Add Read the Docs configuration and update documentation setup

- Added .readthedocs.yaml configuration file for RTD builds
- Updated docs/conf.py version to 0.2.0
- Updated docs/doc_requirements.txt with compatible versions
- Added docs extras_require in setup.py for documentation dependencies
- Verified local documentation builds successfully
- Cleaned up generated documentation artifacts

This should fix the RTD build failure due to missing configuration file.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

Author: jeremymanning

.readthedocs.yaml

@@ -0,0 +1,30 @@
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+  jobs:
+    post_create_environment:
+      # Install the package in editable mode
+      - pip install -e .
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+  fail_on_warning: false
+
+# Optionally declare the Python requirements required to build your docs
+python:
+  install:
+    - requirements: docs/doc_requirements.txt
+    - requirements: requirements.txt
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs

docs/auto_examples/calculate_dynamic_correlations.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Code source: Lucy Owen\n# License: MIT\n\n# load timecorr and other packages\nimport timecorr as tc\nimport numpy as np\n\n\nS = 1\nT = 1000\nK = 10\nB = 5\n\n# define your weights parameters\nwidth = 100\nlaplace = {'name': 'Laplace', 'weights': tc.laplace_weights, 'params': {'scale': width}}\n\n# calculate the dynamic correlation of the two datasets\n\nsubs_data_2 = tc.simulate_data(datagen='ramping', return_corrs=False, set_random_seed=1, S=S, T=T, K=K, B=B)\n\nsubs_data_1 = tc.simulate_data(datagen='ramping', return_corrs=False, set_random_seed=2, S=S, T=T, K=K, B=B)\n\n\n\nwcorred_data = tc.wcorr(np.array(subs_data_1),  np.array(subs_data_2), weights=laplace['weights'](T))"
+        "# Code source: Lucy Owen\n# License: MIT\n\nimport numpy as np\n\n# load timecorr and other packages\nimport timecorr as tc\n\nS = 1\nT = 1000\nK = 10\nB = 5\n\n# define your weights parameters\nwidth = 100\nlaplace = {\"name\": \"Laplace\", \"weights\": tc.laplace_weights, \"params\": {\"scale\": width}}\n\n# calculate the dynamic correlation of the two datasets\n\nsubs_data_2 = tc.simulate_data(\n    datagen=\"ramping\", return_corrs=False, set_random_seed=1, S=S, T=T, K=K, B=B\n)\n\nsubs_data_1 = tc.simulate_data(\n    datagen=\"ramping\", return_corrs=False, set_random_seed=2, S=S, T=T, K=K, B=B\n)\n\n\nwcorred_data = tc.wcorr(\n    np.array(subs_data_1), np.array(subs_data_2), weights=laplace[\"weights\"](T)\n)"
       ]
     }
   ],

docs/auto_examples/calculate_dynamic_correlations.rst

@@ -24,17 +24,17 @@ Calculate dynamic correlations
 
 In this example, we calculate dynamic correlations
 
-.. GENERATED FROM PYTHON SOURCE LINES 10-35
+.. GENERATED FROM PYTHON SOURCE LINES 10-41
 
 .. code-block:: Python
 
     # Code source: Lucy Owen
     # License: MIT
 
-    # load timecorr and other packages
-    import timecorr as tc
     import numpy as np
 
+    # load timecorr and other packages
+    import timecorr as tc
 
     S = 1
     T = 1000
@@ -43,17 +43,23 @@ In this example, we calculate dynamic correlations
 
     # define your weights parameters
     width = 100
-    laplace = {'name': 'Laplace', 'weights': tc.laplace_weights, 'params': {'scale': width}}
+    laplace = {"name": "Laplace", "weights": tc.laplace_weights, "params": {"scale": width}}
 
     # calculate the dynamic correlation of the two datasets
 
-    subs_data_2 = tc.simulate_data(datagen='ramping', return_corrs=False, set_random_seed=1, S=S, T=T, K=K, B=B)
+    subs_data_2 = tc.simulate_data(
+        datagen="ramping", return_corrs=False, set_random_seed=1, S=S, T=T, K=K, B=B
+    )
 
-    subs_data_1 = tc.simulate_data(datagen='ramping', return_corrs=False, set_random_seed=2, S=S, T=T, K=K, B=B)
+    subs_data_1 = tc.simulate_data(
+        datagen="ramping", return_corrs=False, set_random_seed=2, S=S, T=T, K=K, B=B
+    )
 
 
+    wcorred_data = tc.wcorr(
+        np.array(subs_data_1), np.array(subs_data_2), weights=laplace["weights"](T)
+    )
 
-    wcorred_data = tc.wcorr(np.array(subs_data_1),  np.array(subs_data_2), weights=laplace['weights'](T))
 
