diff --git a/.github/workflows/paper.yml b/.github/workflows/paper.yml index 1c0f1990..a10cb3da 100644 --- a/.github/workflows/paper.yml +++ b/.github/workflows/paper.yml @@ -13,7 +13,7 @@ jobs: journal: joss paper-path: paper.md - name: Upload - uses: actions/upload-artifact@v1 + uses: actions/upload-artifact@v4 with: name: paper # This is the output path where Pandoc will write the compiled diff --git a/docs/.gitignore b/docs/.gitignore index 92fd498d..11c2ca0a 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -1,2 +1,3 @@ /_build /autoapi +*.ipynb diff --git a/docs/README.md b/docs/README.md index 92b2f709..a9dfdebe 100644 --- a/docs/README.md +++ b/docs/README.md @@ -6,6 +6,8 @@ To generate a local copy of the documentation, make sure you've installed poetry poetry install --with docs ``` +Additionally, `pandoc` needs to be installed: https://pandoc.org/installing.html + Then navigate to this directory and run: ``` diff --git a/docs/conf.py b/docs/conf.py index 340067e9..b30f3ddd 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -28,6 +28,7 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ + "nbsphinx", "sphinx.ext.napoleon", "sphinx.ext.mathjax", "sphinx_copybutton", @@ -49,13 +50,23 @@ # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. # NOTE: Don't use this for excluding python files, use `autoapi_ignore` below -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints", "**.ipynb", "**README.md"] # -- Global options ---------------------------------------------------------- # Don't mess with double-dash used in CLI options smartquotes_action = "qe" +# -- Notebook rendering ------------------------------------------------- + +# Something to consider: https://dokk.org/documentation/nbsphinx/0.9.3/prolog-and-epilog/ +nbsphinx_epilog = """""" +nbsphinx_prolog = """""" + +nbsphinx_custom_formats = { + ".pct.py": ["jupytext.reads", {"fmt": "py:percent"}], +} + # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for diff --git a/docs/examples/30_degree_rule.pct.py b/docs/examples/30_degree_rule.pct.py new file mode 100644 index 00000000..55b31e5e --- /dev/null +++ b/docs/examples/30_degree_rule.pct.py @@ -0,0 +1,478 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: all +# notebook_metadata_filter: all +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.16.4 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# language_info: +# codemirror_mode: +# name: ipython +# version: 3 +# file_extension: .py +# mimetype: text/x-python +# name: python +# nbconvert_exporter: python +# pygments_lexer: ipython3 +# version: 3.12.4 +# --- + +# %% [markdown] editable=true slideshow={"slide_type": ""} tags=[] +# # The 30 Degree Rule +# +# ## Intro +# +# The 30 degree rule states that the cue ball, when colliding with a ball over a wide range of cut angles, will be deflected roughly 30 degrees from it's initial course after the collision. It is more of a *rule of thumb* used by pool players to improve their game, rather than a truism of pool physics. +# +# In this example, we will setup simulations that test the 30 degree rule and some of the physics equations defined by [Dr. Dave Billiards](https://drdavebilliards.com/). +# +# ## Assumptions +# +# We will use the default pooltool physics engine, which assume perfectly elastic and frictionless ball-ball collisions. Read more [here](https://ekiefl.github.io/2020/04/24/pooltool-theory/#section-ii-ball-ball-interactions). +# +# Importantly, the cue ball must be rolling (without slippage) when it contacts the object ball. Otherwise, the 30-degree rule will not hold. As an extreme example of this, a cue ball with no spin will deflect off the object ball along the [tangent line](https://billiards.colostate.edu/faq/stun/90-degree-rule/). +# +# ## Definitions +# +# **The rule, stated in full:** +# +# > The 30° rule states that for a rolling cue ball (CB) shot, over a wide range of cut angles, between a 1/4-ball hit (49 degree cut) and 3/4-ball hit (14 degree cut), the CB will deflect or carom off by very close to 30° (the “natural angle“) from its original direction after hitting the object ball (OB). If you want to be more precise, the angle is a little more (about 34°) closer to a 1/2-ball hit and a little less (about 27°) closer to a 1/4-ball or 3/4-ball hit. +# +# *(source: [https://billiards.colostate.edu/faq/30-90-rules/30-degree-rule/](https://billiards.colostate.edu/faq/30-90-rules/30-degree-rule/))* +# +# **The carom angle** +# +# The carom