),
),
migrations.CreateModel(
name='ChartOptionsGroupModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(help_text='Save and Reuse Options groups', max_length=100, verbose_name='Options Group Name')),
],
options={
'ordering': ['name'],
'abstract': False,
},
),
migrations.CreateModel(
name='ChartOptionsModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('label', models.CharField(help_text='Include the namespaces for options below root, e.g. hover.mode', max_length=150, verbose_name='Namespace Label')),
('type', models.CharField(default='text', help_text='Select the input data type', max_length=10, verbose_name='Data Type')),
('value', models.TextField(help_text='Value of the option JSON allowed', verbose_name='Option Value')),
('options_group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='options', to='djangocms_charts.ChartOptionsGroupModel')),
],
options={
'ordering': ['label'],
'abstract': False,
},
),
migrations.CreateModel(
name='ChartSpecificOptionsModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('label', models.CharField(help_text='Include the namespaces for options below root, e.g. hover.mode', max_length=150, verbose_name='Namespace Label')),
('type', models.CharField(default='text', help_text='Select the input data type', max_length=10, verbose_name='Data Type')),
('value', models.TextField(help_text='Value of the option JSON allowed', verbose_name='Option Value')),
('options_group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='options', to='djangocms_charts.ChartModel')),
],
options={
'ordering': ['label'],
'abstract': False,
},
),
migrations.CreateModel(
name='ColorGroupModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100, verbose_name='Color Group Name')),
],
options={
'verbose_name': 'Color Groups',
'verbose_name_plural': 'Color Groups',
},
),
migrations.CreateModel(
name='ColorModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('types', models.TextField(verbose_name='Select the Chart Types')),
('labels', models.TextField(verbose_name='Select the Namespace Labels')),
('colors', models.TextField(verbose_name='Select Multiple Colors')),
('color_group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='colors', to='djangocms_charts.ColorGroupModel')),
],
),
migrations.CreateModel(
name='DatasetModel',
fields=[
('cmsplugin_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, related_name='djangocms_charts_datasetmodel', serialize=False, to='cms.CMSPlugin')),
('label', models.CharField(blank=True, max_length=256, null=True, verbose_name='Name')),
('type', models.CharField(choices=[('line', 'line'), ('line_xy', 'line_xy'), ('bar', 'bar'), ('horizontalBar', 'horizontalBar'), ('radar', 'radar'), ('polarArea', 'polarArea'), ('pie', 'pie'), ('doughnut', 'doughnut'), ('bubble', 'bubble'), ('scatter', 'scatter')], max_length=50, verbose_name='Chart Type')),
('table_data', models.TextField(blank=True, verbose_name='Chart Table data')),
('labels_top', models.BooleanField(default=True, verbose_name='Labels top row')),
('labels_left', models.BooleanField(default=True, verbose_name='Labels left column')),
('data_series_format', models.CharField(choices=[('rows', 'rows'), ('cols', 'cols')], default='rows', max_length=10, verbose_name='Multiple Datasets in Rows or Columns')),
('color_by_dataset', models.BooleanField(blank=True, default=False, help_text='True to color each Dataset, False to color each element in a Series', null=True, verbose_name='Color by Dataset')),
('colors', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='datasetmodel_colors', to='djangocms_charts.ColorGroupModel')),
],
options={
'abstract': False,
},
bases=('cms.cmsplugin', models.Model),
),
migrations.CreateModel(
name='DatasetOptionsGroupModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(help_text='Save and Reuse Options groups', max_length=100, verbose_name='Options Group Name')),
],
options={
'ordering': ['name'],
'abstract': False,
},
),
migrations.CreateModel(
name='DatasetOptionsModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('label', models.CharField(help_text='Include the namespaces for options below root, e.g. hover.mode', max_length=150, verbose_name='Namespace Label')),
('type', models.CharField(default='text', help_text='Select the input data type', max_length=10, verbose_name='Data Type')),
('value', models.TextField(help_text='Value of the option JSON allowed', verbose_name='Option Value')),
('options_group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='options', to='djangocms_charts.DatasetOptionsGroupModel')),
],
options={
'ordering': ['label'],
'abstract': False,
},
),
migrations.CreateModel(
name='DatasetSpecificOptionsModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('label', models.CharField(help_text='Include the namespaces for options below root, e.g. hover.mode', max_length=150, verbose_name='Namespace Label')),
('type', models.CharField(default='text', help_text='Select the input data type', max_length=10, verbose_name='Data Type')),
('value', models.TextField(help_text='Value of the option JSON allowed', verbose_name='Option Value')),
('options_group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='options', to='djangocms_charts.DatasetModel')),
],
options={
'ordering': ['label'],
'abstract': False,
},
),
migrations.CreateModel(
name='GlobalOptionsGroupModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(help_text='Save and Reuse Options groups', max_length=100, verbose_name='Options Group Name')),
('enabled', models.BooleanField(blank=True, default=True, verbose_name='Enable Global Settings')),
('colors', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='global_colors', to='djangocms_charts.ColorGroupModel')),
('site', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sites.Site')),
],
options={
'ordering': ['name'],
'abstract': False,
},
),
migrations.CreateModel(
name='GlobalOptionsModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('label', models.CharField(help_text='Include the namespaces for options below root, e.g. hover.mode', max_length=150, verbose_name='Namespace Label')),
('type', models.CharField(default='text', help_text='Select the input data type', max_length=10, verbose_name='Data Type')),
('value', models.TextField(help_text='Value of the option JSON allowed', verbose_name='Option Value')),
('options_group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='options', to='djangocms_charts.GlobalOptionsGroupModel')),
],
options={
'ordering': ['label'],
'abstract': False,
},
),
migrations.AddField(
model_name='datasetmodel',
name='dataset_options_group',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='datasetmodel_options', to='djangocms_charts.DatasetOptionsGroupModel'),
),
migrations.AddField(
model_name='datasetmodel',
name='xAxis',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='datasetmodel_xAxis', to='djangocms_charts.AxisOptionsGroupModel'),
),
migrations.AddField(
model_name='datasetmodel',
name='yAxis',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='datasetmodel_yAxis', to='djangocms_charts.AxisOptionsGroupModel'),
),
migrations.AddField(
model_name='chartmodel',
name='chart_options_group',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='chart_options', to='djangocms_charts.ChartOptionsGroupModel'),
),
migrations.AddField(
model_name='chartmodel',
name='colors',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='chartmodel_colors', to='djangocms_charts.ColorGroupModel'),
),
migrations.AddField(
model_name='chartmodel',
name='dataset_options_group',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='chartmodel_options', to='djangocms_charts.DatasetOptionsGroupModel'),
),
migrations.AddField(
model_name='chartmodel',
name='xAxis',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='chartmodel_xAxis', to='djangocms_charts.AxisOptionsGroupModel'),
),
migrations.AddField(
model_name='chartmodel',
name='yAxis',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='chartmodel_yAxis', to='djangocms_charts.AxisOptionsGroupModel'),
),
]
|
# ─┐ ┬┌─┐┌┐┌┌─┐┌┐ ┬ ┬┌┬┐┌─┐ ─┐ ┬┬ ┬┌─┐
# ┌┴┬┘├┤ ││││ │├┴┐└┬┘ │ ├┤ ┌┴┬┘└┬┘┌─┘
# ┴ └─└─┘┘└┘└─┘└─┘ ┴ ┴ └─┘o┴ └─ ┴ └─┘
# Author: SENEX @ XENOBYTE.XYZ
# License: MIT License
# Website: https://xenobyte.xyz/projects/?nav=dotfiles
from bs4 import BeautifulSoup
import urllib.request
import re
import sys
# How to use
# ----------
# This script is to be called by a second bash / zsh alias that will call wget on the returned list
# of found media. If you're using zsh simply add the following script to .zshrc
# imgdl() {
# if [ ! -d $IMGBOARD_DL_DIR ]; then
# mkdir $IMGBOARD_DL_DIR;
# fi
# rm -rf $IMGBOARD_DL_DIR/*;
# cd $IMGBOARD_DL_DIR;
# python3 $SCRIPTS_DIR"download_images.py" $1 | xargs wget; # Path to script
# }
# This script will download all then media that matches the find_all substring and donwloads them
# to the specified folder.
if __name__ == "__main__":
assert len(sys.argv) == 2, "Invalid number of arguments"
url = sys.argv[1]
con = urllib.request.urlopen(urllib.request.Request(
url, headers={'User-Agent': "Magic Browser"}))
html = BeautifulSoup(con.read(), "html.parser")
img_list = []
# Remove the initial '//' in the href (used by 4chan and some free imageboard engines)
for a in html.find_all("a", {"href": re.compile("([-\w]+\.(?:bmp|jpeg|jpg|gif|png|webm|pdf|epub))")}):
m = str.replace(a["href"], "//", "")
if m not in img_list:
img_list.append(m)
# Print (parse) the processed list of found media to wget
print("\n".join(set(img_list)))
|
__all__ = ['find_fits_files', 'test_fits_keyword', 'test_coordinates', 'test_file_number', 'test_float_input',
'test_float_positive_input', 'test_int_input', 'test_int_positive_input', 'test_int_positive_non_zero_input',
'test_date', 'filter_map']
import numpy as np
import glob
import hops.pylightcurve3 as plc
from astropy.io import fits as pf
def find_fits_files(fits_file):
fits_list = glob.glob('*{0}*.f*t*'.format(fits_file)) + glob.glob('*{0}*.F*T*'.format(fits_file))
fits_list = list(np.unique(fits_list))
fits_list.sort()
return fits_list
def test_fits_keyword(fits_file, keyword):
if len(fits_file) == 0:
return [False, 'No keyword found']
else:
try:
fits_file = find_fits_files(fits_file)[0]
fits = pf.open(fits_file)
try:
fits = [fits['SCI']]
except KeyError:
sci_id = 0
for sci_id in range(len(fits)):
try:
if (fits[sci_id].data).all():
break
except:
pass
fits = [fits[sci_id]]
if fits[0].header[str(keyword)]:
return [True, 'Keyword found', fits[0].header[str(keyword)]]
else:
return [False, 'No keyword found']
except (KeyError, IndexError):
return [False, 'No keyword found']
def test_file_number(fits_file):
if len(fits_file) == 0:
test = 0
else:
test = len(find_fits_files(fits_file))
if test > 0:
return [True, '{0} files found'.format(test)]
else:
return [False, 'No files found']
def test_coordinates(ra_dec_string, single_line=False):
try:
ra_dec_string = ra_dec_string.split(' ')[0].split(':') + ra_dec_string.split(' ')[1].split(':')
target = plc.Target(plc.Hours(ra_dec_string[0], ra_dec_string[1], ra_dec_string[2]),
plc.Degrees(ra_dec_string[3], ra_dec_string[4], ra_dec_string[5]))
if single_line:
return[True, 'Coordinates accepted']
else:
return[True, 'Coordinates\naccepted']
except:
if single_line:
return [False, 'Wrong coordinates']
else:
return [False, 'Wrong\ncoordinates']
def test_float_input(input_str, typing):
if typing == '1':
try:
if float(input_str) >= 0:
return True
elif float(input_str) < 0:
return True
else:
return False
except ValueError:
return False
else:
return True
def test_float_positive_input(input_str, typing):
if typing == '1':
try:
if float(input_str) >= 0:
return True
else:
return False
except ValueError:
return False
else:
return True
def test_int_input(input_str, typing):
if typing == '1':
try:
if int(input_str):
return True
else:
return False
except ValueError:
return False
else:
return True
def test_int_positive_input(input_str, typing):
if typing == '1':
try:
if int(input_str) >= 0:
return True
else:
return False
except ValueError:
return False
else:
return True
def test_int_positive_non_zero_input(input_str, typing):
if typing == '1':
try:
if int(input_str) > 0:
return True
else:
return False
except ValueError:
return False
else:
return True
def test_date(year_month_string):
pass_test = True
if len(year_month_string.split()) != 2:
pass_test = False
elif len(year_month_string.split()[0]) != 4:
pass_test = False
elif len(year_month_string.split()[1]) != 2:
pass_test = False
else:
try:
year = int(year_month_string.split()[0])
month = int(year_month_string.split()[1])
if int(month) < 1 or int(month) > 12:
pass_test = False
if int(year) < 0:
pass_test = False
except:
pass_test = False
if pass_test:
return[True, 'Date\naccepted']
else:
return [False, 'Wrong\ndate']
filter_map = {'Clear': 'V', 'Luminance': 'V',
'U': 'U', 'B': 'B', 'V': 'V', 'R': 'R', 'I': 'I', 'H': 'H', 'J': 'J', 'K': 'K',
'u': 'u', 'b': 'b', 'v': 'v', 'y': 'y',
'u\'': 'u,', 'g\'': 'g,', 'r\'': 'r,', 'i\'': 'i,', 'z\'': 'z,',
'Astrodon ExoPlanet-BB': 'R',
'UV': 'U', 'Rc': 'R', 'Ic': 'I', 'Re': 'R', 'Ie': 'I', 'Y': 'y,', 'r': 'r,', 'z': 'z,', 'i': 'i,',
}
|
#
# 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 os
import sys
from benchmark.benchmark_stat import BenchmarkStat
class HdfsLogStat(BenchmarkStat):
def __init__(self, log_file="logs/log.txt"):
self._total_bytes = 0
self._file = log_file
self.header = "DFSClient:bytes"
def start(self):
# This stat relies on a log file already being generated ()
pass
def end(self):
if os.path.isfile(self._file):
self.parse()
def parse(self):
with open(self._file, 'r') as fd:
total = 0
for line in fd.readlines():
if "DFSClient readNextPacket" in line:
items = line.rstrip("\n").split(" ")
cur_bytes = int(items[10].split("=")[1])
total += cur_bytes
self._total_bytes = total
def __str__(self):
if self._total_bytes > 0:
return f"{self._total_bytes}"
return ""
if __name__ == "__main__":
file = sys.argv[1]
stat = HdfsLogStat(file)
stat.end()
print(stat)
|
# Generated by Django 4.0.1 on 2022-03-19 13:31
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('lab', '0016_run_new_status_squashed_0019_rename_new_status_run_status'),
('lab', '0019_alter_run_end_date_alter_run_start_date'),
]
operations = [
]
|
__all__ = ['WebClient']
import inspect
import input_helper as ih
import webclient_helper as wh
class WebClient(object):
"""Interact with an API on the web
If you need to obtain a token from a login endpoint, define a "login"
method when you subclass WebClient and set self._token and self._token_type
Example:
def login(self):
headers = {'Content-Type': 'application/json'}
data = {'email': self._username, 'password': self._password}
response = self.session.post(
self._base_url + '/api/login',
headers=headers,
json=data
)
self._token = response.json().get('access_token')
self._token_type = 'Bearer'
"""
def __init__(self, username=None, password=None, token=None, token_type=None,
base_url='', user_agent=None, content_type='application/json',
extra_headers={}):
"""
- username: if specified, set auth on session (requires password)
- password: if specified, set auth on session (requires username)
- token: if specified, use this token in the "Authorization" header
(requires token_type)
- token_type: if specified, use as part of the value in the
"Authorization" header
- base_url: base url for service/API that a subclass would interact with
- user_agent: if specified, set "User-Agent" header
- content_type: content type for requests
- extra_headers: a dict of extra headers to set on the session
If no login method is defined, any supplied username/password will be
passed to new_requests_session (for basic auth)
"""
self._username = username
self._password = password
self._token = token
self._token_type = token_type
self._base_url = base_url.strip('/')
self._user_agent = user_agent
self._content_type = content_type
self._extra_headers = extra_headers
self._history = []
self.set_session()
def login(self):
pass
def is_login_defined(self):
"""Return True if a login method is defined"""
return inspect.getsource(self.login) != ' def login(self):\n pass\n'
def _set_auth_header(self):
"""Add "Authorization" header on session if self._token is set"""
if self._token:
self.session.headers.update({
'Authorization': '{} {}'.format(self._token_type, self._token)
})
def set_session(self):
"""Get a new session object for self.session and invoke login method"""
if self.is_login_defined():
self.session = wh.new_requests_session(
user_agent=self._user_agent,
content_type=self._content_type,
extra_headers=self._extra_headers
)
self.login()
else:
self.session = wh.new_requests_session(
username=self._username,
password=self._password,
user_agent=self._user_agent,
content_type=self._content_type,
extra_headers=self._extra_headers
)
if self._token:
assert self._token_type is not None, "self._token is set, but not self._token_type"
self._set_auth_header()
def OPTIONS(self, url, headers=None, debug=False, **kwargs):
"""Send a OPTIONS request and return response object
- url: url/endpoint
- headers: dict of headers to update on the session before making request
- debug: if True, enter debugger before returning
Other kwargs are passed to webclient_helper.session_method
"""
response = wh.session_method(
'options',
url,
session=self.session,
headers=headers,
debug=debug,
**kwargs
)
self._history.append({
'caller': inspect.getouterframes(inspect.currentframe(), 2)[1][3],
'summary': wh.get_summary_from_response(response),
'response': response
})
return response
def HEAD(self, url, headers=None, debug=False, **kwargs):
"""Send a HEAD request and return response object
- url: url/endpoint
- headers: dict of headers to update on the session before making request
- debug: if True, enter debugger before returning
Other kwargs are passed to webclient_helper.session_method
"""
response = wh.session_method(
'head',
url,
session=self.session,
headers=headers,
debug=debug,
**kwargs
)
self._history.append({
'caller': inspect.getouterframes(inspect.currentframe(), 2)[1][3],
'summary': wh.get_summary_from_response(response),
'response': response
})
return response
def GET(self, url, headers=None, params=None, debug=False, **kwargs):
"""Send a GET request and return response object
- url: url/endpoint
- headers: dict of headers to update on the session before making request
- params: a dict with query string vars and values
- debug: if True, enter debugger before returning
Other kwargs are passed to webclient_helper.session_method
"""
response = wh.session_method(
'get',
url,
session=self.session,
headers=headers,
params=params,
debug=debug,
**kwargs
)
self._history.append({
'caller': inspect.getouterframes(inspect.currentframe(), 2)[1][3],
'summary': wh.get_summary_from_response(response),
'response': response
})
return response
def POST(self, url, headers=None, data=None, json=None, debug=False, **kwargs):
"""Send a POST request and return response object
- url: url/endpoint
- headers: dict of headers to update on the session before making request
- data: a dict to send in the body (non-JSON)
- json: a dict to send in the body
- debug: if True, enter debugger before returning
Other kwargs are passed to webclient_helper.session_method
"""
response = wh.session_method(
'post',
url,
session=self.session,
headers=headers,
data=data,
json=json,
debug=debug,
**kwargs
)
self._history.append({
'caller': inspect.getouterframes(inspect.currentframe(), 2)[1][3],
'summary': wh.get_summary_from_response(response),
'response': response
})
return response
def PUT(self, url, headers=None, data=None, debug=False, **kwargs):
"""Send a PUT request and return response object
- url: url/endpoint
- headers: dict of headers to update on the session before making request
- data: a dict to send in the body (non-JSON)
- debug: if True, enter debugger before returning
Other kwargs are passed to webclient_helper.session_method
"""
response = wh.session_method(
'put',
url,
session=self.session,
headers=headers,
data=data,
debug=debug,
**kwargs
)
self._history.append({
'caller': inspect.getouterframes(inspect.currentframe(), 2)[1][3],
'summary': wh.get_summary_from_response(response),
'response': response
})
return response
def PATCH(self, url, headers=None, data=None, debug=False, **kwargs):
"""Send a PATCH request and return response object
- url: url/endpoint
- headers: dict of headers to update on the session before making request
- data: a dict to send in the body (non-JSON)
- debug: if True, enter debugger before returning
Other kwargs are passed to webclient_helper.session_method
"""
response = wh.session_method(
'patch',
url,
session=self.session,
headers=headers,
data=data,
debug=debug,
**kwargs
)
self._history.append({
'caller': inspect.getouterframes(inspect.currentframe(), 2)[1][3],
'summary': wh.get_summary_from_response(response),
'response': response
})
return response
def DELETE(self, url, headers=None, debug=False, **kwargs):
"""Send a DELETE request and return response object
- url: url/endpoint
- headers: dict of headers to update on the session before making request
- debug: if True, enter debugger before returning
Other kwargs are passed to webclient_helper.session_method
"""
response = wh.session_method(
'delete',
url,
session=self.session,
headers=headers,
debug=debug,
**kwargs
)
self._history.append({
'caller': inspect.getouterframes(inspect.currentframe(), 2)[1][3],
'summary': wh.get_summary_from_response(response),
'response': response
})
return response
def history_explorer(self, return_selections=False):
"""Select responses from history to explore in ipython (if ipython installed)
- return_selections: if True, return the selections from history
"""
selected_responses = ih.make_selections(
self._history,
item_format='{summary} (called by {caller})',
prompt='Select responses to inspect',
wrap=False,
)
if selected_responses:
ih.start_ipython(warn=True, selected_responses=selected_responses)
if return_selections:
return selected_responses
|
### imports ###
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from scipy.integrate import odeint
import random as rd
### parameters ###
### E.coli parameters
growth_rate = 0.04
e_max_size = 2
e_min_size = 1
### minicell parameters
minicell_production_rate = 0
m_max_size = 1/5 * e_max_size
m_min_size = 1/5 * e_min_size
######### implementation ###########
### initialisation
cell_count = [1]
minicell_count = []
tmax = 300
time = np.linspace(0,tmax,1000)
tspan = tmax/len(time)
counts = [] # [[[]=cell_count,[]=minicell_count]=t0 , []=cell_count,[]=minicell_count]=t1 , ...]
div = []
mini = []
''' a) exponential growth, constant minicells production'''
minicell_production_rate = 0.03
### functions ###
def minicell_production(cell,t,tspan):
minicell = 0
if cell > e_min_size + m_max_size:
if ((t-tspan)*minicell_production_rate)%1 > (t*minicell_production_rate)%1 :
minicell = m_max_size
cell = cell - minicell
return cell, minicell
### simulation
for t in time:
for i in range(len(cell_count)):
cell_count[i] = round(cell_count[i] + cell_count[i]*growth_rate*tspan,5)
cell_count[i], minicell = minicell_production(cell_count[i],t,tspan)
if minicell > 0:
minicell_count.append(minicell)
mini.append(t)
if cell_count[i] >= 2:
div.append(t)
cell_count[i] = cell_count[i]/2
new_cell = cell_count[i]
cell_count.append(new_cell)
counts.append([cell_count.copy(),minicell_count.copy()])
### plot
plt.figure()
prev = 0
col = 0
for i in range(len(counts)):
if prev < len(counts[i][0]):
style = ["-","--","-."]
prev = len(counts[i][0])
life = time[i:]
plt.plot(life, [l[0][col] for l in counts[i:]], style[col%3], label='cell'+str(prev))
col = col + 1
for t in div:
plt.axvline(x=t, color='lightgrey', linestyle=':')
for t in mini:
plt.axvline(x=t, color='darkgrey', linestyle='--')
plt.ylim([1,2.5])
plt.xlabel("time (m)")
plt.ylabel("size of the bacteria (micron)")
plt.legend()
plt.show()
plt.close()
## counts
final_population = counts[-1]
print("cellule=", len(final_population[0])," minicellule=", len(final_population[1]))
plt.figure()
plt.plot(time,[len(x[0]) for x in counts], label='n of cells')
plt.plot(time,[len(x[1]) for x in counts], label='n of minicells')
plt.legend()
plt.show()
plt.close()
''' b) exponential growth, exponential minicell production'''
minicell_production_rate = 0.02
rate_sum = 0
### functions ###
def minicell_production(cell,t,tspan,rate_sum):
minicell = 0
if cell > e_min_size + m_max_size:
if (rate_sum - (minicell_production_rate*cell*tspan))%1 > rate_sum%1 :
minicell = m_max_size
cell = cell - minicell
return cell, minicell
### simulation
for t in time:
for i in range(len(cell_count)):
cell_count[i] = round(cell_count[i] + cell_count[i]*growth_rate*tspan,5)
if i == 0:
rate_sum = rate_sum + (minicell_production_rate*cell_count[i]*tspan)
cell_count[i], minicell = minicell_production(cell_count[i],t,tspan,rate_sum)
if minicell > 0:
minicell_count.append(minicell)
mini.append(t)
if cell_count[i] >= 2:
div.append(t)
cell_count[i] = cell_count[i]/2
new_cell = cell_count[i]
cell_count.append(new_cell)
counts.append([cell_count.copy(),minicell_count.copy()])
### plot
plt.figure()
prev = 0
col = 0
for i in range(len(counts)):
if prev < len(counts[i][0]):
style = ["-","--","-."]
prev = len(counts[i][0])
life = time[i:]
plt.plot(life, [l[0][col] for l in counts[i:]], style[col%3], label='cell'+str(prev))
col = col + 1
for t in div:
plt.axvline(x=t, color='lightgrey', linestyle=':')
for t in mini:
plt.axvline(x=t, color='darkgrey', linestyle='--')
plt.legend()
plt.ylim([1,2.5])
plt.xlabel("time (m)")
plt.ylabel("size of the bacteria (micron)")
plt.show()
plt.close()
### counts
final_population = counts[-1]
print("cellule=", len(final_population[0])," minicellule=", len(final_population[1]))
plt.figure()
plt.plot(time,[len(x[0]) for x in counts], label='n of cells')
plt.plot(time,[len(x[1]) for x in counts], label='n of minicells')
plt.legend()
plt.show()
plt.close()
''' c) exponential growth, constant minicells '''
minicell_production_rate = 0.03
### functions ###
def minicell_production(cell,tspan):
minicell = 0
if cell > e_min_size + m_max_size:
threshold = minicell_production_rate*tspan*10000
random = rd.randint(1,10000)
if random <= threshold:
minicell = m_max_size
cell = cell - minicell
return cell, minicell
### simulation
for t in time:
for i in range(len(cell_count)):
cell_count[i] = round(cell_count[i] + cell_count[i]*growth_rate*tspan,5)
cell_count[i], minicell = minicell_production(cell_count[i],tspan)
if minicell > 0:
minicell_count.append(minicell)
mini.append(t)
if cell_count[i] >= 2:
div.append(t)
cell_count[i] = cell_count[i]/2
new_cell = cell_count[i]
cell_count.append(new_cell)
counts.append([cell_count.copy(),minicell_count.copy()])
### plot
plt.figure()
prev = 0
col = 0
for i in range(len(counts)):
if prev < len(counts[i][0]):
style = ["-","--","-."]
prev = len(counts[i][0])
life = time[i:]
plt.plot(life, [l[0][col] for l in counts[i:]], style[col%3], label='cell'+str(prev))
col = col + 1
for t in div:
plt.axvline(x=t, color='lightgrey', linestyle=':')
for t in mini:
plt.axvline(x=t, color='darkgrey', linestyle='--')
plt.ylim([1,2.5])
plt.xlabel("time (m)")
plt.ylabel("size of the bacteria (micron)")
plt.legend()
plt.show()
plt.close()
## counts
final_population = counts[-1]
print("cellule=", len(final_population[0])," minicellule=", len(final_population[1]))
plt.figure()
plt.plot(time,[len(x[0]) for x in counts], label='n of cells')
plt.plot(time,[len(x[1]) for x in counts], label='n of minicells')
plt.yscale('log')
plt.legend()
plt.show()
plt.close()
''' d) exponential growth, constant minicells '''
minicell_production_rate = 0.02
## functions ###
def minicell_production(cell,tspan):
minicell = 0
if cell > e_min_size + m_max_size:
threshold = minicell_production_rate*tspan*10000*cell
random = rd.randint(1,10000)
if random <= threshold:
minicell = m_max_size
cell = cell - minicell
return cell, minicell
## simulation
for t in time:
for i in range(len(cell_count)):
cell_count[i] = round(cell_count[i] + cell_count[i]*growth_rate*tspan,5)
cell_count[i], minicell = minicell_production(cell_count[i],tspan)
if minicell > 0:
minicell_count.append(minicell)
mini.append(t)
if cell_count[i] >= 2:
div.append(t)
cell_count[i] = cell_count[i]/2
new_cell = cell_count[i]
cell_count.append(new_cell)
counts.append([cell_count.copy(),minicell_count.copy()])
### plot
plt.figure()
prev = 0
col = 0
for i in range(len(counts)):
if prev < len(counts[i][0]):
style = ["-","--","-."]
prev = len(counts[i][0])
life = time[i:]
plt.plot(life, [l[0][col] for l in counts[i:]], style[col%3], label='cell'+str(prev))
col = col + 1
for t in div:
plt.axvline(x=t, color='lightgrey', linestyle=':')
for t in mini:
plt.axvline(x=t, color='darkgrey', linestyle='--')
plt.ylim([1,2.5])
plt.xlabel("time (m)")
plt.ylabel("size of the bacteria (micron)")
plt.legend()
plt.show()
plt.close()
## counts
final_population = counts[-1]
print("cellule=", len(final_population[0])," minicellule=", len(final_population[1]))
plt.figure()
plt.plot(time,[len(x[0]) for x in counts], label='n of cells')
plt.plot(time,[len(x[1]) for x in counts], label='n of minicells')
plt.yscale('log')
plt.legend()
plt.xlabel('time')
plt.ylabel('count')
plt.show()
plt.close()
# plot ration
plt.figure()
plt.plot(time,[len(x[1])/len(x[0]) for x in counts], label='ratio minicells/cells')
plt.legend()
plt.xlabel('time')
plt.ylabel('ratio')
plt.show()
plt.close()
|
# -*- coding: utf-8 -*-
from optimus.assets.registry import register_assets
from optimus.pages.builder import PageBuilder
from optimus.utils import initialize
def builder_interface(settings, views):
"""
Build all enabled pages from given views module.
Arguments:
settings (optimus.conf.model.SettingsModel): Settings object which defines
everything required for building.
views (object): Module which defines page views to build, in fact the module
object require only a ``PAGES`` attribute that is a list of Page view.
Returns:
dict: A dictionnary with initialized builder (``builder`` item), asset manager
(``assets_env`` item) and the list of builded pages (``builded`` item).
"""
# Initialize required structure according to settings
initialize(settings)
# Init asset manager
assets_env = register_assets(settings)
# Init page builder
builder = PageBuilder(settings, assets_env=assets_env)
# Proceed to page building from registered pages
builded = builder.build_bulk(views.PAGES)
return {
"assets_env": assets_env,
"builded": builded,
"builder": builder,
}
|
from django.urls import path
from . import views
app_name='menu'
urlpatterns = [
path("",views.menuPage,name='menu'),
path("logout",views.logoutPage,name='logout')
] |
import cv2
import os
import argparse
# References: https://blog.csdn.net/bryant_meng/article/details/110079285
# Check test_video.sh in the main directory
# Step1: Run video_to_frame to convert low light video to images
# Step2: Run test.py to convert low light images to enhanced images
# Step3: Run image_to_video to convert enhanced images to enhanced videos
def parse_args():
parser = argparse.ArgumentParser(description="Video_Demo")
parser.add_argument('--video_path', type=str, default='demo/Movie/Ari.mp4')
parser.add_argument("--image_lowlight_folder", type=str, default='data/test_data/Frames/%d.jpg')
parser.add_argument('--image_folder', type=str, default='data/test_output/Frames/')
parser.add_argument('--save_path', type=str, default='demo/Movie/Res.mp4')
parser.add_argument('--choice', type=str, required=True, choices = ['V2I', 'I2V'], default='V2I')
args = parser.parse_args()
return args
def cal_frame(args):
video_cap = cv2.VideoCapture(args.video_path)
frame_count = 0
while True:
ret, frame = video_cap.read()
if ret is False:
break
frame_count = frame_count + 1
print(frame_count)
def cal_fps(args):
video = cv2.VideoCapture(args.video_path)
fps = video.get(cv2.CAP_PROP_FPS)
# print("Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(fps))
video.release()
return fps
def video_to_frame(args):
vidcap = cv2.VideoCapture(args.video_path) # TODO: replace with url like youtube link
success, image = vidcap.read()
count = 0
while success:
cv2.imwrite(args.image_lowlight_folder % count, image)
success, image = vidcap.read()
print('Read a new frame: ', success)
count += 1
print("We have %2d images" % count)
def image_to_video(args):
img = cv2.imread(args.image_folder + '0.jpg')
fps = cal_fps(args)
size = (img.shape[1], img.shape[0])
print(size)
fourcc = cv2.VideoWriter_fourcc(*"mp4v") # For mp4 only
videoWrite = cv2.VideoWriter(args.save_path, fourcc, fps, size)
files = os.listdir(args.image_folder)
out_num = len(files)
for i in range(0, out_num):
fileName = args.image_folder + str(i) + '.jpg'
img = cv2.imread(fileName)
videoWrite.write(img)
def main(args):
if args.choice == 'V2I':
video_to_frame(args)
elif args.choice == 'I2V':
image_to_video(args)
else:
raise TypeError
if __name__ == "__main__":
args = parse_args()
main(args)
|
from cs50 import SQL
from flask import Flask, flash, redirect, render_template, request, session, url_for
from flask_session import Session
from passlib.apps import custom_app_context as pwd_context
from tempfile import mkdtemp
from passlib.context import CryptContext
from helpers import *
import helpers
# configure application
app = Flask(__name__)
# ensure responses aren't cached
if app.config["DEBUG"]:
@app.after_request
def after_request(response):
response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate"
response.headers["Expires"] = 0
response.headers["Pragma"] = "no-cache"
return response
# custom filter
app.jinja_env.filters["usd"] = usd
# configure session to use filesystem (instead of signed cookies)
app.config["SESSION_FILE_DIR"] = mkdtemp()
app.config["SESSION_PERMANENT"] = False
app.config["SESSION_TYPE"] = "filesystem"
Session(app)
# configure CS50 Library to use SQLite database
db = SQL("sqlite:///finance.db")
@app.route("/")
@login_required
def index():
return render_template("index.html")
@app.route("/regs")
def regs():
rows = db.execute("SELECT * from users")
return render_template("registrants.html", registrants=rows)
@app.route("/unregister", methods=["GET", "POST"])
def unregister():
if request.method == "GET":
rows = db.execute("SELECT * from users")
return render_template("unregister.html", registrants=rows)
elif request.method == "POST":
if request.form["id"]:
id=request.form.get("id")
db.execute("DELETE FROM users WHERE id=:id;", id=id)
return redirect(url_for("regs"))
@app.route("/play")
@login_required
def play():
"""Show history of transactions."""
return render_template("play.html")
@app.route("/leaderboard")
@login_required
def play2():
"""Show history of transactions."""
return render_template("play2.html")
@app.route("/login", methods=["GET", "POST"])
def login():
"""Log user in."""
# forget any user_id
session.clear()
# if user reached route via POST (as by submitting a form via POST)
if request.method == "POST":
# ensure username was submitted
if not request.form.get("username"):
return apology("must provide username")
# ensure password was submitted
elif not request.form.get("password"):
return apology("must provide password")
# query database for username
rows = db.execute("SELECT * FROM users WHERE username = :username", username=request.form.get("username"))
# ensure username exists and password is correct
if len(rows) != 1 or not pwd_context.verify(request.form.get("password"), rows[0]["hash"]):
return apology("invalid username and/or password")
# query database for username
db.execute("SELECT * FROM users WHERE username = :username", username=request.form.get("username"))
# remember which user has logged in
session["user_id"] = rows[0]["id"]
flash('Welcome back!')
# redirect user to home page
return redirect(url_for("index"))
# else if user reached route via GET (as by clicking a link or via redirect)
else:
return render_template("login.html")
@app.route("/logout")
def logout():
"""Log user out."""
# forget any user_id
session.clear()
# redirect user to login form
return redirect(url_for("login"))
@app.route("/register", methods=["GET", "POST"])
def register():
# forget any user_id
session.clear()
# if user reached route via POST (as by submitting a form via POST)
if request.method == "POST":
# ensure username was submitted
if not request.form.get("username"):
return apology("must provide username")
# ensure password was submitted
elif not request.form.get("password"):
return apology("must provide password")
# ensure password was submitted
elif not request.form.get("password2"):
return apology("must provide password")
# ensure password was submitted
if request.form.get("password") != request.form.get("password2"):
return apology("passwords should be the same!")
