diff --git a/docs/6_dust_evolution.html b/docs/6_dust_evolution.html index 9f4d981..aeccc67 100644 --- a/docs/6_dust_evolution.html +++ b/docs/6_dust_evolution.html @@ -195,42 +195,23 @@

6. Dust Evolution
fig = plt.figure(dpi=150)
 ax = fig.add_subplot(111)
 ax.imshow(np.where(sim.dust.Sigma.jacobian().toarray() != 0., 1., 0.), cmap="Blues")
-ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color="gray", alpha=0.5)
-ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color="gray", alpha=0.5)
-ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color="gray", alpha=0.25, lw=0.5)
-ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color="gray", alpha=0.25, lw=0.5)
+ax.hlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], sim.grid.Nm[0])-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color="gray", alpha=0.5)
+ax.vlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], sim.grid.Nm[0])-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color="gray", alpha=0.5)
+ax.hlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], 1)-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color="gray", alpha=0.25, lw=0.5)
+ax.vlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], 1)-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color="gray", alpha=0.25, lw=0.5)
 ax.get_xaxis().set_visible(False)
 ax.get_yaxis().set_visible(False)
-ax.set_title("Structure of Jacobian $\mathbb{J}$")
+ax.set_title(r"Structure of Jacobian $\mathbb{J}$")
 fig.tight_layout()
 plt.show()
-
-
-
-
-
-<>:10: SyntaxWarning: invalid escape sequence '\m'
-<>:10: SyntaxWarning: invalid escape sequence '\m'
-/tmp/ipykernel_93912/1018857805.py:10: SyntaxWarning: invalid escape sequence '\m'
-  ax.set_title("Structure of Jacobian $\mathbb{J}$")
-/tmp/ipykernel_93912/1018857805.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color="gray", alpha=0.5)
-/tmp/ipykernel_93912/1018857805.py:5: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color="gray", alpha=0.5)
-/tmp/ipykernel_93912/1018857805.py:6: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color="gray", alpha=0.25, lw=0.5)
-/tmp/ipykernel_93912/1018857805.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color="gray", alpha=0.25, lw=0.5)
-
-
-_images/6_dust_evolution_9_1.png +_images/6_dust_evolution_9_0.png

This is a the Jacobian for a simulation with 5 radial grid cells and 8 mass bins. This Jacobian acts on the raveled dust surface densities Simulation.dust.Sigma.ravel() and has a shape of (Simulation.grid.Nr*Simulation.grid.Nm, Simulation.grid.Nr*Simulation.grid.Nm)

diff --git a/docs/6_dust_evolution.ipynb b/docs/6_dust_evolution.ipynb index d7869f4..7748c27 100644 --- a/docs/6_dust_evolution.ipynb +++ b/docs/6_dust_evolution.ipynb @@ -22,10 +22,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:51.821080Z", - "iopub.status.busy": "2023-11-30T11:27:51.820360Z", - "iopub.status.idle": "2023-11-30T11:27:52.816431Z", - "shell.execute_reply": "2023-11-30T11:27:52.815314Z" + "iopub.execute_input": "2023-12-01T18:27:28.518913Z", + "iopub.status.busy": "2023-12-01T18:27:28.518278Z", + "iopub.status.idle": "2023-12-01T18:27:30.247124Z", + "shell.execute_reply": "2023-12-01T18:27:30.245045Z" } }, "outputs": [], @@ -50,10 +50,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:52.822858Z", - "iopub.status.busy": "2023-11-30T11:27:52.822389Z", - "iopub.status.idle": "2023-11-30T11:27:52.830552Z", - "shell.execute_reply": "2023-11-30T11:27:52.829770Z" + "iopub.execute_input": "2023-12-01T18:27:30.255522Z", + "iopub.status.busy": "2023-12-01T18:27:30.254370Z", + "iopub.status.idle": "2023-12-01T18:27:30.270145Z", + "shell.execute_reply": "2023-12-01T18:27:30.268847Z" } }, "outputs": [ @@ -100,10 +100,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:52.882368Z", - "iopub.status.busy": "2023-11-30T11:27:52.881683Z", - "iopub.status.idle": "2023-11-30T11:27:52.890284Z", - "shell.execute_reply": "2023-11-30T11:27:52.889187Z" + "iopub.execute_input": "2023-12-01T18:27:30.339093Z", + "iopub.status.busy": "2023-12-01T18:27:30.338441Z", + "iopub.status.idle": "2023-12-01T18:27:30.346704Z", + "shell.execute_reply": "2023-12-01T18:27:30.345623Z" } }, "outputs": [ @@ -134,10 +134,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:52.894501Z", - "iopub.status.busy": "2023-11-30T11:27:52.893870Z", - "iopub.status.idle": "2023-11-30T11:27:52.899681Z", - "shell.execute_reply": "2023-11-30T11:27:52.898307Z" + "iopub.execute_input": "2023-12-01T18:27:30.350519Z", + "iopub.status.busy": "2023-12-01T18:27:30.349883Z", + "iopub.status.idle": "2023-12-01T18:27:30.355640Z", + "shell.execute_reply": "2023-12-01T18:27:30.354256Z" } }, "outputs": [], @@ -151,31 +151,13 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:52.904162Z", - "iopub.status.busy": "2023-11-30T11:27:52.903512Z", - "iopub.status.idle": "2023-11-30T11:27:53.248440Z", - "shell.execute_reply": "2023-11-30T11:27:53.247433Z" + "iopub.execute_input": "2023-12-01T18:27:30.359770Z", + "iopub.status.busy": "2023-12-01T18:27:30.359146Z", + "iopub.status.idle": "2023-12-01T18:27:30.690036Z", + "shell.execute_reply": "2023-12-01T18:27:30.689052Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:10: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:10: SyntaxWarning: invalid escape sequence '\\m'\n", - "/tmp/ipykernel_93912/1018857805.py:10: SyntaxWarning: invalid escape sequence '\\m'\n", - " ax.set_title(\"Structure of Jacobian $\\mathbb{J}$\")\n", - "/tmp/ipykernel_93912/1018857805.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.5)\n", - "/tmp/ipykernel_93912/1018857805.py:5: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.5)\n", - "/tmp/ipykernel_93912/1018857805.py:6: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", - "/tmp/ipykernel_93912/1018857805.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n" - ] - }, { "data": { "image/png": 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", @@ -191,13 +173,13 @@ "fig = plt.figure(dpi=150)\n", "ax = fig.add_subplot(111)\n", "ax.imshow(np.where(sim.dust.Sigma.jacobian().toarray() != 0., 1., 0.), cmap=\"Blues\")\n", - "ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.5)\n", - "ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.5)\n", - "ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", - "ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", + "ax.hlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], sim.grid.Nm[0])-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color=\"gray\", alpha=0.5)\n", + "ax.vlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], sim.grid.Nm[0])-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color=\"gray\", alpha=0.5)\n", + "ax.hlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], 1)-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", + "ax.vlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], 1)-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", "ax.get_xaxis().set_visible(False)\n", "ax.get_yaxis().set_visible(False)\n", - "ax.set_title(\"Structure of Jacobian $\\mathbb{J}$\")\n", + "ax.set_title(r\"Structure of Jacobian $\\mathbb{J}$\")\n", "fig.tight_layout()\n", "plt.show()" ] @@ -259,10 +241,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.255283Z", - "iopub.status.busy": "2023-11-30T11:27:53.255001Z", - "iopub.status.idle": "2023-11-30T11:27:53.260322Z", - "shell.execute_reply": "2023-11-30T11:27:53.259340Z" + "iopub.execute_input": "2023-12-01T18:27:30.696556Z", + "iopub.status.busy": "2023-12-01T18:27:30.696322Z", + "iopub.status.idle": "2023-12-01T18:27:30.700814Z", + "shell.execute_reply": "2023-12-01T18:27:30.699855Z" } }, "outputs": [], @@ -293,10 +275,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.265575Z", - "iopub.status.busy": "2023-11-30T11:27:53.265286Z", - "iopub.status.idle": "2023-11-30T11:27:53.272063Z", - "shell.execute_reply": "2023-11-30T11:27:53.271041Z" + "iopub.execute_input": "2023-12-01T18:27:30.705249Z", + "iopub.status.busy": "2023-12-01T18:27:30.705010Z", + "iopub.status.idle": "2023-12-01T18:27:30.711141Z", + "shell.execute_reply": "2023-12-01T18:27:30.710138Z" } }, "outputs": [], @@ -318,10 +300,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.277290Z", - "iopub.status.busy": "2023-11-30T11:27:53.276925Z", - "iopub.status.idle": "2023-11-30T11:27:53.282901Z", - "shell.execute_reply": "2023-11-30T11:27:53.281741Z" + "iopub.execute_input": "2023-12-01T18:27:30.716319Z", + "iopub.status.busy": "2023-12-01T18:27:30.715869Z", + "iopub.status.idle": "2023-12-01T18:27:30.721345Z", + "shell.execute_reply": "2023-12-01T18:27:30.719928Z" } }, "outputs": [], @@ -350,10 +332,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.288861Z", - "iopub.status.busy": "2023-11-30T11:27:53.288191Z", - "iopub.status.idle": "2023-11-30T11:27:53.296884Z", - "shell.execute_reply": "2023-11-30T11:27:53.295569Z" + "iopub.execute_input": "2023-12-01T18:27:30.726823Z", + "iopub.status.busy": "2023-12-01T18:27:30.726267Z", + "iopub.status.idle": "2023-12-01T18:27:30.734362Z", + "shell.execute_reply": "2023-12-01T18:27:30.733138Z" } }, "outputs": [ @@ -404,10 +386,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.303313Z", - "iopub.status.busy": "2023-11-30T11:27:53.302638Z", - "iopub.status.idle": "2023-11-30T11:27:53.309399Z", - "shell.execute_reply": "2023-11-30T11:27:53.308023Z" + "iopub.execute_input": "2023-12-01T18:27:30.740421Z", + "iopub.status.busy": "2023-12-01T18:27:30.739718Z", + "iopub.status.idle": "2023-12-01T18:27:30.746149Z", + "shell.execute_reply": "2023-12-01T18:27:30.744852Z" } }, "outputs": [], @@ -451,10 +433,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.315817Z", - "iopub.status.busy": "2023-11-30T11:27:53.315090Z", - "iopub.status.idle": "2023-11-30T11:27:53.322085Z", - "shell.execute_reply": "2023-11-30T11:27:53.320699Z" + "iopub.execute_input": "2023-12-01T18:27:30.752328Z", + "iopub.status.busy": "2023-12-01T18:27:30.751635Z", + "iopub.status.idle": "2023-12-01T18:27:30.757981Z", + "shell.execute_reply": "2023-12-01T18:27:30.756709Z" } }, "outputs": [], @@ -477,10 +459,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.328324Z", - "iopub.status.busy": "2023-11-30T11:27:53.327577Z", - "iopub.status.idle": "2023-11-30T11:27:53.335211Z", - "shell.execute_reply": "2023-11-30T11:27:53.333820Z" + "iopub.execute_input": "2023-12-01T18:27:30.763875Z", + "iopub.status.busy": "2023-12-01T18:27:30.763217Z", + "iopub.status.idle": "2023-12-01T18:27:30.796947Z", + "shell.execute_reply": "2023-12-01T18:27:30.793949Z" } }, "outputs": [], @@ -523,10 +505,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.341930Z", - "iopub.status.busy": "2023-11-30T11:27:53.341180Z", - "iopub.status.idle": "2023-11-30T11:27:53.350654Z", - "shell.execute_reply": "2023-11-30T11:27:53.349324Z" + "iopub.execute_input": "2023-12-01T18:27:30.804683Z", + "iopub.status.busy": "2023-12-01T18:27:30.804188Z", + "iopub.status.idle": "2023-12-01T18:27:30.814646Z", + "shell.execute_reply": "2023-12-01T18:27:30.813611Z" } }, "outputs": [ @@ -551,10 +533,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.357056Z", - "iopub.status.busy": "2023-11-30T11:27:53.356291Z", - "iopub.status.idle": "2023-11-30T11:27:53.363722Z", - "shell.execute_reply": "2023-11-30T11:27:53.362111Z" + "iopub.execute_input": "2023-12-01T18:27:30.819620Z", + "iopub.status.busy": "2023-12-01T18:27:30.819264Z", + "iopub.status.idle": "2023-12-01T18:27:30.824995Z", + "shell.execute_reply": "2023-12-01T18:27:30.823800Z" } }, "outputs": [], @@ -567,10 +549,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.370077Z", - "iopub.status.busy": "2023-11-30T11:27:53.369346Z", - "iopub.status.idle": "2023-11-30T11:27:53.378582Z", - "shell.execute_reply": "2023-11-30T11:27:53.377225Z" + "iopub.execute_input": "2023-12-01T18:27:30.830522Z", + "iopub.status.busy": "2023-12-01T18:27:30.829668Z", + "iopub.status.idle": "2023-12-01T18:27:30.838221Z", + "shell.execute_reply": "2023-12-01T18:27:30.837012Z" } }, "outputs": [ @@ -621,10 +603,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.385437Z", - "iopub.status.busy": "2023-11-30T11:27:53.384678Z", - "iopub.status.idle": "2023-11-30T11:27:53.396860Z", - "shell.execute_reply": "2023-11-30T11:27:53.395289Z" + "iopub.execute_input": "2023-12-01T18:27:30.844269Z", + "iopub.status.busy": "2023-12-01T18:27:30.843636Z", + "iopub.status.idle": "2023-12-01T18:27:30.860535Z", + "shell.execute_reply": "2023-12-01T18:27:30.859239Z" } }, "outputs": [ @@ -654,10 +636,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.403395Z", - "iopub.status.busy": "2023-11-30T11:27:53.402736Z", - "iopub.status.idle": "2023-11-30T11:27:53.414116Z", - "shell.execute_reply": "2023-11-30T11:27:53.412439Z" + "iopub.execute_input": "2023-12-01T18:27:30.866514Z", + "iopub.status.busy": "2023-12-01T18:27:30.865869Z", + "iopub.status.idle": "2023-12-01T18:27:30.876385Z", + "shell.execute_reply": "2023-12-01T18:27:30.874935Z" } }, "outputs": [], @@ -684,11 +666,12 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.420910Z", - "iopub.status.busy": "2023-11-30T11:27:53.420212Z", - "iopub.status.idle": "2023-11-30T11:27:53.428916Z", - "shell.execute_reply": "2023-11-30T11:27:53.427291Z" - } + "iopub.execute_input": "2023-12-01T18:27:30.882493Z", + "iopub.status.busy": "2023-12-01T18:27:30.881838Z", + "iopub.status.idle": "2023-12-01T18:27:30.892066Z", + "shell.execute_reply": "2023-12-01T18:27:30.890770Z" + }, + "tags": [] }, "outputs": [ { @@ -735,7 +718,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/7_gas_evolution.html b/docs/7_gas_evolution.html index 8532543..6739991 100644 --- a/docs/7_gas_evolution.html +++ b/docs/7_gas_evolution.html @@ -155,36 +155,21 @@

Hydrodynamics
fig = plt.figure(dpi=150)
 ax = fig.add_subplot(111)
 ax.imshow(np.where(sim.gas.Sigma.jacobian().toarray() != 0., 1., 0.), cmap="Blues")
-ax.hlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color="gray", alpha=0.5)
-ax.vlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color="gray", alpha=0.5)
+ax.hlines(np.arange(0., sim.grid.Nr[0])-0.5, -0.5, sim.grid.Nr[0]-0.5, color="gray", alpha=0.5)
+ax.vlines(np.arange(0., sim.grid.Nr[0])-0.5, -0.5, sim.grid.Nr[0]-0.5, color="gray", alpha=0.5)
 ax.get_xaxis().set_visible(False)
 ax.get_yaxis().set_visible(False)
-ax.set_title("Structure of Jacobian $\mathbb{J}$")
+ax.set_title(r"Structure of Jacobian $\mathbb{J}$")
 fig.tight_layout()
 plt.show()
-
-
-
-
-
-<>:8: SyntaxWarning: invalid escape sequence '\m'
-<>:8: SyntaxWarning: invalid escape sequence '\m'
-/tmp/ipykernel_93989/2308441433.py:8: SyntaxWarning: invalid escape sequence '\m'
-  ax.set_title("Structure of Jacobian $\mathbb{J}$")
-/tmp/ipykernel_93989/2308441433.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  ax.hlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color="gray", alpha=0.5)
-/tmp/ipykernel_93989/2308441433.py:5: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  ax.vlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color="gray", alpha=0.5)
-
-
-_images/7_gas_evolution_5_1.png +_images/7_gas_evolution_5_0.png

Notable exceptions are the first and the last row, which are used to set the boundary conditions. They require up to three elements to set the gradients if needed.

diff --git a/docs/7_gas_evolution.ipynb b/docs/7_gas_evolution.ipynb index 25cf5f2..366d93a 100644 --- a/docs/7_gas_evolution.ipynb +++ b/docs/7_gas_evolution.ipynb @@ -35,10 +35,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:23.237559Z", - "iopub.status.busy": "2023-11-30T11:28:23.236864Z", - "iopub.status.idle": "2023-11-30T11:28:24.180435Z", - "shell.execute_reply": "2023-11-30T11:28:24.178773Z" + "iopub.execute_input": "2023-12-01T18:27:33.313774Z", + "iopub.status.busy": "2023-12-01T18:27:33.313154Z", + "iopub.status.idle": "2023-12-01T18:27:34.363317Z", + "shell.execute_reply": "2023-12-01T18:27:34.361722Z" } }, "outputs": [], @@ -78,10 +78,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.187660Z", - "iopub.status.busy": "2023-11-30T11:28:24.186921Z", - "iopub.status.idle": "2023-11-30T11:28:24.193101Z", - "shell.execute_reply": "2023-11-30T11:28:24.192072Z" + "iopub.execute_input": "2023-12-01T18:27:34.371494Z", + "iopub.status.busy": "2023-12-01T18:27:34.370345Z", + "iopub.status.idle": "2023-12-01T18:27:34.377508Z", + "shell.execute_reply": "2023-12-01T18:27:34.376670Z" } }, "outputs": [], @@ -95,27 +95,14 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.198475Z", - "iopub.status.busy": "2023-11-30T11:28:24.197910Z", - "iopub.status.idle": "2023-11-30T11:28:24.493119Z", - "shell.execute_reply": "2023-11-30T11:28:24.492056Z" - } + "iopub.execute_input": "2023-12-01T18:27:34.382015Z", + "iopub.status.busy": "2023-12-01T18:27:34.381784Z", + "iopub.status.idle": "2023-12-01T18:27:34.664024Z", + "shell.execute_reply": "2023-12-01T18:27:34.663052Z" + }, + "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:8: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:8: SyntaxWarning: invalid escape sequence '\\m'\n", - "/tmp/ipykernel_93989/2308441433.py:8: SyntaxWarning: invalid escape sequence '\\m'\n", - " ax.set_title(\"Structure of Jacobian $\\mathbb{J}$\")\n", - "/tmp/ipykernel_93989/2308441433.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.hlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color=\"gray\", alpha=0.5)\n", - "/tmp/ipykernel_93989/2308441433.py:5: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.vlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color=\"gray\", alpha=0.5)\n" - ] - }, { "data": { "image/png": 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", @@ -131,11 +118,11 @@ "fig = plt.figure(dpi=150)\n", "ax = fig.add_subplot(111)\n", "ax.imshow(np.where(sim.gas.Sigma.jacobian().toarray() != 0., 1., 0.), cmap=\"Blues\")\n", - "ax.hlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color=\"gray\", alpha=0.5)\n", - "ax.vlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color=\"gray\", alpha=0.5)\n", + "ax.hlines(np.arange(0., sim.grid.Nr[0])-0.5, -0.5, sim.grid.Nr[0]-0.5, color=\"gray\", alpha=0.5)\n", + "ax.vlines(np.arange(0., sim.grid.Nr[0])-0.5, -0.5, sim.grid.Nr[0]-0.5, color=\"gray\", alpha=0.5)\n", "ax.get_xaxis().set_visible(False)\n", "ax.get_yaxis().set_visible(False)\n", - "ax.set_title(\"Structure of Jacobian $\\mathbb{J}$\")\n", + "ax.set_title(r\"Structure of Jacobian $\\mathbb{J}$\")\n", "fig.tight_layout()\n", "plt.show()" ] @@ -191,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.497327Z", - "iopub.status.busy": "2023-11-30T11:28:24.497053Z", - "iopub.status.idle": "2023-11-30T11:28:24.505822Z", - "shell.execute_reply": "2023-11-30T11:28:24.504900Z" + "iopub.execute_input": "2023-12-01T18:27:34.720211Z", + "iopub.status.busy": "2023-12-01T18:27:34.718848Z", + "iopub.status.idle": "2023-12-01T18:27:34.732454Z", + "shell.execute_reply": "2023-12-01T18:27:34.731139Z" } }, "outputs": [ @@ -241,10 +228,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.510064Z", - "iopub.status.busy": "2023-11-30T11:28:24.509824Z", - "iopub.status.idle": "2023-11-30T11:28:24.514775Z", - "shell.execute_reply": "2023-11-30T11:28:24.513726Z" + "iopub.execute_input": "2023-12-01T18:27:34.736284Z", + "iopub.status.busy": "2023-12-01T18:27:34.735799Z", + "iopub.status.idle": "2023-12-01T18:27:34.741789Z", + "shell.execute_reply": "2023-12-01T18:27:34.740386Z" } }, "outputs": [], @@ -258,10 +245,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.518863Z", - "iopub.status.busy": "2023-11-30T11:28:24.518590Z", - "iopub.status.idle": "2023-11-30T11:28:24.527562Z", - "shell.execute_reply": "2023-11-30T11:28:24.526435Z" + "iopub.execute_input": "2023-12-01T18:27:34.746216Z", + "iopub.status.busy": "2023-12-01T18:27:34.745619Z", + "iopub.status.idle": "2023-12-01T18:27:34.755321Z", + "shell.execute_reply": "2023-12-01T18:27:34.754195Z" } }, "outputs": [ @@ -312,10 +299,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.533296Z", - "iopub.status.busy": "2023-11-30T11:28:24.532696Z", - "iopub.status.idle": "2023-11-30T11:28:24.539000Z", - "shell.execute_reply": "2023-11-30T11:28:24.537584Z" + "iopub.execute_input": "2023-12-01T18:27:34.760296Z", + "iopub.status.busy": "2023-12-01T18:27:34.759708Z", + "iopub.status.idle": "2023-12-01T18:27:34.765477Z", + "shell.execute_reply": "2023-12-01T18:27:34.764249Z" } }, "outputs": [], @@ -338,10 +325,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.544909Z", - "iopub.status.busy": "2023-11-30T11:28:24.544284Z", - "iopub.status.idle": "2023-11-30T11:28:24.553180Z", - "shell.execute_reply": "2023-11-30T11:28:24.551897Z" + "iopub.execute_input": "2023-12-01T18:27:34.770985Z", + "iopub.status.busy": "2023-12-01T18:27:34.770357Z", + "iopub.status.idle": "2023-12-01T18:27:34.778730Z", + "shell.execute_reply": "2023-12-01T18:27:34.777526Z" } }, "outputs": [ @@ -366,10 +353,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.559812Z", - "iopub.status.busy": "2023-11-30T11:28:24.558316Z", - "iopub.status.idle": "2023-11-30T11:28:24.565185Z", - "shell.execute_reply": "2023-11-30T11:28:24.563771Z" + "iopub.execute_input": "2023-12-01T18:27:34.784596Z", + "iopub.status.busy": "2023-12-01T18:27:34.783885Z", + "iopub.status.idle": "2023-12-01T18:27:34.789985Z", + "shell.execute_reply": "2023-12-01T18:27:34.788520Z" } }, "outputs": [], @@ -382,10 +369,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.571273Z", - "iopub.status.busy": "2023-11-30T11:28:24.570565Z", - "iopub.status.idle": "2023-11-30T11:28:24.580307Z", - "shell.execute_reply": "2023-11-30T11:28:24.578799Z" + "iopub.execute_input": "2023-12-01T18:27:34.795709Z", + "iopub.status.busy": "2023-12-01T18:27:34.795037Z", + "iopub.status.idle": "2023-12-01T18:27:34.803322Z", + "shell.execute_reply": "2023-12-01T18:27:34.802107Z" } }, "outputs": [ @@ -428,7 +415,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/B_publications.ipynb b/docs/B_publications.ipynb index 321817d..abfb685 100644 --- a/docs/B_publications.ipynb +++ b/docs/B_publications.ipynb @@ -292,7 +292,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/E_changelog.html b/docs/E_changelog.html index 1437f2b..7a21dea 100644 --- a/docs/E_changelog.html +++ b/docs/E_changelog.html @@ -128,7 +128,7 @@

Appendix E: ChangelogThis is a list of changes made to DustPy since version v1.0.0 including discussions of their influence on the simulations.

v1.0.5

-

Release date: 2nd December 2023

+

Release date: 3rd December 2023

Using Meson as build system

Due to the deprecation of numpy.distutils, DustPy is now using Meson as build system.

@@ -143,7 +143,7 @@

Bugfix to plotting script

Preparation for the addition of multiple gas species

-

In order to add multiple gas species in future versions, the Jacobian of the gas surface density has been modified. All previous model that have not specifically customized the gas Jacobian should be compatible with this version.

+

In order to add multiple gas species in future versions, the Jacobian of the gas surface density has been modified. All previous models that have not specifically customized the gas Jacobian should be compatible with this version.