angle is the angle that the rule claims is 30 degrees. Formally, it is the angle between the velocity of the cue ball directly before impact, and the velocity of the cue ball after the collision once the cue ball is no longer sliding on the cloth. We will see that this angle is independent of the cue's initial speed. +# +# **Ball-hit fraction and cut angle** +# +# - Ball-hit fraction, $f$, describes the fraction of overlap between the cue ball and object ball, projected in the direction of the aiming line. +# - Cut angle, $\phi$, refers to the angle that the cue ball glances the object ball, where $0$ refers to a full ball hit (straight on), and $90$ refers to the lower bound of the thinnest hit possible. +# +# These two are visualized in this diagram, where $f = \text{ball overlap} / (2R)$: +# +# +# +# *(source: [https://billiards.colostate.edu/technical_proofs/new/TP_A-23.pdf](https://billiards.colostate.edu/technical_proofs/new/TP_A-23.pdf))* +# +# Establishing the relationship between these quantities is important, since the 30 degree rule makes reference to both cut angle and ball-hit fraction. One can calculate the ball-hit fraction from cut angle with the following equation: +# +# $$ +# f(\phi) = 1 - \sin{\phi} +# $$ + +# %% [markdown] +# ## Simulating a collision +# +# To start, we'll need to create a billiards system. That means defining a table, a cue stick, and a collection of balls. +# +# We'll start with a table. Since we don't want collisions with cushions to interfere with our trajectory, let's make an unrealistically large $10\text{m} \times 10\text{m}$ [Table](../autoapi/pooltool/index.rst#pooltool.Table). + +# %% trusted=true +import pooltool as pt + +table_specs = pt.objects.BilliardTableSpecs(l=10, w=10) +table = pt.Table.from_table_specs(table_specs) + + +# %% [markdown] +# Next, we'll create two [Ball](../autoapi/pooltool/index.rst#pooltool.Ball) objects. + +# %% trusted=true +cue_ball = pt.Ball.create("cue", xy=(2.5, 1.5)) +obj_ball = pt.Ball.create("obj", xy=(2.5, 3.0)) + +# %% [markdown] +# Next, we'll need a [Cue](../autoapi/pooltool/index.rst#pooltool.Cue). + +# %% trusted=true +cue = pt.Cue(cue_ball_id="cue") + +# %% [markdown] +# Finally, we'll need to wrap these objects up into a [System](../autoapi/pooltool/index.rst#pooltool.System). We'll call this our system *template*, with the intention of reusing it for many different shots. + +# %% trusted=true +system_template = pt.System( + table=table, + cue=cue, + balls=(cue_ball, obj_ball), +) + +# %% [markdown] +# Let's set up a shot by aiming at the object ball with a cut angle of 30 degrees. There is a small clash in terminology here, because in pooltool, `phi` is an angle defined with respect to the table, not the cut angle: +# +# +# +# So in the function call below, `pt.aim.at_ball(system, "obj", cut=30)` returns the angle `phi` that the cue ball should be directed at such that a cut angle of 30 degrees with the object ball is achieved. + +# %% trusted=true +# Creates a deep copy of the template +system = system_template.copy() + +phi = pt.aim.at_ball(system, "obj", cut=30) +system.cue.set_state(V0=3, phi=phi, b=0.4) + +# %% [markdown] +# Now, we [simulate](../autoapi/pooltool/index.rst#pooltool.simulate) the shot and then [continuize](../autoapi/pooltool/evolution/continuize/index.html#pooltool.evolution.continuize.continuize) it to store ball state data (like coordinates) in $10\text{ms}$ timestep intervals. + +# %% trusted=true +pt.simulate(system, inplace=True) +pt.continuize(system, dt=0.01, inplace=True) + +print(f"System simulated: {system.simulated}") + +# %% [markdown] +# ## Visualizing the collision + +# %% [markdown] +# If you have a graphics card, you can immediately visualize this shot in 3D with +# +# ```python +# gui = pt.ShotViewer() +# gui.show(system) +# ``` +# +# Since that can't be embedded into the documentation, we'll instead plot the trajectory of the cue ball and object ball by accessing ther historical states. + +# %% trusted=true +cue_ball = system.balls["cue"] +obj_ball = system.balls["obj"] +cue_history = cue_ball.history_cts +obj_history = obj_ball.history_cts +type(cue_history) + +# %% [markdown] +# The [BallHistory](../autoapi/pooltool/objects/index.rst#pooltool.objects.BallHistory) holds the ball's historical states, each stored as a [BallState](../autoapi/pooltool/objects/index.rst#pooltool.objects.BallState) object. Each attribute of the ball states can be concatenated into numpy arrays