refactor diagram files
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.gitignore
vendored
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.gitignore
vendored
@ -3,3 +3,4 @@ _site/
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/.quarto/
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index_files/
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.jupyter_cache/
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__pycache__/
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@ -1,65 +0,0 @@
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import numpy as np
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import matplotlib.pyplot as plt
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class Carry:
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def __init__(self, arr):
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if not isinstance(arr, np.ndarray):
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arr = np.array(arr)
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overflow = None
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over_pos = None
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coeff_sum = 0
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#find largest carry
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for i, row in enumerate(arr):
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for j, val in enumerate(row):
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coeff_sum += val
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if val > 0:
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if overflow is not None:
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raise ValueError("array supplied has more than one overflow digit")
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overflow = val
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over_pos = (i, j)
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if coeff_sum < 0:
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raise ValueError("sum of coefficients too small")
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self._arr = arr
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self.over_pos = over_pos
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self.overflow = overflow
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def _update(self, base, update_tuples, zerowall):
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#dim_x, dim_y = base.shape
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pos_x, pos_y = self.over_pos
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for (x,y) in update_tuples:
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idx = base[(x,y)]
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dec = (idx // self.overflow) # - 1
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wall = zerowall.copy()
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sh_x, sh_y = self._arr.shape
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x -= pos_x
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y -= pos_y
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wall[x:x + sh_x, y:y + sh_y] = dec * self._arr
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base -= wall
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def update(self, base):
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zerowall = np.zeros(base.shape, dtype=base.dtype)
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while True:
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update_tuples = list(zip(*np.where(base >= self.overflow)))
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if not update_tuples:
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break
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self._update(base, update_tuples, zerowall)
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def times(self, base, val, center=(0,0)):
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#perform multiplication using the carry
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base[center] *= val
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self.update(base)
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return base
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def add(self, base, val=1, center=(0,0)):
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base[center] += val
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self.update(base)
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return base
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laplace = Carry(np.array([[0,-1,0],[-1,4,-1],[0,-1,0]]))
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#test = np.zeros((100, 100))
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#test[50,50] = 4
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0
polycount/cell1/carry2d/__init__.py
Normal file
0
polycount/cell1/carry2d/__init__.py
Normal file
83
polycount/cell1/carry2d/anim.py
Normal file
83
polycount/cell1/carry2d/anim.py
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@ -0,0 +1,83 @@
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from functools import partial
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from typing import Callable, Literal
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from matplotlib import animation, pyplot as plt
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import numpy as np
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from .carry import Carry
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writer = animation.writers["ffmpeg"](fps=15, metadata={"artist": "Me"})
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def times(base: np.ndarray, carry: Carry, val):
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base *= val
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carry.apply(base)
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return base
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def add(base: np.ndarray, carry: Carry, val=1, center=(0, 0)):
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base[center] += val
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carry.apply(base)
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return base
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def animate(
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fig, frames: list[int] | None, **kwargs
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) -> Callable[..., animation.FuncAnimation]:
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# Why isn't this how the function is exposed by default?
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return lambda func: animation.FuncAnimation(fig, func, frames, **kwargs)
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def animate_carry_count(
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carry: Carry,
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dims: int = 100,
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operation: Literal["add", "multiply"] = "add",
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op_val=1,
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center: tuple[int, int] | None = None,
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# Animation parameters
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frames: list[int] | None = None,
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interval=200,
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) -> animation.FuncAnimation:
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if center is None:
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if carry.over_pos != (0, 0):
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center = (dims // 2, dims // 2)
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else:
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center = (0, 0)
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if operation == "add":
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next_func = partial(add, carry=carry, val=op_val, center=center)
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elif operation == "multiply":
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if op_val == 1:
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raise ValueError(f"too small value {op_val} for repeated multiplication")
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next_func = partial(times, carry=carry, val=op_val)
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else:
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raise ValueError(f"Cannot use {repr(operation)} for animation")
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expansion = np.zeros((dims, dims))
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expansion[center] = 1
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title = [1]
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fig = plt.gcf()
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image = plt.imshow(
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expansion,
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extent=[-center[0], dims - center[0], dims - center[1], -center[1]], # type: ignore
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)
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def init():
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plt.title(f"{title[0]}")
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image.set_clim(0, carry.overflow - 1)
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fig.tight_layout()
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@animate(fig, frames, interval=interval, init_func=init)
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def ret(_):
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plt.title(f"{title[0]}")
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image.set_data(expansion)
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fig.tight_layout()
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next_func(expansion)
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if operation == "add":
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title[0] += op_val
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else:
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title[0] *= op_val
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return ret
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81
polycount/cell1/carry2d/carry.py
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81
polycount/cell1/carry2d/carry.py
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@ -0,0 +1,81 @@
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from typing import Sequence
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import numpy as np
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def _validate_carry(arr: np.ndarray) -> tuple[int, tuple[int, int]]:
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"""
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Ensure that `arr` is interpretable as an explicit carry rule.