# query database for username
rows = db.execute("SELECT * FROM users WHERE username = :username", username=request.form.get("username"))
# ensure username exists and password is correct
if len(rows) == 1:
return apology("username exists")
# ensure doesn't exist
if len(rows) != 1:
#encrypt pass and save them to db
encrypted=pwd_context.encrypt(request.form.get("password"))
db.execute("INSERT INTO users (username, hash) VALUES(:username, :hash)",username=request.form.get("username"),hash=encrypted)
# query database for username
rows = db.execute("SELECT * FROM users WHERE username = :username", username=request.form.get("username"))
# remember which user has logged in
session["user_id"] = rows[0]["id"]
# redirect user to home page
return redirect(url_for("index"))
# else if user reached route via GET (as by clicking a link or via redirect)
else:
return render_template("register.html")
@app.route("/change", methods=["GET", "POST"])
@login_required
def change():
#return apology("TODO")
id = session["user_id"]
# if user reached route via POST (as by submitting a form via POST)
if request.method == "POST":
# ensure password was submitted
if not request.form.get("password"):
return apology("must provide the old password")
# query database for username
rows = db.execute("SELECT * FROM users WHERE id = :id", id=id)
# ensure username exists and password is correct
if len(rows) != 1 or not pwd_context.verify(request.form.get('password'), rows[0]['hash']):
return apology("old password invalid")
# ensure password2 was submitted
elif not request.form.get("password2"):
return apology("must provide the new password")
encrypted2=pwd_context.encrypt(request.form.get("password2"))
db.execute("UPDATE users SET hash=:hashes WHERE id=:id",id=id, hashes=encrypted2)
return redirect(url_for('logout'))
# else if user reached route via GET (as by clicking a link or via redirect)
else:
return render_template("change.html")
@app.route("/uchange", methods=["GET", "POST"])
@login_required
def uchange():
#return apology("TODO")
id = session["user_id"]
if request.method == "POST":
# ensure password was submitted
if not request.form.get("name"):
return apology("must provide the old name")
# query database for username
rows = db.execute("SELECT * FROM users WHERE id = :id", id=id)
# ensure username exists and password is correct
if len(rows) != 1 or not request.form.get('name') == rows[0]['username']:
return apology("old username invalid")
# ensure password2 was submitted
elif not request.form.get("name2"):
return apology("must provide the new username")
# query database for username
rowss = db.execute("SELECT * FROM users WHERE username = :username", username=request.form.get("name2"))
# ensure username exists and password is correct
if len(rowss) == 1:
return apology("username already taken")
else:
db.execute("UPDATE users SET username=:username WHERE id=:id",id=id, username=request.form.get('name2'))
return redirect(url_for('logout'))
# else if user reached route via GET (as by clicking a link or via redirect)
else:
return render_template("uchange.html")
|
from django.core.management.base import BaseCommand
from ...utils import get_user_model
class Command(BaseCommand):
help = "Sync customer data"
def handle(self, *args, **options):
User = get_user_model()
qs = User.objects.exclude(customer__isnull=True)
count = 0
total = qs.count()
for user in qs:
count += 1
perc = int(round(100 * (float(count) / float(total))))
__username = ''
if hasattr(user, 'username'):
__username = user.username
elif hasattr(user, 'email'):
__username = user.email
print("[{0}/{1} {2}%] Syncing {3} [{4}]".format(
count, total, perc, __username, user.pk
))
customer = user.customer
cu = customer.stripe_customer
customer.sync(cu=cu)
customer.sync_current_subscription(cu=cu)
customer.sync_invoices(cu=cu)
customer.sync_charges(cu=cu)
|
#!/usr/bin/python
import os
os.system('geany')
|
from __future__ import print_function
from potentiostat import Potentiostat
import sys
import time
if len(sys.argv) > 1:
port = sys.argv[1]
else:
port = '/dev/ttyACM0'
num_sample = 10
sleep_dt = 1.0
dev = Potentiostat(port)
dev.set_volt_range('5V')
dev.set_curr_range('100uA')
dev.set_volt(0.0)
volt_list = []
curr_list = []
for i in range(num_sample):
if i < num_sample/2:
volt = -3.0
else:
volt = 3.0
dev.set_volt(volt)
curr = dev.get_curr()
volt_list.append(volt)
curr_list.append(curr)
print('({0}/{1}) volt: {2}, curr: {3}'.format(i+1, num_sample, volt,curr))
time.sleep(sleep_dt)
print()
print('volt_list: ', volt_list)
print('curr_list: ', curr_list)
|
import time
import logging
import cfg
import sensorlogger
import logentry
import batteryManager
import circularlist
#import actor abstract
import absactor
class Actor(absactor.Actor):
def __init__(self):
self.logger = logging.getLogger("PB.actor.real")
self.logger.info("Starting up")
self.batteryManagerObj = batteryManager.BatteryManager()
self.batteryManagerObj.startThreads()
self.sensorLogger = sensorlogger.Logger()
self.keepRunning = True
self.batteryCur = False
self.batteryMax = False
self.currentBuf = circularlist.CircularList(3)
def run(self):
try:
while self.keepRunning:
currentIn, currentOut, voltage, lux = self.batteryManagerObj.getReadings()
if lux < cfg.cfg["actor.real.luxThreshold"]:
if currentOut > cfg.cfg["actor.real.currentOutThreshold"]:
if self.getSoC() < 1.0:
rStatus = self.batteryManagerObj.relay.getStatus(cfg.cfg["actor.real.relay.charge"])
if rStatus[1] > cfg.cfg["actor.real.relay.chargeTimeout"]:
self.batteryManagerObj.relay.disable(cfg.cfg["actor.real.relay.charge"])
else:
self.batteryManagerObj.relay.enable(cfg.cfg["actor.real.relay.charge"])
if currentIn < cfg.cfg["actor.real.currentInThreshold"]:
self.batteryMax = self.batteryCur
self.currentBuf.addElement(currentIn-currentOut)
self.batteryCur += self.currentBuf.sRule(cfg.cfg["actor.real.interval"])
relays = repr(self.batteryManagerObj.relay.status)
logEntry = logentry.Entry(currentInReading=currentIn, currentOutReading=currentOut, voltageReading=voltage, luxReading=lux, SoCalc=self.getSoC(), ampSeconds=self.batteryCur, relayStatus=relays)
self.sensorLogger.recordEntry(logEntry)
time.sleep(cfg.cfg["actor.real.interval"])
except KeyboardInterrupt:
self.batteryManagerObj.shutdown()
self.sensorLogger.fileObj.flush()
if "sensorlogger.upload.connDetails" in cfg.cfg:
self.sensorLogger.uploadFile(self.sensorLogger.logFileName)
def getSoC(self):
if self.batteryCur and self.batteryMax:
return self.batteryCur/self.batteryMax
else:
return False
|
import os
import traceback
from time import time
import click
from site_speaker.speech.CrtAdapter import CrtAdapter
from site_speaker.utils.export import export
from site_speaker.utils.files import get_all_files_with_extension, get_base_filename
from site_speaker.utils.parsing import get_posts
from site_speaker.utils.queries import query
from site_speaker.utils.string import stringify_number
@click.group()
def main():
pass
@main.command()
@click.argument('url', type=str)
@click.option('--output-path', '-o', type=str)
def read(url: str, output_path: str):
page = query(url, as_html=True)
posts = get_posts(html=page, class_name='post__text')
export(output_path=output_path, posts=posts)
@main.command()
@click.argument('url_pattern', type=str)
@click.option('--n_pages', '-n', default=None, type=int)
@click.option('--output-path', '-o', type=str)
def read_many(url_pattern: str, n_pages: int, output_path: str):
posts = []
i = 0
n_new_posts = None
try:
while (n_new_posts is None or n_new_posts > 0) and (n_pages is None or i < n_pages):
start = time()
page = query(url_pattern.format(i=i), as_html=True)
new_posts = get_posts(html=page, class_name='post__text')
n_new_posts = len(new_posts)
print(f'Handled {stringify_number(i + 1)} page in {time() - start:.3f} seconds (fetched {n_new_posts} new posts)')
if n_new_posts == 0:
break
posts.extend(new_posts)
i += 1
except Exception:
print(f'Oops, an exception occurred:')
print(traceback.format_exc())
export(output_path=output_path, posts=posts)
@main.command()
@click.option('--input-path', '-i', type=str)
@click.option('--output-path', '-o', type=str)
def tts(input_path: str, output_path: str):
adapter = CrtAdapter()
adapter.generate_audio(input_file_path=input_path, output_file_path=output_path)
@main.command()
@click.option('--input-path', '-i', type=str)
@click.option('--src-extension', '-s', default='txt', type=str)
@click.option('--output-path', '-o', type=str)
@click.option('--dst-extension', '-d', default='mp3', type=str)
def tts_many(input_path: str, output_path: str, src_extension: str, dst_extension: str):
os.makedirs(output_path, exist_ok=True)
adapter = CrtAdapter()
files = get_all_files_with_extension(input_path, src_extension)
n_files = len(files)
for i, input_file_path in enumerate(sorted(files)):
base_input_filename = get_base_filename(input_file_path)
output_file_path = os.path.join(output_path, f'{base_input_filename}.{dst_extension}')
adapter.generate_audio(input_file_path=input_file_path, output_file_path=output_file_path)
print(f'Handled {i + 1} / {n_files} files')
if __name__ == '__main__':
main()
|
# Code listing #8
def rms(varray=[]):
""" RMS velocity """
squares = map(lambda x: x*x, varray)
return pow(sum(squares), 0.5)
def rms(varray=[]):
""" Root mean squared velocity. Returns
square root of sum of squares of velocities """
squares = map(lambda x: x*x, varray)
return pow(sum(squares), 0.5)
|
from flask import render_template, Blueprint, request, redirect, url_for
from flask_login import login_required, current_user
from project.dataset.form import DatasetForm
from project.models import Dataset, College
from project import db
dataset_blueprint = Blueprint(
'dataset', __name__,
template_folder='templates'
)
@dataset_blueprint.route('/new_data', methods=['GET', 'POST'])
@login_required
def newDataset():
user = current_user
form = DatasetForm(request.form)
if request.method == 'POST':
if form.validate_on_submit():
dataset = Dataset()
dataset.college_id = form.college.data.id
dataset.accept_transfer = form.acceptTransfers.data
dataset.year = form.year.data
dataset.transfer_applicants = form.applied.data
dataset.transfer_admitted = form.admitted.data
dataset.transfer_enrolled = form.enrolled.data
dataset.reference = form.reference.data
db.session.add(dataset)
db.session.commit()
return redirect(url_for('home.home'))
else:
print(form.errors)
return render_template('edit_dataset.html', user=user, form=form)
@dataset_blueprint.route('/edit_dataset/<string:id>', methods=['GET', 'POST'])
@login_required
def editDataset(id):
user = current_user
form = DatasetForm(request.form)
dataset = Dataset.query.filter_by(id=id).first()
if request.method == 'GET':
form.college.data = College.query.filter_by(id=dataset.college_id).first()
form.acceptTransfers.data = dataset.accept_transfer
form.year.data = dataset.year
form.applied.data = dataset.transfer_applicants
form.admitted.data = dataset.transfer_admitted
form.enrolled.data = dataset.transfer_enrolled
form.reference.data = dataset.reference
form.totalEnrollment.data = dataset.total_enrollment
form.minTransferGrade.data = dataset.min_transfer_grade
form.maxCredits2Yr.data = dataset.max_credits_2yr
form.maxCredits4Yr.data = dataset.max_credits_4yr
if request.method == 'POST':
if form.validate_on_submit():
#dataset.college_id = form.college.data.id
dataset.accept_transfer = form.acceptTransfers.data
dataset.year = form.year.data
dataset.transfer_applicants = form.applied.data
dataset.transfer_admitted = form.admitted.data
dataset.transfer_enrolled = form.enrolled.data
dataset.reference = form.reference.data
dataset.total_enrollment = form.totalEnrollment.data
dataset.min_transfer_grade = form.minTransferGrade.data
dataset.max_credits_2yr = form.maxCredits2Yr.data
dataset.max_credits_4yr = form.maxCredits4Yr.data
db.session.commit()
return redirect(url_for('home.home'))
else:
print(form.errors)
return render_template('edit_dataset.html', user=user, form=form)
@dataset_blueprint.route('/data/<string:id>')
def viewDataset(id):
user = current_user
dataset = Dataset.query.filter_by(id=id).first()
college = College.query.filter_by(id=dataset.college_id).first()
return render_template('view_dataset.html', user=user, dataset=dataset, col=college) |
"""Definition of a hook.
Logger hooks are functions or callable classes that receive all the logging information that should be logged
by the instance in any of the calls of the debug, info, warning, error, fatal or critical methods.
Example:
def hook_function(context: HookContext) -> NoReturn:
if 'foo' in context.extra_data:
del context.extra_data['foo']
logger.add_hook(hook_function)
Example2:
class HookClass:
def __call__(self, context: HookContext) -> NoReturn:
if 'bar' in context.extra_data:
context.extra_data['bar'] = context.extra_data['foo']
logger.add_hook(HookClass())
"""
from typing import Callable, NoReturn
from elasticlogger.hooks.hook_context import HookContext
Hook = Callable[[HookContext], NoReturn]
|
#!/usr/bin/python
"""
Copyright 2013 Jack Kelly (aka Daniel)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import print_function, division
import unittest
import slicedpy.utils as utils
import pandas as pd
class TestUtils(unittest.TestCase):
def test_find_nearest(self):
N = 24
idx = pd.date_range('2013-01-01', periods=N, freq='H')
series = pd.Series(None, index=idx)
for i in range(N):
nearest = utils.find_nearest(series, idx[i])
self.assertEqual(nearest, i)
idx_ten_mins = pd.date_range('2013-01-01 00:10', periods=N, freq='H')
for i in range(N):
nearest = utils.find_nearest(series, idx_ten_mins[i])
self.assertEqual(nearest, i)
idx_fifty_mins = pd.date_range('2013-01-01 00:50', periods=N, freq='H')
for i in range(N-1):
nearest = utils.find_nearest(series, idx_fifty_mins[i])
self.assertEqual(nearest, i+1)
nearest = utils.find_nearest(series, idx_fifty_mins[-1])
self.assertEqual(nearest, N-1)
# create events of duration = 50 mins so we can test align='end'
dicts = []
for i in range(N):
dicts.append({'end':idx_fifty_mins[i]})
df = pd.DataFrame(dicts, index=idx)
for i in range(N):
nearest = utils.find_nearest(df, idx_fifty_mins[i], align='end')
self.assertEqual(nearest, i)
for i in range(0,N):
nearest = utils.find_nearest(df, idx[i], align='end')
self.assertEqual(nearest, i-1 if i > 0 else 0)
if __name__ == '__main__':
unittest.main()
|
import numpy as np
import os
try:
import netCDF4 as netCDF
except:
import netCDF3 as netCDF
import matplotlib.pyplot as plt
import time
import datetime as dt
from matplotlib.dates import date2num, num2date
import pyroms
import pyroms_toolbox
from pyroms import _remapping
class nctime(object):
pass
def remap(src_varname, src_file, src_grd, dst_grd, dst_file, dmax=0, cdepth=0, kk=0, dst_dir='./'):
# ARCTIC4 grid sub-sample
# xrange=src_grd.xrange; yrange=src_grd.yrange
ystart=235
# get time
nctime.long_name = 'time'
nctime.units = 'days since 1900-01-01 00:00:00'
# create IC/CLM file
print 'Creating ROMS file', dst_file
if os.path.exists(dst_file) is True:
os.remove(dst_file)
pyroms_toolbox.nc_create_roms_file(dst_file, dst_grd, nctime)
# open IC file
nc = netCDF.Dataset(dst_file, 'a', format='NETCDF3_64BIT')
#load var
cdf = netCDF.Dataset(src_file)
src_var = cdf.variables[src_varname]
tmp = cdf.variables['time'][:]
if len(tmp) > 1:
print 'error : multiple frames in input file' ; exit()
else:
time = tmp[0]
# we need to correct the time axis
ref_soda = dt.datetime(1980,1,1,0,0)
ref_roms = dt.datetime(1900,1,1,0,0)
ndays = (ref_soda - ref_roms).days
time = time + ndays
#get missing value
spval = src_var.missing_value
# determine variable dimension
ndim = len(src_var.dimensions) - 1
# ARCTIC4 grid sub-sample
if ndim == 3:
# src_var = src_var[0,:, yrange[0]:yrange[1]+1, xrange[0]:xrange[1]+1]
print 'subgrid 3d', src_var.shape
src_var = np.squeeze(src_var, axis=(0,))
src_var = src_var[:,np.r_[ystart:np.size(src_var,1),-1],:]
print 'subgrid 3d', src_var.shape
elif ndim == 2:
# src_var = src_var[0,yrange[0]:yrange[1]+1, xrange[0]:xrange[1]+1]
print 'subgrid 2d', src_var.shape
src_var = np.squeeze(src_var, axis=(0,))
src_var = src_var[np.r_[ystart:np.size(src_var,0),-1],:]
print 'subgrid 2d', src_var.shape
vartime = 'ocean_time'
if src_varname == 'ssh':
Bpos = 't'
Cpos = 'rho'
z = src_grd.z_t
Mp, Lp = dst_grd.hgrid.mask_rho.shape
wts_file = 'remap_weights_' + src_grd.name + '_to_' + dst_grd.name + '_bilinear_t_to_rho.nc'
dst_varname = 'zeta'
dimensions = ('ocean_time', 'eta_rho', 'xi_rho')
long_name = 'free-surface'
units = 'meter'
field = 'zeta, scalar, series'
elif src_varname == 'temp':
src_var = src_var
Bpos = 't'
Cpos = 'rho'
z = src_grd.z_t
Mp, Lp = dst_grd.hgrid.mask_rho.shape
wts_file = 'remap_weights_' + src_grd.name + '_to_' + dst_grd.name + '_bilinear_t_to_rho.nc'
dst_varname = 'temp'
dimensions = ('ocean_time', 's_rho', 'eta_rho', 'xi_rho')
long_name = 'potential temperature'
units = 'Celsius'
field = 'temp, scalar, series'
elif src_varname == 'salt':
Bpos = 't'
Cpos = 'rho'
z = src_grd.z_t
Mp, Lp = dst_grd.hgrid.mask_rho.shape
wts_file = 'remap_weights_' + src_grd.name + '_to_' + dst_grd.name + '_bilinear_t_to_rho.nc'
dst_varname = 'salt'
dimensions = ('ocean_time', 's_rho', 'eta_rho', 'xi_rho')
long_name = 'salinity'
units = 'none'
field = 'salt_north, scalar, series'
else:
raise ValueError, 'Undefined src_varname'
if ndim == 3:
# build intermediate zgrid
zlevel = -z[::-1]
nzlevel = len(zlevel)
dst_zcoord = pyroms.vgrid.z_coordinate(dst_grd.vgrid.h, zlevel, nzlevel)
dst_grdz = pyroms.grid.ROMS_Grid(dst_grd.name+'_Z', dst_grd.hgrid, dst_zcoord)
# create variable in file
print 'Creating variable', dst_varname
nc.createVariable(dst_varname, 'f8', dimensions, fill_value=spval)
nc.variables[dst_varname].long_name = long_name
nc.variables[dst_varname].units = units
nc.variables[dst_varname].field = field
nc.variables[dst_varname].time = 'ocean_time'
# remapping
print 'remapping', dst_varname, 'from', src_grd.name, \
'to', dst_grd.name
if ndim == 3:
# flood the grid
print 'flood the grid'
src_varz = pyroms_toolbox.BGrid_GFDL.flood(src_var, src_grd, Bpos=Bpos, spval=spval, \
dmax=dmax, cdepth=cdepth, kk=kk)
else:
src_varz = src_var
# horizontal interpolation using scrip weights
print 'horizontal interpolation using scrip weights'
dst_varz = pyroms.remapping.remap(src_varz, wts_file, spval=spval)
if ndim == 3:
# vertical interpolation from standard z level to sigma
print 'vertical interpolation from standard z level to sigma'
dst_var= pyroms.remapping.z2roms(dst_varz[::-1, :, :], \
dst_grdz, dst_grd, Cpos=Cpos, spval=spval, \
flood=False, irange=(0,Lp), jrange=(0,Mp))
else:
dst_var = dst_varz
# write data in destination file
print 'write data in destination file\n'
nc.variables['ocean_time'][0] = time
nc.variables[dst_varname][0] = np.squeeze(dst_var)
# close file
nc.close()
cdf.close()
if src_varname == 'ssh':
return dst_varz
|
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import numpy as np
import os
import sys
from observations.util import maybe_download_and_extract
def ducks(path):
"""Behavioral and Plumage Characteristics of Hybrid Ducks
The `ducks` data frame has 11 rows and 2 columns.
Each row of the data frame represents a male duck who is a second
generation cross of mallard and pintail ducks. For 11 such ducks a
behavioural and plumage index were calculated. These were measured on
scales devised for this experiment which was to examine whether there
was any link between which species the ducks resembled physically and
which they resembled in behaviour. The scale for the physical appearance
ranged from 0 (identical in appearance to a mallard) to 20 (identical to
a pintail). The behavioural traits of the ducks were on a scale from 0
to 15 with lower numbers indicating closer to mallard-like in behaviour.
This data frame contains the following columns:
`plumage`
The index of physical appearance based on the plumage of individual
ducks.
`behaviour`
The index of behavioural characteristics of the ducks.
The data were obtained from
Larsen, R.J. and Marx, M.L. (1986) *An Introduction to Mathematical
Statistics and its Applications* (Second Edition). Prentice-Hall.
Args:
path: str.
Path to directory which either stores file or otherwise file will
be downloaded and extracted there.
Filename is `ducks.csv`.
Returns:
Tuple of np.ndarray `x_train` with 11 rows and 2 columns and
dictionary `metadata` of column headers (feature names).
"""
import pandas as pd
path = os.path.expanduser(path)
filename = 'ducks.csv'
if not os.path.exists(os.path.join(path, filename)):
url = 'http://dustintran.com/data/r/boot/ducks.csv'
maybe_download_and_extract(path, url,
save_file_name='ducks.csv',
resume=False)
data = pd.read_csv(os.path.join(path, filename), index_col=0,
parse_dates=True)
x_train = data.values
metadata = {'columns': data.columns}
return x_train, metadata
|
def bitIsSet( value, bit ):
bitSet = value & (2 ** bit)
return True if bitSet > 0 else False
def chunks(lst, n):
for i in range(0, len(lst), n):
yield lst[i:i + n]
def getRange(lst, start, length):
return lst[start:start + length]
def getNiceGlyphName(unicode):
glyphNames = [
'space', 'exclam', 'quotedbl', 'numbersign', 'dollar', 'percent', 'ampersand',
'quotesingle', 'parenleft', 'parenright', 'asterisk', 'plus', 'comma', 'hyphen',
'period', 'slash', 'zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven',
'eight', 'nine', 'colon', 'semicolon', 'less', 'equal', 'greater', 'question',
'at', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O',
'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'bracketleft', 'backslash',
'bracketright', 'asciicircum', 'underscore', 'grave', 'a', 'b', 'c', 'd', 'e', 'f',
'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v',
'w', 'x', 'y', 'z', 'braceleft', 'bar', 'braceright', 'asciitilde', 'DEL', 'uni0080',
'uni0081', 'uni0082', 'uni0083', 'uni0084', 'uni0085', 'uni0086', 'uni0087',
'uni0088', 'uni0089', 'uni008a', 'uni008b', 'uni008c', 'uni008d', 'uni008e',
'uni008f', 'uni0090', 'uni0091', 'uni0092', 'uni0093', 'uni0094', 'uni0095',
'uni0096', 'uni0097', 'uni0098', 'uni0099', 'uni009a', 'uni009b', 'uni009c',
'uni009d', 'uni009e', 'uni009f', 'nbspace', 'exclamdown', 'cent', 'sterling',
'currency', 'yen', 'brokenbar', 'section', 'dieresis', 'copyright', 'ordfeminine',
'guillemotleft', 'logicalnot', 'softhyphen', 'registered', 'macron', 'degree',
'plusminus', 'twosuperior', 'threesuperior', 'acute', 'micro', 'paragraph',
'periodcentered', 'cedilla', 'onesuperior', 'ordmasculine', 'guillemotright',
'onequarter', 'onehalf', 'threequarters', 'questiondown', 'Agrave', 'Aacute',
'Acircumflex', 'Atilde', 'Adieresis', 'Aring', 'AE', 'Ccedilla', 'Egrave',
'Eacute', 'Ecircumflex', 'Edieresis', 'Igrave', 'Iacute', 'Icircumflex', 'Idieresis',
'Eth', 'Ntilde', 'Ograve', 'Oacute', 'Ocircumflex', 'Otilde', 'Odieresis',
'multiply', 'Oslash', 'Ugrave', 'Uacute', 'Ucircumflex', 'Udieresis', 'Yacute',
'Thorn', 'germandbls', 'agrave', 'aacute', 'acircumflex', 'atilde', 'adieresis',
'aring', 'ae', 'ccedilla', 'egrave', 'eacute', 'ecircumflex', 'edieresis', 'igrave',
'iacute', 'icircumflex', 'idieresis', 'eth', 'ntilde', 'ograve', 'oacute',
'ocircumflex', 'otilde', 'odieresis', 'divide', 'oslash', 'ugrave', 'uacute',
'ucircumflex', 'udieresis', 'yacute', 'thorn', 'ydieresis'
]
return glyphNames[unicode - 32]
|
from ..utils import Object
class RemoveNotificationGroup(Object):
"""
Removes a group of active notifications. Needs to be called only if the notification group is removed by the current user
Attributes:
ID (:obj:`str`): ``RemoveNotificationGroup``
Args:
notification_group_id (:obj:`int`):
Notification group identifier
max_notification_id (:obj:`int`):
The maximum identifier of removed notifications
Returns:
Ok
Raises:
:class:`telegram.Error`
"""
ID = "removeNotificationGroup"
def __init__(self, notification_group_id, max_notification_id, extra=None, **kwargs):
self.extra = extra
self.notification_group_id = notification_group_id # int
self.max_notification_id = max_notification_id # int
@staticmethod
def read(q: dict, *args) -> "RemoveNotificationGroup":
notification_group_id = q.get('notification_group_id')
max_notification_id = q.get('max_notification_id')
return RemoveNotificationGroup(notification_group_id, max_notification_id)
|
import json
import math
import random
import webcolors
from alive_progress import alive_bar
import cairo
def do_nothing():
"""Do nothing. A dummy function to use in place of alive_bar."""
pass
class CairoPainter:
"""
A class to interface with the Cairo library to draw the map for a given save file.
"""
def __init__(self, parent):
"""
:param parent: The SaveFileParser used to get useful information.
:type parent: SaveFileParser.
"""
self.parent = parent
self.player_colors = [
(200, 0, 0),
(0, 0, 200),
(100, 0, 0),
(0, 0, 100),
(0, 200, 200),
(200, 0, 200),
(200, 200, 0),
(0, 100, 100),
(100, 0, 100),
(100, 100, 0),
(200, 200, 200),
(100, 100, 100),
(50, 50, 50),
(0, 0, 0),
(0, 200, 0),
(0, 100, 0),
]
def log_message(self, message):
"""
Send a message to the logger.
:param message: The message to log.
:type message: string.
"""
self.parent.log_message(message)
def load_rgb(self, rgb_name, default_value):
"""
Update the settings, given a config file path.
:param rgb_name: The name to look up.
:type rgb_name: string.
:param default_value: A tuple of 3 ints for the RGB values.
:type default_value: (integer, integer, integer).
"""
value = self.load_settings.get(rgb_name, default_value)
if isinstance(value, str):
if value in self.load_settings_rgb_values:
return self.load_settings_rgb_values[value]
try:
return webcolors.name_to_rgb(value)
except ValueError:
return (0, 0, 0)
return value
def load_settings(self, file_path, tile_size):
"""
Update the settings, given a config file path.
:param file_path: The path to the config file.
:type file_path: string.
:param tile_size: Override for tile size.
:type tile_size: integer.
"""
with open("config/rgb_values.json") as file_handle:
self.load_settings_rgb_values = json.load(file_handle)
with open(file_path) as file_handle:
self.load_settings = json.load(file_handle)
self.ds = self.load_settings.get("ds", 25)
self.ss = 2 * self.ds
self.ocean_noise = self.load_settings.get("ocean_noise", 50)
self.height_rgb_low = self.load_rgb("height_rgb_low", (255, 255, 255))
self.height_rgb_high = self.load_rgb("height_rgb_high", (200, 200, 200))
self.road_tile_rgb = self.load_rgb("road_tile_rgb", (100, 100, 100))
self.rail_rgb = self.load_rgb("rail_rgb", (255, 255, 255))
self.road_rgb = self.load_rgb("road_rgb", (255, 255, 255))
self.tram_rgb = self.load_rgb("tram_rgb", (255, 255, 255))
self.railway_rgb = self.load_rgb("railway_rgb", (100, 100, 100))
self.electrified_railway_rgb = self.load_rgb("electrified_railway_rgb", (200, 200, 200))
self.monorail_rgb = self.load_rgb("monorail_rgb", (150, 150, 150))
self.maglev_rgb = self.load_rgb("maglev_rgb", (100, 100, 100))
self.town_building_rgb = self.load_rgb("town_building_rgb", (255, 255, 255))
self.industry_rgb = self.load_rgb("industry_rgb", (255, 165, 0))
self.industry_edge_rgb = self.load_rgb("industry_edge_rgb", (0, 0, 0))
self.torb_rgb = self.load_rgb("torb_rgb", (150, 150, 150))
self.objects_rgb = self.load_rgb("objects_rgb", (150, 150, 150))
self.torb_edge_rgb = self.load_rgb("torb_edge_rgb", (0, 100, 100))
self.water_edge_rgb = self.load_rgb("water_edge_rgb", (0, 0, 255))
self.station_rgb = self.load_rgb("station_rgb", (255, 0, 255))
self.rail_station_rgb = self.load_rgb("rail_station_rgb", (255, 0, 255))
self.bus_station_rgb = self.load_rgb("bus_station_rgb", (255, 0, 255))
self.truck_station_rgb = self.load_rgb("truck_station_rgb", (255, 0, 255))
self.airport_rgb = self.load_rgb("airport_rgb", (255, 255, 0))
self.seaport_rgb = self.load_rgb("seaport_rgb", (0, 255, 255))
self.heliport_rgb = self.load_rgb("heliport_rgb", (255, 255, 0))
self.rail_depot_rgb = self.load_rgb("rail_depot_rgb", (0, 100, 100))
self.road_depot_rgb = self.load_rgb("road_depot_rgb", (0, 100, 100))
self.ship_depot_rgb = self.load_rgb("ship_depot_rgb", (0, 100, 100))
self.screen_mode = self.load_settings.get("screen_mode", "normal")
self.player_colors = self.load_settings.get("player_colors", self.player_colors)
self.reverse_track_rgb = self.load_settings.get("reverse_track_rgb", False)
self.show_signals = self.load_settings.get("show_signals", True)
self.show_roads = self.load_settings.get("show_roads", True)
for index, rgb in enumerate(self.player_colors):
if not isinstance(rgb, str):
continue
if rgb in self.load_settings_rgb_values:
self.player_colors[index] = self.load_settings_rgb_values[rgb]
else:
try:
self.player_colors[index] = webcolors.name_to_rgb(rgb)
except ValueError:
self.player_colors[index] = (0, 0, 0)
if tile_size:
self.ss = tile_size
self.ds = (tile_size - 1) // 2
# Thicknesses of various elements.
self.rail_width = self.ds / 3
self.road_width = self.ds / 2
self.tram_width = self.ds / 5
self.bridge_edge_width = self.ss / 10
self.edge_width = int(0.1 * self.ss)
if self.edge_width % 2 == 0:
self.edge_width += 1
def set_rgb(self, rgb):
"""
Set the RGB for the context.
:param rgb: The fill color, expressed as a tuple in the range (0-255, 0-255, 0-255).
:type rgb: (integer, integer, integer).
"""
ctx = self.context
(rgb_r, rgb_g, rgb_b) = rgb
ctx.set_source_rgb(rgb_r / 255, rgb_g / 255, rgb_b / 255)
def set_rgba(self, rgba):
"""
Set the RGBA for the context.
:param rgba: The fill color, expressed as a tuple in the range (0-255, 0-255, 0-255, 0-1).
:type rgba: (integer, integer, integer, integer).
"""
ctx = self.context
(rgba_r, rgba_g, rgba_b, rgba_a) = rgba
ctx.set_source_rgba(rgba_r / 255, rgba_g / 255, rgba_b / 255, rgba_a)
def draw_line(self, x1, y1, x2, y2, rgb, width, round_cap=True):
"""
Draw a line on the context.
:param x1: The x coordinate of the top left corner.
:type x1: float
:param y1: The y coordinate of the top left corner.
:type y1: float
:param x2: The x coordinate of the bottom right corner.
:type x2: float
:param y2: The y coordinate of the bottom right corner.
:type y2: float
:param rgb: The fill color, expressed as a tuple in the range (0-255, 0-255, 0-255).
:type rgb: (integer, integer, integer).
:param width: The width of the line.
:type width: float
:param round_cap: If True, set the line cap to round. Defaults to False.
:type round_cap: Boolean
"""
ctx = self.context
ctx.save()
self.set_rgb(rgb)
ctx.set_line_width(width)
if round_cap:
ctx.set_line_cap(cairo.LINE_CAP_ROUND)
ctx.move_to(x1, y1)
ctx.line_to(x2, y2)
ctx.stroke()
ctx.restore()
def draw_rectangle(self, x, y, w, h, rgb_fill, rgb_stroke=None):
"""
Fill a rectangle on the context.
:param x: The x coordinate of the top left corner.
:type x: float
:param y: The y coordinate of the top left corner.
:type y: float
:param w: The width of the rectangle.
:type w: float
:param h: The height of the rectangle.
:type h: float
:param rgb_fill: The fill color, expressed as a tuple in the range (0-255, 0-255, 0-255).
:type rgb_fill: (integer, integer, integer).
:param rgb_stroke: The stroke color, expressed as a tuple in the range (0-255, 0-255, 0-255).
:type rgb_stroke: (integer, integer, integer).
"""
ctx = self.context
ctx.save()
self.set_rgb(rgb_fill)
ctx.rectangle(x, y, w, h)
ctx.fill()
if rgb_stroke is not None:
self.set_rgb(rgb_stroke)
ctx.rectangle(x, y, w, h)
ctx.stroke()
ctx.restore()
def draw_rectangle_rgba(self, x, y, w, h, rgba):
"""
Fill a rectangle on the context.
:param x: The x coordinate of the top left corner.
:type x: float
:param y: The y coordinate of the top left corner.
:type y: float
:param w: The width of the rectangle.
:type w: float
:param h: The height of the rectangle.
:type h: float
:param rgba: The fill color, expressed as a tuple in the range (0-255, 0-255, 0-255, 0-1).
:type rgba: (integer, integer, integer, integer).
"""
ctx = self.context
ctx.save()
self.set_rgba(rgba)
ctx.rectangle(x, y, w, h)
ctx.fill()
ctx.restore()
def cxy_from_rc(self, row, col):
"""
Get the centre of a tile, given its row and column.
:param row: The row of the tile.
:type row: integer
:param col: The column of the tile.
:type col: integer
:return: The coordinates of the centre of the tile, a tuple of floats.
:rtype: (float, float)
"""
x = (self.parent.ncols - col - 1.0 - 0.5) * self.ss
y = (row - 0.5) * self.ss
cx = int(x + 0.5 * self.ss)
cy = int(y + 0.5 * self.ss)
return cx, cy
def xy_from_tile(self, tile):
"""
Get the top left corner of a tile.
:param tile: The tile to consider.
:type tile: TileObject
:return: The coordinates of the centre of the tile, a tuple of floats.
:rtype: (float, float)
"""
x = int((self.parent.ncols - tile.col - 1 - 0.5) * self.ss)
y = int((tile.row - 0.5) * self.ss)
return x, y
def cxy_from_tile(self, tile):
"""
Get the centre of a tile.
:param tile: The tile to consider.
:type tile: TileObject
:return: The coordinates of the centre of the tile, a tuple of floats.
:rtype: (float, float)
"""
return self.cxy_from_rc(tile.row, tile.col)
def transform_to_tile(self, tile, rotation):
"""
Transform the context to a given tile, with a rotation.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the triangle, in the range 0, 3.
:type rotation: integer
"""
ctx = self.context
ctx.save()
cx, cy = self.cxy_from_tile(tile)
ctx.translate(cx + 0.5, cy + 0.5)
ctx.rotate(0.5 * math.pi * rotation)
def end_transform_to_tile(self):
"""Restore the context. This should be called after transform_to_tile."""
self.context.restore()
def draw_square(self, tile, rgb_fill, rgb_stroke=None):
"""
Draw a square for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rgb: The fill color, expressed as a tuple in the range (0-255, 0-255, 0-255).
:type rgb: (integer, integer, integer).
:param rgb_stroke: The stroke color, expressed as a tuple in the range (0-255, 0-255, 0-255). Default is None.
:type rgb_stroke: (integer, integer, integer).
"""
x, y = self.xy_from_tile(tile)
w = self.ss
h = self.ss
if rgb_stroke is None:
rgb_stroke = rgb_fill
self.draw_rectangle(x, y, w, h, rgb_fill, rgb_stroke=rgb_stroke)
def draw_rail_background(self, tile):
"""
Draw the background for a given tile that contains railway track.
:param tile: The tile to consider.
:type tile: TileObject
"""
if self.screen_mode == "martin":
return
rgb = self.player_colors[tile.owner]
self.draw_square(tile, rgb)
def draw_rail_line(self, x1, y1, x2, y2, track_type, line_mode="outer", round_cap=False, owner=0):
"""
Draw a line for a railway track.
:param x1: The x coordinate of the start of the line.
:type x1: float
:param y1: The y coordinate of the start of the line.
:type y1: float
:param x2: The x coordinate of the end of the line.
:type x2: float
:param y2: The y coordinate of the end of the line.
:type y2: float
:param track_type: The type of track (railway, electrified railway, monorail, maglev).
:type track_type: integer
:param line_mode: Whether to draw the outer or inner line for the payload. Default is False.
:type line_mode: Boolean
:param round_cap: If True, set the line cap to round. Defaults to False.
:type round_cap: Boolean
:param owner: The index of the owner. Defaults to 0.
:type owner: Integer
"""
do_draw_inner = line_mode in ["inner", "both"]
do_draw_outer = line_mode in ["outer", "both"]
outer_rgbs = [
self.railway_rgb,
self.electrified_railway_rgb,
self.monorail_rgb,
self.maglev_rgb
]
outer_rgb = outer_rgbs[track_type]
if owner is None:
inner_rgb = self.rail_rgb
else:
inner_rgb = self.player_colors[owner]
if self.reverse_track_rgb:
outer_rgb = inner_rgb
inner_rgb = outer_rgbs[track_type]
if do_draw_outer:
rgb = outer_rgb
self.draw_line(
x1, y1, x2, y2, rgb, 2.5 * self.rail_width, round_cap
)
if do_draw_inner:
rgb = inner_rgb
self.draw_line(x1, y1, x2, y2, rgb, self.rail_width, round_cap)
def draw_rail_XY(self, tile, rotation, line_mode="outer"):
"""
Draw a railway track in the X or Y direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the railway track, in the range 0, 1.
:type rotation: integer
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean, Default is False.