with the [BallHistory.vectorize](../autoapi/pooltool/objects/index.rst#pooltool.objects.BallHistory.vectorize) method. + +# %% trusted=true +rvw_cue, s_cue, t_cue = cue_history.vectorize() +rvw_obj, s_obj, t_obj = obj_history.vectorize() + +print(rvw_cue.shape) +print(s_cue.shape) +print(t_cue.shape) + +# %% [markdown] +# We can grab the xy-coordinates from the `rvw` array by with the following. + +# %% trusted=true +coords_cue = rvw_cue[:, 0, :2] +coords_obj = rvw_obj[:, 0, :2] +coords_cue.shape + +# %% trusted=true editable=true slideshow={"slide_type": ""} tags=[] +import plotly.graph_objects as go +import plotly.io as pio + +pio.renderers.default = "sphinx_gallery" + +fig = go.Figure() +fig.add_trace( + go.Scatter(x=coords_cue[:, 0], y=coords_cue[:, 1], mode="lines", name="cue") +) +fig.add_trace( + go.Scatter(x=coords_obj[:, 0], y=coords_obj[:, 1], mode="lines", name="obj") +) +fig.update_layout( + title="Ball trajectories", + xaxis_title="X [m]", + yaxis_title="Y [m]", + yaxis_scaleanchor="x", + yaxis_scaleratio=1, + width=600, + template="presentation", +) + +fig.show() + + +# %% [markdown] +# ## Calculating the carom angle +# +# One could calculate the carom angle for the above trajectory by manually splicing the trajectory coordinates of the cue ball and determining ball direction by comparing temporally adjacent coordinates. However, pooltool has much more precise methods for dissecting shot dynamics. +# +# As mentioned before, the carom angle is the angle between the cue ball velocity right before collision, and the cue ball velocity post-collision, once the ball has stopped sliding on the cloth. Hidden somewhere in the system **event list** one can find the events corresponding to these precise moments in time: + +# %% trusted=true +system.events[:6] + +# %% [markdown] +# Programatically, we can pick out these two events of interest with event selection syntax. +# +# Since there is only one ball-ball collision, it's easy to select with [filter_type](../autoapi/pooltool/events/index.rst#pooltool.events.filter_type): + +# %% trusted=true +collision = pt.events.filter_type(system.events, pt.EventType.BALL_BALL)[0] +collision + +# %% [markdown] +# To get the event when the cue ball stops sliding, we can similarly try filtering by the sliding to rolling transition event: + +# %% trusted=true +pt.events.filter_type(system.events, pt.EventType.SLIDING_ROLLING) + +# %% [markdown] +# But there are many sliding to rolling transition events, and to make matters worse, they are shared by both the cue ball and the object ball. What we need is the **first** **sliding to rolling** transition that the **cue ball** undergoes **after** the **ball-ball** collision. We can achieve this multi-criteria query with [filter_events](../autoapi/pooltool/events/index.rst#pooltool.events.filter_events): + +# %% trusted=true +transition = pt.events.filter_events( + system.events, + pt.events.by_time(t=collision.time, after=True), + pt.events.by_ball("cue"), + pt.events.by_type(pt.EventType.SLIDING_ROLLING), +)[0] +transition + +# %% [markdown] +# Now, we can dive into these two events and pull out the cue ball velocities we need to calculate the carom angle. + +# %% trusted=true +# Velocity prior to impact +for agent in collision.agents: + if agent.id == "cue": + # agent.initial is a copy of the Ball before resolving the collision + velocity_initial = agent.initial.state.rvw[1, :2] + +# Velocity post sliding +# We choose `final` here for posterity, but the velocity is the same both before and after resolving the transition. +velocity_final = transition.agents[0].final.state.rvw[1, :2] + +carom_angle = pt.ptmath.utils.angle_between_vectors(velocity_final, velocity_initial) + +print(f"The carom angle is {carom_angle:.1f} degrees") + + +# %% [markdown] +# ## Carom angle as a function of cut angle + +# %% [markdown] +# We calculated the carom angle for a single cut angle, 30 degrees. Let's write a function called `get_carom_angle` so we can do that repeatedly for different cut angles. + + +# %% trusted=true +def get_carom_angle(system: pt.System) -> float: + assert system.simulated + + collision = pt.events.filter_type(system.events, pt.EventType.BALL_BALL)[0] + transition = pt.events.filter_events( + system.events, + pt.events.by_time(t=collision.time, after=True), + pt.events.by_ball("cue"), + pt.events.by_type(pt.EventType.SLIDING_ROLLING), + )[0] + + velocity_final = transition.agents[0].final.state.rvw[1, :2] + for