Restore multimodal example

.gitignore

@@ -26,6 +26,7 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
+*.DS_STORE
 
 # PyInstaller
 #  Usually these files are written by a python script from a template
@@ -69,7 +70,9 @@ instance/
 .scrapy
 
 # Sphinx documentation
-docs/_build/
+doc/_build/
+doc/auto_examples/
+doc/generated/
 
 # PyBuilder
 target/
@@ -117,13 +120,13 @@ venv.bak/
 # Rope project settings
 .ropeproject
 
-# mkdocs documentation
-/site
-
 # mypy
 .mypy_cache/
 .dmypy.json
 dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# examples
+examples/*_data/*

doc/API/datatypes.rst

@@ -1,12 +1,12 @@
 Data Types
 ===================================
 
-.. autoclass:: whobpyt.datatypes.Recording
+.. autoclass:: whobpyt.datatypes.Timeseries
    :members:
    :undoc-members:
    :special-members: __init__
 
-.. autoclass:: whobpyt.datatypes.par
+.. autoclass:: whobpyt.datatypes.Parameter
    :members:
    :undoc-members:
    :special-members: __init__

doc/API/models.rst

@@ -1,13 +1,13 @@
 Models
 ===================================
 
-.. autoclass:: whobpyt.models.JansenRit.RNNJANSEN
-   :members: info, createIC, createDelayIC, setModelParameters, forward
+.. autoclass:: whobpyt.models.jansen_rit.JansenRitModel
+   :members: info, createIC, createDelayIC, setModelSCParameters, forward
    :undoc-members:
    :special-members: __init__
 
-.. autoclass:: whobpyt.models.RWW.RNNRWW
-   :members: info, createIC, setModelParameters, forward
+.. autoclass:: whobpyt.models.wong_wang.ReducedWongWangModel
+   :members: info, createIC, createDelayIC, setModelSCParameters, forward
    :undoc-members:
    :special-members: __init__
 

doc/API/run.rst

@@ -1,17 +1,17 @@
 Run
 ===================================
 
-.. autoclass:: whobpyt.run.Model_fitting
+.. autoclass:: whobpyt.run.ModelFitting
    :members: train, evaluate, save
    :undoc-members:
    :special-members: __init__
 
-.. autoclass:: whobpyt.run.Fitting_Batch
+.. autoclass:: whobpyt.run.FittingBatch
    :members: train
    :undoc-members:
    :special-members: __init__
 
-.. autoclass:: whobpyt.run.Fitting_FNGFPG
+.. autoclass:: whobpyt.run.FittingFNGFPG
    :members: train
    :undoc-members:
    :special-members: __init__

doc/Makefile

@@ -9,7 +9,6 @@
 # You can set these variables from the command line.
 SPHINXOPTS    =
 SPHINXBUILD   = sphinx-build
-#SPHINXPROJ    = fooof
 SPHINXPROJ    = whobpyt
 SOURCEDIR     = .
 BUILDDIR      = _build
@@ -50,7 +49,6 @@ install:
 	#   --no-checkout just fetches the root folder without content
 	#   --depth 1 is a speed optimization since we don't need the history prior to the last commit
 	#   -b gh-pages fetches only the branch for the gh-pages
-	#git clone -b gh-pages --single-branch --no-checkout --depth 1 https://github.com/fooof-tools/fooof _build/gh_pages
 	git clone -b gh-pages --single-branch --no-checkout --depth 1 git@github.com:griffithslab/whobpyt _build/gh_pages
 	# A .nojekyll file tells Github pages to bypass Jekyll processing
 	touch _build/gh_pages/.nojekyll

doc/conf.py

@@ -59,24 +59,18 @@ def is_development_build():
 }
 
 html_theme = 'sphinx_rtd_theme'
-#html_theme_options = {
-#    'logo_only': False,#True,
-#    'navigation_depth': 5,
-#}
 html_context = {}
 
-if not 'READTHEDOCS' in os.environ:
-    html_static_path = ['_static/']
-    html_js_files = ['debug.js']
+# if not 'READTHEDOCS' in os.environ:
+#     html_static_path = ['_static/']
+#     html_js_files = ['debug.js']
 
-    # Add fake versions for local QA of the menu
-    html_context['test_versions'] = list(map(
-        lambda x: str(x / 10),
-        range(1, 100)
-    ))
+#     # Add fake versions for local QA of the menu
+#     html_context['test_versions'] = list(map(
+#         lambda x: str(x / 10),
+#         range(1, 100)
+#     ))
 
-#html_logo = "demo/static/logo-wordmark-light.svg"
-#html_logo = "img/eeg-notebooks_logo.png"
 html_show_sourcelink = True
 
 htmlhelp_basename = slug

doc/getting_started/running_in_codespaces.rst

@@ -4,23 +4,24 @@ Running whobpyt in GitHub Codespaces
 
 GitHub Codespaces is a new feature that allows you to run a full VS Code environment in the cloud and access it from your browser.
 
