Convert Jupyter to Marimo

.gitignore

@@ -119,6 +119,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+*-venv/
 
 # Spyder project settings
 .spyderproject
@@ -150,3 +151,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+.DS_Store
+/Task6/Data

README.md

@@ -1,2 +1,21 @@
-# ascript
- Advanced Smart Charting Infrastructure Planning Tool
+# ASCRIPT - Advanced Smart Charting Infrastructure Planning Tool
+
+To setup, do the following:
+
+~~~
+python3 -m venv .venv
+. .venv/bin/activate
+python3 -m pip install pip --upgrade -r requirements.txt
+mkdir .venv/src
+cd .venv/src
+git clone https://github.com/slacgismo/speech --depth 1
+python3 -m pip install speech
+cd -
+~~~
+
+To run the scenario UI, do the following:
+
+~~~
+cd Task6
+marimo run scenario.py
+~~~

Task2/optimize_placement.py

@@ -0,0 +1,54 @@
+import marimo
+
+__generated_with = "0.6.16"
+app = marimo.App()
+
+
+@app.cell
+def __(mo, np):
+    S = [1.0,0,0,0.1] # node supply capacity (MW)
+    D = [0,0.1,0.2,0.1] # node demand (MW)
+    R = [0.5,0.6,0.7] # line resistances (Ohm)
+    G = [[0,1],[1,2],[1,3]] # graph of network
+
+    M = len(G) # number of lines
+    N = len(S) # number of nodes
+
+    mo.stop(len(R)!=M,"incorrect number of line resistances")
+    mo.stop(len(D)!=N,"incorrect number of demands")
+    mo.stop(min(min(G))!=0 or max(max(G))!=N-1,"graph contains invalid nodes")
+
+    A = np.zeros((N,N)) # admittance matrix
+    for n,tf in enumerate(G):
+        A[tf[0],tf[1]] = A[tf[1],tf[0]] = 1/R[n]
+    return A, D, G, M, N, R, S, n, tf
+
+
+@app.cell
+def __(A):
+    A
+    return
+
+
+@app.cell
+def __(A, M, N, cp):
+    f = cp.Variable(M) # line flows
+    v = cp.Variable(N) # node voltages
+
+    objective = cp.Minimize(v*A*v)
+    constraints = [
+        f == A*v
+    ]
+    return constraints, f, objective, v
+
+
+@app.cell
+def __():
+    import marimo as mo
+    import cvxpy as cp
+    import numpy as np
+    return cp, mo, np
+
+
+if __name__ == "__main__":
+    app.run()