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See `Carry` for more information.
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"""
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overflow = None
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over_pos = None
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coeff_sum = 0
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# find largest digit for carry
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for i, row in enumerate(arr):
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for j, val in enumerate(row):
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coeff_sum += val
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if val < 0:
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if overflow is not None:
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raise ValueError("Array supplied has more than one overflow digit")
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overflow = -val
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over_pos = (i, j)
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if coeff_sum < 0:
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raise ValueError("Sum of coefficients too small")
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if overflow is None or over_pos is None:
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raise ValueError("No dominating term supplied")
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return overflow, over_pos
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class Carry:
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"""
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Two-dimensional carry rule.
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This is an (integer) Numpy array which is greedily "applied" to another (integer) Numpy array to
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rid it of integers.
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Only "explicit" carries are allowed -- these are carries that are all negative except for a
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single positive term, such that the sum of all terms is non-negative.
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For example, the Laplacian kernel, as a carry rule is:
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`laplace = Carry([[0,1,0],[1,-4,1],[0,1,0]])`
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Interpreted as a carry rule, this spreads integers above 4 into their neighboring cells.
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"""
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def __init__(self, arr: np.ndarray | Sequence):
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if not isinstance(arr, np.ndarray):
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arr = np.array(arr)
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overflow, over_pos = _validate_carry(arr)
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self._arr: np.ndarray = arr
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self.over_pos: tuple[int, int] = over_pos
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self.overflow: int = overflow
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def _apply(
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self,
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base: np.ndarray,
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update_tuples: list[tuple[int, int]],
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zerowall: np.ndarray,
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):
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"""Apply the carry at the locations in `base` specified by `update_tuples`."""
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pos_x, pos_y = self.over_pos
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for x, y in update_tuples:
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idx = base[(x, y)]
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dec = idx // self.overflow # - 1
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wall = zerowall.copy()
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sh_x, sh_y = self._arr.shape
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x -= pos_x
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y -= pos_y
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wall[x : x + sh_x, y : y + sh_y] = dec * self._arr
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base += wall
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def apply(self, base: np.ndarray) -> np.ndarray:
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"""Update an expansion `base` according to this carry."""
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zerowall = np.zeros(base.shape, dtype=base.dtype)
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while update_tuples := list(zip(*np.where(base >= self.overflow))):
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self._apply(base, update_tuples, zerowall)
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return base
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88
polycount/cell1/carry2d/curve.py
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88
polycount/cell1/carry2d/curve.py
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@ -0,0 +1,88 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from sympy.abc import x, y
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from sympy.plotting import plot_implicit
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from .anim import animate
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def _first_nonzero(arr: np.ndarray, axis: int):
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mask = arr!=0
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return min(np.where(mask.any(axis=axis), mask.argmax(axis=axis), arr.shape[axis] + 1))
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def latex_polynumber(
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arr: np.ndarray,
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center: tuple[int, int] | None = None,
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show_zero: bool = False
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):
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upper_left = _first_nonzero(arr, 0), _first_nonzero(arr, 1)
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lower_right = (
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len(arr) - _first_nonzero(np.flip(arr, 0), 0) - 1,
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len(arr[1]) - _first_nonzero(np.flip(arr, 1), 1) - 1
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)
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center_left, center_top = center or (0, 0)
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if center is not None:
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# this does not need offset, since we iterate over this range
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center_top = center[0]
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# but this does for the array environment argument
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center_left = center[1] - upper_left[1]
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num_columns = lower_right[1] - upper_left[1]
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column_layout = ("c" * center_left) + "|" + ("c" * (num_columns - center_left))
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# build array output
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ret = "\\begin{array}{" + column_layout + "}"
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for i in range(upper_left[0], lower_right[0] + 1):
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if i == center_top:
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ret += " \\hline "
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ret += " & ".join([
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str(arr[i,j] if arr[i,j] != 0 or show_zero else "")
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for j in range(upper_left[1], lower_right[1] + 1)
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])
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# next row
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ret += " \\\\ "
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return ret + "\\end{array}"
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def poly_from_array(array):
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ret = 0
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for i, row in enumerate(array):
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for j, val in enumerate(row):
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ret += val*(x**i * y**j)
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return ret
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def bindfig(fig):
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def ret(**kwargs):
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for i, j in kwargs.items():
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if i == "figsize":
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if j is not None:
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fig.set_figwidth(j[0])
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fig.set_figheight(j[1])
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continue
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fig.__dict__["set_" + i](j)
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return fig
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return ret
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def anim_curves(next_func, dims=25, invalid=2, frames=None, interval=200):