-We have added pre-made configurations for you to use `whobpyt` in GitHub Codespaces, which takes care of cloning, installing python, setting up the environment, and installing the pertinent packages for you.
+We have added pre-made configurations for you to use ``whobpyt`` in GitHub Codespaces, which takes care of cloning, installing python, setting up the environment, and installing the pertinent packages for you.
+
+Steps to use ``whobpyt`` in GitHub Codespaces:
 
-Steps to use `whobpyt` in GitHub Codespaces:
 1. Go to [the GitHub page for the repo](https://github.com/griffithslab/whobpyt) (or any specific branch you want to explore).
 2. Click on the green "Code" button on the top right and select the second tab "Codespaces".
 3. In front of the first option "Codespaces", click on the three-dot button.
 4. Select "New with options".
 5. In the new page, select your desired branch. From the second field, choose either of the three configurations we have provided:
-    a. `WhoBPyT-CPU` for a CPU-only environment.
-    b. `WhoBPyT-GPU` for a GPU-enabled environment.
-    c. `WhoBPyT-docs` for a CPU-only environment that also builds the `html` documentation (useful for exploring and debugging the documentation)
+    a. ``WhoBPyT-CPU`` for a CPU-only environment.
+    b. ``WhoBPyT-GPU`` for a GPU-enabled environment.
+    c. ``WhoBPyT-docs`` for a CPU-only environment that also builds the ``html`` documentation (useful for exploring and debugging the documentation)
 6. Choose your desired region and machine type.
 7. Click on "Create codespace" and wait for the environment to be created. This should take 1-2 minutes.
 8. Once the environment is created, the python environment will install the necessary packages. This should take 4-5 minutes.
 9. Your configured codespace should be ready. Now you get a full VS Code environment in your browser. You can now explore the code, run the examples, and even edit the code and commit your changes back to the repo.
 
-Now you should be good to continue with the rest of the example code in `.py` files, `.ipynb` notebooks, and experiment with new ideas. 
+Now you should be good to continue with the rest of the example code in ``.py`` files, ``.ipynb`` notebooks, and experiment with new ideas. 
 
 
 **Notes**:

doc/index.rst

@@ -38,4 +38,6 @@ Welcome to WhoBPyT documentation!
    :glob:
 
    auto_examples/eg__replicate_Momi2023
+   auto_examples/eg__RWW_example
+   auto_examples/eg__multimodal_simulation
 