"""
track_type = tile.occupant.track_type
self.transform_to_tile(tile, rotation)
self.draw_rail_line(
-0.5 * self.ss, 0, 0.5 * self.ss, 0, track_type,
line_mode=line_mode, round_cap=False, owner=tile.owner
)
self.end_transform_to_tile()
def draw_rail_NSEW(self, tile, rotation, line_mode="outer"):
"""
Draw a railway track in the N, S, E or W direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the railway track, in the range 0, 3.
:type rotation: integer
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is False.
"""
track_type = tile.occupant.track_type
self.transform_to_tile(tile, rotation)
self.draw_rail_line(
0, 0.5 * self.ss, 0.5 * self.ss, 0, track_type,
line_mode=line_mode, round_cap=True, owner=tile.owner
)
self.end_transform_to_tile()
def draw_rail_X(self, tile, line_mode="outer"):
"""
Draw a railway track in the X direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is False.
"""
self.draw_rail_XY(tile, 0, line_mode=line_mode)
def draw_rail_Y(self, tile, line_mode="outer"):
"""
Draw a railway track in the Y direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is False.
"""
self.draw_rail_XY(tile, 1, line_mode=line_mode)
def draw_rail_N(self, tile, line_mode="outer"):
"""
Draw a railway track in the N direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is False.
"""
self.draw_rail_NSEW(tile, 3, line_mode=line_mode)
def draw_rail_W(self, tile, line_mode="outer"):
"""
Draw a railway track in the W direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is False.
"""
self.draw_rail_NSEW(tile, 2, line_mode=line_mode)
def draw_rail_S(self, tile, line_mode="outer"):
"""
Draw a railway track in the S direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is False.
"""
self.draw_rail_NSEW(tile, 1, line_mode=line_mode)
def draw_rail_E(self, tile, line_mode="outer"):
"""
Draw a railway track in the E direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is False.
"""
self.draw_rail_NSEW(tile, 0, line_mode=line_mode)
def draw_rail_depot(self, tile, rotation):
"""
Draw a railway depot for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the depot entrance, in the range 0, 3.
:type rotation: integer
"""
track_type = tile.occupant.track_type
ss = self.ss
self.transform_to_tile(tile, rotation)
self.draw_rail_line(
-0.2 * ss, 0, 0.5 * ss, 0, track_type,
line_mode="both", round_cap=False, owner=tile.owner
)
self.draw_line(-0.2 * ss, 0.3 * ss, -0.2 * ss, -0.3 * ss, self.rail_rgb, 0.2 * ss)
self.end_transform_to_tile()
def draw_signal(self, cx, cy, signal_era, signal_type):
"""
Draw a railway signal for a given tile.
:param cx: The x position of the signal.
:type cx: float
:param cy: The y position of the signal.
:type cy: float
:param signal_era: The era of the signal (0 for semaphone, 1 for electric).
:type signal_era: integer
:param signal_type: The type of the signal.
:type signal_type: integer
"""
ctx = self.context
r = 0.1 * self.ss
lw = 0.025 * self.ss
stroke_rgb = (0, 0, 0)
fill_rgbs = [
(255, 255, 255), # Block signal.
(255, 255, 0), # Pre signal.
(100, 100, 100), # Exit signal.
(255, 255, 0), # Combo signal.
(255, 0, 0), # Path signal.
(255, 255, 255), # One way path signal.
]
self.set_rgb(fill_rgbs[signal_type])
if signal_era == 0:
ctx.arc(cx, cy, r, 0, 2 * math.pi)
else:
ctx.rectangle(cx - r, cy - r, 2 * r, 2 * r)
ctx.fill()
if signal_type == 5: # One way path signal.
self.set_rgb((255, 0, 0))
ctx.move_to(cx - r, cy)
ctx.line_to(cx + r, cy)
ctx.set_line_width(0.75 * r)
ctx.stroke()
elif signal_type == 3: # Combo signal.
self.set_rgb((100, 100, 100))
ctx.move_to(cx, cy - r)
ctx.line_to(cx, cy + r)
ctx.set_line_width(0.75 * r)
ctx.stroke()
self.set_rgb(stroke_rgb)
ctx.set_line_width(lw)
if signal_era == 0:
ctx.arc(cx, cy, r, 0, 2 * math.pi)
else:
ctx.rectangle(cx - r, cy - r, 2 * r, 2 * r)
ctx.stroke()
def draw_rail_signals_tile(self, tile, rotation):
"""
Draw a railway signals for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the depot entrance, in the range 0, 3.
:type rotation: integer
"""
ss = self.ss
d1 = 0.1 * ss
d2 = 0.2 * ss
d3 = 0.4 * ss
rail = tile.occupant
s01e = rail.signal_01_era
s23e = rail.signal_23_era
s01t = rail.signal_01_type
s23t = rail.signal_23_type
self.transform_to_tile(tile, rotation)
if rail.track_X:
if rail.signal_2_present:
self.draw_signal(d3, -d2, s23e, s23t)
if rail.signal_3_present:
self.draw_signal(-d3, d2, s23e, s23t)
if rail.track_Y:
if rail.signal_2_present:
self.draw_signal(d2, d3, s23e, s23t)
if rail.signal_3_present:
self.draw_signal(-d2, -d3, s23e, s23t)
if rail.track_W:
if rail.signal_2_present:
self.draw_signal(-d3, -d3, s23e, s23t)
if rail.signal_3_present:
self.draw_signal(-d1, -d1, s23e, s23t)
if rail.track_E:
if rail.signal_0_present:
self.draw_signal(d1, d1, s01e, s01t)
if rail.signal_1_present:
self.draw_signal(d3, d3, s01e, s01t)
if rail.track_N:
if rail.signal_2_present:
self.draw_signal(d3, -d3, s23e, s23t)
if rail.signal_3_present:
self.draw_signal(d1, -d1, s23e, s23t)
if rail.track_S:
if rail.signal_0_present:
self.draw_signal(-d1, d1, s01e, s01t)
if rail.signal_1_present:
self.draw_signal(-d3, d3, s01e, s01t)
self.end_transform_to_tile()
def draw_bridge_ramp(self, tile, rotation, payload):
"""
Draw a bridge ramp for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the bridge ramp, in the range 0, 3.
:type rotation: integer
:param payload: The payload of the bridge. It should be one of 'rail', 'road'.
:type payload: string.
"""
bec = self.torb_edge_rgb
bew = self.bridge_edge_width
d = self.ss
bd = 0.25 * d
self.transform_to_tile(tile, rotation)
self.draw_line(-0.25 * d, -bd, 0.5 * d, -bd, bec, bew)
self.draw_line(-0.25 * d, bd, 0.5 * d, bd, bec, bew)
if payload == "road":
self.draw_road_line(-0.5 * d, 0, 0.5 * d, 0, line_mode="both", owner=tile.owner)
if tile.occupant.tram_type == 1:
self.draw_tram_line(-0.5 * d, 0, 0.5 * d, 0, owner=tile.owner_tram)
else:
track_type = tile.occupant.track_type
self.draw_rail_line(
-0.5 * d, 0, 0.5 * d, 0,
track_type, line_mode="both", owner=tile.owner
)
self.end_transform_to_tile()
def draw_bridge_over(self, tile, rotation, payload, track_type, has_tram, source_tile_owner):
"""
Draw a bridge over a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the bridge, in the range 0, 1.
:type rotation: integer
:param payload: The payload of the bridge. It should be one of 'rail', 'road'.
:type payload: string.
:param has_tram: Whether the payload contains a tram.
:type has_tram: Boolean
"""
bec = self.torb_edge_rgb
bew = self.bridge_edge_width
d = self.ss
bd = 0.25 * d
self.transform_to_tile(tile, rotation)
self.draw_line(-0.5 * d, -bd, 0.5 * d, -bd, bec, bew)
self.draw_line(-0.5 * d, bd, 0.5 * d, bd, bec, bew)
if payload == "road":
self.draw_road_line(
-0.5 * d, 0, 0.5 * d, 0,
line_mode="both", owner=source_tile_owner
)
if has_tram:
self.draw_tram_line(-0.5 * d, 0, 0.5 * d, 0, owner=source_tile_owner)
else:
self.draw_rail_line(
-0.5 * d, 0, 0.5 * d, 0, track_type,
line_mode="both", owner=source_tile_owner
)
self.end_transform_to_tile()
def draw_tunnel_mouth(self, tile, rotation, payload):
"""
Draw a tunnel mouth for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the tunnel mouth, in the range 0, 3.
:type rotation: integer
:param payload: The payload of the tunnel. It should be one of 'rail', 'road'.
:type payload: string.
"""
bec = self.torb_edge_rgb
bew = self.bridge_edge_width
d = self.ss
self.transform_to_tile(tile, rotation)
if payload == "road":
self.draw_road_line(-0.5 * d, 0, 0.25 * d, 0, line_mode="both")
else:
track_type = tile.occupant.track_type
self.draw_rail_line(
-0.5 * d, 0, 0.25 * d, 0, track_type,
line_mode="both", owner=tile.owner
)
bd = 0.3 * d
self.draw_line(0.25 * d, -bd, 0.25 * d, bd, bec, bew)
self.draw_line(0.25 * d, -bd, 0, -bd, bec, bew)
self.draw_line(0.25 * d, bd, 0, bd, bec, bew)
self.end_transform_to_tile()
def draw_rail_bridge_ramp(self, tile, rotation):
"""
Draw a rail bridge ramp for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the rail bridge ramp, in the range 0, 3.
:type rotation: integer
"""
self.draw_bridge_ramp(tile, rotation, "rail")
def draw_rail_bridge_over(self, tile, rotation, track_type, source_tile_owner):
"""
Draw a rail bridge over a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the rail bridge, in the range 0, 1.
:type rotation: integer
"""
self.draw_bridge_over(tile, rotation, "rail", track_type, None, source_tile_owner)
def draw_rail_tunnel_mouth(self, tile, rotation):
"""
Draw a rail tunnel mouth for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the tunnel mouth, in the range 0, 3.
:type rotation: integer
"""
self.draw_tunnel_mouth(tile, rotation, "rail")
def draw_road_line(self, x1, y1, x2, y2, line_mode="outer", round_cap=False, owner=None):
"""
Draw a line for a road.
:param x1: The x coordinate of the start of the line.
:type x1: float
:param y1: The y coordinate of the start of the line.
:type y1: float
:param x2: The x coordinate of the end of the line.
:type x2: float
:param y2: The y coordinate of the end of the line.
:type y2: float
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
:param round_cap: If True, set the line cap to round. Defaults to False.
:type round_cap: Boolean
:param owner: Owner of the road. Defaults to None.
:type owner: Boolean
"""
rw1 = 1.75 * self.road_width
rw2 = 1.25 * self.road_width
do_draw_inner = line_mode in ["inner", "both"]
do_draw_outer = line_mode in ["outer", "both"]
rgb = self.road_tile_rgb
if owner is not None:
rgb = self.player_colors[owner]
if do_draw_outer:
self.draw_line(x1, y1, x2, y2, rgb, rw1, round_cap)
if do_draw_inner:
self.draw_line(x1, y1, x2, y2, self.road_rgb, rw2, round_cap)
def draw_tram_line(self, x1, y1, x2, y2, line_mode="outer", round_cap=False, owner=None):
"""
Draw a line for a road.
:param x1: The x coordinate of the start of the line.
:type x1: float
:param y1: The y coordinate of the start of the line.
:type y1: float
:param x2: The x coordinate of the end of the line.
:type x2: float
:param y2: The y coordinate of the end of the line.
:type y2: float
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
:param round_cap: If True, set the line cap to round. Defaults to False.
:type round_cap: Boolean
:param owner: Owner of the road. Defaults to None.
:type owner: Boolean
"""
tw = self.tram_width
rgb = self.tram_rgb
if owner is not None:
rgb = self.player_colors[owner]
if self.screen_mode == "martin":
rgb = self.tram_rgb
self.draw_line(x1, y1, x2, y2, rgb, tw, round_cap)
def draw_road_NSEW(self, tile, rotation, line_mode, round_cap):
"""
Draw a road for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the road, in the range 0, 3.
:type rotation: integer
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
:param round_cap: If True, set the line cap to round. Defaults to False.
:type round_cap: Boolean
"""
self.transform_to_tile(tile, rotation)
self.draw_road_line(0, 0.5 * self.ss, 0, 0, line_mode=line_mode, round_cap=False, owner=tile.owner)
self.end_transform_to_tile()
def draw_road_NE(self, tile, line_mode):
"""
Draw a road in the NE direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_road_NSEW(tile, 3, line_mode=line_mode, round_cap=False)
def draw_road_NW(self, tile, line_mode):
"""
Draw a road in the NW direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_road_NSEW(tile, 2, line_mode=line_mode, round_cap=False)
def draw_road_SE(self, tile, line_mode):
"""
Draw a road in the SE direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_road_NSEW(tile, 0, line_mode=line_mode, round_cap=False)
def draw_road_SW(self, tile, line_mode):
"""
Draw a road in the SW direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_road_NSEW(tile, 1, line_mode=line_mode, round_cap=False)
def draw_road_through(self, tile, line_mode, rotation):
"""
Draw a road that goes across a given tile in the NE-SW direction.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean
:param rotation: The direction of the road. Should be in the range [0, 1].
:type rotation: integer
"""
d = 0.5 * self.ss
self.transform_to_tile(tile, rotation)
self.draw_road_line(0, -d, 0, d, line_mode=line_mode, round_cap=False, owner=tile.owner)
self.end_transform_to_tile()
def draw_road_NE_to_SW(self, tile, line_mode):
"""
Draw a road that goes across a given tile in the NE-SW direction.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_road_through(tile, line_mode, 1)
def draw_road_NW_to_SE(self, tile, line_mode):
"""
Draw a road that goes across a given tile in the NE-SW direction.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_road_through(tile, line_mode, 0)
def draw_tram_NSEW(self, tile, rotation, line_mode, round_cap):
"""
Draw a tram for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the road, in the range 0, 3.
:type rotation: integer
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
:param round_cap: If True, set the line cap to round. Defaults to False.
:type round_cap: Boolean
"""
self.transform_to_tile(tile, rotation)
self.draw_tram_line(0, 0.5 * self.ss, 0, 0, line_mode=line_mode, round_cap=True, owner=tile.owner_tram)
self.end_transform_to_tile()
def draw_tram_NE(self, tile, line_mode):
"""
Draw a road in the NE direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_tram_NSEW(tile, 3, line_mode=line_mode, round_cap=False)
def draw_tram_NW(self, tile, line_mode):
"""
Draw a road in the NW direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_tram_NSEW(tile, 2, line_mode=line_mode, round_cap=False)
def draw_tram_SE(self, tile, line_mode):
"""
Draw a road in the SE direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_tram_NSEW(tile, 0, line_mode=line_mode, round_cap=False)
def draw_tram_SW(self, tile, line_mode):
"""
Draw a road in the SW direction for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is "outer".
"""
self.draw_tram_NSEW(tile, 1, line_mode=line_mode, round_cap=False)
def draw_road_depot(self, tile, rotation, line_mode):
"""
Draw a road depot for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the depot, in the range 0, 3.
:type rotation: integer
"""
ss = self.ss
if line_mode == "outer":
self.draw_square(tile, self.road_depot_rgb, rgb_stroke=self.player_colors[tile.owner])
self.transform_to_tile(tile, rotation)
self.draw_road_line(0, 0, 0, 0, line_mode=line_mode, round_cap=True, owner=tile.owner)
self.draw_road_line(0, 0, 0.5 * ss, 0, round_cap=False, line_mode=line_mode, owner=tile.owner)
if tile.occupant.tram_type == 1:
self.draw_tram_line(0, 0, 0.5 * ss, 0, round_cap=True, line_mode="outer", owner=tile.owner)
self.end_transform_to_tile()
def draw_road_bridge_ramp(self, tile, rotation):
"""
Draw a road bridge ramp for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the triangle, in the range 0, 1.
:type rotation: integer
"""
self.draw_bridge_ramp(tile, rotation, "road")
def draw_road_bridge_over(self, tile, rotation, has_tram, source_tile_owner):
"""
Draw a road bridge over a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the bridge, in the range 0, 1.
:type rotation: integer
"""
self.draw_bridge_over(tile, rotation, "road", None, has_tram, source_tile_owner)
def draw_road_tunnel_mouth(self, tile, rotation):
"""
Draw a road tunnel mouth for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the tunnel mouth, in the range 0, 3.
:type rotation: integer
"""
self.draw_tunnel_mouth(tile, rotation, "road")
def draw_level_crossing(self, tile, rotation):
"""
Draw a level crossing for a given tile.
:param tile: The tile to consider.
:type tile: TileObject
:param rotation: The direction of the level crossing, in the range 0, 1.
:type rotation: integer
"""
if rotation == 0:
self.draw_road_NE_to_SW(tile, line_mode="both")
if tile.owner_tram:
self.draw_tram_NE(tile, line_mode="both")
self.draw_tram_SW(tile, line_mode="both")
self.draw_rail_Y(tile, line_mode="both")
else:
self.draw_road_NW_to_SE(tile, line_mode="both")
if tile.owner_tram:
self.draw_tram_NW(tile, line_mode="both")
self.draw_tram_SE(tile, line_mode="both")
self.draw_rail_X(tile, line_mode="both")
def seek_bridge_ramp(self, tiles, row, col, rotation):
"""
Recursively step through tiles to find a bridge ramp, and return the bridge ramp tile.
:param tiles: The list of all TileObjects in the map.
:type tiles: list of TileObject.
:param row: The row of the current tile.
:type row: integer
:param col: The col of the current tile.
:type col: integer
:param rotation: The direction of the bridge, in the range 0, 1.
:type rotation: integer
:return: The TileObject to the NE of the given tile.
:rtype: TileObject
"""
if rotation == 0:
drow = 0
dcol = 1
else:
drow = 1
dcol = 0
while True:
index = self.parent.ncols * row + col
try:
tile = tiles[index]
except IndexError:
return None
if tile.kind == 9:
return tile
row += drow
col += dcol
def draw_unknown_bridge_over(self, tiles, tile, rotation, line_mode="outer"):
"""
Draw a bridge over a tile when we we don't know what the payload of the bridge is.
:param tiles: The list of all TileObjects in the map.
:type tiles: list of TileObject.
:param tile: The current tile.
:type tile: integer
:param rotation: The direction of the bridge, in the range 0, 1.
:type rotation: integer
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean Default is False.
"""
source_tile = self.seek_bridge_ramp(tiles, tile.row, tile.col, rotation)
payload_kind = 0
track_type = 0
has_tram = False
source_tile_owner = None
if source_tile:
payload_kind = source_tile.occupant.payload_kind
source_tile_owner = source_tile.owner
if payload_kind == 0:
track_type = source_tile.occupant.track_type
if source_tile.occupant.tram_type == 1:
has_tram = True
if payload_kind == 0:
self.draw_rail_bridge_over(tile, rotation, track_type, source_tile_owner)
elif payload_kind == 1:
self.draw_road_bridge_over(tile, rotation, has_tram, source_tile_owner)
def draw_tile_edge(self, tile, rotation, owner):
"""
Draw the edge of a tile.
:param tile: The current tile.
:type tile: integer
:param rotation: The direction of the edge, in the range [0, 7].
:type rotation: integer
:param owner: The owner index. Can be None.
:type owner: integer
"""
d = self.ds + 0.5
ew = self.edge_width
dew = (ew - 1) // 2
rgb = self.industry_edge_rgb
if owner == "water":
rgb = self.water_edge_rgb
elif owner is not None:
rgb = self.player_colors[owner]
self.transform_to_tile(tile, rotation)
if rotation == 0:
self.draw_rectangle(-d, -d - dew, self.ss, ew, rgb)
elif rotation < 4:
self.draw_rectangle(-d, -d, self.ss, ew, rgb)
elif rotation == 4:
self.draw_rectangle(-d, -d - dew, ew, ew, rgb)
elif rotation == 5:
self.draw_rectangle(-d - dew, -d, ew, ew, rgb)
else:
self.draw_rectangle(-d, -d, ew, ew, rgb)
self.end_transform_to_tile()
def draw_industry_edge(self, tile, rotation):
"""
Draw the edge of an industry tile.
:param tile: The current tile.
:type tile: integer
:param rotation: The direction of the edge, in the range [0, 7].
:type rotation: integer
"""
self.draw_tile_edge(tile, rotation, None)
def draw_station_edge(self, tile, rotation):
"""
Draw the edge of a station tile.
:param tile: The current tile.
:type tile: integer
:param rotation: The direction of the edge, in the range [0, 7].
:type rotation: integer
"""
self.draw_tile_edge(tile, rotation, tile.owner)
def draw_water_edge(self, tile, rotation):
"""
Draw the edge of a water tile.
:param tile: The current tile.
:type tile: integer
:param rotation: The direction of the edge, in the range [0, 7].
:type rotation: integer
"""
self.draw_tile_edge(tile, rotation, "water")
def draw_industry_edges(self, tile, tiles):
"""
Draw all the edges of an industry tile.
:param tile: The current tile.
:type tile: integer
:param tiles: The list of all TileObjects in the map.
:type tiles: list of TileObject.
"""
industry_id = tile.occupant.industry_id
for direction in range(8):
other_tile = self.parent.tile_grid.get_tile_dir(tile, direction)
if not other_tile:
continue
is_same_industry = other_tile.kind == 8 and other_tile.occupant.industry_id == industry_id
if is_same_industry:
continue
self.draw_industry_edge(tile, direction)
def draw_station_edges(self, tile, tiles):
"""
Draw all the edges of a station tile.
:param tile: The current tile.
:type tile: integer
:param tiles: The list of all TileObjects in the map.
:type tiles: list of TileObject.
"""
station_id = tile.occupant.station_id
for direction in range(8):
other_tile = self.parent.tile_grid.get_tile_dir(tile, direction)
if not other_tile:
continue
is_same_station = other_tile.kind == 5 and other_tile.occupant.station_id == station_id
if is_same_station:
continue
self.draw_station_edge(tile, direction)
def draw_water_edges(self, tile, tiles):
"""
Draw all the edges of a water tile.
:param tile: The current tile.
:type tile: integer
:param tiles: The list of all TileObjects in the map.
:type tiles: list of TileObject.
"""
for direction in range(8):
other_tile = self.parent.tile_grid.get_tile_dir(tile, direction)
if not other_tile:
continue
if other_tile.kind == 6:
continue
self.draw_water_edge(tile, direction)
def make_industry_shapes(self, industry_tiles):
"""
Return a list of shapes for all the industries on the map, one shape per industry.
:param industry_tiles: The list of all TileObjects in the map that contains an industry.
:type industry_tiles: list of TileObject.
:return: A list of list of TileObjects, one list per industry.
:rtype: list of lists of TileObjects
"""
industry_shapes_dict = {}
for industry_tile in industry_tiles:
industry_shapes_dict[industry_tile.occupant.industry_id] = []
for industry_tile in industry_tiles:
industry_shapes_dict[industry_tile.occupant.industry_id].append(industry_tile)
industry_shapes_list = []
for industry_id in industry_shapes_dict:
industry_shapes_list.append(industry_shapes_dict[industry_id])
return industry_shapes_list
def make_station_shapes(self, station_tiles):
"""
Return a list of shapes for all the stations on the map, one shape per station.
:param station_tiles: The list of all TileObjects in the map that contains a station.
:type station_tiles: list of TileObject.
:return: A list of list of TileObjects, one list per station.
:rtype: list of lists of TileObjects
"""
station_shapes_dict = {}
for station_tile in station_tiles:
station_shapes_dict[station_tile.occupant.station_id] = []
for station_tile in station_tiles:
station_shapes_dict[station_tile.occupant.station_id].append(station_tile)
station_shapes_list = []
for station_id in station_shapes_dict:
station_shapes_list.append(station_shapes_dict[station_id])
return station_shapes_list
def draw_tile_backgrounds(self, tiles):
"""
Draw the background squares for all the tiles.
:param tiles: The list of all TileObjects in the map.
:type tiles: list of TileObject.
"""
def process_tile(tile):
h = tile.height
h_index = (h - self.parent.min_height) / (self.parent.max_height - self.parent.min_height)
rgb_rand_1 = random.randint(0, self.ocean_noise)
height_rgb = [0, 0, 0]
height_rgb[0] = self.height_rgb_low[0] + h_index * (self.height_rgb_high[0] - self.height_rgb_low[0])
height_rgb[1] = self.height_rgb_low[1] + h_index * (self.height_rgb_high[1] - self.height_rgb_low[1])
height_rgb[2] = self.height_rgb_low[2] + h_index * (self.height_rgb_high[2] - self.height_rgb_low[2])
water_rgb = (rgb_rand_1, rgb_rand_1, 255)
if self.screen_mode == "dark":
water_rgb = (rgb_rand_1 // 2, rgb_rand_1 // 2, 150)
if self.screen_mode == "martin":
water_rgb = (195 + rgb_rand_1 * 0.5, 234 + rgb_rand_1 * 0.5, 251)
fillColors = [
height_rgb, # Ground
height_rgb, # Rail
self.road_tile_rgb, # Road
height_rgb, # Town building
height_rgb, # Trees
self.station_rgb, # Stations
water_rgb, # Water
height_rgb, # Void
self.industry_rgb, # Industries
self.torb_rgb, # Tunnel/bridge
height_rgb, # Objects
]
fillColor = fillColors[tile.kind % len(fillColors)]
if tile.kind == 1:
rail = tile.occupant
if rail.is_depot:
fillColor = self.rail_depot_rgb
if tile.kind == 5:
station = tile.occupant
if station.station_type == 0:
fillColor = self.rail_station_rgb
if station.station_type == 1:
fillColor = self.airport_rgb
if station.station_type == 2:
fillColor = self.bus_station_rgb
if station.station_type == 3:
fillColor = self.truck_station_rgb
if station.station_type == 4:
fillColor = self.heliport_rgb
if station.station_type == 5:
fillColor = self.seaport_rgb
self.draw_square(tile, fillColor)
if tile.kind == 1:
rail = tile.occupant
if not rail.is_depot:
self.draw_rail_background(tile)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile)
abar()
else:
for tile in tiles:
process_tile(tile)
def draw_rail_tile_lines(self, tiles, line_mode):
"""
Draw the rail lines.
:param tiles: The list of rail TileObjects in the map.
:type tiles: list of TileObject.
:param line_mode: Whether to draw the outer or inner line for the rail.
:type line_mode: Boolean
"""
def process_tile(tile, line_mode):
rail = tile.occupant
if rail.is_depot:
self.draw_rail_depot(tile, rail.depot_direction)
else:
if rail.track_X:
self.draw_rail_X(tile, line_mode=line_mode)
if rail.track_Y:
self.draw_rail_Y(tile, line_mode=line_mode)
if rail.track_N:
self.draw_rail_N(tile, line_mode=line_mode)
if rail.track_S:
self.draw_rail_S(tile, line_mode=line_mode)
if rail.track_W:
self.draw_rail_W(tile, line_mode=line_mode)
if rail.track_E:
self.draw_rail_E(tile, line_mode=line_mode)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile, line_mode)
abar()
else:
for tile in tiles:
process_tile(tile, line_mode)
def draw_rail_signals(self, tiles):
"""
Draw the rail lines.
:param tiles: The list of rail TileObjects in the map.
:type tiles: list of TileObject.
"""
def process_tile(tile):
self.draw_rail_signals_tile(tile, rotation=0)
tiles = [t for t in tiles if t.occupant.has_signals]
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile)
abar()
else:
for tile in tiles:
process_tile(tile)
def draw_road_tile_lines(self, tiles, line_mode):
"""
Draw the road lines.
:param tiles: The list of road TileObjects in the map.
:type tiles: list of TileObject.
"""
def process_tile(tile, line_mode):
road = tile.occupant
if road.is_depot:
self.draw_road_depot(tile, road.depot_direction, line_mode=line_mode)
elif road.is_level_crossing:
self.draw_level_crossing(tile, road.level_crossing_direction)
else:
has_stub = False
if road.road_NW and not road.road_SE:
has_stub = True
if road.road_SW and not road.road_NE:
has_stub = True
if road.road_SE and not road.road_NW:
has_stub = True
if road.road_NE and not road.road_SW:
has_stub = True
if has_stub:
self.transform_to_tile(tile, 0)
self.draw_road_line(0, 0, 0, 0, line_mode=line_mode, round_cap=True, owner=tile.owner)
self.end_transform_to_tile()
if road.road_NW and not road.road_SE:
self.draw_road_NW(tile, line_mode)
if road.road_SW and not road.road_NE:
self.draw_road_SW(tile, line_mode)
if road.road_SE and not road.road_NW:
self.draw_road_SE(tile, line_mode)
if road.road_NE and not road.road_SW:
self.draw_road_NE(tile, line_mode)
if road.road_NW and road.road_SE:
self.draw_road_NW_to_SE(tile, line_mode)
if road.road_SW and road.road_NE:
self.draw_road_NE_to_SW(tile, line_mode)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile, line_mode)
abar()
else:
for tile in tiles:
process_tile(tile, line_mode)
def draw_tram_tile_lines(self, tiles, line_mode):
"""
Draw the tram lines.
:param tiles: The list of road TileObjects in the map.
:type tiles: list of TileObject.
"""
def process_tile(tiles, line_mode):
road = tile.occupant
if road.is_depot:
return
elif road.is_level_crossing:
return
else:
if road.tram_NW:
self.draw_tram_NW(tile, line_mode)
if road.tram_SW:
self.draw_tram_SW(tile, line_mode)
if road.tram_SE:
self.draw_tram_SE(tile, line_mode)
if road.tram_NE:
self.draw_tram_NE(tile, line_mode)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile, line_mode)
abar()
else:
for tile in tiles:
process_tile(tile, line_mode)
def draw_stations_with_lines(self, tiles, all_tiles, line_mode="both"):
"""
Draw the stations with lines.
:param tiles: The list of stations TileObjects in the map.
:type tiles: list of TileObject.
:param all_tiles: The list of all TileObjects in the map.
:type tiles: list of TileObject.
:param line_mode: Whether to draw the outer or inner line for the payload.
:type line_mode: Boolean
"""
def process_tile(tile, all_tiles, line_mode):
station = tile.occupant
self.draw_station_edges(tile, all_tiles)
if station.station_type == 0:
if station.track_direction == 0:
self.draw_rail_X(tile, line_mode="both")
else:
self.draw_rail_Y(tile, line_mode="both")
elif station.station_type in [2, 3]:
has_stub = False
if station.road_NW and not station.road_SE:
has_stub = True
if station.road_SW and not station.road_NE:
has_stub = True
if station.road_SE and not station.road_NW:
has_stub = True
if station.road_NE and not station.road_SW:
has_stub = True
if has_stub:
self.transform_to_tile(tile, 0)
self.draw_road_line(0, 0, 0, 0, line_mode=line_mode, round_cap=True, owner=tile.owner)
self.end_transform_to_tile()
if station.road_NW and not station.road_SE:
self.draw_road_NW(tile, line_mode="both")
if station.road_SW and not station.road_NE:
self.draw_road_SW(tile, line_mode="both")
if station.road_SE and not station.road_NW:
self.draw_road_SE(tile, line_mode="both")
if station.road_NE and not station.road_SW:
self.draw_road_NE(tile, line_mode="both")
if station.road_NW and station.road_SE:
self.draw_road_NW_to_SE(tile, line_mode="both")
if station.road_SW and station.road_NE:
self.draw_road_NE_to_SW(tile, line_mode="both")
if station.station_type == 7:
if station.track_direction == 0:
self.draw_rail_X(tile, line_mode="inner")
else:
self.draw_rail_Y(tile, line_mode="inner")
# Draw tram lines last, to make sure they fit over the roads.
def process_tile_tram(tile, all_tiles):
station = tile.occupant
if station.station_type in [2, 3]:
if tile.occupant.tram_type == 1:
if station.road_NW:
self.draw_tram_NW(tile, line_mode="both")
if station.road_SW:
self.draw_tram_SW(tile, line_mode="both")
if station.road_SE:
self.draw_tram_SE(tile, line_mode="both")
if station.road_NE:
self.draw_tram_NE(tile, line_mode="both")
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile, all_tiles, line_mode)
abar()
else:
for tile in tiles:
process_tile(tile, all_tiles, line_mode)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile_tram(tile, all_tiles)
abar()
else:
for tile in tiles:
process_tile_tram(tile, all_tiles)
def draw_tunnel_mouths_and_bridge_ramps(self, tiles):
"""
Draw the tunnel mouths and bridge ramps.
:param tiles: The list of tunnel or bridge TileObjects in the map.
:type tiles: list of TileObject.
"""
def process_tile(tile):
torb = tile.occupant
if torb.is_tunnel:
if torb.payload_kind == 0:
self.draw_rail_tunnel_mouth(tile, torb.entrance_direction)
elif torb.payload_kind == 1:
self.draw_road_tunnel_mouth(tile, torb.entrance_direction)
else:
if torb.payload_kind == 0:
self.draw_rail_bridge_ramp(tile, torb.entrance_direction)
elif torb.payload_kind == 1:
self.draw_road_bridge_ramp(tile, torb.entrance_direction)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile)
abar()
else:
for tile in tiles:
process_tile(tile)
def draw_bridges_over(self, tiles):
"""
Draw the bridges over tiles, where they exist.
:param tiles: The list of all TileObjects in the map.
:type tiles: list of TileObject.
"""
def process_tile(tile):
if not tile.bridge:
return
self.draw_unknown_bridge_over(tiles, tile, tile.bridge - 1)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile)
abar()
else:
for tile in tiles:
process_tile(tile)
def draw_industry_tiles(self, tiles, all_tiles):
"""
Draw the industry tiles.
:param tiles: The list of industry TileObjects in the map.
:type tiles: list of TileObject.
:param all_tiles: The list of all TileObjects in the map.
:type all_tiles: list of TileObject.
"""
def process_tile(tile, all_tiles):
self.draw_industry_edges(tile, all_tiles)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile, all_tiles)
abar()
else:
for tile in tiles:
process_tile(tile, all_tiles)
def draw_water_tiles(self, tiles, all_tiles):
"""
Draw the water tiles.
:param tiles: The list of water TileObjects in the map.
:type tiles: list of TileObject.
:param all_tiles: The list of all TileObjects in the map.
:type all_tiles: list of TileObject.
"""
def process_tile(tile, all_tiles):
self.draw_water_edges(tile, all_tiles)
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile, all_tiles)
abar()
else:
for tile in tiles:
process_tile(tile, all_tiles)
def draw_building_tiles(self, tiles):
"""
Draw the industry tiles.
:param tiles: The list of building TileObjects in the map.
:type tiles: list of TileObject.
"""
def process_tile(tile):
d = 0.3 * self.ss
self.transform_to_tile(tile, 0)
self.draw_rectangle(-d, -d, 2 * d, 2 * d, self.town_building_rgb)
self.end_transform_to_tile()
if self.parent.show_progress_bar:
with alive_bar(len(tiles)) as abar:
for tile in tiles:
process_tile(tile)
abar()
else:
for tile in tiles:
process_tile(tile)
def draw_label(self, text, font_size, cx, cy):
"""
Draw a label.
:param text: The text of the label.
:type text: string.
:param font_size: The font size of the label.
:type font_size: float.
:param cx: The centre x value of the label.
:type cx: float.
:param cy: The centre y value of the label.
:type cy: float.
"""
context = self.context
context.select_font_face(
"Arial", cairo.FONT_SLANT_NORMAL, cairo.FONT_WEIGHT_NORMAL
)
context.set_font_size(font_size)
(tx, ty, tw, th, tdx, tdy) = context.text_extents(text)
padding = 0.1 * self.ss
x = cx - tw / 2 - padding
y = cy - th / 2 - padding
self.draw_rectangle_rgba(x, y, tw + 2 * padding, th + 2 * padding, (255, 255, 255, 0.5))
context.set_source_rgb(0, 0, 0)
context.move_to(cx - tw / 2, cy + 0.25 * font_size)
context.show_text(text)
def draw_industry_labels(self):
"""Draw the industry labels."""
for shape in self.industry_shapes:
ave_row = sum([t.row for t in shape]) / len(shape)
ave_col = sum([t.col for t in shape]) / len(shape)
cx, cy = self.cxy_from_rc(ave_row, ave_col)
font_size = 0.5 * self.ss
context = self.context
context.save()
label = shape[0].occupant.industry_type
label = label.replace("_", " ")
label = label.lower().capitalize()
self.draw_label(label, font_size, cx, cy)
context.restore()
def draw_station_labels(self):
"""Draw the station labels."""
for shape in self.station_shapes:
ave_row = sum([t.row for t in shape]) / len(shape)
ave_col = sum([t.col for t in shape]) / len(shape)
cx, cy = self.cxy_from_rc(ave_row, ave_col)
font_size = 0.5 * self.ss
context = self.context
context.save()
label = "Station"
label = label.replace("_", " ")
label = label.lower().capitalize()
self.draw_label(label, font_size, cx, cy)
context.restore()
def save_image(self, image_file_path, filetype="PNG"):
"""
Save the image to file.
:param image_file_path: The path to the file, excluding the extension.
:type image_file_path: string.
:param filetype: The filetype to the image. Defaults to 'PNG'.
:type filetype: string.
:return: A list of list of TileObjects, one list per industry.
