Source code for tvb.core.adapters.abcuploader

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.. moduleauthor:: Robert Vincze <>
.. moduleauthor:: Mihai Andrei <>

import os
import numpy
from abc import ABCMeta
from scipy import io as scipy_io

from tvb.basic.logger.builder import get_logger
from tvb.basic.profile import TvbProfile
from tvb.core.adapters.abcadapter import AdapterLaunchModeEnum, ABCAdapterForm, ABCAdapter
from tvb.core.adapters.exceptions import LaunchException
from tvb.core.neotraits.forms import StrField, TraitUploadField
from tvb.core.neotraits.uploader_view_model import UploaderViewModel

[docs]class ABCUploaderForm(ABCAdapterForm): def __init__(self): super(ABCUploaderForm, self).__init__() self.subject_field = StrField(UploaderViewModel.data_subject, name='Data_Subject') # Show Encryption field only when the current TVB installation is capable of decryption supports_encrypted_files = (TvbProfile.current.UPLOAD_KEY_PATH is not None and os.path.exists(TvbProfile.current.UPLOAD_KEY_PATH)) if supports_encrypted_files: self.encrypted_aes_key = TraitUploadField(UploaderViewModel.encrypted_aes_key, '.pem', 'encrypted_aes_key')
[docs] @staticmethod def get_required_datatype(): return None
[docs] @staticmethod def get_filters(): return None
[docs] @staticmethod def get_input_name(): return None
[docs] def get_upload_field_names(self): for field in self.trait_fields: if isinstance(field, TraitUploadField): yield field.trait_attribute.field_name
[docs]class ABCUploader(ABCAdapter, metaclass=ABCMeta): """ Base class of the uploading algorithms """ LOGGER = get_logger(__name__) launch_mode = AdapterLaunchModeEnum.SYNC_DIFF_MEM def _prelaunch(self, operation, view_model, available_disk_space=0): """ Before going with the usual prelaunch, get from input parameters the 'subject'. """ self.generic_attributes.subject = view_model.data_subject if view_model.encrypted_aes_key is not None: trait_upload_field_names = list(self.get_form_class().get_upload_information().keys()) if TvbProfile.current.UPLOAD_KEY_PATH is None or not os.path.exists(TvbProfile.current.UPLOAD_KEY_PATH): raise LaunchException("TVB can not process Encrypted files at this moment!" " Please contact the administrator!") for upload_field_name in trait_upload_field_names: upload_path = getattr(view_model, upload_field_name) decrypted_download_path = self.storage_interface.get_import_export_encryption_handler().decrypt_content( view_model.encrypted_aes_key, [upload_path], TvbProfile.current.UPLOAD_KEY_PATH)[0] setattr(view_model, upload_field_name, decrypted_download_path) return ABCAdapter._prelaunch(self, operation, view_model, available_disk_space)
[docs] def get_required_memory_size(self, view_model): """ Return the required memory to run this algorithm. As it is an upload algorithm and we do not have information about data, we can not approximate this. """ return -1
[docs] def get_required_disk_size(self, view_model): """ As it is an upload algorithm and we do not have information about data, we can not approximate this. """ return 0
[docs] @staticmethod def read_list_data(full_path, dimensions=None, dtype=numpy.float64, skiprows=0, usecols=None): """ Read numpy.array from a text file or a npy/npz file. """ try: if full_path.endswith(".npy") or full_path.endswith(".npz"): array_result = numpy.load(full_path) else: array_result = numpy.loadtxt(full_path, dtype=dtype, skiprows=skiprows, usecols=usecols) if dimensions: return array_result.reshape(dimensions) return array_result except ValueError as exc: file_ending = os.path.split(full_path)[1] exc.args = (exc.args[0] + " In file: " + file_ending,) raise
[docs] @staticmethod def read_matlab_data(path, matlab_data_name=None): """ Read array from matlab file. """ try: matlab_data = scipy_io.matlab.loadmat(path) except NotImplementedError: ABCUploader.LOGGER.error("Could not read Matlab content from: " + path) ABCUploader.LOGGER.error("Matlab files must be saved in a format <= -V7...") raise try: return matlab_data[matlab_data_name] except KeyError: def double__(n): n = str(n) return n.startswith('__') and n.endswith('__') available = [s for s in matlab_data if not double__(s)] raise KeyError("Could not find dataset named %s. Available datasets: %s" % (matlab_data_name, available))
[docs] @staticmethod def get_upload_information(): return NotImplementedError