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zero = np.zeros((dims, dims), dtype=np.int32)
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#zero[0,0] = 1
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fig = plt.gcf()
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#plt.colorbar()
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#temp = plt.figure
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#I hate doing it, but there's no other way to get the figure before it's plotted
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plt.figure = bindfig(fig)
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@animate(fig, frames, interval=interval)
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def ret(fr):
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next_func(zero, invalid)
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fig.clf()
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plot_implicit(
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poly_from_array(zero) - fr,
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backend='matplotlib',
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adaptive=False
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)
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plt.title(f"{fr+1}")
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print(fr)
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return ret
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78
polycount/cell1/carry2d/extra.py
Normal file
78
polycount/cell1/carry2d/extra.py
Normal file
@ -0,0 +1,78 @@
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import numpy as np
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from .carry import Carry
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xy_2 = Carry([[2,-1],[-1,0]])
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xy_3 = Carry([[3,-1],[-1,0]])
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x2y_3 = Carry([[3,-2],[-1,0]])
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x3y_4 = Carry([[4,-3],[-1,0]])
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laplace = Carry([[0,-1,0],[-1,4,-1],[0,-1,0]])
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laplace3 = Carry([[0,0,0],[-1,3,-1],[0,-1,0]])
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almost_folium = Carry([[0,0,-1], [0,2,0], [-1,0,0]])
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folium = Carry([
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[ 0,0,0,-1],
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[ 0,2,0, 0],
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[ 0,0,0, 0],
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[-1,0,0, 0]
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])
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folium3 = Carry([
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[ 0,0,0,-1],
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[ 0,3,0, 0],
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[ 0,0,0, 0],
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[-1,0,0, 0]
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])
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folium4 = Carry([
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[ 0, 0, 0,-1, 0],
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[ 0, 0, 0, 0,-1],
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[ 0, 0, 4, 0, 0],
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[-1, 0, 0, 0, 0],
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[ 0,-1, 0, 0, 0]
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])
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def triangle_spread(n):
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ret = np.zeros((n + 1, n + 1), dtype=np.int32)
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ret[0, 0] = -1
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ret[1, 1] = 3
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ret[n, 0] = -1
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ret[0, n] = -1
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return ret
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triangle1 = Carry(triangle_spread(1))
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triangle2 = Carry(triangle_spread(2))
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triangle3 = Carry(triangle_spread(3)) #factorable!
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triangle4 = Carry(triangle_spread(4))
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tri3_rot = Carry([
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[ 0, 0,-1, 0, 0],
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[ 0, 0, 3, 0, 0],
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[-1, 0, 0, 0,-1]
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])
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tri3_rot_real = Carry([
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[ 0, 0, 0,-1, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 3, 0, 0, 0],
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[-1, 0, 0, 0, 0, 0,-1]
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])
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tri3_rot2 = Carry([
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[ 0,-1, 0],
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[ 0, 3, 0],
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[-1, 0,-1]
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])
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tri3_tall = Carry([
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[ 0,-1, 0],
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[ 0, 0, 0],
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[ 0, 3, 0],
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[-1, 0,-1]
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])
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tri3_tall2 = Carry([
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[ 0,-1, 0],
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[ 0, 3, 0],
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[ 0, 0, 0],
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[-1, 0,-1]
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])
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@ -1,148 +0,0 @@
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from functools import partial
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import numpy as np
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import sympy
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import matplotlib.pyplot as plt
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from sympy.plotting import plot_implicit
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from matplotlib import animation
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from carry import Carry
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def make_animation(fig, frames, **kwargs):
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fig.tight_layout()
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#why isn't this how the function is exposed by default
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return lambda func: animation.FuncAnimation(fig, func, frames, init_func=lambda: None, **kwargs)
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def poly_from_array(array):
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ret = 0
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for i, row in enumerate(array):
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for j, val in enumerate(row):
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ret += val*(x**i * y**j)
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return ret
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x, y = sympy.symbols("x y")
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xy_2 = Carry([[2,-1],[-1,0]])
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xy_3 = Carry([[3,-1],[-1,0]])
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x2y_3 = Carry([[3,-2],[-1,0]])
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x3y_4 = Carry([[4,-3],[-1,0]])
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laplace = Carry([[0,-1,0],[-1,4,-1],[0,-1,0]])
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laplace3 = Carry([[0,0,0],[-1,3,-1],[0,-1,0]])
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almost_folium = Carry([[0,0,-1], [0,2,0], [-1,0,0]])
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folium = Carry([[0,0,0,-1], [0,2,0,0], [0,0,0,0], [-1,0,0,0]])
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folium3 = Carry([[0,0,0,-1], [0,3,0,0], [0,0,0,0], [-1,0,0,0]])
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folium4 = Carry([ [ 0, 0,0,-1, 0],
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[ 0, 0,0, 0,-1],
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[ 0, 0,4, 0, 0],
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[-1, 0,0, 0, 0],
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[ 0,-1,0, 0, 0]])
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def triangle_spread(n):
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ret = np.zeros((n + 1, n + 1), dtype=np.int32)
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ret[0, 0] = -1
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ret[1, 1] = 3
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ret[n, 0] = -1
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ret[0, n] = -1
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return ret
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triangle1 = Carry(triangle_spread(1))
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triangle2 = Carry(triangle_spread(2))
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triangle3 = Carry(triangle_spread(3)) #factorable!
