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Add answer key to yet another OOP lesson #143

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Add answer key to yet another OOP lesson #143

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6c39d42
Add notebook
ethan-pierce Jul 31, 2024
0c21701
Add media
ethan-pierce Jul 31, 2024
622c7e1
Add answers to yet_another_oop notebook
ethan-pierce Aug 1, 2024
501aa15
Hide answers in OOP lesson
ethan-pierce Aug 1, 2024
6c0ea66
Merge branch 'main' into ethan-pierce/add_new_oop_lesson
mdpiper Aug 9, 2024
8bf7cdb
Make pretty
mdpiper Aug 9, 2024
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164 changes: 154 additions & 10 deletions lessons/python/yet_another_oop.ipynb
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Original file line number Diff line number Diff line change
@@ -507,7 +507,9 @@
"id": "4d4b08d0-7027-4fae-909e-4fe8d43c2113",
"metadata": {},
"source": [
"Just kidding - here's a template, but feel free to draw your own owl :)"
"Just kidding - here's a template, but feel free to draw your own owl :)\n",
"\n",
"Note: where I've written <tt>pass</tt>, you will probably want to <tt>return</tt> something or save a new class attribute (<tt>self.whatever</tt>), which means you can remove the <tt>pass</tt> statement."
]
},
{
@@ -535,19 +537,27 @@
" self.temperature = initial_temperature\n",
"\n",
" # We probably want to calculate dz --> try using np.gradient\n",
" # 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",
" # 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",
" # Maybe we should go ahead and calculate diffusivity right away?\n",
"\n",
" # Let's keep track of the elapsed time\n",
"\n",
" def calc_diffusivity(self):\n",
" def calc_diffusivity(self) -> float:\n",
" \"\"\"From above, kappa = k / (rho * c).\"\"\"\n",
" pass\n",
"\n",
" def calc_heat_flux(self):\n",
" def calc_heat_flux(self) -> np.ndarray:\n",
" \"\"\"The heat flux is -kappa * dT / dz.\"\"\"\n",
"\n",
" # How should we calculate the difference in temperature with depth? (hint: see dz, above)\n",
@@ -622,31 +632,165 @@
"source": [
"model = SimpleGlacier(z, T0)\n",
"\n",
"dt = 60 * 60 * 24\n",
"dt = 60 * 60 * 24 * 20\n",
"\n",
"for i in range(365):\n",
"for i in range(30000):\n",
" model.run_one_step(dt)\n",
" print(f\"{model.time_elapsed / 31556926} years.\")\n",
"\n",
"model.plot()"
" if i % 1000 == 0:\n",
" print(f\"{model.time_elapsed / 31556926} years.\")\n",
"\n",
"plt.plot(model.temperature, model.z)\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()"
]
},
{
"cell_type": "markdown",
"id": "7ae3c5ca-a9eb-4481-961f-1cc0d07911a5",
"metadata": {},
"source": [
"Once you've done that, we could think about how to add a better boundary condition at the bed. A typical geothermal heat flux in Greenland is $0.04$ W m$^{-2}$ - but don't forget to also divide by $(\\rho * c)$ to keep the units intact! Maybe we should also let the user pass their own geothermal heat flux to our model. At this point, I bet you can figure out how to do that."
"Once you've done that, we could think about how to add a better boundary condition at the bed. Let's add a geothermal heat flux of $0.01$ W m$^{-2}$. Maybe we should also let the user pass their own geothermal heat flux to our model. At this point, I bet you can figure out how to do that."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ebbb16c-f568-4358-992f-4bfe38817afc",
"id": "dccba120-feb5-4ae6-b726-99146df0700f",
"metadata": {},
"outputs": [],
"source": [
"# Initialize your model with a new bottom boundary condition"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4cc607e6-10bb-4dd5-9994-b3076be2a093",
"metadata": {},
"outputs": [],
"source": [
"dt = 60 * 60 * 24 * 20\n",
"\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",
"\n",
"plt.plot(model.temperature, model.z)\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()"
]
},
{
"cell_type": "markdown",
"id": "038ac06d-9307-4717-987a-5dece0a6b682",
"metadata": {},
"source": [
"### Spoilers below!"
]
},
{
"cell_type": "markdown",
"id": "038be991-e91b-488f-b423-f9ad2d833d8b",
"metadata": {},
"source": [
"This is one example of how you could choose to write this class."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a74ed31d-6be6-40cb-a018-89830b53b923",
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"# Plot results here!"
"class SimpleGlacier:\n",
" \"\"\"Models the temperature profile with a glacier.\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",
" Attributes:\n",
" z: an array of z-coordinates\n",
" temperature: an array representing the temperature profile with depth\n",
" \"\"\"\n",
"\n",
" def __init__(self, z: np.ndarray, initial_temperature: np.ndarray):\n",
" \"\"\"Initialize the model with arrays of z-coordinates and initial temperature profile.\"\"\"\n",
" self.z = z\n",
" self.temperature = initial_temperature\n",
"\n",
" # We probably want to calculate dz --> try using np.gradient\n",
" 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",
" # 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",
" self.surface_temperature = -10.0\n",
" self.basal_heat_flux = 0.0\n",
"\n",
" # Maybe we should go ahead and calculate diffusivity right away?\n",
" self.kappa = self.calc_diffusivity()\n",
"\n",
" # Let's keep track of the elapsed time\n",
" self.time_elapsed = 0.0\n",
"\n",
" def calc_diffusivity(self) -> float:\n",
" \"\"\"From above, kappa = k / (rho * c).\"\"\"\n",
" return self.k / (self.rho * self.c)\n",
"\n",
" def calc_heat_flux(self) -> np.ndarray:\n",
" \"\"\"The heat flux is -kappa * dT / dz.\"\"\"\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",
" # Are dT and dz the same size? Are they the same size as z?\n",
" assert temperature_gradient.shape == self.dz.shape == self.z.shape\n",
"\n",
" # Don't forget to apply boundary conditions! The heat flux at the bed should be zero, for now.\n",
" temperature_gradient[-1] = self.basal_heat_flux\n",
"\n",
" return -self.kappa * temperature_gradient\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",
" flux_divergence = np.gradient(heat_flux, self.z)\n",
"\n",
" return flux_divergence\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",
"\n",
" # updated temperature = current temperature - the divergence * dt\n",
" updated_temperature = self.temperature - flux_divergence * dt\n",
"\n",
" # Don't forget to apply boundary conditions! The temperature at the surface is equal to a fixed value.\n",
" updated_temperature[0] = self.surface_temperature\n",
"\n",
" self.temperature = updated_temperature\n",
"\n",
" self.time_elapsed += dt"
]
},
{