Merge pull request #41 from neurolib-dev/fix/python3.6_imports

Fix/python3.6 imports

.travis.yml

@@ -1,11 +1,16 @@
 language: python
 
+os:
+  - linux
+
 python:
+  - "3.6"
   - "3.7"
+  - "3.8"
 
 install:
   - pip install -r requirements.txt
-  - pip install pytest flake8 codecov pytest-cov
+  - pip install pytest flake8 codecov pytest-cov wheel setuptools
   - pip install jupyterlab nbconvert nbformat matplotlib seaborn mne
   - python setup.py sdist bdist_wheel
   - pip install --upgrade dist/*
@@ -16,3 +21,30 @@ script:
 
 after_success:
   - codecov
+
+jobs:
+  allow_failures:
+    - os: osx
+    - os: windows
+
+  include:
+    - stage: test
+      os: osx
+      language: sh
+      addons:
+        homebrew:
+          update: false
+          packages: python3
+      before_install:
+        - pip install virtualenv
+        - virtualenv -p python3 ~/venv
+        - source ~/venv/bin/activate
+
+    # - stage: test
+    #   os: windows
+    #   language: sh
+    #   env:
+    #     - PATH="/c/Python37:/c/Python37/Scripts:$PATH"
+    #   before_install: 
+    #     - choco install python --version 3.7.3
+    #     - python -m pip install --upgrade pip

examples/example-1.1-custom-parameter-exploration.ipynb

@@ -88,7 +88,7 @@
    "source": [
     "parameters = ParameterSpace({\"x\": np.linspace(-2, 2, 2), \"y\": np.linspace(-2, 2, 2)})\n",
     "# info: chose np.linspace(-2, 2, 40) or more, values here are low for testing\n",
-    "search = BoxSearch(evalFunction = explore_me, exploreParameters = parameters)"
+    "search = BoxSearch(evalFunction = explore_me, parameterSpace = parameters)"
    ]
   },
   {

examples/example-1.2-brain-network-exploration.ipynb

@@ -147,7 +147,7 @@
    "source": [
     "parameters = ParameterSpace({\"mue_ext_mean\": np.linspace(0, 3.0, 2), \"mui_ext_mean\": np.linspace(0, 3.0, 2)})\n",
     "# info: chose np.linspace(0, 3, 21) or more, values here are low for testing\n",
-    "search = BoxSearch(evalFunction = evaluateSimulation, model=aln, exploreParameters=parameters)"
+    "search = BoxSearch(evalFunction = evaluateSimulation, model=aln, parameterSpace=parameters)"
    ]
   },
   {

neurolib/models/aln/__init__.py

@@ -1 +1 @@
-from neurolib.models.aln.model import ALNModel
+from .model import ALNModel

neurolib/models/aln/loadDefaultParams.py

@@ -2,7 +2,7 @@
 import numpy as np
 import h5py
 
-from neurolib.utils.collections import dotdict
+from ...utils.collections import dotdict
 
 
 def loadDefaultParams(Cmat=None, Dmat=None, lookupTableFileName=None, seed=None):

neurolib/models/aln/model.py

@@ -1,8 +1,8 @@
 import numpy as np
 
-import neurolib.models.aln.loadDefaultParams as dp
-import neurolib.models.aln.timeIntegration as ti
-from neurolib.models.model import Model
+from . import loadDefaultParams as dp
+from . import timeIntegration as ti
+from ..model import Model
 
 
 class ALNModel(Model):

neurolib/models/aln/timeIntegration.py

@@ -1,7 +1,7 @@
 import numpy as np
 import numba
 
-import neurolib.models.aln.loadDefaultParams as dp
+from . import loadDefaultParams as dp
 
 
 def timeIntegration(params):

neurolib/models/bold/__init__.py

@@ -1,2 +1,2 @@
-from neurolib.models.bold.model import BOLDModel
-from neurolib.models.bold.timeIntegration import simulateBOLD
+from .model import BOLDModel
+from .timeIntegration import simulateBOLD

neurolib/models/bold/model.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from neurolib.models.bold.timeIntegration import simulateBOLD
+from .timeIntegration import simulateBOLD
 
 
 class BOLDModel:

neurolib/models/bold/timeIntegration.py

@@ -28,15 +28,14 @@ def simulateBOLD(Z, dt, voxelCounts, X=None, F=None, Q=None, V=None):
 
     N = np.shape(Z)[0]
 
-    if not "voxelCounts" in globals():
+    if "voxelCounts" not in globals():
         voxelCounts = np.ones((N,))
 
-    voxelCountsSqrtInv = 1 / np.sqrt(voxelCounts)
-
     # Balloon-Windkessel model parameters (Deco 2013, Friston 2003):
     # Note: the distribution of each Balloon-Windkessel models parameters are given per voxel
     # Since we usually average the empirical fMRI of each voxel for a given area, the standard
     # deviation of the gaussian distribution should be divided by the number of voxels in each area
+    # voxelCountsSqrtInv = 1 / np.sqrt(voxelCounts)
     #
     # rho     = np.random.normal(0.34, np.sqrt(0.0024) / np.sqrt( sum(voxelCounts) ) )    # Capillary resting net oxygen extraction
     # alpha   = np.random.normal(0.32, np.sqrt(0.0015) / np.sqrt( sum(voxelCounts) ) )    # Grubb's vessel stiffness exponent

neurolib/models/fhn/__init__.py

@@ -1 +1 @@
-from neurolib.models.fhn.model import FHNModel
+from .model import FHNModel

neurolib/models/fhn/loadDefaultParams.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from neurolib.utils.collections import dotdict
+from ...utils.collections import dotdict
 
 
 def loadDefaultParams(Cmat=None, Dmat=None, seed=None):