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triangle4 = Carry(triangle_spread(4))
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tri3_rot = Carry([ [ 0, 0,-1, 0, 0],
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[ 0, 0, 3, 0, 0],
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[-1, 0, 0, 0,-1] ])
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|
||||
tri3_rot_real = Carry([ [ 0, 0, 0,-1, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 3, 0, 0, 0],
|
||||
[-1, 0, 0, 0, 0, 0,-1] ])
|
||||
|
||||
tri3_rot2 = Carry([ [ 0,-1, 0],
|
||||
[ 0, 3, 0],
|
||||
[-1, 0,-1] ])
|
||||
|
||||
tri3_tall = Carry([ [ 0,-1, 0],
|
||||
[ 0, 0, 0],
|
||||
[ 0, 3, 0],
|
||||
[-1, 0,-1] ])
|
||||
|
||||
tri3_tall2 = Carry([ [ 0,-1, 0],
|
||||
[ 0, 3, 0],
|
||||
[ 0, 0, 0],
|
||||
[-1, 0,-1] ])
|
||||
|
||||
def start_anim(dims=100, carry=xy_2, frames=None, interval=200, inter='add', i_val=1, center=(0,0)):
|
||||
if center == (0,0) and carry.over_pos != center:
|
||||
center = (dims // 2, dims // 2)
|
||||
|
||||
if inter == 'add':
|
||||
next_func = partial(carry.add, val=i_val, center=center)
|
||||
elif inter == 'mult':
|
||||
if i_val == 1:
|
||||
raise ValueError(f"too small value {i_val} for repeated multiplication")
|
||||
next_func = partial(carry.mult, val=i_val, center=center)
|
||||
else:
|
||||
raise ValueError(f"Cannot use {repr(inter)} for animation")
|
||||
|
||||
zero = np.zeros((dims, dims))
|
||||
zero[center] = 1
|
||||
val = [1]
|
||||
|
||||
fig = plt.gcf()
|
||||
plt.title('0')
|
||||
image = plt.imshow(zero)
|
||||
image.set_clim(0, carry.overflow-1)
|
||||
|
||||
@make_animation(fig, frames, interval=interval)
|
||||
def ret(fr):
|
||||
next_func(zero)
|
||||
# if next_func != xy_2.add:
|
||||
# fr = sum(i*(invalid**j) for i,j in zip(zero[0], range(dims))) - 1
|
||||
if inter == 'add':
|
||||
val[0] += i_val
|
||||
else:
|
||||
val[0] *= i_val
|
||||
plt.title(f"{val[0]}")
|
||||
image.set_data(zero)
|
||||
fig.tight_layout()
|
||||
|
||||
return ret
|
||||
|
||||
def anim_curves(dims=25, invalid=2, frames=None, interval=200, next_func=xy_2.add):
|
||||
zero = np.zeros((dims, dims), dtype=np.int32)
|
||||
#zero[0,0] = 1
|
||||
fig = plt.gcf()
|
||||
#plt.colorbar()
|
||||
|
||||
#temp = plt.figure
|
||||
#I hate doing it, but there's no other way to get the figure before it's plotted
|
||||
plt.figure = bindfig(fig)
|
||||
|
||||
@make_animation(fig, frames, interval=interval)
|
||||
def ret(fr):
|
||||
next_func(zero, invalid)
|
||||
fig.clf()
|
||||
plot = plot_implicit(poly_from_array(zero) - fr, backend='matplotlib')
|
||||
plt.title(f"{fr+1}")
|
||||
print(fr)
|
||||
|
||||
return ret
|
||||
|
||||
writer = animation.writers['ffmpeg'](fps=15, metadata={'artist': 'Me'})
|
||||
|
||||
def bindfig(fig):
|
||||
def ret(**kwargs):
|
||||
for i, j in kwargs.items():
|
||||
if i == "figsize":
|
||||
if j is not None:
|
||||
fig.set_figwidth(j[0])
|
||||
fig.set_figheight(j[1])
|
||||
continue
|
||||
fig.__dict__["set_" + i](j)
|
||||
return fig
|
||||
return ret
|
||||
Loading…
x
Reference in New Issue
Block a user