In [1]:
import PIL
import os
import pandas as pd
import numpy as np
import IPython.display as ip
import requests
from io import BytesIO
from lets_plot import *
LetsPlot.setup_html()
LetsPlot.set({'magick_export': True})
mpg = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv')
airquality_df = pd.read_csv("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/airquality.csv")
pie_df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot/refs/heads/master/docs/f-25a/data/gdp_forecast_2025_trillion_usd.csv", encoding ='utf-8')
markdown_df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv")
markdown_df.drop(columns=["Unnamed: 0"], inplace=True)
boat_response = requests.get('https://github.com/JetBrains/lets-plot-docs/raw/master/source/examples/cookbook/images/fisher_boat.png')
boat_raster = PIL.Image.open(BytesIO(boat_response.content))
boat = np.asarray(boat_raster)
def show(p, name, w=None, h=None, dpi=None, unit=None, scale=1, show_svg=True, out=None):
if (out is None) or isinstance(out, str):
out = name + ".png"
png_img = p.to_png(out, w=w, h=h, dpi=dpi, unit=unit, scale=scale)
out_dir = os.path.dirname(png_img)
if show_svg:
svg_img = os.path.join(out_dir, name + '.svg')
svg = p.to_svg(svg_img)
ip.display(ip.HTML("<h4>SVG</h4>"))
ip.display(ip.SVG(p.to_svg()))
ip.display(ip.HTML("<h4>ImageMagick</h4>"))
ip.display(ip.Image(png_img))
elif isinstance(out, BytesIO):
# file-like object
svg = BytesIO()
p.to_svg(svg)
p.to_png(out, w=w, h=h, dpi=dpi, unit=unit, scale=scale)
if show_svg:
ip.display(ip.HTML("<h4>SVG</h4>"))
ip.display(ip.SVG(svg.getvalue()))
ip.display(ip.HTML("<h4>ImageMagick</h4>"))
ip.display(ip.Image(out.getvalue()))
else:
raise ValueError("Unsupported out type: " + type(out))
return
Basic plots¶
In [2]:
barplot_df = {
'val': ['3', '1', '2', '3', '1', '3', '2', '1', '3', '2', '2', '2', '2', '2', '2'],
'vai': [3, 1,2, 3, 1, 3, 2, 1, 3, 2, 2, 2, 2, 2, 2]
}
p = ggplot(barplot_df) + geom_bar(aes(x=as_discrete('vai'), fill=as_discrete('vai', levels=[1, 2, 3])))
show(p, "magick_barplot")
Path¶
In [3]:
common_args = {'color': "black", 'alpha': .95}
quantiles = np.linspace(0, 1, 15)
fill_diverging = scale_fill_gradient2(low="white", mid="#3F8F77", high="white", midpoint=0.5)
p = ggplot(mpg, aes("hwy", group="drv"))
p1 = p + geom_density(**common_args) + \
ggtitle("geom_density(): fill=None in aes (default)")
p2 = p + geom_density(aes(fill='..quantile..'), quantiles=quantiles, **common_args) + fill_diverging + \
ggtitle("geom_density(): fill='..quantile..' in aes")
p = gggrid([p1, p2], ncol=1) + ggsize(700, 500)
show(p, "magick_path")
Variadic Path¶
In [4]:
p = ggplot(airquality_df) \
+ geom_line(
aes(
x = 'Day',
y = 'Temp',
size = 'Wind', # <--- wind varies by day
color = as_discrete('Month')
)
) \
+ scale_size([0.5, 5.0]) \
+ ggsize(700, 500)
show(p, "magick_variadic_path")
Fonts¶
In [5]:
fonts = {
"y": [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ],
"label": [ "Arial", "Calibri", "Garamond", "Geneva", "Georgia", "Helvetica", "Lucida Grande", "Rockwell", "Times New Roman", "Verdana", "sans-serif", "serif", "monospace (imi)" ],
"family": [ "Arial", "Calibri", "Garamond", "Geneva", "Georgia", "Helvetica", "Lucida Grande", "Rockwell", "Times New Roman", "Verdana", "sans-serif", "serif", "monospace" ]
}
p = ggplot(fonts, aes(y='y', label='label', family='family')) + geom_text(size=10)
show(p, "magick_fonts.bmp")
Markdown¶
In [6]:
p = (ggplot(markdown_df)
+ geom_point(aes(x='displ', y='cty', color='drv'), size=8)
+ scale_color_manual(['#66c2a5', '#fc8d62', '#8da0cb'], guide='none')
# Enable Markdown in all titles
+ theme(title=element_markdown())
# Adjust style of title and subtitle
+ theme(plot_title=element_text(size=30, family='Georgia', hjust=0.5),
plot_subtitle=element_text(family='Georgia', hjust=0.5))
+ labs(
# Span styling, mixing style and emphasis
title=
"""<span style="color:#66c2a5">**Forward**</span>, """
"""<span style="color:#8da0cb">**Rear**</span> and """
"""<span style="color:#fc8d62">**4WD**</span> Drivetrain""",
# Simple emphasis
subtitle='**City milage** *vs* **displacement**',
# multiline caption, multiline style span, links
caption="<span style='color:grey'>"
"Powered by <a href='https://lets-plot.org'>Lets-Plot</a>. \n"
"Visit the <a href='https://github.com/jetbrains/lets-plot/issues'>issue tracker</a> for feedback."