agent in collision.agents: + if agent.id == "cue": + velocity_initial = agent.initial.state.rvw[1, :2] + + return pt.ptmath.utils.angle_between_vectors(velocity_final, velocity_initial) + + +# %% [markdown] +# `get_carom_angle` assumes the passed system has already been simulated, so we'll need another function to take care of that. We'll cue stick speed and cut angle as parameters. + + +# %% trusted=true +def simulate_experiment(V0: float, cut_angle: float) -> pt.System: + system = system_template.copy() + phi = pt.aim.at_ball(system, "obj", cut=cut_angle) + system.cue.set_state(V0=V0, phi=phi, b=0.4) + pt.simulate(system, inplace=True) + return system + + +# %% [markdown] +# We'll also want the ball hit fraction: + + +# %% trusted=true +import numpy as np + +def get_ball_hit_fraction(cut_angle: float) -> float: + return 1 - np.sin(cut_angle * np.pi / 180) + + +# %% [markdown] +# With these functions, we are ready to simulate how carom angle varies as a function of cut angle. + +# %% trusted=true +import pandas as pd + +data = { + "phi": [], + "f": [], + "theta": [], +} + +V0 = 2.5 + +for cut_angle in np.linspace(0, 88, 50): + system = simulate_experiment(V0, cut_angle) + data["theta"].append(get_carom_angle(system)) + data["f"].append(get_ball_hit_fraction(cut_angle)) + data["phi"].append(cut_angle) + +frame = pd.DataFrame(data) +frame.head(10) + +# %% [markdown] +# From this dataframe we can make some plots. On top of the ball-hit fraction, plot, I'll create a box between a $1/4$ ball hit and a $3/4$ ball hit, since this is the carom angle range that the 30-degree rule is defined with respect to. + +# %% trusted=true editable=true slideshow={"slide_type": ""} tags=["nbsphinx-thumbnail"] +import matplotlib.pyplot as plt + +x_min = 0.25 +x_max = 0.75 +y_min = frame.loc[(frame.f >= x_min) & (frame.f <= x_max), "theta"].min() +y_max = frame.loc[(frame.f >= x_min) & (frame.f <= x_max), "theta"].max() +box_data_x = [x_min, x_min, x_max, x_max, x_min] +box_data_y = [y_min, y_max, y_max, y_min, y_min] + +fig, ax = plt.subplots() +ax.plot(box_data_x, box_data_y, linestyle='--', color='gray', label='30-degree range') +ax.scatter(frame['f'], frame['theta'], color='#1f77b4', label='Simulation') +ax.set_title('Carom Angle vs Ball Hit Fraction', fontsize=20) +ax.set_xlabel('Ball Hit Fraction (f)', fontsize=16) +ax.set_ylabel('Carom Angle (theta, degrees)', fontsize=16) +ax.tick_params(axis='both', which='major', labelsize=14) +ax.legend(fontsize=14) +plt.show() + +# %% [markdown] +# Between a $1/4$ and $3/4$ ball hit, there is a relative invariance in the carom angle, with a range of around 6 degrees. +# +# For your reference, here is the same plot but with cut angle $\phi$ as the x-axis: + +# %% trusted=true +fig, ax = plt.subplots() +ax.scatter(frame['phi'], frame['theta'], color='#1f77b4') +ax.set_title('Carom Angle vs Cut Angle', fontsize=20) +ax.set_xlabel('Cut Angle (phi)', fontsize=16) +ax.set_ylabel('Carom Angle (theta, degrees)', fontsize=16) +ax.tick_params(axis='both', which='major', labelsize=14) +plt.show() + + +# %% [markdown] +# ## Comparison to theory +# +# Under the assumption of a perfectly elastic and frictionless ball-ball collision, Dr. Dave has calculated the theoretical carom angle $\theta$ to be +# +# $$ +# \theta_{\text{ideal}}(\phi) = \arctan{\frac{\sin\phi \times \cos\phi}{\sin^2\phi + \frac{2}{5}}} +# $$ +# +# *(source: [https://billiards.colostate.edu/technical_proofs/new/TP_B-13.pdf](https://billiards.colostate.edu/technical_proofs/new/TP_B-13.pdf))* +# +# Since pooltool's baseline physics engine makes the same assumptions, we should expect the results to be the same. Let's directly compare: + + +# %% trusted=true +def get_theoretical_carom_angle(phi) -> float: + return np.atan2(np.sin(phi) * np.cos(phi), (np.sin(phi) ** 2 + 2 / 5)) + +phi_theory = np.linspace(0, np.pi / 2, 500) +theta_theory = get_theoretical_carom_angle(phi_theory) + +phi_theory *= 180 / np.pi +theta_theory *= 180 / np.pi +f_theory = get_ball_hit_fraction(phi_theory) + +fig = go.Figure() +fig.add_trace( + go.Scatter( + x=box_data_x, + y=box_data_y, + mode="lines", + name="30-degree range", + line=dict(dash="dash", color="gray"), + ) +) +fig.add_trace( + go.Scatter( + x=frame["f"], + y=frame["theta"], + mode="markers", + name="Simulation", + marker=dict(color="#1f77b4"), + ) +) +fig.add_trace(go.Scatter(x=f_theory, y=theta_theory, mode="lines", name="Theory")) +fig.update_layout( + title="Carom Angle vs Ball Hit