update for winter term 2024/2025

fneum · Oct 15, 2024 · 37add99 · 37add99
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diff --git a/data-science-for-esm/01-workshop-python.ipynb b/data-science-for-esm/01-workshop-python.ipynb
@@ -1608,12 +1608,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "69786ae8c799a179d39b18fbc76c1e4e4ca1662535939a7096f8f45ee1440c35"
-   }
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

diff --git a/data-science-for-esm/02-workshop-numpy.ipynb b/data-science-for-esm/02-workshop-numpy.ipynb
@@ -264,7 +264,8 @@
    "metadata": {},
    "source": [
     "Numpy also has some utilities for helping us generate multi-dimensional arrays.\n",
-    "For instance, `meshgrid` creates 2D arrays out of a combination of 1D arrays."
+    "For instance, `meshgrid` creates 2D arrays out of a combination of 1D arrays.\n",
+    "It takes two arrays representing x and y values and creates two 2D grids: one for all the x coordinates and another for all the y coordinates, which can be used to evaluate functions on a complete grid covering the x-y plane."
    ]
   },
   {
@@ -1338,7 +1339,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

diff --git a/data-science-for-esm/03-workshop-pandas.ipynb b/data-science-for-esm/03-workshop-pandas.ipynb
@@ -940,7 +940,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ts.resample(\"M\").mean().head()"
+    "ts.resample(\"ME\").mean().head()"
    ]
   },
   {
@@ -949,7 +949,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ts.resample(\"M\").mean().plot()"
+    "ts.resample(\"ME\").mean().plot()"
    ]
   },
   {
@@ -1559,12 +1559,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "69786ae8c799a179d39b18fbc76c1e4e4ca1662535939a7096f8f45ee1440c35"
-   }
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

diff --git a/data-science-for-esm/04-workshop-geopandas.ipynb b/data-science-for-esm/04-workshop-geopandas.ipynb
@@ -1406,7 +1406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

diff --git a/data-science-for-esm/05-workshop-pysheds.ipynb b/data-science-for-esm/05-workshop-pysheds.ipynb
@@ -128,8 +128,8 @@
    "outputs": [],
    "source": [
     "fn = \"output_COP30.tif\"\n",
-    "grid = Grid.from_raster(fn)\n",
-    "dem = grid.read_raster(fn)"
+    "grid = Grid.from_raster(fn, nodata=0)\n",
+    "dem = grid.read_raster(fn, nodata=0)"
    ]
   },
   {
@@ -173,8 +173,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pit_filled_dem = grid.fill_pits(dem)\n",
-    "flooded_dem = grid.fill_depressions(pit_filled_dem)\n",
+    "pit_filled_dem = grid.fill_pits(dem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flooded_dem = grid.fill_depressions(pit_filled_dem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "conditioned_dem = grid.resolve_flats(flooded_dem)"
    ]
   },
@@ -431,7 +447,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

diff --git a/data-science-for-esm/06-workshop-atlite.ipynb b/data-science-for-esm/06-workshop-atlite.ipynb
@@ -425,7 +425,7 @@
    "outputs": [],
    "source": [
     "url = \"https://tubcloud.tu-berlin.de/s/XoDpBcweJHmYKgF/download/GEBCO_2014_2D-PT.nc\"\n",
-    "gebco = rasterio.open(url)"
+    "gebco = rasterio.open(url, driver=\"netcdf\")"
    ]
   },
   {
@@ -1324,7 +1324,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

diff --git a/data-science-for-esm/07-workshop-networkx.ipynb b/data-science-for-esm/07-workshop-networkx.ipynb
@@ -92,7 +92,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Say we want to calculate the Laplacian $L$ of this graph based on its incidence\n",
+    "Say we want to calculate the so-called *Laplacian* $L$ of this graph based on its incidence\n",
     "matrix $K$, which is an $N\\times N$ matrix defined as $L=KK^\\top$ for an\n",
     "undirected graph. The Laplacian matrix of a graph is a representation that\n",
     "captures the connectivity and structure of the graph by quantifying the\n",
@@ -120,13 +120,6 @@
     "K"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To calculate the Laplacian matrix, we form a dot product of the incidence matrix with its transpose:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -700,92 +693,6 @@
     "L"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What is the *degree matrix*?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G.degree()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we need to do some work to produce a diagonal matrix from the output of `G.degree()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "degrees = [val for node, val in G.degree()]\n",
-    "D = np.diag(degrees)\n",
-    "D"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's do a few checks using different formulas for the *Laplacian matrix*:\n",
-    "\n",
-    "$$L = KK^\\top$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "K.dot(K.T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert (K.dot(K.T) == L).all()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$L = D-A$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "D - A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert (D - A == L).all()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1011,7 +918,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's checkout the synchronous zone of Ireland. We want to compute the **Power Transfer Distribution Factor (PTDF)** matrix for this area.\n",
+    "Let's checkout the synchronous zone of Ireland. We want to compute the so-called **Power Transfer Distribution Factor (PTDF)** matrix for this area.\n",
     "\n",
     "The **PTDF** matrix measures the sensitivity of power flows in each transmission line relative to incremental changes in nodal power injections or withdrawals throughout the electricity network. It is a matrix representation showing how changes in power injections or withdrawals at various nodes in a power grid affect the flow in each of its transmission lines. It is an alternative formulation for linearised power flow to the cycle-based approache from the lecture.\n",
     "\n",
@@ -1653,6 +1560,13 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1677,7 +1591,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

diff --git a/data-science-for-esm/08-workshop-pyomo.ipynb b/data-science-for-esm/08-workshop-pyomo.ipynb
@@ -1856,6 +1856,20 @@
     "- What parameters of the storage unit would have to be changed to reduce the objective? What's the sensitivity?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1880,7 +1894,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,