Make pretty

Author: mdpiper

lessons/python/yet_another_oop.ipynb

@@ -540,17 +540,17 @@
     "        # Note: I didn't realize this when I originally wrote the class, but we won't actually need dz\n",
     "        # Want to know why? Check out the documentation for np.gradient -> https://numpy.org/doc/stable/reference/generated/numpy.gradient.html\n",
     "        # (What does the second argument to np.gradient do?)\n",
-    "        \n",
+    "\n",
     "        # We'll need attributes for rho, c, and k\n",
     "        # You could also store info about boundary conditions at this point, if you want\n",
     "        self.rho = 917\n",
     "        self.c = 2093\n",
     "        self.k = 2.1\n",
     "\n",
     "        # Let's store boundary conditions too\n",
-    "    \n",
+    "\n",
     "        # Maybe we should go ahead and calculate diffusivity right away?\n",
-    "    \n",
+    "\n",
     "        # Let's keep track of the elapsed time\n",
     "\n",
     "    def calc_diffusivity(self) -> float:\n",
@@ -559,7 +559,7 @@
     "\n",
     "    def calc_heat_flux(self) -> np.ndarray:\n",
     "        \"\"\"The heat flux is -kappa * dT / dz.\"\"\"\n",
-    "        \n",
+    "\n",
     "        # How should we calculate the difference in temperature with depth? (hint: see dz, above)\n",
     "\n",
     "        # Are dT and dz the same size? Are they the same size as z?\n",
@@ -634,16 +634,16 @@
     "\n",
     "dt = 60 * 60 * 24 * 20\n",
     "\n",
-    "for i in range(30000): \n",
+    "for i in range(30000):\n",
     "    model.run_one_step(dt)\n",
     "\n",
     "    if i % 1000 == 0:\n",
-    "        print(f'{model.time_elapsed / 31556926} years.')\n",
+    "        print(f\"{model.time_elapsed / 31556926} years.\")\n",
     "\n",
     "plt.plot(model.temperature, model.z)\n",
-    "plt.xlabel('Temperature ($^\\circ$ C)')\n",
-    "plt.ylabel('Depth (m)')\n",
-    "plt.gca().invert_yaxis() # This forces matplotlib to invert the y-axis\n",
+    "plt.xlabel(r\"Temperature ($^\\circ$ C)\")\n",
+    "plt.ylabel(\"Depth (m)\")\n",
+    "plt.gca().invert_yaxis()  # This forces matplotlib to invert the y-axis\n",
     "plt.show()"
    ]
   },
@@ -662,8 +662,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Initialize your model with a new bottom boundary condition\n",
-    "\n"
+    "# Initialize your model with a new bottom boundary condition"
    ]
   },
   {
@@ -675,16 +674,16 @@
    "source": [
     "dt = 60 * 60 * 24 * 20\n",
     "\n",
-    "for i in range(30000): \n",
+    "for i in range(30000):\n",
     "    model.run_one_step(dt)\n",
     "\n",
     "    if i % 1000 == 0:\n",
-    "        print(f'{model.time_elapsed / 31556926} years.')\n",
+    "        print(f\"{model.time_elapsed / 31556926} years.\")\n",
     "\n",
     "plt.plot(model.temperature, model.z)\n",
-    "plt.xlabel('Temperature ($^\\circ$ C)')\n",
-    "plt.ylabel('Depth (m)')\n",
-    "plt.gca().invert_yaxis() # This forces matplotlib to invert the y-axis\n",
+    "plt.xlabel(r\"Temperature ($^\\circ$ C)\")\n",
+    "plt.ylabel(\"Depth (m)\")\n",
+    "plt.gca().invert_yaxis()  # This forces matplotlib to invert the y-axis\n",
     "plt.show()"
    ]
   },
@@ -717,8 +716,8 @@
    "source": [
     "class SimpleGlacier:\n",
     "    \"\"\"Models the temperature profile with a glacier.\n",
-    "    \n",
-    "    This model is based off of: \n",
+    "\n",
+    "    This model is based off of:\n",
     "    The Physics of Glaciers (Cuffey and Paterson, 2010).\n",
     "    Lecture notes from Andy Aschwanden (McCarthy school, summer 2012).\n",
     "\n",
@@ -736,7 +735,7 @@
     "        self.dz = np.gradient(self.z)\n",
     "        # Note: I didn't realize this when I originally wrote the class, but we won't need dz\n",
     "        # Want to know why? Check out the documentation for np.gradient -> https://numpy.org/doc/stable/reference/generated/numpy.gradient.html\n",
-    "        \n",
+    "\n",
     "        # We'll need attributes for rho, c, and k\n",
     "        # You could also store info about boundary conditions at this point, if you want\n",
     "        self.rho = 917\n",
@@ -746,10 +745,10 @@
     "        # Let's store boundary conditions too\n",
     "        self.surface_temperature = -10.0\n",
     "        self.basal_heat_flux = 0.0\n",
-    "    \n",
+    "\n",
     "        # Maybe we should go ahead and calculate diffusivity right away?\n",
     "        self.kappa = self.calc_diffusivity()\n",
-    "    \n",
+    "\n",
     "        # Let's keep track of the elapsed time\n",
     "        self.time_elapsed = 0.0\n",
     "\n",
@@ -759,7 +758,7 @@
     "\n",
     "    def calc_heat_flux(self) -> np.ndarray:\n",
     "        \"\"\"The heat flux is -kappa * dT / dz.\"\"\"\n",
-    "        \n",
+    "\n",
     "        # How should we calculate the difference in temperature with depth? (hint: see dz, above)\n",
     "        temperature_gradient = np.gradient(self.temperature, self.z)\n",
     "\n",
@@ -770,15 +769,15 @@
     "        temperature_gradient[-1] = self.basal_heat_flux\n",
     "\n",
     "        return -self.kappa * temperature_gradient\n",
-    "        \n",
+    "\n",
     "    def calc_divergence(self) -> np.ndarray:\n",
     "        \"\"\"In 1D, divergence is just the derivative. yay!\"\"\"\n",
     "        heat_flux = self.calc_heat_flux()\n",
-    "        \n",
+    "\n",
     "        flux_divergence = np.gradient(heat_flux, self.z)\n",
-    "        \n",
+    "\n",
     "        return flux_divergence\n",
-    "    \n",
+    "\n",
     "    def run_one_step(self, dt: float):\n",
     "        \"\"\"Advance the model by one step of size dt.\"\"\"\n",
     "        flux_divergence = self.calc_divergence()\n",