Fraction", + xaxis_title="Ball Hit Fraction (f)", + yaxis_title="Carom Angle (theta, degrees)", + template="presentation", +) +fig.show() + +# %% [markdown] +# A perfect match. + +# %% [markdown] +# ## Impact speed independence +# +# Interestingly, the carom angle is independent of the speed: + +# %% trusted=true +for V0 in np.linspace(1, 4, 20): + system = simulate_experiment(V0, 30) + carom_angle = get_carom_angle(system) + print(f"Carom angle for V0={V0:2f} is {carom_angle:4f}") + +# %% [markdown] +# This doesn't mean that the trajectories are the same though. Here are the trajectories: + +# %% trusted=true +import numpy as np +import plotly.graph_objects as go + + +def get_coordinates(system: pt.System): + rvw, s, t = system.balls["cue"].history_cts.vectorize() + xy = rvw[:, 0, :2] + + return xy, s, t + + +fig = go.Figure() + +for V0 in np.linspace(1, 3, 6): + system = simulate_experiment(V0, 30) + pt.continuize(system, dt=0.03, inplace=True) + rvw, s, t = system.balls["cue"].history_cts.vectorize() + xy = rvw[:, 0, :2] + + fig.add_trace( + go.Scatter( + x=xy[:, 0], + y=xy[:, 1], + mode="lines", + name=f"Speed {V0}", + showlegend=True, + ) + ) + +fig.update_layout( + title="Ball trajectories", + xaxis_title="X [m]", + yaxis_title="Y [m]", + yaxis_scaleanchor="x", + yaxis_scaleratio=1, + width=600, + xaxis=dict(range=[1.5, 4.5]), + yaxis=dict(range=[1.5, 4.5]), + template="presentation", +) +fig.show() + +# %% [markdown] +# Harder shots follow the *tangent line* (aka the line perpendicular to the line connected the balls' centers during contact) for longer, but they all converge to the same outgoing angle. diff --git a/docs/examples/README.md b/docs/examples/README.md new file mode 100644 index 00000000..ca403696 --- /dev/null +++ b/docs/examples/README.md @@ -0,0 +1,7 @@ +# Summary + +All the examples are jupyter notebooks represented in percent format: https://jupytext.readthedocs.io/en/latest/formats-scripts.html#the-percent-format + +They can be opened as notebooks by running `jupyter lab`, right clicking the file, and selecting "Open as notebook" + +If you have a notebook, and would like to add it to the documentation, convert it to the percent format `jupytext --sync `. It must be suffixed with `.pct.py`. Then open `index.md` and add it. diff --git a/docs/examples/assets/30_degree_rule/diagram1.png b/docs/examples/assets/30_degree_rule/diagram1.png new file mode 100644 index 00000000..d9cfaab1 Binary files /dev/null and b/docs/examples/assets/30_degree_rule/diagram1.png differ diff --git a/docs/examples/index.md b/docs/examples/index.md new file mode 100644 index 00000000..022fb097 --- /dev/null +++ b/docs/examples/index.md @@ -0,0 +1,7 @@ +# Examples + +```{eval-rst} +.. nbgallery:: + 30_degree_rule +``` + diff --git a/docs/getting_started/script.md b/docs/getting_started/script.md index f7dd45a3..61670dca 100644 --- a/docs/getting_started/script.md +++ b/docs/getting_started/script.md @@ -90,5 +90,5 @@ interface.show(shot) ## Next -Obviously this script is just the beginning. Pooltool offers much more than this, which means you have much more to learn. From here, I suggest you check out the [Resources](resources) page and dive into whatever topic interests you. +Obviously this script is just the beginning. Pooltool offers much more than this, which means you have much more to learn. From here, I suggest you check out the [Examples](examples) page and dive into whatever topic interests you. diff --git a/docs/index.md b/docs/index.md index aadb4d6c..bdceb33c 100644 --- a/docs/index.md +++ b/docs/index.md @@ -11,6 +11,7 @@ Its core design principles focus on speed, flexibility, and the ease of visualiz self getting_started/index + examples/index resources/index autoapi/index license/index diff --git a/docs/resources/index.md b/docs/resources/index.md index 22225577..2b287323 100644 --- a/docs/resources/index.md +++ b/docs/resources/index.md @@ -1,12 +1,8 @@ # Resources -This is the landing page for all the exposition-style documentation available for pooltool. +This is the landing page for resources you may find useful. -:::{note} -All current development bandwidth is being spent on documenting the API. Once that is complete, resource documents will start to take shape (and priority). Until then, this page will remain somewhat bare. If what you're looking for isn't here, hopefully you can find it in the [API Reference](#pooltool) or the [blog](https://ekiefl.github.io/projects/pooltool/). -::: - -## Contents +## Deep dives ```{eval-rst} .. toctree:: @@ -16,3 +12,7 @@ All current development bandwidth is being spent on documenting the API. Once th table_specs custom_physics ``` + +## The blog + +A lot of the early work done on pooltool was written in tandem with a blog you can find [here](https://ekiefl.github.io/projects/pooltool/). diff --git a/poetry.lock b/poetry.lock index 53c3411b..ef25801b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -11,15 +11,124 @@ files = [ {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"}, ] +[[package]] +name = "anyio" +version = "4.4.