Fix latex (#35)

cosmopower/trained_models/CP_paper/CMB/README.md

@@ -4,12 +4,12 @@
 
 | Parameter  | Range |
 | ---------  | ----- |
-| <img src="https://render.githubusercontent.com/render/math?math=\omega_{\mathrm{b}}"> | <img src="https://render.githubusercontent.com/render/math?math=[0.005, 0.04]"> |
-| <img src="https://render.githubusercontent.com/render/math?math=\omega_{\mathrm{cdm}}"> | <img src="https://render.githubusercontent.com/render/math?math=[0.001, 0.99]"> |
-| <img src="https://render.githubusercontent.com/render/math?math=h"> | <img src="https://render.githubusercontent.com/render/math?math=[0.2, 1.0]"> |
-| <img src="https://render.githubusercontent.com/render/math?math=\tau_{\mathrm{reio}}"> | <img src="https://render.githubusercontent.com/render/math?math=[0.01, 0.8]"> |
-| <img src="https://render.githubusercontent.com/render/math?math=n_s"> | <img src="https://render.githubusercontent.com/render/math?math=[0.7, 1.3]"> |
-| <img src="https://render.githubusercontent.com/render/math?math=\mathrm{ln}10^{10}A_s"> | <img src="https://render.githubusercontent.com/render/math?math=[1.61, 5]"> |
+| <img src="https://latex.codecogs.com/gif.latex?\omega_{\mathrm{b}}"> | [0.005, 0.04] |
+| <img src="https://latex.codecogs.com/gif.latex?\omega_{\mathrm{cdm}}"> | [0.001, 0.99] |
+| <img src="https://latex.codecogs.com/gif.latex?h"> | [0.2, 1.0] |
+| <img src="https://latex.codecogs.com/gif.latex?\tau_{\mathrm{reio}}"> | [0.01, 0.8] |
+| <img src="https://latex.codecogs.com/gif.latex?n_{\mathrm{s}}"> | [0.7, 1.3] |
+| <img src="https://latex.codecogs.com/gif.latex?\mathrm{ln}10^{10}A_{\mathrm{s}}"> | [1.61, 5] |
 
 
 ## Features

notebooks/training_notebooks/README.md

@@ -2,6 +2,6 @@
 
 - [cosmopower_NN_CMB_training.ipynb](https://github.com/alessiospuriomancini/cosmopower/blob/main/notebooks/training_notebooks/cosmopower_NN_CMB_training.ipynb) shows how to train a ``cosmopower_NN`` model. The example in this notebook is an emulation of CMB TT power spectra. However, the procedure is analogous to every other type of spectrum, including the matter power spectrum. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eiDX_P0fxcuxv530xr2iceaPbY4CA5pD?usp=sharing)
 
-- [cosmopower_PCAplusNN_CMB_training.ipynb](https://github.com/alessiospuriomancini/cosmopower/blob/main/notebooks/training_notebooks/cosmopower_PCAplusNN_CMB_training.ipynb) shows how to train a ``cosmopower_PCAplusNN`` model. The example in this notebook is an emulation of CMB <img src="https://render.githubusercontent.com/render/math?math=\phi \phi"> power spectra. However, the procedure is again analogous to every other type of spectrum. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1G8vABcUk9yztXYDx8bDFaNhJrtVIA5ei?usp=sharing)
+- [cosmopower_PCAplusNN_CMB_training.ipynb](https://github.com/alessiospuriomancini/cosmopower/blob/main/notebooks/training_notebooks/cosmopower_PCAplusNN_CMB_training.ipynb) shows how to train a ``cosmopower_PCAplusNN`` model. The example in this notebook is an emulation of CMB <img src="https://latex.codecogs.com/gif.latex?\phi\phi"> power spectra. However, the procedure is again analogous to every other type of spectrum. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1G8vABcUk9yztXYDx8bDFaNhJrtVIA5ei?usp=sharing)
 
 - [cosmopower_NN_3times2pt.ipynb](https://github.com/alessiospuriomancini/cosmopower/blob/main/notebooks/training_notebooks/cosmopower_NN_3times2pt.ipynb) shows how to train a ``cosmopower_NN`` model, with the same preprocessing of the input features and postprocessing of the input, which can significantly improve the emulator accuracy. The example in this notebook shows an emulation of the 3x2pt statistic from DES Y3 models.