"</span>",
# Axis titles
x='Displacement (***inches***)',
y='Miles per gallon (***cty***)'
)
)
show(p, "magick_markdown")
Pie¶
In [7]:
p = ggplot(pie_df, aes(fill='Country', slice='GDP_2025_Trillion_USD')) \
+ geom_pie(size=.6, size_unit='x', stat='identity') \
+ ggtitle('GDP forecast 2025 (trillion US$) by country',
subtitle='Source: <a href="https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)">Wikipedia</a>') \
+ scale_fill_gradient(low="blue", high="yellow") \
+ theme_void() + theme(plot_title=element_text(hjust=0.5), plot_subtitle=element_text(hjust=0.5))
show(p, "magick_pie")
Power degree¶
In [8]:
power_df = {'x': list(range(-10, 11))}
p = ggplot() + \
geom_function(aes(x='x'), data=power_df, fun=lambda x: x**3 - 100 * x) + \
ggtitle("Graph of the \( y = x^3 - 100 x \)") + \
theme(text=element_text(family="Times New Roman"), \
plot_title=element_text(size=20, face='bold'))
show(p, "magick_power")
Polar plot¶
In [9]:
p = ggplot(mpg) \
+ geom_bar(aes(x='model', y='cty', fill='cty'), stat='identity', position='dodge') \
+ scale_fill_gradient(low='red', high='white', limits=(5,40)) \
+ theme(
axis_line_y=element_line(color='red', size=2),
axis_line_x=element_line(color='blue', size=2),
axis_ticks_length_y=5,
axis_ticks_length_x=10,
axis_ticks_y=element_line(size=5, color='red'),
axis_ticks_x=element_line(size=3, color='blue'),
axis_text_x=element_text(color='blue', angle=10),
axis_text_y=element_text(color='red'),
panel_inset=[20, 140, 30, 135] # New! Expand the panel to fit axis labels.
) \
+ ggsize(900, 500) \
+ coord_polar(transform_bkgr=False) # Keep the old school rectangular background.
ggsave(p, "magick_polar.svg")
show(p, "magick_polar")
Curve¶
In [10]:
def curve_plot(curvature=0.5, angle=90.0, ncp=5):
return ggplot() \
+ geom_curve(x=-10, y=1, xend=10, yend=-1,
curvature=curvature, angle=angle, ncp=ncp,
arrow=arrow(ends='both')) \
+ ggtitle("curvature={0}, angle={1}, ncp={2}".format(curvature, angle, ncp)) \
+ xlim(-15,15)
p = gggrid([
curve_plot(angle=0),
curve_plot(ncp=1),
curve_plot(angle=45),
curve_plot(curvature=-1, angle=45),
curve_plot(curvature=0.7, angle=30),
curve_plot(curvature=-0.7, angle=30),
], ncol=2)
show(p, "magick_curve")
Clip Path¶
In [11]:
p = ggplot(mpg) \
+ geom_bar(aes(fill=as_discrete('cyl')), size=0, position='dodge', show_legend=False) + coord_cartesian(ylim=[0, 70]) + ggtitle("Title", "Subtitle")
show(p, "magick_clip")
In [12]:
p = ggplot(mpg) \
+ geom_bar(aes(fill=as_discrete('cyl')), size=0, position='dodge', show_legend=False) + coord_cartesian(ylim=[0, 70]) + ggtitle("Title", "Subtitle") \
+ coord_polar()
show(p, "magick_clip_polar")
imshow¶
greyscale¶
In [13]:
arr = np.array([[150, 75, 0],
[ 200, 150, 75]])
arr_nans = np.array([
[150, np.nan ,0],
[np.nan, 150 ,75]
])
p = gggrid([
ggplot() + geom_imshow(arr, norm=False),
ggplot() + geom_imshow(arr, cmap="viridis", norm=False),
ggplot() + geom_imshow(arr_nans, cmap="viridis", norm=False),
], ncol=3) + ggsize(1000, 210)
show(p, "magick_imshow_greyscale")
rgb¶
In [14]:
# An 2x2 pix image by default has extent [-0.5, 1.5, -0.5, 1.5]
arr = np.array([
[[150, 0, 0, 255], [0, 150, 0, 255]],
[[0, 0, 150, 255], [150, 150, 0, 255]]
])
p = ggplot() + geom_imshow(arr) + ggsize(300, 200)
show(p, "magick_imshow")
In [15]:
p = ggplot() + geom_imshow(image_data=boat)
show(p, "magick_boat")
In [16]:
dim = 100
ext_base = [0, dim, 0, dim]
ext_flip_x = [dim, 0, 0, dim]
ext_flip_y = [dim, 0, dim, 0]
ext_transpose = [0, dim, dim, 0]
label_data = dict(
x = [50, 150, 50, 150],
y = [200, 200, 0, 0],