diff --git a/docs/E_changelog.ipynb b/docs/E_changelog.ipynb index 36becf2..b055499 100644 --- a/docs/E_changelog.ipynb +++ b/docs/E_changelog.ipynb @@ -22,7 +22,7 @@ "metadata": {}, "source": [ "### **v1.0.5**\n", - "**Release date: 2nd December 2023**\n", + "**Release date: 3rd December 2023**\n", "\n", "#### Using Meson as build system\n", "\n", @@ -38,7 +38,7 @@ "\n", "#### Preparation for the addition of multiple gas species\n", "\n", - "In order to add multiple gas species in future versions, the Jacobian of the gas surface density has been modified. All previous model that have not specifically customized the gas Jacobian should be compatible with this version." + "In order to add multiple gas species in future versions, the Jacobian of the gas surface density has been modified. All previous models that have not specifically customized the gas Jacobian should be compatible with this version." ] }, { @@ -134,7 +134,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/_images/6_dust_evolution_9_1.png b/docs/_images/6_dust_evolution_9_0.png similarity index 100% rename from docs/_images/6_dust_evolution_9_1.png rename to docs/_images/6_dust_evolution_9_0.png diff --git a/docs/_images/7_gas_evolution_5_1.png b/docs/_images/7_gas_evolution_5_0.png similarity index 100% rename from docs/_images/7_gas_evolution_5_1.png rename to docs/_images/7_gas_evolution_5_0.png diff --git a/docs/_images/example_planetary_gaps_52_1.png b/docs/_images/example_planetary_gaps_52_0.png similarity index 100% rename from docs/_images/example_planetary_gaps_52_1.png rename to docs/_images/example_planetary_gaps_52_0.png diff --git a/docs/_images/example_planetesimal_formation_48_1.png b/docs/_images/example_planetesimal_formation_48_0.png similarity index 100% rename from docs/_images/example_planetesimal_formation_48_1.png rename to docs/_images/example_planetesimal_formation_48_0.png diff --git a/docs/_images/test_analytical_coagulation_kernels_29_0.png b/docs/_images/test_analytical_coagulation_kernels_29_0.png new file mode 100644 index 0000000..267f28c Binary files /dev/null and b/docs/_images/test_analytical_coagulation_kernels_29_0.png differ diff --git a/docs/_sources/6_dust_evolution.ipynb.txt b/docs/_sources/6_dust_evolution.ipynb.txt index d7869f4..7748c27 100644 --- a/docs/_sources/6_dust_evolution.ipynb.txt +++ b/docs/_sources/6_dust_evolution.ipynb.txt @@ -22,10 +22,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:51.821080Z", - "iopub.status.busy": "2023-11-30T11:27:51.820360Z", - "iopub.status.idle": "2023-11-30T11:27:52.816431Z", - "shell.execute_reply": "2023-11-30T11:27:52.815314Z" + "iopub.execute_input": "2023-12-01T18:27:28.518913Z", + "iopub.status.busy": "2023-12-01T18:27:28.518278Z", + "iopub.status.idle": "2023-12-01T18:27:30.247124Z", + "shell.execute_reply": "2023-12-01T18:27:30.245045Z" } }, "outputs": [], @@ -50,10 +50,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:52.822858Z", - "iopub.status.busy": "2023-11-30T11:27:52.822389Z", - "iopub.status.idle": "2023-11-30T11:27:52.830552Z", - "shell.execute_reply": "2023-11-30T11:27:52.829770Z" + "iopub.execute_input": "2023-12-01T18:27:30.255522Z", + "iopub.status.busy": "2023-12-01T18:27:30.254370Z", + "iopub.status.idle": "2023-12-01T18:27:30.270145Z", + "shell.execute_reply": "2023-12-01T18:27:30.268847Z" } }, "outputs": [ @@ -100,10 +100,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:52.882368Z", - "iopub.status.busy": "2023-11-30T11:27:52.881683Z", - "iopub.status.idle": "2023-11-30T11:27:52.890284Z", - "shell.execute_reply": "2023-11-30T11:27:52.889187Z" + "iopub.execute_input": "2023-12-01T18:27:30.339093Z", + "iopub.status.busy": "2023-12-01T18:27:30.338441Z", + "iopub.status.idle": "2023-12-01T18:27:30.346704Z", + "shell.execute_reply": "2023-12-01T18:27:30.345623Z" } }, "outputs": [ @@ -134,10 +134,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:52.894501Z", - "iopub.status.busy": "2023-11-30T11:27:52.893870Z", - "iopub.status.idle": "2023-11-30T11:27:52.899681Z", - "shell.execute_reply": "2023-11-30T11:27:52.898307Z" + "iopub.execute_input": "2023-12-01T18:27:30.350519Z", + "iopub.status.busy": "2023-12-01T18:27:30.349883Z", + "iopub.status.idle": "2023-12-01T18:27:30.355640Z", + "shell.execute_reply": "2023-12-01T18:27:30.354256Z" } }, "outputs": [], @@ -151,31 +151,13 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:52.904162Z", - "iopub.status.busy": "2023-11-30T11:27:52.903512Z", - "iopub.status.idle": "2023-11-30T11:27:53.248440Z", - "shell.execute_reply": "2023-11-30T11:27:53.247433Z" + "iopub.execute_input": "2023-12-01T18:27:30.359770Z", + "iopub.status.busy": "2023-12-01T18:27:30.359146Z", + "iopub.status.idle": "2023-12-01T18:27:30.690036Z", + "shell.execute_reply": "2023-12-01T18:27:30.689052Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:10: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:10: SyntaxWarning: invalid escape sequence '\\m'\n", - "/tmp/ipykernel_93912/1018857805.py:10: SyntaxWarning: invalid escape sequence '\\m'\n", - " ax.set_title(\"Structure of Jacobian $\\mathbb{J}$\")\n", - "/tmp/ipykernel_93912/1018857805.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.5)\n", - "/tmp/ipykernel_93912/1018857805.py:5: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.5)\n", - "/tmp/ipykernel_93912/1018857805.py:6: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", - "/tmp/ipykernel_93912/1018857805.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n" - ] - }, { "data": { "image/png": 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", @@ -191,13 +173,13 @@ "fig = plt.figure(dpi=150)\n", "ax = fig.add_subplot(111)\n", "ax.imshow(np.where(sim.dust.Sigma.jacobian().toarray() != 0., 1., 0.), cmap=\"Blues\")\n", - "ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.5)\n", - "ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, sim.grid.Nm)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.5)\n", - "ax.hlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", - "ax.vlines(np.arange(0., sim.grid.Nr*sim.grid.Nm, 1)-0.5, -0.5, sim.grid.Nr*sim.grid.Nm-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", + "ax.hlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], sim.grid.Nm[0])-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color=\"gray\", alpha=0.5)\n", + "ax.vlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], sim.grid.Nm[0])-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color=\"gray\", alpha=0.5)\n", + "ax.hlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], 1)-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", + "ax.vlines(np.arange(0., sim.grid.Nr[0]*sim.grid.Nm[0], 1)-0.5, -0.5, sim.grid.Nr[0]*sim.grid.Nm[0]-0.5, color=\"gray\", alpha=0.25, lw=0.5)\n", "ax.get_xaxis().set_visible(False)\n", "ax.get_yaxis().set_visible(False)\n", - "ax.set_title(\"Structure of Jacobian $\\mathbb{J}$\")\n", + "ax.set_title(r\"Structure of Jacobian $\\mathbb{J}$\")\n", "fig.tight_layout()\n", "plt.show()" ] @@ -259,10 +241,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.255283Z", - "iopub.status.busy": "2023-11-30T11:27:53.255001Z", - "iopub.status.idle": "2023-11-30T11:27:53.260322Z", - "shell.execute_reply": "2023-11-30T11:27:53.259340Z" + "iopub.execute_input": "2023-12-01T18:27:30.696556Z", + "iopub.status.busy": "2023-12-01T18:27:30.696322Z", + "iopub.status.idle": "2023-12-01T18:27:30.700814Z", + "shell.execute_reply": "2023-12-01T18:27:30.699855Z" } }, "outputs": [], @@ -293,10 +275,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.265575Z", - "iopub.status.busy": "2023-11-30T11:27:53.265286Z", - "iopub.status.idle": "2023-11-30T11:27:53.272063Z", - "shell.execute_reply": "2023-11-30T11:27:53.271041Z" + "iopub.execute_input": "2023-12-01T18:27:30.705249Z", + "iopub.status.busy": "2023-12-01T18:27:30.705010Z", + "iopub.status.idle": "2023-12-01T18:27:30.711141Z", + "shell.execute_reply": "2023-12-01T18:27:30.710138Z" } }, "outputs": [], @@ -318,10 +300,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.277290Z", - "iopub.status.busy": "2023-11-30T11:27:53.276925Z", - "iopub.status.idle": "2023-11-30T11:27:53.282901Z", - "shell.execute_reply": "2023-11-30T11:27:53.281741Z" + "iopub.execute_input": "2023-12-01T18:27:30.716319Z", + "iopub.status.busy": "2023-12-01T18:27:30.715869Z", + "iopub.status.idle": "2023-12-01T18:27:30.721345Z", + "shell.execute_reply": "2023-12-01T18:27:30.719928Z" } }, "outputs": [], @@ -350,10 +332,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.288861Z", - "iopub.status.busy": "2023-11-30T11:27:53.288191Z", - "iopub.status.idle": "2023-11-30T11:27:53.296884Z", - "shell.execute_reply": "2023-11-30T11:27:53.295569Z" + "iopub.execute_input": "2023-12-01T18:27:30.726823Z", + "iopub.status.busy": "2023-12-01T18:27:30.726267Z", + "iopub.status.idle": "2023-12-01T18:27:30.734362Z", + "shell.execute_reply": "2023-12-01T18:27:30.733138Z" } }, "outputs": [ @@ -404,10 +386,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.303313Z", - "iopub.status.busy": "2023-11-30T11:27:53.302638Z", - "iopub.status.idle": "2023-11-30T11:27:53.309399Z", - "shell.execute_reply": "2023-11-30T11:27:53.308023Z" + "iopub.execute_input": "2023-12-01T18:27:30.740421Z", + "iopub.status.busy": "2023-12-01T18:27:30.739718Z", + "iopub.status.idle": "2023-12-01T18:27:30.746149Z", + "shell.execute_reply": "2023-12-01T18:27:30.744852Z" } }, "outputs": [], @@ -451,10 +433,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.315817Z", - "iopub.status.busy": "2023-11-30T11:27:53.315090Z", - "iopub.status.idle": "2023-11-30T11:27:53.322085Z", - "shell.execute_reply": "2023-11-30T11:27:53.320699Z" + "iopub.execute_input": "2023-12-01T18:27:30.752328Z", + "iopub.status.busy": "2023-12-01T18:27:30.751635Z", + "iopub.status.idle": "2023-12-01T18:27:30.757981Z", + "shell.execute_reply": "2023-12-01T18:27:30.756709Z" } }, "outputs": [], @@ -477,10 +459,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.328324Z", - "iopub.status.busy": "2023-11-30T11:27:53.327577Z", - "iopub.status.idle": "2023-11-30T11:27:53.335211Z", - "shell.execute_reply": "2023-11-30T11:27:53.333820Z" + "iopub.execute_input": "2023-12-01T18:27:30.763875Z", + "iopub.status.busy": "2023-12-01T18:27:30.763217Z", + "iopub.status.idle": "2023-12-01T18:27:30.796947Z", + "shell.execute_reply": "2023-12-01T18:27:30.793949Z" } }, "outputs": [], @@ -523,10 +505,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.341930Z", - "iopub.status.busy": "2023-11-30T11:27:53.341180Z", - "iopub.status.idle": "2023-11-30T11:27:53.350654Z", - "shell.execute_reply": "2023-11-30T11:27:53.349324Z" + "iopub.execute_input": "2023-12-01T18:27:30.804683Z", + "iopub.status.busy": "2023-12-01T18:27:30.804188Z", + "iopub.status.idle": "2023-12-01T18:27:30.814646Z", + "shell.execute_reply": "2023-12-01T18:27:30.813611Z" } }, "outputs": [ @@ -551,10 +533,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.357056Z", - "iopub.status.busy": "2023-11-30T11:27:53.356291Z", - "iopub.status.idle": "2023-11-30T11:27:53.363722Z", - "shell.execute_reply": "2023-11-30T11:27:53.362111Z" + "iopub.execute_input": "2023-12-01T18:27:30.819620Z", + "iopub.status.busy": "2023-12-01T18:27:30.819264Z", + "iopub.status.idle": "2023-12-01T18:27:30.824995Z", + "shell.execute_reply": "2023-12-01T18:27:30.823800Z" } }, "outputs": [], @@ -567,10 +549,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.370077Z", - "iopub.status.busy": "2023-11-30T11:27:53.369346Z", - "iopub.status.idle": "2023-11-30T11:27:53.378582Z", - "shell.execute_reply": "2023-11-30T11:27:53.377225Z" + "iopub.execute_input": "2023-12-01T18:27:30.830522Z", + "iopub.status.busy": "2023-12-01T18:27:30.829668Z", + "iopub.status.idle": "2023-12-01T18:27:30.838221Z", + "shell.execute_reply": "2023-12-01T18:27:30.837012Z" } }, "outputs": [ @@ -621,10 +603,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.385437Z", - "iopub.status.busy": "2023-11-30T11:27:53.384678Z", - "iopub.status.idle": "2023-11-30T11:27:53.396860Z", - "shell.execute_reply": "2023-11-30T11:27:53.395289Z" + "iopub.execute_input": "2023-12-01T18:27:30.844269Z", + "iopub.status.busy": "2023-12-01T18:27:30.843636Z", + "iopub.status.idle": "2023-12-01T18:27:30.860535Z", + "shell.execute_reply": "2023-12-01T18:27:30.859239Z" } }, "outputs": [ @@ -654,10 +636,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.403395Z", - "iopub.status.busy": "2023-11-30T11:27:53.402736Z", - "iopub.status.idle": "2023-11-30T11:27:53.414116Z", - "shell.execute_reply": "2023-11-30T11:27:53.412439Z" + "iopub.execute_input": "2023-12-01T18:27:30.866514Z", + "iopub.status.busy": "2023-12-01T18:27:30.865869Z", + "iopub.status.idle": "2023-12-01T18:27:30.876385Z", + "shell.execute_reply": "2023-12-01T18:27:30.874935Z" } }, "outputs": [], @@ -684,11 +666,12 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:27:53.420910Z", - "iopub.status.busy": "2023-11-30T11:27:53.420212Z", - "iopub.status.idle": "2023-11-30T11:27:53.428916Z", - "shell.execute_reply": "2023-11-30T11:27:53.427291Z" - } + "iopub.execute_input": "2023-12-01T18:27:30.882493Z", + "iopub.status.busy": "2023-12-01T18:27:30.881838Z", + "iopub.status.idle": "2023-12-01T18:27:30.892066Z", + "shell.execute_reply": "2023-12-01T18:27:30.890770Z" + }, + "tags": [] }, "outputs": [ { @@ -735,7 +718,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/_sources/7_gas_evolution.ipynb.txt b/docs/_sources/7_gas_evolution.ipynb.txt index 25cf5f2..366d93a 100644 --- a/docs/_sources/7_gas_evolution.ipynb.txt +++ b/docs/_sources/7_gas_evolution.ipynb.txt @@ -35,10 +35,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:23.237559Z", - "iopub.status.busy": "2023-11-30T11:28:23.236864Z", - "iopub.status.idle": "2023-11-30T11:28:24.180435Z", - "shell.execute_reply": "2023-11-30T11:28:24.178773Z" + "iopub.execute_input": "2023-12-01T18:27:33.313774Z", + "iopub.status.busy": "2023-12-01T18:27:33.313154Z", + "iopub.status.idle": "2023-12-01T18:27:34.363317Z", + "shell.execute_reply": "2023-12-01T18:27:34.361722Z" } }, "outputs": [], @@ -78,10 +78,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.187660Z", - "iopub.status.busy": "2023-11-30T11:28:24.186921Z", - "iopub.status.idle": "2023-11-30T11:28:24.193101Z", - "shell.execute_reply": "2023-11-30T11:28:24.192072Z" + "iopub.execute_input": "2023-12-01T18:27:34.371494Z", + "iopub.status.busy": "2023-12-01T18:27:34.370345Z", + "iopub.status.idle": "2023-12-01T18:27:34.377508Z", + "shell.execute_reply": "2023-12-01T18:27:34.376670Z" } }, "outputs": [], @@ -95,27 +95,14 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.198475Z", - "iopub.status.busy": "2023-11-30T11:28:24.197910Z", - "iopub.status.idle": "2023-11-30T11:28:24.493119Z", - "shell.execute_reply": "2023-11-30T11:28:24.492056Z" - } + "iopub.execute_input": "2023-12-01T18:27:34.382015Z", + "iopub.status.busy": "2023-12-01T18:27:34.381784Z", + "iopub.status.idle": "2023-12-01T18:27:34.664024Z", + "shell.execute_reply": "2023-12-01T18:27:34.663052Z" + }, + "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:8: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:8: SyntaxWarning: invalid escape sequence '\\m'\n", - "/tmp/ipykernel_93989/2308441433.py:8: SyntaxWarning: invalid escape sequence '\\m'\n", - " ax.set_title(\"Structure of Jacobian $\\mathbb{J}$\")\n", - "/tmp/ipykernel_93989/2308441433.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.hlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color=\"gray\", alpha=0.5)\n", - "/tmp/ipykernel_93989/2308441433.py:5: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.vlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color=\"gray\", alpha=0.5)\n" - ] - }, { "data": { "image/png": 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", @@ -131,11 +118,11 @@ "fig = plt.figure(dpi=150)\n", "ax = fig.add_subplot(111)\n", "ax.imshow(np.where(sim.gas.Sigma.jacobian().toarray() != 0., 1., 0.), cmap=\"Blues\")\n", - "ax.hlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color=\"gray\", alpha=0.5)\n", - "ax.vlines(np.arange(0., sim.grid.Nr)-0.5, -0.5, sim.grid.Nr-0.5, color=\"gray\", alpha=0.5)\n", + "ax.hlines(np.arange(0., sim.grid.Nr[0])-0.5, -0.5, sim.grid.Nr[0]-0.5, color=\"gray\", alpha=0.5)\n", + "ax.vlines(np.arange(0., sim.grid.Nr[0])-0.5, -0.5, sim.grid.Nr[0]-0.5, color=\"gray\", alpha=0.5)\n", "ax.get_xaxis().set_visible(False)\n", "ax.get_yaxis().set_visible(False)\n", - "ax.set_title(\"Structure of Jacobian $\\mathbb{J}$\")\n", + "ax.set_title(r\"Structure of Jacobian $\\mathbb{J}$\")\n", "fig.tight_layout()\n", "plt.show()" ] @@ -191,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.497327Z", - "iopub.status.busy": "2023-11-30T11:28:24.497053Z", - "iopub.status.idle": "2023-11-30T11:28:24.505822Z", - "shell.execute_reply": "2023-11-30T11:28:24.504900Z" + "iopub.execute_input": "2023-12-01T18:27:34.720211Z", + "iopub.status.busy": "2023-12-01T18:27:34.718848Z", + "iopub.status.idle": "2023-12-01T18:27:34.732454Z", + "shell.execute_reply": "2023-12-01T18:27:34.731139Z" } }, "outputs": [ @@ -241,10 +228,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.510064Z", - "iopub.status.busy": "2023-11-30T11:28:24.509824Z", - "iopub.status.idle": "2023-11-30T11:28:24.514775Z", - "shell.execute_reply": "2023-11-30T11:28:24.513726Z" + "iopub.execute_input": "2023-12-01T18:27:34.736284Z", + "iopub.status.busy": "2023-12-01T18:27:34.735799Z", + "iopub.status.idle": "2023-12-01T18:27:34.741789Z", + "shell.execute_reply": "2023-12-01T18:27:34.740386Z" } }, "outputs": [], @@ -258,10 +245,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.518863Z", - "iopub.status.busy": "2023-11-30T11:28:24.518590Z", - "iopub.status.idle": "2023-11-30T11:28:24.527562Z", - "shell.execute_reply": "2023-11-30T11:28:24.526435Z" + "iopub.execute_input": "2023-12-01T18:27:34.746216Z", + "iopub.status.busy": "2023-12-01T18:27:34.745619Z", + "iopub.status.idle": "2023-12-01T18:27:34.755321Z", + "shell.execute_reply": "2023-12-01T18:27:34.754195Z" } }, "outputs": [ @@ -312,10 +299,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.533296Z", - "iopub.status.busy": "2023-11-30T11:28:24.532696Z", - "iopub.status.idle": "2023-11-30T11:28:24.539000Z", - "shell.execute_reply": "2023-11-30T11:28:24.537584Z" + "iopub.execute_input": "2023-12-01T18:27:34.760296Z", + "iopub.status.busy": "2023-12-01T18:27:34.759708Z", + "iopub.status.idle": "2023-12-01T18:27:34.765477Z", + "shell.execute_reply": "2023-12-01T18:27:34.764249Z" } }, "outputs": [], @@ -338,10 +325,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.544909Z", - "iopub.status.busy": "2023-11-30T11:28:24.544284Z", - "iopub.status.idle": "2023-11-30T11:28:24.553180Z", - "shell.execute_reply": "2023-11-30T11:28:24.551897Z" + "iopub.execute_input": "2023-12-01T18:27:34.770985Z", + "iopub.status.busy": "2023-12-01T18:27:34.770357Z", + "iopub.status.idle": "2023-12-01T18:27:34.778730Z", + "shell.execute_reply": "2023-12-01T18:27:34.777526Z" } }, "outputs": [ @@ -366,10 +353,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.559812Z", - "iopub.status.busy": "2023-11-30T11:28:24.558316Z", - "iopub.status.idle": "2023-11-30T11:28:24.565185Z", - "shell.execute_reply": "2023-11-30T11:28:24.563771Z" + "iopub.execute_input": "2023-12-01T18:27:34.784596Z", + "iopub.status.busy": "2023-12-01T18:27:34.783885Z", + "iopub.status.idle": "2023-12-01T18:27:34.789985Z", + "shell.execute_reply": "2023-12-01T18:27:34.788520Z" } }, "outputs": [], @@ -382,10 +369,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:28:24.571273Z", - "iopub.status.busy": "2023-11-30T11:28:24.570565Z", - "iopub.status.idle": "2023-11-30T11:28:24.580307Z", - "shell.execute_reply": "2023-11-30T11:28:24.578799Z" + "iopub.execute_input": "2023-12-01T18:27:34.795709Z", + "iopub.status.busy": "2023-12-01T18:27:34.795037Z", + "iopub.status.idle": "2023-12-01T18:27:34.803322Z", + "shell.execute_reply": "2023-12-01T18:27:34.802107Z" } }, "outputs": [ @@ -428,7 +415,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/_sources/B_publications.ipynb.txt b/docs/_sources/B_publications.ipynb.txt index 321817d..abfb685 100644 --- a/docs/_sources/B_publications.ipynb.txt +++ b/docs/_sources/B_publications.ipynb.txt @@ -292,7 +292,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/_sources/E_changelog.ipynb.txt b/docs/_sources/E_changelog.ipynb.txt index 36becf2..b055499 100644 --- a/docs/_sources/E_changelog.ipynb.txt +++ b/docs/_sources/E_changelog.ipynb.txt @@ -22,7 +22,7 @@ "metadata": {}, "source": [ "### **v1.0.5**\n", - "**Release date: 2nd December 2023**\n", + "**Release date: 3rd December 2023**\n", "\n", "#### Using Meson as build system\n", "\n", @@ -38,7 +38,7 @@ "\n", "#### Preparation for the addition of multiple gas species\n", "\n", - "In order to add multiple gas species in future versions, the Jacobian of the gas surface density has been modified. All previous model that have not specifically customized the gas Jacobian should be compatible with this version." + "In order to add multiple gas species in future versions, the Jacobian of the gas surface density has been modified. All previous models that have not specifically customized the gas Jacobian should be compatible with this version." ] }, { @@ -134,7 +134,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/_sources/example_planetary_gaps.ipynb.txt b/docs/_sources/example_planetary_gaps.ipynb.txt index 94abf2e..1297c15 100644 --- a/docs/_sources/example_planetary_gaps.ipynb.txt +++ b/docs/_sources/example_planetary_gaps.ipynb.txt @@ -63,10 +63,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:52.129232Z", - "iopub.status.busy": "2023-11-30T11:29:52.128558Z", - "iopub.status.idle": "2023-11-30T11:29:52.240310Z", - "shell.execute_reply": "2023-11-30T11:29:52.239369Z" + "iopub.execute_input": "2023-12-01T18:14:24.933354Z", + "iopub.status.busy": "2023-12-01T18:14:24.932702Z", + "iopub.status.idle": "2023-12-01T18:14:25.198137Z", + "shell.execute_reply": "2023-12-01T18:14:25.196846Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:52.246190Z", - "iopub.status.busy": "2023-11-30T11:29:52.245867Z", - "iopub.status.idle": "2023-11-30T11:29:53.098662Z", - "shell.execute_reply": "2023-11-30T11:29:53.097265Z" + "iopub.execute_input": "2023-12-01T18:14:25.204221Z", + "iopub.status.busy": "2023-12-01T18:14:25.203467Z", + "iopub.status.idle": "2023-12-01T18:14:26.675995Z", + "shell.execute_reply": "2023-12-01T18:14:26.674206Z" } }, "outputs": [], @@ -151,10 +151,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.105337Z", - "iopub.status.busy": "2023-11-30T11:29:53.104607Z", - "iopub.status.idle": "2023-11-30T11:29:53.110972Z", - "shell.execute_reply": "2023-11-30T11:29:53.110058Z" + "iopub.execute_input": "2023-12-01T18:14:26.683152Z", + "iopub.status.busy": "2023-12-01T18:14:26.682003Z", + "iopub.status.idle": "2023-12-01T18:14:26.690435Z", + "shell.execute_reply": "2023-12-01T18:14:26.689043Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.115852Z", - "iopub.status.busy": "2023-11-30T11:29:53.115517Z", - "iopub.status.idle": "2023-11-30T11:29:53.121117Z", - "shell.execute_reply": "2023-11-30T11:29:53.119815Z" + "iopub.execute_input": "2023-12-01T18:14:26.695921Z", + "iopub.status.busy": "2023-12-01T18:14:26.695357Z", + "iopub.status.idle": "2023-12-01T18:14:26.700826Z", + "shell.execute_reply": "2023-12-01T18:14:26.699758Z" } }, "outputs": [], @@ -183,10 +183,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.126888Z", - "iopub.status.busy": "2023-11-30T11:29:53.126275Z", - "iopub.status.idle": "2023-11-30T11:29:53.607704Z", - "shell.execute_reply": "2023-11-30T11:29:53.606152Z" + "iopub.execute_input": "2023-12-01T18:14:26.706354Z", + "iopub.status.busy": "2023-12-01T18:14:26.705722Z", + "iopub.status.idle": "2023-12-01T18:14:27.206810Z", + "shell.execute_reply": "2023-12-01T18:14:27.205424Z" } }, "outputs": [ @@ -236,10 +236,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.670755Z", - "iopub.status.busy": "2023-11-30T11:29:53.670083Z", - "iopub.status.idle": "2023-11-30T11:29:53.680716Z", - "shell.execute_reply": "2023-11-30T11:29:53.679175Z" + "iopub.execute_input": "2023-12-01T18:14:27.265710Z", + "iopub.status.busy": "2023-12-01T18:14:27.265385Z", + "iopub.status.idle": "2023-12-01T18:14:27.273687Z", + "shell.execute_reply": "2023-12-01T18:14:27.272642Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.685358Z", - "iopub.status.busy": "2023-11-30T11:29:53.684684Z", - "iopub.status.idle": "2023-11-30T11:29:53.690957Z", - "shell.execute_reply": "2023-11-30T11:29:53.689590Z" + "iopub.execute_input": "2023-12-01T18:14:27.276686Z", + "iopub.status.busy": "2023-12-01T18:14:27.276224Z", + "iopub.status.idle": "2023-12-01T18:14:27.281222Z", + "shell.execute_reply": "2023-12-01T18:14:27.280185Z" } }, "outputs": [], @@ -280,10 +280,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.695785Z", - "iopub.status.busy": "2023-11-30T11:29:53.695160Z", - "iopub.status.idle": "2023-11-30T11:29:53.702913Z", - "shell.execute_reply": "2023-11-30T11:29:53.701549Z" + "iopub.execute_input": "2023-12-01T18:14:27.284821Z", + "iopub.status.busy": "2023-12-01T18:14:27.284358Z", + "iopub.status.idle": "2023-12-01T18:14:27.291069Z", + "shell.execute_reply": "2023-12-01T18:14:27.290060Z" } }, "outputs": [], @@ -296,10 +296,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.708369Z", - "iopub.status.busy": "2023-11-30T11:29:53.707712Z", - "iopub.status.idle": "2023-11-30T11:29:53.715484Z", - "shell.execute_reply": "2023-11-30T11:29:53.714085Z" + "iopub.execute_input": "2023-12-01T18:14:27.294845Z", + "iopub.status.busy": "2023-12-01T18:14:27.294494Z", + "iopub.status.idle": "2023-12-01T18:14:27.300831Z", + "shell.execute_reply": "2023-12-01T18:14:27.299752Z" } }, "outputs": [], @@ -315,10 +315,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.721308Z", - "iopub.status.busy": "2023-11-30T11:29:53.720704Z", - "iopub.status.idle": "2023-11-30T11:29:54.366573Z", - "shell.execute_reply": "2023-11-30T11:29:54.365260Z" + "iopub.execute_input": "2023-12-01T18:14:27.305992Z", + "iopub.status.busy": "2023-12-01T18:14:27.305024Z", + "iopub.status.idle": "2023-12-01T18:14:27.709338Z", + "shell.execute_reply": "2023-12-01T18:14:27.708252Z" } }, "outputs": [], @@ -331,10 +331,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.372540Z", - "iopub.status.busy": "2023-11-30T11:29:54.372302Z", - "iopub.status.idle": "2023-11-30T11:29:54.376927Z", - "shell.execute_reply": "2023-11-30T11:29:54.376175Z" + "iopub.execute_input": "2023-12-01T18:14:27.714952Z", + "iopub.status.busy": "2023-12-01T18:14:27.714734Z", + "iopub.status.idle": "2023-12-01T18:14:27.721263Z", + "shell.execute_reply": "2023-12-01T18:14:27.720401Z" } }, "outputs": [], @@ -349,10 +349,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.381330Z", - "iopub.status.busy": "2023-11-30T11:29:54.381106Z", - "iopub.status.idle": "2023-11-30T11:29:54.385672Z", - "shell.execute_reply": "2023-11-30T11:29:54.384888Z" + "iopub.execute_input": "2023-12-01T18:14:27.725605Z", + "iopub.status.busy": "2023-12-01T18:14:27.725381Z", + "iopub.status.idle": "2023-12-01T18:14:27.729979Z", + "shell.execute_reply": "2023-12-01T18:14:27.729131Z" } }, "outputs": [], @@ -366,10 +366,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.390085Z", - "iopub.status.busy": "2023-11-30T11:29:54.389800Z", - "iopub.status.idle": "2023-11-30T11:29:54.394596Z", - "shell.execute_reply": "2023-11-30T11:29:54.393718Z" + "iopub.execute_input": "2023-12-01T18:14:27.734591Z", + "iopub.status.busy": "2023-12-01T18:14:27.734228Z", + "iopub.status.idle": "2023-12-01T18:14:27.738978Z", + "shell.execute_reply": "2023-12-01T18:14:27.738015Z" } }, "outputs": [], @@ -390,10 +390,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.398882Z", - "iopub.status.busy": "2023-11-30T11:29:54.398592Z", - "iopub.status.idle": "2023-11-30T11:29:54.404735Z", - "shell.execute_reply": "2023-11-30T11:29:54.403711Z" + "iopub.execute_input": "2023-12-01T18:14:27.743530Z", + "iopub.status.busy": "2023-12-01T18:14:27.743038Z", + "iopub.status.idle": "2023-12-01T18:14:27.750131Z", + "shell.execute_reply": "2023-12-01T18:14:27.749029Z" } }, "outputs": [ @@ -431,10 +431,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.410128Z", - "iopub.status.busy": "2023-11-30T11:29:54.409278Z", - "iopub.status.idle": "2023-11-30T11:29:54.415072Z", - "shell.execute_reply": "2023-11-30T11:29:54.413698Z" + "iopub.execute_input": "2023-12-01T18:14:27.755419Z", + "iopub.status.busy": "2023-12-01T18:14:27.754773Z", + "iopub.status.idle": "2023-12-01T18:14:27.760588Z", + "shell.execute_reply": "2023-12-01T18:14:27.759350Z" } }, "outputs": [], @@ -447,10 +447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.420387Z", - "iopub.status.busy": "2023-11-30T11:29:54.419781Z", - "iopub.status.idle": "2023-11-30T11:29:54.429622Z", - "shell.execute_reply": "2023-11-30T11:29:54.428189Z" + "iopub.execute_input": "2023-12-01T18:14:27.766119Z", + "iopub.status.busy": "2023-12-01T18:14:27.765483Z", + "iopub.status.idle": "2023-12-01T18:14:27.775588Z", + "shell.execute_reply": "2023-12-01T18:14:27.774155Z" } }, "outputs": [], @@ -503,10 +503,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.435684Z", - "iopub.status.busy": "2023-11-30T11:29:54.435090Z", - "iopub.status.idle": "2023-11-30T11:29:54.847760Z", - "shell.execute_reply": "2023-11-30T11:29:54.846880Z" + "iopub.execute_input": "2023-12-01T18:14:27.781304Z", + "iopub.status.busy": "2023-12-01T18:14:27.780719Z", + "iopub.status.idle": "2023-12-01T18:14:28.105352Z", + "shell.execute_reply": "2023-12-01T18:14:28.104528Z" } }, "outputs": [ @@ -546,10 +546,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.854636Z", - "iopub.status.busy": "2023-11-30T11:29:54.854352Z", - "iopub.status.idle": "2023-11-30T11:29:54.860494Z", - "shell.execute_reply": "2023-11-30T11:29:54.859667Z" + "iopub.execute_input": "2023-12-01T18:14:28.111151Z", + "iopub.status.busy": "2023-12-01T18:14:28.110966Z", + "iopub.status.idle": "2023-12-01T18:14:28.115775Z", + "shell.execute_reply": "2023-12-01T18:14:28.114977Z" } }, "outputs": [], @@ -571,10 +571,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.864805Z", - "iopub.status.busy": "2023-11-30T11:29:54.864520Z", - "iopub.status.idle": "2023-11-30T11:29:54.869978Z", - "shell.execute_reply": "2023-11-30T11:29:54.869117Z" + "iopub.execute_input": "2023-12-01T18:14:28.120046Z", + "iopub.status.busy": "2023-12-01T18:14:28.119808Z", + "iopub.status.idle": "2023-12-01T18:14:28.124553Z", + "shell.execute_reply": "2023-12-01T18:14:28.123653Z" } }, "outputs": [], @@ -614,10 +614,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.874735Z", - "iopub.status.busy": "2023-11-30T11:29:54.874447Z", - "iopub.status.idle": "2023-11-30T11:29:54.880600Z", - "shell.execute_reply": "2023-11-30T11:29:54.879559Z" + "iopub.execute_input": "2023-12-01T18:14:28.128841Z", + "iopub.status.busy": "2023-12-01T18:14:28.128550Z", + "iopub.status.idle": "2023-12-01T18:14:28.133840Z", + "shell.execute_reply": "2023-12-01T18:14:28.132934Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.886172Z", - "iopub.status.busy": "2023-11-30T11:29:54.885536Z", - "iopub.status.idle": "2023-11-30T11:29:54.891995Z", - "shell.execute_reply": "2023-11-30T11:29:54.890577Z" + "iopub.execute_input": "2023-12-01T18:14:28.138099Z", + "iopub.status.busy": "2023-12-01T18:14:28.137674Z", + "iopub.status.idle": "2023-12-01T18:14:28.143092Z", + "shell.execute_reply": "2023-12-01T18:14:28.141997Z" } }, "outputs": [], @@ -665,10 +665,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.897916Z", - "iopub.status.busy": "2023-11-30T11:29:54.897253Z", - "iopub.status.idle": "2023-11-30T11:29:54.904396Z", - "shell.execute_reply": "2023-11-30T11:29:54.902939Z" + "iopub.execute_input": "2023-12-01T18:14:28.148089Z", + "iopub.status.busy": "2023-12-01T18:14:28.147547Z", + "iopub.status.idle": "2023-12-01T18:14:28.154368Z", + "shell.execute_reply": "2023-12-01T18:14:28.152946Z" } }, "outputs": [], @@ -690,10 +690,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.910598Z", - "iopub.status.busy": "2023-11-30T11:29:54.909905Z", - "iopub.status.idle": "2023-11-30T11:29:54.916561Z", - "shell.execute_reply": "2023-11-30T11:29:54.915143Z" + "iopub.execute_input": "2023-12-01T18:14:28.160403Z", + "iopub.status.busy": "2023-12-01T18:14:28.159754Z", + "iopub.status.idle": "2023-12-01T18:14:28.166115Z", + "shell.execute_reply": "2023-12-01T18:14:28.164895Z" } }, "outputs": [], @@ -713,10 +713,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.922641Z", - "iopub.status.busy": "2023-11-30T11:29:54.921985Z", - "iopub.status.idle": "2023-11-30T11:29:54.928330Z", - "shell.execute_reply": "2023-11-30T11:29:54.926886Z" + "iopub.execute_input": "2023-12-01T18:14:28.171925Z", + "iopub.status.busy": "2023-12-01T18:14:28.171296Z", + "iopub.status.idle": "2023-12-01T18:14:28.177279Z", + "shell.execute_reply": "2023-12-01T18:14:28.176070Z" } }, "outputs": [], @@ -736,10 +736,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.934651Z", - "iopub.status.busy": "2023-11-30T11:29:54.933479Z", - "iopub.status.idle": "2023-11-30T11:29:55.184886Z", - "shell.execute_reply": "2023-11-30T11:29:55.183295Z" + "iopub.execute_input": "2023-12-01T18:14:28.182440Z", + "iopub.status.busy": "2023-12-01T18:14:28.182157Z", + "iopub.status.idle": "2023-12-01T18:14:28.423102Z", + "shell.execute_reply": "2023-12-01T18:14:28.422108Z" } }, "outputs": [], @@ -759,10 +759,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:55.191648Z", - "iopub.status.busy": "2023-11-30T11:29:55.191272Z", - "iopub.status.idle": "2023-11-30T11:29:55.199251Z", - "shell.execute_reply": "2023-11-30T11:29:55.198305Z" + "iopub.execute_input": "2023-12-01T18:14:28.428017Z", + "iopub.status.busy": "2023-12-01T18:14:28.427785Z", + "iopub.status.idle": "2023-12-01T18:14:28.433172Z", + "shell.execute_reply": "2023-12-01T18:14:28.432251Z" } }, "outputs": [ @@ -793,10 +793,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:55.204892Z", - "iopub.status.busy": "2023-11-30T11:29:55.204501Z", - "iopub.status.idle": "2023-11-30T11:29:55.210546Z", - "shell.execute_reply": "2023-11-30T11:29:55.209203Z" + "iopub.execute_input": "2023-12-01T18:14:28.438068Z", + "iopub.status.busy": "2023-12-01T18:14:28.437819Z", + "iopub.status.idle": "2023-12-01T18:14:28.442328Z", + "shell.execute_reply": "2023-12-01T18:14:28.441414Z" } }, "outputs": [], @@ -809,10 +809,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:55.216331Z", - "iopub.status.busy": "2023-11-30T11:29:55.215855Z", - "iopub.status.idle": "2023-11-30T12:42:39.054199Z", - "shell.execute_reply": "2023-11-30T12:42:39.052575Z" + "iopub.execute_input": "2023-12-01T18:14:28.446729Z", + "iopub.status.busy": "2023-12-01T18:14:28.446416Z", + "iopub.status.idle": "2023-12-01T19:32:37.549949Z", + "shell.execute_reply": "2023-12-01T19:32:37.548932Z" } }, "outputs": [ @@ -892,7 +892,7 @@ "Writing dump file \u001b[94mexample_planetary_gaps/frame.dmp\u001b[0m\n", "Writing file \u001b[94mexample_planetary_gaps/data0021.hdf5\u001b[0m\n", "Writing dump file \u001b[94mexample_planetary_gaps/frame.dmp\u001b[0m\n", - "Execution time: \u001b[94m1:12:43\u001b[0m\n" + "Execution time: \u001b[94m1:18:09\u001b[0m\n" ] } ], @@ -912,10 +912,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T12:42:39.062113Z", - "iopub.status.busy": "2023-11-30T12:42:39.061747Z", - "iopub.status.idle": "2023-11-30T12:42:39.068126Z", - "shell.execute_reply": "2023-11-30T12:42:39.067085Z" + "iopub.execute_input": "2023-12-01T19:32:37.555286Z", + "iopub.status.busy": "2023-12-01T19:32:37.554994Z", + "iopub.status.idle": "2023-12-01T19:32:37.559853Z", + "shell.execute_reply": "2023-12-01T19:32:37.559014Z" } }, "outputs": [], @@ -928,10 +928,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T12:42:39.073220Z", - "iopub.status.busy": "2023-11-30T12:42:39.072860Z", - "iopub.status.idle": "2023-11-30T12:42:40.238967Z", - "shell.execute_reply": "2023-11-30T12:42:40.237997Z" + "iopub.execute_input": "2023-12-01T19:32:37.564722Z", + "iopub.status.busy": "2023-12-01T19:32:37.564409Z", + "iopub.status.idle": "2023-12-01T19:32:38.569154Z", + "shell.execute_reply": "2023-12-01T19:32:38.568275Z" } }, "outputs": [ @@ -964,10 +964,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T12:42:40.248751Z", - "iopub.status.busy": "2023-11-30T12:42:40.248458Z", - "iopub.status.idle": "2023-11-30T12:42:40.283547Z", - "shell.execute_reply": "2023-11-30T12:42:40.282564Z" + "iopub.execute_input": "2023-12-01T19:32:38.577757Z", + "iopub.status.busy": "2023-12-01T19:32:38.577529Z", + "iopub.status.idle": "2023-12-01T19:32:38.610772Z", + "shell.execute_reply": "2023-12-01T19:32:38.609417Z" } }, "outputs": [], @@ -982,23 +982,13 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T12:42:40.288954Z", - "iopub.status.busy": "2023-11-30T12:42:40.288698Z", - "iopub.status.idle": "2023-11-30T12:42:40.455284Z", - "shell.execute_reply": "2023-11-30T12:42:40.454334Z" + "iopub.execute_input": "2023-12-01T19:32:38.615196Z", + "iopub.status.busy": "2023-12-01T19:32:38.615028Z", + "iopub.status.idle": "2023-12-01T19:32:38.758988Z", + "shell.execute_reply": "2023-12-01T19:32:38.757633Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:9: SyntaxWarning: invalid escape sequence '\\o'\n", - "<>:9: SyntaxWarning: invalid escape sequence '\\o'\n", - "/tmp/ipykernel_94188/1466386948.py:9: SyntaxWarning: invalid escape sequence '\\o'\n", - " ax.set_ylabel(\"Mass [$M_\\oplus$]\")\n" - ] - }, { "data": { "image/png": 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", @@ -1018,8 +1008,8 @@ "ax.legend()\n", "ax.set_xlim(t[0]/c.year, t[-1]/c.year)\n", "ax.grid(visible=True)\n", - "ax.set_xlabel(\"Time [years]\")\n", - "ax.set_ylabel(\"Mass [$M_\\oplus$]\")\n", + "ax.set_xlabel(r\"Time [years]\")\n", + "ax.set_ylabel(r\"Mass [$M_\\oplus$]\")\n", "fig.tight_layout()\n", "plt.show()" ] @@ -1041,7 +1031,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/_sources/example_planetesimal_formation.ipynb.txt b/docs/_sources/example_planetesimal_formation.ipynb.txt index 547ec4a..0426658 100644 --- a/docs/_sources/example_planetesimal_formation.ipynb.txt +++ b/docs/_sources/example_planetesimal_formation.ipynb.txt @@ -43,10 +43,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:11.769745Z", - "iopub.status.busy": "2023-11-30T11:30:11.769049Z", - "iopub.status.idle": "2023-11-30T11:30:12.965161Z", - "shell.execute_reply": "2023-11-30T11:30:12.963939Z" + "iopub.execute_input": "2023-12-01T18:14:27.375259Z", + "iopub.status.busy": "2023-12-01T18:14:27.374595Z", + "iopub.status.idle": "2023-12-01T18:14:28.251107Z", + "shell.execute_reply": "2023-12-01T18:14:28.249797Z" } }, "outputs": [], @@ -60,10 +60,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:12.971730Z", - "iopub.status.busy": "2023-11-30T11:30:12.971010Z", - "iopub.status.idle": "2023-11-30T11:30:12.977418Z", - "shell.execute_reply": "2023-11-30T11:30:12.976543Z" + "iopub.execute_input": "2023-12-01T18:14:28.257208Z", + "iopub.status.busy": "2023-12-01T18:14:28.256740Z", + "iopub.status.idle": "2023-12-01T18:14:28.261674Z", + "shell.execute_reply": "2023-12-01T18:14:28.260905Z" } }, "outputs": [], @@ -104,10 +104,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:12.982434Z", - "iopub.status.busy": "2023-11-30T11:30:12.982088Z", - "iopub.status.idle": "2023-11-30T11:30:12.989335Z", - "shell.execute_reply": "2023-11-30T11:30:12.988307Z" + "iopub.execute_input": "2023-12-01T18:14:28.266124Z", + "iopub.status.busy": "2023-12-01T18:14:28.265900Z", + "iopub.status.idle": "2023-12-01T18:14:28.270964Z", + "shell.execute_reply": "2023-12-01T18:14:28.270097Z" } }, "outputs": [], @@ -134,10 +134,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:12.994722Z", - "iopub.status.busy": "2023-11-30T11:30:12.994344Z", - "iopub.status.idle": "2023-11-30T11:30:13.002669Z", - "shell.execute_reply": "2023-11-30T11:30:13.001647Z" + "iopub.execute_input": "2023-12-01T18:14:28.275586Z", + "iopub.status.busy": "2023-12-01T18:14:28.275310Z", + "iopub.status.idle": "2023-12-01T18:14:28.283460Z", + "shell.execute_reply": "2023-12-01T18:14:28.282494Z" } }, "outputs": [], @@ -189,10 +189,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:13.008079Z", - "iopub.status.busy": "2023-11-30T11:30:13.007683Z", - "iopub.status.idle": "2023-11-30T11:30:13.013947Z", - "shell.execute_reply": "2023-11-30T11:30:13.012885Z" + "iopub.execute_input": "2023-12-01T18:14:28.288258Z", + "iopub.status.busy": "2023-12-01T18:14:28.287883Z", + "iopub.status.idle": "2023-12-01T18:14:28.294388Z", + "shell.execute_reply": "2023-12-01T18:14:28.293087Z" } }, "outputs": [], @@ -206,10 +206,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:13.019212Z", - "iopub.status.busy": "2023-11-30T11:30:13.018802Z", - "iopub.status.idle": "2023-11-30T11:30:13.981525Z", - "shell.execute_reply": "2023-11-30T11:30:13.978957Z" + "iopub.execute_input": "2023-12-01T18:14:28.299786Z", + "iopub.status.busy": "2023-12-01T18:14:28.299193Z", + "iopub.status.idle": "2023-12-01T18:14:28.902999Z", + "shell.execute_reply": "2023-12-01T18:14:28.902057Z" } }, "outputs": [], @@ -229,10 +229,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:13.988177Z", - "iopub.status.busy": "2023-11-30T11:30:13.987833Z", - "iopub.status.idle": "2023-11-30T11:30:13.993344Z", - "shell.execute_reply": "2023-11-30T11:30:13.992457Z" + "iopub.execute_input": "2023-12-01T18:14:28.908058Z", + "iopub.status.busy": "2023-12-01T18:14:28.907785Z", + "iopub.status.idle": "2023-12-01T18:14:28.912382Z", + "shell.execute_reply": "2023-12-01T18:14:28.911566Z" } }, "outputs": [], @@ -254,10 +254,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:13.998175Z", - "iopub.status.busy": "2023-11-30T11:30:13.997845Z", - "iopub.status.idle": "2023-11-30T11:30:14.003026Z", - "shell.execute_reply": "2023-11-30T11:30:14.002140Z" + "iopub.execute_input": "2023-12-01T18:14:28.916973Z", + "iopub.status.busy": "2023-12-01T18:14:28.916706Z", + "iopub.status.idle": "2023-12-01T18:14:28.921477Z", + "shell.execute_reply": "2023-12-01T18:14:28.920648Z" } }, "outputs": [], @@ -280,10 +280,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.008770Z", - "iopub.status.busy": "2023-11-30T11:30:14.007920Z", - "iopub.status.idle": "2023-11-30T11:30:14.014090Z", - "shell.execute_reply": "2023-11-30T11:30:14.012758Z" + "iopub.execute_input": "2023-12-01T18:14:28.926195Z", + "iopub.status.busy": "2023-12-01T18:14:28.925904Z", + "iopub.status.idle": "2023-12-01T18:14:28.930521Z", + "shell.execute_reply": "2023-12-01T18:14:28.929628Z" } }, "outputs": [], @@ -296,10 +296,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.019780Z", - "iopub.status.busy": "2023-11-30T11:30:14.019126Z", - "iopub.status.idle": "2023-11-30T11:30:14.027004Z", - "shell.execute_reply": "2023-11-30T11:30:14.025550Z" + "iopub.execute_input": "2023-12-01T18:14:28.950944Z", + "iopub.status.busy": "2023-12-01T18:14:28.950172Z", + "iopub.status.idle": "2023-12-01T18:14:28.957944Z", + "shell.execute_reply": "2023-12-01T18:14:28.956527Z" } }, "outputs": [], @@ -313,10 +313,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.033093Z", - "iopub.status.busy": "2023-11-30T11:30:14.032428Z", - "iopub.status.idle": "2023-11-30T11:30:14.047997Z", - "shell.execute_reply": "2023-11-30T11:30:14.047036Z" + "iopub.execute_input": "2023-12-01T18:14:28.963483Z", + "iopub.status.busy": "2023-12-01T18:14:28.962811Z", + "iopub.status.idle": "2023-12-01T18:14:28.977729Z", + "shell.execute_reply": "2023-12-01T18:14:28.976677Z" } }, "outputs": [ @@ -351,10 +351,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.104990Z", - "iopub.status.busy": "2023-11-30T11:30:14.104177Z", - "iopub.status.idle": "2023-11-30T11:30:14.111269Z", - "shell.execute_reply": "2023-11-30T11:30:14.109982Z" + "iopub.execute_input": "2023-12-01T18:14:29.040512Z", + "iopub.status.busy": "2023-12-01T18:14:29.039648Z", + "iopub.status.idle": "2023-12-01T18:14:29.047136Z", + "shell.execute_reply": "2023-12-01T18:14:29.045655Z" } }, "outputs": [], @@ -368,10 +368,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.115282Z", - "iopub.status.busy": "2023-11-30T11:30:14.114607Z", - "iopub.status.idle": "2023-11-30T11:30:14.120860Z", - "shell.execute_reply": "2023-11-30T11:30:14.119633Z" + "iopub.execute_input": "2023-12-01T18:14:29.051288Z", + "iopub.status.busy": "2023-12-01T18:14:29.050567Z", + "iopub.status.idle": "2023-12-01T18:14:29.056794Z", + "shell.execute_reply": "2023-12-01T18:14:29.055583Z" } }, "outputs": [], @@ -391,10 +391,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.125135Z", - "iopub.status.busy": "2023-11-30T11:30:14.124447Z", - "iopub.status.idle": "2023-11-30T11:30:14.131450Z", - "shell.execute_reply": "2023-11-30T11:30:14.130030Z" + "iopub.execute_input": "2023-12-01T18:14:29.061106Z", + "iopub.status.busy": "2023-12-01T18:14:29.060463Z", + "iopub.status.idle": "2023-12-01T18:14:29.067295Z", + "shell.execute_reply": "2023-12-01T18:14:29.065915Z" } }, "outputs": [], @@ -417,10 +417,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.136597Z", - "iopub.status.busy": "2023-11-30T11:30:14.135878Z", - "iopub.status.idle": "2023-11-30T11:30:14.145371Z", - "shell.execute_reply": "2023-11-30T11:30:14.143896Z" + "iopub.execute_input": "2023-12-01T18:14:29.072276Z", + "iopub.status.busy": "2023-12-01T18:14:29.071617Z", + "iopub.status.idle": "2023-12-01T18:14:29.080164Z", + "shell.execute_reply": "2023-12-01T18:14:29.078819Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.151220Z", - "iopub.status.busy": "2023-11-30T11:30:14.150592Z", - "iopub.status.idle": "2023-11-30T11:30:14.156975Z", - "shell.execute_reply": "2023-11-30T11:30:14.155598Z" + "iopub.execute_input": "2023-12-01T18:14:29.085594Z", + "iopub.status.busy": "2023-12-01T18:14:29.084997Z", + "iopub.status.idle": "2023-12-01T18:14:29.090903Z", + "shell.execute_reply": "2023-12-01T18:14:29.089517Z" } }, "outputs": [], @@ -469,10 +469,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.162551Z", - "iopub.status.busy": "2023-11-30T11:30:14.161896Z", - "iopub.status.idle": "2023-11-30T11:30:14.168088Z", - "shell.execute_reply": "2023-11-30T11:30:14.166912Z" + "iopub.execute_input": "2023-12-01T18:14:29.104711Z", + "iopub.status.busy": "2023-12-01T18:14:29.102144Z", + "iopub.status.idle": "2023-12-01T18:14:29.112033Z", + "shell.execute_reply": "2023-12-01T18:14:29.110741Z" } }, "outputs": [], @@ -486,10 +486,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.173683Z", - "iopub.status.busy": "2023-11-30T11:30:14.173036Z", - "iopub.status.idle": "2023-11-30T11:30:14.179253Z", - "shell.execute_reply": "2023-11-30T11:30:14.177859Z" + "iopub.execute_input": "2023-12-01T18:14:29.117637Z", + "iopub.status.busy": "2023-12-01T18:14:29.117087Z", + "iopub.status.idle": "2023-12-01T18:14:29.123305Z", + "shell.execute_reply": "2023-12-01T18:14:29.122077Z" } }, "outputs": [], @@ -509,10 +509,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.185311Z", - "iopub.status.busy": "2023-11-30T11:30:14.184699Z", - "iopub.status.idle": "2023-11-30T11:30:14.190486Z", - "shell.execute_reply": "2023-11-30T11:30:14.189359Z" + "iopub.execute_input": "2023-12-01T18:14:29.129389Z", + "iopub.status.busy": "2023-12-01T18:14:29.128721Z", + "iopub.status.idle": "2023-12-01T18:14:29.135120Z", + "shell.execute_reply": "2023-12-01T18:14:29.133723Z" } }, "outputs": [], @@ -533,10 +533,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.196220Z", - "iopub.status.busy": "2023-11-30T11:30:14.195566Z", - "iopub.status.idle": "2023-11-30T11:30:14.201984Z", - "shell.execute_reply": "2023-11-30T11:30:14.200602Z" + "iopub.execute_input": "2023-12-01T18:14:29.141063Z", + "iopub.status.busy": "2023-12-01T18:14:29.140397Z", + "iopub.status.idle": "2023-12-01T18:14:29.147153Z", + "shell.execute_reply": "2023-12-01T18:14:29.145722Z" } }, "outputs": [], @@ -559,10 +559,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.207926Z", - "iopub.status.busy": "2023-11-30T11:30:14.207254Z", - "iopub.status.idle": "2023-11-30T11:30:14.213434Z", - "shell.execute_reply": "2023-11-30T11:30:14.212276Z" + "iopub.execute_input": "2023-12-01T18:14:29.154105Z", + "iopub.status.busy": "2023-12-01T18:14:29.152811Z", + "iopub.status.idle": "2023-12-01T18:14:29.159466Z", + "shell.execute_reply": "2023-12-01T18:14:29.158084Z" } }, "outputs": [], @@ -582,10 +582,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.219397Z", - "iopub.status.busy": "2023-11-30T11:30:14.218765Z", - "iopub.status.idle": "2023-11-30T11:30:14.227674Z", - "shell.execute_reply": "2023-11-30T11:30:14.226317Z" + "iopub.execute_input": "2023-12-01T18:14:29.165473Z", + "iopub.status.busy": "2023-12-01T18:14:29.164814Z", + "iopub.status.idle": "2023-12-01T18:14:29.174294Z", + "shell.execute_reply": "2023-12-01T18:14:29.172917Z" } }, "outputs": [ @@ -620,10 +620,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.233600Z", - "iopub.status.busy": "2023-11-30T11:30:14.232956Z", - "iopub.status.idle": "2023-11-30T11:30:14.495415Z", - "shell.execute_reply": "2023-11-30T11:30:14.494445Z" + "iopub.execute_input": "2023-12-01T18:14:29.180271Z", + "iopub.status.busy": "2023-12-01T18:14:29.179574Z", + "iopub.status.idle": "2023-12-01T18:14:29.415923Z", + "shell.execute_reply": "2023-12-01T18:14:29.415034Z" } }, "outputs": [], @@ -637,10 +637,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.500674Z", - "iopub.status.busy": "2023-11-30T11:30:14.500392Z", - "iopub.status.idle": "2023-12-01T09:48:07.108521Z", - "shell.execute_reply": "2023-12-01T09:48:07.107540Z" + "iopub.execute_input": "2023-12-01T18:14:29.420841Z", + "iopub.status.busy": "2023-12-01T18:14:29.420612Z", + "iopub.status.idle": "2023-12-02T20:09:19.909195Z", + "shell.execute_reply": "2023-12-02T20:09:19.907871Z" } }, "outputs": [ @@ -740,7 +740,7 @@ "Writing dump file \u001b[94mexample_planetesimal_formation/frame.dmp\u001b[0m\n", "Writing file \u001b[94mexample_planetesimal_formation/data0031.hdf5\u001b[0m\n", "Writing dump file \u001b[94mexample_planetesimal_formation/frame.dmp\u001b[0m\n", - "Execution time: \u001b[94m22:17:52\u001b[0m\n" + "Execution time: \u001b[94m1 day, 1:54:50\u001b[0m\n" ] } ], @@ -760,10 +760,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:07.114558Z", - "iopub.status.busy": "2023-12-01T09:48:07.114307Z", - "iopub.status.idle": "2023-12-01T09:48:07.118715Z", - "shell.execute_reply": "2023-12-01T09:48:07.117955Z" + "iopub.execute_input": "2023-12-02T20:09:19.914589Z", + "iopub.status.busy": "2023-12-02T20:09:19.914314Z", + "iopub.status.idle": "2023-12-02T20:09:19.919544Z", + "shell.execute_reply": "2023-12-02T20:09:19.918758Z" } }, "outputs": [], @@ -776,10 +776,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:07.123221Z", - "iopub.status.busy": "2023-12-01T09:48:07.122948Z", - "iopub.status.idle": "2023-12-01T09:48:08.199010Z", - "shell.execute_reply": "2023-12-01T09:48:08.197703Z" + "iopub.execute_input": "2023-12-02T20:09:19.924164Z", + "iopub.status.busy": "2023-12-02T20:09:19.923845Z", + "iopub.status.idle": "2023-12-02T20:09:21.119701Z", + "shell.execute_reply": "2023-12-02T20:09:21.118158Z" } }, "outputs": [ @@ -810,10 +810,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:08.208190Z", - "iopub.status.busy": "2023-12-01T09:48:08.207519Z", - "iopub.status.idle": "2023-12-01T09:48:08.275422Z", - "shell.execute_reply": "2023-12-01T09:48:08.274700Z" + "iopub.execute_input": "2023-12-02T20:09:21.128905Z", + "iopub.status.busy": "2023-12-02T20:09:21.128167Z", + "iopub.status.idle": "2023-12-02T20:09:21.197907Z", + "shell.execute_reply": "2023-12-02T20:09:21.197017Z" } }, "outputs": [], @@ -836,10 +836,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:08.280160Z", - "iopub.status.busy": "2023-12-01T09:48:08.279973Z", - "iopub.status.idle": "2023-12-01T09:48:08.283928Z", - "shell.execute_reply": "2023-12-01T09:48:08.283178Z" + "iopub.execute_input": "2023-12-02T20:09:21.202679Z", + "iopub.status.busy": "2023-12-02T20:09:21.202467Z", + "iopub.status.idle": "2023-12-02T20:09:21.206941Z", + "shell.execute_reply": "2023-12-02T20:09:21.206151Z" } }, "outputs": [], @@ -853,23 +853,13 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:08.288344Z", - "iopub.status.busy": "2023-12-01T09:48:08.288122Z", - "iopub.status.idle": "2023-12-01T09:48:08.871279Z", - "shell.execute_reply": "2023-12-01T09:48:08.870525Z" + "iopub.execute_input": "2023-12-02T20:09:21.211199Z", + "iopub.status.busy": "2023-12-02T20:09:21.210974Z", + "iopub.status.idle": "2023-12-02T20:09:21.846183Z", + "shell.execute_reply": "2023-12-02T20:09:21.845405Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:21: SyntaxWarning: invalid escape sequence '\\o'\n", - "<>:21: SyntaxWarning: invalid escape sequence '\\o'\n", - "/tmp/ipykernel_94249/2126622601.py:21: SyntaxWarning: invalid escape sequence '\\o'\n", - " ax10.set_ylabel(\"Mass [$M_\\oplus$]\")\n" - ] - }, { "data": { "image/png": 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", @@ -901,8 +891,8 @@ "ax10.loglog(t/c.year, Mplan/c.M_earth, label=\"Planetesimals\")\n", "ax10.set_xlim(t[1]/c.year, t[-1]/c.year)\n", "ax10.set_ylim(1.e-1, 1.e5)\n", - "ax10.set_xlabel(\"Time [yrs]\")\n", - "ax10.set_ylabel(\"Mass [$M_\\oplus$]\")\n", + "ax10.set_xlabel(r\"Time [yrs]\")\n", + "ax10.set_ylabel(r\"Mass [$M_\\oplus$]\")\n", "\n", "fig.tight_layout()\n", "plt.show()" @@ -925,7 +915,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/_sources/test_analytical_coagulation_kernels.ipynb.txt b/docs/_sources/test_analytical_coagulation_kernels.ipynb.txt index 959f9b9..5b470a8 100644 --- a/docs/_sources/test_analytical_coagulation_kernels.ipynb.txt +++ b/docs/_sources/test_analytical_coagulation_kernels.ipynb.txt @@ -69,10 +69,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.840855Z", - "iopub.status.busy": "2023-11-30T11:31:10.840075Z", - "iopub.status.idle": "2023-11-30T11:31:10.956938Z", - "shell.execute_reply": "2023-11-30T11:31:10.955974Z" + "iopub.execute_input": "2023-12-01T18:14:32.210905Z", + "iopub.status.busy": "2023-12-01T18:14:32.210249Z", + "iopub.status.idle": "2023-12-01T18:14:32.312616Z", + "shell.execute_reply": "2023-12-01T18:14:32.311787Z" } }, "outputs": [], @@ -85,10 +85,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.962938Z", - "iopub.status.busy": "2023-11-30T11:31:10.962523Z", - "iopub.status.idle": "2023-11-30T11:31:10.968394Z", - "shell.execute_reply": "2023-11-30T11:31:10.967499Z" + "iopub.execute_input": "2023-12-01T18:14:32.317453Z", + "iopub.status.busy": "2023-12-01T18:14:32.317214Z", + "iopub.status.idle": "2023-12-01T18:14:32.321078Z", + "shell.execute_reply": "2023-12-01T18:14:32.320365Z" } }, "outputs": [], @@ -124,10 +124,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.973465Z", - "iopub.status.busy": "2023-11-30T11:31:10.973124Z", - "iopub.status.idle": "2023-11-30T11:31:10.979670Z", - "shell.execute_reply": "2023-11-30T11:31:10.978696Z" + "iopub.execute_input": "2023-12-01T18:14:32.325515Z", + "iopub.status.busy": "2023-12-01T18:14:32.325340Z", + "iopub.status.idle": "2023-12-01T18:14:32.329848Z", + "shell.execute_reply": "2023-12-01T18:14:32.329087Z" } }, "outputs": [], @@ -171,10 +171,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.985163Z", - "iopub.status.busy": "2023-11-30T11:31:10.984775Z", - "iopub.status.idle": "2023-11-30T11:31:10.994639Z", - "shell.execute_reply": "2023-11-30T11:31:10.993639Z" + "iopub.execute_input": "2023-12-01T18:14:32.335764Z", + "iopub.status.busy": "2023-12-01T18:14:32.334906Z", + "iopub.status.idle": "2023-12-01T18:14:32.347139Z", + "shell.execute_reply": "2023-12-01T18:14:32.345777Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.999763Z", - "iopub.status.busy": "2023-11-30T11:31:10.999409Z", - "iopub.status.idle": "2023-11-30T11:31:11.899428Z", - "shell.execute_reply": "2023-11-30T11:31:11.898038Z" + "iopub.execute_input": "2023-12-01T18:14:32.352831Z", + "iopub.status.busy": "2023-12-01T18:14:32.351748Z", + "iopub.status.idle": "2023-12-01T18:14:33.104455Z", + "shell.execute_reply": "2023-12-01T18:14:33.103163Z" } }, "outputs": [], @@ -244,10 +244,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:11.903963Z", - "iopub.status.busy": "2023-11-30T11:31:11.903361Z", - "iopub.status.idle": "2023-11-30T11:31:11.910083Z", - "shell.execute_reply": "2023-11-30T11:31:11.909162Z" + "iopub.execute_input": "2023-12-01T18:14:33.109672Z", + "iopub.status.busy": "2023-12-01T18:14:33.109241Z", + "iopub.status.idle": "2023-12-01T18:14:33.114376Z", + "shell.execute_reply": "2023-12-01T18:14:33.113635Z" } }, "outputs": [], @@ -267,10 +267,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:11.913615Z", - "iopub.status.busy": "2023-11-30T11:31:11.913336Z", - "iopub.status.idle": "2023-11-30T11:31:11.918292Z", - "shell.execute_reply": "2023-11-30T11:31:11.917346Z" + "iopub.execute_input": "2023-12-01T18:14:33.118849Z", + "iopub.status.busy": "2023-12-01T18:14:33.118630Z", + "iopub.status.idle": "2023-12-01T18:14:33.122410Z", + "shell.execute_reply": "2023-12-01T18:14:33.121670Z" } }, "outputs": [], @@ -290,10 +290,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:11.921980Z", - "iopub.status.busy": "2023-11-30T11:31:11.921663Z", - "iopub.status.idle": "2023-11-30T11:31:11.927279Z", - "shell.execute_reply": "2023-11-30T11:31:11.925961Z" + "iopub.execute_input": "2023-12-01T18:14:33.126578Z", + "iopub.status.busy": "2023-12-01T18:14:33.126305Z", + "iopub.status.idle": "2023-12-01T18:14:33.130292Z", + "shell.execute_reply": "2023-12-01T18:14:33.129441Z" } }, "outputs": [], @@ -306,10 +306,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:11.931141Z", - "iopub.status.busy": "2023-11-30T11:31:11.930734Z", - "iopub.status.idle": "2023-11-30T11:31:12.032900Z", - "shell.execute_reply": "2023-11-30T11:31:12.031908Z" + "iopub.execute_input": "2023-12-01T18:14:33.135035Z", + "iopub.status.busy": "2023-12-01T18:14:33.134753Z", + "iopub.status.idle": "2023-12-01T18:14:33.199007Z", + "shell.execute_reply": "2023-12-01T18:14:33.198136Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.036763Z", - "iopub.status.busy": "2023-11-30T11:31:12.036466Z", - "iopub.status.idle": "2023-11-30T11:31:12.041041Z", - "shell.execute_reply": "2023-11-30T11:31:12.040203Z" + "iopub.execute_input": "2023-12-01T18:14:33.203686Z", + "iopub.status.busy": "2023-12-01T18:14:33.203478Z", + "iopub.status.idle": "2023-12-01T18:14:33.207310Z", + "shell.execute_reply": "2023-12-01T18:14:33.206505Z" } }, "outputs": [], @@ -346,10 +346,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.044145Z", - "iopub.status.busy": "2023-11-30T11:31:12.043895Z", - "iopub.status.idle": "2023-11-30T11:31:12.052779Z", - "shell.execute_reply": "2023-11-30T11:31:12.051866Z" + "iopub.execute_input": "2023-12-01T18:14:33.211594Z", + "iopub.status.busy": "2023-12-01T18:14:33.211365Z", + "iopub.status.idle": "2023-12-01T18:14:33.218717Z", + "shell.execute_reply": "2023-12-01T18:14:33.217859Z" } }, "outputs": [], @@ -369,10 +369,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.056354Z", - "iopub.status.busy": "2023-11-30T11:31:12.056077Z", - "iopub.status.idle": "2023-11-30T11:31:12.060962Z", - "shell.execute_reply": "2023-11-30T11:31:12.060062Z" + "iopub.execute_input": "2023-12-01T18:14:33.223512Z", + "iopub.status.busy": "2023-12-01T18:14:33.223243Z", + "iopub.status.idle": "2023-12-01T18:14:33.227888Z", + "shell.execute_reply": "2023-12-01T18:14:33.226928Z" } }, "outputs": [], @@ -393,10 +393,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.064438Z", - "iopub.status.busy": "2023-11-30T11:31:12.064172Z", - "iopub.status.idle": "2023-11-30T11:31:12.068940Z", - "shell.execute_reply": "2023-11-30T11:31:12.068060Z" + "iopub.execute_input": "2023-12-01T18:14:33.232666Z", + "iopub.status.busy": "2023-12-01T18:14:33.232318Z", + "iopub.status.idle": "2023-12-01T18:14:33.236979Z", + "shell.execute_reply": "2023-12-01T18:14:33.236052Z" } }, "outputs": [], @@ -416,10 +416,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.072414Z", - "iopub.status.busy": "2023-11-30T11:31:12.072125Z", - "iopub.status.idle": "2023-11-30T11:31:16.157163Z", - "shell.execute_reply": "2023-11-30T11:31:16.155774Z" + "iopub.execute_input": "2023-12-01T18:14:33.242496Z", + "iopub.status.busy": "2023-12-01T18:14:33.241919Z", + "iopub.status.idle": "2023-12-01T18:14:36.495278Z", + "shell.execute_reply": "2023-12-01T18:14:36.493807Z" } }, "outputs": [ @@ -469,7 +469,7 @@ "Writing dump file \u001b[94mtest_constant_kernel/frame.dmp\u001b[0m\n", "Writing file \u001b[94mtest_constant_kernel/data0006.hdf5\u001b[0m\n", "Writing dump file \u001b[94mtest_constant_kernel/frame.dmp\u001b[0m\n", - "Execution time: \u001b[94m0:00:04\u001b[0m\n" + "Execution time: \u001b[94m0:00:03\u001b[0m\n" ] } ], @@ -489,10 +489,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:16.162688Z", - "iopub.status.busy": "2023-11-30T11:31:16.162387Z", - "iopub.status.idle": "2023-11-30T11:31:16.179777Z", - "shell.execute_reply": "2023-11-30T11:31:16.178865Z" + "iopub.execute_input": "2023-12-01T18:14:36.500773Z", + "iopub.status.busy": "2023-12-01T18:14:36.500552Z", + "iopub.status.idle": "2023-12-01T18:14:36.515973Z", + "shell.execute_reply": "2023-12-01T18:14:36.515250Z" } }, "outputs": [], @@ -507,10 +507,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:16.184774Z", - "iopub.status.busy": "2023-11-30T11:31:16.184470Z", - "iopub.status.idle": "2023-11-30T11:31:16.189503Z", - "shell.execute_reply": "2023-11-30T11:31:16.188646Z" + "iopub.execute_input": "2023-12-01T18:14:36.520249Z", + "iopub.status.busy": "2023-12-01T18:14:36.520072Z", + "iopub.status.idle": "2023-12-01T18:14:36.523926Z", + "shell.execute_reply": "2023-12-01T18:14:36.523179Z" } }, "outputs": [], @@ -523,45 +523,16 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:16.194346Z", - "iopub.status.busy": "2023-11-30T11:31:16.194028Z", - "iopub.status.idle": "2023-11-30T11:31:18.872666Z", - "shell.execute_reply": "2023-11-30T11:31:18.870601Z" + "iopub.execute_input": "2023-12-01T18:14:36.528350Z", + "iopub.status.busy": "2023-12-01T18:14:36.528127Z", + "iopub.status.idle": "2023-12-01T18:14:37.644400Z", + "shell.execute_reply": "2023-12-01T18:14:37.643405Z" } }, "outputs": [ - { - "ename": "TypeError", - "evalue": "must be real number, not str", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 338\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 150\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backend_bases.py:2193\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2189\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2190\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m 2191\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m 2192\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2193\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2194\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2195\u001b[0m \u001b[43m \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2196\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2197\u001b[0m \u001b[43m \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2198\u001b[0m \u001b[43m \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2199\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2200\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 2201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2039\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m 2040\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2041\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 2042\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2043\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2044\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mskip\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2045\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[1;32m 2046\u001b[0m print_method \u001b[38;5;241m=\u001b[39m meth\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:497\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 451\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m Write the figure to a PNG file.\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124;03m *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[1;32m 496\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:445\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_print_pil\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, fmt, pil_kwargs, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 441\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;124;03m Draw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;124;03m *pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 445\u001b[0m \u001b[43mFigureCanvasAgg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 446\u001b[0m mpl\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimsave(\n\u001b[1;32m 447\u001b[0m filename_or_obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer_rgba(), \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39mfmt, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 448\u001b[0m dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:388\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[0;32m--> 388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 97\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3151\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3155\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m 3158\u001b[0m sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/axes/_base.py:3070\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m 3068\u001b[0m _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3070\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3071\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3073\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3074\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/text.py:1988\u001b[0m, in \u001b[0;36mAnnotation.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1986\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marrow_patch\u001b[38;5;241m.\u001b[39mfigure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1987\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marrow_patch\u001b[38;5;241m.\u001b[39mfigure \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\n\u001b[0;32m-> 1988\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marrow_patch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1989\u001b[0m \u001b[38;5;66;03m# Draw text, including FancyBboxPatch, after FancyArrowPatch.\u001b[39;00m\n\u001b[1;32m 1990\u001b[0m \u001b[38;5;66;03m# Otherwise, a wedge arrowstyle can land partly on top of the Bbox.\u001b[39;00m\n\u001b[1;32m 1991\u001b[0m Text\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m, renderer)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:39\u001b[0m, in \u001b[0;36m_prevent_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n\u001b[1;32m 37\u001b[0m renderer\u001b[38;5;241m.\u001b[39m_rasterizing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/patches.py:4388\u001b[0m, in \u001b[0;36mFancyArrowPatch.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 4384\u001b[0m fillable \u001b[38;5;241m=\u001b[39m [fillable]\n\u001b[1;32m 4386\u001b[0m affine \u001b[38;5;241m=\u001b[39m transforms\u001b[38;5;241m.\u001b[39mIdentityTransform()\n\u001b[0;32m-> 4388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_paths_with_artist_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4389\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4390\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_facecolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mand\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_facecolor\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 4391\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfillable\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/patches.py:573\u001b[0m, in \u001b[0;36mPatch._draw_paths_with_artist_properties\u001b[0;34m(self, renderer, draw_path_args_list)\u001b[0m\n\u001b[1;32m 570\u001b[0m renderer \u001b[38;5;241m=\u001b[39m PathEffectRenderer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_path_effects(), renderer)\n\u001b[1;32m 572\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m draw_path_args \u001b[38;5;129;01min\u001b[39;00m draw_path_args_list:\n\u001b[0;32m--> 573\u001b[0m \u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdraw_path_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 575\u001b[0m gc\u001b[38;5;241m.\u001b[39mrestore()\n\u001b[1;32m 576\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpatch\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:132\u001b[0m, in \u001b[0;36mRendererAgg.draw_path\u001b[0;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 132\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_renderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrgbFace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m cant_chunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n", - "\u001b[0;31mTypeError\u001b[0m: must be real number, not str" - ] - }, { "data": { + "image/png": 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", "text/plain": [ "
" ] @@ -579,7 +550,7 @@ " cstr = \"C\" + str(i-1)\n", " ax.loglog(m[i, ...], convert(SigmaConstant[i, 1, :], m[i, ...]), lw=1, c=cstr, label=\"t = {:3.1e}\".format(t[i]))\n", " ax.loglog(m[i, ...], solution_constant_kernel(t[i], m[i, ...], a, S0), \":\", lw=1, c=cstr)\n", - "ax.annotate('', xy=(1.1e2, 1.e-5), xytext=(1.e3, 1.e-4), arrowprops=dict(facecolor='red', lw=\"0\", width=6))\n", + "ax.annotate('', xy=(1.1e2, 1.e-5), xytext=(1.e3, 1.e-4), arrowprops=dict(facecolor='red', lw=0, width=6))\n", "ax.legend(ncol=2)\n", "ax.set_xlim(m[0, 0], m[0, -1])\n", "ax.set_ylim(1.e-6, 1.e3)\n", @@ -604,10 +575,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:18.879271Z", - "iopub.status.busy": "2023-11-30T11:31:18.878736Z", - "iopub.status.idle": "2023-11-30T11:31:18.887975Z", - "shell.execute_reply": "2023-11-30T11:31:18.886866Z" + "iopub.execute_input": "2023-12-01T18:14:37.653043Z", + "iopub.status.busy": "2023-12-01T18:14:37.652718Z", + "iopub.status.idle": "2023-12-01T18:14:37.658924Z", + "shell.execute_reply": "2023-12-01T18:14:37.657897Z" } }, "outputs": [], @@ -620,10 +591,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:18.892940Z", - "iopub.status.busy": "2023-11-30T11:31:18.892568Z", - "iopub.status.idle": "2023-11-30T11:31:18.898679Z", - "shell.execute_reply": "2023-11-30T11:31:18.897293Z" + "iopub.execute_input": "2023-12-01T18:14:37.663201Z", + "iopub.status.busy": "2023-12-01T18:14:37.662838Z", + "iopub.status.idle": "2023-12-01T18:14:37.668059Z", + "shell.execute_reply": "2023-12-01T18:14:37.666970Z" } }, "outputs": [], @@ -637,10 +608,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:18.904303Z", - "iopub.status.busy": "2023-11-30T11:31:18.903804Z", - "iopub.status.idle": "2023-11-30T11:31:18.910362Z", - "shell.execute_reply": "2023-11-30T11:31:18.908897Z" + "iopub.execute_input": "2023-12-01T18:14:37.673275Z", + "iopub.status.busy": "2023-12-01T18:14:37.672684Z", + "iopub.status.idle": "2023-12-01T18:14:37.678207Z", + "shell.execute_reply": "2023-12-01T18:14:37.677003Z" } }, "outputs": [], @@ -653,10 +624,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:18.916336Z", - "iopub.status.busy": "2023-11-30T11:31:18.915606Z", - "iopub.status.idle": "2023-11-30T11:31:22.023129Z", - "shell.execute_reply": "2023-11-30T11:31:22.022123Z" + "iopub.execute_input": "2023-12-01T18:14:37.683723Z", + "iopub.status.busy": "2023-12-01T18:14:37.683056Z", + "iopub.status.idle": "2023-12-01T18:14:42.005690Z", + "shell.execute_reply": "2023-12-01T18:14:42.004173Z" } }, "outputs": [], @@ -669,10 +640,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:22.028452Z", - "iopub.status.busy": "2023-11-30T11:31:22.028220Z", - "iopub.status.idle": "2023-11-30T11:31:22.081379Z", - "shell.execute_reply": "2023-11-30T11:31:22.080371Z" + "iopub.execute_input": "2023-12-01T18:14:42.010930Z", + "iopub.status.busy": "2023-12-01T18:14:42.010677Z", + "iopub.status.idle": "2023-12-01T18:14:42.078765Z", + "shell.execute_reply": "2023-12-01T18:14:42.076730Z" } }, "outputs": [], @@ -685,10 +656,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:22.086503Z", - "iopub.status.busy": "2023-11-30T11:31:22.086286Z", - "iopub.status.idle": "2023-11-30T11:31:22.090867Z", - "shell.execute_reply": "2023-11-30T11:31:22.089940Z" + "iopub.execute_input": "2023-12-01T18:14:42.084584Z", + "iopub.status.busy": "2023-12-01T18:14:42.084254Z", + "iopub.status.idle": "2023-12-01T18:14:42.090493Z", + "shell.execute_reply": "2023-12-01T18:14:42.089558Z" } }, "outputs": [], @@ -701,10 +672,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:22.095367Z", - "iopub.status.busy": "2023-11-30T11:31:22.095133Z", - "iopub.status.idle": "2023-11-30T11:31:22.099594Z", - "shell.execute_reply": "2023-11-30T11:31:22.098682Z" + "iopub.execute_input": "2023-12-01T18:14:42.094896Z", + "iopub.status.busy": "2023-12-01T18:14:42.094675Z", + "iopub.status.idle": "2023-12-01T18:14:42.099155Z", + "shell.execute_reply": "2023-12-01T18:14:42.098229Z" } }, "outputs": [], @@ -717,10 +688,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:22.103743Z", - "iopub.status.busy": "2023-11-30T11:31:22.103513Z", - "iopub.status.idle": "2023-11-30T11:32:30.249482Z", - "shell.execute_reply": "2023-11-30T11:32:30.247673Z" + "iopub.execute_input": "2023-12-01T18:14:42.103587Z", + "iopub.status.busy": "2023-12-01T18:14:42.103304Z", + "iopub.status.idle": "2023-12-01T18:15:43.927715Z", + "shell.execute_reply": "2023-12-01T18:15:43.926295Z" } }, "outputs": [ @@ -770,7 +741,7 @@ "Writing dump file \u001b[94mtest_constant_kernel_high_res/frame.dmp\u001b[0m\n", "Writing file \u001b[94mtest_constant_kernel_high_res/data0006.hdf5\u001b[0m\n", "Writing dump file \u001b[94mtest_constant_kernel_high_res/frame.dmp\u001b[0m\n", - "Execution time: \u001b[94m0:01:08\u001b[0m\n" + "Execution time: \u001b[94m0:01:01\u001b[0m\n" ] } ], @@ -783,10 +754,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:30.254879Z", - "iopub.status.busy": "2023-11-30T11:32:30.254581Z", - "iopub.status.idle": "2023-11-30T11:32:30.272954Z", - "shell.execute_reply": "2023-11-30T11:32:30.271720Z" + "iopub.execute_input": "2023-12-01T18:15:43.934071Z", + "iopub.status.busy": "2023-12-01T18:15:43.933813Z", + "iopub.status.idle": "2023-12-01T18:15:43.950078Z", + "shell.execute_reply": "2023-12-01T18:15:43.949135Z" } }, "outputs": [], @@ -801,10 +772,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:30.278121Z", - "iopub.status.busy": "2023-11-30T11:32:30.277823Z", - "iopub.status.idle": "2023-11-30T11:32:31.113415Z", - "shell.execute_reply": "2023-11-30T11:32:31.112241Z" + "iopub.execute_input": "2023-12-01T18:15:43.954967Z", + "iopub.status.busy": "2023-12-01T18:15:43.954715Z", + "iopub.status.idle": "2023-12-01T18:15:44.814975Z", + "shell.execute_reply": "2023-12-01T18:15:44.814060Z" } }, "outputs": [ @@ -870,10 +841,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.122675Z", - "iopub.status.busy": "2023-11-30T11:32:31.122339Z", - "iopub.status.idle": "2023-11-30T11:32:31.130318Z", - "shell.execute_reply": "2023-11-30T11:32:31.129063Z" + "iopub.execute_input": "2023-12-01T18:15:44.825063Z", + "iopub.status.busy": "2023-12-01T18:15:44.824771Z", + "iopub.status.idle": "2023-12-01T18:15:44.830708Z", + "shell.execute_reply": "2023-12-01T18:15:44.829744Z" } }, "outputs": [], @@ -910,10 +881,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.135502Z", - "iopub.status.busy": "2023-11-30T11:32:31.134976Z", - "iopub.status.idle": "2023-11-30T11:32:31.146699Z", - "shell.execute_reply": "2023-11-30T11:32:31.145156Z" + "iopub.execute_input": "2023-12-01T18:15:44.834590Z", + "iopub.status.busy": "2023-12-01T18:15:44.834323Z", + "iopub.status.idle": "2023-12-01T18:15:44.843690Z", + "shell.execute_reply": "2023-12-01T18:15:44.842647Z" } }, "outputs": [], @@ -959,10 +930,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.152609Z", - "iopub.status.busy": "2023-11-30T11:32:31.151974Z", - "iopub.status.idle": "2023-11-30T11:32:31.159343Z", - "shell.execute_reply": "2023-11-30T11:32:31.158026Z" + "iopub.execute_input": "2023-12-01T18:15:44.848922Z", + "iopub.status.busy": "2023-12-01T18:15:44.848473Z", + "iopub.status.idle": "2023-12-01T18:15:44.853630Z", + "shell.execute_reply": "2023-12-01T18:15:44.852680Z" } }, "outputs": [], @@ -975,10 +946,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.165198Z", - "iopub.status.busy": "2023-11-30T11:32:31.164557Z", - "iopub.status.idle": "2023-11-30T11:32:31.170982Z", - "shell.execute_reply": "2023-11-30T11:32:31.169604Z" + "iopub.execute_input": "2023-12-01T18:15:44.859214Z", + "iopub.status.busy": "2023-12-01T18:15:44.858083Z", + "iopub.status.idle": "2023-12-01T18:15:44.863415Z", + "shell.execute_reply": "2023-12-01T18:15:44.862391Z" } }, "outputs": [], @@ -992,10 +963,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.177130Z", - "iopub.status.busy": "2023-11-30T11:32:31.176403Z", - "iopub.status.idle": "2023-11-30T11:32:31.257094Z", - "shell.execute_reply": "2023-11-30T11:32:31.255811Z" + "iopub.execute_input": "2023-12-01T18:15:44.868768Z", + "iopub.status.busy": "2023-12-01T18:15:44.868187Z", + "iopub.status.idle": "2023-12-01T18:15:44.950117Z", + "shell.execute_reply": "2023-12-01T18:15:44.949154Z" } }, "outputs": [], @@ -1008,10 +979,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.262354Z", - "iopub.status.busy": "2023-11-30T11:32:31.262062Z", - "iopub.status.idle": "2023-11-30T11:32:31.271367Z", - "shell.execute_reply": "2023-11-30T11:32:31.270249Z" + "iopub.execute_input": "2023-12-01T18:15:44.954661Z", + "iopub.status.busy": "2023-12-01T18:15:44.954441Z", + "iopub.status.idle": "2023-12-01T18:15:44.962464Z", + "shell.execute_reply": "2023-12-01T18:15:44.961569Z" } }, "outputs": [], @@ -1024,10 +995,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.276399Z", - "iopub.status.busy": "2023-11-30T11:32:31.276113Z", - "iopub.status.idle": "2023-11-30T11:32:31.281460Z", - "shell.execute_reply": "2023-11-30T11:32:31.280305Z" + "iopub.execute_input": "2023-12-01T18:15:44.967018Z", + "iopub.status.busy": "2023-12-01T18:15:44.966783Z", + "iopub.status.idle": "2023-12-01T18:15:44.971150Z", + "shell.execute_reply": "2023-12-01T18:15:44.970294Z" } }, "outputs": [], @@ -1041,10 +1012,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.286147Z", - "iopub.status.busy": "2023-11-30T11:32:31.285835Z", - "iopub.status.idle": "2023-11-30T11:32:31.291226Z", - "shell.execute_reply": "2023-11-30T11:32:31.290002Z" + "iopub.execute_input": "2023-12-01T18:15:44.975524Z", + "iopub.status.busy": "2023-12-01T18:15:44.975249Z", + "iopub.status.idle": "2023-12-01T18:15:44.979740Z", + "shell.execute_reply": "2023-12-01T18:15:44.978767Z" } }, "outputs": [], @@ -1057,10 +1028,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.296445Z", - "iopub.status.busy": "2023-11-30T11:32:31.295881Z", - "iopub.status.idle": "2023-11-30T11:32:32.610902Z", - "shell.execute_reply": "2023-11-30T11:32:32.609663Z" + "iopub.execute_input": "2023-12-01T18:15:44.983911Z", + "iopub.status.busy": "2023-12-01T18:15:44.983639Z", + "iopub.status.idle": "2023-12-01T18:15:46.149188Z", + "shell.execute_reply": "2023-12-01T18:15:46.148219Z" } }, "outputs": [ @@ -1123,10 +1094,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:32.615537Z", - "iopub.status.busy": "2023-11-30T11:32:32.615282Z", - "iopub.status.idle": "2023-11-30T11:32:32.630477Z", - "shell.execute_reply": "2023-11-30T11:32:32.629420Z" + "iopub.execute_input": "2023-12-01T18:15:46.153679Z", + "iopub.status.busy": "2023-12-01T18:15:46.153460Z", + "iopub.status.idle": "2023-12-01T18:15:46.168177Z", + "shell.execute_reply": "2023-12-01T18:15:46.167216Z" } }, "outputs": [], @@ -1141,10 +1112,10 @@ "execution_count": 38, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:32.635528Z", - "iopub.status.busy": "2023-11-30T11:32:32.635305Z", - "iopub.status.idle": "2023-11-30T11:32:33.420498Z", - "shell.execute_reply": "2023-11-30T11:32:33.419069Z" + "iopub.execute_input": "2023-12-01T18:15:46.172880Z", + "iopub.status.busy": "2023-12-01T18:15:46.172655Z", + "iopub.status.idle": "2023-12-01T18:15:46.852690Z", + "shell.execute_reply": "2023-12-01T18:15:46.851648Z" } }, "outputs": [ @@ -1190,10 +1161,10 @@ "execution_count": 39, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:33.428530Z", - "iopub.status.busy": "2023-11-30T11:32:33.428256Z", - "iopub.status.idle": "2023-11-30T11:32:33.434278Z", - "shell.execute_reply": "2023-11-30T11:32:33.433129Z" + "iopub.execute_input": "2023-12-01T18:15:46.861893Z", + "iopub.status.busy": "2023-12-01T18:15:46.861630Z", + "iopub.status.idle": "2023-12-01T18:15:46.866809Z", + "shell.execute_reply": "2023-12-01T18:15:46.865860Z" } }, "outputs": [], @@ -1206,10 +1177,10 @@ "execution_count": 40, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:33.439026Z", - "iopub.status.busy": "2023-11-30T11:32:33.438753Z", - "iopub.status.idle": "2023-11-30T11:32:33.444395Z", - "shell.execute_reply": "2023-11-30T11:32:33.443181Z" + "iopub.execute_input": "2023-12-01T18:15:46.871355Z", + "iopub.status.busy": "2023-12-01T18:15:46.871098Z", + "iopub.status.idle": "2023-12-01T18:15:46.875552Z", + "shell.execute_reply": "2023-12-01T18:15:46.874570Z" } }, "outputs": [], @@ -1223,10 +1194,10 @@ "execution_count": 41, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:33.449333Z", - "iopub.status.busy": "2023-11-30T11:32:33.448864Z", - "iopub.status.idle": "2023-11-30T11:32:33.454189Z", - "shell.execute_reply": "2023-11-30T11:32:33.452858Z" + "iopub.execute_input": "2023-12-01T18:15:46.880353Z", + "iopub.status.busy": "2023-12-01T18:15:46.879884Z", + "iopub.status.idle": "2023-12-01T18:15:46.884531Z", + "shell.execute_reply": "2023-12-01T18:15:46.883546Z" } }, "outputs": [], @@ -1239,10 +1210,10 @@ "execution_count": 42, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:33.459761Z", - "iopub.status.busy": "2023-11-30T11:32:33.459133Z", - "iopub.status.idle": "2023-11-30T11:32:37.943433Z", - "shell.execute_reply": "2023-11-30T11:32:37.942036Z" + "iopub.execute_input": "2023-12-01T18:15:46.889686Z", + "iopub.status.busy": "2023-12-01T18:15:46.889121Z", + "iopub.status.idle": "2023-12-01T18:15:51.223447Z", + "shell.execute_reply": "2023-12-01T18:15:51.222480Z" } }, "outputs": [], @@ -1255,10 +1226,10 @@ "execution_count": 43, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:37.949073Z", - "iopub.status.busy": "2023-11-30T11:32:37.948738Z", - "iopub.status.idle": "2023-11-30T11:32:37.994724Z", - "shell.execute_reply": "2023-11-30T11:32:37.993339Z" + "iopub.execute_input": "2023-12-01T18:15:51.228234Z", + "iopub.status.busy": "2023-12-01T18:15:51.227985Z", + "iopub.status.idle": "2023-12-01T18:15:51.272913Z", + "shell.execute_reply": "2023-12-01T18:15:51.271948Z" } }, "outputs": [], @@ -1271,10 +1242,10 @@ "execution_count": 44, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:38.000195Z", - "iopub.status.busy": "2023-11-30T11:32:37.999870Z", - "iopub.status.idle": "2023-11-30T11:32:38.005020Z", - "shell.execute_reply": "2023-11-30T11:32:38.003948Z" + "iopub.execute_input": "2023-12-01T18:15:51.277542Z", + "iopub.status.busy": "2023-12-01T18:15:51.277320Z", + "iopub.status.idle": "2023-12-01T18:15:51.281623Z", + "shell.execute_reply": "2023-12-01T18:15:51.280774Z" } }, "outputs": [], @@ -1288,10 +1259,10 @@ "execution_count": 45, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:38.009791Z", - "iopub.status.busy": "2023-11-30T11:32:38.009504Z", - "iopub.status.idle": "2023-11-30T11:32:38.014882Z", - "shell.execute_reply": "2023-11-30T11:32:38.013708Z" + "iopub.execute_input": "2023-12-01T18:15:51.286059Z", + "iopub.status.busy": "2023-12-01T18:15:51.285798Z", + "iopub.status.idle": "2023-12-01T18:15:51.289995Z", + "shell.execute_reply": "2023-12-01T18:15:51.289140Z" } }, "outputs": [], @@ -1304,10 +1275,10 @@ "execution_count": 46, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:38.019948Z", - "iopub.status.busy": "2023-11-30T11:32:38.019390Z", - "iopub.status.idle": "2023-11-30T11:32:50.180851Z", - "shell.execute_reply": "2023-11-30T11:32:50.179401Z" + "iopub.execute_input": "2023-12-01T18:15:51.294543Z", + "iopub.status.busy": "2023-12-01T18:15:51.294240Z", + "iopub.status.idle": "2023-12-01T18:16:03.831395Z", + "shell.execute_reply": "2023-12-01T18:16:03.830363Z" } }, "outputs": [ @@ -1370,10 +1341,10 @@ "execution_count": 47, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:50.186604Z", - "iopub.status.busy": "2023-11-30T11:32:50.186202Z", - "iopub.status.idle": "2023-11-30T11:32:50.205883Z", - "shell.execute_reply": "2023-11-30T11:32:50.204953Z" + "iopub.execute_input": "2023-12-01T18:16:03.836294Z", + "iopub.status.busy": "2023-12-01T18:16:03.836031Z", + "iopub.status.idle": "2023-12-01T18:16:03.854278Z", + "shell.execute_reply": "2023-12-01T18:16:03.853316Z" } }, "outputs": [], @@ -1388,10 +1359,10 @@ "execution_count": 48, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:50.211191Z", - "iopub.status.busy": "2023-11-30T11:32:50.210915Z", - "iopub.status.idle": "2023-11-30T11:32:51.006195Z", - "shell.execute_reply": "2023-11-30T11:32:51.005213Z" + "iopub.execute_input": "2023-12-01T18:16:03.858860Z", + "iopub.status.busy": "2023-12-01T18:16:03.858630Z", + "iopub.status.idle": "2023-12-01T18:16:04.643803Z", + "shell.execute_reply": "2023-12-01T18:16:04.642878Z" } }, "outputs": [ @@ -1463,7 +1434,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/example_planetary_gaps.html b/docs/example_planetary_gaps.html index 6fbaa37..ea9b758 100644 --- a/docs/example_planetary_gaps.html +++ b/docs/example_planetary_gaps.html @@ -639,7 +639,7 @@