:rtype: list of lists of TileObjects
"""
iw = self.ss * (self.parent.ncols - 1)
ih = self.ss * (self.parent.nrows - 1)
logline = f"Dimensions of tile size, image before resizing: {self.ss}, {iw} x {ih}"
self.log_message(logline)
max_dimension = max(iw, ih)
if max_dimension > 32767:
rho = 32767 / max_dimension
self.ss = int(rho * self.ss)
self.ds = int(rho * self.ds)
iw = self.ss * (self.parent.ncols - 1)
ih = self.ss * (self.parent.nrows - 1)
logline = f"Dimensions of tile size, image after resizing : {self.ss}, {iw} x {ih}"
self.log_message(logline)
if filetype == "PNG":
self.image = cairo.ImageSurface(cairo.FORMAT_ARGB32, iw, ih)
elif filetype == "SVG":
self.image = cairo.SVGSurface(f"{image_file_path}", iw, ih)
self.context = cairo.Context(self.image)
all_tiles = self.parent.tiles
rail_tiles = [t for t in all_tiles if t.kind == 1]
road_tiles = [t for t in all_tiles if t.kind == 2]
building_tiles = [t for t in all_tiles if t.kind == 3]
stations_tiles = [t for t in all_tiles if t.kind == 5]
water_tiles = [t for t in all_tiles if t.kind == 6]
industry_tiles = [t for t in all_tiles if t.kind == 8]
torb_tiles = [t for t in all_tiles if t.kind == 9]
self.industry_shapes = self.make_industry_shapes(industry_tiles)
self.station_shapes = self.make_station_shapes(stations_tiles)
self.log_message("Drawing tile backgrounds.")
self.draw_tile_backgrounds(all_tiles)
self.log_message("Drawing road tiles.")
self.draw_road_tile_lines(road_tiles, line_mode="outer")
if self.show_roads:
self.log_message("Drawing rail tiles.")
self.draw_rail_tile_lines(rail_tiles, line_mode="outer")
self.log_message("Drawing station tiles.")
self.draw_stations_with_lines(stations_tiles, all_tiles)
self.log_message("Drawing tunnel mouth and bridge ramp tiles.")
self.draw_tunnel_mouths_and_bridge_ramps(torb_tiles)
self.log_message("Drawing building tiles.")
self.draw_building_tiles(building_tiles)
self.log_message("Drawing industry tiles.")
self.draw_industry_tiles(industry_tiles, all_tiles)
self.log_message("Drawing water tiles.")
self.draw_water_tiles(water_tiles, all_tiles)
if self.show_roads:
self.log_message("Drawing road tiles.")
self.draw_road_tile_lines(road_tiles, line_mode="inner")
self.log_message("Drawing tram tiles.")
self.draw_tram_tile_lines(road_tiles, line_mode="inner")
self.log_message("Drawing rail tiles.")
self.draw_rail_tile_lines(rail_tiles, line_mode="inner")
if self.show_signals:
self.log_message("Drawing rail signals.")
self.draw_rail_signals(rail_tiles)
self.log_message("Drawing bridges over tiles.")
self.draw_bridges_over(all_tiles)
self.log_message("Drawing industry labels.")
self.draw_industry_labels()
# Station names don't work yet. I hope to add them in the future.
# self.log_message("Drawing station labels.")
# self.draw_station_labels()
if filetype == "PNG":
self.log_message("Writing PNG file to disk.")
image_file_path = image_file_path.replace(".sav", ".png")
self.image.write_to_png(image_file_path)
self.log_message("All done!")
|
from abstractplayer import *
from element import *
from baseclass import *
__all__ = ['QAlgorithm', 'QPlayer', 'HumanPlayer']
class QAlgorithm(BaseQ):
def __init__(self, alpha, gamma, reward, tie, penalty):
self.qmap = ValueMap()
self.alpha = Value(alpha)
self.gamma = Value(gamma)
self.feedback = {
GameStatus.Victory: Value(reward),
GameStatus.Tie: Value(tie),
GameStatus.Lose: Value(penalty)
}
def setValue(self, pair: BasePair, value: BaseValue):
assert isinstance(pair, BasePair)
assert isinstance(value, BaseValue)
self.qmap[pair] = value
def getValue(self, pair: BasePair):
assert isinstance(pair, BasePair)
return self.qmap[pair]
def getAction(self, state: BaseState):
assert isinstance(state, BaseState)
availPairs = self.getAvailPairs(state)
maxPair = max(availPairs, key=self.getValue)
_ , action = maxPair
return action
def update(self, his: BaseHistory, gs: GameStatus):
assert isinstance(his, BaseHistory)
assert isinstance(gs , GameStatus)
assert gs is not GameStatus.InProgress
post = self.feedback[gs] / self.gamma
while his:
pair = his.pop()
prev = self.getValue(pair)
new = (Value(1.0) - self.alpha) * prev + self.alpha * (
self.gamma * post)
self.setValue(pair, new)
post = new
@staticmethod
def getAvailPairs(state: BaseState) -> BasePair:
assert isinstance(state, BaseState)
r = len(state)
c = len(state[0])
rec = Record(state)
allActs = (Location(i,j) for i in range(r) for j in range(c))
availPairs = (Pair(rec, act) for act in allActs if state[act] == 0)
return availPairs
class QPlayer(ComputerPlayer):
def __init__(self, index: int, alg: BaseAlgorithm):
self.index = Index(index)
self.alg = alg
self.his = History()
@staticmethod
def convert(state: BaseState):
assert isinstance(state, BaseState)
return Record(state)
def decide(self, state: BaseState):
assert isinstance(state, BaseState)
action = self.alg.getAction(state)
return action
def update(self, pair: BasePair):
assert isinstance(pair, BasePair)
self.his.append(pair)
def move(self, state: BaseState):
assert isinstance(state, BaseState)
action = self.decide(state)
record = self.convert(state)
pair = Pair(record, action)
self.update(pair)
return action
def recv(self, gs: GameStatus):
assert isinstance(gs, GameStatus)
if gs is GameStatus.InProgress:
pass
else:
self.alg.update(self.his, gs)
def getIndex(self):
return self.index
class HumanPlayer(BasePlayer):
def __init__(self, index: int):
self.index = Index(index)
def move(self, state:BaseState):
while True:
x = input('x: ')
y = input('y: ')
try:
action = Location(int(x), int(y))
assert state[action] == 0
return action
except:
print("Retype valid x, y")
def recv(self, gs:GameStatus):
pass
def getIndex(self):
return self.index
|
from __future__ import absolute_import, division, print_function, unicode_literals
from gittip.elsewhere import PlatformOAuth2
from gittip.elsewhere._extractors import key
from gittip.elsewhere._paginators import header_links_paginator
class GitHub(PlatformOAuth2):
# Platform attributes
name = 'github'
display_name = 'GitHub'
account_url = 'https://github.com/{user_name}'
# Auth attributes
auth_url = 'https://github.com/login/oauth'
oauth_email_scope = 'user:email'
# API attributes
api_format = 'json'
api_paginator = header_links_paginator()
api_url = 'https://api.github.com'
api_user_info_path = '/users/{user_name}'
api_user_self_info_path = '/user'
api_team_members_path = '/orgs/{user_name}/public_members'
ratelimit_headers_prefix = 'x-ratelimit-'
# User info extractors
x_user_id = key('id')
x_user_name = key('login')
x_display_name = key('name')
x_email = key('email')
x_gravatar_id = key('gravatar_id')
x_avatar_url = key('avatar_url')
x_is_team = key('type', clean=lambda t: t.lower() == 'organization')
|
from PIL import Image, ImageOps
import numpy as np
import skimage.io as io
from src.models.class_patcher import patcher
from src.utils.imgproc import *
from skimage.color import rgb2hsv, hsv2rgb
class patcher(patcher):
def __init__(self, body='./body/body_glaze.png', **options):
super().__init__('ぐれーず', body=body, pantie_position=[265, 2019], **options)
self.mask = io.imread('./mask/mask_glaze.png')
self.pantie_edge = io.imread('./material/glaze_pantie_edge.png') / 255.
try:
self.with_bra = self.options['with_bra']
except:
self.with_bra = self.ask(question='With bra?', default=True)
if self.with_bra:
self.bra_mask = io.imread('./mask/bra_glaze_center.png')
self.bra_others = io.imread('./material/glaze_others.png') / 255.
def convert(self, image):
pantie = np.array(image)
patch = np.copy(pantie[-140:-5, 546:, :])
[pr, pc, d] = patch.shape
arrx = np.zeros(25) - 10
arry = np.zeros(25) - 5
arrx[6:-10] += np.sin(np.linspace(0, np.pi, 9)) * 4
arrx[-15:] -= np.sin(np.linspace(0, np.pi / 2, 15)) * 5
arry[1:5] -= np.sin(np.linspace(0, np.pi, 4)) * 25
arry[5:] += np.sin(np.linspace(0, np.pi / 2, 20)) * 40
pantie = affine_transform_by_arr(pantie, arrx, arry)
pantie[125:125 + pr, :pc, :] = np.float32(patch[::-1, ::-1] / 255)
pantie = np.uint8(resize(pantie[:320, :-50], [3.25, 3.15]) * 255)[:, 38:]
pantie = np.bitwise_and(pantie, self.mask)
pantie = np.concatenate((pantie[:, ::-1], pantie), axis=1)
return Image.fromarray(pantie)
def gen_bra_center(self, image):
pantie = np.array(image)
center = np.uint8(resize(pantie[15:150, 450:], [7.85, 7.85]) * 255)
center = np.bitwise_and(center, self.bra_mask)
return Image.fromarray(center)
def gen_colored_tex(self, image, texture):
pantie = np.array(image)
tex = np.copy(texture)
front = pantie[20:100, 30:80, :3] / 255.0
front_color = np.mean(np.mean(front, axis=0), axis=0)
front_shade = pantie[130:150, 0:40, :3] / 255.0
front_shade_color = np.mean(np.mean(front_shade, axis=0), axis=0)
# tex[:, :, :3] = tex[:, :, :3] * front_shade_color
tex[:, :, :3] = tex[:, :, :3] * front_color
return Image.fromarray(np.uint8(tex * 255))
def patch(self, image, transparent=False):
pantie = self.convert(image)
if transparent:
patched = Image.new("RGBA", (4096, 4096))
else:
patched = self.body.copy()
patched = self.paste(patched, pantie, self.pantie_position)
patched = self.paste(patched, self.gen_colored_tex(image, self.pantie_edge), [258, 1907])
if self.with_bra:
bra_center = self.gen_bra_center(image)
patched = self.paste(patched, bra_center, [528, 162])
patched = self.paste(patched, ImageOps.mirror(bra_center), [2173, 162])
patched = self.paste(patched, self.gen_colored_tex(image, self.bra_others), [251, 921])
return patched
|
class Solution:
def reverseBits(self, nums: int) -> int:
result = 0
for i in range(32):
result <<= 1
if (nums & 1) == 1:
result ^= 1
nums >>= 1
return result
|
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 29 15:06:21 2019
@author: Ahmed Ali Mohamed
-------
Chapter 4 section 3.1 (4.3.1)
-------
*******
Finger exercise: When the implementation of fib in Figure 4.7 is used to compute
fib(5), how many times does it compute the value of fib(2) on the way to computing fib(5)?
*******
"""
'''
The solution is '3' three times
'''
# code to track how many times the specific has been called
number_to_fib = int(input('Please, Enter a positive integer: '))
# the umber user want to track how many it is called during getting Fibonacci of the input
specific_number_to_be_counted = int(input('Please, Enter a positive integer to track how many it is called? '))
def fib(n):
'''
Assumes, n int >= 0
return fibbonachi of n
'''
global numFibCalls
numFibCalls = 0
numFibCalls += 1
global numFibTwo
if n == specific_number_to_be_counted:
numFibTwo += 1
if n == 1 or n == 0:
return 1
else:
return fib(n-1) + fib(n-2)
def testFib(x):
global numFibCalls
numFibCalls = 0
global numFibTwo
numFibTwo = 0
print('the Fibbonaci number of:', x, 'is', fib(x))
print('Fib function has been called', numFibCalls, 'times')
print('Fib(',specific_number_to_be_counted,') function has been called', numFibTwo, 'times')
return None
testFib(number_to_fib)
|
# Given an integer array arr, sort the integers in arr in ascending order by the number of 1’s in their binary representation and return the sorted array.
#
# Examples:
# $ sortBits([0,1,2,3,4,5,6,7,8])
# $ [0,1,2,4,8,3,5,6,7]
def sort_bits(num_list):
nums_counted = {}
miny = 0
maxy = 0
for num in num_list:
if num > maxy:
maxy = num
if num < miny:
miny = num
ones = (str(bin(num))).count('1')
if ones in nums_counted:
nums_counted[ones].append(num)
else:
nums_counted[ones] = [num]
output_list = []
for key in range(miny, maxy+1):
if key in nums_counted:
output_list.extend(nums_counted[key])
return output_list
assert sort_bits([0,1,2,3,4,5,6,7,8]) == [0,1,2,4,8,3,5,6,7], f"Did not sort correctly, was not: [0,1,2,4,8,3,5,6,7]"
assert sort_bits([8,7,6,5,4,3,2,1,0]) == [0,8,4,2,1,6,5,3,7], f"Did not sort correctly, was not: [0,8,4,2,1,6,5,3,7], it was: {sort_bits([8,7,6,5,4,3,2,1,0])}"
assert sort_bits([0,0,1,2,3,4,5,6,7,8]) == [0,0,1,2,4,8,3,5,6,7], f"Did not sort correctly, was not: [0,0,1,2,4,8,3,5,6,7]"
assert sort_bits([0,1,2,3,4,5,6,7,8, 256, 256]) == [0,1,2,4,8,256,256,3,5,6,7], f"Did not sort correctly, was not: [0,1,2,4,8,256,256,3,5,6,7]"
assert sort_bits([0,1,2,3,4,5,6,7,8, 256, 7]) != [0,1,2,4,8,256,7,3,5,6,7], f"Did not sort correctly, should have failed: [0,1,2,4,8,256,7,3,5,6,7]"
assert sort_bits([]) == [], f"Did not sort correctly, should have been []"
|
import timeit
import numpy as np
from bingo.symbolic_regression.agraph \
import agraph as agraph_module
from bingo.symbolic_regression.agraph.evaluation_backend import \
evaluation_backend as pyBackend
from bingo.local_optimizers.continuous_local_opt \
import ContinuousLocalOptimization
from bingocpp import evaluation_backend as cppBackend
from benchmark_data import StatsPrinter, \
generate_random_individuals, \
copy_to_cpp, \
TEST_EXPLICIT_REGRESSION, \
TEST_EXPLICIT_REGRESSION_CPP, \
TEST_IMPLICIT_REGRESSION, \
TEST_IMPLICIT_REGRESSION_CPP, \
CLO_TIMING_NUMBER, \
CLO_TIMING_REPEATS, \
NUM_AGRAPHS_INDVS
import benchmark_data as benchmark_data
TEST_EXPLICIT_REGRESSION_OPTIMIZATION \
= ContinuousLocalOptimization(TEST_EXPLICIT_REGRESSION)
TEST_IMPLICIT_REGRESSION_OPTIMIZATION \
= ContinuousLocalOptimization(TEST_IMPLICIT_REGRESSION)
TEST_EXPLICIT_REGRESSION_OPTIMIZATION_CPP \
= ContinuousLocalOptimization(TEST_EXPLICIT_REGRESSION_CPP)
TEST_IMPLICIT_REGRESSION_OPTIMIZATION_CPP \
= ContinuousLocalOptimization(TEST_IMPLICIT_REGRESSION_CPP)
TEST_ITERATION = 0
DEBUG = False
TEST_AGRAPHS = generate_random_individuals(benchmark_data.NUM_AGRAPHS_INDVS,
benchmark_data.COMMAND_ARRAY_SIZE,
True)
TEST_AGRAPHS_CPP = copy_to_cpp(TEST_AGRAPHS)
BENCHMARK_LISTS = []
BENCHMARK_LISTS_CPP = []
def benchmark_explicit_regression_with_optimization():
np.random.seed(0)
for i, test_run in enumerate(BENCHMARK_LISTS):
for indv in test_run:
_ = TEST_EXPLICIT_REGRESSION_OPTIMIZATION.__call__(indv)
def benchmark_explicit_regression_cpp_with_optimization():
np.random.seed(0)
for i, test_run in enumerate(BENCHMARK_LISTS_CPP):
for indv in test_run:
_ = TEST_EXPLICIT_REGRESSION_OPTIMIZATION_CPP.__call__(indv)
def benchmark_implicit_regression_with_optimization():
np.random.seed(0)
for i, test_run in enumerate(BENCHMARK_LISTS):
for indv in test_run:
_ = TEST_IMPLICIT_REGRESSION_OPTIMIZATION.__call__(indv)
def benchmark_implicit_regression_cpp_with_optimization():
np.random.seed(0)
for i, test_run in enumerate(BENCHMARK_LISTS_CPP):
for indv in test_run:
_ = TEST_IMPLICIT_REGRESSION_OPTIMIZATION_CPP.__call__(indv)
def reset_test_data():
_reset_test_data_helper(TEST_AGRAPHS, BENCHMARK_LISTS)
def reset_test_data_cpp():
_reset_test_data_helper(TEST_AGRAPHS_CPP, BENCHMARK_LISTS_CPP)
def _reset_test_data_helper(agraph_list, benchmarking_array):
global TEST_ITERATION
if DEBUG:
print("...Executing iteration:", TEST_ITERATION)
_create_fresh_benchmarking_array(agraph_list, benchmarking_array)
TEST_ITERATION += 1
def _create_fresh_benchmarking_array(agraph_list, benchmarking_array):
benchmarking_array.clear()
for num_runs in range(0, CLO_TIMING_NUMBER):
run_list = []
for agraph in agraph_list:
run_list.append(agraph.copy())
benchmarking_array.append(run_list)
def do_benchmarking(debug = False):
if debug:
global DEBUG
DEBUG = True
benchmarks = [
[benchmark_explicit_regression_with_optimization,
benchmark_explicit_regression_cpp_with_optimization,
"LOCAL OPTIMIZATION (EXPLICIT REGRESSION) BENCHMARKS"],
[benchmark_implicit_regression_with_optimization,
benchmark_implicit_regression_cpp_with_optimization,
"LOCAL OPTIMIZATION (IMPLICIT REGRESSION) BENCHMARKS"]]
stats_printer_list = []
for regression, regression_cpp, reg_name in benchmarks:
printer = StatsPrinter(reg_name)
_run_benchmarks(printer, regression, regression_cpp)
stats_printer_list.append(printer)
return stats_printer_list
def _run_benchmarks(printer, regression, regression_cpp):
for backend, name in [[pyBackend, " py"], [cppBackend, "c++"]]:
agraph_module.evaluation_backend = backend
_add_stats_and_log_intermediate_steps(
printer,
regression,
"py: fitness " +name + ": evaluate ",
reset_test_data
)
_add_stats_and_log_intermediate_steps(
printer,
regression_cpp,
"c++: fitness c++: evaluate ",
reset_test_data_cpp
)
def _add_stats_and_log_intermediate_steps(stats_printer,
regression,
run_name,
setup_function):
if DEBUG:
print("Running", regression.__name__, "...................")
stats_printer.add_stats(
run_name,
timeit.repeat(regression, setup=setup_function, number=1,
repeat=CLO_TIMING_REPEATS),
number=CLO_TIMING_NUMBER * NUM_AGRAPHS_INDVS,
unit_mult=1000
)
if DEBUG:
print(regression.__name__, "finished\n")
_reset_iteration_count()
def _reset_iteration_count():
global TEST_ITERATION
TEST_ITERATION = 0
def _print_stats(printer_list):
for printer in printer_list:
printer.print()
if __name__ == '__main__':
do_benchmarking(False) |
import json
import schedule
import time as t
import webbrowser
import datetime
import argparse
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
options = webdriver.ChromeOptions()
options.add_argument("--disable-infobars")
options.add_argument("--window-size=1280,720")
options.add_experimental_option("prefs", { \
"profile.default_content_setting_values.media_stream_mic": 1, # 1:allow, 2:block
"profile.default_content_setting_values.media_stream_camera": 1, # 1:allow, 2:block
"profile.default_content_setting_values.geolocation": 2, # 1:allow, 2:block
"profile.default_content_setting_values.notifications": 2 # 1:allow, 2:block
})
driver = webdriver.Chrome('./chromedriver', chrome_options=options)
# Opening JSON file
f = open('slots.json')
g = open('your_slots.json')
h = open('secret.json')
def login():
secret = json.load(h)
#login
driver.get('https://teams.microsoft.com')
t.sleep(2)
driver.find_element_by_xpath('//*[@id="i0116"]').send_keys(secret['username'])
t.sleep(2)
driver.find_element_by_xpath('//*[@id="idSIButton9"]').click()
t.sleep(2)
driver.find_element_by_xpath('//*[@id="i0118"]').send_keys(secret['password'])
t.sleep(2)
driver.find_element_by_xpath('//*[@id="idSIButton9"]').click()
t.sleep(2)
driver.find_element_by_xpath('//*[@id="idSIButton9"]').click()
def start_meeting(meeting_link, meeting_type):
print("Opening meeting link {}".format(meeting_link))
if meeting_type == 'browser':
webbrowser.open(meeting_link, new=2)
elif meeting_type == 'microsoft_teams':
driver.get(meeting_link)
t.sleep(2)
try:
driver.find_element_by_xpath('//*[@id="openTeamsClientInBrowser"]').click()
except:
pass
join_meeting_button = None
while join_meeting_button is None:
try:
join_meeting_button = driver.find_element_by_xpath('/html/body/div[1]/div[2]/div[2]/div[1]/middle-messages-stripe/div/messages-header/div[2]/div/message-pane/div/div[1]/div/div/message-list/div/virtual-repeat/div/div[8]/div/thread/div/div[2]/calling-thread-header/div/calling-join-button/button')
except:
print('wating for meeting to start...')
t.sleep(2)
pass
join_meeting_button.click()
t.sleep(5)
video_toggle = driver.find_element_by_xpath('//*[@id="page-content-wrapper"]/div[1]/div/calling-pre-join-screen/div/div/div[2]/div[1]/div[2]/div/div/section/div[2]/toggle-button[1]/div/button/span[1]')
mic_toggle = driver.find_element_by_xpath('//*[@id="preJoinAudioButton"]/div/button/span[1]')
if video_toggle.get_attribute('title') == 'Turn camera off':
video_toggle.click()
if mic_toggle.get_attribute('title') == 'Mute microphone':
mic_toggle.click()
t.sleep(2)
driver.find_element_by_xpath('//*[@id="page-content-wrapper"]/div[1]/div/calling-pre-join-screen/div/div/div[2]/div[1]/div[2]/div/div/section/div[1]/div/div/button').click()
else:
print('Invalid Option')
return schedule.CancelJob
def main():
# Initialize parser
parser = argparse.ArgumentParser()
# Adding optional argument
parser.add_argument("--today", action='store_true', help = "Schedule classses today")
parser.add_argument("--tomorrow", action='store_true', help = "Schedule classses tomorrow")
# Read arguments from command line
args = parser.parse_args()
if not (args.tomorrow or args.today):
parser.error('No action requested, add --today or --tomorrow')
if args.tomorrow and args.today:
parser.error('Both actions requested, add either --today or --tomorrow')
today = datetime.datetime.now().strftime("%A").lower()
tomorrow = ""
days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']
for i in range(len(days)):
if(today == days[i]):
tomorrow = days[(i+1) % 7]
# returns JSON object as
# a dictionary
slots = json.load(f)
your_slots = json.load(g)
for your_slot in your_slots:
slot_name = your_slot['slot_name']
course_name = your_slot['course_name']
meeting_type = your_slot['meeting_type']
meeting_link = your_slot['meeting_link']
periods = slots[slot_name]
required_day = ""
for period in periods:
if args.today:
required_day = today
if args.tomorrow:
required_day = tomorrow
day = period['day']
time = period['time']
if day == required_day:
print("Scheduling {} at slot {} on {} at {}. Meeting type is {}".format(course_name, slot_name, day, time, meeting_type))
schedule.every().day.at(time).do(start_meeting, meeting_link=meeting_link, meeting_type=meeting_type)
login()
while True:
# Checks whether a scheduled task
# is pending to run or not
schedule.run_pending()
t.sleep(30)
# Closing file
f.close()
if __name__ == "__main__":
main() |
from datetime import datetime, timedelta
import jwt
from django.conf import settings
from django.contrib.auth.models import (
AbstractBaseUser,
BaseUserManager,
PermissionsMixin,
)
from django.db import models
from sendit.apps.core.base_model import CommonFieldsMixin
class UserManager(BaseUserManager):
"""
Django requires that custom users define their own Manager class. By
inheriting from `BaseUserManager`, we get a lot of the same code used by
Django to create a `User` for free.
All we have to do is override the `create_user` function which we will use
to create `User` objects.
"""
def create_user(self, full_name, email, password=None):
"""Create and return a `User` with an email, full name and password."""
if full_name is None:
raise TypeError("Users must have a name.")
if email is None:
raise TypeError("Users must have an email address.")
user = self.model(full_name=full_name, email=self.normalize_email(email))
user.set_password(password)
user.save()
return user
def create_superuser(self, full_name, email, password):
"""
Create and return a `User` with superuser powers.
Superuser powers means that this use is an admin that can do anything
they want.
"""
if password is None:
raise TypeError("Superusers must have a password.")
user = self.create_user(full_name, email, password)
user.is_superuser = True
user.is_staff = True
user.is_admin = True
user.save()
return user
class User(AbstractBaseUser, PermissionsMixin, CommonFieldsMixin):
full_name = models.CharField(db_index=True, max_length=255)
email = models.EmailField(db_index=True, unique=True)
is_active = models.BooleanField(default=False)
is_staff = models.BooleanField(default=False)
is_verified = models.BooleanField(default=False)
is_admin = models.BooleanField(default=False)
USERNAME_FIELD = "email"
objects = UserManager()
def __str__(self):
"""
Returns a string representation of this `User`.
This string is used when a `User` is printed in the console.
"""
return self.email
@property
def get_full_name(self):
"""
This method is required by Django for things like handling emails.
Typically, this would be the user's first and last name. Since we do
not store the user's real name, we return their full name instead.
"""
return self.full_name
def get_short_name(self):
"""
This method is required by Django for things like handling emails.
Typically, this would be the user's first name. Since we do not store
the user's real name, we return their full name instead.
"""
return self.full_name
@property
def token(self):
"""
Allows us to get a user's token by calling `user.token`
"""
return self._generate_jwt_token()
def _generate_jwt_token(self):
"""
Generates a JWT that stores this user's email and has an expiry
date set to 1 day into the future.
"""
dt = datetime.now() + timedelta(days=1)
data = {
"email": self.email,
"active": self.is_active,
"exp": int(dt.strftime("%s")),
}
token = jwt.encode(data, settings.SECRET_KEY, algorithm="HS256")
return token.decode("utf-8")
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.general.attention.additive import AdditiveAttention
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class NewsEncoder(torch.nn.Module):
def __init__(self, config, pretrained_word_embedding, writer):
super(NewsEncoder, self).__init__()
self.config = config
if pretrained_word_embedding is None:
self.word_embedding = nn.Embedding(config.num_words,
config.word_embedding_dim,
padding_idx=0)
else:
self.word_embedding = nn.Embedding.from_pretrained(
pretrained_word_embedding, freeze=False, padding_idx=0)
self.category_embedding = nn.Embedding(config.num_categories,
config.category_embedding_dim,
padding_idx=0)
self.category_linear = nn.Linear(config.category_embedding_dim,
config.num_filters)
self.subcategory_linear = nn.Linear(config.category_embedding_dim,
config.num_filters)
assert config.window_size >= 1 and config.window_size % 2 == 1
self.title_CNN = nn.Conv2d(
1,
config.num_filters,
(config.window_size, config.word_embedding_dim),
padding=(int((config.window_size - 1) / 2), 0))
self.abstract_CNN = nn.Conv2d(
1,
config.num_filters,
(config.window_size, config.word_embedding_dim),
padding=(int((config.window_size - 1) / 2), 0))
self.title_attention = AdditiveAttention(config.query_vector_dim,
config.num_filters)
self.abstract_attention = AdditiveAttention(config.query_vector_dim,
config.num_filters)
self.final_attention = AdditiveAttention(config.query_vector_dim,
config.num_filters, writer,
'Train/NewsAttentionWeight',
['category', 'subcategory',
'title', 'abstract'])
def forward(self, news):
"""
Args:
news:
{
"category": Tensor(batch_size),
"subcategory": Tensor(batch_size),
"title": Tensor(batch_size) * num_words_title,
"abstract": Tensor(batch_size) * num_words_abstract
}
Returns:
(shape) batch_size, num_filters
"""
# Part 1: calculate activated_category_vector
# batch_size, category_embedding_dim
category_vector = self.category_embedding(news['category'].to(device))
# batch_size, num_filters
activated_category_vector = F.relu(
self.category_linear(category_vector))
# Part 2: calculate activated_subcategory_vector
# batch_size, category_embedding_dim
subcategory_vector = self.category_embedding(
news['subcategory'].to(device))
# batch_size, num_filters
activated_subcategory_vector = F.relu(
self.subcategory_linear(subcategory_vector))
# Part 3: calculate weighted_title_vector
# batch_size, num_words_title, word_embedding_dim
title_vector = F.dropout(self.word_embedding(
torch.stack(news['title'], dim=1).to(device)),
p=self.config.dropout_probability,
training=self.training)
# batch_size, num_filters, num_words_title
convoluted_title_vector = self.title_CNN(
title_vector.unsqueeze(dim=1)).squeeze(dim=3)
# batch_size, num_filters, num_words_title
activated_title_vector = F.dropout(F.relu(convoluted_title_vector),
p=self.config.dropout_probability,
training=self.training)
# batch_size, num_filters
weighted_title_vector = self.title_attention(
activated_title_vector.transpose(1, 2))
# Part 4: calculate weighted_abstract_vector
# batch_size, num_words_abstract, word_embedding_dim
abstract_vector = F.dropout(self.word_embedding(
torch.stack(news['abstract'], dim=1).to(device)),
p=self.config.dropout_probability,
training=self.training)
# batch_size, num_filters, num_words_abstract
convoluted_abstract_vector = self.abstract_CNN(
abstract_vector.unsqueeze(dim=1)).squeeze(dim=3)
# batch_size, num_filters, num_words_abstract
activated_abstract_vector = F.dropout(
F.relu(convoluted_abstract_vector),
p=self.config.dropout_probability,
training=self.training)
# batch_size, num_filters
weighted_abstract_vector = self.abstract_attention(
activated_abstract_vector.transpose(1, 2))
# batch_size, 4, num_filters
stacked_news_vector = torch.stack([
activated_category_vector, activated_subcategory_vector,
weighted_title_vector, weighted_abstract_vector
],
dim=1)
# batch_size, num_filters
news_vector = self.final_attention(stacked_news_vector)
return news_vector
|
import torch.nn as nn
def CrossEntropyLoss(output, target):
criterion = nn.CrossEntropyLoss()
return criterion(output, target)
|
from graph4nlp.pytorch.modules.evaluation.cider import CIDEr
if __name__ == "__main__":
import json
scorer = CIDEr(df="corpus")
pred_file_path = "/home/shiina/shiina/question/iq/pred.json"
gt_file_path = "/home/shiina/shiina/question/iq/gt.json"
with open(gt_file_path, "r") as f:
gt = json.load(f)
print(gt[0])
gts = []
for i in gt:
for j in i:
gts.append(str(j))
with open(pred_file_path, "r") as f:
pred = json.load(f)
print(pred[1])
preds = []
for i in pred:
for j in i:
preds.append(str(j))
print(len(gts), len(preds))
score, scores = scorer.calculate_scores(gts, preds)
print(score)
print(len(scores))
|
import os
import openai
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
start_sequence = "\nIntelli_AGENT:"
restart_sequence = "\n\nUser:"
session_prompt = "Intelli_AGENT is a chatbot that reluctantly answers questions.\n\n###\nUser: How many pounds are in a kilogram?\nIntelli_AGENT: This again? There are 2.2 pounds in a kilogram. Please make a note of this.\n###\nUser: What does HTML stand for?\nMarv: Was Google too busy? Hypertext Markup Language. The T is for try to ask better questions in the future.\n###\nIntelli_AGENT: When did the first airplane fly?\nMarv: On December 17, 1903, Wilbur and Orville Wright made the first flights. I wish they’d come and take me away.\n###\nUser: Who was the first man in space?\nIntelli_AGENT:"
def ask(question, chat_log=None):
prompt_text = f'{chat_log}{restart_sequence}: {question}{start_sequence}:'
response = openai.Completion.create(
engine="davinci",
prompt=prompt_text,
temperature=0.8,
max_tokens=150,
top_p=1,
frequency_penalty=0,
presence_penalty=0.3,
stop=["\n"],
)
story = response['choices'][0]['text']
return str(story)
def append_interaction_to_chat_log(question, answer, chat_log=None):
if chat_log is None:
chat_log = session_prompt
return f'{chat_log}{restart_sequence} {question}{start_sequence}{answer}'
|
try:
from urllib.request import urlopen
except ImportError:
from urllib import urlopen
import re
import string
def format_normalized_package_name(package_name):
# type: (str) -> str
"""Normalize a package name"""
return package_name.replace(".", "-").replace("_", "-").lower()
def format_description(description):
# type: (str) -> str
"""Normalize whitespace, remove punctuation, and capitalize first letter"""
if len(description) == 0:
return ""
description = re.sub("\s+", description.strip(string.punctuation), " ")
return description[0].upper() + description[1:]
def format_homepage(homepage):
# type: (str) -> str
"""Use https url if possible"""
if re.match("https://", homepage):
return homepage
https_homepage = homepage.replace("http://", "https://")
try:
response = urlopen(https_homepage)
return https_homepage
except:
return ""
def format_license(license):
# type: (str) -> str
"""Convert python setup.py license to nix license
These licenses account for about 95% of all licenses. The
following snippet gathers licenses of top 5,000 packages.
.. code-block:: python
import urllib.request
import json
from collections import defaultdict
projects = {r['project'] for r in requests.get("https://hugovk.github.io/top-pypi-packages/top-pypi-packages-30-days.json").json()['rows']}
licenses = defaultdict(lambda:0)
for p in projects:
try:
data = requests.get('https://pypi.org/pypi/%s/json' % p).json()
license = data.get('info', {}).get('license')
licenses[license] += 1
except:
pass
sorted([(k, v) for k, v in licenses.items()], key=lambda t: -t[1])
"""
case_sensitive_license_nix_map = {
"3-clause BSD": "bsd3",
"AGPL": "agpl3",
"Apache": "asl20",
"Apache 2": "asl20",
"Apache2": "asl20",
"Apache-2": "asl20",
"Apache 2.0": "asl20",
"Apache-2.0": "asl20",
"Apache License": "asl20",
"Apache License (2.0)": "asl20",
"Apache License 2.0": "asl20",
"Apache License, Version 2.0": "asl20",
"Apache License Version 2.0": "asl20",
"Apache Software License": "asl20",
"Apache Software License 2.0": "asl20",
"BSD license": " # lookup BSD license being used: bsd0, bsd2, bsd3, or bsdOriginal ",
"BSD": " # lookup BSD license being used: bsd0, bsd2, bsd3, or bsdOriginal ",
"BSD-3": "bsd3",
"BSD 3-clause": "bsd3",
"BSD 3-Clause License": "bsd3",
"GNU GPL": "gpl1",
"GNU LGPL": "lgpl2Plus",
"GNU GPLv2 or any later version": "gpl2Plus",
"GNU General Public License (GPL)": "gpl1",
"GNU General Public License v2 or later (GPLv2+)": "gpl2Plus",
"GPL": "gpl1",
"GPLv2 or later": "gpl2Plus",
"GPLv2": "gpl2",
"GPLv2+": "gpl2Plus",
"GPLv3": "gpl3",
"GPL v3": "gpl3",
"GPLv3+": "gpl3Plus",
"ISC": "isc",
"ISC License": "isc",
"LGPL": "lgpl2Plus",
"LGPLv2+": "lgpl2Plus",
"LGPLv2.1 or later": "lgpl21Plus",
"LGPLv3": "lgpl3",
"LGPLv3+": "lgpl3Plus",
"License :: OSI Approved :: MIT License": "mit",
"MIT": "mit",
"MIT License": "mit",
"The MIT License: http://www.opensource.org/licenses/mit-license.php": "mit",
"Mozilla Public License 2.0 (MPL 2.0)": "mpl20",
"MPL": "mpl10",
"MPL2": "mpl20",
"MPL 2.0": "mpl20",
"New BSD": "bsd3",
"New BSD License": "bsd3",
"PSF License": "psfl",
"PSF": "psfl",
"Python Software Foundation License": "psfl",
"Python style": "psfl",
"Public Domain": "publicDomain",
"Two-clause BSD license": "bsd2",
"Unlicense": "unlicense",
"ZPL 2.1": "zpl21",
"ZPL": "zpl21",
"Zope Public License": "zpl21",
}
license_nix_map = {
name.lower(): nix_attr
for name, nix_attr in case_sensitive_license_nix_map.items()
}
return license_nix_map.get(license.lower())
|
import threading
class Neighbor(threading.Thread):
"""The class Neighbor contains the client node socket, ip and host and hold the server node id. Communication
is done by this class. The server node sending message, pass through to the main node.