 .. _sphx_glr_download_auto_examples_calculate_dynamic_correlations.py:
 

docs/auto_examples/decode_by_level.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Code source: Lucy Owen\n# License: MIT\n\n# load timecorr and other packages\nimport timecorr as tc\nimport hypertools as hyp\nimport numpy as np\n\nprint(\"Timepoint Decoding Example\")\nprint(\"=\"*30)\nprint(\"NOTE: This example currently has compatibility issues.\")\nprint(\"Please see docs/auto_examples/decode_by_level.py for a working version.\")\nprint(\"=\"*30)\n\n# load example data\ndata = hyp.load('weights').get_data()\n\n# Convert to numpy array format required by timepoint_decoder\n# timepoint_decoder expects a numpy array with shape (n_subjects, T, K)\ndata_array = np.array(data)\nprint(f\"Data shape: {data_array.shape} (subjects, timepoints, features)\")\n\n# define your weights parameters\nwidth = 10\nlaplace = {'name': 'Laplace', 'weights': tc.laplace_weights, 'params': {'scale': width}}\n\n# set your number of levels\n# if integer, returns decoding accuracy, error, and rank for specified level\nlevel = 2\n\nprint(f\"\\nAttempting timepoint decoding at level {level}...\")\n\ntry:\n    # run timecorr with specified functions for calculating correlations, as well as combining and reducing\n    results = tc.timepoint_decoder(data_array, level=level, combine=tc.corrmean_combine,\n                                   cfun=tc.isfc, rfun='eigenvector_centrality', weights_fun=laplace['weights'],\n                                   weights_params=laplace['params'])\n    \n    # returns only decoding results for level 2\n    print(\"\u2713 SUCCESS: Level 2 decoding results:\")\n    print(results)\n    \nexcept Exception as e:\n    print(f\"\u2717 ERROR: {e}\")\n    print(\"This function has compatibility issues with the current version.\")\n\n# set your number of levels\n# if list or array of integers, returns decoding accuracy, error, and rank for all levels\nlevels = np.arange(int(level) + 1)\n\nprint(f\"\\nAttempting multi-level decoding for levels {levels}...\")\n\ntry:\n    # run timecorr with specified functions for calculating correlations, as well as combining and reducing\n    results = tc.timepoint_decoder(data_array, level=levels, combine=tc.corrmean_combine,\n                                   cfun=tc.isfc, rfun='eigenvector_centrality', weights_fun=laplace['weights'],\n                                   weights_params=laplace['params'])\n    \n    # returns decoding results for all levels up to level 2\n    print(\"\u2713 SUCCESS: Multi-level decoding results:\")\n    print(results)\n    \nexcept Exception as e:\n    print(f\"\u2717 ERROR: {e}\")\n    print(\"This function has compatibility issues with the current version.\")\n\nprint(\"\\n\" + \"=\"*60)\nprint(\"RECOMMENDATION: Use the enhanced version in docs/auto_examples/decode_by_level.py\")\nprint(\"which uses synthetic data and includes comprehensive error handling.\")"
+        "# Code source: Lucy Owen\n# License: MIT\n\nimport hypertools as hyp\nimport numpy as np\n\n# load timecorr and other packages\nimport timecorr as tc\n\nprint(\"Timepoint Decoding Example\")\nprint(\"=\" * 30)\nprint(\"NOTE: This example currently has compatibility issues.\")\nprint(\"Please see docs/auto_examples/decode_by_level.py for a working version.\")\nprint(\"=\" * 30)\n\n# load example data\ndata = hyp.load(\"weights\").get_data()\n\n# Convert to numpy array format required by timepoint_decoder\n# timepoint_decoder expects a numpy array with shape (n_subjects, T, K)\ndata_array = np.array(data)\nprint(f\"Data shape: {data_array.shape} (subjects, timepoints, features)\")\n\n# define your weights parameters\nwidth = 10\nlaplace = {\"name\": \"Laplace\", \"weights\": tc.laplace_weights, \"params\": {\"scale\": width}}\n\n# set your number of levels\n# if integer, returns decoding accuracy, error, and rank for specified level\nlevel = 2\n\nprint(f\"\\nAttempting timepoint decoding at level {level}...\")\n\ntry:\n    # run timecorr with specified functions for calculating correlations, as well as combining and reducing\n    results = tc.timepoint_decoder(\n        data_array,\n        level=level,\n        combine=tc.corrmean_combine,\n        cfun=tc.isfc,\n        rfun=\"eigenvector_centrality\",\n        weights_fun=laplace[\"weights\"],\n        weights_params=laplace[\"params\"],\n    )\n\n    # returns only decoding results for level 2\n    print(\"\u2713 SUCCESS: Level 2 decoding results:\")\n    print(results)\n\nexcept Exception as e:\n    print(f\"\u2717 ERROR: {e}\")\n    print(\"This function has compatibility issues with the current version.\")\n\n# set your number of levels\n# if list or array of integers, returns decoding accuracy, error, and rank for all levels\nlevels = np.arange(int(level) + 1)\n\nprint(f\"\\nAttempting multi-level decoding for levels {levels}...\")\n\ntry:\n    # run timecorr with specified functions for calculating correlations, as well as combining and reducing\n    results = tc.timepoint_decoder(\n        data_array,\n        level=levels,\n        combine=tc.corrmean_combine,\n        cfun=tc.isfc,\n        rfun=\"eigenvector_centrality\",\n        weights_fun=laplace[\"weights\"],\n        weights_params=laplace[\"params\"],\n    )\n\n    # returns decoding results for all levels up to level 2\n    print(\"\u2713 SUCCESS: Multi-level decoding results:\")\n    print(results)\n\nexcept Exception as e:\n    print(f\"\u2717 ERROR: {e}\")\n    print(\"This function has compatibility issues with the current version.\")\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\n    \"RECOMMENDATION: Use the enhanced version in docs/auto_examples/decode_by_level.py\"\n)\nprint(\"which uses synthetic data and includes comprehensive error handling.\")"
       ]
     }
   ],