examples/eg__RWW_example.py

@@ -0,0 +1,177 @@
+"""
+
+==================================
+Fitting Wong_Wang model (HCP data)
+==================================
+
+"""
+
+# sphinx_gallery_thumbnail_number = 1
+
+# %%
+# Importage
+# ---------
+#
+
+# os stuff
+import os
+import sys
+sys.path.append('..')
+
+# whobpyt stuff
+import whobpyt
+from whobpyt.datatypes import Parameter, Timeseries
+from whobpyt.models.wong_wang import ReducedWongWangModel, ReducedWongWangParams
+from whobpyt.optimization.custom_cost_RWW import CostsRWW
+from whobpyt.run import ModelFitting
+from whobpyt.datasets import fetch_hcpl2k8
+
+# array and pd stuff
+import numpy as np
+import pandas as pd
+
+# viz stuff
+import matplotlib.pyplot as plt
+
+# gdown
+import gdown
+
+# %%
+# define destination path and download the data
+
+
+"""
+des_dir = '../'
+if not os.path.exists(des_dir):
+    os.makedirs(des_dir)  # create folder if it does not exist
+url = "https://drive.google.com/drive/folders/18smy3ElTd4VksoL4Z15dhwT5l3yjk6xS"
+os.chdir(des_dir)
+gdown.download_folder(url, remaining_ok = True)
+"""
+
+# Go to examples folder
+#os.chdir('examples')
+
+
+# Download the data for this example to default location ~/.whobpyt/data
+data_dir = fetch_hcpl2k8()
+
+base_dir = os.path.join(data_dir, 'HCP')
+
+# %%
+#get subject list
+#base_dir = '../HCP/'
+
+#subs =sorted([sc_file[-10:-4] for sc_file in os.listdir(base_dir) if sc_file[:8] == 'weights_'])
+
+sub = '100307'
+
+# %%
+# define options for wong-wang model
+node_size = 83
+mask = np.tril_indices(node_size, -1)
+num_epochs = 5
+TPperWindow = 20
+step_size = 0.05
+input_size = 2
+tr = 0.75
+repeat_size = 5
+
+# %%
+# load raw data and get SC empirical BOLD and FC
+sc_file = os.path.join(base_dir, 'weights_' + sub + '.txt')
+ts_file = os.path.join(base_dir, sub + '_rfMRI_REST1_LR_hpc200_clean__l2k8_sc33_ts.pkl')  # out_dir+'sub_'+sub+'simBOLD_idt.txt'
+
+sc = np.loadtxt(sc_file)
+SC = (sc + sc.T) * 0.5
+sc = np.log1p(SC) / np.linalg.norm(np.log1p(SC))
+
+ts_pd = pd.read_pickle(ts_file)
+ts = ts_pd.values
+ts = ts / np.max(ts)
+fc_emp = np.corrcoef(ts.T)
+
+# %%
+# prepare data structure for model
+print(ts.T.shape)
+fMRIstep = tr
+data_mean = Timeseries(ts.T, fMRIstep)
+
+# %%
+# get model parameters structure and define the fitted parameters by setting non-zero variance for the model
+params = ReducedWongWangParams(g=Parameter(400, 400, 1/np.sqrt(10), True), g_EE=Parameter(1.5, 1.5, 1/np.sqrt(50), True), \
+g_EI =Parameter(0.8, 0.8, 1/np.sqrt(50), True), \
+                   g_IE=Parameter(0.6, 0.6, 1/np.sqrt(50), True), I_0 =Parameter(0.2), \
+                   std_in=Parameter(np.log(0.1), np.log(0.1), 0.1, True, True, lb =0.02))
+
+# %%
+# call model want to fit
+model = ReducedWongWangModel(params, node_size =node_size, TRs_per_window =TPperWindow, step_size=step_size, tr=tr, sc=sc, use_fit_gains=True)
+
+# %%
+# create objective function
+ObjFun = CostsRWW(model)
+
+# %%
+# call model fit
+F = ModelFitting(model, ObjFun)
+
+
+# %%
+# Model Training
+# ---------------------------------------------------
+#
+F.train(u = 0, empRecs = [data_mean], num_epochs = num_epochs, TPperWindow = TPperWindow, learningrate = 0.05)
+
+# %%
+# Plots of loss over Training
+plt.plot(np.arange(1,len(F.trainingStats.loss)+1), F.trainingStats.loss)
+plt.title("Total Loss over Training Epochs")
+
+# %%
+# Plots of parameters values over Training
+plt.plot(F.trainingStats.fit_params['g_EE'], label = "g_EE")
+plt.plot(F.trainingStats.fit_params['g_EI'], label = "g_EI")
+plt.plot(F.trainingStats.fit_params['g_IE'], label = "g_IE")
+plt.legend()
+plt.title("Local Coupling Variables Changing Over Training Epochs")
+
+# %%
+# Model Evaluation (with 20 window for warmup)
+# ---------------------------------------------------
+#
+F.evaluate(u = 0, empRec = data_mean, TPperWindow = TPperWindow, base_window_num = 20)
+
+# %%
+# Plot SC and fitted SC
+
+fig, ax = plt.subplots(1, 2, figsize=(5, 4))
+im0 = ax[0].imshow(sc, cmap='bwr', vmin = 0.0, vmax = 0.05)
+ax[0].set_title('The empirical SC')
+fig.colorbar(im0, ax=ax[0], fraction=0.046, pad=0.04)
+im1 = ax[1].imshow(F.model.sc_fitted.detach().numpy(), cmap='bwr', vmin = 0.0, vmax = 0.05)
+ax[1].set_title('The fitted SC')
+fig.colorbar(im1, ax=ax[1], fraction=0.046, pad=0.04)
+plt.show()
+
+# %%
+# Plot E I and simulated BOLD
+fig, ax = plt.subplots(1, 3, figsize=(12, 8))
+ax[0].plot(F.lastRec['E'].npTS().T)
+ax[0].set_title('Test: E')
+ax[1].plot(F.lastRec['I'].npTS().T)
+ax[1].set_title('Test: I')
+ax[2].plot(F.lastRec['bold'].npTS().T)
+ax[2].set_title('Test: BOLD')
+plt.show()
+
+# %%
+# Plot the FC and the test FC
+fig, ax = plt.subplots(1, 2, figsize=(5, 4))
+im0 = ax[0].imshow(fc_emp, cmap='bwr')
+ax[0].set_title('The empirical FC')
+fig.colorbar(im0, ax=ax[0], fraction=0.046, pad=0.04)
+im1 = ax[1].imshow(np.corrcoef(F.lastRec['bold'].npTS()), cmap='bwr')
+ax[1].set_title('The simulated FC')
+fig.colorbar(im1, ax=ax[1], fraction=0.046, pad=0.04)
+plt.show()