Growing planetsexample_planetary_gaps/frame.dmp Writing file example_planetary_gaps/data0021.hdf5 Writing dump file example_planetary_gaps/frame.dmp -Execution time: 1:12:43 +Execution time: 1:18:09

This is the result of our simulation.

@@ -689,29 +689,18 @@

Growing planetsax.legend() ax.set_xlim(t[0]/c.year, t[-1]/c.year) ax.grid(visible=True) -ax.set_xlabel("Time [years]") -ax.set_ylabel("Mass [$M_\oplus$]") +ax.set_xlabel(r"Time [years]") +ax.set_ylabel(r"Mass [$M_\oplus$]") fig.tight_layout() plt.show() -
-
-
-
-
-<>:9: SyntaxWarning: invalid escape sequence '\o'
-<>:9: SyntaxWarning: invalid escape sequence '\o'
-/tmp/ipykernel_94188/1466386948.py:9: SyntaxWarning: invalid escape sequence '\o'
-  ax.set_ylabel("Mass [$M_\oplus$]")
-
-
-_images/example_planetary_gaps_52_1.png +_images/example_planetary_gaps_52_0.png

diff --git a/docs/example_planetary_gaps.ipynb b/docs/example_planetary_gaps.ipynb index 94abf2e..1297c15 100644 --- a/docs/example_planetary_gaps.ipynb +++ b/docs/example_planetary_gaps.ipynb @@ -63,10 +63,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:52.129232Z", - "iopub.status.busy": "2023-11-30T11:29:52.128558Z", - "iopub.status.idle": "2023-11-30T11:29:52.240310Z", - "shell.execute_reply": "2023-11-30T11:29:52.239369Z" + "iopub.execute_input": "2023-12-01T18:14:24.933354Z", + "iopub.status.busy": "2023-12-01T18:14:24.932702Z", + "iopub.status.idle": "2023-12-01T18:14:25.198137Z", + "shell.execute_reply": "2023-12-01T18:14:25.196846Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:52.246190Z", - "iopub.status.busy": "2023-11-30T11:29:52.245867Z", - "iopub.status.idle": "2023-11-30T11:29:53.098662Z", - "shell.execute_reply": "2023-11-30T11:29:53.097265Z" + "iopub.execute_input": "2023-12-01T18:14:25.204221Z", + "iopub.status.busy": "2023-12-01T18:14:25.203467Z", + "iopub.status.idle": "2023-12-01T18:14:26.675995Z", + "shell.execute_reply": "2023-12-01T18:14:26.674206Z" } }, "outputs": [], @@ -151,10 +151,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.105337Z", - "iopub.status.busy": "2023-11-30T11:29:53.104607Z", - "iopub.status.idle": "2023-11-30T11:29:53.110972Z", - "shell.execute_reply": "2023-11-30T11:29:53.110058Z" + "iopub.execute_input": "2023-12-01T18:14:26.683152Z", + "iopub.status.busy": "2023-12-01T18:14:26.682003Z", + "iopub.status.idle": "2023-12-01T18:14:26.690435Z", + "shell.execute_reply": "2023-12-01T18:14:26.689043Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.115852Z", - "iopub.status.busy": "2023-11-30T11:29:53.115517Z", - "iopub.status.idle": "2023-11-30T11:29:53.121117Z", - "shell.execute_reply": "2023-11-30T11:29:53.119815Z" + "iopub.execute_input": "2023-12-01T18:14:26.695921Z", + "iopub.status.busy": "2023-12-01T18:14:26.695357Z", + "iopub.status.idle": "2023-12-01T18:14:26.700826Z", + "shell.execute_reply": "2023-12-01T18:14:26.699758Z" } }, "outputs": [], @@ -183,10 +183,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.126888Z", - "iopub.status.busy": "2023-11-30T11:29:53.126275Z", - "iopub.status.idle": "2023-11-30T11:29:53.607704Z", - "shell.execute_reply": "2023-11-30T11:29:53.606152Z" + "iopub.execute_input": "2023-12-01T18:14:26.706354Z", + "iopub.status.busy": "2023-12-01T18:14:26.705722Z", + "iopub.status.idle": "2023-12-01T18:14:27.206810Z", + "shell.execute_reply": "2023-12-01T18:14:27.205424Z" } }, "outputs": [ @@ -236,10 +236,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.670755Z", - "iopub.status.busy": "2023-11-30T11:29:53.670083Z", - "iopub.status.idle": "2023-11-30T11:29:53.680716Z", - "shell.execute_reply": "2023-11-30T11:29:53.679175Z" + "iopub.execute_input": "2023-12-01T18:14:27.265710Z", + "iopub.status.busy": "2023-12-01T18:14:27.265385Z", + "iopub.status.idle": "2023-12-01T18:14:27.273687Z", + "shell.execute_reply": "2023-12-01T18:14:27.272642Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.685358Z", - "iopub.status.busy": "2023-11-30T11:29:53.684684Z", - "iopub.status.idle": "2023-11-30T11:29:53.690957Z", - "shell.execute_reply": "2023-11-30T11:29:53.689590Z" + "iopub.execute_input": "2023-12-01T18:14:27.276686Z", + "iopub.status.busy": "2023-12-01T18:14:27.276224Z", + "iopub.status.idle": "2023-12-01T18:14:27.281222Z", + "shell.execute_reply": "2023-12-01T18:14:27.280185Z" } }, "outputs": [], @@ -280,10 +280,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.695785Z", - "iopub.status.busy": "2023-11-30T11:29:53.695160Z", - "iopub.status.idle": "2023-11-30T11:29:53.702913Z", - "shell.execute_reply": "2023-11-30T11:29:53.701549Z" + "iopub.execute_input": "2023-12-01T18:14:27.284821Z", + "iopub.status.busy": "2023-12-01T18:14:27.284358Z", + "iopub.status.idle": "2023-12-01T18:14:27.291069Z", + "shell.execute_reply": "2023-12-01T18:14:27.290060Z" } }, "outputs": [], @@ -296,10 +296,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.708369Z", - "iopub.status.busy": "2023-11-30T11:29:53.707712Z", - "iopub.status.idle": "2023-11-30T11:29:53.715484Z", - "shell.execute_reply": "2023-11-30T11:29:53.714085Z" + "iopub.execute_input": "2023-12-01T18:14:27.294845Z", + "iopub.status.busy": "2023-12-01T18:14:27.294494Z", + "iopub.status.idle": "2023-12-01T18:14:27.300831Z", + "shell.execute_reply": "2023-12-01T18:14:27.299752Z" } }, "outputs": [], @@ -315,10 +315,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:53.721308Z", - "iopub.status.busy": "2023-11-30T11:29:53.720704Z", - "iopub.status.idle": "2023-11-30T11:29:54.366573Z", - "shell.execute_reply": "2023-11-30T11:29:54.365260Z" + "iopub.execute_input": "2023-12-01T18:14:27.305992Z", + "iopub.status.busy": "2023-12-01T18:14:27.305024Z", + "iopub.status.idle": "2023-12-01T18:14:27.709338Z", + "shell.execute_reply": "2023-12-01T18:14:27.708252Z" } }, "outputs": [], @@ -331,10 +331,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.372540Z", - "iopub.status.busy": "2023-11-30T11:29:54.372302Z", - "iopub.status.idle": "2023-11-30T11:29:54.376927Z", - "shell.execute_reply": "2023-11-30T11:29:54.376175Z" + "iopub.execute_input": "2023-12-01T18:14:27.714952Z", + "iopub.status.busy": "2023-12-01T18:14:27.714734Z", + "iopub.status.idle": "2023-12-01T18:14:27.721263Z", + "shell.execute_reply": "2023-12-01T18:14:27.720401Z" } }, "outputs": [], @@ -349,10 +349,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.381330Z", - "iopub.status.busy": "2023-11-30T11:29:54.381106Z", - "iopub.status.idle": "2023-11-30T11:29:54.385672Z", - "shell.execute_reply": "2023-11-30T11:29:54.384888Z" + "iopub.execute_input": "2023-12-01T18:14:27.725605Z", + "iopub.status.busy": "2023-12-01T18:14:27.725381Z", + "iopub.status.idle": "2023-12-01T18:14:27.729979Z", + "shell.execute_reply": "2023-12-01T18:14:27.729131Z" } }, "outputs": [], @@ -366,10 +366,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.390085Z", - "iopub.status.busy": "2023-11-30T11:29:54.389800Z", - "iopub.status.idle": "2023-11-30T11:29:54.394596Z", - "shell.execute_reply": "2023-11-30T11:29:54.393718Z" + "iopub.execute_input": "2023-12-01T18:14:27.734591Z", + "iopub.status.busy": "2023-12-01T18:14:27.734228Z", + "iopub.status.idle": "2023-12-01T18:14:27.738978Z", + "shell.execute_reply": "2023-12-01T18:14:27.738015Z" } }, "outputs": [], @@ -390,10 +390,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.398882Z", - "iopub.status.busy": "2023-11-30T11:29:54.398592Z", - "iopub.status.idle": "2023-11-30T11:29:54.404735Z", - "shell.execute_reply": "2023-11-30T11:29:54.403711Z" + "iopub.execute_input": "2023-12-01T18:14:27.743530Z", + "iopub.status.busy": "2023-12-01T18:14:27.743038Z", + "iopub.status.idle": "2023-12-01T18:14:27.750131Z", + "shell.execute_reply": "2023-12-01T18:14:27.749029Z" } }, "outputs": [ @@ -431,10 +431,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.410128Z", - "iopub.status.busy": "2023-11-30T11:29:54.409278Z", - "iopub.status.idle": "2023-11-30T11:29:54.415072Z", - "shell.execute_reply": "2023-11-30T11:29:54.413698Z" + "iopub.execute_input": "2023-12-01T18:14:27.755419Z", + "iopub.status.busy": "2023-12-01T18:14:27.754773Z", + "iopub.status.idle": "2023-12-01T18:14:27.760588Z", + "shell.execute_reply": "2023-12-01T18:14:27.759350Z" } }, "outputs": [], @@ -447,10 +447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.420387Z", - "iopub.status.busy": "2023-11-30T11:29:54.419781Z", - "iopub.status.idle": "2023-11-30T11:29:54.429622Z", - "shell.execute_reply": "2023-11-30T11:29:54.428189Z" + "iopub.execute_input": "2023-12-01T18:14:27.766119Z", + "iopub.status.busy": "2023-12-01T18:14:27.765483Z", + "iopub.status.idle": "2023-12-01T18:14:27.775588Z", + "shell.execute_reply": "2023-12-01T18:14:27.774155Z" } }, "outputs": [], @@ -503,10 +503,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.435684Z", - "iopub.status.busy": "2023-11-30T11:29:54.435090Z", - "iopub.status.idle": "2023-11-30T11:29:54.847760Z", - "shell.execute_reply": "2023-11-30T11:29:54.846880Z" + "iopub.execute_input": "2023-12-01T18:14:27.781304Z", + "iopub.status.busy": "2023-12-01T18:14:27.780719Z", + "iopub.status.idle": "2023-12-01T18:14:28.105352Z", + "shell.execute_reply": "2023-12-01T18:14:28.104528Z" } }, "outputs": [ @@ -546,10 +546,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.854636Z", - "iopub.status.busy": "2023-11-30T11:29:54.854352Z", - "iopub.status.idle": "2023-11-30T11:29:54.860494Z", - "shell.execute_reply": "2023-11-30T11:29:54.859667Z" + "iopub.execute_input": "2023-12-01T18:14:28.111151Z", + "iopub.status.busy": "2023-12-01T18:14:28.110966Z", + "iopub.status.idle": "2023-12-01T18:14:28.115775Z", + "shell.execute_reply": "2023-12-01T18:14:28.114977Z" } }, "outputs": [], @@ -571,10 +571,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.864805Z", - "iopub.status.busy": "2023-11-30T11:29:54.864520Z", - "iopub.status.idle": "2023-11-30T11:29:54.869978Z", - "shell.execute_reply": "2023-11-30T11:29:54.869117Z" + "iopub.execute_input": "2023-12-01T18:14:28.120046Z", + "iopub.status.busy": "2023-12-01T18:14:28.119808Z", + "iopub.status.idle": "2023-12-01T18:14:28.124553Z", + "shell.execute_reply": "2023-12-01T18:14:28.123653Z" } }, "outputs": [], @@ -614,10 +614,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.874735Z", - "iopub.status.busy": "2023-11-30T11:29:54.874447Z", - "iopub.status.idle": "2023-11-30T11:29:54.880600Z", - "shell.execute_reply": "2023-11-30T11:29:54.879559Z" + "iopub.execute_input": "2023-12-01T18:14:28.128841Z", + "iopub.status.busy": "2023-12-01T18:14:28.128550Z", + "iopub.status.idle": "2023-12-01T18:14:28.133840Z", + "shell.execute_reply": "2023-12-01T18:14:28.132934Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.886172Z", - "iopub.status.busy": "2023-11-30T11:29:54.885536Z", - "iopub.status.idle": "2023-11-30T11:29:54.891995Z", - "shell.execute_reply": "2023-11-30T11:29:54.890577Z" + "iopub.execute_input": "2023-12-01T18:14:28.138099Z", + "iopub.status.busy": "2023-12-01T18:14:28.137674Z", + "iopub.status.idle": "2023-12-01T18:14:28.143092Z", + "shell.execute_reply": "2023-12-01T18:14:28.141997Z" } }, "outputs": [], @@ -665,10 +665,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.897916Z", - "iopub.status.busy": "2023-11-30T11:29:54.897253Z", - "iopub.status.idle": "2023-11-30T11:29:54.904396Z", - "shell.execute_reply": "2023-11-30T11:29:54.902939Z" + "iopub.execute_input": "2023-12-01T18:14:28.148089Z", + "iopub.status.busy": "2023-12-01T18:14:28.147547Z", + "iopub.status.idle": "2023-12-01T18:14:28.154368Z", + "shell.execute_reply": "2023-12-01T18:14:28.152946Z" } }, "outputs": [], @@ -690,10 +690,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.910598Z", - "iopub.status.busy": "2023-11-30T11:29:54.909905Z", - "iopub.status.idle": "2023-11-30T11:29:54.916561Z", - "shell.execute_reply": "2023-11-30T11:29:54.915143Z" + "iopub.execute_input": "2023-12-01T18:14:28.160403Z", + "iopub.status.busy": "2023-12-01T18:14:28.159754Z", + "iopub.status.idle": "2023-12-01T18:14:28.166115Z", + "shell.execute_reply": "2023-12-01T18:14:28.164895Z" } }, "outputs": [], @@ -713,10 +713,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.922641Z", - "iopub.status.busy": "2023-11-30T11:29:54.921985Z", - "iopub.status.idle": "2023-11-30T11:29:54.928330Z", - "shell.execute_reply": "2023-11-30T11:29:54.926886Z" + "iopub.execute_input": "2023-12-01T18:14:28.171925Z", + "iopub.status.busy": "2023-12-01T18:14:28.171296Z", + "iopub.status.idle": "2023-12-01T18:14:28.177279Z", + "shell.execute_reply": "2023-12-01T18:14:28.176070Z" } }, "outputs": [], @@ -736,10 +736,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:54.934651Z", - "iopub.status.busy": "2023-11-30T11:29:54.933479Z", - "iopub.status.idle": "2023-11-30T11:29:55.184886Z", - "shell.execute_reply": "2023-11-30T11:29:55.183295Z" + "iopub.execute_input": "2023-12-01T18:14:28.182440Z", + "iopub.status.busy": "2023-12-01T18:14:28.182157Z", + "iopub.status.idle": "2023-12-01T18:14:28.423102Z", + "shell.execute_reply": "2023-12-01T18:14:28.422108Z" } }, "outputs": [], @@ -759,10 +759,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:55.191648Z", - "iopub.status.busy": "2023-11-30T11:29:55.191272Z", - "iopub.status.idle": "2023-11-30T11:29:55.199251Z", - "shell.execute_reply": "2023-11-30T11:29:55.198305Z" + "iopub.execute_input": "2023-12-01T18:14:28.428017Z", + "iopub.status.busy": "2023-12-01T18:14:28.427785Z", + "iopub.status.idle": "2023-12-01T18:14:28.433172Z", + "shell.execute_reply": "2023-12-01T18:14:28.432251Z" } }, "outputs": [ @@ -793,10 +793,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:55.204892Z", - "iopub.status.busy": "2023-11-30T11:29:55.204501Z", - "iopub.status.idle": "2023-11-30T11:29:55.210546Z", - "shell.execute_reply": "2023-11-30T11:29:55.209203Z" + "iopub.execute_input": "2023-12-01T18:14:28.438068Z", + "iopub.status.busy": "2023-12-01T18:14:28.437819Z", + "iopub.status.idle": "2023-12-01T18:14:28.442328Z", + "shell.execute_reply": "2023-12-01T18:14:28.441414Z" } }, "outputs": [], @@ -809,10 +809,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:29:55.216331Z", - "iopub.status.busy": "2023-11-30T11:29:55.215855Z", - "iopub.status.idle": "2023-11-30T12:42:39.054199Z", - "shell.execute_reply": "2023-11-30T12:42:39.052575Z" + "iopub.execute_input": "2023-12-01T18:14:28.446729Z", + "iopub.status.busy": "2023-12-01T18:14:28.446416Z", + "iopub.status.idle": "2023-12-01T19:32:37.549949Z", + "shell.execute_reply": "2023-12-01T19:32:37.548932Z" } }, "outputs": [ @@ -892,7 +892,7 @@ "Writing dump file \u001b[94mexample_planetary_gaps/frame.dmp\u001b[0m\n", "Writing file \u001b[94mexample_planetary_gaps/data0021.hdf5\u001b[0m\n", "Writing dump file \u001b[94mexample_planetary_gaps/frame.dmp\u001b[0m\n", - "Execution time: \u001b[94m1:12:43\u001b[0m\n" + "Execution time: \u001b[94m1:18:09\u001b[0m\n" ] } ], @@ -912,10 +912,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T12:42:39.062113Z", - "iopub.status.busy": "2023-11-30T12:42:39.061747Z", - "iopub.status.idle": "2023-11-30T12:42:39.068126Z", - "shell.execute_reply": "2023-11-30T12:42:39.067085Z" + "iopub.execute_input": "2023-12-01T19:32:37.555286Z", + "iopub.status.busy": "2023-12-01T19:32:37.554994Z", + "iopub.status.idle": "2023-12-01T19:32:37.559853Z", + "shell.execute_reply": "2023-12-01T19:32:37.559014Z" } }, "outputs": [], @@ -928,10 +928,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T12:42:39.073220Z", - "iopub.status.busy": "2023-11-30T12:42:39.072860Z", - "iopub.status.idle": "2023-11-30T12:42:40.238967Z", - "shell.execute_reply": "2023-11-30T12:42:40.237997Z" + "iopub.execute_input": "2023-12-01T19:32:37.564722Z", + "iopub.status.busy": "2023-12-01T19:32:37.564409Z", + "iopub.status.idle": "2023-12-01T19:32:38.569154Z", + "shell.execute_reply": "2023-12-01T19:32:38.568275Z" } }, "outputs": [ @@ -964,10 +964,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T12:42:40.248751Z", - "iopub.status.busy": "2023-11-30T12:42:40.248458Z", - "iopub.status.idle": "2023-11-30T12:42:40.283547Z", - "shell.execute_reply": "2023-11-30T12:42:40.282564Z" + "iopub.execute_input": "2023-12-01T19:32:38.577757Z", + "iopub.status.busy": "2023-12-01T19:32:38.577529Z", + "iopub.status.idle": "2023-12-01T19:32:38.610772Z", + "shell.execute_reply": "2023-12-01T19:32:38.609417Z" } }, "outputs": [], @@ -982,23 +982,13 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T12:42:40.288954Z", - "iopub.status.busy": "2023-11-30T12:42:40.288698Z", - "iopub.status.idle": "2023-11-30T12:42:40.455284Z", - "shell.execute_reply": "2023-11-30T12:42:40.454334Z" + "iopub.execute_input": "2023-12-01T19:32:38.615196Z", + "iopub.status.busy": "2023-12-01T19:32:38.615028Z", + "iopub.status.idle": "2023-12-01T19:32:38.758988Z", + "shell.execute_reply": "2023-12-01T19:32:38.757633Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:9: SyntaxWarning: invalid escape sequence '\\o'\n", - "<>:9: SyntaxWarning: invalid escape sequence '\\o'\n", - "/tmp/ipykernel_94188/1466386948.py:9: SyntaxWarning: invalid escape sequence '\\o'\n", - " ax.set_ylabel(\"Mass [$M_\\oplus$]\")\n" - ] - }, { "data": { "image/png": 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", @@ -1018,8 +1008,8 @@ "ax.legend()\n", "ax.set_xlim(t[0]/c.year, t[-1]/c.year)\n", "ax.grid(visible=True)\n", - "ax.set_xlabel(\"Time [years]\")\n", - "ax.set_ylabel(\"Mass [$M_\\oplus$]\")\n", + "ax.set_xlabel(r\"Time [years]\")\n", + "ax.set_ylabel(r\"Mass [$M_\\oplus$]\")\n", "fig.tight_layout()\n", "plt.show()" ] @@ -1041,7 +1031,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/example_planetesimal_formation.html b/docs/example_planetesimal_formation.html index 57d40cb..bda0cae 100644 --- a/docs/example_planetesimal_formation.html +++ b/docs/example_planetesimal_formation.html @@ -543,7 +543,7 @@

Adding planetesimal formationexample_planetesimal_formation/frame.dmp Writing file example_planetesimal_formation/data0031.hdf5 Writing dump file example_planetesimal_formation/frame.dmp -Execution time: 22:17:52 +Execution time: 1 day, 1:54:50

We can plot our results now.

