Merge pull request #136 from deepskies/paper-edits

Paper edits

Author: voetberg

README.md

@@ -1,23 +1,21 @@
 ![GitHub Workflow Status](https://github.com/deepskies/DeepBench/actions/workflows/test-bench.yml/badge.svg?label=test)
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![PyPI version](https://badge.fury.io/py/deepbench.svg)](https://badge.fury.io/py/deepbench)
-
+[![Documentation Status](https://readthedocs.org/projects/deepbench/badge/?version=latest)](https://deepbench.readthedocs.io/en/latest/?badge=latest)
 
 ### What is it?
 Simulation library for very simple simulations to *benchmark* machine learning algorithms.
 
-
-
 ### Why do we need it? Why is it useful?
 1. There are very universally recognized scientifically meaningful benchmark data sets, or methods with which to generate them.
 2. A very simple data set will have objects, patterns, and signals that are intuitively quanitifiable and will be fast to generate.
 3. A very simple data set will be a great testing ground for new networks and for newcomers to practice with the technology.
 
 ## Documentation
 
-#### Readthedocs link coming soon!!
+#### [ReadTheDocs](https://deepbench.readthedocs.io/en/latest/)
 
-#### To build
+#### To build from source
 ```
 pip install sphinx
 cd docs

paper/figures/00EF172E-27F9-44CD-9A48-1C16CA8882A0_1_201_a.jpeg

@@ -0,0 +1,137 @@
+from deepbench.image import SkyImage, ShapeImage
+from deepbench.physics_object import HamiltonianPendulum, Pendulum
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Each image is 480,480
+image_shape = (480, 480)
+
+# Total images N and figure size
+fig, subplots = plt.subplots(2, 4, figsize=(12, 6))
+
+# Center of all images is at 480/2, 480/2
+center = image_shape[0] / 2
+
+
+# Parameters for each ellipse
+ellipse_params = {
+    "center": (center, center),
+    "width": 100,
+    "height": 200,
+    "fill": True,
+    "angle": 30,
+}
+shape_single = ShapeImage(image_shape, object_noise_level=0.0)
+single_shape_noiseless = shape_single.combine_objects(
+    ["ellipse"], object_params=[ellipse_params]
+)
+
+subplots[0, 0].imshow(single_shape_noiseless)
+
+# Use the same parameters to make an ellipse with noise
+shape_single = ShapeImage(image_shape, object_noise_level=0.4)
+shape_single_noisy = shape_single.combine_objects(
+    ["ellipse"], object_params=[ellipse_params]
+)
+
+subplots[0, 1].imshow(shape_single_noisy)
+
+# Produce a rectangle with specified line widths
+line_params = {
+    "center": (center + int(center / 2), center),
+    "width": 120,
+    "height": 200,
+    "line_width": 20,
+}
+shape_two = ShapeImage(image_shape, object_noise_level=0)
+# Use the combine objects method to make ellipses and rectangles with the above prameters
+shape_two_noiseless = shape_two.combine_objects(
+    ["ellipse", "rectangle"], object_params=[ellipse_params, line_params]
+)
+
+subplots[0, 2].imshow(shape_two_noiseless)
+
+# Do it with a noise argument now
+shape_two = ShapeImage(image_shape, object_noise_level=0.2)
+shape_two_noisy = shape_single.combine_objects(
+    ["ellipse", "rectangle"], object_params=[ellipse_params, line_params]
+)
+
+subplots[0, 3].imshow(shape_two_noisy)
+
+# Read the process with specifiations for astronomy objects
+star_instance = {"radius": 100.0, "amplitude": 100.0}
+star_params = {"center_x": center - int(center / 2), "center_y": center}
+
+galaxy_instance = {"radius": 30.0, "amplitude": 200.0, "ellipse": 0.8, "theta": 0.2}
+galaxy_params = {"center_x": center, "center_y": center + int(center / 2)}
+subplots[1, 0].set_ylabel("Astronomy", labelpad=8.0)
+
+one_image_sky = SkyImage(image_shape)
+one_sky = one_image_sky.combine_objects(
+    ["star"], instance_params=[star_instance], object_params=[star_params]
+)
+
+subplots[1, 0].imshow(one_sky)
+
+
+one_sky_noise = SkyImage(image_shape, object_noise_level=0.4)
+one_image_sky_noise = one_sky_noise.combine_objects(
+    ["star"], instance_params=[star_instance], object_params=[star_params]
+)
+
+subplots[1, 1].imshow(one_image_sky_noise)
+
+one_image_sky = SkyImage(image_shape)
+one_sky = one_image_sky.combine_objects(
+    ["star", "galaxy"],
+    instance_params=[star_instance, galaxy_instance],
+    object_params=[star_params, galaxy_params],
+)
+
+subplots[1, 2].imshow(one_sky)
+
+
+one_sky_noise = SkyImage(image_shape, object_noise_level=0.4)