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < 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(>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "04a6d5859d7eb66caf0b2099295895f644f458f39840d25fb97c1b3e77ad44a4" +content-hash = "a94a3f69c36fd0d980a7ac8fe7e812b52b0a1f115ed2a3e7bcfef97723229357" diff --git a/pooltool/ani/modes/calculate.py b/pooltool/ani/modes/calculate.py index 0e0aa83c..e16fe9dd 100644 --- a/pooltool/ani/modes/calculate.py +++ b/pooltool/ani/modes/calculate.py @@ -9,9 +9,20 @@ from pooltool.ani.modes.datatypes import BaseMode, Mode from pooltool.ani.mouse import MouseMode, mouse from pooltool.evolution import simulate +from pooltool.physics.engine import PhysicsEngine +from pooltool.physics.resolve.stick_ball.instantaneous_point import InstantaneousPoint from pooltool.system.datatypes import System, multisystem +def _interactive_physics_engine() -> PhysicsEngine: + engine = PhysicsEngine() + engine.resolver.stick_ball = InstantaneousPoint(english_throttle=0.5) + return engine + + +ENGINE = _interactive_physics_engine() + + class CalculateMode(BaseMode): name = Mode.calculate keymap = { @@ -87,6 +98,7 @@ def run_simulation(self, system: System, task): system, continuous=True, inplace=True, + engine=ENGINE, ) tasks.remove("run_simulation") diff --git a/pooltool/constants.py b/pooltool/constants.py index 9792340f..b92b4fb3 100644 --- a/pooltool/constants.py +++ b/pooltool/constants.py @@ -56,5 +56,3 @@ on_table = {stationary, spinning, sliding, rolling} nontranslating = {stationary, spinning, pocketed} energetic = {spinning, sliding, rolling} - -english_fraction = 0.5000001 diff --git a/pooltool/objects/ball/datatypes.py b/pooltool/objects/ball/datatypes.py index b2ab82ee..417c6ce7 100644 --- a/pooltool/objects/ball/datatypes.py +++ b/pooltool/objects/ball/datatypes.py @@ -237,7 +237,7 @@ def vectorize( plt.show() See Also: - - :meth:`vectorize` + - :meth:`from_vectorization` """ if self.empty: return None @@ -284,7 +284,7 @@ def from_vectorization( True See Also: - - :meth:`from_vectorization` + - :meth:`vectorize` """ history = BallHistory() @@ -365,7 +365,7 @@ class Ball: :math:`t_{final}` densely sampled with respect to time. See Also: - - See :func:`pooltool.evolution.event_based.continuize.continuize` for a + - See :func:`pooltool.evolution.continuize.continuize` for a details about continuizing a simulated system. - See the *Important* section in :class:`Ball` for a description of the role of ``history_cts`` during simulation. diff --git a/pooltool/physics/resolve/resolver.py b/pooltool/physics/resolve/resolver.py index bbd35091..242c10a9 100644 --- a/pooltool/physics/resolve/resolver.py +++ b/pooltool/physics/resolve/resolver.py @@ -3,6 +3,7 @@ from __future__ import annotations from pathlib import Path +from typing import Optional import attrs @@ -39,10 +40,16 @@ from pooltool.physics.resolve.types import ModelArgs from pooltool.serialize import Pathish, conversion from pooltool.system.datatypes import System +from pooltool.terminal import Run RESOLVER_CONFIG_PATH = pooltool.user_config.PHYSICS_DIR / "resolver.yaml" """The location of the resolver config path YAML""" +VERSION: int = 1 + + +run = Run() + @attrs.define class ResolverConfig: @@ -66,6 +73,8 @@ class ResolverConfig: transition: BallTransitionModel transition_params: ModelArgs + version: Optional[int] = None + def save(self, path: Pathish) -> Path: path = Path(path) conversion.unstructure_to(self, path) @@ -79,7 +88,16 @@ def load(cls, path: Pathish) -> ResolverConfig: def default(cls) -> ResolverConfig: """Load ~/.config/pooltool/physics/resolver.yaml if exists, create otherwise""" if RESOLVER_CONFIG_PATH.exists(): - return cls.load(RESOLVER_CONFIG_PATH) + config = cls.load(RESOLVER_CONFIG_PATH) + + if config.version == VERSION: + return config + else: + run.info_single( + f"{RESOLVER_CONFIG_PATH} is has version {config.version}, which is not up to " + f"date with the most current version: {VERSION}. It will be replaced with the " + f"default." + ) config = cls( ball_ball=BallBallModel.FRICTIONLESS_ELASTIC, @@ -91,9 +109,10 @@ def default(cls) -> ResolverConfig: ball_pocket=BallPocketModel.CANONICAL, ball_pocket_params={}, stick_ball=StickBallModel.INSTANTANEOUS_POINT, - stick_ball_params={"throttle_english": True}, + stick_ball_params={"english_throttle": 1.0}, transition=BallTransitionModel.CANONICAL, transition_params={}, + version=VERSION, ) config.save(RESOLVER_CONFIG_PATH) diff --git a/pooltool/physics/resolve/stick_ball/__init__.py b/pooltool/physics/resolve/stick_ball/__init__.py index b81abc50..a2c13d19 100644 --- a/pooltool/physics/resolve/stick_ball/__init__.py +++ b/pooltool/physics/resolve/stick_ball/__init__.py @@ -40,6 +40,6 @@ def get_stick_ball_model( protocol. """ if model is None: - return InstantaneousPoint(throttle_english=True) + return InstantaneousPoint() return _stick_ball_models[model](**params) diff --git a/pooltool/physics/resolve/stick_ball/instantaneous_point/__init__.py b/pooltool/physics/resolve/stick_ball/instantaneous_point/__init__.py index d46e682c..324b188e 100644 --- a/pooltool/physics/resolve/stick_ball/instantaneous_point/__init__.py +++ b/pooltool/physics/resolve/stick_ball/instantaneous_point/__init__.py @@ -10,7 +10,7 @@ from pooltool.physics.resolve.stick_ball.core import CoreStickBallCollision -def cue_strike(m, M, R, V0, phi, theta, a, b, throttle_english: bool): +def cue_strike(m, M, R, V0, phi, theta, a, b, english_throttle: float): """Strike a ball .. code:: @@ -57,18 +57,18 @@ def cue_strike(m, M, R, V0, phi, theta, a, b, throttle_english: bool): How much vertical english should be put on? -1 being bottom-most side of ball, +1 being topmost side of ball - throttle_english: - This function creates unrealistic magnitudes of spin. To compensate, this flag - exists. If True, a 'fake' factor is added that scales down the passed a and b - values, by an amount defined by pooltool.english_fraction + english_throttle: + This modulates the amount of spin that is generated from a cue strike, where + english_throttle < 1 produces less spin than the model's default, and + english_throttle > 1 produces more. In the interactive interface, + english_throttle of 0.5 produces somewhat realistic seeming spin. """ a *= R b *= R - if throttle_english: - a *= const.english_fraction - b *= const.english_fraction + a *= english_throttle + b *= english_throttle phi *= np.pi / 180 theta *= np.pi / 180 @@ -121,7 +121,7 @@ class InstantaneousPoint(CoreStickBallCollision): we can in good faith assume this is their own equation. """ - throttle_english: bool + english_throttle: float = 1.0 def solve(self, cue: Cue, ball: Ball) -> Tuple[Cue, Ball]: v, w = cue_strike( @@ -133,7 +133,7 @@ def solve(self, cue: Cue, ball: Ball) -> Tuple[Cue, Ball]: cue.theta, cue.a, cue.b, - throttle_english=self.throttle_english, + english_throttle=self.english_throttle, ) rvw = np.array([ball.state.rvw[0], v, w]) diff --git a/pooltool/ptmath/utils.py b/pooltool/ptmath/utils.py index 7ddc8eb8..5583efd8 100644 --- a/pooltool/ptmath/utils.py +++ b/pooltool/ptmath/utils.py @@ -63,7 +63,7 @@ def convert_2D_to_3D(array: NDArray[np.float64]) -> NDArray[np.float64]: def angle_between_vectors(v1: NDArray[np.float64], v2: NDArray[np.float64]) -> float: """Returns angles between [-180, 180]""" - angle = np.math.atan2(np.linalg.det([v1, v2]), np.dot(v1, v2)) # type: ignore + angle = np.atan2(np.linalg.det([v1, v2]), np.dot(v1, v2)) # type: ignore return degrees(angle) diff --git a/pooltool/system/datatypes.py b/pooltool/system/datatypes.py index d5a6c120..22947e1e 100644 --- a/pooltool/system/datatypes.py +++ b/pooltool/system/datatypes.py @@ -2,7 +2,7 @@ from __future__ import annotations -from typing import Dict, Iterator, List, Optional +from typing import Any, Dict, Iterator, List, Optional import numpy as np from attrs import define, field @@ -18,6 +18,13 @@ from pooltool.serialize.serializers import Pathish +def _convert_balls(balls: Any) -> Dict[str, Ball]: + if isinstance(balls, dict): + return balls + + return {ball.id: ball for ball in balls} + + @define class System: """A class representing the billiards system. @@ -49,6 +56,10 @@ class System: Warning: Each key must match each value's ``id`` (`e.g.` ``{"2": Ball(id="1")}`` is invalid). + + Note: + If, during construction, a sequence (`e.g.