@@ -615,30 +615,19 @@

Adding planetesimal formationax10.loglog(t/c.year, Mplan/c.M_earth, label="Planetesimals") ax10.set_xlim(t[1]/c.year, t[-1]/c.year) ax10.set_ylim(1.e-1, 1.e5) -ax10.set_xlabel("Time [yrs]") -ax10.set_ylabel("Mass [$M_\oplus$]") +ax10.set_xlabel(r"Time [yrs]") +ax10.set_ylabel(r"Mass [$M_\oplus$]") fig.tight_layout() plt.show() -
-
-
-
-
-<>:21: SyntaxWarning: invalid escape sequence '\o'
-<>:21: SyntaxWarning: invalid escape sequence '\o'
-/tmp/ipykernel_94249/2126622601.py:21: SyntaxWarning: invalid escape sequence '\o'
-  ax10.set_ylabel("Mass [$M_\oplus$]")
-
-
-_images/example_planetesimal_formation_48_1.png +_images/example_planetesimal_formation_48_0.png
diff --git a/docs/example_planetesimal_formation.ipynb b/docs/example_planetesimal_formation.ipynb index 547ec4a..0426658 100644 --- a/docs/example_planetesimal_formation.ipynb +++ b/docs/example_planetesimal_formation.ipynb @@ -43,10 +43,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:11.769745Z", - "iopub.status.busy": "2023-11-30T11:30:11.769049Z", - "iopub.status.idle": "2023-11-30T11:30:12.965161Z", - "shell.execute_reply": "2023-11-30T11:30:12.963939Z" + "iopub.execute_input": "2023-12-01T18:14:27.375259Z", + "iopub.status.busy": "2023-12-01T18:14:27.374595Z", + "iopub.status.idle": "2023-12-01T18:14:28.251107Z", + "shell.execute_reply": "2023-12-01T18:14:28.249797Z" } }, "outputs": [], @@ -60,10 +60,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:12.971730Z", - "iopub.status.busy": "2023-11-30T11:30:12.971010Z", - "iopub.status.idle": "2023-11-30T11:30:12.977418Z", - "shell.execute_reply": "2023-11-30T11:30:12.976543Z" + "iopub.execute_input": "2023-12-01T18:14:28.257208Z", + "iopub.status.busy": "2023-12-01T18:14:28.256740Z", + "iopub.status.idle": "2023-12-01T18:14:28.261674Z", + "shell.execute_reply": "2023-12-01T18:14:28.260905Z" } }, "outputs": [], @@ -104,10 +104,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:12.982434Z", - "iopub.status.busy": "2023-11-30T11:30:12.982088Z", - "iopub.status.idle": "2023-11-30T11:30:12.989335Z", - "shell.execute_reply": "2023-11-30T11:30:12.988307Z" + "iopub.execute_input": "2023-12-01T18:14:28.266124Z", + "iopub.status.busy": "2023-12-01T18:14:28.265900Z", + "iopub.status.idle": "2023-12-01T18:14:28.270964Z", + "shell.execute_reply": "2023-12-01T18:14:28.270097Z" } }, "outputs": [], @@ -134,10 +134,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:12.994722Z", - "iopub.status.busy": "2023-11-30T11:30:12.994344Z", - "iopub.status.idle": "2023-11-30T11:30:13.002669Z", - "shell.execute_reply": "2023-11-30T11:30:13.001647Z" + "iopub.execute_input": "2023-12-01T18:14:28.275586Z", + "iopub.status.busy": "2023-12-01T18:14:28.275310Z", + "iopub.status.idle": "2023-12-01T18:14:28.283460Z", + "shell.execute_reply": "2023-12-01T18:14:28.282494Z" } }, "outputs": [], @@ -189,10 +189,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:13.008079Z", - "iopub.status.busy": "2023-11-30T11:30:13.007683Z", - "iopub.status.idle": "2023-11-30T11:30:13.013947Z", - "shell.execute_reply": "2023-11-30T11:30:13.012885Z" + "iopub.execute_input": "2023-12-01T18:14:28.288258Z", + "iopub.status.busy": "2023-12-01T18:14:28.287883Z", + "iopub.status.idle": "2023-12-01T18:14:28.294388Z", + "shell.execute_reply": "2023-12-01T18:14:28.293087Z" } }, "outputs": [], @@ -206,10 +206,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:13.019212Z", - "iopub.status.busy": "2023-11-30T11:30:13.018802Z", - "iopub.status.idle": "2023-11-30T11:30:13.981525Z", - "shell.execute_reply": "2023-11-30T11:30:13.978957Z" + "iopub.execute_input": "2023-12-01T18:14:28.299786Z", + "iopub.status.busy": "2023-12-01T18:14:28.299193Z", + "iopub.status.idle": "2023-12-01T18:14:28.902999Z", + "shell.execute_reply": "2023-12-01T18:14:28.902057Z" } }, "outputs": [], @@ -229,10 +229,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:13.988177Z", - "iopub.status.busy": "2023-11-30T11:30:13.987833Z", - "iopub.status.idle": "2023-11-30T11:30:13.993344Z", - "shell.execute_reply": "2023-11-30T11:30:13.992457Z" + "iopub.execute_input": "2023-12-01T18:14:28.908058Z", + "iopub.status.busy": "2023-12-01T18:14:28.907785Z", + "iopub.status.idle": "2023-12-01T18:14:28.912382Z", + "shell.execute_reply": "2023-12-01T18:14:28.911566Z" } }, "outputs": [], @@ -254,10 +254,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:13.998175Z", - "iopub.status.busy": "2023-11-30T11:30:13.997845Z", - "iopub.status.idle": "2023-11-30T11:30:14.003026Z", - "shell.execute_reply": "2023-11-30T11:30:14.002140Z" + "iopub.execute_input": "2023-12-01T18:14:28.916973Z", + "iopub.status.busy": "2023-12-01T18:14:28.916706Z", + "iopub.status.idle": "2023-12-01T18:14:28.921477Z", + "shell.execute_reply": "2023-12-01T18:14:28.920648Z" } }, "outputs": [], @@ -280,10 +280,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.008770Z", - "iopub.status.busy": "2023-11-30T11:30:14.007920Z", - "iopub.status.idle": "2023-11-30T11:30:14.014090Z", - "shell.execute_reply": "2023-11-30T11:30:14.012758Z" + "iopub.execute_input": "2023-12-01T18:14:28.926195Z", + "iopub.status.busy": "2023-12-01T18:14:28.925904Z", + "iopub.status.idle": "2023-12-01T18:14:28.930521Z", + "shell.execute_reply": "2023-12-01T18:14:28.929628Z" } }, "outputs": [], @@ -296,10 +296,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.019780Z", - "iopub.status.busy": "2023-11-30T11:30:14.019126Z", - "iopub.status.idle": "2023-11-30T11:30:14.027004Z", - "shell.execute_reply": "2023-11-30T11:30:14.025550Z" + "iopub.execute_input": "2023-12-01T18:14:28.950944Z", + "iopub.status.busy": "2023-12-01T18:14:28.950172Z", + "iopub.status.idle": "2023-12-01T18:14:28.957944Z", + "shell.execute_reply": "2023-12-01T18:14:28.956527Z" } }, "outputs": [], @@ -313,10 +313,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.033093Z", - "iopub.status.busy": "2023-11-30T11:30:14.032428Z", - "iopub.status.idle": "2023-11-30T11:30:14.047997Z", - "shell.execute_reply": "2023-11-30T11:30:14.047036Z" + "iopub.execute_input": "2023-12-01T18:14:28.963483Z", + "iopub.status.busy": "2023-12-01T18:14:28.962811Z", + "iopub.status.idle": "2023-12-01T18:14:28.977729Z", + "shell.execute_reply": "2023-12-01T18:14:28.976677Z" } }, "outputs": [ @@ -351,10 +351,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.104990Z", - "iopub.status.busy": "2023-11-30T11:30:14.104177Z", - "iopub.status.idle": "2023-11-30T11:30:14.111269Z", - "shell.execute_reply": "2023-11-30T11:30:14.109982Z" + "iopub.execute_input": "2023-12-01T18:14:29.040512Z", + "iopub.status.busy": "2023-12-01T18:14:29.039648Z", + "iopub.status.idle": "2023-12-01T18:14:29.047136Z", + "shell.execute_reply": "2023-12-01T18:14:29.045655Z" } }, "outputs": [], @@ -368,10 +368,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.115282Z", - "iopub.status.busy": "2023-11-30T11:30:14.114607Z", - "iopub.status.idle": "2023-11-30T11:30:14.120860Z", - "shell.execute_reply": "2023-11-30T11:30:14.119633Z" + "iopub.execute_input": "2023-12-01T18:14:29.051288Z", + "iopub.status.busy": "2023-12-01T18:14:29.050567Z", + "iopub.status.idle": "2023-12-01T18:14:29.056794Z", + "shell.execute_reply": "2023-12-01T18:14:29.055583Z" } }, "outputs": [], @@ -391,10 +391,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.125135Z", - "iopub.status.busy": "2023-11-30T11:30:14.124447Z", - "iopub.status.idle": "2023-11-30T11:30:14.131450Z", - "shell.execute_reply": "2023-11-30T11:30:14.130030Z" + "iopub.execute_input": "2023-12-01T18:14:29.061106Z", + "iopub.status.busy": "2023-12-01T18:14:29.060463Z", + "iopub.status.idle": "2023-12-01T18:14:29.067295Z", + "shell.execute_reply": "2023-12-01T18:14:29.065915Z" } }, "outputs": [], @@ -417,10 +417,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.136597Z", - "iopub.status.busy": "2023-11-30T11:30:14.135878Z", - "iopub.status.idle": "2023-11-30T11:30:14.145371Z", - "shell.execute_reply": "2023-11-30T11:30:14.143896Z" + "iopub.execute_input": "2023-12-01T18:14:29.072276Z", + "iopub.status.busy": "2023-12-01T18:14:29.071617Z", + "iopub.status.idle": "2023-12-01T18:14:29.080164Z", + "shell.execute_reply": "2023-12-01T18:14:29.078819Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.151220Z", - "iopub.status.busy": "2023-11-30T11:30:14.150592Z", - "iopub.status.idle": "2023-11-30T11:30:14.156975Z", - "shell.execute_reply": "2023-11-30T11:30:14.155598Z" + "iopub.execute_input": "2023-12-01T18:14:29.085594Z", + "iopub.status.busy": "2023-12-01T18:14:29.084997Z", + "iopub.status.idle": "2023-12-01T18:14:29.090903Z", + "shell.execute_reply": "2023-12-01T18:14:29.089517Z" } }, "outputs": [], @@ -469,10 +469,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.162551Z", - "iopub.status.busy": "2023-11-30T11:30:14.161896Z", - "iopub.status.idle": "2023-11-30T11:30:14.168088Z", - "shell.execute_reply": "2023-11-30T11:30:14.166912Z" + "iopub.execute_input": "2023-12-01T18:14:29.104711Z", + "iopub.status.busy": "2023-12-01T18:14:29.102144Z", + "iopub.status.idle": "2023-12-01T18:14:29.112033Z", + "shell.execute_reply": "2023-12-01T18:14:29.110741Z" } }, "outputs": [], @@ -486,10 +486,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.173683Z", - "iopub.status.busy": "2023-11-30T11:30:14.173036Z", - "iopub.status.idle": "2023-11-30T11:30:14.179253Z", - "shell.execute_reply": "2023-11-30T11:30:14.177859Z" + "iopub.execute_input": "2023-12-01T18:14:29.117637Z", + "iopub.status.busy": "2023-12-01T18:14:29.117087Z", + "iopub.status.idle": "2023-12-01T18:14:29.123305Z", + "shell.execute_reply": "2023-12-01T18:14:29.122077Z" } }, "outputs": [], @@ -509,10 +509,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.185311Z", - "iopub.status.busy": "2023-11-30T11:30:14.184699Z", - "iopub.status.idle": "2023-11-30T11:30:14.190486Z", - "shell.execute_reply": "2023-11-30T11:30:14.189359Z" + "iopub.execute_input": "2023-12-01T18:14:29.129389Z", + "iopub.status.busy": "2023-12-01T18:14:29.128721Z", + "iopub.status.idle": "2023-12-01T18:14:29.135120Z", + "shell.execute_reply": "2023-12-01T18:14:29.133723Z" } }, "outputs": [], @@ -533,10 +533,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.196220Z", - "iopub.status.busy": "2023-11-30T11:30:14.195566Z", - "iopub.status.idle": "2023-11-30T11:30:14.201984Z", - "shell.execute_reply": "2023-11-30T11:30:14.200602Z" + "iopub.execute_input": "2023-12-01T18:14:29.141063Z", + "iopub.status.busy": "2023-12-01T18:14:29.140397Z", + "iopub.status.idle": "2023-12-01T18:14:29.147153Z", + "shell.execute_reply": "2023-12-01T18:14:29.145722Z" } }, "outputs": [], @@ -559,10 +559,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.207926Z", - "iopub.status.busy": "2023-11-30T11:30:14.207254Z", - "iopub.status.idle": "2023-11-30T11:30:14.213434Z", - "shell.execute_reply": "2023-11-30T11:30:14.212276Z" + "iopub.execute_input": "2023-12-01T18:14:29.154105Z", + "iopub.status.busy": "2023-12-01T18:14:29.152811Z", + "iopub.status.idle": "2023-12-01T18:14:29.159466Z", + "shell.execute_reply": "2023-12-01T18:14:29.158084Z" } }, "outputs": [], @@ -582,10 +582,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.219397Z", - "iopub.status.busy": "2023-11-30T11:30:14.218765Z", - "iopub.status.idle": "2023-11-30T11:30:14.227674Z", - "shell.execute_reply": "2023-11-30T11:30:14.226317Z" + "iopub.execute_input": "2023-12-01T18:14:29.165473Z", + "iopub.status.busy": "2023-12-01T18:14:29.164814Z", + "iopub.status.idle": "2023-12-01T18:14:29.174294Z", + "shell.execute_reply": "2023-12-01T18:14:29.172917Z" } }, "outputs": [ @@ -620,10 +620,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.233600Z", - "iopub.status.busy": "2023-11-30T11:30:14.232956Z", - "iopub.status.idle": "2023-11-30T11:30:14.495415Z", - "shell.execute_reply": "2023-11-30T11:30:14.494445Z" + "iopub.execute_input": "2023-12-01T18:14:29.180271Z", + "iopub.status.busy": "2023-12-01T18:14:29.179574Z", + "iopub.status.idle": "2023-12-01T18:14:29.415923Z", + "shell.execute_reply": "2023-12-01T18:14:29.415034Z" } }, "outputs": [], @@ -637,10 +637,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:30:14.500674Z", - "iopub.status.busy": "2023-11-30T11:30:14.500392Z", - "iopub.status.idle": "2023-12-01T09:48:07.108521Z", - "shell.execute_reply": "2023-12-01T09:48:07.107540Z" + "iopub.execute_input": "2023-12-01T18:14:29.420841Z", + "iopub.status.busy": "2023-12-01T18:14:29.420612Z", + "iopub.status.idle": "2023-12-02T20:09:19.909195Z", + "shell.execute_reply": "2023-12-02T20:09:19.907871Z" } }, "outputs": [ @@ -740,7 +740,7 @@ "Writing dump file \u001b[94mexample_planetesimal_formation/frame.dmp\u001b[0m\n", "Writing file \u001b[94mexample_planetesimal_formation/data0031.hdf5\u001b[0m\n", "Writing dump file \u001b[94mexample_planetesimal_formation/frame.dmp\u001b[0m\n", - "Execution time: \u001b[94m22:17:52\u001b[0m\n" + "Execution time: \u001b[94m1 day, 1:54:50\u001b[0m\n" ] } ], @@ -760,10 +760,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:07.114558Z", - "iopub.status.busy": "2023-12-01T09:48:07.114307Z", - "iopub.status.idle": "2023-12-01T09:48:07.118715Z", - "shell.execute_reply": "2023-12-01T09:48:07.117955Z" + "iopub.execute_input": "2023-12-02T20:09:19.914589Z", + "iopub.status.busy": "2023-12-02T20:09:19.914314Z", + "iopub.status.idle": "2023-12-02T20:09:19.919544Z", + "shell.execute_reply": "2023-12-02T20:09:19.918758Z" } }, "outputs": [], @@ -776,10 +776,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:07.123221Z", - "iopub.status.busy": "2023-12-01T09:48:07.122948Z", - "iopub.status.idle": "2023-12-01T09:48:08.199010Z", - "shell.execute_reply": "2023-12-01T09:48:08.197703Z" + "iopub.execute_input": "2023-12-02T20:09:19.924164Z", + "iopub.status.busy": "2023-12-02T20:09:19.923845Z", + "iopub.status.idle": "2023-12-02T20:09:21.119701Z", + "shell.execute_reply": "2023-12-02T20:09:21.118158Z" } }, "outputs": [ @@ -810,10 +810,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:08.208190Z", - "iopub.status.busy": "2023-12-01T09:48:08.207519Z", - "iopub.status.idle": "2023-12-01T09:48:08.275422Z", - "shell.execute_reply": "2023-12-01T09:48:08.274700Z" + "iopub.execute_input": "2023-12-02T20:09:21.128905Z", + "iopub.status.busy": "2023-12-02T20:09:21.128167Z", + "iopub.status.idle": "2023-12-02T20:09:21.197907Z", + "shell.execute_reply": "2023-12-02T20:09:21.197017Z" } }, "outputs": [], @@ -836,10 +836,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:08.280160Z", - "iopub.status.busy": "2023-12-01T09:48:08.279973Z", - "iopub.status.idle": "2023-12-01T09:48:08.283928Z", - "shell.execute_reply": "2023-12-01T09:48:08.283178Z" + "iopub.execute_input": "2023-12-02T20:09:21.202679Z", + "iopub.status.busy": "2023-12-02T20:09:21.202467Z", + "iopub.status.idle": "2023-12-02T20:09:21.206941Z", + "shell.execute_reply": "2023-12-02T20:09:21.206151Z" } }, "outputs": [], @@ -853,23 +853,13 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2023-12-01T09:48:08.288344Z", - "iopub.status.busy": "2023-12-01T09:48:08.288122Z", - "iopub.status.idle": "2023-12-01T09:48:08.871279Z", - "shell.execute_reply": "2023-12-01T09:48:08.870525Z" + "iopub.execute_input": "2023-12-02T20:09:21.211199Z", + "iopub.status.busy": "2023-12-02T20:09:21.210974Z", + "iopub.status.idle": "2023-12-02T20:09:21.846183Z", + "shell.execute_reply": "2023-12-02T20:09:21.845405Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:21: SyntaxWarning: invalid escape sequence '\\o'\n", - "<>:21: SyntaxWarning: invalid escape sequence '\\o'\n", - "/tmp/ipykernel_94249/2126622601.py:21: SyntaxWarning: invalid escape sequence '\\o'\n", - " ax10.set_ylabel(\"Mass [$M_\\oplus$]\")\n" - ] - }, { "data": { "image/png": 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", @@ -901,8 +891,8 @@ "ax10.loglog(t/c.year, Mplan/c.M_earth, label=\"Planetesimals\")\n", "ax10.set_xlim(t[1]/c.year, t[-1]/c.year)\n", "ax10.set_ylim(1.e-1, 1.e5)\n", - "ax10.set_xlabel(\"Time [yrs]\")\n", - "ax10.set_ylabel(\"Mass [$M_\\oplus$]\")\n", + "ax10.set_xlabel(r\"Time [yrs]\")\n", + "ax10.set_ylabel(r\"Mass [$M_\\oplus$]\")\n", "\n", "fig.tight_layout()\n", "plt.show()" @@ -925,7 +915,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/searchindex.js b/docs/searchindex.js index 005a379..711798a 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["1_basics", "2_simple_customization", "3_advanced_customization", "4_standard_model", "5_dust_coagulation", "6_dust_evolution", "7_gas_evolution", "A_citation", "B_publications", "C_contrib_bug_feature", "D_discussions", "E_changelog", "api", "api/dustpy.Simulation", 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"api/dustpy.std.gas.lyndenbellpringle1974.rst", "api/dustpy.std.gas.mfp_midplane.rst", "api/dustpy.std.gas.n_midplane.rst", "api/dustpy.std.gas.nu.rst", "api/dustpy.std.gas.prepare.rst", "api/dustpy.std.gas.rho_midplane.rst", "api/dustpy.std.gas.set_implicit_boundaries.rst", "api/dustpy.std.gas.vrad.rst", "api/dustpy.std.gas.vvisc.rst", "api/dustpy.std.grid.OmegaK.rst", "api/dustpy.std.sim.dt.rst", "api/dustpy.std.sim.dt_adaptive.rst", "api/dustpy.std.sim.finalize_explicit_dust.rst", "api/dustpy.std.sim.finalize_implicit_dust.rst", "api/dustpy.std.sim.prepare_explicit_dust.rst", "api/dustpy.std.sim.prepare_implicit_dust.rst", "api/dustpy.std.star.luminosity.rst", "api/dustpy.utils.Boundary.rst", "api/dustpy.utils.print_version_warning.rst", "dustpylib.ipynb", "example_ice_lines.ipynb", "example_planetary_gaps.ipynb", "example_planetesimal_formation.ipynb", "index.rst", "test_analytical_coagulation_kernels.ipynb", "test_gas_evolution.ipynb"], "titles": ["1. Basic usage", "2. Simple Customization", "3. Advanced Customization", "4. The Standard Model", "5. Dust Coagulation", "6. Dust Evolution", "7. Gas Evolution", "Appendix A: Citation", "Appendix B: List of Publications", "Appendix C: Contributing/Bugs/Features", "Appendix D: DustPy Discussions", "Appendix E: Changelog", "Module Reference", "Simulation", "ipanel", "panel", "D", "F_adv", "F_diff", "F_tot", "H", "MRN_distribution", "S_coag", "S_hyd", "S_tot", "SigmaFloor", "Sigma_deriv", "St_Epstein_StokesI", "a", "boundary", "coagulation_parameters", "dt", "dt_adaptive", "enforce_floor_value", "eps", "finalize_explicit", "finalize_implicit", "impl_1_direct", "jacobian", "kernel", "p_frag", "p_stick", "prepare", "rho_midplane", "set_implicit_boundaries", "vdriftmax", "vrad", "vrel_azimuthal_drift", "vrel_brownian_motion", "vrel_radial_drift", "vrel_tot", "vrel_turbulent_motion", "vrel_vertical_settling", "Fi", "Hp", "P_midplane", "S_hyd", "S_tot", "T_passive", "boundary", "cs_adiabatic", "dt", "enforce_floor_value", "eta_midplane", "finalize", "impl_1_direct", "jacobian", "lyndenbellpringle1974", "mfp_midplane", "n_midplane", "nu", "prepare", "rho_midplane", "set_implicit_boundaries", "vrad", "vvisc", "OmegaK", "dt", "dt_adaptive", "finalize_explicit_dust", "finalize_implicit_dust", "prepare_explicit_dust", "prepare_implicit_dust", "luminosity", "Boundary", "print_version_warning", "Library: dustpylib", "Example: Ice Lines", "Example: Planetary Gaps", "Example: Planetesimal Formation", "DustPy Documentation", "Test: Analytical Coagulation Kernels", "Test: Gas Evolution"], "terms": {"dustpi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91], "i": [0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 17, 21, 25, 27, 30, 37, 38, 41, 48, 65, 66, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92], "us": [0, 1, 2, 3, 4, 5, 6, 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Basic usage": [[0, "1.-Basic-usage"]], "The Simulation Frame": [[0, "The-Simulation-Frame"]], "Initializing": [[0, "Initializing"]], "Running a Simulation": [[0, "Running-a-Simulation"]], "Plotting": [[0, "Plotting"]], "Code Units": [[0, "Code-Units"]], "Reading data files": [[0, "Reading-data-files"]], "Reading Dump Files": [[0, "Reading-Dump-Files"]], "2. Simple Customization": [[1, "2.-Simple-Customization"]], "Stellar parameters": [[1, "Stellar-parameters"]], "Grid parameters": [[1, "Grid-parameters"]], "Gas Parameters": [[1, "Gas-Parameters"]], "Dust Parameters": [[1, "Dust-Parameters"]], "Changing the Initial Conditions": [[1, "Changing-the-Initial-Conditions"]], "Changing the Snapshots": [[1, "Changing-the-Snapshots"]], "Changing the Output Directory": [[1, "Changing-the-Output-Directory"]], "3. 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The Standard Model": [[3, "4.-The-Standard-Model"]], "Dust": [[3, "Dust"]], "Simulation.dust.backreaction": [[3, "Simulation.dust.backreaction"]], "Simulation.dust.boundary": [[3, "Simulation.dust.boundary"]], "Simulation.dust.coagulation": [[3, "Simulation.dust.coagulation"]], "Simulation.dust.delta": [[3, "Simulation.dust.delta"]], "Simulation.dust.delta.rad": [[3, "Simulation.dust.delta.rad"]], "Simulation.dust.delta.turb": [[3, "Simulation.dust.delta.turb"]], "Simulation.dust.delta.vert": [[3, "Simulation.dust.delta.vert"]], "Simulation.dust.Fi": [[3, "Simulation.dust.Fi"]], "Simulation.dust.Fi.adv": [[3, "Simulation.dust.Fi.adv"]], "Simulation.dust.Fi.diff": [[3, "Simulation.dust.Fi.diff"]], "Simulation.dust.Fi.tot": [[3, "Simulation.dust.Fi.tot"]], "Simulation.dust.p": [[3, "Simulation.dust.p"]], "Simulation.dust.p.frag": [[3, "Simulation.dust.p.frag"]], "Simulation.dust.p.stick": [[3, "Simulation.dust.p.stick"]], "Simulation.dust.S": [[3, "Simulation.dust.S"]], "Simulation.dust.S.coag": [[3, "Simulation.dust.S.coag"]], "Simulation.dust.S.ext": [[3, "Simulation.dust.S.ext"]], "Simulation.dust.S.hyd": [[3, "Simulation.dust.S.hyd"]], "Simulation.dust.S.tot": [[3, "Simulation.dust.S.tot"]], "Simulation.dust.v": [[3, "Simulation.dust.v"]], "Simulation.dust.v.rel": [[3, "Simulation.dust.v.rel"]], "Simulation.dust.v.rel.azi": [[3, "Simulation.dust.v.rel.azi"]], "Simulation.dust.v.brown": [[3, "Simulation.dust.v.brown"]], "Simulation.dust.v.rel.rad": [[3, "Simulation.dust.v.rel.rad"]], "Simulation.dust.v.rel.tot": [[3, "Simulation.dust.v.rel.tot"]], "Simulation.dust.v.rel.turb": [[3, "Simulation.dust.v.rel.turb"]], "Simulation.dust.v.rel.vert": [[3, "Simulation.dust.v.rel.vert"]], "Simulation.dust.v.driftmax": [[3, "Simulation.dust.v.driftmax"]], "Simulation.dust.v.frag": [[3, "Simulation.dust.v.frag"]], "Simulation.dust.v.rad": [[3, "Simulation.dust.v.rad"]], "Simulation.dust.a": [[3, "Simulation.dust.a"]], "Simulation.dust.D": [[3, "Simulation.dust.D"]], "Simulation.dust.eps": [[3, "Simulation.dust.eps"]], "Simulation.dust.fill": [[3, "Simulation.dust.fill"]], "Simulation.dust.H": [[3, "Simulation.dust.H"]], "Simulation.dust.kernel": [[3, "Simulation.dust.kernel"]], "Simulation.dust.rho": [[3, "Simulation.dust.rho"]], "Simulation.dust.rhos": [[3, "Simulation.dust.rhos"]], "Simulation.dust.Sigma": [[3, "Simulation.dust.Sigma"]], "Simulation.dust.SigmaFloor": [[3, "Simulation.dust.SigmaFloor"]], "Simulation.dust.St": [[3, "Simulation.dust.St"]], "Update order": [[3, "Update-order"], [3, "id1"], [3, "id2"], [3, "id3"]], "Gas": [[3, "Gas"]], "Simulation.gas.boundary": [[3, "Simulation.gas.boundary"]], "Simulation.gas.S": [[3, "Simulation.gas.S"]], "Simulation.gas.S.ext": [[3, "Simulation.gas.S.ext"]], "Simulation.gas.S.hyd": [[3, "Simulation.gas.S.hyd"]], "Simulation.gas.S.tot": [[3, "Simulation.gas.S.tot"]], "Simulation.gas.v": [[3, "Simulation.gas.v"]], "Simulation.gas.v.rad": [[3, "Simulation.gas.v.rad"]], "Simulation.gas.v.visc": [[3, "Simulation.gas.v.visc"]], "Simulation.gas.alpha": [[3, "Simulation.gas.alpha"]], "Simulation.gas.cs": [[3, "Simulation.gas.cs"]], "Simulation.gas.eta": [[3, "Simulation.gas.eta"]], "Simulation.gas.Fi": [[3, "Simulation.gas.Fi"]], "Simulation.gas.gamma": [[3, "Simulation.gas.gamma"]], "Simulation.gas.Hp": [[3, "Simulation.gas.Hp"]], "Simulation.gas.mfp": [[3, "Simulation.gas.mfp"]], "Simulation.gas.mu": [[3, "Simulation.gas.mu"]], "Simulation.gas.n": [[3, "Simulation.gas.n"]], "Simulation.gas.nu": [[3, "Simulation.gas.nu"]], "Simulation.gas.P": [[3, "Simulation.gas.P"]], "Simulation.gas.rho": [[3, "Simulation.gas.rho"]], "Simulation.gas.Sigma": [[3, "Simulation.gas.Sigma"]], "Simulation.gas.SigmaFloor": [[3, "Simulation.gas.SigmaFloor"]], "Simulation.gas.T": [[3, "Simulation.gas.T"]], "Grid": [[3, "Grid"]], "Simulation.grid.A": [[3, "Simulation.grid.A"]], "Simulation.grid.m": [[3, "Simulation.grid.m"]], "Simulation.grid.Nm": [[3, "Simulation.grid.Nm"]], "Simulation.grid.Nr": [[3, "Simulation.grid.Nr"]], "Simulation.grid.OmegaK": [[3, "Simulation.grid.OmegaK"]], "Simulation.grid.r": [[3, "Simulation.grid.r"]], "Simulation.grid.ri": [[3, "Simulation.grid.ri"]], "Star": [[3, "Star"]], "Simulation.star.L": [[3, "Simulation.star.L"]], "Simulation.star.M": [[3, "Simulation.star.M"]], "Simulation.star.R": [[3, "Simulation.star.R"]], "Simulation.star.T": [[3, "Simulation.star.T"]], "Time": [[3, "Time"]], "Integrator": [[3, "Integrator"]], "Writer": [[3, "Writer"]], "5. Dust Coagulation": [[4, "5.-Dust-Coagulation"]], "Sticking": [[4, "Sticking"]], "Fragmentation & Erosion": [[4, "Fragmentation-&-Erosion"]], "Probabilities": [[4, "Probabilities"]], "Bouncing": [[4, "Bouncing"]], "Collision Rates": [[4, "Collision-Rates"]], "Coagulation Sources": [[4, "Coagulation-Sources"]], "6. Dust Evolution": [[5, "6.-Dust-Evolution"]], "Coagulation": [[5, "Coagulation"]], "Turning off Fragmentation": [[5, "Turning-off-Fragmentation"]], "Turning off Coagulation": [[5, "Turning-off-Coagulation"]], "Hydrodynamics": [[5, "Hydrodynamics"], [6, "Hydrodynamics"]], "Advection": [[5, "Advection"]], "Turning off Advection": [[5, "Turning-off-Advection"]], "Diffusion": [[5, "Diffusion"]], "Turning off Diffusion": [[5, "Turning-off-Diffusion"]], "Turning off Hydrodynamics": [[5, "Turning-off-Hydrodynamics"], [6, "Turning-off-Hydrodynamics"]], "External Sources": [[5, "External-Sources"], [6, "External-Sources"]], "Turning off Dust Evolution": [[5, "Turning-off-Dust-Evolution"], [92, "Turning-off-Dust-Evolution"]], "Changing the Dust Integrator": [[5, "Changing-the-Dust-Integrator"]], "7. Gas Evolution": [[6, "7.-Gas-Evolution"]], "Turning off External Sources": [[6, "Turning-off-External-Sources"]], "Turning off Gas Evolution": [[6, "Turning-off-Gas-Evolution"]], "Appendix A: Citation": [[7, "Appendix-A:-Citation"]], "Appendix B: List of Publications": [[8, "Appendix-B:-List-of-Publications"]], "Appendix C: Contributing/Bugs/Features": [[9, "Appendix-C:-Contributing/Bugs/Features"]], "Contributing": [[9, "Contributing"]], "Bug Reports": [[9, "Bug-Reports"]], "Feature Requests": [[9, "Feature-Requests"]], "Appendix D: DustPy Discussions": [[10, "Appendix-D:-DustPy-Discussions"]], "Appendix E: Changelog": [[11, "Appendix-E:-Changelog"]], "v1.0.5": [[11, "v1.0.5"]], "Using Meson as build system": [[11, "Using-Meson-as-build-system"]], "Bugfix to velocity distribution": [[11, "Bugfix-to-velocity-distribution"]], "Bugfix to plotting script": [[11, "Bugfix-to-plotting-script"]], "Preparation for the addition of multiple gas species": [[11, "Preparation-for-the-addition-of-multiple-gas-species"]], "v1.0.4": [[11, "v1.0.4"]], "Bugfix to boundary conditions": [[11, "Bugfix-to-boundary-conditions"]], "v1.0.3": [[11, "v1.0.3"]], "Correction to inital particle size distribution": [[11, "Correction-to-inital-particle-size-distribution"]], "Removal of non-ASCII characters": [[11, "Removal-of-non-ASCII-characters"]], "v1.0.2": [[11, "v1.0.2"]], "Change in default temperature profile": [[11, "Change-in-default-temperature-profile"]], "v1.0.1": [[11, "v1.0.1"]], "Change to Collision Kernel": [[11, "Change-to-Collision-Kernel"]], "v1.0.0": [[11, "v1.0.0"]], "Module Reference": [[12, "module-reference"]], "dustpy Package": [[12, "module-dustpy"]], "Classes": [[12, "classes"], [12, "id2"], [12, "id4"], [12, "id9"]], "dustpy.constants Package": [[12, "module-dustpy.constants"]], "Constants": [[12, "constants"]], "dustpy.plot Package": [[12, "module-dustpy.plot"]], "Functions": [[12, "functions"], [12, "id1"], [12, "id3"], [12, "id5"], [12, "id6"], [12, "id7"], [12, "id8"]], "dustpy.std Package": [[12, "module-dustpy.std"]], "dustpy.std.dust Module": [[12, "module-dustpy.std.dust"]], "dustpy.std.gas Module": [[12, "module-dustpy.std.gas"]], "dustpy.std.grid Module": [[12, "module-dustpy.std.grid"]], "dustpy.std.sim Module": [[12, "module-dustpy.std.sim"]], "dustpy.std.star Module": [[12, "module-dustpy.std.star"]], "dustpy.utils Package": [[12, "module-dustpy.utils"]], "Simulation": [[13, "simulation"]], "ipanel": [[14, "ipanel"]], "panel": [[15, "panel"]], "D": [[16, "d"]], "F_adv": [[17, "f-adv"]], "F_diff": [[18, "f-diff"]], "F_tot": [[19, "f-tot"]], "H": [[20, "h"]], "MRN_distribution": [[21, "mrn-distribution"]], "S_coag": [[22, "s-coag"]], "S_hyd": [[23, "s-hyd"], [56, "s-hyd"]], "S_tot": [[24, "s-tot"], [57, "s-tot"]], "SigmaFloor": [[25, "sigmafloor"]], "Sigma_deriv": [[26, "sigma-deriv"]], "St_Epstein_StokesI": [[27, "st-epstein-stokesi"]], "a": [[28, "a"]], "boundary": [[29, "boundary"], [59, "boundary"]], "coagulation_parameters": [[30, "coagulation-parameters"]], "dt": [[31, "dt"], [61, "dt"], [77, "dt"]], "dt_adaptive": [[32, "dt-adaptive"], [78, "dt-adaptive"]], "enforce_floor_value": [[33, "enforce-floor-value"], [62, "enforce-floor-value"]], "eps": [[34, "eps"]], "finalize_explicit": [[35, "finalize-explicit"]], "finalize_implicit": [[36, "finalize-implicit"]], "impl_1_direct": [[37, "impl-1-direct"], [65, "impl-1-direct"]], "jacobian": [[38, "jacobian"], [66, "jacobian"]], "kernel": [[39, "kernel"]], "p_frag": [[40, "p-frag"]], "p_stick": [[41, "p-stick"]], "prepare": [[42, "prepare"], [71, "prepare"]], "rho_midplane": [[43, "rho-midplane"], [72, "rho-midplane"]], "set_implicit_boundaries": [[44, "set-implicit-boundaries"], [73, "set-implicit-boundaries"]], "vdriftmax": [[45, "vdriftmax"]], "vrad": [[46, "vrad"], [74, "vrad"]], "vrel_azimuthal_drift": [[47, "vrel-azimuthal-drift"]], "vrel_brownian_motion": [[48, "vrel-brownian-motion"]], "vrel_radial_drift": [[49, "vrel-radial-drift"]], "vrel_tot": [[50, "vrel-tot"]], "vrel_turbulent_motion": [[51, "vrel-turbulent-motion"]], "vrel_vertical_settling": [[52, "vrel-vertical-settling"]], "Fi": [[53, "fi"]], "Hp": [[54, "hp"]], "P_midplane": [[55, "p-midplane"]], "T_passive": [[58, "t-passive"]], "cs_adiabatic": [[60, "cs-adiabatic"]], "eta_midplane": [[63, "eta-midplane"]], "finalize": [[64, "finalize"]], "lyndenbellpringle1974": [[67, "lyndenbellpringle1974"]], "mfp_midplane": [[68, "mfp-midplane"]], "n_midplane": [[69, "n-midplane"]], "nu": [[70, "nu"]], "vvisc": [[75, "vvisc"]], "OmegaK": [[76, "omegak"]], "finalize_explicit_dust": [[79, "finalize-explicit-dust"]], "finalize_implicit_dust": [[80, "finalize-implicit-dust"]], "prepare_explicit_dust": [[81, "prepare-explicit-dust"]], "prepare_implicit_dust": [[82, "prepare-implicit-dust"]], "luminosity": [[83, "luminosity"]], "Boundary": [[84, "boundary"]], "print_version_warning": [[85, "print-version-warning"]], "Library: dustpylib": [[86, "Library:-dustpylib"]], "Example: Ice Lines": [[87, "Example:-Ice-Lines"]], "Example: Planetary