Example usage of neuralib.plot
[1]:
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from neuralib.io.dataset import load_example_rois
from neuralib.plot import (
dotplot,
violin_boxplot,
scatter_histogram,
scatter_binx_plot,
grid_subplots,
VennDiagram
)
from neuralib.util.verbose import printdf
[2]:
%load_ext autoreload
%autoreload
Plot the data with violin and box with dots
[3]:
df = load_example_rois()
_ = printdf(df, nrows=10)
shape: (45_163, 17)
┌─────────────────────────────────┬─────────┬─────────────┬─────────────┬─────────────┬─────────┬─────────┬────────┬───────────────────────────┬──────────────┬────────┬────────────┬────────────┬────────────┬────────────┬────────────┬───────────┐
│ name ┆ acronym ┆ AP_location ┆ DV_location ┆ ML_location ┆ avIndex ┆ channel ┆ source ┆ abbr ┆ acronym_abbr ┆ hemi. ┆ merge_ac_0 ┆ merge_ac_1 ┆ merge_ac_2 ┆ merge_ac_3 ┆ merge_ac_4 ┆ family │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ i64 ┆ str ┆ str ┆ str ┆ str ┆ str ┆ str ┆ str ┆ str ┆ str ┆ str ┆ str │
╞═════════════════════════════════╪═════════╪═════════════╪═════════════╪═════════════╪═════════╪═════════╪════════╪═══════════════════════════╪══════════════╪════════╪════════════╪════════════╪════════════╪════════════╪════════════╪═══════════╡
│ Ectorhinal area/Layer 5 ┆ ECT5 ┆ -3.03 ┆ 4.34 ┆ -4.5 ┆ 377 ┆ gfp ┆ aRSC ┆ Ectorhinal area ┆ ECT ┆ contra ┆ ECT ┆ ECT ┆ ECT ┆ ECT ┆ ECT ┆ ISOCORTEX │
│ Perirhinal area layer 6a ┆ PERI6a ┆ -3.03 ┆ 4.42 ┆ -4.37 ┆ 372 ┆ gfp ┆ aRSC ┆ Perirhinal area ┆ PERI ┆ contra ┆ PERI ┆ PERI ┆ PERI ┆ PERI ┆ PERI ┆ ISOCORTEX │
│ Perirhinal area layer 6a ┆ PERI6a ┆ -3.03 ┆ 4.55 ┆ -4.37 ┆ 372 ┆ gfp ┆ aRSC ┆ Perirhinal area ┆ PERI ┆ contra ┆ PERI ┆ PERI ┆ PERI ┆ PERI ┆ PERI ┆ ISOCORTEX │
│ Perirhinal area layer 6a ┆ PERI6a ┆ -3.03 ┆ 4.5 ┆ -4.36 ┆ 372 ┆ gfp ┆ aRSC ┆ Perirhinal area ┆ PERI ┆ contra ┆ PERI ┆ PERI ┆ PERI ┆ PERI ┆ PERI ┆ ISOCORTEX │
│ Entorhinal area lateral part l… ┆ ENTl5 ┆ -3.03 ┆ 4.92 ┆ -4.31 ┆ 504 ┆ gfp ┆ aRSC ┆ Entorhinal area ┆ ENTl ┆ contra ┆ HPF ┆ RHP ┆ ENT ┆ ENT ┆ ENTl ┆ HPF │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Temporal association areas lay… ┆ TEa6a ┆ -2.91 ┆ 3.79 ┆ 4.45 ┆ 366 ┆ rfp ┆ pRSC ┆ Temporal association area ┆ TEa ┆ ipsi ┆ TEa ┆ TEa ┆ TEa ┆ TEa ┆ TEa ┆ ISOCORTEX │
│ Ectorhinal area/Layer 6a ┆ ECT6a ┆ -2.91 ┆ 4.31 ┆ 4.45 ┆ 378 ┆ rfp ┆ pRSC ┆ Ectorhinal area ┆ ECT ┆ ipsi ┆ ECT ┆ ECT ┆ ECT ┆ ECT ┆ ECT ┆ ISOCORTEX │
│ Ventral auditory area layer 6a ┆ AUDv6a ┆ -2.91 ┆ 3.52 ┆ 4.46 ┆ 156 ┆ rfp ┆ pRSC ┆ Ventral auditory area ┆ AUDv ┆ ipsi ┆ AUD ┆ AUD ┆ AUD ┆ AUD ┆ AUDv ┆ ISOCORTEX │