Instantiates a new Neighbor.
main_node: The Node class that received a connection.
sock: The socket that is associated with the client connection.
id: The id of the connected node.
host: The ip of the main node.
port: The port of the server of the main node."""
def __init__(self, main_node, sock, id, host, port, last_hello_time):
super(Neighbor, self).__init__()
self.host = host
self.port = port
self.main_node = main_node
self.sock = sock
self.last_hello_time = last_hello_time
self.terminate_flag = threading.Event()
# The connected node index
self.id = id
self.main_node.debug_print(
"Neighbor Node:" + str(self.id) + " initialized for Node:" + str(self.main_node.id))
def stop(self):
"""Terminates the connection and the thread is stopped."""
self.terminate_flag.set()
|
import pytest
from tests.utils import file_response
from documenters_aggregator.spiders.chi_animal import Chi_animalSpider
test_response = file_response('files/chi_animal.html', url='https://www.cityofchicago.org/city/en/depts/cacc/supp_info/public_notice.html')
spider = Chi_animalSpider()
parsed_items = [item for item in spider.parse(test_response) if isinstance(item, dict)]
def test_len():
assert len(parsed_items) == 3
def test_name():
assert parsed_items[0]['name'] == 'Commission meeting'
def test_start_time():
assert parsed_items[0]['start_time'].isoformat() == '2017-09-21T00:00:00-05:00'
@pytest.mark.parametrize('item', parsed_items)
def test_sources(item):
assert item['sources'] == [{'url': 'https://www.cityofchicago.org/city/en/depts/cacc/supp_info/public_notice.html',
'note': ''}]
@pytest.mark.parametrize('item', parsed_items)
def test_timezone(item):
assert item['timezone'] == 'America/Chicago'
|
#!/usr/bin/env python
"""
OCRL HW2
Simple: first fit a spline for received waypoints, then a path tracking or PID controller to follow
"""
from common import *
from nav_msgs.msg import Odometry, Path
from geometry_msgs.msg import PoseArray, Pose, Twist, PoseStamped
from ackermann_msgs.msg import AckermannDriveStamped
from scipy.interpolate import interp1d
import dubins
from angles import *
import tf
class TrajectoryFollowerNode:
"""base class for processing waypoints to give control output"""
def __init__(self):
# Parameters
self.nominal_speed = 2
# Initialize Publishers
self.cmd_pub = rospy.Publisher('/ackermann_vehicle/ackermann_cmd', AckermannDriveStamped, queue_size=10)
self.track_point_pub = rospy.Publisher('/track_point', PoseStamped, queue_size=10)
# Initialize Subscribers and relevant variables
self.got_spline = False
rospy.Subscriber("/spline_path",Path,self.trajectoryCallback)
self.rear_axle_center = Pose()
self.rear_axle_velocity = Twist()
self.rear_axle_theta = 0
rospy.Subscriber("/ackermann_vehicle/ground_truth/state",
Odometry, self.vehicleStateCallback)
# Marks time we get first spline path as spline_start_time, and starts outputting tracking point and associated commands
rospy.wait_for_message("/spline_path", Path, 10)
self.got_spline = True
self.spline_start_time = rospy.Time.now()
self.track_pt_timer = rospy.Timer(rospy.Duration(0.02), self.trackPointTimerCallback) # track point based on time from spline_path start tim
def trajectoryCallback(self,msg):
path_list = []
for i in range(len(msg.poses)):
pose_i = msg.poses[i].pose
theta = euler_from_quaternion([pose_i.orientation.x, pose_i.orientation.y, pose_i.orientation.z, pose_i.orientation.w])[2]
path_list.append([pose_i.position.x, pose_i.position.y, theta, msg.poses[i].header.stamp.secs])
#pose.pose.position.x = path_list[i,0]
#pose.pose.position.y = path_list[i,1]
path_list = np.array(path_list)
print(path_list)
self.spline_points = path_list
self.spline_distance = np.sum(np.sqrt(np.sum(np.diff(path_list[:,:2], axis=0)**2, axis=1)))
self.spline_cum_dist = np.cumsum(np.sqrt(np.sum(np.diff(path_list[:,:2], axis=0)**2, axis=1)))
# Keep this from pure_pursuit.py
def vehicleStateCallback(self,msg):
self.rear_axle_center.position.x = msg.pose.pose.position.x
self.rear_axle_center.position.y = msg.pose.pose.position.y
self.rear_axle_center.orientation = msg.pose.pose.orientation
self.rear_axle_theta = euler_from_quaternion(
[self.rear_axle_center.orientation.x, self.rear_axle_center.orientation.y, self.rear_axle_center.orientation.z,
self.rear_axle_center.orientation.w])[2]
self.rear_axle_velocity.linear = msg.twist.twist.linear
self.rear_axle_velocity.angular = msg.twist.twist.angular
def trackPointTimerCallback(self, event):
time_since_start = (rospy.Time.now() - self.spline_start_time).to_sec()
dist_along_spline = self.nominal_speed * time_since_start
track_point_ind = np.argwhere(self.spline_cum_dist > dist_along_spline)[0]
track_point_x = self.spline_points[track_point_ind, 0]
track_point_y = self.spline_points[track_point_ind, 1]
# Publish track point pose
track_pose_msg = PoseStamped()
track_pose_msg.header.stamp = rospy.Time.now()
track_pose_msg.header.frame_id = '/map'
track_pose_msg.pose.position.x = track_point_x
track_pose_msg.pose.position.y = track_point_y
self.track_point_pub.publish(track_pose_msg)
# Calculate Commands based on Tracking Point
dx = track_point_x - self.rear_axle_center.position.x
dy = track_point_y - self.rear_axle_center.position.y
lookahead_dist = np.sqrt(dx * dx + dy * dy)
lookahead_theta = math.atan2(dy, dx)
alpha = shortest_angular_distance(self.rear_axle_theta, lookahead_theta)
cmd = AckermannDriveStamped()
cmd.header.stamp = rospy.Time.now()
cmd.header.frame_id = "base_link"
# Publishing constant speed of 1m/s
cmd.drive.speed = self.nominal_speed
# Reactive steering
if alpha < 0:
st_ang = max(-max_steering_angle, alpha)
else:
st_ang = min(max_steering_angle, alpha)
cmd.drive.steering_angle = st_ang
self.cmd_pub.publish(cmd) # CMD includes steering_angle
if __name__ == '__main__':
rospy.init_node('trajectory_follower_node')
rospy.loginfo('trajectory_follower_node initialized')
node = TrajectoryFollowerNode()
rospy.spin()
|
import argparse
import os.path as osp
import warnings
import numpy as np
import onnx
import onnxruntime as rt
import torch
from mmdet.core import (build_model_from_cfg, generate_inputs_and_wrap_model,
preprocess_example_input)
def pytorch2onnx(config_path,
checkpoint_path,
input_img,
input_shape,
opset_version=11,
show=False,
output_file='tmp.onnx',
verify=False,
normalize_cfg=None,
dataset='coco',
test_img=None,
do_simplify=False):
input_config = {
'input_shape': input_shape,
'input_path': input_img,
'normalize_cfg': normalize_cfg
}
# prepare original model and meta for verifying the onnx model
orig_model = build_model_from_cfg(config_path, checkpoint_path)
one_img, one_meta = preprocess_example_input(input_config)
model, tensor_data = generate_inputs_and_wrap_model(
config_path, checkpoint_path, input_config)
output_names = ['boxes']
if model.with_bbox:
output_names.append('labels')
if model.with_mask:
output_names.append('masks')
torch.onnx.export(
model,
tensor_data,
output_file,
input_names=['input'],
output_names=output_names,
export_params=True,
keep_initializers_as_inputs=True,
do_constant_folding=True,
verbose=show,
opset_version=opset_version)
model.forward = orig_model.forward
# simplify onnx model
if do_simplify:
from mmdet import digit_version
import mmcv
min_required_version = '1.2.5'
assert digit_version(mmcv.__version__) >= digit_version(
min_required_version
), f'Requires to install mmcv>={min_required_version}'
from mmcv.onnx.simplify import simplify
input_dic = {'input': one_img.detach().cpu().numpy()}
_ = simplify(output_file, [input_dic], output_file)
print(f'Successfully exported ONNX model: {output_file}')
if verify:
from mmdet.core import get_classes, bbox2result
from mmdet.apis import show_result_pyplot
ort_custom_op_path = ''
try:
from mmcv.ops import get_onnxruntime_op_path
ort_custom_op_path = get_onnxruntime_op_path()
except (ImportError, ModuleNotFoundError):
warnings.warn('If input model has custom op from mmcv, \
you may have to build mmcv with ONNXRuntime from source.')
model.CLASSES = get_classes(dataset)
num_classes = len(model.CLASSES)
# check by onnx
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model)
if test_img is not None:
input_config['input_path'] = test_img
one_img, one_meta = preprocess_example_input(input_config)
tensor_data = [one_img]
# check the numerical value
# get pytorch output
pytorch_results = model(tensor_data, [[one_meta]], return_loss=False)
pytorch_results = pytorch_results[0]
# get onnx output
input_all = [node.name for node in onnx_model.graph.input]
input_initializer = [
node.name for node in onnx_model.graph.initializer
]
net_feed_input = list(set(input_all) - set(input_initializer))
assert (len(net_feed_input) == 1)
session_options = rt.SessionOptions()
# register custom op for onnxruntime
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
sess = rt.InferenceSession(output_file, session_options)
onnx_outputs = sess.run(None,
{net_feed_input[0]: one_img.detach().numpy()})
output_names = [_.name for _ in sess.get_outputs()]
output_shapes = [_.shape for _ in onnx_outputs]
print(f'onnxruntime output names: {output_names}, \
output shapes: {output_shapes}')
nrof_out = len(onnx_outputs)
assert nrof_out > 0, 'Must have output'
with_mask = nrof_out == 3
if nrof_out == 1:
onnx_results = onnx_outputs[0]
else:
det_bboxes, det_labels = onnx_outputs[:2]
onnx_results = bbox2result(det_bboxes, det_labels, num_classes)
if with_mask:
segm_results = onnx_outputs[2].squeeze(1)
cls_segms = [[] for _ in range(num_classes)]
for i in range(det_bboxes.shape[0]):
cls_segms[det_labels[i]].append(segm_results[i])
onnx_results = (onnx_results, cls_segms)
# visualize predictions
if show:
show_result_pyplot(
model, one_meta['show_img'], pytorch_results, title='Pytorch')
show_result_pyplot(
model, one_meta['show_img'], onnx_results, title='ONNX')
# compare a part of result
if with_mask:
compare_pairs = list(zip(onnx_results, pytorch_results))
else:
compare_pairs = [(onnx_results, pytorch_results)]
for onnx_res, pytorch_res in compare_pairs:
for o_res, p_res in zip(onnx_res, pytorch_res):
np.testing.assert_allclose(
o_res,
p_res,
rtol=1e-03,
atol=1e-05,
)
print('The numerical values are the same between Pytorch and ONNX')
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MMDetection models to ONNX')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--input-img', type=str, help='Images for input')
parser.add_argument('--show', action='store_true', help='show onnx graph')
parser.add_argument('--output-file', type=str, default='tmp.onnx')
parser.add_argument('--opset-version', type=int, default=11)
parser.add_argument(
'--test-img', type=str, default=None, help='Images for test')
parser.add_argument(
'--dataset', type=str, default='coco', help='Dataset name')
parser.add_argument(
'--verify',
action='store_true',
help='verify the onnx model output against pytorch output')
parser.add_argument(
'--simplify',
action='store_true',
help='Whether to simplify onnx model.')
parser.add_argument(
'--shape',
type=int,
nargs='+',
default=[800, 1216],
help='input image size')
parser.add_argument(
'--mean',
type=float,
nargs='+',
default=[123.675, 116.28, 103.53],
help='mean value used for preprocess input data')
parser.add_argument(
'--std',
type=float,
nargs='+',
default=[58.395, 57.12, 57.375],
help='variance value used for preprocess input data')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
assert args.opset_version == 11, 'MMDet only support opset 11 now'
if not args.input_img:
args.input_img = osp.join(
osp.dirname(__file__), '../../tests/data/color.jpg')
if len(args.shape) == 1:
input_shape = (1, 3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (1, 3) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
assert len(args.mean) == 3
assert len(args.std) == 3
normalize_cfg = {'mean': args.mean, 'std': args.std}
# convert model to onnx file
pytorch2onnx(
args.config,
args.checkpoint,
args.input_img,
input_shape,
opset_version=args.opset_version,
show=args.show,
output_file=args.output_file,
verify=args.verify,
normalize_cfg=normalize_cfg,
dataset=args.dataset,
test_img=args.test_img,
do_simplify=args.simplify)
|
import sys
import argparse
import math
import almath as m # python's wrapping of almath
import time
#import numpy as np
from naoqi import ALProxy
degree = math.pi/180.0 # radians per degree
pi = math.pi
exp = math.exp
atan2 = math.atan2
def FTarget(target_distance, target_angle):
# Attractor Behaviors
#fr1 = -sin(psi-Psi1)
#fr3 = -exp(-dt)*sin(psi-Psi3)
#frf = fr1+fr3
Ftar=0
return Ftar
def FObstacle(obs_distance, obs_angle):
# too_far=2.5 #m
# Repulsive Behaviors
#fr2 = exp(((-psi-Psi2)^2)/(2(sb^2)))*(psi-Psi2)*exp(-do/b2)
#do something useful here
# if obs_distance < too_far:
#do something useful here
#X = 0.2
#Y = 0.0
#Theta = 0.0
#Frequency =0.5 # low speed
#try:
# motionProxy.moveToward(X, Y, Theta, [["Frequency", Frequency]])
# tts.say("There has been an \\emph=2\\ earthquake. \\eos=1\\Don't panic!")
#except (Exception, errorMsg):
#print (str(errorMsg))
#print ("This example is not allowed on this robot.")
#exit()
# time.sleep(3)
# Fobs=0 # needs replacing !
#else:
# Fobs=0
# return Fobs
b1 = 6
#b1 = 8
b2 = 18
#b2 = 15
##
z = pi/6
sig0 = dist[0]* atan2(30*pi/180)
sig1 = dist[1]* atan2(30*pi/180)
lamb1 = b1*exp(-(dist[0]-r0)/b2)
lamb2 = b1*exp(-(dist[1]-r0)/b2)
fr1 = -(lamb1*z*exp(-(z**2)/(sig0)**2))
fr2 = -(lamb2*z*exp(-(z**2)/(sig0)**2))
Fobs = fr1 + fr2
return = Fobs
#def FStochastic():
#do something useful here
# Fstoch=0
# return Fstoch
#def FOrienting():
#do something useful here
# Forient=0
# return Forient
Q = .00001
S = 999 # sensor at t=0
R = 0.1**2 # var(sensor)
def Kalman(S, Bel):
# from MATLAB code
# s.x = s.A*s.x + s.B*s.u;
# s.P = s.A * s.P * s.A' + s.Q;
# % Compute Kalman gain factor:
# K = s.P*s.H'*inv(s.H*s.P*s.H'+s.R);
# % Correction based on observation:
# s.x = s.x + K*(s.z-s.H*s.x);
# s.P = s.P - K*s.H*s.P;
x_prev = S
x = S
p_prev = Bel
p = Bel + Q
K = p_prev/(p_prev + R)
x = x_prev + K * x_prev
p = (1 - K) * p_prev
S = x
Bel = p
return S, Bel
def compute_velocity(sonar_distance_left, sonar_distance_right):
max_velocity = 1.0
max_distance = 0.5 #m
min_distance = 0.2 #m
if sonar_distance_left>max_distance and sonar_distance_right > max_distance:
velocity = max_velocity
obs=False
stop=False
print "Nominal movements"
elif sonar_distance_left<min_distance or sonar_distance_right < min_distance:
velocity = 0.0
obs=True
stop=True
print "Imminent obstacle detected"
elif sonar_distance_left<sonar_distance_right:
velocity = max_velocity*sonar_distance_left/max_distance
else:
velocity = max_velocity*sonar_distance_right/max_distance
return velocity
def compute_turnrate(target_dist, target_angle, sonar_distance_left, sonar_distance_right):
max_turnrate = 0.349 #rad/s
delta_t = 0.01 # may need adjustment!
sonar_angle_left = 30 * degree
sonar_angle_right = -30 * degree
Fobs_left = FObstacle(sonar_distance_left, sonar_angle_left)
Fobs_right = FObstacle(sonar_distance_right, sonar_angle_right)
FTotal = FTarget(target_dist, target_angle) + Fobs_left + Fobs_right + FOrienting() + FStochastic()
# turnrate: d phi(t) / dt = sum( forces )
turnrate = Ftotal*delta_t
#normalise turnrate value
if turnrate>max_turnrate:
turnrate=1.0
else:
turnrate=turnrate/max_turnrate
return turnrate
def StiffnessOn(proxy):
# We use the "Body" name to signify the collection of all joints
pNames = "Body"
pStiffnessLists = 1.0
pTimeLists = 1.0
proxy.stiffnessInterpolation(pNames, pStiffnessLists, pTimeLists)
def main(robotIP):
try:
motionProxy = ALProxy("ALMotion", robotIP, 9559)
except Exception, e:
print ("Could not create proxy to ALMotion")
print ("Error was: ")
# Set NAO in stiffness On
StiffnessOn(motionProxy)
#####################
## Enable arms control by move algorithm
#####################
motionProxy.setWalkArmsEnabled(True, True)
#~ motionProxy.setWalkArmsEnabled(False, False)
#####################
## FOOT CONTACT PROTECTION
#####################
#~ motionProxy.setMotionConfig([["ENABLE_FOOT_CONTACT_PROTECTION",False]])
motionProxy.setMotionConfig([["ENABLE_FOOT_CONTACT_PROTECTION", True]])
#####################
## get robot position before move
#####################
initRobotPosition = m.Pose2D(motionProxy.getRobotPosition(False))
X = 0.3
Y = 0.0
Theta = math.pi/2.0
motionProxy.post.moveTo(X, Y, Theta)
# wait is useful because with post moveTo is not blocking function
motionProxy.waitUntilMoveIsFinished()
#####################
## get robot position after move
#####################
endRobotPosition = m.Pose2D(motionProxy.getRobotPosition(False))
#####################
## compute and print the robot motion
#####################
robotMove = m.pose2DInverse(initRobotPosition)*endRobotPosition
print ("Robot Move :")
if __name__ == "__main__":
robotIp = "127.0.0.1"
if len(sys.argv) <= 1:
print ("Usage python motion_moveTo.py robotIP (optional default: 127.0.0.1)")
else:
robotIp = sys.argv[1]
main(robotIp)
|
try:
import tilde
except ImportError:
import os, sys
chk_path = os.path.realpath(os.path.normpath(os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"../"
)))
if not chk_path in sys.path: sys.path.insert(0, chk_path)
|
from matplotlib import mlab
import numpy as np
def estimate_decoder(time,s,r,dt,nfft=2**12):
'''
Estimates the decoding kernel K and reconstructs the original stimuls
Inputs: time: time vector
s: stimulus
r: response
dt: sampling period
nfft: number of datapoints to be used for spectral estimation (window length)
Outputs: k: the decoding kernel (length of the nfft)
k_time: the time vector of the decoding kernel (length of the nfft)
s_est: the estimated stimulus (length of s and r minus a window length)
s_est_time: time vector for s_est
NOTE: we discard data points 0:nfft/2 and -nnft2:end due to boundary effects
'''
Fs=1/dt # sampling frequency
# compute the cross spectrum between response and stimulus
Qf_rs, freqs = mlab.csd(r,s,nfft,Fs=Fs)
Qf_rs*=dt
# computes the response power spectrum
Qf_rr, freqs = mlab.psd(r,nfft,Fs=Fs)
Qf_rr*=dt
# take the ratio
Kf = Qf_rs/Qf_rr
# we need to add negative frequency with (conjugate values) to comply to the input
# requirements of the ifft function
Kf= np.hstack([Kf,Kf[::-1].conj()[1:-1]])
k = np.fft.fftshift(np.fft.ifft(Kf).real)/dt
k_time=(np.arange(nfft)-int(nfft/2))*dt
# compute estimated stimulus
s_est=np.convolve(r,k,'same')*dt
# crop s_est and time vector appropriately
s_est = s_est[int(nfft/2):int(-nfft/2)]
s_est_time = time[int(nfft/2):int(-nfft/2)]-dt
return k,k_time,s_est,s_est_time |
from pydantic import BaseModel
import json
class KubeConfigRequest(BaseModel):
userId: str
configString: str
def toJSON(self):
return json.dumps(self, default=lambda o: o.__dict__,
sort_keys=True, indent=4)
|
from __future__ import print_function
from hashlib import sha1
from rdflib import Graph
import re
import requests
import sys
import os
import ssl
from urllib.parse import urlparse, quote
from urllib.request import urlopen
from .constants import EXT_BINARY_INTERNAL, EXT_BINARY_EXTERNAL, \
LDP_NON_RDF_SOURCE, MINIMAL_HEADER
from .utils import get_data_dir, replace_strings_in_file
class Resource(object):
"""Common properties of any resource."""
def __init__(self, inputpath, config, logger, console):
self.config = config
self.origpath = inputpath
self.logger = logger
self.console = console
self.data_dir = get_data_dir(config)
def fetch_headers(self, origpath, auth):
return requests.head(url=origpath, auth=auth)
def _calculate_sha1(self, stream):
sh = sha1()
while True:
data = stream.read(8192)
if not data:
break
sh.update(data)
return sh.hexdigest()
class FedoraResource(Resource):
"""Properties and methods for a resource in a Fedora repository."""
def __init__(self, inputpath, config, logger, console):
Resource.__init__(self, inputpath, config, logger, console)
self.location = "fedora"
self.relpath = urlparse(self.origpath).path.rstrip("/")
head_response = self.fetch_headers(self.origpath, self.config.auth)
# handle various HTTP responses
if head_response.status_code == 200:
self.is_reachable = True
self.headers = head_response.headers
if inputpath.endswith("/fcr:versions"):
# before fcrepo-4.8.0, fcr:versions does have ldp_type in
# the header
# todo remove when FCREPO-2511 is resolved in all supported
# versions of fcrepo4 core.
self.ldp_type = "http://www.w3.org/ns/ldp#RDFSource"
else:
self.ldp_type = head_response.links["type"]["url"]
self.external = False
elif head_response.status_code == 307:
self.is_reachable = True
self.headers = head_response.headers
self.ldp_type = head_response.links["type"]["url"]
self.external = True
elif head_response.status_code in [401, 403, 404, 405]:
self.is_reachable = False
self.type = "unknown"
else:
self.console.error("Unexpected response from Fedora")
sys.exit(1)
# analyze resources that can be reached
if self.is_binary():
self.type = "binary"
self.metadata = self.origpath + "/fcr:metadata"
if self.external:
content_type = self.headers["Content-Type"]
p = re.compile('.*url=\"(.*)\"')
url = p.match(content_type).group(1)
self.sha1 = self._calculate_sha1_from_uri(url)
else:
self.sha1 = self.lookup_sha1()
if self.external:
self.destpath = quote(
(self.data_dir + self.relpath + EXT_BINARY_EXTERNAL)
)
else:
self.destpath = quote(
(self.data_dir + self.relpath + EXT_BINARY_INTERNAL)
)
else:
self.type = "rdf"
self.destpath = quote(
(self.data_dir + self.relpath + self.config.ext)
)
response = requests.get(self.origpath, auth=self.config.auth)
minimal_resp = requests.get(
self.origpath, auth=self.config.auth, headers=MINIMAL_HEADER
)
if response.status_code == 200 and minimal_resp.status_code == 200:
self.graph = Graph().parse(
data=response.text, format="text/turtle"
)
self.minimal = Graph().parse(
data=minimal_resp.text, format="text/turtle"
)
self.server_managed = self.graph - self.minimal
else:
self.console.error("Cannot verify RDF resource!")
def is_binary(self):
return self.ldp_type == LDP_NON_RDF_SOURCE
def filter_binary_refs(self):
for (s, p, o) in self.graph:
if o.startswith(self.config.repobase) and \
FedoraResource(o, self.config, self.logger).is_binary():
self.graph.remove((s, p, o))
def lookup_sha1(self):
result = ""
response = requests.get(self.metadata, auth=self.config.auth)
if response.status_code == 200:
m = re.search(
r"premis:hasMessageDigest[\s]+<urn:sha1:(.+?)>", response.text
)
result = m.group(1) if m else ""
return result
def _calculate_sha1_from_uri(self, uri):
gc_context = ssl.SSLContext(ssl.PROTOCOL_TLSv1)
with urlopen(uri, context=gc_context) as u:
return self._calculate_sha1(u)
class LocalResource(Resource):
"""Properties and methods for a resource serialized to disk."""
def __init__(self, inputpath, config, logger, console):
Resource.__init__(self, inputpath, config, logger, console)
self.location = "local"
self.relpath = self.origpath[len(self.data_dir):]
self.mapfrom = self.config.mapFrom
self.mapto = self.config.mapTo
self.type = "unknown"
if self.is_binary():
self.type = "binary"
self.external = False
if self.origpath.endswith(EXT_BINARY_EXTERNAL):
self.external = True
if self.external:
self.destpath = self._resolve_dest_path(EXT_BINARY_EXTERNAL)
else:
self.destpath = self._resolve_dest_path(EXT_BINARY_INTERNAL)
self.sha1 = self._calculate_sha1_from_file(self.origpath)
elif self.origpath.startswith(self.data_dir) and \
self.origpath.endswith(config.ext):
self.type = "rdf"
self.destpath = self._resolve_dest_path(config.ext)
# replace mapfrom with mapto if mapped otherwise load origpath
localfilepath = self.origpath
if self.config.mapFrom is not None:
localfilepath = replace_strings_in_file(self.origpath,
self.mapfrom,
self.mapto)
self.graph = Graph().parse(
location=localfilepath, format=config.lang
)
if self.config.mapFrom is not None:
os.remove(localfilepath)
else:
msg = "RDF resource lacks expected extension!".format(
self.origpath)
self.logger.error(msg)
def _resolve_dest_path(self, suffix):
# remove suffix of relpath
relative_path = self.relpath[:-len(suffix)]
# if mapped
if self.mapfrom is not None:
# reconstruct source uri
sourceuri = self._get_base_uri(urlparse(self.mapfrom)) + \
relative_path
return sourceuri.replace(self.mapfrom, self.mapto)
else:
desturlinfo = urlparse(self.config.repo)
return self._get_base_uri(desturlinfo) + relative_path
def _get_base_uri(self, urlinfo):
return urlinfo.scheme + "://" + urlinfo.netloc
def is_binary(self):
if self.origpath.endswith(EXT_BINARY_INTERNAL) or \
self.origpath.endswith(EXT_BINARY_EXTERNAL):
return True
else:
return False
def _calculate_sha1_from_file(self, file_path):
with open(file_path, "rb") as f:
return self._calculate_sha1(f)
|
from unittest.mock import MagicMock
from pytest import fixture
from slacknewsbot.app import GetHN
SLACK_TEXT = "Top 3 from HackerNews:\n>*<https://news.ycombinator.com/item?id=1|1>* - <abc|bcd>"
@fixture()
def obj():
obj = GetHN.__new__(GetHN)
obj.logger = MagicMock()
return obj
def test_should_create_hn_text(obj):
obj.get_top_stories = MagicMock(
return_value=[{
"id": 1,
"score": 1,
"url": "abc",
"title": "bcd"
}]
)
assert obj.create_hn_text() == SLACK_TEXT
# TODO: Add more tests
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# vim: set et sw=4 ts=4 sts=4 ff=unix fenc=utf8:
# Author: Binux<i@binux.me>
# http://binux.me
# Created on 2014-07-30 12:22:47
import os,sys
sys.path.append(os.path.abspath(os.path.dirname(os.path.dirname(__file__)))) |
"""Notelist CLI Setup script."""
import setuptools as st
if __name__ == "__main__":
with open("README.md") as f:
long_desc = f.read()
st.setup(
name="notelist-cli",
version="0.3.0",
description="Command line interface for the Notelist API",
author="Jose A. Jimenez",
author_email="jajimenezcarm@gmail.com",
license="MIT",
long_description=long_desc,
long_description_content_type="text/markdown",
url="https://github.com/jajimenez/notelist-cli",
classifiers=[
"Programming Language :: Python :: 3",
"Operating System :: OS Independent",
"License :: OSI Approved :: MIT License"
],
python_requires=">=3.9.0",
install_requires=[
"click==8.0.3",
"requests==2.26.0",
"userconf==0.4.0"
],
packages=[
"notelist_cli"
],
package_dir={
"notelist_cli": "src/notelist_cli"
},
entry_points={
"console_scripts": [
"notelist-cli=notelist_cli:main"
]
}
)
|
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional
import psycopg2
from sqlalchemy.dialects import postgresql
from local_data_api.models import ColumnMetadata
from local_data_api.resources.resource import Resource, register_resource_type
if TYPE_CHECKING: # pragma: no cover
from local_data_api.resources.resource import ConnectionMaker, Cursor
@register_resource_type
class PostgresSQL(Resource):
def create_column_metadata_set(
self, cursor: Cursor
) -> List[ColumnMetadata]: # pragma: no cover
raise NotImplementedError
DIALECT = postgresql.dialect(paramstyle='named')
@classmethod
def create_connection_maker(
cls,
host: Optional[str] = None,
port: Optional[int] = None,
user_name: Optional[str] = None,
password: Optional[str] = None,
engine_kwargs: Dict[str, Any] = None,
) -> ConnectionMaker:
kwargs: Dict[str, Any] = {}
if host:
kwargs['host'] = host
if port:
kwargs['port'] = port
if user_name:
kwargs['user'] = user_name
if password:
kwargs['password'] = password
if engine_kwargs:
kwargs.update(engine_kwargs)
def connect(database: Optional[str] = None): # type: ignore
if database:
kwargs['dbname'] = database
return psycopg2.connect(**kwargs)
return connect
|
import collections
import bs4
import tornado
import tornado.httpclient
import tornado.ioloop
from tqdm import tqdm
import common.mongo
import common.settings
# taken from https://github.com/tornadoweb/tornado/issues/1400
class BacklogClient(object):
def __init__(self):
self.max_concurrent_requests = common.settings.urls.getint("MaxConcurrentRequests")
self.client = tornado.httpclient.AsyncHTTPClient(max_clients=self.max_concurrent_requests)
self.client.configure(None, defaults=dict(connect_timeout=common.settings.urls.getint("ConnectTimeout"),
request_timeout=common.settings.urls.getint("RequestTimeout")))
self.backlog = collections.deque()
self.concurrent_requests = 0
def __get_callback(self, func):
def wrapped(*args, **kwargs):
self.concurrent_requests -= 1
self.try_run_request()
return func(*args, **kwargs)
return wrapped
def try_run_request(self):
while self.backlog and self.concurrent_requests < self.max_concurrent_requests:
request, callback = self.backlog.popleft()
self.client.fetch(request, callback=callback)
self.concurrent_requests += 1
def fetch(self, request, callback=None):
wrapped = self.__get_callback(callback)
self.backlog.append((request, wrapped))
self.try_run_request()
class UrlFetcher:
def __init__(self):
self.backlog = BacklogClient()
def handle_request(self, response, url):
self.tqdm.update(1)
try:
soup = bs4.BeautifulSoup(response.body)
url["text"] = soup.title.string
common.mongo.urls_collection.update_one({"id_str": url["id_str"]}, {"$set": url})
except Exception:
if common.settings.urls.getboolean("RemoveUrlNotFound"):
common.mongo.urls_collection.remove({"id_str": url["id_str"]})
if not self.backlog.backlog and self.backlog.concurrent_requests == 0:
tornado.ioloop.IOLoop.instance().stop()
def _start_fetch(self, url):
self.backlog.fetch(tornado.httpclient.HTTPRequest(url["expanded_url"], method='GET', headers=None),
lambda response: self.handle_request(response, url))
def launch(self, urls):
print("filling urls")
self.tqdm = tqdm(total=len(urls))
for url in urls:
self._start_fetch(url)
tornado.ioloop.IOLoop.current().start()
def urls():
UrlFetcher().launch(common.mongo.get_urls())
if __name__ == "__main__":
urls()
|
# encoding: utf-8
"""
@author: yp
@software: PyCharm
@file: num_to_rmb.py
@time: 2019/8/21 0021 10:17
"""
'''
把一个浮点数分解成整数部分和小数部分字符串
num 需要被分解的浮点数
返回分解出来的整数部分和小数部分。
第一个数组元素是整数部分，第二个数组元素是小数部分
'''
def divide(num):
# 将一个浮点数强制类型转换为int型，即得到它的整数部分
integer = int(num)
# 浮点数减去整数部分，得到小数部分，小数部分乘以100后再取整得到2位小数
fraction = round((num - integer) * 100)
# 下面把整数转换为字符串
return (str(integer), str(fraction))
han_list = ["零", "壹", "贰", "叁", "肆", "伍", "陆", "柒", "捌", "玖"]
unit_list = ["十", "百", "千"]
'''
把一个四位的数字字符串变成汉字字符串
num_str 需要被转换的四位的数字字符串
返回四位的数字字符串被转换成汉字字符串
'''
def four_to_hanstr(num_str):
result = ""
num_len = len(num_str)
# 依次遍历数字字符串的每一位数字
for i in range(num_len):
# 把字符串转成数值
num = int(num_str[i])
# 如果不是最后一位数字，而且数字不是零，则需要添加单位（千、百、十）
if i != num_len - 1 and num != 0:
result += han_list[num] + unit_list[num_len - 2 - i]
# 否则不要添加单位
else:
result += han_list[num]
return result
'''
把数字字符串变成汉字字符串
num_str 需要被转换的数字字符串
返回数字字符串被转换成汉字字符串
'''
def integer_to_sfr(num_str):
str_len = len(num_str)
if str_len > 12:
print('数字太大，翻译不了')
return
# 如果大于8位，包含单位亿
elif str_len > 8:
return four_to_hanstr(num_str[:-8]) + "亿" + \
four_to_hanstr(num_str[-8: -4]) + "万" + \
four_to_hanstr(num_str[-4:])
# 如果大于4位，包含单位万
elif str_len > 4:
return four_to_hanstr(num_str[:-4]) + "万" + \
four_to_hanstr(num_str[-4:])
else:
return four_to_hanstr(num_str)
num = float(input("请输入一个浮点数: "))
# 测试把一个浮点数分解成整数部分和小数部分
integer, fraction = divide(num)
# 测试把一个四位的数字字符串变成汉字字符串
print(integer_to_str(integer))
print(fraction)
|
#No phi nodes are placed
# we need a case when no phi function placed for different blocks
c = 10
if c:
print(c)
else:
t = 0
print(t)
|
from django.core.management.base import BaseCommand
from edge.models.genome import Genome
from edge.io import IO
class Command(BaseCommand):
def handle(self, *args, **options):
if len(args) != 2:
raise Exception("Expecting integer genome ID and filename as arguments")
try:
genome_id = int(args[0])
except BaseException:
raise Exception("Expecting integer genome ID and filename as arguments")
io = IO(Genome.objects.get(pk=genome_id))
io.to_gff(args[1])
|
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
from copy import deepcopy
from dataclasses import asdict
from typing import List, Dict
import os
from pre_wigs_validation.enums import (
ValidationConfig,
ValidationResult,
ValidationEnforcement,
)
from pre_wigs_validation.dataclasses import ValidationOutput, FinalOutput
def sanitize_json(final_output: FinalOutput) -> Dict:
"""Remove newline characters from data before writing to JSON."""
final_output_copy = deepcopy(final_output)
for output in final_output_copy.validation_outputs:
if output.message is not None:
output.message = output.message.replace("\n", "")
return asdict(final_output_copy)
def check_validation_config(
default_params: List, local_params: List
) -> ValidationConfig:
"""
Compare the passed parameter values of a validation function to its
default parameter values.
"""
custom = False
for kw in default_params:
if not (default_params[kw] == local_params[kw]):
custom = True
return ValidationConfig.CUSTOM if custom else ValidationConfig.DEFAULT
def get_final_output(outputs: List[ValidationOutput]) -> FinalOutput:
"""Return the final result of the pre-validation."""
custom = False
partial_pass = False
failed = False
pass_count = 0
errored = False
for validation_output in outputs:
if validation_output.config is ValidationConfig.CUSTOM:
custom = True
if validation_output.result is ValidationResult.PASS:
pass_count += 1
elif validation_output.result is ValidationResult.ERROR:
errored = True
else:
if validation_output.enforcement is ValidationEnforcement.RECOMMENDED:
partial_pass = True
else:
failed = True
if errored:
final_result = ValidationResult.ERROR
message = "Unexpected error during validation"
elif failed:
final_result = ValidationResult.FAIL
message = "Not ready for ingestion"
elif partial_pass:
final_result = ValidationResult.PASS
message = (
"Passed base requirements for ingestion," " but not may be fully optimized"
)
else:
final_result = ValidationResult.PASS
message = "Ready for ingestion"
if custom:
config = ValidationConfig.CUSTOM
message += ". Warning, custom config may affect final result"
else:
config = ValidationConfig.DEFAULT
return FinalOutput(
final_result=final_result,
config=config,
pass_count=pass_count,
total_count=len(outputs),
message=message,
validation_outputs=outputs,
)
def get_ld_library_orig() -> Dict:
env = dict(os.environ) # make a copy of the environment
lp_key = "LD_LIBRARY_PATH" # for GNU/Linux and *BSD.
lp_orig = env.get(lp_key + "_ORIG")
if lp_orig is not None:
env[lp_key] = lp_orig # restore the original, unmodified value
else:
# This happens when LD_LIBRARY_PATH was not set.
# Remove the env var as a last resort:
env.pop(lp_key, None)
return env
|
"""Tests for the AsyncJob web API."""
import json
import re
import time
import pytest
from yeti.core.asyncjob import get_active_jobs
@pytest.yield_fixture(autouse=True)
def wait_for_jobs():
"""Faits for active jobs to complete before each test run."""
while get_active_jobs():
time.sleep(0.1)
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_execute(authenticated_client):
"""Test that an existing feed can be executed"""
rv = authenticated_client.post('/api/async/FastDummyFeed/execute')
response = json.loads(rv.data)
assert response['status'] == 'queued'
assert 'FastDummyFeed' in response['msg']
# expecting a UUID: d18e0132-7271-4597-8fb3-8a2624f4059e
assert re.match(r'[a-f0-9-]+', response['job_id'])
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_execute_duplicate(authenticated_client):
"""Test that a feed cannot be run in parallel"""
rv = authenticated_client.post('/api/async/FastDummyFeed/execute')
rv = authenticated_client.post('/api/async/FastDummyFeed/execute')
rv = authenticated_client.post('/api/async/FastDummyFeed/execute')
response = json.loads(rv.data)
assert 'GenericYetiError' in response
assert rv.status_code == 409
assert '~"FastDummyFeed"' in response['GenericYetiError']
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_execute_nonexistent(authenticated_client):
"""Test that executing a nonexistent feed generates an error."""
rv = authenticated_client.post('/api/async/nonexistent/execute')
response = json.loads(rv.data)
assert response['GenericYetiError'] == \
'nonexistent is not a registered AsyncJob'
assert rv.status_code == 404
@pytest.mark.usefixtures('clean_db')
def test_filter(populate_feeds, authenticated_client):
"""Test that asyncJobs can be filtered by name."""
rv = authenticated_client.post('/api/async/filter', json={'name': 'DummyFeed'})
response = json.loads(rv.data)
assert len(response) == len(populate_feeds)
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_filter_errors(authenticated_client):
"""Test that incorrect filtering generates a ValidationError / HTTP 400."""
rv = authenticated_client.post('/api/async/filter', json={})
response = json.loads(rv.data)
assert 'ValidationError' in response
assert rv.status_code == 400
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_toggle(authenticated_client):
"""Test that a feed can be toggled."""
rv = authenticated_client.post('/api/async/FastDummyFeed/toggle')
response = json.loads(rv.data)
assert response['enabled']
assert 'FastDummyFeed' in response['msg']
rv = authenticated_client.post('/api/async/filter', json={'name': 'FastDummyFeed'})
response = json.loads(rv.data)
assert response[0]['enabled']
rv = authenticated_client.post('/api/async/filter', json={'name': 'SlowDummyFeed'})
response = json.loads(rv.data)
assert not response[0]['enabled']
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_toggle_nonexistent(authenticated_client):
"""Test that toggling a nonexistent feed generates an error."""
rv = authenticated_client.post('/api/async/nonexistent/toggle')
response = json.loads(rv.data)
assert 'GenericYetiError' in response
assert rv.status_code == 404
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_info(authenticated_client):
"""Test that we can get information on a job."""
rv = authenticated_client.post('/api/async/FastDummyFeed/execute')
response = json.loads(rv.data)
assert 'job_id' in response
jobid = response['job_id']
time.sleep(0.1)
rv = authenticated_client.get('/api/async/info/' + jobid)
response = json.loads(rv.data)
assert response['meta']['name'] == 'FastDummyFeed'
assert response['status'] in ['queued', 'started']
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_info_nonexistent(authenticated_client):
"""Test that getting information on a nonexistent job generates an error."""
rv = authenticated_client.get('/api/async/info/d18e0132-0000-0000-0000-8a2624f4059e')
response = json.loads(rv.data)
assert 'GenericYetiError' in response
assert response['GenericYetiError'] == \
'Job ID d18e0132-0000-0000-0000-8a2624f4059e is not an active job'
assert rv.status_code == 404
@pytest.mark.usefixtures('clean_db', 'populate_feeds')
def test_get_activejobs(authenticated_client):
"""Test that getting information on a nonexistent job generates an error."""
rv = authenticated_client.post('/api/async/SlowDummyFeed/execute')
time.sleep(0.1)
rv = authenticated_client.get('/api/async/active')
response = json.loads(rv.data)
assert len(response) == 1
|
"""
26. Remove Duplicates from Sorted Array
Given a sorted array nums, remove the duplicates in-place such that
each element appear only once and return the new length.