Gaps": [[88, "Example:-Planetary-Gaps"]], "Gap Profiles": [[88, "Gap-Profiles"]], "Adding planets": [[88, "Adding-planets"]], "Viscosity pertubation": [[88, "Viscosity-pertubation"]], "Growing planets": [[88, "Growing-planets"]], "Example: Planetesimal Formation": [[89, "Example:-Planetesimal-Formation"]], "Refining the radial grid": [[89, "Refining-the-radial-grid"]], "Adding gap": [[89, "Adding-gap"]], "Adding planetesimals": [[89, "Adding-planetesimals"]], "Adding planetesimal formation": [[89, "Adding-planetesimal-formation"]], "DustPy Documentation": [[90, "dustpy-documentation"]], "Contents:": [[90, null]], "Indices and tables": [[90, "indices-and-tables"]], "Test: Analytical Coagulation Kernels": [[91, "Test:-Analytical-Coagulation-Kernels"]], "The Constant Kernel": [[91, "The-Constant-Kernel"]], "The Linear Kernel": [[91, "The-Linear-Kernel"]], "The Product Kernel": [[91, "The-Product-Kernel"]], "Test: Gas Evolution": [[92, "Test:-Gas-Evolution"]], "Analytical Solution": [[92, "Analytical-Solution"]], "Setting up DustPy": [[92, "Setting-up-DustPy"]], "Modifying Integration Variable": [[92, "Modifying-Integration-Variable"]], "Setting Writer Options": [[92, "Setting-Writer-Options"]], "Reading and Plotting Data": [[92, "Reading-and-Plotting-Data"]], "Modifying the Power Law Index": [[92, "Modifying-the-Power-Law-Index"]]}, "indexentries": {"dustpy": [[12, "module-dustpy"]], "dustpy.constants": [[12, "module-dustpy.constants"]], "dustpy.plot": [[12, "module-dustpy.plot"]], "dustpy.std": [[12, "module-dustpy.std"]], "dustpy.std.dust": [[12, "module-dustpy.std.dust"]], "dustpy.std.gas": [[12, "module-dustpy.std.gas"]], "dustpy.std.grid": [[12, "module-dustpy.std.grid"]], "dustpy.std.sim": [[12, "module-dustpy.std.sim"]], "dustpy.std.star": [[12, "module-dustpy.std.star"]], "dustpy.utils": [[12, "module-dustpy.utils"]], "module": [[12, "module-dustpy"], [12, "module-dustpy.constants"], [12, "module-dustpy.plot"], [12, "module-dustpy.std"], [12, "module-dustpy.std.dust"], [12, "module-dustpy.std.gas"], [12, "module-dustpy.std.grid"], [12, "module-dustpy.std.sim"], [12, "module-dustpy.std.star"], [12, "module-dustpy.utils"]], "simulation (class in dustpy)": [[13, "dustpy.Simulation"]], "checkmassconservation() (dustpy.simulation method)": [[13, "dustpy.Simulation.checkmassconservation"]], "ini (dustpy.simulation attribute)": [[13, "dustpy.Simulation.ini"]], "initialize() (dustpy.simulation method)": [[13, "dustpy.Simulation.initialize"]], "makegrids() (dustpy.simulation method)": [[13, "dustpy.Simulation.makegrids"]], "run() (dustpy.simulation method)": [[13, "dustpy.Simulation.run"]], "setdustintegrator() (dustpy.simulation method)": [[13, "dustpy.Simulation.setdustintegrator"]], "ipanel() (in module dustpy.plot)": [[14, "dustpy.plot.ipanel"]], "panel() (in module dustpy.plot)": [[15, "dustpy.plot.panel"]], "d() (in module dustpy.std.dust)": [[16, "dustpy.std.dust.D"]], "f_adv() (in module dustpy.std.dust)": [[17, "dustpy.std.dust.F_adv"]], "f_diff() (in module dustpy.std.dust)": [[18, "dustpy.std.dust.F_diff"]], "f_tot() (in module dustpy.std.dust)": [[19, "dustpy.std.dust.F_tot"]], "h() (in module dustpy.std.dust)": [[20, "dustpy.std.dust.H"]], "mrn_distribution() (in module dustpy.std.dust)": [[21, "dustpy.std.dust.MRN_distribution"]], "s_coag() (in module dustpy.std.dust)": [[22, "dustpy.std.dust.S_coag"]], "s_hyd() (in module dustpy.std.dust)": [[23, "dustpy.std.dust.S_hyd"]], "s_tot() (in module dustpy.std.dust)": [[24, "dustpy.std.dust.S_tot"]], "sigmafloor() (in module dustpy.std.dust)": [[25, "dustpy.std.dust.SigmaFloor"]], "sigma_deriv() (in module dustpy.std.dust)": [[26, "dustpy.std.dust.Sigma_deriv"]], "st_epstein_stokesi() (in module dustpy.std.dust)": [[27, "dustpy.std.dust.St_Epstein_StokesI"]], "a() (in module dustpy.std.dust)": [[28, "dustpy.std.dust.a"]], "boundary() (in module dustpy.std.dust)": [[29, "dustpy.std.dust.boundary"]], "coagulation_parameters() (in module dustpy.std.dust)": [[30, "dustpy.std.dust.coagulation_parameters"]], "dt() (in module dustpy.std.dust)": [[31, "dustpy.std.dust.dt"]], "dt_adaptive() (in module dustpy.std.dust)": [[32, "dustpy.std.dust.dt_adaptive"]], "enforce_floor_value() (in module dustpy.std.dust)": [[33, "dustpy.std.dust.enforce_floor_value"]], "eps() (in module dustpy.std.dust)": [[34, "dustpy.std.dust.eps"]], "finalize_explicit() (in module dustpy.std.dust)": [[35, "dustpy.std.dust.finalize_explicit"]], "finalize_implicit() (in module dustpy.std.dust)": [[36, "dustpy.std.dust.finalize_implicit"]], "impl_1_direct (class in dustpy.std.dust)": [[37, "dustpy.std.dust.impl_1_direct"]], "jacobian() (in module dustpy.std.dust)": [[38, "dustpy.std.dust.jacobian"]], "kernel() (in module dustpy.std.dust)": [[39, "dustpy.std.dust.kernel"]], "p_frag() (in module dustpy.std.dust)": [[40, "dustpy.std.dust.p_frag"]], "p_stick() (in module dustpy.std.dust)": [[41, "dustpy.std.dust.p_stick"]], "prepare() (in module dustpy.std.dust)": [[42, "dustpy.std.dust.prepare"]], "rho_midplane() (in module dustpy.std.dust)": [[43, "dustpy.std.dust.rho_midplane"]], "set_implicit_boundaries() (in module dustpy.std.dust)": [[44, "dustpy.std.dust.set_implicit_boundaries"]], "vdriftmax() (in module dustpy.std.dust)": [[45, "dustpy.std.dust.vdriftmax"]], "vrad() (in module dustpy.std.dust)": [[46, "dustpy.std.dust.vrad"]], "vrel_azimuthal_drift() (in module dustpy.std.dust)": [[47, "dustpy.std.dust.vrel_azimuthal_drift"]], "vrel_brownian_motion() (in module dustpy.std.dust)": [[48, "dustpy.std.dust.vrel_brownian_motion"]], "vrel_radial_drift() (in module dustpy.std.dust)": [[49, "dustpy.std.dust.vrel_radial_drift"]], "vrel_tot() (in module dustpy.std.dust)": [[50, "dustpy.std.dust.vrel_tot"]], "vrel_turbulent_motion() (in module dustpy.std.dust)": [[51, "dustpy.std.dust.vrel_turbulent_motion"]], "vrel_vertical_settling() (in module dustpy.std.dust)": [[52, "dustpy.std.dust.vrel_vertical_settling"]], "fi() (in module dustpy.std.gas)": [[53, "dustpy.std.gas.Fi"]], "hp() (in module dustpy.std.gas)": [[54, "dustpy.std.gas.Hp"]], "p_midplane() (in module dustpy.std.gas)": [[55, "dustpy.std.gas.P_midplane"]], "s_hyd() (in module dustpy.std.gas)": [[56, "dustpy.std.gas.S_hyd"]], "s_tot() (in module dustpy.std.gas)": [[57, "dustpy.std.gas.S_tot"]], "t_passive() (in module dustpy.std.gas)": [[58, "dustpy.std.gas.T_passive"]], "boundary() (in module dustpy.std.gas)": [[59, "dustpy.std.gas.boundary"]], "cs_adiabatic() (in module dustpy.std.gas)": [[60, "dustpy.std.gas.cs_adiabatic"]], "dt() (in module dustpy.std.gas)": [[61, "dustpy.std.gas.dt"]], "enforce_floor_value() (in module dustpy.std.gas)": [[62, "dustpy.std.gas.enforce_floor_value"]], "eta_midplane() (in module dustpy.std.gas)": [[63, "dustpy.std.gas.eta_midplane"]], "finalize() (in module dustpy.std.gas)": [[64, "dustpy.std.gas.finalize"]], "impl_1_direct (class in dustpy.std.gas)": [[65, "dustpy.std.gas.impl_1_direct"]], "jacobian() (in module dustpy.std.gas)": [[66, "dustpy.std.gas.jacobian"]], "lyndenbellpringle1974() (in module dustpy.std.gas)": [[67, "dustpy.std.gas.lyndenbellpringle1974"]], "mfp_midplane() (in module dustpy.std.gas)": [[68, "dustpy.std.gas.mfp_midplane"]], "n_midplane() (in module dustpy.std.gas)": [[69, "dustpy.std.gas.n_midplane"]], "nu() (in module dustpy.std.gas)": [[70, "dustpy.std.gas.nu"]], "prepare() (in module dustpy.std.gas)": [[71, "dustpy.std.gas.prepare"]], "rho_midplane() (in module dustpy.std.gas)": [[72, "dustpy.std.gas.rho_midplane"]], "set_implicit_boundaries() (in module dustpy.std.gas)": [[73, "dustpy.std.gas.set_implicit_boundaries"]], "vrad() (in module dustpy.std.gas)": [[74, "dustpy.std.gas.vrad"]], "vvisc() (in module dustpy.std.gas)": [[75, "dustpy.std.gas.vvisc"]], "omegak() (in module dustpy.std.grid)": [[76, "dustpy.std.grid.OmegaK"]], "dt() (in module dustpy.std.sim)": [[77, "dustpy.std.sim.dt"]], "dt_adaptive() (in module dustpy.std.sim)": [[78, "dustpy.std.sim.dt_adaptive"]], "finalize_explicit_dust() (in module dustpy.std.sim)": [[79, "dustpy.std.sim.finalize_explicit_dust"]], "finalize_implicit_dust() (in module dustpy.std.sim)": [[80, "dustpy.std.sim.finalize_implicit_dust"]], "prepare_explicit_dust() (in module dustpy.std.sim)": [[81, "dustpy.std.sim.prepare_explicit_dust"]], "prepare_implicit_dust() (in module dustpy.std.sim)": [[82, "dustpy.std.sim.prepare_implicit_dust"]], "luminosity() (in module dustpy.std.star)": [[83, "dustpy.std.star.luminosity"]], "boundary (class in dustpy.utils)": [[84, "dustpy.utils.Boundary"]], "condition (dustpy.utils.boundary attribute)": [[84, "dustpy.utils.Boundary.condition"]], "setboundary() (dustpy.utils.boundary method)": [[84, "dustpy.utils.Boundary.setboundary"]], "setcondition() (dustpy.utils.boundary method)": [[84, "dustpy.utils.Boundary.setcondition"]], "value (dustpy.utils.boundary attribute)": [[84, 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"final": 64, "lyndenbellpringle1974": 67, "mfp_midplan": 68, "n_midplan": 69, "vvisc": 75, "finalize_explicit_dust": 79, "finalize_implicit_dust": 80, "prepare_explicit_dust": 81, "prepare_implicit_dust": 82, "luminos": 83, "print_version_warn": 85, "librari": 86, "dustpylib": 86, "exampl": [87, 88, 89], "ic": 87, "line": 87, "planetari": 88, "gap": [88, 89], "planet": 88, "viscos": 88, "pertub": 88, "grow": 88, "planetesim": 89, "format": 89, "refin": 89, "document": 90, "content": 90, "indic": 90, "tabl": 90, "test": [91, 92], "analyt": [91, 92], "linear": 91, "product": 91, "solut": 92, "set": 92, "up": 92, "variabl": 92, "option": 92, "power": 92, "law": 92, "index": 92}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1, "nbsphinx": 4, "sphinx": 60}, "alltitles": {"1. Basic usage": [[0, "1.-Basic-usage"]], "The Simulation Frame": [[0, "The-Simulation-Frame"]], "Initializing": [[0, "Initializing"]], "Running a Simulation": [[0, "Running-a-Simulation"]], "Plotting": [[0, "Plotting"]], "Code Units": [[0, "Code-Units"]], "Reading data files": [[0, "Reading-data-files"]], "Reading Dump Files": [[0, "Reading-Dump-Files"]], "2. Simple Customization": [[1, "2.-Simple-Customization"]], "Stellar parameters": [[1, "Stellar-parameters"]], "Grid parameters": [[1, "Grid-parameters"]], "Gas Parameters": [[1, "Gas-Parameters"]], "Dust Parameters": [[1, "Dust-Parameters"]], "Changing the Initial Conditions": [[1, "Changing-the-Initial-Conditions"]], "Changing the Snapshots": [[1, "Changing-the-Snapshots"]], "Changing the Output Directory": [[1, "Changing-the-Output-Directory"]], "3. Advanced Customization": [[2, "3.-Advanced-Customization"]], "Customizing the Grids": [[2, "Customizing-the-Grids"]], "The Radial Grid": [[2, "The-Radial-Grid"]], "The Mass Grid": [[2, "The-Mass-Grid"]], "Customizing the Physics of a Field": [[2, "Customizing-the-Physics-of-a-Field"]], "Adding Custom Fields": [[2, "Adding-Custom-Fields"]], "Modifying the Update Order": [[2, "Modifying-the-Update-Order"]], "Systoles and Diastoles": [[2, "Systoles-and-Diastoles"]], "Customizing the Snapshots": [[2, "Customizing-the-Snapshots"]], "4. The Standard Model": [[3, "4.-The-Standard-Model"]], "Dust": [[3, "Dust"]], "Simulation.dust.backreaction": [[3, "Simulation.dust.backreaction"]], "Simulation.dust.boundary": [[3, "Simulation.dust.boundary"]], "Simulation.dust.coagulation": [[3, "Simulation.dust.coagulation"]], "Simulation.dust.delta": [[3, "Simulation.dust.delta"]], "Simulation.dust.delta.rad": [[3, "Simulation.dust.delta.rad"]], "Simulation.dust.delta.turb": [[3, "Simulation.dust.delta.turb"]], "Simulation.dust.delta.vert": [[3, "Simulation.dust.delta.vert"]], "Simulation.dust.Fi": [[3, "Simulation.dust.Fi"]], "Simulation.dust.Fi.adv": [[3, "Simulation.dust.Fi.adv"]], "Simulation.dust.Fi.diff": [[3, "Simulation.dust.Fi.diff"]], "Simulation.dust.Fi.tot": [[3, "Simulation.dust.Fi.tot"]], "Simulation.dust.p": [[3, "Simulation.dust.p"]], "Simulation.dust.p.frag": [[3, "Simulation.dust.p.frag"]], "Simulation.dust.p.stick": [[3, "Simulation.dust.p.stick"]], "Simulation.dust.S": [[3, "Simulation.dust.S"]], "Simulation.dust.S.coag": [[3, "Simulation.dust.S.coag"]], "Simulation.dust.S.ext": [[3, "Simulation.dust.S.ext"]], "Simulation.dust.S.hyd": [[3, "Simulation.dust.S.hyd"]], "Simulation.dust.S.tot": [[3, "Simulation.dust.S.tot"]], "Simulation.dust.v": [[3, "Simulation.dust.v"]], "Simulation.dust.v.rel": [[3, "Simulation.dust.v.rel"]], "Simulation.dust.v.rel.azi": [[3, "Simulation.dust.v.rel.azi"]], "Simulation.dust.v.brown": [[3, "Simulation.dust.v.brown"]], "Simulation.dust.v.rel.rad": [[3, "Simulation.dust.v.rel.rad"]], "Simulation.dust.v.rel.tot": [[3, "Simulation.dust.v.rel.tot"]], "Simulation.dust.v.rel.turb": [[3, "Simulation.dust.v.rel.turb"]], "Simulation.dust.v.rel.vert": [[3, "Simulation.dust.v.rel.vert"]], "Simulation.dust.v.driftmax": [[3, "Simulation.dust.v.driftmax"]], "Simulation.dust.v.frag": [[3, "Simulation.dust.v.frag"]], "Simulation.dust.v.rad": [[3, "Simulation.dust.v.rad"]], "Simulation.dust.a": [[3, "Simulation.dust.a"]], "Simulation.dust.D": [[3, "Simulation.dust.D"]], "Simulation.dust.eps": [[3, "Simulation.dust.eps"]], "Simulation.dust.fill": [[3, "Simulation.dust.fill"]], "Simulation.dust.H": [[3, "Simulation.dust.H"]], "Simulation.dust.kernel": [[3, "Simulation.dust.kernel"]], "Simulation.dust.rho": [[3, "Simulation.dust.rho"]], "Simulation.dust.rhos": [[3, "Simulation.dust.rhos"]], "Simulation.dust.Sigma": [[3, "Simulation.dust.Sigma"]], "Simulation.dust.SigmaFloor": [[3, "Simulation.dust.SigmaFloor"]], "Simulation.dust.St": [[3, "Simulation.dust.St"]], "Update order": [[3, "Update-order"], [3, "id1"], [3, "id2"], [3, "id3"]], "Gas": [[3, "Gas"]], "Simulation.gas.boundary": [[3, "Simulation.gas.boundary"]], "Simulation.gas.S": [[3, "Simulation.gas.S"]], "Simulation.gas.S.ext": [[3, "Simulation.gas.S.ext"]], "Simulation.gas.S.hyd": [[3, "Simulation.gas.S.hyd"]], "Simulation.gas.S.tot": [[3, "Simulation.gas.S.tot"]], "Simulation.gas.v": [[3, "Simulation.gas.v"]], "Simulation.gas.v.rad": [[3, "Simulation.gas.v.rad"]], "Simulation.gas.v.visc": [[3, "Simulation.gas.v.visc"]], "Simulation.gas.alpha": [[3, "Simulation.gas.alpha"]], "Simulation.gas.cs": [[3, "Simulation.gas.cs"]], "Simulation.gas.eta": [[3, "Simulation.gas.eta"]], "Simulation.gas.Fi": [[3, "Simulation.gas.Fi"]], "Simulation.gas.gamma": [[3, "Simulation.gas.gamma"]], "Simulation.gas.Hp": [[3, "Simulation.gas.Hp"]], "Simulation.gas.mfp": [[3, "Simulation.gas.mfp"]], "Simulation.gas.mu": [[3, "Simulation.gas.mu"]], "Simulation.gas.n": [[3, "Simulation.gas.n"]], "Simulation.gas.nu": [[3, "Simulation.gas.nu"]], "Simulation.gas.P": [[3, "Simulation.gas.P"]], "Simulation.gas.rho": [[3, "Simulation.gas.rho"]], "Simulation.gas.Sigma": [[3, "Simulation.gas.Sigma"]], "Simulation.gas.SigmaFloor": [[3, "Simulation.gas.SigmaFloor"]], "Simulation.gas.T": [[3, "Simulation.gas.T"]], "Grid": [[3, "Grid"]], "Simulation.grid.A": [[3, "Simulation.grid.A"]], "Simulation.grid.m": [[3, "Simulation.grid.m"]], "Simulation.grid.Nm": [[3, "Simulation.grid.Nm"]], "Simulation.grid.Nr": [[3, "Simulation.grid.Nr"]], "Simulation.grid.OmegaK": [[3, "Simulation.grid.OmegaK"]], "Simulation.grid.r": [[3, "Simulation.grid.r"]], "Simulation.grid.ri": [[3, "Simulation.grid.ri"]], "Star": [[3, "Star"]], "Simulation.star.L": [[3, "Simulation.star.L"]], "Simulation.star.M": [[3, "Simulation.star.M"]], "Simulation.star.R": [[3, "Simulation.star.R"]], "Simulation.star.T": [[3, "Simulation.star.T"]], "Time": [[3, "Time"]], "Integrator": [[3, "Integrator"]], "Writer": [[3, "Writer"]], "5. Dust Coagulation": [[4, "5.-Dust-Coagulation"]], "Sticking": [[4, "Sticking"]], "Fragmentation & Erosion": [[4, "Fragmentation-&-Erosion"]], "Probabilities": [[4, "Probabilities"]], "Bouncing": [[4, "Bouncing"]], "Collision Rates": [[4, "Collision-Rates"]], "Coagulation Sources": [[4, "Coagulation-Sources"]], "6. Dust Evolution": [[5, "6.-Dust-Evolution"]], "Coagulation": [[5, "Coagulation"]], "Turning off Fragmentation": [[5, "Turning-off-Fragmentation"]], "Turning off Coagulation": [[5, "Turning-off-Coagulation"]], "Hydrodynamics": [[5, "Hydrodynamics"], [6, "Hydrodynamics"]], "Advection": [[5, "Advection"]], "Turning off Advection": [[5, "Turning-off-Advection"]], "Diffusion": [[5, "Diffusion"]], "Turning off Diffusion": [[5, "Turning-off-Diffusion"]], "Turning off Hydrodynamics": [[5, "Turning-off-Hydrodynamics"], [6, "Turning-off-Hydrodynamics"]], "External Sources": [[5, "External-Sources"], [6, "External-Sources"]], "Turning off Dust Evolution": [[5, "Turning-off-Dust-Evolution"], [92, "Turning-off-Dust-Evolution"]], "Changing the Dust Integrator": [[5, "Changing-the-Dust-Integrator"]], "7. Gas Evolution": [[6, "7.-Gas-Evolution"]], "Turning off External Sources": [[6, "Turning-off-External-Sources"]], "Turning off Gas Evolution": [[6, "Turning-off-Gas-Evolution"]], "Appendix A: Citation": [[7, "Appendix-A:-Citation"]], "Appendix B: List of Publications": [[8, "Appendix-B:-List-of-Publications"]], "Appendix C: Contributing/Bugs/Features": [[9, "Appendix-C:-Contributing/Bugs/Features"]], "Contributing": [[9, "Contributing"]], "Bug Reports": [[9, "Bug-Reports"]], "Feature Requests": [[9, "Feature-Requests"]], "Appendix D: DustPy Discussions": [[10, "Appendix-D:-DustPy-Discussions"]], "Appendix E: Changelog": [[11, "Appendix-E:-Changelog"]], "v1.0.5": [[11, "v1.0.5"]], "Using Meson as build system": [[11, "Using-Meson-as-build-system"]], "Bugfix to velocity distribution": [[11, "Bugfix-to-velocity-distribution"]], "Bugfix to plotting script": [[11, "Bugfix-to-plotting-script"]], "Preparation for the addition of multiple gas species": [[11, "Preparation-for-the-addition-of-multiple-gas-species"]], "v1.0.4": [[11, "v1.0.4"]], "Bugfix to boundary conditions": [[11, "Bugfix-to-boundary-conditions"]], "v1.0.3": [[11, "v1.0.3"]], "Correction to inital particle size distribution": [[11, "Correction-to-inital-particle-size-distribution"]], "Removal of non-ASCII characters": [[11, "Removal-of-non-ASCII-characters"]], "v1.0.2": [[11, "v1.0.2"]], "Change in default temperature profile": [[11, "Change-in-default-temperature-profile"]], "v1.0.1": [[11, "v1.0.1"]], "Change to Collision Kernel": [[11, "Change-to-Collision-Kernel"]], "v1.0.0": [[11, "v1.0.0"]], "Module Reference": [[12, "module-reference"]], "dustpy Package": [[12, "module-dustpy"]], "Classes": [[12, "classes"], [12, "id2"], [12, "id4"], [12, "id9"]], "dustpy.constants Package": [[12, "module-dustpy.constants"]], "Constants": [[12, "constants"]], "dustpy.plot Package": [[12, "module-dustpy.plot"]], "Functions": [[12, "functions"], [12, "id1"], [12, "id3"], [12, "id5"], [12, "id6"], [12, "id7"], [12, "id8"]], "dustpy.std Package": [[12, "module-dustpy.std"]], "dustpy.std.dust Module": [[12, "module-dustpy.std.dust"]], "dustpy.std.gas Module": [[12, "module-dustpy.std.gas"]], "dustpy.std.grid Module": [[12, "module-dustpy.std.grid"]], "dustpy.std.sim Module": [[12, "module-dustpy.std.sim"]], "dustpy.std.star Module": [[12, "module-dustpy.std.star"]], "dustpy.utils Package": [[12, "module-dustpy.utils"]], "Simulation": [[13, "simulation"]], "ipanel": [[14, "ipanel"]], "panel": [[15, "panel"]], "D": [[16, "d"]], "F_adv": [[17, "f-adv"]], "F_diff": [[18, "f-diff"]], "F_tot": [[19, "f-tot"]], "H": [[20, "h"]], "MRN_distribution": [[21, "mrn-distribution"]], "S_coag": [[22, "s-coag"]], "S_hyd": [[23, "s-hyd"], [56, "s-hyd"]], "S_tot": [[24, "s-tot"], [57, "s-tot"]], "SigmaFloor": [[25, "sigmafloor"]], "Sigma_deriv": [[26, "sigma-deriv"]], "St_Epstein_StokesI": [[27, "st-epstein-stokesi"]], "a": [[28, "a"]], "boundary": [[29, "boundary"], [59, "boundary"]], "coagulation_parameters": [[30, "coagulation-parameters"]], "dt": [[31, "dt"], [61, "dt"], [77, "dt"]], "dt_adaptive": [[32, "dt-adaptive"], [78, "dt-adaptive"]], "enforce_floor_value": [[33, "enforce-floor-value"], [62, "enforce-floor-value"]], "eps": [[34, "eps"]], "finalize_explicit": [[35, "finalize-explicit"]], "finalize_implicit": [[36, "finalize-implicit"]], "impl_1_direct": [[37, "impl-1-direct"], [65, "impl-1-direct"]], "jacobian": [[38, "jacobian"], [66, "jacobian"]], "kernel": [[39, "kernel"]], "p_frag": [[40, "p-frag"]], "p_stick": [[41, "p-stick"]], "prepare": [[42, "prepare"], [71, "prepare"]], "rho_midplane": [[43, "rho-midplane"], [72, "rho-midplane"]], "set_implicit_boundaries": [[44, "set-implicit-boundaries"], [73, "set-implicit-boundaries"]], "vdriftmax": [[45, "vdriftmax"]], "vrad": [[46, "vrad"], [74, "vrad"]], "vrel_azimuthal_drift": [[47, "vrel-azimuthal-drift"]], "vrel_brownian_motion": [[48, "vrel-brownian-motion"]], "vrel_radial_drift": [[49, "vrel-radial-drift"]], "vrel_tot": [[50, "vrel-tot"]], "vrel_turbulent_motion": [[51, "vrel-turbulent-motion"]], "vrel_vertical_settling": [[52, "vrel-vertical-settling"]], "Fi": [[53, "fi"]], "Hp": [[54, "hp"]], "P_midplane": [[55, "p-midplane"]], "T_passive": [[58, "t-passive"]], "cs_adiabatic": [[60, "cs-adiabatic"]], "eta_midplane": [[63, "eta-midplane"]], "finalize": [[64, "finalize"]], "lyndenbellpringle1974": [[67, "lyndenbellpringle1974"]], "mfp_midplane": [[68, "mfp-midplane"]], "n_midplane": [[69, "n-midplane"]], "nu": [[70, "nu"]], "vvisc": [[75, "vvisc"]], "OmegaK": [[76, "omegak"]], "finalize_explicit_dust": [[79, "finalize-explicit-dust"]], "finalize_implicit_dust": [[80, "finalize-implicit-dust"]], "prepare_explicit_dust": [[81, "prepare-explicit-dust"]], "prepare_implicit_dust": [[82, "prepare-implicit-dust"]], "luminosity": [[83, "luminosity"]], "Boundary": [[84, "boundary"]], "print_version_warning": [[85, "print-version-warning"]], "Library: dustpylib": [[86, "Library:-dustpylib"]], "Example: Ice Lines": [[87, "Example:-Ice-Lines"]], "Example: Planetary Gaps": [[88, "Example:-Planetary-Gaps"]], "Gap Profiles": [[88, "Gap-Profiles"]], "Adding planets": [[88, "Adding-planets"]], "Viscosity pertubation": [[88, "Viscosity-pertubation"]], "Growing planets": [[88, "Growing-planets"]], "Example: Planetesimal Formation": [[89, "Example:-Planetesimal-Formation"]], "Refining the radial grid": [[89, "Refining-the-radial-grid"]], "Adding gap": [[89, "Adding-gap"]], "Adding planetesimals": [[89, "Adding-planetesimals"]], "Adding planetesimal formation": [[89, "Adding-planetesimal-formation"]], "DustPy Documentation": [[90, "dustpy-documentation"]], "Contents:": [[90, null]], "Indices and tables": [[90, "indices-and-tables"]], "Test: Analytical Coagulation Kernels": [[91, "Test:-Analytical-Coagulation-Kernels"]], "The Constant Kernel": [[91, "The-Constant-Kernel"]], "The Linear Kernel": [[91, "The-Linear-Kernel"]], "The Product Kernel": [[91, "The-Product-Kernel"]], "Test: Gas Evolution": [[92, "Test:-Gas-Evolution"]], "Analytical Solution": [[92, "Analytical-Solution"]], "Setting up DustPy": [[92, "Setting-up-DustPy"]], "Modifying Integration Variable": [[92, "Modifying-Integration-Variable"]], "Setting Writer Options": [[92, "Setting-Writer-Options"]], "Reading and Plotting Data": [[92, "Reading-and-Plotting-Data"]], "Modifying the Power Law Index": [[92, "Modifying-the-Power-Law-Index"]]}, "indexentries": {"dustpy": [[12, "module-dustpy"]], "dustpy.constants": [[12, "module-dustpy.constants"]], "dustpy.plot": [[12, "module-dustpy.plot"]], "dustpy.std": [[12, "module-dustpy.std"]], "dustpy.std.dust": [[12, "module-dustpy.std.dust"]], "dustpy.std.gas": [[12, "module-dustpy.std.gas"]], "dustpy.std.grid": [[12, "module-dustpy.std.grid"]], "dustpy.std.sim": [[12, "module-dustpy.std.sim"]], "dustpy.std.star": [[12, "module-dustpy.std.star"]], "dustpy.utils": [[12, "module-dustpy.utils"]], "module": [[12, "module-dustpy"], [12, "module-dustpy.constants"], [12, "module-dustpy.plot"], [12, "module-dustpy.std"], [12, "module-dustpy.std.dust"], [12, "module-dustpy.std.gas"], [12, "module-dustpy.std.grid"], [12, "module-dustpy.std.sim"], [12, "module-dustpy.std.star"], [12, "module-dustpy.utils"]], "simulation (class in dustpy)": [[13, "dustpy.Simulation"]], "checkmassconservation() (dustpy.simulation method)": [[13, "dustpy.Simulation.checkmassconservation"]], "ini (dustpy.simulation attribute)": [[13, "dustpy.Simulation.ini"]], "initialize() (dustpy.simulation method)": [[13, "dustpy.Simulation.initialize"]], "makegrids() (dustpy.simulation method)": [[13, "dustpy.Simulation.makegrids"]], "run() (dustpy.simulation method)": [[13, "dustpy.Simulation.run"]], "setdustintegrator() (dustpy.simulation method)": [[13, "dustpy.Simulation.setdustintegrator"]], "ipanel() (in module dustpy.plot)": [[14, "dustpy.plot.ipanel"]], "panel() (in module dustpy.plot)": [[15, "dustpy.plot.panel"]], "d() (in module dustpy.std.dust)": [[16, "dustpy.std.dust.D"]], "f_adv() (in module dustpy.std.dust)": [[17, "dustpy.std.dust.F_adv"]], "f_diff() (in module dustpy.std.dust)": [[18, "dustpy.std.dust.F_diff"]], "f_tot() (in module dustpy.std.dust)": [[19, "dustpy.std.dust.F_tot"]], "h() (in module dustpy.std.dust)": [[20, "dustpy.std.dust.H"]], "mrn_distribution() (in module dustpy.std.dust)": [[21, "dustpy.std.dust.MRN_distribution"]], "s_coag() (in module dustpy.std.dust)": [[22, "dustpy.std.dust.S_coag"]], "s_hyd() (in module dustpy.std.dust)": [[23, "dustpy.std.dust.S_hyd"]], "s_tot() (in module dustpy.std.dust)": [[24, "dustpy.std.dust.S_tot"]], "sigmafloor() (in module dustpy.std.dust)": [[25, "dustpy.std.dust.SigmaFloor"]], "sigma_deriv() (in module dustpy.std.dust)": [[26, "dustpy.std.dust.Sigma_deriv"]], "st_epstein_stokesi() (in module dustpy.std.dust)": [[27, "dustpy.std.dust.St_Epstein_StokesI"]], "a() (in module dustpy.std.dust)": [[28, "dustpy.std.dust.a"]], "boundary() (in module dustpy.std.dust)": [[29, "dustpy.std.dust.boundary"]], "coagulation_parameters() (in module dustpy.std.dust)": [[30, "dustpy.std.dust.coagulation_parameters"]], "dt() (in module dustpy.std.dust)": [[31, "dustpy.std.dust.dt"]], "dt_adaptive() (in module dustpy.std.dust)": [[32, "dustpy.std.dust.dt_adaptive"]], "enforce_floor_value() (in module dustpy.std.dust)": [[33, "dustpy.std.dust.enforce_floor_value"]], "eps() (in module dustpy.std.dust)": [[34, "dustpy.std.dust.eps"]], "finalize_explicit() (in module dustpy.std.dust)": [[35, "dustpy.std.dust.finalize_explicit"]], "finalize_implicit() (in module dustpy.std.dust)": [[36, "dustpy.std.dust.finalize_implicit"]], "impl_1_direct (class in dustpy.std.dust)": [[37, "dustpy.std.dust.impl_1_direct"]], "jacobian() (in module dustpy.std.dust)": [[38, "dustpy.std.dust.jacobian"]], "kernel() (in module dustpy.std.dust)": [[39, "dustpy.std.dust.kernel"]], "p_frag() (in module dustpy.std.dust)": [[40, "dustpy.std.dust.p_frag"]], "p_stick() (in module dustpy.std.dust)": [[41, "dustpy.std.dust.p_stick"]], "prepare() (in module dustpy.std.dust)": [[42, "dustpy.std.dust.prepare"]], "rho_midplane() (in module dustpy.std.dust)": [[43, "dustpy.std.dust.rho_midplane"]], "set_implicit_boundaries() (in module dustpy.std.dust)": [[44, "dustpy.std.dust.set_implicit_boundaries"]], "vdriftmax() (in module dustpy.std.dust)": [[45, "dustpy.std.dust.vdriftmax"]], "vrad() (in module dustpy.std.dust)": [[46, "dustpy.std.dust.vrad"]], "vrel_azimuthal_drift() (in module dustpy.std.dust)": [[47, "dustpy.std.dust.vrel_azimuthal_drift"]], "vrel_brownian_motion() (in module dustpy.std.dust)": [[48, "dustpy.std.dust.vrel_brownian_motion"]], "vrel_radial_drift() (in module dustpy.std.dust)": [[49, "dustpy.std.dust.vrel_radial_drift"]], "vrel_tot() (in module dustpy.std.dust)": [[50, "dustpy.std.dust.vrel_tot"]], "vrel_turbulent_motion() (in module dustpy.std.dust)": [[51, "dustpy.std.dust.vrel_turbulent_motion"]], "vrel_vertical_settling() (in module dustpy.std.dust)": [[52, "dustpy.std.dust.vrel_vertical_settling"]], "fi() (in module dustpy.std.gas)": [[53, "dustpy.std.gas.Fi"]], "hp() (in module dustpy.std.gas)": [[54, "dustpy.std.gas.Hp"]], "p_midplane() (in module dustpy.std.gas)": [[55, "dustpy.std.gas.P_midplane"]], "s_hyd() (in module dustpy.std.gas)": [[56, "dustpy.std.gas.S_hyd"]], "s_tot() (in module dustpy.std.gas)": [[57, "dustpy.std.gas.S_tot"]], "t_passive() (in module dustpy.std.gas)": [[58, "dustpy.std.gas.T_passive"]], "boundary() (in module dustpy.std.gas)": [[59, "dustpy.std.gas.boundary"]], "cs_adiabatic() (in module dustpy.std.gas)": [[60, "dustpy.std.gas.cs_adiabatic"]], "dt() (in module dustpy.std.gas)": [[61, "dustpy.std.gas.dt"]], "enforce_floor_value() (in module dustpy.std.gas)": [[62, "dustpy.std.gas.enforce_floor_value"]], "eta_midplane() (in module dustpy.std.gas)": [[63, "dustpy.std.gas.eta_midplane"]], "finalize() (in module dustpy.std.gas)": [[64, "dustpy.std.gas.finalize"]], "impl_1_direct (class in dustpy.std.gas)": [[65, "dustpy.std.gas.impl_1_direct"]], "jacobian() (in module dustpy.std.gas)": [[66, "dustpy.std.gas.jacobian"]], "lyndenbellpringle1974() (in module dustpy.std.gas)": [[67, "dustpy.std.gas.lyndenbellpringle1974"]], "mfp_midplane() (in module dustpy.std.gas)": [[68, "dustpy.std.gas.mfp_midplane"]], "n_midplane() (in module dustpy.std.gas)": [[69, "dustpy.std.gas.n_midplane"]], "nu() (in module dustpy.std.gas)": [[70, "dustpy.std.gas.nu"]], "prepare() (in module dustpy.std.gas)": [[71, "dustpy.std.gas.prepare"]], "rho_midplane() (in module dustpy.std.gas)": [[72, "dustpy.std.gas.rho_midplane"]], "set_implicit_boundaries() (in module dustpy.std.gas)": [[73, "dustpy.std.gas.set_implicit_boundaries"]], "vrad() (in module dustpy.std.gas)": [[74, "dustpy.std.gas.vrad"]], "vvisc() (in module dustpy.std.gas)": [[75, "dustpy.std.gas.vvisc"]], "omegak() (in module dustpy.std.grid)": [[76, "dustpy.std.grid.OmegaK"]], "dt() (in module dustpy.std.sim)": [[77, "dustpy.std.sim.dt"]], "dt_adaptive() (in module dustpy.std.sim)": [[78, "dustpy.std.sim.dt_adaptive"]], "finalize_explicit_dust() (in module dustpy.std.sim)": [[79, "dustpy.std.sim.finalize_explicit_dust"]], "finalize_implicit_dust() (in module dustpy.std.sim)": [[80, "dustpy.std.sim.finalize_implicit_dust"]], "prepare_explicit_dust() (in module dustpy.std.sim)": [[81, "dustpy.std.sim.prepare_explicit_dust"]], "prepare_implicit_dust() (in module dustpy.std.sim)": [[82, "dustpy.std.sim.prepare_implicit_dust"]], "luminosity() (in module dustpy.std.star)": [[83, "dustpy.std.star.luminosity"]], "boundary (class in dustpy.utils)": [[84, "dustpy.utils.Boundary"]], "condition (dustpy.utils.boundary attribute)": [[84, "dustpy.utils.Boundary.condition"]], "setboundary() (dustpy.utils.boundary method)": [[84, "dustpy.utils.Boundary.setboundary"]], "setcondition() (dustpy.utils.boundary method)": [[84, "dustpy.utils.Boundary.setcondition"]], "value (dustpy.utils.boundary attribute)": [[84, "dustpy.utils.Boundary.value"]], "print_version_warning() (in module dustpy.utils)": [[85, "dustpy.utils.print_version_warning"]]}}) \ No newline at end of file diff --git a/docs/test_analytical_coagulation_kernels.html b/docs/test_analytical_coagulation_kernels.html index 51a02f3..08b88c5 100644 --- a/docs/test_analytical_coagulation_kernels.html +++ b/docs/test_analytical_coagulation_kernels.html @@ -367,7 +367,7 @@

The Constant Kerneltest_constant_kernel/frame.dmp Writing file test_constant_kernel/data0006.hdf5 Writing dump file test_constant_kernel/frame.dmp -Execution time: 0:00:04 +Execution time: 0:00:03

We now read the necessary data and plot it.

@@ -401,7 +401,7 @@

The Constant Kernelcstr = "C" + str(i-1) ax.loglog(m[i, ...], convert(SigmaConstant[i, 1, :], m[i, ...]), lw=1, c=cstr, label="t = {:3.1e}".format(t[i])) ax.loglog(m[i, ...], solution_constant_kernel(t[i], m[i, ...], a, S0), ":", lw=1, c=cstr) -ax.annotate('', xy=(1.1e2, 1.e-5), xytext=(1.e3, 1.e-4), arrowprops=dict(facecolor='red', lw="0", width=6)) +ax.annotate('', xy=(1.1e2, 1.e-5), xytext=(1.e3, 1.e-4), arrowprops=dict(facecolor='red', lw=0, width=6)) ax.legend(ncol=2) ax.set_xlim(m[0, 0], m[0, -1]) ax.set_ylim(1.e-6, 1.e3) @@ -413,187 +413,12 @@

The Constant Kernel -
-
-
-
----------------------------------------------------------------------------
-TypeError                                 Traceback (most recent call last)
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/IPython/core/formatters.py:340, in BaseFormatter.__call__(self, obj)
-    338     pass
-    339 else:
---> 340     return printer(obj)
-    341 # Finally look for special method names
-    342 method = get_real_method(obj, self.print_method)
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/IPython/core/pylabtools.py:152, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
-    149     from matplotlib.backend_bases import FigureCanvasBase
-    150     FigureCanvasBase(fig)
---> 152 fig.canvas.print_figure(bytes_io, **kw)
-    153 data = bytes_io.getvalue()
-    154 if fmt == 'svg':
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backend_bases.py:2193, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
-   2189 try:
-   2190     # _get_renderer may change the figure dpi (as vector formats
-   2191     # force the figure dpi to 72), so we need to set it again here.
-   2192     with cbook._setattr_cm(self.figure, dpi=dpi):
--> 2193         result = print_method(
-   2194             filename,
-   2195             facecolor=facecolor,
-   2196             edgecolor=edgecolor,
-   2197             orientation=orientation,
-   2198             bbox_inches_restore=_bbox_inches_restore,
-   2199             **kwargs)
-   2200 finally:
-   2201     if bbox_inches and restore_bbox:
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backend_bases.py:2043, in FigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>(*args, **kwargs)
-   2039     optional_kws = {  # Passed by print_figure for other renderers.
-   2040         "dpi", "facecolor", "edgecolor", "orientation",
-   2041         "bbox_inches_restore"}
-   2042     skip = optional_kws - {*inspect.signature(meth).parameters}
--> 2043     print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(
-   2044         *args, **{k: v for k, v in kwargs.items() if k not in skip}))
-   2045 else:  # Let third-parties do as they see fit.
-   2046     print_method = meth
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:497, in FigureCanvasAgg.print_png(self, filename_or_obj, metadata, pil_kwargs)
-    450 def print_png(self, filename_or_obj, *, metadata=None, pil_kwargs=None):
-    451     """
-    452     Write the figure to a PNG file.
-    453
-   (...)
-    495         *metadata*, including the default 'Software' key.
-    496     """
---> 497     self._print_pil(filename_or_obj, "png", pil_kwargs, metadata)
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:445, in FigureCanvasAgg._print_pil(self, filename_or_obj, fmt, pil_kwargs, metadata)
-    440 def _print_pil(self, filename_or_obj, fmt, pil_kwargs, metadata=None):
-    441     """
-    442     Draw the canvas, then save it using `.image.imsave` (to which
-    443     *pil_kwargs* and *metadata* are forwarded).
-    444     """
---> 445     FigureCanvasAgg.draw(self)
-    446     mpl.image.imsave(
-    447         filename_or_obj, self.buffer_rgba(), format=fmt, origin="upper",
-    448         dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:388, in FigureCanvasAgg.draw(self)
-    385 # Acquire a lock on the shared font cache.
-    386 with (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar
-    387       else nullcontext()):
---> 388     self.figure.draw(self.renderer)
-    389     # A GUI class may be need to update a window using this draw, so
-    390     # don't forget to call the superclass.
-    391     super().draw()
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:95, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
-     93 @wraps(draw)
-     94 def draw_wrapper(artist, renderer, *args, **kwargs):
----> 95     result = draw(artist, renderer, *args, **kwargs)
-     96     if renderer._rasterizing:
-     97         renderer.stop_rasterizing()
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/figure.py:3154, in Figure.draw(self, renderer)
-   3151         # ValueError can occur when resizing a window.
-   3153 self.patch.draw(renderer)
--> 3154 mimage._draw_list_compositing_images(
-   3155     renderer, self, artists, self.suppressComposite)
-   3157 for sfig in self.subfigs:
-   3158     sfig.draw(renderer)
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
-    130 if not_composite or not has_images:
-    131     for a in artists:
---> 132         a.draw(renderer)
-    133 else:
-    134     # Composite any adjacent images together
-    135     image_group = []
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/axes/_base.py:3070, in _AxesBase.draw(self, renderer)
-   3067 if artists_rasterized:
-   3068     _draw_rasterized(self.figure, artists_rasterized, renderer)
--> 3070 mimage._draw_list_compositing_images(
-   3071     renderer, self, artists, self.figure.suppressComposite)
-   3073 renderer.close_group('axes')
-   3074 self.stale = False
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
-    130 if not_composite or not has_images:
-    131     for a in artists:
---> 132         a.draw(renderer)
-    133 else:
-    134     # Composite any adjacent images together
-    135     image_group = []
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/text.py:1988, in Annotation.draw(self, renderer)
-   1986     if self.arrow_patch.figure is None and self.figure is not None:
-   1987         self.arrow_patch.figure = self.figure
--> 1988     self.arrow_patch.draw(renderer)
-   1989 # Draw text, including FancyBboxPatch, after FancyArrowPatch.
-   1990 # Otherwise, a wedge arrowstyle can land partly on top of the Bbox.
-   1991 Text.draw(self, renderer)
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:39, in _prevent_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
-     36     renderer.stop_rasterizing()
-     37     renderer._rasterizing = False
----> 39 return draw(artist, renderer, *args, **kwargs)
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/patches.py:4388, in FancyArrowPatch.draw(self, renderer)
-   4384     fillable = [fillable]
-   4386 affine = transforms.IdentityTransform()
--> 4388 self._draw_paths_with_artist_properties(
-   4389     renderer,
-   4390     [(p, affine, self._facecolor if f and self._facecolor[3] else None)
-   4391      for p, f in zip(path, fillable)])
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/patches.py:573, in Patch._draw_paths_with_artist_properties(self, renderer, draw_path_args_list)
-    570     renderer = PathEffectRenderer(self.get_path_effects(), renderer)
-    572 for draw_path_args in draw_path_args_list:
---> 573     renderer.draw_path(gc, *draw_path_args)
-    575 gc.restore()
-    576 renderer.close_group('patch')
-
-File ~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:132, in RendererAgg.draw_path(self, gc, path, transform, rgbFace)
-    130 else:
-    131     try:
---> 132         self._renderer.draw_path(gc, path, transform, rgbFace)
-    133     except OverflowError:
-    134         cant_chunk = ''
-
-TypeError: must be real number, not str
-
-
-
-<Figure size 960x720 with 1 Axes>
-
+_images/test_analytical_coagulation_kernels_29_0.png +

As marked by the red arrow, the upper end of the mass distribution is a bit off from the analytical solution. The reason for that is, that on a logarithmic mass grid the sum of two colliding masses will lie between two mass bins and has to be distributed between them. That means that a mass bin will be filled that is larger than the combined mass of both collision partners. This leads to accelerated particles growth.

To counteract this, the mass resolution can be increased. In the following example we are running the same simulation again but with 28 mass bins per mass decade. Default is 7.