│ Ectorhinal area/Layer 6a ┆ ECT6a ┆ -2.91 ┆ 4.14 ┆ 4.47 ┆ 378 ┆ rfp ┆ pRSC ┆ Ectorhinal area ┆ ECT ┆ ipsi ┆ ECT ┆ ECT ┆ ECT ┆ ECT ┆ ECT ┆ ISOCORTEX │
│ Temporal association areas lay… ┆ TEa5 ┆ -2.91 ┆ 4.02 ┆ 4.55 ┆ 365 ┆ rfp ┆ pRSC ┆ Temporal association area ┆ TEa ┆ ipsi ┆ TEa ┆ TEa ┆ TEa ┆ TEa ┆ TEa ┆ ISOCORTEX │
└─────────────────────────────────┴─────────┴─────────────┴─────────────┴─────────────┴─────────┴─────────┴────────┴───────────────────────────┴──────────────┴────────┴────────────┴────────────┴────────────┴────────────┴────────────┴───────────┘
[4]:
sns.set(style='white', font_scale=1.2)
fig, ax = plt.subplots()
violin_boxplot(data=df, x='source', y='AP_location', hue='source', ax=ax)
plt.show()
See correlation and histogram of two variables
[5]:
iris = sns.load_dataset('iris')
x = iris['petal_width']
y = iris['sepal_width']
scatter_histogram(x, y, linear_reg=True, bins=20)
See y as a function of x
dotted line indicates the binned median or mean y values
[6]:
_, ax = plt.subplots()
scatter_binx_plot(ax=ax, x=x, y=y, bins=10, bin_func='median')
Dotsplot with colormap
[7]:
# (X,): different animals
xlabel = ['animal_A', 'animal_B', 'animal_C', 'animal_D', 'animal_E']
# (Y,): subregion in Visual Cortex as an example
ylabel = ['VISam', 'VISp', 'VISpm', 'VISpor', 'VISl', 'VISal', 'VISli', 'VISpl']
# values: Array[float, [X, Y]]
nx = len(xlabel)
ny = len(ylabel)
values = np.random.sample((nx, ny)) * 100
dotplot(xlabel, ylabel, values,
scale='area',
max_marker_size=700,
with_color=True,
figure_title='example of dotplot')
Venn Diagram
[8]:
# 2 sets
subsets = {'setA': 10, 'setB': 20}
vd = VennDiagram(subsets, colors=('pink', 'palegreen'))
vd.add_intersection('setA & setB', 5)
vd.add_total(100)
vd.plot()
vd.show()
[9]:
# 3 sets
subsets = {'setA': 20, 'setB': 100, 'setC': 50}
vd = VennDiagram(subsets)
vd.add_intersection('setA & setB', 10)
vd.add_intersection('setB & setC', 10)
vd.add_intersection('setA & setC', 10)
vd.add_intersection('setA & setB & setC', 2)
vd.add_total(200)
vd.plot()
vd.show()
Grid subplots
[10]:
data = np.random.sample((30, 10, 2))
grid_subplots(data, 5, 'plot', dtype='xy', ls='dotted')
[11]:
def generate_example_2d_tuning(blobs: int = 6) -> np.ndarray:
"""example mimic place cell place cell firing"""
x, y = np.meshgrid(np.linspace(0, 6, 50), np.linspace(0, 6, 50))
z = np.zeros_like(x)
for _ in range(blobs):
x0 = np.random.uniform(0, 6)
y0 = np.random.uniform(0, 6)
sigma = 0.5
z += np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
return z
data = [generate_example_2d_tuning() for i in range(30)]
grid_subplots(data, images_per_row=6, plot_func='imshow', dtype='img')