Do not allocate extra space for another array, you must do this by modifying
the input array in-place with O(1) extra memory.
"""
class Solution:
# Runtime: 84 ms, faster than 95.52% of Python3 online submissions for Remove Duplicates from Sorted Array.
# Memory Usage: 14.4 MB, less than 100.00% of Python3 online submissions for Remove Duplicates from Sorted Array.
def removeDuplicates(self, nums):
# nums: List[int]
# return: int
length = len(nums)
# Arrays of none or one values do not need to be de-duplicated
if length in (0,1):
return length
# Keep track of uniques
unique = 0
# Iterate over list.
# If the next value is different from current, this marks the end
# of repetitions of the current value, and we can set it to the
# unique counter's position.
# In this way, all values behind current will be uniques in order.
for i in range(length-1):
if nums[i] != nums[i+1]:
nums[unique] = nums[i]
unique += 1
# Thus the i+1 value from above is unique too,
# and is placed at the next position.
nums[unique] = nums[length-1]
# Slice list to include only uniques
nums = nums[:unique+1]
return len(nums)
if __name__ == '__main__':
solution = Solution()
print(solution.removeDuplicates([1,1,2]))
print(solution.removeDuplicates([0,0,1,1,1,2,2,3,3,4]))
print(solution.removeDuplicates([1,1]))
print(solution.removeDuplicates([1,1,1])) |
import asyncio
import pytest
from click.testing import CliRunner
from unittest.mock import Mock
from asynctest import CoroutineMock
from netcfgbu.cli import probe
@pytest.fixture(autouse=True)
def _always(netcfgbu_envars, files_dir, monkeypatch):
test_inv = files_dir.joinpath("test-small-inventory.csv")
monkeypatch.setenv("NETCFGBU_INVENTORY", str(test_inv))
def test_cli_probe_pass(monkeypatch):
mock_probe = Mock()
monkeypatch.setattr(probe, "exec_probe", mock_probe)
runner = CliRunner()
res = runner.invoke(probe.cli_check, obj={})
assert res.exit_code == 0
assert mock_probe.called
call_args = mock_probe.mock_calls[0].args
inv_rec = call_args[0]
assert len(inv_rec) == 6
def test_cli_probe_pass_exec(monkeypatch, log_vcr):
mock_probe = CoroutineMock()
monkeypatch.setattr(probe, "probe", mock_probe)
monkeypatch.setattr(probe, "get_logger", Mock(return_value=log_vcr))
runner = CliRunner()
res = runner.invoke(probe.cli_check, obj={})
assert res.exit_code == 0
logs = log_vcr.handlers[0].records[1:]
assert all("PASS" in log.msg for log in logs)
def test_cli_probe_fail_exec(monkeypatch, log_vcr):
mock_probe = CoroutineMock()
mock_probe.side_effect = asyncio.TimeoutError
monkeypatch.setattr(probe, "probe", mock_probe)
monkeypatch.setattr(probe, "get_logger", Mock(return_value=log_vcr))
runner = CliRunner()
res = runner.invoke(probe.cli_check, obj={})
assert res.exit_code == 0
logs = log_vcr.handlers[0].records[1:]
assert all("FAIL" in log.msg for log in logs)
|
import mablane.bandits
from mablane.bandits import BinomialBandit
def test_binomial_bandit():
mablane.bandits._BinomialBandit.binomial = lambda n, p: n + p
bandit = BinomialBandit(1)
assert bandit.pull() == 2
bandit = BinomialBandit(1, 2)
assert bandit.pull() == 3 |
import argparse
import datetime
import os
import pickle
import subprocess
import numpy as np
import pandas as pd
from src.general.directory_handling import make_and_cd
from simulation_parameters import BindingParameters
class ParameterTesting(object):
def __init__(self, steps=8, lf=30):
self.binding_constants = BindingParameters()
self.lf = lf
self.sub_directories = ["Ls", "Ls_Lf_{0}".format(self.lf)]
self.steps = steps
self.paths = []
self.parameters = []
self.home_directory = os.getcwd()
self.simulation_name = "ODE_steps_" + str(self.steps)
self.simulation_time = 10
def generate_qsub(self, self_foreign=False):
q = open("qsub.sh", "w")
q.write("#PBS -m ae\n")
q.write("#PBS -q short\n")
q.write("#PBS -V\n")
q.write("#PBS -l walltime={1},nodes=1:ppn=2 -N {0}\n\n".format(self.simulation_name,
datetime.timedelta(
minutes=self.simulation_time)))
q.write("cd $PBS_O_WORKDIR\n")
q.write("echo $PBS_JOBID > job_id\n\n")
if self_foreign:
q.write(
"python ~/SSC_python_modules/pysb_t_cell_network.py --steps {0} --lf {1}\n".format(
self.steps,
self.lf))
else:
q.write("python ~/SSC_python_modules/pysb_t_cell_network.py --steps {0}".format(self.steps))
q.close()
def launch(self):
(stdout, stderr) = subprocess.Popen(["qsub {0}".format("qsub.sh")], shell=True, stdout=subprocess.PIPE,
cwd=os.getcwd()).communicate()
def make_launch_simulations(self, set):
home = os.getcwd()
for directory in self.sub_directories:
make_and_cd(directory)
pickle_out = open("parameters.pickle", "wb")
pickle.dump(set, pickle_out)
pickle_out.close()
if directory == "Ls_Lf_{0}".format(self.lf):
self.generate_qsub(self_foreign=True)
else:
self.generate_qsub()
if args.run:
self.launch()
os.chdir(home)
def create_submit(self, count, param_grid):
file_path = "{0}_step_{1}".format(self.steps, count)
self.paths.append(file_path)
print("param_grid " + str(param_grid))
self.parameters.append(str(param_grid))
make_and_cd(file_path)
self.make_launch_simulations(param_grid)
os.chdir(self.home_directory)
def run_neg_fb_parameter_search(self):
count = 0
on_rates = [0.5, 1.0, 2.0, 3.0, 5.0]
negative_fb = [0.0, 0.001, 0.01, 0.05, 0.1]
param_grid = {}
for on_rate in on_rates:
param_grid['k_lck_on_RL'] = on_rate / self.binding_constants.initial.lck_0
param_grid['k_p_on_R_pmhc'] = on_rate
param_grid['k_zap_on_R_pmhc'] = on_rate / self.binding_constants.initial.zap_0
param_grid['k_p_on_zap_species'] = on_rate
param_grid['k_lat_on_species'] = on_rate / self.binding_constants.initial.lat_0
param_grid['kp_on_lat1'] = on_rate
param_grid['k_p_lat_on_species'] = on_rate / 10.0
for i in negative_fb:
param_grid['k_neg_fb'] = i
param_grid['k_lcki'] = 0.01
self.create_submit(count, param_grid)
count += 1
df = pd.DataFrame({'file_path': self.paths})
df.to_csv("./file_paths", sep='\t')
df_2 = pd.DataFrame({'file_path': self.paths, 'parameters': self.parameters})
df_2.to_csv("./parameters", sep='\t')
pickle_out = open("parameter_range.pickle", "wb")
pickle.dump(param_grid, pickle_out)
pickle_out.close()
def run_real_neg_fb_parameter_search(self):
count = 0
on_rates = [0.5, 1.0, 2.0, 3.0, 5.0]
negative_fb = [0.01, 0.005, 0.001, 0.0005, 0.0001]
param_grid = {}
for on_rate in on_rates:
param_grid['k_lck_on_RL'] = on_rate / self.binding_constants.initial.lck_0
param_grid['k_p_on_R_pmhc'] = on_rate
param_grid['k_zap_on_R_pmhc'] = on_rate / self.binding_constants.initial.zap_0
param_grid['k_p_on_zap_species'] = on_rate
param_grid['k_lat_on_species'] = on_rate / self.binding_constants.initial.lat_0
param_grid['kp_on_lat1'] = on_rate
param_grid['k_p_lat_on_species'] = on_rate / 10.0
for i in negative_fb:
param_grid['k_lcki'] = i
self.create_submit(count, param_grid)
count += 1
df = pd.DataFrame({'file_path': self.paths})
df.to_csv("./file_paths", sep='\t')
df_2 = pd.DataFrame({'file_path': self.paths, 'parameters': self.parameters})
df_2.to_csv("./parameters", sep='\t')
pickle_out = open("parameter_range.pickle", "wb")
pickle.dump(param_grid, pickle_out)
pickle_out.close()
def run_parameter_search(self):
count = 0
on_rates = np.linspace(0.5, 5.0, 10)
discount = [1.0, 5.0, 10.0, 50.0, 100.0]
param_grid = {}
for on_rate in on_rates:
param_grid['k_lck_on_RL'] = on_rate / self.binding_constants.initial.lck_0
param_grid['k_p_on_R_pmhc'] = on_rate
param_grid['k_zap_on_R_pmhc'] = on_rate / self.binding_constants.initial.zap_0
param_grid['k_p_on_zap_species'] = on_rate
param_grid['k_lat_on_species'] = on_rate / self.binding_constants.initial.lat_0
param_grid['kp_on_lat1'] = on_rate
if self.steps > 7:
for i in discount:
param_grid['kp_on_lat2'] = on_rate / i
self.create_submit(count, param_grid)
count += 1
else:
self.create_submit(count, param_grid)
count += 1
df = pd.DataFrame({'file_path': self.paths})
df.to_csv("./file_paths", sep='\t')
df_2 = pd.DataFrame({'file_path': self.paths, 'parameters': self.parameters})
df_2.to_csv("./parameters", sep='\t')
pickle_out = open("parameter_range.pickle", "wb")
pickle.dump(param_grid, pickle_out)
pickle_out.close()
# def run_tests(self):
# paths = []
# parameters = []
#
# count = 0
# home_directory = os.getcwd()
#
# for set in list(self.grid):
# file_path = "{0}_step_{1}".format(self.steps, count)
# paths.append(file_path)
# parameters.append(str(set))
#
# make_and_cd(file_path)
# self.make_launch_simulations(set)
# os.chdir(home_directory)
#
# count += 1
#
# df = pd.DataFrame({'file_path': paths})
# df.to_csv("./file_paths", sep='\t')
#
# df_2 = pd.DataFrame({'file_path': paths, 'parameters': parameters})
# df_2.to_csv("./parameters", sep='\t')
#
# pickle_out = open("parameter_range.pickle", "wb")
# pickle.dump(self.param_grid, pickle_out)
# pickle_out.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Submitting ode calculations as function of steps",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--steps', dest='steps', action='store', type=int, default=8, help="number of KP steps.")
parser.add_argument('--run', action='store_true', default=False, help='Flag for submitting simulations.')
parser.add_argument('--lf', dest='lf', action='store', type=int, default=10, help="number of foreign ligands.")
args = parser.parse_args()
p_test = ParameterTesting(steps=args.steps, lf=args.lf)
p_test.run_real_neg_fb_parameter_search()
|
#!/usr/bin/env python
# Copyright 2016-2019 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from sklearn.base import BaseEstimator
from sklearn.feature_selection.base import SelectorMixin
import numpy as np
class SelectGroups(BaseEstimator, SelectorMixin):
'''
Object to fit feature selection based on the type group the feature belongs
to. The label for the feature is used for this procedure.
'''
def __init__(self, parameters):
'''
Parameters
----------
parameters: dict, mandatory
Contains the settings for the groups to be selected. Should
contain the settings for the following groups:
- histogram_features
- shape_features
- orientation_features
- semantic_features
- patient_features
- coliage_features
- phase_features
- vessel_features
- log_features
- texture_Gabor_features
- texture_GLCM_features
- texture_GLCMMS_features
- texture_GLRLM_features
- texture_GLSZM_features
- texture_NGTDM_features
- texture_LBP_features
'''
params = list()
if parameters['histogram_features'] == 'True':
params.append('hf_')
if parameters['shape_features'] == 'True':
params.append('sf_')
if parameters['orientation_features'] == 'True':
params.append('of_')
if parameters['semantic_features'] == 'True':
params.append('semf_')
if parameters['patient_features'] == 'True':
params.append('pf_')
if parameters['coliage_features'] == 'True':
params.append('cf_')
if parameters['phase_features'] == 'True':
params.append('phasef_')
if parameters['vessel_features'] == 'True':
params.append('vf_')
if parameters['log_features'] == 'True':
params.append('logf_')
if 'texture_features' in parameters.keys():
# Backwards compatability
if parameters['texture_features'] == 'True':
params.append('tf_')
elif parameters['texture_features'] == 'False':
pass
else:
params.append('tf_' + parameters['texture_features'])
else:
# Hyperparameter per feature group
if parameters['texture_gabor_features'] == 'True':
params.append('tf_Gabor')
if parameters['texture_glcm_features'] == 'True':
params.append('tf_GLCM_')
if parameters['texture_glcmms_features'] == 'True':
params.append('tf_GLCMMS')
if parameters['texture_glrlm_features'] == 'True':
params.append('tf_GLRLM')
if parameters['texture_glszm_features'] == 'True':
params.append('tf_GLSZM')
if parameters['texture_ngtdm_features'] == 'True':
params.append('tf_NGTDM')
if parameters['texture_lbp_features'] == 'True':
params.append('tf_LBP')
self.parameters = params
def fit(self, feature_labels):
'''
Select only features specificed by parameters per patient.
Parameters
----------
feature_labels: list, optional
Contains the labels of all features used. The index in this
list will be used in the transform funtion to select features.
'''
# Remove NAN
selectrows = list()
for num, l in enumerate(feature_labels):
if any(x in l for x in self.parameters):
selectrows.append(num)
self.selectrows = selectrows
def transform(self, inputarray):
'''
Transform the inputarray to select only the features based on the
result from the fit function.
Parameters
----------
inputarray: numpy array, mandatory
Array containing the items to use selection on. The type of
item in this list does not matter, e.g. floats, strings etc.
'''
return np.asarray([np.asarray(x)[self.selectrows].tolist() for x in inputarray])
def _get_support_mask(self):
# NOTE: Method is required for the Selector class, but can be empty
pass
|
# Copyright 2021 IBM All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
URLS used by Alvearie FHIR extensions
These are defined by https://alvearie.io/alvearie-fhir-ig/artifacts.html
"""
INSIGHT_CLASSIFICATION_URL = "http://ibm.com/fhir/cdm/insight/classification"
INSIGHT_RESULT_URL = "http://ibm.com/fhir/cdm/StructureDefinition/insight-result"
INSIGHT_SPAN_URL = "http://ibm.com/fhir/cdm/StructureDefinition/span"
INSIGHT_SPAN_OFFSET_BEGIN_URL = (
"http://ibm.com/fhir/cdm/StructureDefinition/offset-begin"
)
INSIGHT_SPAN_OFFSET_END_URL = "http://ibm.com/fhir/cdm/StructureDefinition/offset-end"
INSIGHT_SPAN_COVERED_TEXT_URL = (
"http://ibm.com/fhir/cdm/StructureDefinition/covered-text"
)
INSIGHT_CONFIDENCE_URL = (
"http://ibm.com/fhir/cdm/StructureDefinition/insight-confidence"
)
INSIGHT_CONFIDENCE_SCORE_URL = "http://ibm.com/fhir/cdm/StructureDefinition/score"
INSIGHT_CONFIDENCE_DESCRIPTION_URL = (
"http://ibm.com/fhir/cdm/StructureDefinition/description"
)
INSIGHT_CONFIDENCE_SCORING_METHOD_URL = (
"http://ibm.com/fhir/cdm/StructureDefinition/method"
)
INSIGHT_CATEGORY_URL = "http://ibm.com/fhir/cdm/StructureDefinition/category"
INSIGHT_URL = "http://ibm.com/fhir/cdm/StructureDefinition/insight"
INSIGHT_REFERENCE_URL = "http://ibm.com/fhir/cdm/StructureDefinition/reference"
INSIGHT_DETAIL_URL = "http://ibm.com/fhir/cdm/StructureDefinition/insight-detail"
INSIGHT_ID_URL = "http://ibm.com/fhir/cdm/StructureDefinition/insight-id"
INSIGHT_EVALUATED_OUTPUT_URL = (
"http://ibm.com/fhir/cdm/StructureDefinition/evaluated-output"
)
INSIGHT_PATH_URL = "http://ibm.com/fhir/cdm/StructureDefinition/path"
INSIGHT_REFERENCE_PATH_URL = (
"http://ibm.com/fhir/cdm/StructureDefinition/reference-path"
)
INSIGHT_SUMMARY_URL = "http://ibm.com/fhir/cdm/StructureDefinition/insight-summary"
|
#!/usr/bin/env python
# Copyright 2014 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This is a complete rewrite of a file licensed as follows:
#
# Copyright (c) 2010-2013, Even Rouault <even . rouault at mines-paris dot org>
# Frank Warmerdam <warmerdam@pobox.com>
#
# 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.
"""Test the Bathymetry Attributed Grid (BAG) driver.
Rewrite of:
http://trac.osgeo.org/gdal/browser/trunk/autotest/gdrivers/bagf.py
"""
import unittest
from osgeo import gdal
from autotest2.gdrivers import gdrivers_util
EXT = '.bag'
@gdrivers_util.SkipIfDriverMissing(gdrivers_util.BAG_DRIVER)
class BagTest(gdrivers_util.DriverTestCase):
def setUp(self):
super(BagTest, self).setUp(gdrivers_util.BAG_DRIVER, EXT)
self.dst = None
def testBag02TrueNorthNominal(self):
filepath = gdrivers_util.GetTestFilePath('true_n_nominal.bag')
self.CheckOpen(filepath)
for band_num, checksum, nodata in ((1, 1072, 1e6), (2, 150, 1e6),
(3, 1315, 1e6)):
self.CheckBand(band_num, checksum, gdal.GDT_Float32, nodata)
band1 = self.src.GetRasterBand(1)
self.assertAlmostEqual(10, band1.GetMinimum())
self.assertAlmostEqual(19.8, band1.GetMaximum(), delta=0.000001)
xml_bag = self.src.GetMetadata('xml:BAG')[0]
self.assertIn('<?xml', xml_bag)
# TODO(schwehr): Do we need to have the check for closing the file?
def testBag03SouthernHemisphereFalseNorthing(self):
filepath = gdrivers_util.GetTestFilePath('southern_hemi_false_northing.bag')
self.CheckOpen(filepath)
self.assertEqual(self.src.RasterCount, 2)
for band_num, checksum, nodata in ((1, 21402, 1e6), (2, 33216, 1e6)):
self.CheckBand(band_num, checksum, gdal.GDT_Float32, nodata)
geotransform = (615037.5, 75.0, 0.0, 9559387.5, 0.0, -75.0)
self.CheckGeoTransform(geotransform)
self.CheckProjection(
'PROJCS["UTM Zone 13, Southern Hemisphere",'
' GEOGCS["WGS 84",'
' DATUM["WGS_1984",'
' SPHEROID["WGS 84",6378137,298.257223563,'
' AUTHORITY["EPSG","7030"]],'
' TOWGS84[0,0,0,0,0,0,0],'
' AUTHORITY["EPSG","6326"]],'
' PRIMEM["Greenwich",0,'
' AUTHORITY["EPSG","8901"]],'
' UNIT["degree",0.0174532925199433,'
' AUTHORITY["EPSG","9108"]],'
' AUTHORITY["EPSG","4326"]],'
' PROJECTION["Transverse_Mercator"],'
' PARAMETER["latitude_of_origin",0],'
' PARAMETER["central_meridian",-105],'
' PARAMETER["scale_factor",0.9996],'
' PARAMETER["false_easting",500000],'
' PARAMETER["false_northing",10000000],'
' UNIT["Meter",1]]'
)
# TODO(schwehr): Test BAG version 1.5.
if __name__ == '__main__':
unittest.main()
|
def bar():
pass
bar()
|
#!/usr/bin/env python2
from hermes_python.hermes import Hermes
from datetime import datetime
from pytz import timezone
import requests
MQTT_IP_ADDR = "localhost"
MQTT_PORT = 1883
MQTT_ADDR = "{}:{}".format(MQTT_IP_ADDR, str(MQTT_PORT))
def intent_received(hermes, intent_message):
print()
print(intent_message.intent.intent_name)
print ()
if intent_message.intent.intent_name == 'Clemac81:ActivationAlarme':
r = requests.get("http://192.168.1.38:8080/json.htm?type=command¶m=switchlight&idx=119&switchcmd=On")
print(r.json)
# hermes.publish_continue_session(intent_message.session_id, sentence, ["Joseph:greetings"])
hermes.publish_end_session(intent_message.session_id, sentence)
with Hermes(MQTT_ADDR) as h:
h.subscribe_intents(intent_received).start()
#if intentName ==
#r = requests.get("http://192.168.1.38:8080/json.htm?type=command¶m=switchlight&idx=119&switchcmd=On")
# print(r.json)
#current_session_id = intentMessage.session_id
#hermes.publish_end_session(current_session_id, result_sentence)
|
# coding=utf-8
import time
import hashlib
import oss2
import os
import codecs
import json
from mov_sdk.nft_api import NftApi, Net
def load_json(filename):
"""
Load data from json file in temp path.
"""
if os.path.exists(filename):
with codecs.open(filename, mode="r", encoding="utf-8") as f:
data = json.load(f)
return data
else:
return {}
def oss_upload(input_path_name, suffix_name=".png"):
js_data = load_json("key.json")
regin = "oss-cn-shanghai"
accessKeyId = js_data["accessKeyId"]
accessKeySecret = js_data["accessKeySecret"]
bucket_name = "bycoin"
use_dir = "prod-t-nft/"
url_path = f'http://{regin}.aliyuncs.com'
auth = oss2.Auth(accessKeyId, accessKeySecret)
bucket = oss2.Bucket(auth, url_path, bucket_name)
new_save_name = use_dir + time.strftime("%Y-%m-%d_%H%M%S", time.localtime()) + str(int((time.time()*1000)) % 1000) + suffix_name
result = bucket.put_object_from_file(new_save_name, input_path_name)
image_url = f"https://{bucket_name}.{regin}.aliyuncs.com/" + new_save_name
# HTTP返回码。
print('http status: {0} {1}'.format(result.status, image_url))
# # 请求ID。请求ID是请求的唯一标识，强烈建议在程序日志中添加此参数。
# print('request_id: {0}'.format(result.request_id))
return new_save_name
def get_md5_02(file_path):
f = open(file_path, 'rb')
md5_obj = hashlib.md5()
while True:
d = f.read(8096)
if not d:
break
md5_obj.update(d)
hash_code = md5_obj.hexdigest()
f.close()
md5 = str(hash_code).lower()
return md5
def run():
filepath_dic = {}
mnemonic_str = "major skull dinner crucial trip weird thumb grunt absent note motion primary"
# obj = NftApi(mnemonic_str=mnemonic_str, network=Net.MAIN.value)
obj = NftApi(mnemonic_str=mnemonic_str, network=Net.TEST.value)
#description = "sdds !!"
description = '''Bearwood-Rabbit:The rabbit comes from the Earth, wearing the M-BEAR jersey, with blue bows and green ears representing its blessings and expectations for a clean earth.
Bearwood-Ox: The ox is from Venus, wearing M-BEAR jersey, honest and hardworking, it welcomes every guest who comes to Venus and prepares sweet and delicious milk for them.
Bearwood-Horse: The horse comes from Jupiter and wears the M-BEAR jersey, which has a gorgeous rose-red skin and a commanding mane. The horse has an inexhaustible energy, and it will always run freely on Jupiter.
Bearwood-Rat: The rat is from Mercury, wearing the M-BEAR jersey, it has dark skin and a pair of short ears of different colors, who knows how much food it stores in the Mercury dungeon Woolen cloth?
Bearwood-Lamb: The lamb is from Mars, wearing the M-BEAR jersey, born with natural curls, kind and simple in nature. The lamb has this pair of quirky horns, and will squint comfortably when being stroked.
Bearwood-Puppy: The puppy is from Saturn and wears the M-BEAR jersey. 8 has a very auspicious meaning in Chinese society. If you see a bear puppy on Saturn, it means that good luck will come soon!
Bearwood-Tiger: The tiger is from Uranus and wears the M-BEAR jersey. Tiger is the guardian of Uranus, and also expressed his best wishes to bear rabbit in the Year of the Tiger on Earth.
Bearwood-Dragon: The dragon is from Neptune and wears the M-BEAR jersey. Dragon loves to swim. He roams freely on Neptune every day. Sometimes you can hear dragon's long cry in the lonely late night. Do you know what it is calling?
Bearwood-Pig: The pig is from Pluto and wears the M-BEAR jersey. The biggest joy of pig is to eat snacks every day. As long as there is delicious food, he is always very happy. Do you want to go to Pluto and have a snack with pig?
Bearwood-Monkey: The monkey comes from the moon and wears the M-BEAR jersey. The moon is so beautiful, but the little bear monkey can be very lonely at times. It looks at the earth from the moon every da
'''
description = '''Earth, wearing the M-BEAR №2 jersey, with blue bows and green ears representing its blessings and expectations for a clean earth.
Bearwood-Ox: The ox is from Venus, wearing M-BEAR №35 jersey, honest and hardworking, it welcomes every guest who comes to Venus and prepares sweet and delicious milk for them.
Bearwood-Horse: The horse comes from Jupiter and wears the M-BEAR №20 jersey, which has a gorgeous rose-red skin and a commanding mane. The horse has an inexhaustible energy, and it will always run freely on Jupiter.
Bearwood-Rat: The rat is from Mercury, wearing the M-BEAR №14 jersey, it has dark skin and a pair of short ears of different colors, who knows how much food it stores in the Mercury dungeon Woolen cloth?
Bearwood-Lamb: The lamb is from Mars, wearing the M-BEAR №24 jersey, born with natural curls, kind and simple in nature. The lamb has this pair of quirky horns, and will squint comfortably when being stroked.
Bearwood-Puppy: The puppy is from Saturn and wears the M-BEAR №8 jersey. 8 has a very auspicious meaning in Chinese society. If you see a bear puppy on Saturn, it means that good luck will come soon!
Bearwood-Tiger: The tiger is from Uranus and wears the M-BEAR №40 jersey. Tiger is the guardian of Uranus, and also expressed his best wishes to bear rabbit in the Year of the Tiger on Earth.
Bearwood-Dragon: The dragon is from Neptune and wears the M-BEAR №5 jersey. Dragon loves to swim. He roams freely on Neptune every day. Sometimes you can hear dragon’s long cry in the lonely late night. Do you know what it is calling?
Bearwood-Pig: The pig is from Pluto and wears the M-BEAR №12 jersey. The biggest joy of pig is to eat snacks every day. As long as there is delicious food, he is always very happy. Do you want to go to Pluto and have a snack with pig?
Bearwood-Monkey: The monkey comes from the moon and wears the M-BEAR №23 jersey. The moon is so beautiful, but the little bear monkey can be very lonely at times. It looks at the earth from the moon every da
"我是傻逼"
'''
#description = "我是傻逼"
royalty_rate = "15"
margin_amount = "0.1"
btm_asset = 'ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff'
error_out_f = open("error.log", "a")
error_out_f.write("new upload task!\n")
flag = True
f = open("input.txt", "r")
for line in f:
if flag:
flag = False
continue
name, code, pic_name = line.strip().split(',')
if pic_name in filepath_dic.keys():
filepath = filepath_dic[pic_name]
else:
filepath = oss_upload(pic_name, suffix_name=".jpg")
filepath_dic[pic_name] = filepath
i = 0
while i < 10:
i = i + 1
try:
content_md5 = get_md5_02(pic_name)
data = obj.issue_nft(name + code, filepath, content_md5, royalty_rate, btm_asset, margin_amount, description)
print(data)
if int(data["code"]) == 200 and data["data"]["nft_asset"]:
print(f"upload {name} {code} {pic_name} successily!")
break
else:
print(f"error {name} {code} {pic_name} failed!")
line = ','.join([str(x) for x in [name, code, pic_name]])
error_out_f.write(line + "\n")
time.sleep(1)
except Exception as ex:
print(ex)
time.sleep(1)
f.close()
error_out_f.close()
if __name__ == "__main__":
run()
|
# ref.
# https://docs.python.org/3/library/weakref.html#finalizer-objects
# https://docs.python.org/3/library/weakref.html#module-weakref
# A primary use for weak references is to implement caches or mappings holding large objects,
# where it’s desired that a large object not be kept alive solely
# because it appears in a cache or mapping.
# for garbage collector(GC)
import weakref
class Object:
pass
def example_1():
twtrubiks = Object()
weakref.finalize(twtrubiks, print, "You killed twtrubiks!")
del twtrubiks
def callback(x, y, z):
print("hello")
return x + y + z
def example_2():
obj = Object()
f = weakref.finalize(obj, callback, 1, 2, 3)
print('f()', f())
if __name__ == "__main__":
example_1()
# example_2() |
"""
RD Project
"""
from reorder import *
from fp_growth import find_frequent_itemsets
import glob
import itertools
import math
import sys
def assemble_Cohorte():
"""
-> Assemble cohorte (i.e list of list)
of patient
-> create index file for variable in PARAMETERS folder
-> return a list of patient patient vector
"""
#####################
# Create index file #
#####################
listOfParameters = get_allParam("ALL")
indexFile = open("PARAMETERS/variable_index.csv", "w")
cmpt = 0
for parameter in listOfParameters:
cmpt = cmpt + 1
indexFile.write(parameter+";"+"p"+str(cmpt)+"\n")
indexFile.close()
#########################
# Assemblage en cohorte #
#########################
listOfPatientFiles = glob.glob("DATA/PATIENT/*.csv")
cohorte = []
for patientFile in listOfPatientFiles:
patientVector = []
data = open(patientFile, "r")
for line in data:
variable = ""
lineInArray = line.split("\n")
lineInArray = lineInArray[0]
lineInArray = lineInArray.split(";")
typeOfParameter = lineInArray[2]
parameterName = ""
parameterValue = lineInArray[4]
if(typeOfParameter == "PROPORTION"):
parameterName = lineInArray[1]+"_IN_"+lineInArray[3]
elif(typeOfParameter == "MFI"):
parameterName = lineInArray[1]+"_MFI_"+lineInArray[3]
else:
parameterName = lineInArray[1]
indexFile = open("PARAMETERS/variable_index.csv", "r")
for lineInIndexFile in indexFile:
lineInIndexFileInArray = lineInIndexFile.split("\n")
lineInIndexFileInArray = lineInIndexFileInArray[0]
lineInIndexFileInArray = lineInIndexFileInArray.split(";")
parameterNameInIndexFile = lineInIndexFileInArray[0]
parameterId = lineInIndexFileInArray[1]
if(parameterName == parameterNameInIndexFile):
variable = parameterId+"_"+str(parameterValue)
patientVector.append(variable)
indexFile.close()
data.close()
cohorte.append(patientVector)
return cohorte
def alleviate_cohorte(cohorte, threshold_alleviate):
"""
-> alleviate patient vector in cohorte : check the
discrete value of all parameter, if the count of
"normal" value is above the threshold_alleviate
the variable is deleted from all patient vector.
-> cohorte is a list of patient vector (i.e a list of lists)
-> threshold_alleviate is an int
-> return a cohorte ( i.e a list of lists)
"""
###################################
# Denombrement des variables avec #
# possedant une valeur normal #
###################################
paramToNormalCount = {}
for variable in cohorte[0]:
variableInArray = variable.split("_")
variableId = variableInArray[0]
paramToNormalCount[variableId] = 0
for patient in cohorte:
for variable in patient:
variableInArray = variable.split("_")
variableValue = variableInArray[1]
variableId = variableInArray[0]
if(variableValue == "normal"):
paramToNormalCount[variableId] = paramToNormalCount[variableId] + 1
###########################################
# Suppression des variables "trop normal" #
###########################################
listOfParameterToDelete = []
for variable in paramToNormalCount.keys():
if(paramToNormalCount[variable] > threshold_alleviate):
listOfParameterToDelete.append(variable)
for patient in cohorte:
for variable in patient:
variableInArray = variable.split("_")
variableValue = variableInArray[1]
variableId = variableInArray[0]
if(variableId in listOfParameterToDelete):
patient.remove(variable)
return cohorte
def get_listOfNormalParameters(cohorte, threshold):
"""
-> check the discrete value of all parameter, if the count of
"normal" value is above the threshold the variable is added
to the list of "Normal Parameters"
-> cohorte is a list of patient vector (i.e a list of lists)
-> threshold is an int
-> return a list
"""
###################################
# Denombrement des variables avec #
# possedant une valeur normal #
###################################
paramToNormalCount = {}
for variable in cohorte[0]:
variableInArray = variable.split("_")
variableId = variableInArray[0]
paramToNormalCount[variableId] = 0
for patient in cohorte:
for variable in patient:
variableInArray = variable.split("_")
variableValue = variableInArray[1]
variableId = variableInArray[0]
if(variableValue == "normal"):
paramToNormalCount[variableId] = paramToNormalCount[variableId] + 1
###########################
# Selection des variables #
###########################
listOfParameterToDelete = []
for variable in paramToNormalCount.keys():
if(paramToNormalCount[variable] > threshold):
listOfParameterToDelete.append(variable)
####################################
# Conversion des variables avec le #
# fichier index #
####################################
listOfVariableToReturn = []
for variable in listOfParameterToDelete:
variableName = "undef"
variableIndex = open("PARAMETERS/variable_index.csv")
for line in variableIndex:
lineInArray = line.split("\n")
lineInArray = lineInArray[0]
lineInArray = lineInArray.split(";")
parameterName = lineInArray[0]
parameterId = lineInArray[1]
if(parameterId == variable):
variableName = parameterName
listOfVariableToReturn.append(variableName)
variableIndex.close()
return listOfVariableToReturn
def get_optimalValueOfThreshold(cohorte, maxTry, expectedNumberOfRemovedParameter):
"""
-> search the optimal value of threshold for the function
get_listOfNormalParameters(). The optimal value is the
one providing the expectedNumberOfRemovedParameter
-> maxTry is an int, maximum number of try
-> expectedNumberOfRemovedParameter is an int
"""
listOfParameters = get_allParam("ALL")
threshold = 0
listOfNormalParameters = []
numberOfTry = 0
while(1 > 0):
if(numberOfTry >= maxTry):
print "=> reach maximum number of tries"
threshold = -1
break
listOfNormalParameters = get_listOfNormalParameters(cohorte, threshold)
if(len(listOfNormalParameters) == len(listOfParameters)):
#print "=> Nothing left to proceed"
if(threshold + 1 <= len(cohorte[0])):
threshold = threshold + 1
else:
#print "Can't do better" # i.e everything is super normal
break
elif(len(listOfNormalParameters) == 0):
#print "=> No reduction performed"
if(threshold > 0):
threshold = threshold - 1
else:
#print "Can't do better" # i.e nothing is normal
threshold = len(cohorte[0]) + 1
break;
elif(len(listOfNormalParameters) < expectedNumberOfRemovedParameter):
#print "=> Not enough parameter removed"
threshold = threshold - 1
else:
#print "=> Good enough"
break
numberOfTry = numberOfTry + 1
return threshold
def get_controledValueOfThreshold(cohorte, maxTry, minNumberOfParamToRemove, minNumberOfParamToLeft):
"""
-> Control the result return by get_optimalValueOfThreshold function,
make sure that at least minNumberOfParamToLeft parameters are left
for further analysis
-> cochorte is list of list
-> maxTry is an int, max number of tries
-> minNumberOfParamToRemove is an int, could be modify in the function
to fit minNumberOfParamToLeft
-> minNumberOfParamToLeft is an int, tje minimum number of parameter to left if
all parameters present in listOfNormalParameters are deleted from the data
-> return threshold, the best "safe" value to use in function
get_listOfNormalParameters.
