def __init__(self, pid, mem, cpu, length=100): self.__dict__.update(locals()) self.cpus = [] self.mems = [] self.br = False super(StatProcessor, self).__init__() self.logger = logger.setup_custom_logger("stats", logging.DEBUG)
def __init__(self, pid, mem, cpu, length=100): self.__dict__.update(locals()) self.cpus = [] self.mems = [] self.br = False super(StatProcessor, self).__init__() self.logger = logger.setup_custom_logger("stats", logging.DEBUG)
def __init__(self, experiment, out_path, processing_flag, mem, cpu, last_processed, dbdescr=None, job_id=None): self.__dict__.update(locals()) self.on = False self.channels = [] self.collect_channels = False self.channel_groups = {} self.d = { "name": experiment.name, "yaml": None, "results_of_interest": experiment.results_of_interest +\ ["cpu", "mem"], "stats": { "status": "STARTING", "pid": None, "user": None, "host": socket.gethostname(), "create_time": None, "last_heard": None, "port": None }, "processing": False, "last_processed": "Never", "hyperparams": None, "logs": { "cpu": {"cpu": []}, "mem": {"mem": []} }, "log_stream": "" } self.logger = logger.setup_custom_logger("pl2mind", logging.DEBUG) log_file = path.join(out_path, "model.log") formatter = logging.Formatter(fmt="%(asctime)s:%(levelname)s:" "%(module)s:%(message)s") fh = logging.FileHandler(log_file, mode="w") fh.setLevel(logging.DEBUG) fh.setFormatter(formatter) self.logger.addHandler(fh) h = logging.StreamHandler(MetaLogHandler(self.d)) h.setLevel(logging.DEBUG) h.setFormatter(formatter) self.logger.addHandler(h) self.write_json()
def __init__(self, experiment, checkpoint, ep, flag, last_processed): self.__dict__.update(locals()) self.socket = None self.out_path = "/".join(checkpoint.split("/")[:-1]) self.best_checkpoint = path.join( self.out_path, checkpoint.split("/")[-1].split(".")[0] + "_best.pkl") self.persistent = False super(ModelProcessor, self).__init__() self.logger = logger.setup_custom_logger("processor", logging.DEBUG)
def __init__(self, experiment, checkpoint, ep, flag, last_processed): self.__dict__.update(locals()) self.socket = None self.out_path = "/".join(checkpoint.split("/")[:-1]) self.best_checkpoint = path.join(self.out_path, checkpoint.split( "/")[-1].split( ".")[0] + "_best.pkl") self.persistent = False super(ModelProcessor, self).__init__() self.logger = logger.setup_custom_logger("processor", logging.DEBUG)
def __init__(self, experiment, out_path, processing_flag, mem, cpu, last_processed, dbdescr=None, job_id=None): self.__dict__.update(locals()) self.on = False self.channels = [] self.collect_channels = False self.channel_groups = {} self.d = { "name": experiment.name, "yaml": None, "results_of_interest": experiment.results_of_interest +\ ["cpu", "mem"], "stats": { "status": "STARTING", "pid": None, "user": None, "host": socket.gethostname(), "create_time": None, "last_heard": None, "port": None }, "processing": False, "last_processed": "Never", "hyperparams": None, "logs": { "cpu": {"cpu": []}, "mem": {"mem": []} }, "log_stream": "" } self.logger = logger.setup_custom_logger("pl2mind", logging.DEBUG) log_file = path.join(out_path, "model.log") formatter = logging.Formatter(fmt="%(asctime)s:%(levelname)s:" "%(module)s:%(message)s") fh = logging.FileHandler(log_file, mode="w") fh.setLevel(logging.DEBUG) fh.setFormatter(formatter) self.logger.addHandler(fh) h = logging.StreamHandler(MetaLogHandler(self.d)) h.setLevel(logging.DEBUG) h.setFormatter(formatter) self.logger.addHandler(h) self.write_json()
