Python IQData示例

示例#1

文件： networks.py 项目： NilsWinter/Predicting-Intelligence-From-Brain-Gray-Matter-Volume

def run_network(network_index):
    network_index = int(network_index)
    network_names = {
        0: "visual",
        1: "somatomotor",
        2: "dorsal_attention",
        3: "ventral_attention",
        4: "limbic",
        5: "fronto_parietal",
        6: "default_mode",
        7: "subcortical",
        8: "cerebellum"
    }

    analysis_name = network_names[network_index]
    project_folder = '.'

    # get data
    data = IQData()
    covariates = np.asarray([data.age, data.gender, data.handedness]).T
    data.load_single_networks(use_cached=False)
    X = data.networks[network_index]
    y = data.fsiq
    del data

    # run analysis
    pipe = construct_hyperpipe(analysis_name, project_folder)
    pipe.fit(X, y, **{'covariates': covariates})
    os.remove(pipe.output_settings.pretrained_model_filename)

示例#2

文件： perm_schaefer_networks.py 项目： NilsWinter/Predicting-Intelligence-From-Brain-Gray-Matter-Volume

def run_perm_test(row):
    row_array = row.split(';')
    perm_ind = row_array[0]
    perm_y = row_array[1]
    perm_y = np.asarray(perm_y.split(',')).astype(int)

    networks = [
        'visual', 'somatomotor', 'dorsal_attention', 'ventral_attention',
        'limbic', 'default_mode', 'fronto_parietal'
    ]

    for network in networks:
        analysis_name = 'schaefer_{}_noTiv_perm_'.format(network) + str(
            perm_ind)
        data_folder = '/scratch/tmp/wintern/iq_frankfurt/'
        project_folder = '/scratch/tmp/wintern/iq_frankfurt/results/noTivRescaling/perm/schaefer_{}'.format(
            network)
        os.makedirs(project_folder, exist_ok=True)
        cache_dir = '/scratch/tmp/wintern/cache'

        # get data
        data = IQData(data_folder=data_folder, tiv_rescaled=False)
        covariates = np.asarray([data.age, data.gender, data.handedness]).T
        data.load_schaefer_network(network, use_cached=True)

        # run analysis
        pipe = construct_hyperpipe_schaefer(analysis_name, project_folder,
                                            cache_dir)
        pipe.groups = data.fsiq
        pipe.fit(data.schaefer_network, perm_y, **{'covariates': covariates})
        os.remove(pipe.output_settings.pretrained_model_filename)

示例#3

文件： perm_networks.py 项目： NilsWinter/Predicting-Intelligence-From-Brain-Gray-Matter-Volume

def run_perm_test(row, network_index):
    network_index = int(network_index)
    network_names = {
        0: "visual",
        1: "somatomotor",
        2: "dorsal_attention",
        3: "ventral_attention",
        4: "limbic",
        5: "fronto_parietal",
        6: "default_mode",
        7: "subcortical",
        8: "cerebellum"
    }

    row_array = row.split(';')
    perm_ind = row_array[0]
    perm_y = row_array[1]
    perm_y = np.asarray(perm_y.split(',')).astype(int)

    analysis_name = network_names[network_index] + '_perm_' + str(perm_ind)
    project_folder = './perms/'

    # get data
    data = IQData()
    covariates = np.asarray([data.age, data.gender, data.handedness]).T
    data.load_single_networks(use_cached=True)
    X = data.networks[network_index]
    y = data.fsiq
    del data

    # run analysis
    pipe = construct_hyperpipe(analysis_name, project_folder)
    pipe.groups = y
    pipe.fit(X, perm_y, **{'covariates': covariates})
    os.remove(pipe.output_settings.pretrained_model_filename)

示例#4

def run_perm_test(row):
    row_array = row.split(';')
    perm_ind = row_array[0]
    perm_y = row_array[1]
    perm_y = np.asarray(perm_y.split(',')).astype(int)

    analysis_name = 'whole_brain_noTiv_perm_' + str(perm_ind)
    project_folder = './perm/'

    # get data
    data = IQData(tiv_rescaled=False)
    covariates = np.asarray([data.age, data.gender, data.handedness]).T
    data.load_whole_brain(use_cached=True)

    # run analysis
    pipe = construct_hyperpipe(analysis_name, project_folder)
    pipe.groups = data.fsiq
    pipe.fit(data.whole_brain, perm_y, **{'covariates': covariates})
    os.remove(pipe.output_settings.pretrained_model_filename)

示例#5

def run_perm_test(row):
    row_array = row.split(';')
    perm_ind = row_array[0]
    perm_y = row_array[1]
    perm_y = np.asarray(perm_y.split(',')).astype(int)

    analysis_name = 'whole_brain_perm_' + str(perm_ind)
    data_folder = '/scratch/tmp/wintern/iq_frankfurt/'
    project_folder = '/scratch/tmp/wintern/iq_frankfurt/results/perm/whole_brain/'
    cache_dir = '/scratch/tmp/wintern/cache'

    # get data
    data = IQData(data_folder=data_folder)
    covariates = np.asarray([data.age, data.gender, data.handedness]).T
    data.load_whole_brain(use_cached=True)

    # run analysis
    pipe = construct_hyperpipe(analysis_name, project_folder, cache_dir)
    pipe.groups = data.fsiq
    pipe.fit(data.whole_brain, perm_y, **{'covariates': covariates})
    os.remove(pipe.output_settings.pretrained_model_filename)

