Merge pull request #121 from IamShubhamGupto/add/gp

GP notebook import

Author: dhruvbalwada

.github/workflows/deploy.yml

@@ -42,6 +42,10 @@ jobs:
           conda list
       - name: 📦 Installing graphviz
         uses: ts-graphviz/setup-graphviz@v1
+      - name: 👀 Check Tensorflow installation
+        shell: bash -l {0}
+        run: |
+          python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
       # - name: 📦 Setting up PySR
       #   shell: bash -l {0}
       #   run: |

_config.yml

@@ -6,6 +6,7 @@ title: Learning Machine Learning with Lorenz-96
 author: The M<sup>2</sup>LInES Community
 logo: newlogo.png
 
+exclude_patterns: ['env/*', '_build/*']
 # Force re-execution of notebooks on each build.
 # See https://jupyterbook.org/content/execute.html
 execute:

_toc.yml

@@ -21,6 +21,7 @@ parts:
       - file: notebooks/Neural-Network-Saliency-Maps.ipynb
       - file: notebooks/Neural-Network-Advection.ipynb
       - file: notebooks/random_forest_parameterization.ipynb
+      - file: notebooks/GP_lorenz96.ipynb
   - caption: Data Assimilation with Lorenz-96
     chapters:
       - file: notebooks/DA_demo_L96.ipynb

conda-linux-64.lock

@@ -23,3 +23,4 @@ dependencies:
   - pip
   - xarray
   - pysr
+  - nomkl

environment.yaml

@@ -6,7 +6,7 @@
 from numba import jit
 
 
-@jit
+@jit(nopython=False)
 def Lin3dvar(ub, w, H, R, B, opt):
     # The solution of the 3DVAR problem in the linear case requires
     # the solution of a linear system of equations.
@@ -35,7 +35,7 @@ def Lin3dvar(ub, w, H, R, B, opt):
     return ua
 
 
-@jit
+@jit(nopython=False)
 def ens_inflate(posterior, prior, opt, factor):
     inflated = np.zeros(posterior.shape)
     n, N = prior.shape
@@ -55,7 +55,7 @@ def ens_inflate(posterior, prior, opt, factor):
     return inflated
 
 
-@jit
+@jit(nopython=False)
 def EnKF(prior, obs, H, R, B):
     # The analysis step for the (stochastic) ensemble Kalman filter
     # with virtual observations

notebooks/DA_methods.py

@@ -340,7 +340,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

notebooks/GP_lorenz96.ipynb

@@ -8,7 +8,7 @@
 from numba import jit
 
 
-@jit
+@jit(nopython=False)
 def L96_eq1_xdot(X, F, advect=True):
     """
     Calculate the time rate of change for the X variables for the Lorenz '96, equation 1:
@@ -33,7 +33,7 @@ def L96_eq1_xdot(X, F, advect=True):
     return Xdot
 
 
-@jit
+@jit(nopython=False)
 def L96_2t_xdot_ydot(X, Y, F, h, b, c):
     """
     Calculate the time rate of change for the X and Y variables for the Lorenz '96, two time-scale

notebooks/L96-two-scale-description.ipynb

@@ -244,3 +244,20 @@ @article{Bach2015
   pages = {1-46},
   number = {7},
 }
+
+@book{Rasmussen_Williams_2005,
+  title={Gaussian Processes for Machine Learning},
+  url={http://dx.doi.org/10.7551/mitpress/3206.001.0001},
+  doi={10.7551/mitpress/3206.001.0001},
+  publisher={The MIT Press},
+  author={Rasmussen, Carl Edward and Williams, Christopher K. I.},
+  year={2005}
+}
+@book{Bishop2006,
+  author = {Bishop, Christopher M.},
+  title = {Pattern Recognition and Machine Learning (Information Science and Statistics)},
+  year = {2006},
+  isbn = {0387310738},
+  publisher = {Springer-Verlag},
+  address = {Berlin, Heidelberg}
+}

notebooks/L96_model.py

@@ -3,3 +3,4 @@ pydot
 pysindy
 torch-lr-finder
 gplearn
+gpflow