import multiprocessing as mp import networkx as nx import numpy as np import os from os import path import pickle from pl2mind.analysis import feature_extraction as fe from pl2mind.datasets import MRI from pl2mind import logger from pl2mind.tools import simtb_viewer from pylearn2.datasets.transformer_dataset import TransformerDataset from pylearn2.utils import serial logger = logger.setup_custom_logger("pl2mind", logging.ERROR) def save_simtb_montage(dataset, features, out_file, feature_dict, target_stat=None, target_value=None): """ Saves a simtb montage. """ logger.info("Saving simtb montage") weights_view = dataset.get_weights_view(features) simtb_viewer.montage(weights_view, out_file=out_file, feature_dict=feature_dict, target_stat=target_stat, target_value=target_value) def save_helper(args):
__licence__ = "3-clause BSD" __email__ = "*****@*****.**" __maintainer__ = "Alvaro Ulloa" import argparse import logging import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np from pl2mind import logger import scipy.signal as ss logger = logger.setup_custom_logger("pl2mind", logging.WARNING) def qea(im): """ Quasi-eigen approximation function. Parameters ---------- im: array_like 1d vector that contains a time series Returns ------- ia: array_like instantaneous amplitude
__maintainer__ = "Alvaro Ulloa" import argparse import logging import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np from pl2mind import logger import scipy.signal as ss logger = logger.setup_custom_logger("pl2mind", logging.WARNING) def qea(im): """ Quasi-eigen approximation function. Parameters ---------- im: array_like 1d vector that contains a time series Returns ------- ia: array_like instantaneous amplitude ip: array_like
from os import path import pickle from pl2mind import logger from pylearn2.utils import serial from random import shuffle import re from scipy import io from scipy.stats import kurtosis from scipy.stats import skew import sys from sys import stdout logger = logger.setup_custom_logger("pl2mind", logging.ERROR) def natural_sort(l): convert = lambda text: int(text) if text.isdigit() else text.lower() alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] return sorted(l, key = alphanum_key) def save_variance_map(dataset, save_path): logger.info("Saving variance file") variance_map = dataset.X.std(axis=0) np.save(save_path, variance_map) def pull_niftis(source_directory, *args): """ Pull healthy and schizophrenia nitfi files from a source_directory. Uses glob to get multiple files.
import logging import multiprocessing as mp from nipy import load_image from nipy import save_image import numpy as np import pickle from pl2mind import logger import pprint import re from scipy import (reshape, zeros, where, std, argmax, sqrt, ceil, floor, sign, negative, linspace, double, float16) import subprocess from sys import stdout logger = logger.setup_custom_logger("pl2mind", logging.DEBUG) # These are general names of regions for use elsewhere. singles = ["Postcentral Gyrus", "Cingulate Gyrus", "Thalamus", "Superior Frontal Gyrus", "Pyramis", "Caudate", "Declive", "Cuneus", "Ulvula", "Medial Frontal Gyrus", "Precuneus", "Lingual Gyrus", "Paracentral Lobule",
import itertools import logging import multiprocessing as mp from nipy import load_image from nipy import save_image import numpy as np import pickle from pl2mind import logger import pprint import re from scipy import (reshape, zeros, where, std, argmax, sqrt, ceil, floor, sign, negative, linspace, double, float16) import subprocess from sys import stdout logger = logger.setup_custom_logger("pl2mind", logging.DEBUG) # These are general names of regions for use elsewhere. singles = [ "Postcentral Gyrus", "Cingulate Gyrus", "Thalamus", "Superior Frontal Gyrus", "Pyramis", "Caudate", "Declive", "Cuneus", "Ulvula", "Medial Frontal Gyrus", "Precuneus", "Lingual Gyrus", "Paracentral Lobule", "Semi-Lunar Lobule", "Posterior Cingulate", "Culmen", "Cerebellar Tonsil", "Cingulate Gyrus", "Middle Frontal Gyrus", "Anterior Cingulate" ] # Larger functional regions. Not used here, but can be referenced. SC = [ "Caudate", "Putamen", "Thalamus", "Caudate Tail", "Caudate Body", "Caudate Head"