neurolib/models/fhn/model.py

@@ -1,6 +1,6 @@
-import neurolib.models.fhn.loadDefaultParams as dp
-import neurolib.models.fhn.timeIntegration as ti
-from neurolib.models.model import Model
+from . import loadDefaultParams as dp
+from . import timeIntegration as ti
+from ..model import Model
 
 
 class FHNModel(Model):

neurolib/models/fhn/timeIntegration.py

@@ -1,7 +1,7 @@
 import numpy as np
 import numba
 
-import neurolib.models.hopf.loadDefaultParams as dp
+from . import loadDefaultParams as dp
 
 
 def timeIntegration(params):

neurolib/models/hopf/__init__.py

@@ -1 +1 @@
-from neurolib.models.hopf.model import HopfModel
+from .model import HopfModel

neurolib/models/hopf/loadDefaultParams.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from neurolib.utils.collections import dotdict
+from ...utils.collections import dotdict
 
 
 def loadDefaultParams(Cmat=None, Dmat=None, seed=None):

neurolib/models/hopf/model.py

@@ -1,6 +1,6 @@
-import neurolib.models.hopf.loadDefaultParams as dp
-import neurolib.models.hopf.timeIntegration as ti
-from neurolib.models.model import Model
+from . import loadDefaultParams as dp
+from . import timeIntegration as ti
+from ..model import Model
 
 
 class HopfModel(Model):

neurolib/models/hopf/timeIntegration.py

@@ -1,7 +1,7 @@
 import numpy as np
 import numba
 
-import neurolib.models.hopf.loadDefaultParams as dp
+from . import loadDefaultParams as dp
 
 
 def timeIntegration(params):

neurolib/models/model.py

@@ -2,9 +2,9 @@
 import xarray as xr
 import numpy as np
 
-from neurolib.models import bold
+from ..models import bold
 
-from neurolib.utils.collections import dotdict
+from ..utils.collections import dotdict
 
 
 class Model:

neurolib/optimize/evolution/__init__.py

@@ -1 +1 @@
-from neurolib.optimize.evolution.evolution import Evolution
+from .evolution import Evolution

neurolib/optimize/evolution/evolution.py

@@ -13,11 +13,11 @@
 import pypet as pp
 import pandas as pd
 
-import neurolib.utils.paths as paths
+from ...utils import paths as paths
+from ...utils import pypetUtils as pu
 
-import neurolib.optimize.evolution.evolutionaryUtils as eu
-import neurolib.optimize.evolution.deapUtils as du
-import neurolib.utils.pypetUtils as pu
+from . import evolutionaryUtils as eu
+from . import deapUtils as du
 
 
 class Evolution:

neurolib/optimize/evolution/evolutionaryUtils.py

@@ -1,16 +1,11 @@
 import logging
-import datetime
 import os
-import random
-import copy
 
-import deap
 import numpy as np
-import pypet as pp
 import pandas as pd
 
-import neurolib.utils.paths as paths
-import neurolib.optimize.evolution.deapUtils as du
+from ...utils import paths as paths
+from . import deapUtils as du
 
 
 def saveToPypet(traj, pop, gIdx):

neurolib/optimize/exploration/__init__.py

@@ -1 +1 @@
-from neurolib.optimize.exploration.exploration import BoxSearch
+from .exploration import BoxSearch

neurolib/optimize/exploration/exploration.py

@@ -9,18 +9,18 @@
 import pandas as pd
 import tqdm
 
-import neurolib.utils.paths as paths
-import neurolib.utils.pypetUtils as pu
+from ...utils import paths
+from ...utils import pypetUtils as pu
 
-from neurolib.utils.collections import dotdict
+from ...utils.collections import dotdict
 
 
 class BoxSearch:
     """
     Paremter box search for a given model and a range of parameters.
     """
 
-    def __init__(self, model=None, exploreParameters=None, evalFunction=None):
+    def __init__(self, model=None, parameterSpace=None, evalFunction=None):
         """Either a model has to be passed, or an evalFunction. If an evalFunction
         is passed, then the evalFunction will be called and the model is accessible to the 
         evalFunction via `self.getModelFromTraj(traj)`. The parameters of the current 
@@ -31,8 +31,8 @@ def __init__(self, model=None, exploreParameters=None, evalFunction=None):
 
         :param model: Model to run for each parameter (or model to pass to the evaluation funciton if an evaluation function is used), defaults to None
         :type model: `neurolib.models.model.Model`, optional
-        :param exploreParameters: Parameter space to explore, defaults to None
-        :type exploreParameters: `neurolib.utils.parameterSpace.ParameterSpace`, optional
+        :param parameterSpace: Parameter space to explore, defaults to None
+        :type parameterSpace: `neurolib.utils.parameterSpace.ParameterSpace`, optional
         :param evalFunction: Evaluation function to call for each run., defaults to None
         :type evalFunction: function, optional
         """
@@ -46,10 +46,10 @@ def __init__(self, model=None, exploreParameters=None, evalFunction=None):
             model is not None
         ), "Either a model has to be specified or an evalFunction."
 
-        assert exploreParameters is not None, "No parameters to explore."
+        assert parameterSpace is not None, "No parameters to explore."
 
-        self.parameterSpace = exploreParameters
-        self.exploreParameters = exploreParameters.dict()
+        self.parameterSpace = parameterSpace
+        self.exploreParameters = parameterSpace.dict()
 
         # todo: use random ICs for every explored point or rather reuse the ones that are generated at model initialization
         self.useRandomICs = False

neurolib/utils/loadData.py

@@ -7,8 +7,8 @@
 import h5py
 import scipy.io
 
-import neurolib.utils.functions as func
-from neurolib.utils.collections import dotdict
+from . import functions as func
+from .collections import dotdict
 
 
 class Dataset: