Merge pull request #113 from ajati/plot-fix2

Plot fixes and minor readme fixes
ibm-granite · Aug 21, 2024 · 2021c10 · 2021c10
2 parents cb3982e + 224d683
commit 2021c10
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diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@ The core TSFM time series models have been made available on Hugging Face -- det
 [here](wiki.md). Information on the services component can be found [here](services/inference/README.md).
 
 
-# Python Version
+## Python Version
 The current Python versions supported are 3.9, 3.10, 3.11.
 
 ## Initial Setup
@@ -23,13 +23,13 @@ To install use `pip`:
 pip install ".[notebooks]"
 ```
 
-### Notebooks links
+### 🔗 Links to the notebooks
 - Getting started with `PatchTSMixer` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb)
 - Transfer learning with `PatchTSMixer` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tsmixer_transfer.ipynb)
 - Transfer learning with `PatchTST` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tst_transfer.ipynb)
 - Getting started with `TinyTimeMixer (TTM)` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb)
 
-## 📗 Google Colab
+## 📗 Google Colab Tutorials
 Run the TTM tutorial in Google Colab, and quickly build a forecasting application with pre-trained TSFM models.
 - [TTM Colab Tutorial](https://colab.research.google.com/github/IBM/tsfm/blob/main/notebooks/tutorial/ttm_tutorial.ipynb) 
 
@@ -40,10 +40,12 @@ The demo presented at NeurIPS 2023 is available in `tsfmhfdemos`. This demo requ
 pip install ".[demos]"
 ```
 
-## Issues
+## 🪲 Issues
 If you encounter an issue with this project, you are welcome to submit a [bug report](https://github.com/ibm-granite/granite-tsfm/issues).
 Before opening a new issue, please search for similar issues. It's possible that someone has already reported it.
 
+## 🌏 Wiki 
+[Wiki Page](wiki.md)
 
 # Notice
 The intention of this repository is to make it easier to use and demonstrate Granite TimeSeries components that have been made available in the [Hugging Face transformers library](https://huggingface.co/docs/transformers/main/en/index). As we continue to develop these capabilities we will update the code here.

diff --git a/notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb b/notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb
diff --git a/notebooks/hfdemo/ttm_getting_started.ipynb b/notebooks/hfdemo/ttm_getting_started.ipynb
diff --git a/tsfm_public/toolkit/visualization.py b/tsfm_public/toolkit/visualization.py
@@ -232,7 +232,11 @@ def plot_predictions(
     plt.style.use("seaborn-v0_8-whitegrid")
 
     # Adjust figure size and subplot spacing
+    assert num_plots >= 1
     fig, axs = plt.subplots(num_plots, 1, figsize=(10, 2 * num_plots))
+    if num_plots == 1:
+        axs = [axs]
+
     for i, ri in enumerate(random_indices):
         batch = dset[ri]