"""
numberOfTry = 0
while(1 > 0):
numberOfTry += 1
if(numberOfTry > maxTry):
print "=> reach maximum number of tries"
threshold = len(cohorte[0]) + 1
break
threshold = get_optimalValueOfThreshold(cohorte, 60, minNumberOfParamToRemove)
listOfNormalParameters = get_listOfNormalParameters(cohorte, threshold)
listOfParameters = get_allParam("ALL")
if(len(listOfParameters) - len(listOfNormalParameters) < minNumberOfParamToLeft):
if(minNumberOfParamToRemove - 1 < 0):
print "=> Can't remove parameters"
threshold = len(cohorte[0]) + 1
break
else:
minNumberOfParamToRemove = minNumberOfParamToRemove - 1
else:
print "=> Found a good threshold :"+str(threshold)
print "=> "+str(minNumberOfParamToRemove) + " parameters removed"
break
return threshold
def searchForPattern(cohorte, maxTry, maxNumberOfFrequentPattern, patternSaveFileName):
"""
-> Generate pattern (i.e frequent itemsets) from cohorte, with maxTry.
results are saved in a .csv file (patternSaveFileName).
-> cohorte is a list of list
-> maxTry is an int
-> patternSaveFileName is a string, save file should be located in
DATA/PATTERN folder.
-> maxNumberOfFrequentPattern is an int, the max number of frequent pattern to Generate
(setup ti avoid memory problem)
-> TODO:
- re-check the algorithm
- limit the retrieval of the same patterns
- clean doublon in patternSaveFileName
"""
# Initialisation des parametres
minsup = len(cohorte)
minLenOfPattern = len(cohorte[0])
numberOfTry = 0
tunningPatternLen = 1
tunnigMinSup = 0
pattern_save = open(patternSaveFileName, "a")
pattern_save.close()
while(1>0):
######################################
# controle le nombre de try effectue #
######################################
if(numberOfTry >= maxTry):
break
###########################
# Generation des patterns #
###########################
listOffrequentItemset = []
for itemset in find_frequent_itemsets(cohorte, minsup):
listOffrequentItemset.append(itemset)
######################################################
# controle le nombre de pattern, si aucun pattern #
# n est genere on baisse la valeur du minsup employe #
# pour genere les patterns #
######################################################
if(len(listOffrequentItemset) > 0):
listOfItemSize = []
print "Found "+str(len(listOffrequentItemset))+" frequent itemsets with minsup = "+str(minsup)
##############################################################
# Ecriture des pattern dans un fichier de sauvegarde #
# chaque ligne correspond a un pattern, chaque #
# element du pattern est separe des autres par un ; #
# le dernier terme de la ligne correspond au minsup #
# utilise pour genere le pattern (i.e le support du pattern) #
##############################################################
pattern_save = open(patternSaveFileName, "a")
for element in listOffrequentItemset:
lineToWrite = ""
for item in element:
lineToWrite = lineToWrite + item + ";"
listOfItemSize.append(len(element))
lineToWrite = lineToWrite + str(minsup)
pattern_save.write(lineToWrite+"\n")
pattern_save.close()
if(len(listOffrequentItemset) > maxNumberOfFrequentPattern):
print "max number of patterns reached, cancel mining"
break
######################################################
# controle de la taille des pattern, si la taille #
# du plus gros pattern ne passe pas le controle #
# on adapte alternativement le score minsup employer #
# et la taille des pattern attendues #
######################################################
maxSize = max(listOfItemSize)
if(maxSize < minLenOfPattern):
if(tunnigMinSup):
tunnigMinSup = 0
tunningPatternLen = 1
minsup = minsup - 1
triedToIcreaseMinLenPattern = 0
elif(tunningPatternLen):
tunningPatternLen = 0
tunnigMinSup = 1
minLenOfPattern = minLenOfPattern - 1
else:
if(not triedToIcreaseMinLenPattern):
###################################################
# si la taille des pattern est bonne mais #
# on viens juste de changer de minsup alors #
# on augmente la taille attendue des pattern #
# pour voir si on pas pecher un plus gros pattern #
###################################################
minLenOfPattern = minLenOfPattern + 1
triedToIcreaseMinLenPattern = 1
else:
######################################################
# Si la taille des pattern est bonne et on a deja #
# essayer d augmenter la taille attendue des pattern #
# on arrete la recherche ici #
######################################################
print "found a good pattern"
break
else:
minsup = minsup -1
numberOfTry += 1
def filter_Pattern(fileName):
"""
IN PROGRESS
TODO:
-write doc
"""
filterDataName = fileName.split(".")
heavyFilterName = filterDataName[0] + "_HeavyFilter.csv"
lowFilterName = filterDataName[0] + "_LowFilter.csv"
dataToClean = open(fileName, "r")
dataHeavyFiltered = open(heavyFilterName, "w")
dataLowFiltered = open(lowFilterName, "w")
for line in dataToClean:
lineInArray = line.split("\n")
lineInArray = lineInArray[0]
lineInArray = lineInArray.split(";")
support = lineInArray[-1]
lineInArray = lineInArray[:-1]
listOfDiscreteValue = []
for element in lineInArray:
element = element.split("_")
parameter = element[0]
discreteValue = element[1]
listOfDiscreteValue.append(discreteValue)
# control the line
if("normal" not in listOfDiscreteValue):
dataHeavyFiltered.write(line)
# control the line
saveTheLine = 0
for value in listOfDiscreteValue:
if(value != "normal"):
saveTheLine = 1
if(saveTheLine):
dataLowFiltered.write(line)
dataToClean.close()
dataHeavyFiltered.close()
dataLowFiltered.close()
"""TEST SPACE"""
def extract_parametersFromPattern(fileName, minSupport):
"""
IN PROGRESS
TODO:
- write doc
"""
data = open(fileName, "r")
listOfParameters = []
for line in data:
lineInArray = line.split("\n")
lineInArray = lineInArray[0].split(";")
support = lineInArray[-1]
if(int(support) >= int(minSupport)):
lineInArray = lineInArray[:-1]
for element in lineInArray:
elementInArray = element.split(":")
parameter = elementInArray[0]
if(parameter not in listOfParameters):
listOfParameters.append(parameter)
data.close()
return listOfParameters
from fp_growth import find_frequent_itemsets
cohorte = [["p1_low", "p2_normal", "p3_normal", "p4_normal", "p5_normal"],
["p1_high", "p2_normal", "p3_normal", "p4_normal", "p5_normal"],
["p1_high", "p2_high","p3_high", "p4_normal", "p5_normal"],
["p1_high", "p2_high","p3_high", "p4_normal", "p5_normal"],
["p1_high", "p2_high","p3_high", "p4_normal", "p5_normal"],
["p1_high", "p2_high","p3_high", "p4_normal", "p5_high"],
["p1_high", "p2_high","p3_high", "p4_normal", "p5_high"],
["p1_high", "p2_high","p3_high", "p4_low", "p5_normal"],
["p1_high", "p2_high","p3_high", "p4_low", "p5_normal"],
["p1_high", "p2_high","p3_high", "p4_high", "p5_normal"]]
def search_FrequentItem(cohorte, saveFileName, minSupport):
"""
IN PROGRESS
"""
valueToCount = {}
# initialisation
for patient in cohorte:
for value in patient:
valueToCount[value] = 0
# count
for patient in cohorte:
for value in patient:
for key in valueToCount.keys():
if(value == key):
valueToCount[value] = valueToCount[value] + 1
# write
dataToWrite = open("DATA/PATTERN/"+str(saveFileName), "w")
for key in valueToCount.keys():
support = (float(valueToCount[key])/len(cohorte))*100
if(support >= minSupport):
dataToWrite.write(key+";"+str(valueToCount[key])+"\n")
dataToWrite.close()
#search_FrequentItem(cohorte, "test5.csv", 20)
#searchForPattern(cohorte, 30, 4, "DATA/PATTERN/test2.csv")
#fileName = "DATA/PATTERN/test2.csv"
#--------------------------------------------#
# FUNCTION BUILD FOR CYTOKINES DATA ANALYSIS #######################################
#--------------------------------------------#
def assemble_CohorteFromFile(fileName):
"""
-> Assemble discrete cohorte from cytokine data
-> fileName is the name of the dicrete reduce matrix file
(e. g DATA/CYTOKINES/RA_quantitativeMatrix_discrete.csv)
-> add diagnostic in vectors
-> Write index variable file in PARAMETERS folder
-> add "pX" prefix to scalar in vector
-> return a cohorte (array of arrays)
"""
cohorte = []
listOfVariable = []
data = open(fileName, "r")
fileNameInArray = fileName.split("/CYTOKINES/")
fileNameInArray = fileNameInArray[1].split("_")
diagnostic = fileNameInArray[0]
cmpt = 0
for line in data:
line = line.split("\n")
lineInArray = line[0].split(";")
vector = []
if(cmpt == 0):
for variable in lineInArray:
listOfVariable.append(variable)
else:
index = 1
for scalar in lineInArray:
newScalar = "p"+str(index)+"_"+scalar
vector.append(newScalar)
index += 1
vector.append(diagnostic)
cohorte.append(vector)
cmpt += 1
data.close()
# Write indexFile
indexFile = open("PARAMETERS/"+str(diagnostic)+"_variable_index.csv", "w")
cmpt = 1
for variable in listOfVariable:
indexFile.write("p"+str(cmpt)+";"+variable+"\n")
cmpt += 1
indexFile.close()
return cohorte
def assemble_CohorteFromAllFiles():
"""
-> Assemble a "meta" cohorte, with all discrete reduce files
in CYTOKINES folder
-> return a cohorte
-> Designed for pattern mining
"""
listOfDisease = ["RA", "MCTD", "PAPs", "SjS", "SLE", "SSc", "UCTD", "Control"]
listOfCohorte = []
for diagnostic in listOfDisease:
cohorte = assemble_CohorteFromFile("DATA/CYTOKINES/"+str(diagnostic)+"_quantitativeMatrix_discrete.csv")
listOfCohorte.append(cohorte)
metaCohorte = []
for cohorte in listOfCohorte:
for vector in cohorte:
if(len(vector) > 0):
metaCohorte.append(vector)
return metaCohorte
def extractPatternFromCohorte(cohorte, minsup, tag):
"""
-> Store all frequent pattern (i.e set of items present more than minsup in
cohorte)
-> cohorte is an array of array, discrete value (obtain via the assemble_CohorteFromAllFiles
function)
-> minsup is an int between 0 and 100 (% of support)
-> tag is a string, insert in the output file name
"""
saveFileName = "DATA/PATTERN/"+str(tag)+"_pattern_"+str(minsup)+".csv"
numberOfPatient = len(cohorte)
minsup = int(minsup)
minimumSupport = (minsup * numberOfPatient) / 100
patternFile = open(saveFileName, "w")
for itemset in find_frequent_itemsets(cohorte, minimumSupport):
line = ""
for element in itemset:
line = line + str(element) + ";"
line = line[:-1]
patternFile.write(line+"\n")
patternFile.close()
def get_support(patternToTest, controlFile):
"""
-> get support (i.e proportion of patient
where patternToTest is present)
-> patternToTest is a list of item
-> controlFile is the file conatining the cohorte
(designed for discrete cohorte), could be "all"
for use all the "splited into disease" files, could
be "discrete" when using only discrete data.
"""
if(controlFile == "all"):
cohorte = assemble_CohorteFromAllFiles()
elif(controlFile == "discrete"):
cohorte = assemble_CohorteFromDiscreteAllFiles()
else:
cohorte = assemble_CohorteFromFile(controlFile)
numberOfPatient = len(cohorte)
count = 0
for vector in cohorte:
patternFound = 1
for element in patternToTest:
if element not in vector:
patternFound = 0
if(patternFound):
count +=1
return (float(count)/float(numberOfPatient))*100
def generate_AssociationRulesFromPatternFile(patternFile, rulesFile, confidenceThreshold, display, dataType):
"""
-> generate association rules from a list of pattern
-> patternFile is a fileName (file containing pattern)
-> rulesFile is the file where pattern are write
-> confidenceThreshold is the confidence threshold for
the rules.
-> display is a boolean
-> dataType is a string, could be:
-all
-discrete
-filename
where filename is the name of the file used to compute
support (used as input for get_support() function)
TODO:
-> run a few tests
"""
listOffrequentItemset = []
data = open(patternFile, "r")
for line in data:
frequentItemSet = []
line = line.split("\n")
lineInArray = line[0].split(";")
for item in lineInArray:
frequentItemSet.append(item)
if(len(frequentItemSet) > 1):
listOffrequentItemset.append(frequentItemSet)
data.close()
cmpt_progress = 0
rulesFile = open(rulesFile, "w")
for frequentItemSet in listOffrequentItemset:
progress = float(cmpt_progress / float(len(listOffrequentItemset))) *100
progress = math.ceil(progress)
progress_bar = "#" * int(progress)
progress_bar += "-" * int(50-progress)
display_line = "|"+progress_bar+"|"
sys.stdout.write("\r%d%%" % progress)
sys.stdout.write(display_line)
sys.stdout.flush()
print progress
cmpt_progress += 1
tupleLen = 1
while(tupleLen < len(frequentItemSet)):
for h in itertools.combinations(frequentItemSet, tupleLen):
h = list(h)
complementList = []
for element in frequentItemSet:
if(element not in h and element not in complementList):
complementList.append(element)
rule_confidence = 0
if(get_support(complementList, dataType) != 0):
rule_confidence = (get_support(frequentItemSet, dataType) / get_support(complementList, dataType) * 100)
if(rule_confidence > float(confidenceThreshold)):
if(display):
print"-----------------"
print h
print complementList
print frequentItemSet
rule = ""
for item in complementList:
rule = rule + item + ";"
rule = rule[:-1] + " -> "
for item in h:
rule = rule + item + ";"
rule = rule[:-1] + " ["+str(rule_confidence)+"]"
rulesFile.write(rule+"\n")
if(display):
print "----------------"
tupleLen += 1
rulesFile.close()
def reformat_RulesFile(rulesFileName):
"""
-> Write a new .clp file containing rules
for CLIPS.
-> rulesFileName is a string, the name of
the input file (generated by the generate_AssociationRulesFromPatternFile function)
"""
inputRulesFileName = rulesFileName
outputRulesFileName = inputRulesFileName.split("/")
outputRulesFileName = outputRulesFileName[-1]
outputRulesFileName = outputRulesFileName.split(".")
outputRulesFileName = "DATA/RULES/FORMATED/"+outputRulesFileName[0]+".clp"
inputRulesFile = open(inputRulesFileName, "r")
outputRulesFile = open(outputRulesFileName, "w")
rule_number = 1
for line in inputRulesFile:
line = line.split("\n")
lineInArray = line[0].split(" -> ")
premice = lineInArray[0]
leftPart = lineInArray[1]
leftPartInArray = leftPart.split(" ")
conclusion = leftPartInArray[0]
rule_number+=1
rule = "(defrule rule_"+str(rule_number)+"\n"
for element in premice.split(";"):
rule = rule + " ("+str(element)+")\n"
rule = rule + "=>\n"
for element in conclusion.split(";"):
rule = rule +" (assert ("+str(element)+"))\n"
rule = rule + ")\n"
outputRulesFile.write(rule)
outputRulesFile.close()
inputRulesFile.close()
def filter_associationRules_confidenceValue(inputFileName, confidenceThreshold):
"""
-> Create a filtered file containing rules with
a confidence >= confidenceThreshold.
-> add -cf tag (confidenceThreshold) in output filename
-> inputFileName is the name of the input file (generated by the generate_AssociationRulesFromPatternFile
function)
-> confidenceThreshold is the ... confidenceThreshold
"""
inputRulesFileName = inputFileName
threshold = confidenceThreshold
outputRulesFileName = inputRulesFileName.split(".")
outputRulesFileName = outputRulesFileName[0]+"_cf"+str(threshold)+".csv"
inputRulesFile = open(inputRulesFileName, "r")
outputRulesFile = open(outputRulesFileName, "w")
for line in inputRulesFile:
line = line.split("\n")
lineInArray = line[0].split(" -> ")
premice = lineInArray[0]
leftPart = lineInArray[1]
leftPartInArray = leftPart.split(" ")
conclusion = leftPartInArray[0]
confidence = leftPartInArray[1]
confidence = confidence[1:-1]
if(float(confidence) >= threshold):
outputRulesFile.write(line[0]+"\n")
outputRulesFile.close()
inputRulesFile.close()
def filter_associationRules_paramaterStatus(inputFileName):
"""
-> Create a filtered file containing rules with at least
one parameter (in premice or conclusion) not "normal"
-> add -pf tag (param filtered) in output filename
-> inputFileName is the name of the input file (generated by the generate_AssociationRulesFromPatternFile
function)
"""
inputRulesFileName = inputFileName
outputRulesFileName = inputRulesFileName.split("/")
outputRulesFileName = outputRulesFileName[-1]
outputRulesFileName = outputRulesFileName.split(".")
outputRulesFileName = "DATA/RULES/FILTERED/"+outputRulesFileName[0]+"_pf.csv"
inputRulesFile = open(inputRulesFileName, "r")
outputRulesFile = open(outputRulesFileName, "w")
for line in inputRulesFile:
line = line.split("\n")
lineInArray = line[0].split(" -> ")
premice = lineInArray[0]
leftPart = lineInArray[1]
leftPartInArray = leftPart.split(" ")
conclusion = leftPartInArray[0]
allParametersAreNormalInPremice = 1
for element in premice.split(";"):
elementInArray = element.split("_")
if(len(elementInArray) > 1):
status = elementInArray[1]
if(status != "normal"):
allParametersAreNormalInPremice = 0
allParametersAreNormalInConclusion = 1
for element in conclusion.split(";"):
elementInArray = element.split("_")
if(len(elementInArray) > 1):
status = elementInArray[1]
if(status != "normal"):
allParametersAreNormalInConclusion = 0
if(allParametersAreNormalInPremice and allParametersAreNormalInConclusion):
print "=> Reject rule"
else:
outputRulesFile.write(line[0]+"\n")
outputRulesFile.close()
inputRulesFile.close()
def write_decryptedRulesFiles(inputFileName):
"""
-> write a "decrypted" rule file(in DATA/RULES/DECRYPTED), i.e convert
param_X into real parameter name
-> inputFileName is the name of the input rule file
"""
outputFileName = inputFileName.split("/")
outputFileName = outputFileName[-1]
outputFileName = outputFileName.split(".")
outputFileName = "DATA/RULES/DECRYPTED/"+outputFileName[0]+".csv"
rulesFile = open(inputFileName, "r")
outputFile = open(outputFileName, "w")
for line in rulesFile:
line = line.split("\n")
lineInArray = line[0].split("->")
# Split Rules
leftMember = lineInArray[0]
rightMember = lineInArray[1]
# Parse Premice
leftMemberInArray = leftMember.split(";")
new_leftMember = ""
for element in leftMemberInArray:
elementInArray = element.split("_")
if(len(elementInArray) > 1):
parameter_name = "undef"
parameter = elementInArray[0]
status = elementInArray[1]
indexFile = open("PARAMETERS/RA_variable_index.csv") # diagnostic don't reall matter, all index files are identical
for lineInIndexFile in indexFile:
lineInIndexFile = lineInIndexFile.split("\n")
lineInIndexFileInArray = lineInIndexFile[0].split(";")
index_paramater = lineInIndexFileInArray[0]
if(parameter == index_paramater):
parameter_name = lineInIndexFileInArray[1] +" ("+str(status)+")"
indexFile.close()
else:
parameter_name = element
new_leftMember = new_leftMember + parameter_name + ";"
new_leftMember = new_leftMember[:-1]
# Parse Connclusion
rightMemberInArray = rightMember.split(" ")
conclusion = rightMemberInArray[1]
conclusionInArray = conclusion.split(";")
new_conclusion = ""
for element in conclusionInArray:
elementInArray = element.split("_")
if(len(elementInArray) > 1):
parameter_name = "undef"
parameter = elementInArray[0]
status = elementInArray[1]
indexFile = open("PARAMETERS/RA_variable_index.csv") # diagnostic don't reall matter, all index files are identical
for lineInIndexFile in indexFile:
lineInIndexFile = lineInIndexFile.split("\n")
lineInIndexFileInArray = lineInIndexFile[0].split(";")
index_paramater = lineInIndexFileInArray[0]
if(parameter == index_paramater):
parameter_name = lineInIndexFileInArray[1] +" ("+str(status)+")"
indexFile.close()
else:
parameter_name = element
new_conclusion = new_conclusion + parameter_name + ";"
new_conclusion = new_conclusion[:-1]
# Parse confidence
confidence = rightMemberInArray[2]
# write in outputFile
new_rule = new_leftMember +" -> "+new_conclusion+" "+confidence
outputFile.write(new_rule+"\n")
outputFile.close()
rulesFile.close()
def assemble_CohorteFromDiscreteAllFiles():
"""
-> Assemble a "meta" cohorte, with all discrete files
in CYTOKINES folder (already discrete data, have not pass by
the discretisation procedure)
-> return a cohorte
-> Designed for pattern mining
"""
listOfDisease = ["RA", "MCTD", "PAPs", "SjS", "SLE", "SSc", "UCTD", "Control"]
listOfCohorte = []
for diagnostic in listOfDisease:
cohorte = assemble_CohorteFromFile("DATA/CYTOKINES/"+str(diagnostic)+"_discreteMatrix.csv")
listOfCohorte.append(cohorte)
metaCohorte = []
for cohorte in listOfCohorte:
for vector in cohorte:
if(len(vector) > 0):
metaCohorte.append(vector)
return metaCohorte
def build_cohorte_for_pm(data_file_name):
"""
-> Build a cohorte for pattern mining
-> data_file_name is the name of the file containing data,
supposed to be dichotomized data
-> return a matrix (list of list)
"""
cohorte = []
variables_list = []
file_data = open(data_file_name, "r")
cmpt_line = 0
for line in file_data:
line = line.split("\n")
line = line[0]
line_array = line.split(";")
vector = []
if(cmpt_line == 0):
for variable_name in line_array:
variable_name = variable_name.replace("\t", "")
variable_name = variable_name.replace(" ", "")
variables_list.append(variable_name)
else:
cmpt_index = 0
for scalar_value in line_array:
if(int(scalar_value) == 1):
vector.append(variables_list[cmpt_index])
cmpt_index += 1
if(len(scalar_value) > 4):
vector.append("OMICID_"+str(scalar_value))
cohorte.append(vector)
cmpt_line+=1
file_data.close()
return cohorte |
from brownie import Box, accounts, network
def pp_event(event, prefix='') -> str:
lines = []
for i, log in enumerate(event):
lines.append('{}log[{}]'.format(prefix, i))
for key, value in log.items():
lines.append('{} {}: {}'.format(prefix, key, value))
return '\n'.join(lines)
def pp_events(events, prefix='') -> str:
lines = []
for key, event in events.items():
lines.append('{}{}\n{}'.format(prefix, key, pp_event(event, '{} '.format(prefix))))
return '\n'.join(lines)
def main():
account = accounts[0]
contract = Box.deploy({'from': account})
print('contract successfully deployed on network {}'.format(network.show_active()))
print('initial box value {}'.format(contract.retrieve()))
print('contract address {}'.format(contract.address))
print('owner address {}'.format(account.address))
tx = contract.inc()
events = tx.events
print('new box value {}'.format(contract.retrieve()))
print('recored events\n{}'.format(events))
print('events from tx.inc()\n{}'.format(pp_events(tx.events)))
|
import yaml as ym
import os
import sys
class config():
#loading configuration file
def expose_config(self, gen_conf):
config = self.load_config('config.yaml')
return config[gen_conf]
#function to load configuration file
def load_config(self, config_file):
#base path to file configutation
base_path = os.path.join(sys.path[0], config_file)
with open(base_path, "r") as stream:
try:
return ym.safe_load(stream)
except ym.YAMLError as exc:
print(exc)
|
import os
import shutil
from ievv_opensource.utils.ievvbuildstatic import pluginbase
class Plugin(pluginbase.Plugin):
# Copy specified files from staticsources/<packagename>/node_modules/foo/bar/<libfile>.js to static/<packagename>/<version>/node_modules/<libfile>.js
def __init__(self,
sourcefiles,
destinationfolder=os.path.join('scripts', 'node_modules'),
sourcefolder='node_modules',
**kwargs):
super(Plugin, self).__init__(**kwargs)
self.sourcefiles = sourcefiles
self.destinationfolder = destinationfolder
self.sourcefolder = sourcefolder
def get_sourcefolder_path(self):
return self.app.get_source_path(self.sourcefolder)
def get_destinationfolder_path(self):
return self.app.get_destination_path(self.destinationfolder)
def get_filepaths(self):
files = []
sourcefolder = self.get_sourcefolder_path()
for file in self.sourcefiles:
files.append(os.path.join(sourcefolder, file))
# no idea why regexfilelist wont work..
# regex_file_list = RegexFileList(include_patterns=files)
# file_list = regex_file_list.get_files_as_list(sourcefolder, absolute_paths=True)
# print(file_list)
return files
def run(self):
filepaths = self.get_filepaths()
destinationfolder = self.get_destinationfolder_path()
if os.path.exists(destinationfolder):
self.get_logger().debug('Removing {}'.format(destinationfolder))
shutil.rmtree(destinationfolder)
os.makedirs(destinationfolder)
for sourcepath in filepaths:
if os.path.exists(sourcepath) and os.path.isfile(sourcepath):
filename = os.path.basename(sourcepath)
destinationpath = os.path.join(destinationfolder, filename)
shutil.copyfile(sourcepath, destinationpath)
def __str__(self):
return '{}({})'.format(super(Plugin, self).__str__(), self.sourcefolder)
|
"""This script walks through how to use the Python API.
Covered here: importing GPUdb, instantiating Kinetica, creating a type,
creating a table, inserting records, retrieving records, filtering records,
aggregating/grouping records, and deleting records.
"""
from __future__ import print_function
import collections
import json
import random
import string
import gpudb
def gpudb_example():
print ( "TUTORIAL OUTPUT")
print ( "===============\n")
# all tables/views used in examples below
weather_table_name = "weather"
weather_w_view = "weather_west"
weather_nw_view = "weather_northwest"
weather_country_view = "weather_country"
weather_e_view = "weather_east"
weather_se_view = "weather_southeast"
weather_h_view = "weather_histogram"
""" Establish connection with a locally-running instance of Kinetica,
using binary encoding to save memory """
h_db = gpudb.GPUdb(encoding='BINARY', host='127.0.0.1', port='9191')
print ()
print ( "CREATING A TYPE & TABLE")
print ( "-----------------------")
print ()
""" Create columns; column arguments consist of a list of the name, then type, and then
optional additional properties. E.g., [ "column_name", column_type, column_property1,
column_property2 ]. Note that any number of column properties can be listed as long as
they are not mutually exclusive within themselves or with the primitive type. Also note
that raw string can be used for both the primitive type and the properties; but the user is
also able to use string constants as illustrated in the example below.
"""
columns = [
[ "city", "string", "char16" ],
[ "state_province", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR32 ],
[ "country", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR16 ],
[ "x", "double" ],
[ "y", "double" ],
[ "avg_temp", "double" ],
[ "time_zone", "string", "char8" ]
]
# Clear any existing table with the same name (otherwise we won't be able to
# create the table)
if h_db.has_table( table_name = weather_table_name )['table_exists']:
h_db.clear_table( weather_table_name )
# Create the table from the type
try:
weather_table = gpudb.GPUdbTable( columns, weather_table_name, db = h_db )
print ( "Table successfully created.")
except gpudb.GPUdbException as e:
print ( "Table creation failure: {}".format( str(e) ) )
# We can also create a GPUdbTable object for a table that already exists in
# the database. All we need is the table name (and a GPUdb object). Note how
# we pass None for the type argument
weather_table_duplicate = gpudb.GPUdbTable( None, weather_table_name, db = h_db )
print ( "\n")
print ( "INSERTING DATA")
print ( "--------------")
print ()
# Insert single record example
# Create ordered dictionary for keys & values of record
datum = collections.OrderedDict()
datum["city"] = "Washington, D.C."
datum["state_province"] = "--"
datum["country"] = "USA"
datum["x"] = -77.016389
datum["y"] = 38.904722
datum["avg_temp"] = 58.5
datum["time_zone"] = "UTC-5"
# Insert the record into the table (through the GPUdbTable interface)
weather_table.insert_records( datum )
# Create another record
datum2 = collections.OrderedDict()
datum2["city"] = "Washington, D.C."
datum2["state_province"] = "--"
datum2["country"] = "USA"
datum2["x"] = -77.016389
datum2["y"] = 38.904722
datum2["avg_temp"] = 58.5
datum2["time_zone"] = "UTC-5"
# Insert the second record through the basic GPUdb interface
# Encode record and put into a single element list
weather_record_type = weather_table.get_table_type()
single_record = [ gpudb.GPUdbRecord( weather_record_type, datum ).binary_data ]
# Insert the record into the table
response = h_db.insert_records(table_name = weather_table_name, data = single_record, list_encoding = "binary")
print ( "Number of single records inserted: {}".format(response["count_inserted"]))
# Insert multiple records example
# ===============================
records = []
# Create a list of in-line records
records.append( ["Paris", "TX", "USA", -95.547778, 33.6625, 64.6, "UTC-6"] )
records.append( ["Memphis", "TN", "USA", -89.971111, 35.1175, 63, "UTC-6"] )
records.append( ["Sydney", "Nova Scotia", "Canada", -60.19551, 46.13631, 44.5, "UTC-4"] )
records.append( ["La Paz", "Baja California Sur", "Mexico", -110.310833, 24.142222, 77, "UTC-7"] )
records.append( ["St. Petersburg", "FL", "USA", -82.64, 27.773056, 74.5, "UTC-5"] )
records.append( ["Oslo", "--", "Norway", 10.75, 59.95, 45.5, "UTC+1"] )
records.append( ["Paris", "--", "France", 2.3508, 48.8567, 56.5, "UTC+1"] )
records.append( ["Memphis", "--", "Egypt", 31.250833, 29.844722, 73, "UTC+2"] )
records.append( ["St. Petersburg", "--", "Russia", 30.3, 59.95, 43.5, "UTC+3"] )
records.append( ["Lagos", "Lagos", "Nigeria", 3.384082, 6.455027, 83, "UTC+1"] )
records.append( ["La Paz", "Pedro Domingo Murillo", "Bolivia", -68.15, -16.5, 44, "UTC-4"] )
records.append( ["Sao Paulo", "Sao Paulo", "Brazil", -46.633333, -23.55, 69.5, "UTC-3"] )
records.append( ["Santiago", "Santiago Province", "Chile", -70.666667, -33.45, 62, "UTC-4"] )
records.append( ["Buenos Aires", "--", "Argentina", -58.381667, -34.603333, 65, "UTC-3"] )
records.append( ["Manaus", "Amazonas", "Brazil", -60.016667, -3.1, 83.5, "UTC-4"] )
records.append( ["Sydney", "New South Wales", "Australia", 151.209444, -33.865, 63.5, "UTC+10"] )
records.append( ["Auckland", "--", "New Zealand", 174.74, -36.840556, 60.5, "UTC+12"] )
records.append( ["Jakarta", "--", "Indonesia", 106.816667, -6.2, 83, "UTC+7"] )
records.append( ["Hobart", "--", "Tasmania", 147.325, -42.880556, 56, "UTC+10"] )
records.append( ["Perth", "Western Australia", "Australia", 115.858889, -31.952222, 68, "UTC+8"] )
# Insert the records into the table
weather_table.insert_records( records )
print ( "Number of batch records inserted: {}".format( weather_table.size() ))
print ( "\n")
print ( "RETRIEVING DATA")
print ( "---------------")
print ()
""" Retrieve the second set of ten records from weather_table. Note that
records can be iterated over directly. """
print ( "{:<20s} {:<25s} {:<15s} {:<10s} {:<11s} {:<9s} {:<8s}".format("City","State/Province","Country","Latitude","Longitude","Avg. Temp","Time Zone"))
print ( "{:=<20s} {:=<25s} {:=<15s} {:=<10s} {:=<11s} {:=<9s} {:=<9s}".format("", "", "", "", "", "", ""))
for weatherLoc in weather_table.get_records( offset = 10, limit = 10 ):
print ( "{city:<20s} {state:<25s} {country:<15s} {y:10.6f} {x:11.6f} {avg_temp:9.1f} {time_zone}"
"".format( city = weatherLoc["city"], state = weatherLoc["state_province"], country = weatherLoc["country"],
y = weatherLoc["y"], x = weatherLoc["x"], avg_temp = weatherLoc["avg_temp"], time_zone = weatherLoc["time_zone"] ) )
""" Retrieve no more than 10 records as JSON from weather_table through the GPUdb interface.
Note that records are stringified and have to be parsed if using the 'json' encoding. """
weatherLocs = h_db.get_records( table_name = weather_table_name, offset = 0, limit = 10,
encoding = "json", options = {"sort_by":"city"} )['records_json']
print ( "{:<20s} {:<25s} {:<15s} {:<10s} {:<11s} {:<9s} {:<8s}".format("City","State/Province","Country","Latitude","Longitude","Avg. Temp","Time Zone"))
print ( "{:=<20s} {:=<25s} {:=<15s} {:=<10s} {:=<11s} {:=<9s} {:=<9s}".format("", "", "", "", "", "", ""))
for weatherLoc in weatherLocs:
print ( "{city:<20s} {state_province:<25s} {country:<15s} {y:10.6f} {x:11.6f} {avg_temp:9.1f} {time_zone}".format(**json.loads(weatherLoc)))
""" Retrieve no more than 25 of the remaining records as binary from weather
table. Note that records are binary and have to be decoded. """
response = h_db.get_records( table_name = weather_table_name, offset = 10, limit = 25,
encoding = "binary", options = {"sort_by":"city"})
weatherLocs = gpudb.GPUdbRecord.decode_binary_data(response["type_schema"], response["records_binary"])
for weatherLoc in weatherLocs:
print ( "{city:<20s} {state_province:<25s} {country:<15s} {y:10.6f} {x:11.6f} {avg_temp:9.1f} {time_zone}".format(**weatherLoc))
""" Note that total_number_of_records does not reflect offset/limit; it's
the count of all records or those which match the given expression """
print ( "\nNumber of records in new table: {:d}".format(response["total_number_of_records"]))
print ( "\n")
print ( "FILTERING")
print ( "---------")
print ()
### Filter Example 1
""" Filter records where column x is less than 0, i.e., cities in the
western hemisphere, and store the filter in a view. Note that the GPUdbTable
creates a random view name if one is not supplied. """
view1 = weather_table.filter( expression = "x < 0" )
print ( "Number of records in the western hemisphere: {}".format( view1.size() ))
### Filter Example 2
""" Filter records where column x is less than 0 and column y is greater
than 0, i.e., cities in the northwestern semi-hemisphere, and store
the filter in a view. This filter operation is done through the base
GPUdb interface. """
response = h_db.filter(table_name = weather_table_name, view_name = weather_nw_view,
expression = "x < 0 and y > 0" )
print ( "Number of records in the northwestern semi-hemisphere: {}".format( response["count"] ))
### Filter Example 3
""" Filter records using the same expressions as Example 2, but using
query chaining this time (note that we're using the view created by the
first filter. """
nw_view = view1.filter( expression = "y > 0" )
print ( "Number of records in the northwestern semi-hemisphere (with query chaining): {}"
"".format( nw_view.size() ))
### Filter Example 4
""" Filter by list where country name is USA, Brazil, or Australia. Here we
use the duplicate GPUdbTable object (but it points to the same DB table). """
country_map = {"country": ["USA", "Brazil", "Australia"]}
view3 = weather_table_duplicate.filter_by_list( column_values_map = country_map )
print ( "Number of records where country name is USA, Brazil, or Australia: {}"
"".format( view3.size() ))
### Filter Example 5
""" Filter by range cities that are east of GMT (the Prime Meridian) """
view4 = weather_table.filter_by_range( column_name = "x", lower_bound = 0,
upper_bound = 180 )
print ( "Number of records that are east of the Prime Meridian (x > 0): {}"
"".format( view4.size() ))
print ( "\n")
print ( "AGGREGATING, GROUPING, and HISTOGRAMS")
print ( "-------------------------------------")
print ()
### Aggregate Example 1
""" Aggregate count, min, mean, and max on the average temperature. Note
that unlike the filter functions, the aggregate functions of GPUdbTable
return the response from the database. """
stat_results = weather_table.aggregate_statistics( column_name = "avg_temp",
stats = "count,min,max,mean" )
print ( "Statistics of values in the average temperature column:")
print ( "\tCount: {count:.0f}\n\tMin: {min:4.2f}\n\tMean: {mean:4.2f}\n\tMax: {max:4.2f}"
"\n".format( **stat_results["stats"] ))
### Aggregate Example 2
""" Find unique city names. """
results = weather_table.aggregate_unique( column_name = "city", offset = 0,
limit = 25 )
print ( "Unique city names:")
for weatherLoc in results.data["city"]:
print ( "\t* {}".format( weatherLoc ))
print ()
""" Same operation, but through the base GPUdb interface. Note that the
results have to parsed specially using GPUdb.parse_dynamic_response().
