jonasvdd
commented
1 year ago Code ⬇️ implementation which projects LTTB its buckets to equidistant log buckets.
import numpy as np
from plotly_resampler.aggregation.aggregation_interface import DataPointSelector
from typing import Union
class LogLTTB(DataPointSelector):
@staticmethod
def _argmax_area(prev_x, prev_y, avg_next_x, avg_next_y, x_bucket, y_bucket) -> int:
"""Vectorized triangular area argmax computation.
Parameters
----------
prev_x : float
The previous selected point is x value.
prev_y : float
The previous selected point its y value.
avg_next_x : float
The x mean of the next bucket
avg_next_y : float
The y mean of the next bucket
x_bucket : np.ndarray
All x values in the bucket
y_bucket : np.ndarray
All y values in the bucket
Returns
-------
int
The index of the point with the largest triangular area.
"""
return np.abs(
x_bucket * (prev_y - avg_next_y)
+ y_bucket * (avg_next_x - prev_x)
+ (prev_x * avg_next_y - avg_next_x * prev_y)
).argmax()
def _arg_downsample(
self, x: Union[np.ndarray, None], y: np.ndarray, n_out: int, **kwargs
) -> np.ndarray:
"""TODO complete docs"""
# We need a valid x array to determing the x-range
assert x is not None, "x cannot be None for this downsampler"
# the log function to use
lf = np.log1p
offset = np.unique(
np.searchsorted(
x, np.exp(np.linspace(lf(x[0]), lf(x[-1]), n_out + 1)).astype(np.int64)
)
)
# Construct the output array
sampled_x = np.empty(len(offset) + 1, dtype="int64")
sampled_x[0] = 0
sampled_x[-1] = x.shape[0] - 1
# Convert x & y to int if it is boolean
if x.dtype == np.bool_:
x = x.astype(np.int8)
if y.dtype == np.bool_:
y = y.astype(np.int8)
a = 0
for i in range(len(offset) - 2):
a = (
self._argmax_area(
prev_x=x[a],
prev_y=y[a],
avg_next_x=np.mean(x[offset[i + 1] : offset[i + 2]]),
avg_next_y=y[offset[i + 1] : offset[i + 2]].mean(),
x_bucket=x[offset[i] : offset[i + 1]],
y_bucket=y[offset[i] : offset[i + 1]],
)
+ offset[i]
)
sampled_x[i + 1] = a
# ------------ EDGE CASE ------------
# next-average of last bucket = last point
sampled_x[-2] = (
self._argmax_area(
prev_x=x[a],
prev_y=y[a],
avg_next_x=x[-1], # last point
avg_next_y=y[-1],
x_bucket=x[offset[-2] : offset[-1]],
y_bucket=y[offset[-2] : offset[-1]],
)
+ offset[-2]
)
return sampled_xCode ⬇️ example which creates a log plot:
from plotly_resampler.aggregation import NoGapHandler
n = 100_000
y = np.sin(np.arange(n) / 2_000) + np.random.randn(n) / 10
fr = FigureResampler(False)
fr.add_trace(
go.Scattergl(mode="lines+markers", marker_color=np.abs(y) / np.max(np.abs(y))),
hf_x=np.arange(n),
hf_y=y,
downsampler=LogLTTB(),
gap_handler=NoGapHandler(),
)
fr.update_xaxes(type="log")
fr.update_layout(template='plotly_white', title='log axis demo')
codecov-commenter
jvdd
Partially addresses #206 Closes #190
Specifically, log-axis their zoom functionality will be supported.
TODO: