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Put plotting in package

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Wassim KABALAN 2024-11-14 13:52:59 +01:00
parent 0946842fe5
commit 435c7c848f
6 changed files with 159 additions and 37 deletions
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120
jaxpm/plotting.py Normal file
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@ -0,0 +1,120 @@
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
def plot_fields(fields_dict, sum_over=None):
"""
Plots sum projections of 3D fields along different axes,
slicing only the first `sum_over` elements along each axis.
Args:
- fields: list of 3D arrays representing fields to plot
- names: list of names for each field, used in titles
- sum_over: number of slices to sum along each axis (default: fields[0].shape[0] // 8)
"""
sum_over = sum_over or list(fields_dict.values())[0].shape[0] // 8
nb_rows = len(fields_dict)
nb_cols = 3
fig, axes = plt.subplots(nb_rows, nb_cols, figsize=(15, 5 * nb_rows))
def plot_subplots(proj_axis, field, row, title):
slicing = [slice(None)] * field.ndim
slicing[proj_axis] = slice(None, sum_over)
slicing = tuple(slicing)
# Sum projection over the specified axis and plot
axes[row, proj_axis].imshow(
field[slicing].sum(axis=proj_axis) + 1,
cmap='magma',
extent=[0, field.shape[proj_axis], 0, field.shape[proj_axis]])
axes[row, proj_axis].set_xlabel('Mpc/h')
axes[row, proj_axis].set_ylabel('Mpc/h')
axes[row, proj_axis].set_title(title)
# Plot each field across the three axes
for i, (name, field) in enumerate(fields_dict.items()):
for proj_axis in range(3):
plot_subplots(proj_axis, field, i,
f"{name} projection {proj_axis}")
plt.tight_layout()
plt.show()
def plot_fields_single_projection(fields_dict, sum_over=None, project_axis=0):
"""
Plots a single projection (along axis 0) of 3D fields in a grid,
summing over the first `sum_over` elements along the 0-axis, with 4 images per row.
Args:
- fields_dict: dictionary where keys are field names and values are 3D arrays
- sum_over: number of slices to sum along the projection axis (default: fields[0].shape[0] // 8)
"""
sum_over = sum_over or list(fields_dict.values())[0].shape[0] // 8
nb_fields = len(fields_dict)
nb_cols = 4 # Set number of images per row
nb_rows = (nb_fields + nb_cols - 1) // nb_cols # Calculate required rows
fig, axes = plt.subplots(nb_rows,
nb_cols,
figsize=(5 * nb_cols, 5 * nb_rows))
axes = np.atleast_2d(axes) # Ensure axes is always a 2D array
for i, (name, field) in enumerate(fields_dict.items()):
row, col = divmod(i, nb_cols)
# Define the slice for the 0-axis projection
slicing = [slice(None)] * field.ndim
slicing[project_axis] = slice(None, sum_over)
slicing = tuple(slicing)
# Sum projection over axis 0 and plot
axes[row, col].imshow(field[slicing].sum(axis=project_axis) + 1,
cmap='magma',
extent=[0, field.shape[1], 0, field.shape[2]])
axes[row, col].set_xlabel('Mpc/h')
axes[row, col].set_ylabel('Mpc/h')
axes[row, col].set_title(f"{name} projection 0")
# Remove any empty subplots
for j in range(i + 1, nb_rows * nb_cols):
fig.delaxes(axes.flatten()[j])
plt.tight_layout()
plt.show()
def stack_slices(array):
"""
Stacks 2D slices of an array into a single array based on provided partition dimensions.
Args:
- array_slices: a 2D list of array slices (list of lists format) where
array_slices[i][j] is the slice located at row i, column j in the grid.
- pdims: a tuple representing the grid dimensions (rows, columns).
Returns:
- A single array constructed by stacking the slices.
"""
# Initialize an empty list to store the vertically stacked rows
pdims = array.sharding.mesh.devices.shape
field_slices = []
# Iterate over rows in pdims[0]
for i in range(pdims[0]):
row_slices = []
# Iterate over columns in pdims[1]
for j in range(pdims[1]):
slice_index = i * pdims[0] + j
row_slices.append(array.addressable_data(slice_index))
# Stack the current row of slices vertically
stacked_row = np.hstack(row_slices)
field_slices.append(stacked_row)
# Stack all rows horizontally to form the full array
full_array = np.vstack(field_slices)
return full_array

22
notebooks/01-Introduction.ipynb
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@ -63,17 +63,18 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "281b4d3b",
"metadata": {
"id": "281b4d3b"
},
"outputs": [],
"source": [
"mesh_shape = [256, 256, 256]\n",
"box_size = [256., 256., 256.]\n",
"mesh_shape = [128, 128, 128]\n",
"box_size = [128., 128., 128.]\n",
"snapshots = jnp.array([0.1, 0.5, 1.0])\n",
"\n",
"@jax.jit\n",
"def run_simulation(omega_c, sigma8):\n",
" # Create a small function to generate the matter power spectrum\n",
" k = jnp.logspace(-4, 1, 128)\n",
@ -126,7 +127,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "4e012ce8",
"metadata": {
"colab": {
@ -149,7 +150,7 @@
}
],
"source": [
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"fields = {\"Initial Conditions\" : initial_conditions , \"LPT Field\" : cic_paint(jnp.zeros(mesh_shape) ,lpt_particles)}\n",
"for i , field in enumerate(ode_particles[1:]):\n",
@ -169,7 +170,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "b71824ed",
"metadata": {},
"outputs": [
@ -182,8 +183,8 @@
}
],
"source": [
"mesh_shape = [256, 256, 256]\n",
"box_size = [256., 256., 256.]\n",
"mesh_shape = [128, 128, 128]\n",
"box_size = [128., 128., 128.]\n",
"snapshots = jnp.array([0.1, 0.5, 1.0])\n",
"\n",
"@jax.jit\n",
@ -216,7 +217,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "33b5e684",
"metadata": {},
"outputs": [
@ -232,7 +233,8 @@
}
],
"source": [
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"fields = {\"Initial Conditions\" : initial_conditions , \"LPT Field\" : cic_paint_dx(lpt_displacements)}\n",
"for i , field in enumerate(ode_displacements[1:]):\n",
" fields[f\"field_{i}\"] = cic_paint_dx(field)\n",

34
notebooks/02-Advanced_usage.ipynb
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@ -50,7 +50,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -63,8 +63,8 @@
}
],
"source": [
"mesh_shape = [256, 256, 256]\n",
"box_size = [256., 256., 256.]\n",
"mesh_shape = [128, 128, 128]\n",
"box_size = [128., 128., 128.]\n",
"snapshots = jnp.array([0.5, 1.0])\n",
"\n",
"@jax.jit\n",
@ -111,7 +111,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -126,7 +126,7 @@
}
],
"source": [
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"fields = {\"Initial Conditions\" : initial_conditions , \"LPT Field\" : cic_paint_dx(lpt_displacements)}\n",
"for i , field in enumerate(ode_solutions):\n",
@ -145,7 +145,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -160,8 +160,8 @@
"source": [
"from functools import partial \n",
"\n",
"mesh_shape = [256, 256, 256]\n",
"box_size = [256., 256., 256.]\n",
"mesh_shape = [128, 128, 128]\n",
"box_size = [128., 128., 128.]\n",
"snapshots = jnp.array([0.5,1.])\n",
"\n",
"@partial(jax.jit , static_argnums=(2,))\n",
@ -224,7 +224,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -236,8 +236,8 @@
}
],
"source": [
"mesh_shape = [256, 256, 256]\n",
"box_size = [256., 256., 256.]\n",
"mesh_shape = [128, 128, 128]\n",
"box_size = [128., 128., 128.]\n",
"snapshots = jnp.array([0.1 ,0.5, 1.])\n",
"\n",
"@jax.jit\n",
@ -283,7 +283,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -298,7 +298,7 @@
}
],
"source": [
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"fields = {\"Initial Conditions\" : initial_conditions , \"LPT Field\" : cic_paint(jnp.zeros(mesh_shape) ,lpt_particles)}\n",
"for i , field in enumerate(ode_particles[1:]):\n",
@ -321,7 +321,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -337,7 +337,7 @@
],
"source": [
"from math import prod\n",
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"center = slice(mesh_shape[0] // 4, 3 * mesh_shape[0] // 4 )\n",
"center3d = (slice(None) , center,center) \n",
@ -364,7 +364,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -380,7 +380,7 @@
],
"source": [
"from math import prod\n",
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"center = slice(mesh_shape[0] // 4, 3 * mesh_shape[0] // 4 )\n",
"center3d = (slice(None) , center,center) \n",

14
notebooks/03-MultiGPU_PM_Halo.ipynb
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@ -283,7 +283,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "4e012ce8",
"metadata": {
"colab": {
@ -306,7 +306,7 @@
}
],
"source": [
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"fields = {\"Initial Conditions\" : initial_conditions , \"LPT Field\" : cic_paint_dx(lpt_displacements)}\n",
"for i , field in enumerate(ode_solutions):\n",
@ -342,7 +342,7 @@
"lpt_displacements_g = all_gather(lpt_displacements)\n",
"ode_solutions_g = [all_gather(p) for p in ode_solutions]\n",
"\n",
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"fields = {\"Initial Conditions\" : initial_conditions , \"LPT Field\" : cic_paint_dx(lpt_displacements)}\n",
"for i , field in enumerate(ode_solutions):\n",
@ -447,7 +447,7 @@
},
{
"cell_type": "code",
"execution_count": 78,
"execution_count": null,
"id": "0acb5253",
"metadata": {},
"outputs": [
@ -463,7 +463,7 @@
}
],
"source": [
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"fields = {\"Initial Conditions\" : initial_conditions_g , \"LPT Field\" : lpt_field_g}\n",
"for i , field in enumerate(ode_fields_g):\n",
@ -600,7 +600,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "59cfba84",
"metadata": {},
"outputs": [
@ -652,7 +652,7 @@
],
"source": [
"from math import prod\n",
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"field = ode_solutions[0]\n",
"\n",

4
notebooks/04-MultiGPU_PM_Solvers.ipynb
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@ -159,7 +159,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -181,7 +181,7 @@
}
],
"source": [
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"\n",
"mesh_shape = 1024\n",
"box_size = 1000.\n",

2
notebooks/05-MultiHost_PM.ipynb
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@ -259,7 +259,7 @@
}
],
"source": [
"from visualize import plot_fields_single_projection\n",
"from jaxpm.plotting import plot_fields_single_projection\n",
"fields = {\n",
" \"Initial Conditions\": initial_conditions,\n",
" \"LPT Field\": lpt_displacements,\n",