+one_image_sky_noise = one_sky_noise.combine_objects(
+    ["star", "galaxy"],
+    instance_params=[star_instance, galaxy_instance],
+    object_params=[star_params, galaxy_params],
+)
+
+subplots[1, 3].imshow(one_image_sky_noise)
+
+
+one_sky_noise = SkyImage(image_shape, object_noise_level=0.4)
+one_image_sky_noise = one_sky_noise.combine_objects(
+    ["star", "galaxy"],
+    instance_params=[star_instance, galaxy_instance],
+    object_params=[star_params, galaxy_params],
+)
+
+subplots[1, 3].imshow(one_image_sky_noise)
+
+# Y axis labels for each row
+subplots[0, 0].set_ylabel("Geometry", labelpad=10.0)
+
+# Remove unnecessary ticks, only put them on the 100 pixel marks
+# Flip the images so it starts at 0.,0.
+ticks = np.linspace(0, image_shape[0], int(image_shape[0] / 100))
+for plot in subplots.ravel():
+    plot.autoscale(tight=True)
+    plot.set_yticks(ticks.tolist()[::-1])
+    plot.invert_yaxis()
+    plot.set_xticks(ticks)
+
+# All object titles
+subplots[0, 0].set_title("Noiseless Single Object")
+subplots[0, 2].set_title("Noiseless Multi-Object")
+subplots[0, 1].set_title("Noisy Single Object")
+subplots[0, 3].set_title("Noisy Multi-Object")
+
+# Scale information
+fig.supxlabel("pixel")
+fig.supylabel("pixel")
+
+plt.savefig("../example_objects.png")

paper/figures/0F50EB6B-668C-418F-A449-5B1DD1017780_1_201_a.jpeg

@@ -0,0 +1,89 @@
+from deepbench.physics_object import HamiltonianPendulum, Pendulum
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Define the number of objects in the plot and the total figure size
+fig, subplots = plt.subplots(1, 2, figsize=(int(19 * (3 / 4)), int(7 * 3 / 4)))
+
+# Set the times to calculate the pendulum position over
+# 1 point every second, for 0 to 25 seconds
+time = np.array(np.linspace(0, 25, 25))
+
+# Produce pendulum object
+pendulum = Pendulum(
+    pendulum_arm_length=10.0,
+    starting_angle_radians=np.pi / 4,
+    acceleration_due_to_gravity=9.8,
+    noise_std_percent={
+        "pendulum_arm_length": 0.0,
+        "starting_angle_radians": 0.1,
+        "acceleration_due_to_gravity": 0.1,
+    },
+)
+
+# Use the noiseless argument to make the pendulum w/o noise
+# Plot that against the time and with scatter and line options
+pendulum_noiseless = pendulum.create_object(time, noiseless=True)
+subplots[0].plot(time, pendulum_noiseless, color="black")
+subplots[0].scatter(time, pendulum_noiseless, color="black", label="Noiseless")
+
+# Use the noiseless=False to do the same with a noiseless pendulum
+pendulum_noisy = pendulum.create_object(time, noiseless=False)
+subplots[0].plot(time, pendulum_noisy, color="red")
+subplots[0].scatter(time, pendulum_noisy, color="red", label="Noisy")
+
+
+# Produce noiseless pendulum object for the H
+pendulum = HamiltonianPendulum(
+    pendulum_arm_length=10.0,
+    starting_angle_radians=np.pi / 4,
+    acceleration_due_to_gravity=9.8,
+    noise_std_percent={
+        "pendulum_arm_length": 0.0,
+        "starting_angle_radians": 0.0,
+        "acceleration_due_to_gravity": 0.0,
+    },
+)
+
+# Cacluate the pendulum positions and engeries
+pendulum_data = pendulum.create_object(time)
+
+# Plot the line and scatterplot versions of the position wrt time
+subplots[1].plot(pendulum_data[4], pendulum_data[0], color="black")
+subplots[1].scatter(
+    pendulum_data[4], pendulum_data[0], color="black", label="Noiseless"
+)
+
+# Repeat the process with the noisely pendulum
+pendulum = HamiltonianPendulum(
+    pendulum_arm_length=10.0,
+    starting_angle_radians=np.pi / 4,
+    acceleration_due_to_gravity=9.8,
+    noise_std_percent={
+        "pendulum_arm_length": 0.2,
+        "starting_angle_radians": 0.0,
+        "acceleration_due_to_gravity": 0.0,
+    },
+)
+
+pendulum_data = pendulum.create_object(time)
+
+subplots[1].plot(pendulum_data[4], pendulum_data[0], color="red")
+subplots[1].scatter(pendulum_data[4], pendulum_data[0], color="red", label="Noisy")
+
+# Set plot labels
+subplots[0].set_title("Newtonian")
+subplots[1].set_title("Hamiltonian")
+
+# Set axices labels
+for plot in subplots.ravel():
+    # plot.set(xticks=[], yticks=[])
+
+    plot.set_xlabel("Time (s)")
+    plot.set_ylabel("X Position")
+
+# Assign legend location
+subplots[1].legend(loc="center left", bbox_to_anchor=(1.02, 1))
+
+plt.savefig("../pendulums.png")