` list, tuple, etc.) of balls + is passed instead of a dictionary, it will be converted to a dictionary. t: The elapsed simulation time. If the system is in the process of being simulated, ``t`` is updated to be the number of seconds the system has @@ -96,7 +107,7 @@ class System: cue: Cue = field() table: Table = field() - balls: Dict[str, Ball] = field() + balls: Dict[str, Ball] = field(converter=_convert_balls) t: float = field(default=0.0) events: List[Event] = field(factory=list) diff --git a/pyproject.toml b/pyproject.toml index 112b4fa2..16ce4986 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -111,11 +111,17 @@ pyright = ">=1.1.373" ruff = ">=0.5.5" # Diagnostics psutil = ">=6.0.0" -colored = ">=2.2.4" # TODO switch to something ubiquitous +colored = ">=2.2.4" # TODO switch to something ubiquitous (like rich) ipython = ">=8.18.1" +ipdb = "^0.13.13" +# Vignettes +jupytext = ">=1.15.0" # First release compatible with JupyterLab 4.X +jupyterlab = ">=4.0.0" +pandas = ">=2" +plotly = ">=5" +matplotlib = ">=3.8" # Publishing poetry-dynamic-versioning = {extras = ["plugin"], version = ">=1.4.0"} -ipdb = "^0.13.13" [tool.poetry.group.docs] optional = true @@ -127,9 +133,17 @@ furo = ">=2024.5.6" myst-parser = ">=3.0.1" sphinx-autoapi = ">=3.1.2" sphinx-tabs = ">=3.4.5" +nbsphinx = "^0.9.5" + +[tool.jupytext] +formats = "ipynb,py:percent" +notebook_metadata_filter="all" [tool.ruff] line-length = 88 +exclude = [ + "docs", +] [tool.ruff.lint] extend-select = ["I"] diff --git a/sandbox/30_degree_rule.py b/sandbox/30_degree_rule.py deleted file mode 100644 index bd6b68a4..00000000 --- a/sandbox/30_degree_rule.py +++ /dev/null @@ -1,104 +0,0 @@ -"""This script validates the 30-degree rule proposed by Dr. Dave Billiards - -For more information about the 30-degree rule, see https://billiards.colostate.edu/faq/30-90-rules/30-degree-rule/ -""" - -import numpy as np -import pandas as pd -import plotly.express as px - -import pooltool as pt -import pooltool.constants as constants - - -def _assert_cue_rolling_at_impact(system: pt.System) -> None: - event = pt.events.filter_type(system.events, pt.EventType.BALL_BALL)[0] - for agent in event.agents: - if agent.id != "cue": - continue - assert agent.initial.state.s == constants.rolling, "Cue ball isn't rolling!" - - -def get_deflection_system(cut: float, V0: float = 2, b: float = 0.2) -> pt.System: - ballset = pt.objects.get_ballset("pooltool_pocket") - cue_ball = pt.Ball.create("cue", xy=(50, 50), ballset=ballset) - obj_ball = pt.Ball.create("2", xy=(49, 50), ballset=ballset) - cue = pt.Cue(cue_ball_id="cue") - table = pt.Table.from_table_specs( - specs=pt.objects.BilliardTableSpecs( - l=100, - w=100, - ) - ) - system = pt.System( - cue=cue, - table=table, - balls={"cue": cue_ball, "2": obj_ball}, - ) - system.strike(V0=V0, phi=pt.aim.at_ball(system, "2", cut=cut), b=b) - - # Evolve the shot - _ = pt.simulate(system, inplace=True) - - # The cue ball must be rolling at impact - _assert_cue_rolling_at_impact(system) - - return system - - -def get_deflection_angle(cut: float, V0: float = 2, b: float = 0.2) -> float: - system = get_deflection_system(cut=cut, V0=V0, b=b) - - # Get the ball-ball collision - collision = pt.events.filter_type(system.events, pt.EventType.BALL_BALL)[0] - - # Get the velocity of the cue right before impact - for agent in collision.agents: - if agent.id == "cue": - break - cue_velocity_pre_collision = agent.initial.state.rvw[1] - - # Get event when object ball transitions from sliding to rolling - sliding_to_rolling = pt.events.filter_events( - system.events, - pt.events.by_time(collision.time, after=True), - pt.events.by_ball("cue"), - pt.events.by_type(pt.EventType.SLIDING_ROLLING), - )[0] - - # Get the velocity of the cue after it is done sliding - cue_velocity_post_slide = sliding_to_rolling.agents[0].final.state.rvw[1] - - return np.rad2deg( - np.arccos( - np.dot( - pt.ptmath.unit_vector(cue_velocity_pre_collision), - pt.ptmath.unit_vector(cue_velocity_post_slide), - ) - ) - ) - - -if __name__ == "__main__": - cut_angles = np.linspace(5, 85, 50) - V0s = np.arange(0.5, 3, 0.2).round(2) - deflection_angles_df = pd.DataFrame( - [ - [ - 1 - np.sin(np.deg2rad(cut)), - get_deflection_angle(cut=cut, V0=V0, b=0.8), - V0, - ] - for cut in cut_angles - for V0 in V0s - ], - columns=["fullness", "deflection_angle", "v0"], - ) - fig = px.line( - deflection_angles_df, - x="fullness", - y="deflection_angle", - color="v0", - title="Deflection angles", - ) - fig.show()