label = ["original", "flip columns", "flip rows", "transpose"]
)
p = (ggplot() +
geom_imshow(boat, extent=[v + dim if i in (2, 3) else v for i, v in enumerate(ext_base)]) +
geom_imshow(boat, extent=[v + dim for v in ext_flip_x]) +
geom_imshow(boat, extent=[v + dim if i in (0, 1) else v for i, v in enumerate(ext_flip_y)]) +
geom_imshow(boat, extent=ext_transpose) +
geom_hline(yintercept=100, color="yellow", tooltips="none") +
geom_vline(xintercept=100, color="yellow", tooltips="none") +
geom_label(aes("x", "y", label="label"), data=label_data) +
ggsize(700, 700)
)
show(p, "magick_boat_extent")
image_matrix¶
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from lets_plot.bistro.im import image_matrix
In [18]:
M = 2 # rows
N = 3 # columns
X = np.empty([M, N], dtype=object)
X.fill(boat)
p = image_matrix(X)
show(p, "image_matrix")
In [19]:
X1 = np.empty([M, N], dtype=object)
for row in range(M):
for col in range(N):
v = (row + col + 1) * 10
X1[row][col] = boat[v:-v,v:-v,:]
p = image_matrix(X1)
show(p, "magick_image_matrix_sized")
In [20]:
p = image_matrix(X, scale=.3)
show(p, "magick_image_matrix_scaled")
greyscale¶
In [21]:
img_gs = boat[:,:,0]
def _degrade(grayscale_img:np.ndarray, coef:float):
vmin = grayscale_img.min()
vmax = grayscale_img.max()
middle = vmin + (vmax - vmin) / 2
rows, cols = grayscale_img.shape
for row in range(rows):
for col in range(cols):
v = float(grayscale_img[row][col])
v_new = middle + (v - middle) * coef
grayscale_img[row][col] = int(v_new)
X2 = np.empty([M, N], dtype=object)
img_copy = img_gs.copy()
for row in range(M):
for col in range(N):
X2[row][col] = img_copy
img_copy = img_copy.copy()
_degrade(img_copy, coef=.7)
print("[{}, {}] data range: [{}-{}]".format(row, col, img_copy.min(), img_copy.max()))
In [22]:
p = image_matrix(X2)
show(p, "magick_image_matrix_greyscale")
In [23]:
ggplot() + geom_imshow(X2[0][0], cmap="viridis")
Out[23]:
In [24]:
p = image_matrix(X2, cmap="viridis", norm=False)
show(p, "magick_image_matrix_cmap")
geom_raster¶
In [25]:
# reshape 3-dimentional ndarray to 2-dimentional long-form ndarray
# and then to data frame with columns x,y,r,g,b
cols, rows, _ = boat.shape
img_lf = boat.reshape(cols * rows, -1)
img_df = pd.DataFrame(img_lf,columns=['r','g','b'])
X_mesh, Y_mesh = np.meshgrid(np.arange(rows), np.arange(cols))
img_df['x'] = X_mesh.reshape(-1)
img_df['y'] = Y_mesh.reshape(-1)[::-1]
# Pack color components values to 24-bit RGB values
c_fill = img_df.apply(lambda row: ((int(row['r'] * 255) << 16) +
(int(row['g'] * 255) << 8) +
int(row['b'] * 255)),
axis=1)
# Show image in pseudo colors with only few gradations
p = (ggplot(img_df) + geom_raster(aes('x', 'y', fill=c_fill), show_legend=False)
+ scale_fill_brewer()
+ ggtitle('Raster geom with brewer palette') + ggsize(800,500))
show(p, "magick_raster")
Size and Scale¶
In [26]:
p = ggplot() + geom_blank() + ggsize(200, 200)
show(p, "magick_ggsize_with_scale", scale=2)
In [27]:
p = ggplot() + geom_blank()
show(p, "magick_ggsave", w=10, h=10, unit='cm', dpi=72, show_svg=False)
In [28]:
p = ggplot() + geom_blank()
show(p, "magick_ggsave_with_scale", w=10, h=10, unit='cm', dpi=72, scale=2, show_svg=False)
In [29]:
filelike_obj = BytesIO()
show(p, "magick_filelike", out=filelike_obj)
Performance¶
line¶
In [30]:
n = 50_000
x = list(range(n))
y = [0]*n
p = ggplot({'x': x, 'y': y}) + geom_line(aes(x='x', y='y'))
show(p, "magick_perf_line")
point¶
In [31]:
n = 50_000
x = list(range(n))
y = [0]*n
p = ggplot({'x': x, 'y': y}) + geom_point(aes(x='x', y='y'))
p
show(p, "magick_perf_point")
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