示例#6

文件： shen.py 项目： NilsWinter/Predicting-Intelligence-From-Brain-Gray-Matter-Volume

Last updated:   18-02-2019

Author
------
Nils R. Winter
[email protected]
Translationale Psychiatrie
Universitaetsklinikum Muenster
"""
import sys
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/photonai')
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/')
from analyses.analysis_base import construct_hyperpipe_schaefer
from data.data import IQData
import numpy as np
import os

analysis_name = 'shen_no_tiv'
data_folder = '/scratch/tmp/wintern/iq_frankfurt/'
project_folder = '/scratch/tmp/wintern/iq_frankfurt/results/noTivRescaling/shen'
cache_dir = '/scratch/tmp/wintern/cache'

# get data
data = IQData(data_folder=data_folder, tiv_rescaled=False)
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_shen(use_cached=False)

# run analysis
pipe = construct_hyperpipe_schaefer(analysis_name, project_folder, cache_dir)
pipe.fit(data.shen, data.fsiq, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)

示例#7

Author
------
Nils R. Winter
[email protected]
Translationale Psychiatrie
Universitaetsklinikum Muenster
"""
import sys
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/photonai')
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/')
from analyses.analysis_base import construct_hyperpipe_schaefer
from data.data import IQData
import numpy as np
import os


analysis_name = 'schaefer'
data_folder = '/scratch/tmp/wintern/iq_frankfurt/'
project_folder = '/scratch/tmp/wintern/iq_frankfurt/results'
cache_dir = '/scratch/tmp/wintern/cache'

# get data
data = IQData(data_folder=data_folder)
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_schaefer(use_cached=False)

# run analysis
pipe = construct_hyperpipe_schaefer(analysis_name, project_folder, cache_dir)
pipe.fit(data.schaefer, data.fsiq, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)

示例#8

Last updated:   28-01-2020

Author
------
Nils R. Winter
[email protected]
Translationale Psychiatrie
Universitaetsklinikum Muenster
"""
import sys
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/photonai')
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/')
from analyses.analysis_base import construct_hyperpipe_schaefer
from data.data import IQData
import numpy as np
import os

analysis_name = 'shen'
data_folder = '/scratch/tmp/wintern/iq_frankfurt/'
project_folder = '/scratch/tmp/wintern/iq_frankfurt/results/TivRescaling/shen/'
cache_dir = '/scratch/tmp/wintern/cache'

# get data
data = IQData(data_folder=data_folder)
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_shen(use_cached=False)

# run analysis
pipe = construct_hyperpipe_schaefer(analysis_name, project_folder, cache_dir)
pipe.fit(data.shen, data.fsiq, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)

示例#9

文件： whole_brain.py 项目： NilsWinter/Predicting-Intelligence-From-Brain-Gray-Matter-Volume

This scripts implements the global (whole brain) analysis without TiV rescaling

Version
-------
Created:        28-01-2019
Last updated:   29-09-2019

Author
------
Nils R. Winter
[email protected]
Translationale Psychiatrie
Universitaetsklinikum Muenster
"""
from analyses.analysis_base import construct_hyperpipe
from data.data import IQData
import numpy as np
import os

analysis_name = 'whole_brain_no_tiv_rescaling'
project_folder = '.'

# get data
data = IQData()
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_whole_brain(use_cached=True)

# run analysis
pipe = construct_hyperpipe(analysis_name, project_folder)
pipe.fit(data.whole_brain, data.fsiq, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)

示例#10

文件： schaefer100.py 项目： NilsWinter/Predicting-Intelligence-From-Brain-Gray-Matter-Volume

Last updated:   18-02-2019

Author
------
Nils R. Winter
[email protected]
Translationale Psychiatrie
Universitaetsklinikum Muenster
"""
import sys
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/photonai')
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/')
from analyses.analysis_base import construct_hyperpipe_schaefer
from data.data import IQData
import numpy as np
import os

analysis_name = 'schaefer100_no_tiv'
data_folder = '/scratch/tmp/wintern/iq_frankfurt/'
project_folder = '/scratch/tmp/wintern/iq_frankfurt/results/noTivRescaling/schaefer100'
cache_dir = '/scratch/tmp/wintern/cache'

# get data
data = IQData(data_folder=data_folder, tiv_rescaled=False)
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_schaefer100(use_cached=False)

# run analysis
pipe = construct_hyperpipe_schaefer(analysis_name, project_folder, cache_dir)
pipe.fit(data.schaefer100, data.fsiq, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)

示例#11

Translationale Psychiatrie
Universitaetsklinikum Muenster
"""
import sys
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/photonai')
sys.path.append('/scratch/tmp/wintern/iq_frankfurt/')
from analyses.analysis_base import construct_hyperpipe_schaefer
from data.data import IQData
import numpy as np
import os


networks = ['visual', 'somatomotor', 'dorsal_attention', 'ventral_attention', 'limbic',
            'default_mode', 'fronto_parietal']

for network in networks:
    analysis_name = 'schaefer_{}'.format(network)
    data_folder = '/scratch/tmp/wintern/iq_frankfurt/'
    project_folder = '/scratch/tmp/wintern/iq_frankfurt/results/TivRescaling/schaefer_{}'.format(network)
    os.makedirs(project_folder, exist_ok=True)
    cache_dir = '/scratch/tmp/wintern/cache'

    # get data
    data = IQData(data_folder=data_folder, tiv_rescaled=True)
    covariates = np.asarray([data.age, data.gender, data.handedness]).T
    data.load_schaefer_network(network, use_cached=False)

    # run analysis
    pipe = construct_hyperpipe_schaefer(analysis_name, project_folder, cache_dir)
    pipe.fit(data.schaefer_network, data.fsiq, **{'covariates': covariates})
    os.remove(pipe.output_settings.pretrained_model_filename)