Also, we're using the 'json' encoding in this case (the 'binary' encoding
can also be used). Also note how the data is accessed differently. """
response = h_db.aggregate_unique( table_name = weather_table_name,
column_name = "city", offset = 0,
limit = 25, encoding = "json")
print ( "Unique city names (using the GPUdb class):")
weatherLocs = h_db.parse_dynamic_response(response)['response']['city']
for weatherLoc in weatherLocs:
print ( "\t* {}".format(weatherLoc))
print ()
### Aggregate Example 3
""" Find number of weather locations per country in the northwestern
semi-hemisphere. Note that the data is automatically decoded. """
results = nw_view.aggregate_group_by( column_names = ["country", "count(country)"], offset = 0,
limit = 25 )
print ( "Weather locations per country in the northwest semi-hemisphere:")
for country in zip(results.data["country"], results.data["count(country)"]):
print ( "\t{:<10s}{:2d}".format(country[0] + ":", country[1]))
print ()
""" Find number of weather locations per country in the northwestern
semi-hemisphere; use binary decoding explicitly since we're using
the GPUdb class. """
response = h_db.aggregate_group_by(table_name=weather_nw_view, column_names=["country", "count(country)"], offset=0, limit=25, encoding="binary")
countries = gpudb.GPUdbRecord.decode_binary_data(response["response_schema_str"], response["binary_encoded_response"])[0]
print ( "Weather locations per country in the northwest semi-hemisphere:")
for country in zip(countries["column_1"], countries["column_2"]):
print ( "\t{:<10s}{:2d}".format(country[0] + ":", country[1]))
print ()
### Aggregate Example 4
""" Filter table to southeastern semi-hemisphere records, group by country,
and aggregate min, max, and mean on the average temperature; using the default
binary decoding and the GPUdbTable interface. """
# Do a filter first
se_view = weather_table.filter( expression="x > 0 and y < 0" )
# Then do the aggregation operation (note how we use the 'data' property to get
# the data)
data = se_view.aggregate_group_by( column_names = ["country", "min(avg_temp)", "max(avg_temp)", "mean(avg_temp)"],
offset = 0, limit = 25 ).data
print ( "{:<20s} {:^5s} {:^5s} {:^5s}".format("SE Semi-Hemi Country", "Min", "Mean", "Max"))
print ( "{:=<20s} {:=<5s} {:=<5s} {:=<5s}".format("", "", "", ""))
for countryWeather in zip(data["country"], data["min(avg_temp)"], data["mean(avg_temp)"], data["max(avg_temp)"]):
print ( "{:<20s} {:5.2f} {:5.2f} {:5.2f}".format(*countryWeather))
print ()
""" Filter table to southeastern semi-hemisphere records, group by country,
and aggregate min, max, and mean on the average temperature; using the default
binary decoding and the base GPUdb interface. """
h_db.filter(table_name = weather_table_name, view_name = weather_se_view, expression="x > 0 and y < 0")
response = h_db.aggregate_group_by( table_name = weather_se_view,
column_names = ["country", "min(avg_temp)", "max(avg_temp)", "mean(avg_temp)"],
offset = 0, limit = 25 )
data = h_db.parse_dynamic_response(response)['response']
print ( "{:<20s} {:^5s} {:^5s} {:^5s}".format("SE Semi-Hemi Country", "Min", "Mean", "Max"))
print ( "{:=<20s} {:=<5s} {:=<5s} {:=<5s}".format("", "", "", ""))
for countryWeather in zip(data["country"], data["min(avg_temp)"], data["mean(avg_temp)"], data["max(avg_temp)"]):
print ( "{:<20s} {:5.2f} {:5.2f} {:5.2f}".format(*countryWeather))
print ()
### Aggregate Example 5
""" Filter for southern hemisphere cities and create a histogram for the
average temperature of those cities (divided into every 10 degrees,
e.g., 40s, 50s, 60s, etc.) """
s_view = weather_table.filter( expression = "y < 0" )
histogram_result = s_view.aggregate_histogram( column_name = "avg_temp",
start = 40, end = 90,
interval = 10 )
print ( "Number of southern hemisphere cities with average temps in the given ranges:")
for histogroup in zip([40, 50, 60, 70, 80], histogram_result['counts']):
print ( "\t{}s: {:2.0f}".format(*histogroup))
print()
### Aggregate Example 6
""" Aggregate group by has an option 'result_table' which creates a result table and does not
return the data. Very useful when the data is large and we want to fetch records from it
in batches.
"""
# Create another table with the same type, and generate a lot of random data for it.
# Note that we're allowing GPUdbTable to come up with a random name for the table.
weather_table2 = gpudb.GPUdbTable( columns, db = h_db )
# Create random data (but specify a range for the average temperature column)
weather_table2.insert_records_random( count = 10000,
options = { "avg_temp": {"min": -20, "max": 105 } } )
print()
print ( "Second weather table size: ", weather_table2.size() )
# Create a view on the south-western quadrant of the planet
sw_view = weather_table2.filter( expression="x < 0 and y < 0" )
# Then do the aggregation operation . Note that the column names need
# aliases to utilize th 'result_table' option.
agg_result_table = sw_view.aggregate_group_by( column_names = ["country",
"min(avg_temp) as min_avg_temp",
"max(avg_temp) as max_avg_temp",
"mean(avg_temp) as mean_avg_temp"],
offset = 0, limit = 25,
options = { "result_table": gpudb.GPUdbTable.prefix_name("agg_") } )
print ( "Size of records in the SW quadrant of the planet: ", agg_result_table.size() )
print ( "{:<20s} {:^7s} {:^7s} {:^5s}".format("SW Semi-Hemi Country", "Min", "Mean", "Max"))
print ( "{:=<20s} {:=<6s} {:=<6s} {:=<6s}".format("", "", "", ""))
# Note that we can slice GPUdbTable objects to fetch the data inside
for record in agg_result_table[ 10 : 50 ]:
print ( "{:<20s} {:5.2f} {:5.2f} {:5.2f}".format( record["country"], record["min_avg_temp"], record["mean_avg_temp"], record["max_avg_temp"] ))
print ()
print ( "\n")
print ( "DELETING DATA")
print ( "-------------")
print ()
""" Filter for cities that are either south of latitude -50 or west of
longitude -50 to determine how many records will be deleted; delete
the records, then confirm the deletion by refiltering. """
deleteExpression = "x < -50 or y < -50"
num_records_to_delete = weather_table.filter( expression = deleteExpression ).count
print ( "Number of records that meet deletion criteria before deleting: {}"
"".format( num_records_to_delete ) )
weather_table.delete_records( expressions = [ deleteExpression ] )
# Note that we're using the duplicate GPUdbTable object which points to the
# same table in the DB
num_records_post_delete = weather_table_duplicate.filter( expression = deleteExpression ).count
print ( "Number of records that meet deletion criteria after deleting (expect 0): {}".format( num_records_post_delete ))
print ()
print ( "\n")
print ( "Using Multi-head Ingestion")
print ( "--------------------------")
print ()
"""For tables with primary or shard key columns, it might be useful to use
the multi-head ingestion procedure for inserting records into a table for
heavy ingestion loads. There are benefits and drawbacks of using multi-head
ingestion: the benefit is that if the database is configured for multi-head
ingestion and there is a tremendous ingestion load, then the ingestion will
be faster over all. However, the drawback is that the client has to do some
calculation PER record to find out which worker rank of the database server to
send the record to. So, unless the following parameters are met, it is unwise
to use multi-head ingestion as it will unncessarily slow ingestion down:
* The server is configured to use multi-head ingestion
* The table type has at least one primary or shard key column
* There is a heavy stream of data to be inserted
"""
# Create a type that has some shard keys
sharded_columns = [
[ "city", "string", "char16" ],
[ "state_province", "string", "char2", "shard_key" ], # shard key column
[ "country", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR16 ],
[ "airport", "string", "nullable" ], # a nullable column
[ "x", "double" ],
[ "y", "double" ],
[ "avg_temp", "double" ],
[ "time_zone", "string", "char8", "shard_key" ] # shard key column
]
# Create a table with the multi-head ingestion options
# (the default batch size is 10k)
sharded_table = gpudb.GPUdbTable( sharded_columns, db = h_db,
use_multihead_ingest = True,
multihead_ingest_batch_size = 33 )
# Generate some random data to be inserted
num_records = 100
null_likelihood = 10
alphanum = (string.ascii_letters + string.digits)
for i in range(0, num_records):
record = collections.OrderedDict()
record[ "city" ] = ''.join( [random.choice( alphanum ) for n in range( 0, random.randint( 5, 16 ) )] )
record[ "state_province"] = ''.join( [random.choice( alphanum ) for n in range( 0, random.randint( 0, 2 ) )] )
record[ "country" ] = ''.join( [random.choice( alphanum ) for n in range( 0, random.randint( 5, 16 ) )] )
record[ "airport" ] = None if (random.random() < null_likelihood) \
else ''.join( [random.choice( alphanum ) for n in range( 0, random.randint( 2, 25 ) )] )
record[ "x" ] = random.uniform( -180, 180 )
record[ "y" ] = random.uniform( -90, 90 )
record[ "avg_temp" ] = random.uniform( -40, 110 )
record[ "time_zone" ] = "UTC-{}".format( random.randint( -11, 14 ) )
sharded_table.insert_records( record )
# end loop
print ( "Size of sharded table (expect less than 100 as the batch size is 33 and \n100 is not a multiple of 33): ", sharded_table.size() )
print ()
print ( "Flushing the records remaining in the ingestor queue...")
sharded_table.flush_data_to_server()
print ( "Size of sharded table post forced flush (expect 100): ", sharded_table.size() )
print ()
# end gpudb_example()
if __name__ == '__main__':
gpudb_example()
|
ReLU
[[0.0553801, 0.190198, -0.0377036, -1.32009, -0.704992], [-0.010481, -0.118571, 0.153946, -0.736114, -1.00878], [0.482564, 0.0565022, 0.0443098, 0.0600691, 0.0020876], [-0.0778212, -0.0577902, -0.153575, 0.0605465, 0.450204], [0.0143941, -0.25066, -0.132743, 0.533158, -0.0781143], [-0.00869665, -0.447464, -0.0517125, -0.0990526, 0.204336], [0.010042, -0.00129605, -0.00514204, -0.0715019, -0.0436908], [-0.0696566, 0.215441, -0.00483027, 0.155244, -0.858598], [-0.0412334, 0.845604, 0.238394, 0.166022, -0.0812237], [0.386318, -0.0315554, 0.00926705, -0.369564, -0.731116], [-0.727862, -0.136911, 0.0147124, -0.76237, -0.181072], [0.00181044, -0.131803, 0.0962716, 0.175322, -0.863416], [0.0213996, 0.126362, 0.0304863, -1.13285, 1.17788], [-1.41449, -0.0028435, -0.0144381, -0.0132612, 0.00265051], [-0.126224, 0.115034, -0.124887, -0.754803, -0.478396], [0.00489443, -0.0621162, -0.442564, -0.325057, 0.29953], [0.0238864, -0.294626, 0.437456, 0.167242, -1.57446], [-0.0339431, 0.212746, 0.555427, 0.401026, -0.113187], [0.0534015, 0.505959, -0.695019, -0.597797, 0.757852], [0.0759885, 1.02061, -0.767079, 0.790857, -0.781343], [0.0280475, -0.0544412, 0.492328, -0.388467, 0.188625], [0.00126882, -0.00331824, 0.00365573, -0.0122686, 0.0154782], [0.378386, 2.2269, 0.117052, -0.224842, 0.335837], [0.150166, -2.23327, 0.0177583, 0.146265, -0.332893], [-0.0746921, -0.958603, 0.971528, 0.609819, -0.817018], [-0.00927227, -1.58696, 0.0165695, 0.00278922, 0.0574048], [-0.0132054, -0.849359, 0.548887, 0.310252, -1.48819], [0.0478079, -0.681258, -0.868954, 0.583286, -0.306058], [0.0828738, 0.440471, 0.630704, -0.0333658, 0.00772661], [-0.00806285, 0.0531106, -0.222073, 0.0686884, -1.50157], [-0.114841, 0.919164, 0.190187, 0.0437794, 0.00520963], [-1.76543, -0.0157442, -0.0315514, 0.00938973, -0.000685154], [-0.0530375, -0.450235, 1.76927, 0.5936, -0.6578], [0.0198095, -0.113013, -0.552609, -0.518049, 0.285155], [0.0214781, -1.74124, 1.73425, -0.0566436, 0.459649], [-0.649957, -0.0397138, -0.00584056, -0.00988717, 0.0166818], [0.00680587, 0.0170974, -0.0202533, -1.63096, -0.944574], [-0.116581, 1.9092, -2.42837, -0.107407, 0.248435], [-0.00247847, -0.00046304, 0.00565075, -0.000137539, 0.00251783], [-0.0357091, -0.166897, -0.00179253, -0.579197, -1.04002], [0.344397, -0.939464, 0.00142697, -0.18813, 0.190268], [0.0630945, 1.17492, -1.27231, 0.157916, -0.00482825], [-1.81425, -0.00623258, -0.0159597, 0.00737333, -0.00575161], [-0.00829891, -0.0115763, -0.00241857, 0.00255223, -0.0173112], [-0.077549, -0.28407, 0.00596384, -0.420798, 0.511508], [-0.00162356, -0.621229, 0.599885, -0.170725, 0.68297], [-0.196361, -0.909656, 0.158577, -0.606075, -0.199725], [0.0163317, 0.0189992, 0.00514905, -0.0639583, -0.0439335], [-0.0514028, -1.17772, -0.301449, 0.382633, -0.500615], [-0.936931, -0.203562, 0.279398, -0.20136, 0.320543], [0.0554, 0.1902, -0.0377, -1.32, -0.705], [-0.01048, -0.1186, 0.1539, -0.7363, -1.009], [0.4827, 0.0565, 0.0443, 0.06006, 0.002089], [-0.0778, -0.0578, -0.1536, 0.06055, 0.4502], [0.0144, -0.2507, -0.1327, 0.533, -0.0781], [-0.0087, -0.4475, -0.05173, -0.09906, 0.2043], [0.01004, -0.001296, -0.005142, -0.0715, -0.0437], [-0.06964, 0.2155, -0.00483, 0.1553, -0.8584], [-0.04123, 0.8457, 0.2384, 0.166, -0.08124], [0.3862, -0.03156, 0.00927, -0.3696, -0.731], [-0.728, -0.137, 0.01471, -0.762, -0.181], [0.00181, -0.1318, 0.09625, 0.1753, -0.8633], [0.0214, 0.1263, 0.03049, -1.133, 1.178], [-1.414, -0.002844, -0.014435, -0.01326, 0.002651], [-0.1262, 0.11505, -0.1249, -0.755, -0.4785], [0.004894, -0.0621, -0.4426, -0.325, 0.2996], [0.02388, -0.2947, 0.4375, 0.1672, -1.574], [-0.03394, 0.2128, 0.5557, 0.4011, -0.11316], [0.0534, 0.506, -0.695, -0.5977, 0.758], [0.076, 1.0205, -0.767, 0.791, -0.7812], [0.02805, -0.05444, 0.4924, -0.3884, 0.1886], [0.001268, -0.003319, 0.003656, -0.01227, 0.01548], [0.3784, 2.227, 0.11707, -0.2249, 0.336], [0.1501, -2.232, 0.01776, 0.1462, -0.333], [-0.0747, -0.9585, 0.9717, 0.61, -0.817], [-0.00927, -1.587, 0.01657, 0.002789, 0.0574], [-0.01321, -0.849, 0.549, 0.3103, -1.488], [0.04782, -0.681, -0.869, 0.5835, -0.3062], [0.0829, 0.4404, 0.631, -0.03336, 0.007725], [-0.008064, 0.0531, -0.222, 0.06866, -1.502], [-0.11487, 0.919, 0.1902, 0.0438, 0.00521], [-1.766, -0.01575, -0.03156, 0.00939, -0.000685], [-0.05304, -0.4502, 1.77, 0.5938, -0.6577], [0.0198, -0.11304, -0.5527, -0.518, 0.2852], [0.02148, -1.741, 1.734, -0.05664, 0.4597], [-0.65, -0.0397, -0.00584, -0.00989, 0.01668], [0.006805, 0.01709, -0.02025, -1.631, -0.9443], [-0.1166, 1.909, -2.428, -0.1074, 0.2484], [-0.002478, -0.000463, 0.00565, -0.0001376, 0.002518], [-0.0357, -0.1669, -0.001793, -0.579, -1.04], [0.3445, -0.9395, 0.001427, -0.1881, 0.1903], [0.0631, 1.175, -1.272, 0.158, -0.00483], [-1.814, -0.006233, -0.01596, 0.007374, -0.005753], [-0.0083, -0.01157, -0.002419, 0.002552, -0.01732], [-0.0776, -0.2842, 0.005962, -0.421, 0.5117], [-0.001623, -0.621, 0.6, -0.1708, 0.683], [-0.1964, -0.9097, 0.1586, -0.606, -0.1997], [0.01633, 0.019, 0.00515, -0.06396, -0.04395], [-0.0514, -1.178, -0.3015, 0.3826, -0.5005], [-0.937, -0.2036, 0.2793, -0.2014, 0.3206]]
[-0.557027, -0.317043, 0.138104, 0.189978, 0.188149, 0.0552362, -0.06767, -0.260805, -0.267183, -0.348518, -0.614078, 0.217921, -0.156318, -0.191004, -0.563467, 0.198722, -0.642526, -0.0575729, 0.0511302, -0.187772, -0.0248378, -0.0206649, 0.259386, -0.183478, -0.149923, 0.0451136, -0.371688, -0.0188617, 0.0366777, -0.552902, 0.189979, -0.358897, 0.256364, 0.138195, -0.478434, 0.0133866, -0.133131, -0.349492, -0.0137361, -0.652247, 0.180223, -0.408499, -0.429542, -0.0201813, 0.0710165, 0.0685803, -0.303011, -0.0773268, 0.0294964, -0.20074, -0.557, -0.3171, 0.1381, 0.19, 0.1881, 0.05524, -0.0677, -0.2607, -0.267, -0.3486, -0.6143, 0.2179, -0.1564, -0.191, -0.5635, 0.1987, -0.6426, -0.0576, 0.05112, -0.1877, -0.02484, -0.02066, 0.2593, -0.1835, -0.1499, 0.0451, -0.3716, -0.01886, 0.03668, -0.5527, 0.19, -0.359, 0.2563, 0.1382, -0.4785, 0.01339, -0.1332, -0.3496, -0.01373, -0.6523, 0.1802, -0.4084, -0.4294, -0.02019, 0.07104, 0.0686, -0.303, -0.07733, 0.0295, -0.2007]
ReLU
[[0.0566575, -0.886943, 0.230586, 1.45451, 0.597745, 0.227455, 0.0324496, 0.0962953, 0.633446, -0.321703, 0.614659, 0.513456, -0.207988, -0.106215, 1.56553, 0.258868, -0.735231, -0.95162, -0.087422, 0.0441749, -0.860643, 0.0136313, -0.0763127, -0.0170699, -0.0763528, 0.13214, 0.523823, -0.112733, 0.335358, 1.00594, 0.568737, -1.39865, 0.204936, -0.148696, 0.173248, -0.0788495, 1.17112, 0.0316677, 0.0556427, -0.633747, 0.0171092, -0.0598277, -2.08626, -0.00464231, -0.140753, 0.127757, -0.200616, 0.0578406, -0.0620558, 0.675837, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0112267, -0.0286864, 0.0311223, 0.0419601, -0.0218081, -0.00774441, 0.0556754, -0.0286823, -0.0128888, 0.0377224, 0.0405741, -0.0155171, -0.0286421, -0.0437483, -0.0424458, -0.0465849, 0.0084671, 0.000231055, -0.0260211, -0.0409128, -0.00940972, -0.00526273, -0.0265895, 0.0142803, -0.0252259, -0.0143645, -0.0142295, 0.00947478, 0.0234896, -0.0573354, -0.0293224, -0.0523232, -0.00181744, -0.0131525, -0.0381496, -0.0503052, -0.0087803, -0.0247571, 0.00776736, 0.00488961, -0.00309288, 0.00886165, 0.0121621, 0.0369953, -0.0365977, -0.0112973, 0.043858, -0.00142178, -0.0204356, 0.0231885, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.00504374, -0.0464097, -0.0498462, -0.0304533, 0.00302208, 0.0322248, 0.0296794, 0.0367799, 0.0227954, -0.0383889, 0.0385381, -0.0367588, -0.00679786, -0.0504207, 0.0316609, -0.0427613, -0.0221433, -0.0257581, -0.0399118, -0.0340255, 0.0231971, 0.0224371, -0.0115063, 0.00552454, -0.0018375, 0.0149984, -0.0267956, -0.0538718, 0.034087, 0.035375, -0.0463983, -0.0160521, 0.0271612, -0.0050365, -0.0470514, 0.0291255, 0.0272604, -0.000217995, 0.0356944, -0.016358, 0.00891133, 0.0376687, -0.0475234, 0.0447155, 0.0285667, -0.0252544, 0.00285394, -0.00270473, -0.0268782, -0.00185817, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.377525, 0.112185, -1.1619, -0.444595, -1.71525, -1.61309, -0.0674231, 1.33914, 0.691602, 0.588673, 0.369135, -0.845436, -2.52656, 0.558474, 1.3696, 0.75013, 0.469634, -0.529725, -0.192167, 0.407051, 0.339037, 0.0397048, -0.0926856, -2.22371, -0.0412369, -1.76197, 0.748254, 0.00263616, -0.102593, 1.51041, 0.162677, -0.680222, 0.351902, 0.671622, 0.996997, 1.53587, -0.259994, -0.442276, 0.0293023, 1.78556, 1.01464, 0.156254, 1.66674, 0.0343558, 0.418668, -0.206413, 0.317031, -0.0610785, 0.501862, 0.445306, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.34908, 0.120792, 1.10705, 0.0681482, -0.261093, 0.123956, 0.0834657, -0.113424, -0.0934773, 1.37932, -2.36824, 0.338262, 0.306419, -0.699439, 0.706828, -0.00103402, -0.111298, -0.686809, 0.0104301, -0.0505599, -0.72632, -0.00985458, 0.334483, 1.1772, 0.206715, -0.0663537, -0.215016, 0.365249, 0.159313, 0.306795, -0.0242865, -0.191828, 0.379393, 0.278277, -0.689262, -0.169154, -0.478237, -0.0325523, -0.00222893, 0.919736, 0.482243, -0.4136, 0.336234, -0.0244682, -0.414824, -0.0276796, 0.141941, 0.0379019, -0.351966, 0.166649, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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ReLU
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ReLU
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ReLU
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ReLU
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-5.62e-05, 0.01374, 0.004696, 0.02034, -0.006363, -0.003147, -0.0004945, -6.86e-05, -0.0289, 0.008766, 0.01913, -0.0006, 0.00976, -0.0003934, 0.01788, 0.02165, -0.001986, -0.01723, -0.005234, 0.01002, -0.002165, -0.00053, 0.00427, -0.002611, 0.02272, 0.0005174, -0.006844, 5.98e-05, -0.0001515, 0.01261, -0.01095, 0.01971, -0.01307, -0.0008116], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.005894, 0.003057, 0.002552, -0.002293, 0.015175, -0.002733, 0.000962, 0.01247, -0.01162, 0.002514, 0.006363, -0.0003343, -0.006622, 0.012276, 0.0003254, -0.000875, 0.00085, -0.002926, -0.007763, -0.00609, 0.004444, 0.0426, 0.0003126, -0.000661, 0.006725, 0.003054, 0.03445, -8.23e-05, 0.002523, 0.001471, 0.0331, 0.02953, -0.0002258, 0.00357, -0.00942, 0.01511, -0.000401, -0.0001413, -0.003082, -0.001906, -0.004852, -0.00449, -0.00714, 0.002354, 0.005333, 0.00985, 0.01659, -0.007935, 0.00954, 0.001264]]
[-0.0206515, -0.0190316, 0.0181379, -0.0184323, 0.018364, -0.02065, -0.01903, 0.01814, -0.01843, 0.01837]
|
from gpiozero import MCP3008
import time
adc = MCP3008(channel=1, device=0)
while True:
print(adc.value)
time.sleep(1)
|
# Try to make process close together and short the method name
#
# I DON'T WANT BELOW THESE TRASH UNLESS NECESSARY
#
# # If you do hope use three double quotes(why?),
# # try replace " by &dq&, for example: """ -> &dq&&dq&&dq&
# myString.replace("&dq&", '"')
#
# # To show symbol directly, break each & with &/,
# # they will be remove to keep symbol together
# # For example: &nl& but don't want a line break -> &/ + &nl + &/ + & -> &/&nl&/&
# myString = myString.replace("&/", "")
def _preprocess_message_string(myString):
# .ini config doesn't support space at end of the line,
# they will cap by three double quotes
# '""" """' -> ' '
# &nl& is next line, refer to \n in python CODE
# '&nl&' -> '\n'
return myString.strip('"""').replace("&nl&", "\n")
# get_preprocessed_message
def get_preprocessed_message(message_config):
for group_name in message_config.sections():
for key_name in message_config[group_name]:
message_config[group_name][key_name] = _preprocess_message_string(
message_config[group_name][key_name])
return message_config
|
from django.conf import settings
from django.conf.urls import include, url
from django.conf.urls.static import static
from django.contrib import admin
admin.autodiscover()
urlpatterns = [
url(r'^', include(('misc.urls', 'misc'), namespace='misc')),
url(r'^', include(('meetups.urls', 'meetups'), namespace='meetups')),
url(r'^admin/', admin.site.urls),
] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
|
from django.shortcuts import render
from django.http import HttpResponse
from django.contrib import auth
import pandas as pd
import re
from .forms import getFile,getMessage
from users.models import UsersDB
from Classifiers.QuotientFilter import QuotientFilter
from Classifiers.LSH import LSH
from Classifiers.Classifier4 import pipeline
def handle_uploaded_file(file):
predictions = []
with file.open() as f:
for line in f:
prediction = pipeline.predict([line.decode('utf-8')])
predictions.append([line.decode('utf-8'),prediction[0]])
return predictions
def predictSH(tweet,qf_urls,lsh,qf_users,qf_words):
# user,url,message = tweet[0],tweet[1],tweet[2]
user = tweet[0]
message = tweet[1]
urls_message = re.findall('https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+',message)
# user = user.split(':')[1]
predictions = []
p1 = "Ham"
for url in urls_message:
if qf_urls.lookup(url) == "Spam":
p1 = "Spam"
p2 = lsh.query(message)
p3 = qf_users.lookup(user)
hamW = 0
spamW = 0
for word in message.split(" "):
if qf_words.lookup(word) == "Ham":
hamW += 1
else:
spamW += 1
if hamW > spamW:
p4 = "Ham"
else:
p4 = "Spam"
p5 = pipeline.predict(message)[0]
predictions.append(p1)
predictions.append(p2)
predictions.append(p3)
predictions.append(p4)
predictions.append(p5)
hp,sp = 0,0
for p in predictions:
if p == 'Ham':
hp += 1
else:
sp += 1
if hp > sp:
prediction = "Ham"
else:
prediction = "Spam"
return prediction
# Create your views here.
def classify_spam_ham(request):
f_form = getFile()
m_form = getMessage()
return render(request,"classify/classify_spam_ham.html",{'f_form':f_form,'m_form':m_form})
def ResultView(request):
spam_urls = pd.read_csv('media/SpamUrls.csv')
hsClusters = pd.read_csv('media/Ham_Spam_Clusters.csv')
spammy_words_users = pd.read_csv('media/SpammyWordsUsers.csv')
qf_urls = QuotientFilter()
lsh = LSH()
qf_words = QuotientFilter()
qf_users = QuotientFilter()
if request.user.is_authenticated:
user_id = auth.models.User.objects.filter(username=request.user)[0]
if UsersDB.objects.filter(user=user_id)[0].spamurl_user:
spamurls_file = UsersDB.objects.filter(user=user_id)[0].spamurl_user
spam_urls = pd.read_csv(spamurls_file)
if UsersDB.objects.filter(user=user_id)[0].hamspamtweets_user:
hstweets_file = UsersDB.objects.filter(user=user_id)[0].hamspamtweets_user
hsClusters = pd.read_csv(hstweets_file)
if UsersDB.objects.filter(user=user_id)[0].spammywordsusers_user:
spammy_wu_file = UsersDB.objects.filter(user=user_id)[0].spammywordsusers_user
spammy_words_users = pd.read_csv(spammy_wu_file)
for u in spam_urls['url'][:1000]:
qf_urls.addKey(u)
hamCl = list(hsClusters['Ham'])
spamCl = list(hsClusters['Spam'])
lsh.update(hamCl,spamCl)
spam_words = list(spammy_words_users['spam_words'])
spam_users = list(spammy_words_users['spam_users'])
for w in spam_words[:1000]:
qf_words.addKey(w)
for u in spam_users[:1000]:
qf_users.addKey(u)
form = getFile()
m_form = getMessage();
if request.method == 'POST':
form = getFile(request.POST,request.FILES)
print(form.is_valid())
if form.is_valid():
if 'file' in request.FILES:
inp_file = request.FILES['file']
predictions = []
tweets_file = pd.read_csv(inp_file)
for i in range(len(tweets_file)):
tweet = [tweets_file.iloc[i]['Username'],tweets_file.iloc[i]['Tweet']]
# print(tweet)
prediction = predictSH(tweet,qf_urls,lsh,qf_users,qf_words)
predictions.append(['\n'.join(tweet),prediction])
return render(request,"classify/result.html",context={'predictions':predictions})
else:
tweet = request.POST['message'].split('\n')
predictions = []
prediction = predictSH(tweet,qf_urls,lsh,qf_users,qf_words)
predictions.append(['\n'.join(tweet),prediction])
print(tweet)
return render(request,"classify/result.html",context={'predictions':predictions})
# else:
# print("Here")
# form = getFile()
# m_form = getMessage()
# print(form.errors)
return render(request,"classify/classify_spam_ham.html",{'f_form':form,'m_form':m_form}) |
"""Test the LADRegressor."""
import numpy as np
import pytest
from sklearn.utils.estimator_checks import check_estimator
from .. import LADRegression, QuantileRegression, ImbalancedLinearRegression, LinearRegression
test_batch = [
(np.array([0, 0, 3, 0, 6]), 3),
(np.array([1, 0, -2, 0, 4, 0, -5, 0, 6]), 2),
(np.array([4, -4]), 0),
(np.array([0.1]), 1000),
]
def _create_dataset(coefs, intercept, noise=0.0):
np.random.seed(0)
X = np.random.randn(1000, coefs.shape[0])
y = X @ coefs + intercept + noise * np.random.randn(1000)
return X, y
@pytest.mark.parametrize("coefs, intercept", test_batch)
@pytest.mark.parametrize(
"model", [LADRegression, QuantileRegression, ImbalancedLinearRegression, LinearRegression]
)
def test_coefs_and_intercept__no_noise(coefs, intercept, model):
"""Regression problems without noise."""
X, y = _create_dataset(coefs, intercept)
regressor = model()
regressor.fit(X, y)
assert regressor.score(X, y) > 0.99
@pytest.mark.parametrize("coefs, intercept", test_batch)
@pytest.mark.parametrize(
"model", [LADRegression, QuantileRegression, ImbalancedLinearRegression, LinearRegression]
)
def test_score(coefs, intercept, model):
"""Tests with noise on an easy problem. Parameter reconstruction should be possible."""
X, y = _create_dataset(coefs, intercept, noise=1.0)
regressor = model()
regressor.fit(X, y)
np.testing.assert_almost_equal(regressor.coef_, coefs, decimal=1)
np.testing.assert_almost_equal(regressor.intercept_, intercept, decimal=1)
@pytest.mark.parametrize("coefs, intercept", test_batch)
@pytest.mark.parametrize(
"model", [LADRegression, QuantileRegression, ImbalancedLinearRegression, LinearRegression]
)
def test_coefs_and_intercept__no_noise_positive(coefs, intercept, model):
"""Test with only positive coefficients."""
X, y = _create_dataset(coefs, intercept, noise=0.0)
regressor = model(positive=True)
regressor.fit(X, y)
assert all(regressor.coef_ >= 0)
assert regressor.score(X, y) > 0.3
@pytest.mark.parametrize("coefs, intercept", test_batch)
@pytest.mark.parametrize(
"model", [LADRegression, QuantileRegression, ImbalancedLinearRegression, LinearRegression]
)
def test_fit_intercept_and_copy(coefs, intercept, model):
"""Test if fit_intercept and copy_X work."""
X, y = _create_dataset(coefs, intercept, noise=2.0)
regressor = model(fit_intercept=False, copy_X=False)
regressor.fit(X, y)
assert regressor.intercept_ == 0.0
@pytest.mark.parametrize(
"model", [LADRegression, QuantileRegression, ImbalancedLinearRegression, LinearRegression]
)
def test_check_estimator(model):
"""Conduct all scikit-learn estimator tests."""
regressor = model()
check_estimator(regressor)
|
# Generated by Django 3.2.7 on 2021-10-13 14:36
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
("okr", "0074_auto_20210915_1024"),
]
operations = [
migrations.AddField(
model_name="podcast",
name="ard_audiothek_id",
field=models.CharField(
help_text="ARD Audiothek ID, falls vorhanden",
max_length=32,
null=True,
verbose_name="ARD Audiothek ID",
),
),
migrations.AddField(
model_name="podcastepisode",
name="ard_audiothek_id",
field=models.CharField(
help_text="ARD Audiothek ID, falls vorhanden",
max_length=32,
null=True,
verbose_name="ARD Audiothek ID",
),
),
migrations.CreateModel(
name="PodcastEpisodeDataArdAudiothekPerformance",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"date",
models.DateField(
help_text="Datum der Performance-Daten", verbose_name="Datum"
),
),
(
"starts",
models.IntegerField(
help_text="Wiedergaben der Episode", verbose_name="Wiedergaben"
),
),
(
"playback_time",
models.DurationField(
help_text="Abspieldauer der Episode (HH:MM:SS)",
verbose_name="Spieldauer",
),
),
(
"last_updated",
models.DateTimeField(
auto_now=True,
help_text="Zeitpunkt der Datenaktualisierung",
verbose_name="Zuletzt upgedated",
),
),
(
"episode",
models.ForeignKey(
help_text="Globale ID der Episode",
on_delete=django.db.models.deletion.CASCADE,
related_name="data_ard_audiothek_performance",
related_query_name="data_ard_audiothek_performance",
to="okr.podcastepisode",
verbose_name="Episode",
),
),
],
options={
"verbose_name": "Podcast-Episoden-Performance (ARD Audiothek)",
"verbose_name_plural": "Podcast-Episoden-Performance (ARD Audiothek)",
"db_table": "podcast_episode_data_ard_audiothek_performance",
"ordering": ["-date", "episode"],
"unique_together": {("date", "episode")},
},
),
]
|
from pywick.optimizers.lookahead import *
from torch.optim import SGD
class LookaheadSGD(Lookahead):
def __init__(self, params, lr, alpha=0.5, k=6, momentum=0.9, dampening=0, weight_decay=0.0001, nesterov=False):
"""
Combination of SGD + LookAhead
:param params:
:param lr:
:param alpha:
:param k:
:param momentum:
:param dampening:
:param weight_decay:
:param nesterov:
"""
sgd = SGD(params, lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov)
super().__init__(sgd, alpha, k)
|
from file_convert import convert_file
convert_file('代码整洁之道.md', '代码整洁之道')
|
from django import forms
from .models import MasterField, Source
class SourceDataImportForm(forms.ModelForm):
# source_name = forms.CharField(max_length=255)
# source_file = forms.FileField(
# label="Upload a CSV file containing source data")
# delimiter = forms.CharField(max_length=255, label='CSV delimiter')
class Meta:
model = Source
fields = ['name', 'description',
'reference', 'source_file', 'delimiter']
labels = {
'name': "Source name",
'description': "Description",
'reference': "Reference",
'source_file': "Upload a CSV file containing source data",
'delimiter': "CSV delimiter",
}
class MasterFieldForm(forms.ModelForm):
class Meta:
model = MasterField
fields = ['name', 'description', 'primary_key']
|
from cw_eval.evalfunctions import calculate_iou, process_iou
from cw_eval import data
from shapely.geometry import Polygon
class TestEvalFuncs(object):
def test_overlap(self):
gt_gdf = data.gt_gdf()
pred_poly = Polygon(((736348.0, 3722762.5),
(736353.0, 3722762.0),
(736354.0, 3722759.0),
(736352.0, 3722755.5),
(736348.5, 3722755.5),
(736346.0, 3722757.5),
(736348.0, 3722762.5)))
overlap_pred_gdf = calculate_iou(pred_poly, gt_gdf)
assert overlap_pred_gdf.index[0] == 27
assert overlap_pred_gdf.iou_score.iloc[0] == 0.073499798744833519
def test_process_iou(self):
gt_gdf = data.gt_gdf()
pred_poly = Polygon(((736414.0, 3722573.0),
(736417.5, 3722572.5),
(736420.0, 3722568.0),
(736421.0, 3722556.0),
(736418.5, 3722538.0),
(736424.0, 3722532.5),
(736424.0, 3722527.0),
(736422.5, 3722525.5),
(736412.0, 3722524.0),
(736410.5, 3722521.5),
(736407.0, 3722520.5),
(736383.5, 3722521.0),
(736376.5, 3722528.5),
(736378.0, 3722532.5),
(736402.0, 3722532.0),
(736410.0, 3722539.0),
(736411.0, 3722544.0),
(736408.5, 3722553.5),
(736409.0, 3722569.0),
(736414.0, 3722573.0)))
assert 21 in gt_gdf.index
process_iou(pred_poly, gt_gdf)
assert 21 not in gt_gdf.index
|
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