@@ -709,7 +534,7 @@

The Constant Kerneltest_constant_kernel_high_res/frame.dmp Writing file test_constant_kernel_high_res/data0006.hdf5 Writing dump file test_constant_kernel_high_res/frame.dmp -Execution time: 0:01:08 +Execution time: 0:01:01
diff --git a/docs/test_analytical_coagulation_kernels.ipynb b/docs/test_analytical_coagulation_kernels.ipynb index 959f9b9..5b470a8 100644 --- a/docs/test_analytical_coagulation_kernels.ipynb +++ b/docs/test_analytical_coagulation_kernels.ipynb @@ -69,10 +69,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.840855Z", - "iopub.status.busy": "2023-11-30T11:31:10.840075Z", - "iopub.status.idle": "2023-11-30T11:31:10.956938Z", - "shell.execute_reply": "2023-11-30T11:31:10.955974Z" + "iopub.execute_input": "2023-12-01T18:14:32.210905Z", + "iopub.status.busy": "2023-12-01T18:14:32.210249Z", + "iopub.status.idle": "2023-12-01T18:14:32.312616Z", + "shell.execute_reply": "2023-12-01T18:14:32.311787Z" } }, "outputs": [], @@ -85,10 +85,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.962938Z", - "iopub.status.busy": "2023-11-30T11:31:10.962523Z", - "iopub.status.idle": "2023-11-30T11:31:10.968394Z", - "shell.execute_reply": "2023-11-30T11:31:10.967499Z" + "iopub.execute_input": "2023-12-01T18:14:32.317453Z", + "iopub.status.busy": "2023-12-01T18:14:32.317214Z", + "iopub.status.idle": "2023-12-01T18:14:32.321078Z", + "shell.execute_reply": "2023-12-01T18:14:32.320365Z" } }, "outputs": [], @@ -124,10 +124,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.973465Z", - "iopub.status.busy": "2023-11-30T11:31:10.973124Z", - "iopub.status.idle": "2023-11-30T11:31:10.979670Z", - "shell.execute_reply": "2023-11-30T11:31:10.978696Z" + "iopub.execute_input": "2023-12-01T18:14:32.325515Z", + "iopub.status.busy": "2023-12-01T18:14:32.325340Z", + "iopub.status.idle": "2023-12-01T18:14:32.329848Z", + "shell.execute_reply": "2023-12-01T18:14:32.329087Z" } }, "outputs": [], @@ -171,10 +171,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.985163Z", - "iopub.status.busy": "2023-11-30T11:31:10.984775Z", - "iopub.status.idle": "2023-11-30T11:31:10.994639Z", - "shell.execute_reply": "2023-11-30T11:31:10.993639Z" + "iopub.execute_input": "2023-12-01T18:14:32.335764Z", + "iopub.status.busy": "2023-12-01T18:14:32.334906Z", + "iopub.status.idle": "2023-12-01T18:14:32.347139Z", + "shell.execute_reply": "2023-12-01T18:14:32.345777Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:10.999763Z", - "iopub.status.busy": "2023-11-30T11:31:10.999409Z", - "iopub.status.idle": "2023-11-30T11:31:11.899428Z", - "shell.execute_reply": "2023-11-30T11:31:11.898038Z" + "iopub.execute_input": "2023-12-01T18:14:32.352831Z", + "iopub.status.busy": "2023-12-01T18:14:32.351748Z", + "iopub.status.idle": "2023-12-01T18:14:33.104455Z", + "shell.execute_reply": "2023-12-01T18:14:33.103163Z" } }, "outputs": [], @@ -244,10 +244,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:11.903963Z", - "iopub.status.busy": "2023-11-30T11:31:11.903361Z", - "iopub.status.idle": "2023-11-30T11:31:11.910083Z", - "shell.execute_reply": "2023-11-30T11:31:11.909162Z" + "iopub.execute_input": "2023-12-01T18:14:33.109672Z", + "iopub.status.busy": "2023-12-01T18:14:33.109241Z", + "iopub.status.idle": "2023-12-01T18:14:33.114376Z", + "shell.execute_reply": "2023-12-01T18:14:33.113635Z" } }, "outputs": [], @@ -267,10 +267,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:11.913615Z", - "iopub.status.busy": "2023-11-30T11:31:11.913336Z", - "iopub.status.idle": "2023-11-30T11:31:11.918292Z", - "shell.execute_reply": "2023-11-30T11:31:11.917346Z" + "iopub.execute_input": "2023-12-01T18:14:33.118849Z", + "iopub.status.busy": "2023-12-01T18:14:33.118630Z", + "iopub.status.idle": "2023-12-01T18:14:33.122410Z", + "shell.execute_reply": "2023-12-01T18:14:33.121670Z" } }, "outputs": [], @@ -290,10 +290,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:11.921980Z", - "iopub.status.busy": "2023-11-30T11:31:11.921663Z", - "iopub.status.idle": "2023-11-30T11:31:11.927279Z", - "shell.execute_reply": "2023-11-30T11:31:11.925961Z" + "iopub.execute_input": "2023-12-01T18:14:33.126578Z", + "iopub.status.busy": "2023-12-01T18:14:33.126305Z", + "iopub.status.idle": "2023-12-01T18:14:33.130292Z", + "shell.execute_reply": "2023-12-01T18:14:33.129441Z" } }, "outputs": [], @@ -306,10 +306,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:11.931141Z", - "iopub.status.busy": "2023-11-30T11:31:11.930734Z", - "iopub.status.idle": "2023-11-30T11:31:12.032900Z", - "shell.execute_reply": "2023-11-30T11:31:12.031908Z" + "iopub.execute_input": "2023-12-01T18:14:33.135035Z", + "iopub.status.busy": "2023-12-01T18:14:33.134753Z", + "iopub.status.idle": "2023-12-01T18:14:33.199007Z", + "shell.execute_reply": "2023-12-01T18:14:33.198136Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.036763Z", - "iopub.status.busy": "2023-11-30T11:31:12.036466Z", - "iopub.status.idle": "2023-11-30T11:31:12.041041Z", - "shell.execute_reply": "2023-11-30T11:31:12.040203Z" + "iopub.execute_input": "2023-12-01T18:14:33.203686Z", + "iopub.status.busy": "2023-12-01T18:14:33.203478Z", + "iopub.status.idle": "2023-12-01T18:14:33.207310Z", + "shell.execute_reply": "2023-12-01T18:14:33.206505Z" } }, "outputs": [], @@ -346,10 +346,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.044145Z", - "iopub.status.busy": "2023-11-30T11:31:12.043895Z", - "iopub.status.idle": "2023-11-30T11:31:12.052779Z", - "shell.execute_reply": "2023-11-30T11:31:12.051866Z" + "iopub.execute_input": "2023-12-01T18:14:33.211594Z", + "iopub.status.busy": "2023-12-01T18:14:33.211365Z", + "iopub.status.idle": "2023-12-01T18:14:33.218717Z", + "shell.execute_reply": "2023-12-01T18:14:33.217859Z" } }, "outputs": [], @@ -369,10 +369,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.056354Z", - "iopub.status.busy": "2023-11-30T11:31:12.056077Z", - "iopub.status.idle": "2023-11-30T11:31:12.060962Z", - "shell.execute_reply": "2023-11-30T11:31:12.060062Z" + "iopub.execute_input": "2023-12-01T18:14:33.223512Z", + "iopub.status.busy": "2023-12-01T18:14:33.223243Z", + "iopub.status.idle": "2023-12-01T18:14:33.227888Z", + "shell.execute_reply": "2023-12-01T18:14:33.226928Z" } }, "outputs": [], @@ -393,10 +393,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.064438Z", - "iopub.status.busy": "2023-11-30T11:31:12.064172Z", - "iopub.status.idle": "2023-11-30T11:31:12.068940Z", - "shell.execute_reply": "2023-11-30T11:31:12.068060Z" + "iopub.execute_input": "2023-12-01T18:14:33.232666Z", + "iopub.status.busy": "2023-12-01T18:14:33.232318Z", + "iopub.status.idle": "2023-12-01T18:14:33.236979Z", + "shell.execute_reply": "2023-12-01T18:14:33.236052Z" } }, "outputs": [], @@ -416,10 +416,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:12.072414Z", - "iopub.status.busy": "2023-11-30T11:31:12.072125Z", - "iopub.status.idle": "2023-11-30T11:31:16.157163Z", - "shell.execute_reply": "2023-11-30T11:31:16.155774Z" + "iopub.execute_input": "2023-12-01T18:14:33.242496Z", + "iopub.status.busy": "2023-12-01T18:14:33.241919Z", + "iopub.status.idle": "2023-12-01T18:14:36.495278Z", + "shell.execute_reply": "2023-12-01T18:14:36.493807Z" } }, "outputs": [ @@ -469,7 +469,7 @@ "Writing dump file \u001b[94mtest_constant_kernel/frame.dmp\u001b[0m\n", "Writing file \u001b[94mtest_constant_kernel/data0006.hdf5\u001b[0m\n", "Writing dump file \u001b[94mtest_constant_kernel/frame.dmp\u001b[0m\n", - "Execution time: \u001b[94m0:00:04\u001b[0m\n" + "Execution time: \u001b[94m0:00:03\u001b[0m\n" ] } ], @@ -489,10 +489,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:16.162688Z", - "iopub.status.busy": "2023-11-30T11:31:16.162387Z", - "iopub.status.idle": "2023-11-30T11:31:16.179777Z", - "shell.execute_reply": "2023-11-30T11:31:16.178865Z" + "iopub.execute_input": "2023-12-01T18:14:36.500773Z", + "iopub.status.busy": "2023-12-01T18:14:36.500552Z", + "iopub.status.idle": "2023-12-01T18:14:36.515973Z", + "shell.execute_reply": "2023-12-01T18:14:36.515250Z" } }, "outputs": [], @@ -507,10 +507,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:16.184774Z", - "iopub.status.busy": "2023-11-30T11:31:16.184470Z", - "iopub.status.idle": "2023-11-30T11:31:16.189503Z", - "shell.execute_reply": "2023-11-30T11:31:16.188646Z" + "iopub.execute_input": "2023-12-01T18:14:36.520249Z", + "iopub.status.busy": "2023-12-01T18:14:36.520072Z", + "iopub.status.idle": "2023-12-01T18:14:36.523926Z", + "shell.execute_reply": "2023-12-01T18:14:36.523179Z" } }, "outputs": [], @@ -523,45 +523,16 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:16.194346Z", - "iopub.status.busy": "2023-11-30T11:31:16.194028Z", - "iopub.status.idle": "2023-11-30T11:31:18.872666Z", - "shell.execute_reply": "2023-11-30T11:31:18.870601Z" + "iopub.execute_input": "2023-12-01T18:14:36.528350Z", + "iopub.status.busy": "2023-12-01T18:14:36.528127Z", + "iopub.status.idle": "2023-12-01T18:14:37.644400Z", + "shell.execute_reply": "2023-12-01T18:14:37.643405Z" } }, "outputs": [ - { - "ename": "TypeError", - "evalue": "must be real number, not str", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 338\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 150\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backend_bases.py:2193\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2189\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2190\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m 2191\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m 2192\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2193\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2194\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2195\u001b[0m \u001b[43m \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2196\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2197\u001b[0m \u001b[43m \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2198\u001b[0m \u001b[43m \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2199\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2200\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 2201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2039\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m 2040\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2041\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 2042\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2043\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2044\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m 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\u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:497\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 451\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m Write the figure to a PNG file.\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124;03m *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[1;32m 496\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m 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dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:388\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[0;32m--> 388\u001b[0m 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renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 97\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3151\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3155\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m 3158\u001b[0m sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/axes/_base.py:3070\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m 3068\u001b[0m _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3070\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3071\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3073\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3074\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/text.py:1988\u001b[0m, in \u001b[0;36mAnnotation.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1986\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marrow_patch\u001b[38;5;241m.\u001b[39mfigure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1987\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marrow_patch\u001b[38;5;241m.\u001b[39mfigure \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\n\u001b[0;32m-> 1988\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marrow_patch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1989\u001b[0m \u001b[38;5;66;03m# Draw text, including FancyBboxPatch, after FancyArrowPatch.\u001b[39;00m\n\u001b[1;32m 1990\u001b[0m \u001b[38;5;66;03m# Otherwise, a wedge arrowstyle can land partly on top of the Bbox.\u001b[39;00m\n\u001b[1;32m 1991\u001b[0m Text\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m, renderer)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/artist.py:39\u001b[0m, in \u001b[0;36m_prevent_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n\u001b[1;32m 37\u001b[0m renderer\u001b[38;5;241m.\u001b[39m_rasterizing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/patches.py:4388\u001b[0m, in \u001b[0;36mFancyArrowPatch.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 4384\u001b[0m fillable \u001b[38;5;241m=\u001b[39m [fillable]\n\u001b[1;32m 4386\u001b[0m affine \u001b[38;5;241m=\u001b[39m transforms\u001b[38;5;241m.\u001b[39mIdentityTransform()\n\u001b[0;32m-> 4388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_paths_with_artist_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4389\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4390\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_facecolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mand\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_facecolor\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 4391\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfillable\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/patches.py:573\u001b[0m, in \u001b[0;36mPatch._draw_paths_with_artist_properties\u001b[0;34m(self, renderer, draw_path_args_list)\u001b[0m\n\u001b[1;32m 570\u001b[0m renderer \u001b[38;5;241m=\u001b[39m PathEffectRenderer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_path_effects(), renderer)\n\u001b[1;32m 572\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m draw_path_args \u001b[38;5;129;01min\u001b[39;00m draw_path_args_list:\n\u001b[0;32m--> 573\u001b[0m \u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdraw_path_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 575\u001b[0m gc\u001b[38;5;241m.\u001b[39mrestore()\n\u001b[1;32m 576\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpatch\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/dustpy_docs/lib/python3.12/site-packages/matplotlib/backends/backend_agg.py:132\u001b[0m, in \u001b[0;36mRendererAgg.draw_path\u001b[0;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 132\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_renderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrgbFace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m cant_chunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n", - "\u001b[0;31mTypeError\u001b[0m: must be real number, not str" - ] - }, { "data": { + "image/png": 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", "text/plain": [ "
" ] @@ -579,7 +550,7 @@ " cstr = \"C\" + str(i-1)\n", " ax.loglog(m[i, ...], convert(SigmaConstant[i, 1, :], m[i, ...]), lw=1, c=cstr, label=\"t = {:3.1e}\".format(t[i]))\n", " ax.loglog(m[i, ...], solution_constant_kernel(t[i], m[i, ...], a, S0), \":\", lw=1, c=cstr)\n", - "ax.annotate('', xy=(1.1e2, 1.e-5), xytext=(1.e3, 1.e-4), arrowprops=dict(facecolor='red', lw=\"0\", width=6))\n", + "ax.annotate('', xy=(1.1e2, 1.e-5), xytext=(1.e3, 1.e-4), arrowprops=dict(facecolor='red', lw=0, width=6))\n", "ax.legend(ncol=2)\n", "ax.set_xlim(m[0, 0], m[0, -1])\n", "ax.set_ylim(1.e-6, 1.e3)\n", @@ -604,10 +575,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:18.879271Z", - "iopub.status.busy": "2023-11-30T11:31:18.878736Z", - "iopub.status.idle": "2023-11-30T11:31:18.887975Z", - "shell.execute_reply": "2023-11-30T11:31:18.886866Z" + "iopub.execute_input": "2023-12-01T18:14:37.653043Z", + "iopub.status.busy": "2023-12-01T18:14:37.652718Z", + "iopub.status.idle": "2023-12-01T18:14:37.658924Z", + "shell.execute_reply": "2023-12-01T18:14:37.657897Z" } }, "outputs": [], @@ -620,10 +591,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:18.892940Z", - "iopub.status.busy": "2023-11-30T11:31:18.892568Z", - "iopub.status.idle": "2023-11-30T11:31:18.898679Z", - "shell.execute_reply": "2023-11-30T11:31:18.897293Z" + "iopub.execute_input": "2023-12-01T18:14:37.663201Z", + "iopub.status.busy": "2023-12-01T18:14:37.662838Z", + "iopub.status.idle": "2023-12-01T18:14:37.668059Z", + "shell.execute_reply": "2023-12-01T18:14:37.666970Z" } }, "outputs": [], @@ -637,10 +608,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:18.904303Z", - "iopub.status.busy": "2023-11-30T11:31:18.903804Z", - "iopub.status.idle": "2023-11-30T11:31:18.910362Z", - "shell.execute_reply": "2023-11-30T11:31:18.908897Z" + "iopub.execute_input": "2023-12-01T18:14:37.673275Z", + "iopub.status.busy": "2023-12-01T18:14:37.672684Z", + "iopub.status.idle": "2023-12-01T18:14:37.678207Z", + "shell.execute_reply": "2023-12-01T18:14:37.677003Z" } }, "outputs": [], @@ -653,10 +624,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:18.916336Z", - "iopub.status.busy": "2023-11-30T11:31:18.915606Z", - "iopub.status.idle": "2023-11-30T11:31:22.023129Z", - "shell.execute_reply": "2023-11-30T11:31:22.022123Z" + "iopub.execute_input": "2023-12-01T18:14:37.683723Z", + "iopub.status.busy": "2023-12-01T18:14:37.683056Z", + "iopub.status.idle": "2023-12-01T18:14:42.005690Z", + "shell.execute_reply": "2023-12-01T18:14:42.004173Z" } }, "outputs": [], @@ -669,10 +640,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:22.028452Z", - "iopub.status.busy": "2023-11-30T11:31:22.028220Z", - "iopub.status.idle": "2023-11-30T11:31:22.081379Z", - "shell.execute_reply": "2023-11-30T11:31:22.080371Z" + "iopub.execute_input": "2023-12-01T18:14:42.010930Z", + "iopub.status.busy": "2023-12-01T18:14:42.010677Z", + "iopub.status.idle": "2023-12-01T18:14:42.078765Z", + "shell.execute_reply": "2023-12-01T18:14:42.076730Z" } }, "outputs": [], @@ -685,10 +656,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:22.086503Z", - "iopub.status.busy": "2023-11-30T11:31:22.086286Z", - "iopub.status.idle": "2023-11-30T11:31:22.090867Z", - "shell.execute_reply": "2023-11-30T11:31:22.089940Z" + "iopub.execute_input": "2023-12-01T18:14:42.084584Z", + "iopub.status.busy": "2023-12-01T18:14:42.084254Z", + "iopub.status.idle": "2023-12-01T18:14:42.090493Z", + "shell.execute_reply": "2023-12-01T18:14:42.089558Z" } }, "outputs": [], @@ -701,10 +672,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:22.095367Z", - "iopub.status.busy": "2023-11-30T11:31:22.095133Z", - "iopub.status.idle": "2023-11-30T11:31:22.099594Z", - "shell.execute_reply": "2023-11-30T11:31:22.098682Z" + "iopub.execute_input": "2023-12-01T18:14:42.094896Z", + "iopub.status.busy": "2023-12-01T18:14:42.094675Z", + "iopub.status.idle": "2023-12-01T18:14:42.099155Z", + "shell.execute_reply": "2023-12-01T18:14:42.098229Z" } }, "outputs": [], @@ -717,10 +688,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:31:22.103743Z", - "iopub.status.busy": "2023-11-30T11:31:22.103513Z", - "iopub.status.idle": "2023-11-30T11:32:30.249482Z", - "shell.execute_reply": "2023-11-30T11:32:30.247673Z" + "iopub.execute_input": "2023-12-01T18:14:42.103587Z", + "iopub.status.busy": "2023-12-01T18:14:42.103304Z", + "iopub.status.idle": "2023-12-01T18:15:43.927715Z", + "shell.execute_reply": "2023-12-01T18:15:43.926295Z" } }, "outputs": [ @@ -770,7 +741,7 @@ "Writing dump file \u001b[94mtest_constant_kernel_high_res/frame.dmp\u001b[0m\n", "Writing file \u001b[94mtest_constant_kernel_high_res/data0006.hdf5\u001b[0m\n", "Writing dump file \u001b[94mtest_constant_kernel_high_res/frame.dmp\u001b[0m\n", - "Execution time: \u001b[94m0:01:08\u001b[0m\n" + "Execution time: \u001b[94m0:01:01\u001b[0m\n" ] } ], @@ -783,10 +754,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:30.254879Z", - "iopub.status.busy": "2023-11-30T11:32:30.254581Z", - "iopub.status.idle": "2023-11-30T11:32:30.272954Z", - "shell.execute_reply": "2023-11-30T11:32:30.271720Z" + "iopub.execute_input": "2023-12-01T18:15:43.934071Z", + "iopub.status.busy": "2023-12-01T18:15:43.933813Z", + "iopub.status.idle": "2023-12-01T18:15:43.950078Z", + "shell.execute_reply": "2023-12-01T18:15:43.949135Z" } }, "outputs": [], @@ -801,10 +772,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:30.278121Z", - "iopub.status.busy": "2023-11-30T11:32:30.277823Z", - "iopub.status.idle": "2023-11-30T11:32:31.113415Z", - "shell.execute_reply": "2023-11-30T11:32:31.112241Z" + "iopub.execute_input": "2023-12-01T18:15:43.954967Z", + "iopub.status.busy": "2023-12-01T18:15:43.954715Z", + "iopub.status.idle": "2023-12-01T18:15:44.814975Z", + "shell.execute_reply": "2023-12-01T18:15:44.814060Z" } }, "outputs": [ @@ -870,10 +841,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.122675Z", - "iopub.status.busy": "2023-11-30T11:32:31.122339Z", - "iopub.status.idle": "2023-11-30T11:32:31.130318Z", - "shell.execute_reply": "2023-11-30T11:32:31.129063Z" + "iopub.execute_input": "2023-12-01T18:15:44.825063Z", + "iopub.status.busy": "2023-12-01T18:15:44.824771Z", + "iopub.status.idle": "2023-12-01T18:15:44.830708Z", + "shell.execute_reply": "2023-12-01T18:15:44.829744Z" } }, "outputs": [], @@ -910,10 +881,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.135502Z", - "iopub.status.busy": "2023-11-30T11:32:31.134976Z", - "iopub.status.idle": "2023-11-30T11:32:31.146699Z", - "shell.execute_reply": "2023-11-30T11:32:31.145156Z" + "iopub.execute_input": "2023-12-01T18:15:44.834590Z", + "iopub.status.busy": "2023-12-01T18:15:44.834323Z", + "iopub.status.idle": "2023-12-01T18:15:44.843690Z", + "shell.execute_reply": "2023-12-01T18:15:44.842647Z" } }, "outputs": [], @@ -959,10 +930,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.152609Z", - "iopub.status.busy": "2023-11-30T11:32:31.151974Z", - "iopub.status.idle": "2023-11-30T11:32:31.159343Z", - "shell.execute_reply": "2023-11-30T11:32:31.158026Z" + "iopub.execute_input": "2023-12-01T18:15:44.848922Z", + "iopub.status.busy": "2023-12-01T18:15:44.848473Z", + "iopub.status.idle": "2023-12-01T18:15:44.853630Z", + "shell.execute_reply": "2023-12-01T18:15:44.852680Z" } }, "outputs": [], @@ -975,10 +946,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.165198Z", - "iopub.status.busy": "2023-11-30T11:32:31.164557Z", - "iopub.status.idle": "2023-11-30T11:32:31.170982Z", - "shell.execute_reply": "2023-11-30T11:32:31.169604Z" + "iopub.execute_input": "2023-12-01T18:15:44.859214Z", + "iopub.status.busy": "2023-12-01T18:15:44.858083Z", + "iopub.status.idle": "2023-12-01T18:15:44.863415Z", + "shell.execute_reply": "2023-12-01T18:15:44.862391Z" } }, "outputs": [], @@ -992,10 +963,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.177130Z", - "iopub.status.busy": "2023-11-30T11:32:31.176403Z", - "iopub.status.idle": "2023-11-30T11:32:31.257094Z", - "shell.execute_reply": "2023-11-30T11:32:31.255811Z" + "iopub.execute_input": "2023-12-01T18:15:44.868768Z", + "iopub.status.busy": "2023-12-01T18:15:44.868187Z", + "iopub.status.idle": "2023-12-01T18:15:44.950117Z", + "shell.execute_reply": "2023-12-01T18:15:44.949154Z" } }, "outputs": [], @@ -1008,10 +979,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.262354Z", - "iopub.status.busy": "2023-11-30T11:32:31.262062Z", - "iopub.status.idle": "2023-11-30T11:32:31.271367Z", - "shell.execute_reply": "2023-11-30T11:32:31.270249Z" + "iopub.execute_input": "2023-12-01T18:15:44.954661Z", + "iopub.status.busy": "2023-12-01T18:15:44.954441Z", + "iopub.status.idle": "2023-12-01T18:15:44.962464Z", + "shell.execute_reply": "2023-12-01T18:15:44.961569Z" } }, "outputs": [], @@ -1024,10 +995,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.276399Z", - "iopub.status.busy": "2023-11-30T11:32:31.276113Z", - "iopub.status.idle": "2023-11-30T11:32:31.281460Z", - "shell.execute_reply": "2023-11-30T11:32:31.280305Z" + "iopub.execute_input": "2023-12-01T18:15:44.967018Z", + "iopub.status.busy": "2023-12-01T18:15:44.966783Z", + "iopub.status.idle": "2023-12-01T18:15:44.971150Z", + "shell.execute_reply": "2023-12-01T18:15:44.970294Z" } }, "outputs": [], @@ -1041,10 +1012,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.286147Z", - "iopub.status.busy": "2023-11-30T11:32:31.285835Z", - "iopub.status.idle": "2023-11-30T11:32:31.291226Z", - "shell.execute_reply": "2023-11-30T11:32:31.290002Z" + "iopub.execute_input": "2023-12-01T18:15:44.975524Z", + "iopub.status.busy": "2023-12-01T18:15:44.975249Z", + "iopub.status.idle": "2023-12-01T18:15:44.979740Z", + "shell.execute_reply": "2023-12-01T18:15:44.978767Z" } }, "outputs": [], @@ -1057,10 +1028,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:31.296445Z", - "iopub.status.busy": "2023-11-30T11:32:31.295881Z", - "iopub.status.idle": "2023-11-30T11:32:32.610902Z", - "shell.execute_reply": "2023-11-30T11:32:32.609663Z" + "iopub.execute_input": "2023-12-01T18:15:44.983911Z", + "iopub.status.busy": "2023-12-01T18:15:44.983639Z", + "iopub.status.idle": "2023-12-01T18:15:46.149188Z", + "shell.execute_reply": "2023-12-01T18:15:46.148219Z" } }, "outputs": [ @@ -1123,10 +1094,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:32.615537Z", - "iopub.status.busy": "2023-11-30T11:32:32.615282Z", - "iopub.status.idle": "2023-11-30T11:32:32.630477Z", - "shell.execute_reply": "2023-11-30T11:32:32.629420Z" + "iopub.execute_input": "2023-12-01T18:15:46.153679Z", + "iopub.status.busy": "2023-12-01T18:15:46.153460Z", + "iopub.status.idle": "2023-12-01T18:15:46.168177Z", + "shell.execute_reply": "2023-12-01T18:15:46.167216Z" } }, "outputs": [], @@ -1141,10 +1112,10 @@ "execution_count": 38, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:32.635528Z", - "iopub.status.busy": "2023-11-30T11:32:32.635305Z", - "iopub.status.idle": "2023-11-30T11:32:33.420498Z", - "shell.execute_reply": "2023-11-30T11:32:33.419069Z" + "iopub.execute_input": "2023-12-01T18:15:46.172880Z", + "iopub.status.busy": "2023-12-01T18:15:46.172655Z", + "iopub.status.idle": "2023-12-01T18:15:46.852690Z", + "shell.execute_reply": "2023-12-01T18:15:46.851648Z" } }, "outputs": [ @@ -1190,10 +1161,10 @@ "execution_count": 39, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:33.428530Z", - "iopub.status.busy": "2023-11-30T11:32:33.428256Z", - "iopub.status.idle": "2023-11-30T11:32:33.434278Z", - "shell.execute_reply": "2023-11-30T11:32:33.433129Z" + "iopub.execute_input": "2023-12-01T18:15:46.861893Z", + "iopub.status.busy": "2023-12-01T18:15:46.861630Z", + "iopub.status.idle": "2023-12-01T18:15:46.866809Z", + "shell.execute_reply": "2023-12-01T18:15:46.865860Z" } }, "outputs": [], @@ -1206,10 +1177,10 @@ "execution_count": 40, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:33.439026Z", - "iopub.status.busy": "2023-11-30T11:32:33.438753Z", - "iopub.status.idle": "2023-11-30T11:32:33.444395Z", - "shell.execute_reply": "2023-11-30T11:32:33.443181Z" + "iopub.execute_input": "2023-12-01T18:15:46.871355Z", + "iopub.status.busy": "2023-12-01T18:15:46.871098Z", + "iopub.status.idle": "2023-12-01T18:15:46.875552Z", + "shell.execute_reply": "2023-12-01T18:15:46.874570Z" } }, "outputs": [], @@ -1223,10 +1194,10 @@ "execution_count": 41, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:33.449333Z", - "iopub.status.busy": "2023-11-30T11:32:33.448864Z", - "iopub.status.idle": "2023-11-30T11:32:33.454189Z", - "shell.execute_reply": "2023-11-30T11:32:33.452858Z" + "iopub.execute_input": "2023-12-01T18:15:46.880353Z", + "iopub.status.busy": "2023-12-01T18:15:46.879884Z", + "iopub.status.idle": "2023-12-01T18:15:46.884531Z", + "shell.execute_reply": "2023-12-01T18:15:46.883546Z" } }, "outputs": [], @@ -1239,10 +1210,10 @@ "execution_count": 42, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:33.459761Z", - "iopub.status.busy": "2023-11-30T11:32:33.459133Z", - "iopub.status.idle": "2023-11-30T11:32:37.943433Z", - "shell.execute_reply": "2023-11-30T11:32:37.942036Z" + "iopub.execute_input": "2023-12-01T18:15:46.889686Z", + "iopub.status.busy": "2023-12-01T18:15:46.889121Z", + "iopub.status.idle": "2023-12-01T18:15:51.223447Z", + "shell.execute_reply": "2023-12-01T18:15:51.222480Z" } }, "outputs": [], @@ -1255,10 +1226,10 @@ "execution_count": 43, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:37.949073Z", - "iopub.status.busy": "2023-11-30T11:32:37.948738Z", - "iopub.status.idle": "2023-11-30T11:32:37.994724Z", - "shell.execute_reply": "2023-11-30T11:32:37.993339Z" + "iopub.execute_input": "2023-12-01T18:15:51.228234Z", + "iopub.status.busy": "2023-12-01T18:15:51.227985Z", + "iopub.status.idle": "2023-12-01T18:15:51.272913Z", + "shell.execute_reply": "2023-12-01T18:15:51.271948Z" } }, "outputs": [], @@ -1271,10 +1242,10 @@ "execution_count": 44, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:38.000195Z", - "iopub.status.busy": "2023-11-30T11:32:37.999870Z", - "iopub.status.idle": "2023-11-30T11:32:38.005020Z", - "shell.execute_reply": "2023-11-30T11:32:38.003948Z" + "iopub.execute_input": "2023-12-01T18:15:51.277542Z", + "iopub.status.busy": "2023-12-01T18:15:51.277320Z", + "iopub.status.idle": "2023-12-01T18:15:51.281623Z", + "shell.execute_reply": "2023-12-01T18:15:51.280774Z" } }, "outputs": [], @@ -1288,10 +1259,10 @@ "execution_count": 45, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:38.009791Z", - "iopub.status.busy": "2023-11-30T11:32:38.009504Z", - "iopub.status.idle": "2023-11-30T11:32:38.014882Z", - "shell.execute_reply": "2023-11-30T11:32:38.013708Z" + "iopub.execute_input": "2023-12-01T18:15:51.286059Z", + "iopub.status.busy": "2023-12-01T18:15:51.285798Z", + "iopub.status.idle": "2023-12-01T18:15:51.289995Z", + "shell.execute_reply": "2023-12-01T18:15:51.289140Z" } }, "outputs": [], @@ -1304,10 +1275,10 @@ "execution_count": 46, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:38.019948Z", - "iopub.status.busy": "2023-11-30T11:32:38.019390Z", - "iopub.status.idle": "2023-11-30T11:32:50.180851Z", - "shell.execute_reply": "2023-11-30T11:32:50.179401Z" + "iopub.execute_input": "2023-12-01T18:15:51.294543Z", + "iopub.status.busy": "2023-12-01T18:15:51.294240Z", + "iopub.status.idle": "2023-12-01T18:16:03.831395Z", + "shell.execute_reply": "2023-12-01T18:16:03.830363Z" } }, "outputs": [ @@ -1370,10 +1341,10 @@ "execution_count": 47, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:50.186604Z", - "iopub.status.busy": "2023-11-30T11:32:50.186202Z", - "iopub.status.idle": "2023-11-30T11:32:50.205883Z", - "shell.execute_reply": "2023-11-30T11:32:50.204953Z" + "iopub.execute_input": "2023-12-01T18:16:03.836294Z", + "iopub.status.busy": "2023-12-01T18:16:03.836031Z", + "iopub.status.idle": "2023-12-01T18:16:03.854278Z", + "shell.execute_reply": "2023-12-01T18:16:03.853316Z" } }, "outputs": [], @@ -1388,10 +1359,10 @@ "execution_count": 48, "metadata": { "execution": { - "iopub.execute_input": "2023-11-30T11:32:50.211191Z", - "iopub.status.busy": "2023-11-30T11:32:50.210915Z", - "iopub.status.idle": "2023-11-30T11:32:51.006195Z", - "shell.execute_reply": "2023-11-30T11:32:51.005213Z" + "iopub.execute_input": "2023-12-01T18:16:03.858860Z", + "iopub.status.busy": "2023-12-01T18:16:03.858630Z", + "iopub.status.idle": "2023-12-01T18:16:04.643803Z", + "shell.execute_reply": "2023-12-01T18:16:04.642878Z" } }, "outputs": [ @@ -1463,7 +1434,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.5" } }, "nbformat": 4,