docs/auto_examples/decode_by_level.rst

@@ -27,26 +27,27 @@ In this example, we load in some example data, and decode by level of higher ord
 NOTE: This example currently has compatibility issues with the timepoint_decoder function.
 For a working example, please see the enhanced version in docs/auto_examples/decode_by_level.py
 
-.. GENERATED FROM PYTHON SOURCE LINES 13-81
+.. GENERATED FROM PYTHON SOURCE LINES 13-97
 
 .. code-block:: Python
 
     # Code source: Lucy Owen
     # License: MIT
 
-    # load timecorr and other packages
-    import timecorr as tc
     import hypertools as hyp
     import numpy as np
 
+    # load timecorr and other packages
+    import timecorr as tc
+
     print("Timepoint Decoding Example")
-    print("="*30)
+    print("=" * 30)
     print("NOTE: This example currently has compatibility issues.")
     print("Please see docs/auto_examples/decode_by_level.py for a working version.")
-    print("="*30)
+    print("=" * 30)
 
     # load example data
-    data = hyp.load('weights').get_data()
+    data = hyp.load("weights").get_data()
 
     # Convert to numpy array format required by timepoint_decoder
     # timepoint_decoder expects a numpy array with shape (n_subjects, T, K)
@@ -55,7 +56,7 @@ For a working example, please see the enhanced version in docs/auto_examples/dec
 
     # define your weights parameters
     width = 10
-    laplace = {'name': 'Laplace', 'weights': tc.laplace_weights, 'params': {'scale': width}}
+    laplace = {"name": "Laplace", "weights": tc.laplace_weights, "params": {"scale": width}}
 
     # set your number of levels
     # if integer, returns decoding accuracy, error, and rank for specified level
@@ -65,14 +66,20 @@ For a working example, please see the enhanced version in docs/auto_examples/dec
 
     try:
         # run timecorr with specified functions for calculating correlations, as well as combining and reducing
-        results = tc.timepoint_decoder(data_array, level=level, combine=tc.corrmean_combine,
-                                       cfun=tc.isfc, rfun='eigenvector_centrality', weights_fun=laplace['weights'],
-                                       weights_params=laplace['params'])
-    
+        results = tc.timepoint_decoder(
+            data_array,
+            level=level,
+            combine=tc.corrmean_combine,
+            cfun=tc.isfc,
+            rfun="eigenvector_centrality",
+            weights_fun=laplace["weights"],
+            weights_params=laplace["params"],
+        )
+
         # returns only decoding results for level 2
         print("✓ SUCCESS: Level 2 decoding results:")
         print(results)
-    
+
     except Exception as e:
         print(f"✗ ERROR: {e}")
         print("This function has compatibility issues with the current version.")
@@ -85,22 +92,31 @@ For a working example, please see the enhanced version in docs/auto_examples/dec
 
     try:
         # run timecorr with specified functions for calculating correlations, as well as combining and reducing
-        results = tc.timepoint_decoder(data_array, level=levels, combine=tc.corrmean_combine,
-                                       cfun=tc.isfc, rfun='eigenvector_centrality', weights_fun=laplace['weights'],
-                                       weights_params=laplace['params'])
-    
+        results = tc.timepoint_decoder(
+            data_array,
+            level=levels,
+            combine=tc.corrmean_combine,
+            cfun=tc.isfc,
+            rfun="eigenvector_centrality",
+            weights_fun=laplace["weights"],
+            weights_params=laplace["params"],
+        )
+
         # returns decoding results for all levels up to level 2
         print("✓ SUCCESS: Multi-level decoding results:")
         print(results)
-    
+
     except Exception as e:
         print(f"✗ ERROR: {e}")
         print("This function has compatibility issues with the current version.")
 
-    print("\n" + "="*60)
-    print("RECOMMENDATION: Use the enhanced version in docs/auto_examples/decode_by_level.py")
+    print("\n" + "=" * 60)
+    print(
+        "RECOMMENDATION: Use the enhanced version in docs/auto_examples/decode_by_level.py"
+    )
     print("which uses synthetic data and includes comprehensive error handling.")
 
+
 .. _sphx_glr_download_auto_examples_decode_by_level.py:
 
 .. only:: html