def create_plot(cls, *, data, tiles_params: Dict, points_params: Dict,
labels_params: Dict, reverse_y: bool, show_legend: bool,
labels_map_size: bool, corr_value_format: str,
threshold) -> PlotSpec:
# Adjust options
labels_map_size = _OpUtil.adjust_type_color_size(
tiles_params, points_params, labels_params, labels_map_size)
_OpUtil.adjust_diag(tiles_params, points_params, labels_params,
threshold)
_OpUtil.flip_type(tiles_params, points_params, labels_params,
reverse_y)
tooltips = (layer_tooltips().format(
field='@..corr..', format=corr_value_format).line('@..corr..'))
plot = ggplot(data)
if tiles_params is not None:
plot += geom_tile(stat='corr',
show_legend=show_legend,
size=0.0,
width=1.002,
height=1.002,
tooltips=tooltips,
sampling='none',
**tiles_params)
plot += coord_cartesian()
else:
plot += coord_fixed()
if points_params is not None:
plot += geom_point(stat='corr',
show_legend=show_legend,
size_unit='x',
mapping=aes(size='..corr_abs..'),
tooltips=tooltips,
sampling='none',
**points_params)
if labels_params is not None:
m = None
if labels_map_size:
m = aes(size='..corr_abs..')
else:
labels_params['size'] = 1
plot += geom_text(stat='corr',
show_legend=show_legend,
mapping=m,
na_text='',
label_format=corr_value_format,
tooltips=tooltips,
size_unit='x',
sampling='none',
**labels_params)
return plot
def image_matrix(image_data_array, *, norm: bool = None, scale=1) -> GGBunch:
"""
Display images in a grid.
The grid dimensions are determined by shape of the input 2D ndarray.
Elements of the input 2D array are images specified by ndarrays with shape (n, m) or (n, m, 3) or (n, m, 4).
Parameters
----------
image_data_array : 2D numpy.ndarray containing images
Specifies dimensions of output grid
norm : bool
False - disables default scaling of a luminance (grayscale) images to the (0, 255) range.
scale : scalar, default: 1
Specifies magnification factor
Returns
-------
Plot bunch object (GGBunch)
Examples
--------
>>> import numpy as np
>>> from lets_plot.bistro import im
>>> image = np.random.choice([0.0, 1.0], [64, 64, 3])
>>> X = np.empty([4, 6], dtype=object)
>>> X.fill(image)
>>> im.image_matrix(X)
"""
if not is_ndarray(image_data_array):
raise Exception(
"Invalid image_data_array: 2d ndarray is expacted but was {}".
format(type(image_data_array)))
if image_data_array.ndim != 2:
raise Exception(
"Invalid image_data_array: 2-dimentional ndarray is expacted but was {}-dimentional"
.format(image_data_array.ndim))
rows, cols = image_data_array.shape
if cols * rows <= 0:
return
w_max = 0
h_max = 0
for row in range(rows):
for col in range(cols):
image_data = image_data_array[row][col]
if image_data is None:
continue
_assert_image_data(image_data)
h, w = image_data.shape[0:2]
h, w = _expand_h_w(h, w, scale)
w_max = max(w_max, w)
h_max = max(h_max, h)
# no gaps between image and plot edges
options = scale_x_continuous(expand=[0, 0])
options += scale_y_continuous(expand=[0, 0])
# show no axis
options += theme(axis_line='blank',
axis_title='blank',
axis_ticks='blank',
axis_text='blank')
ggbunch = GGBunch()
for row in range(rows):
for col in range(cols):
image_data = image_data_array[row][col]
if image_data is None:
continue
h, w = image_data.shape[0:2]
h, w = _expand_h_w(h, w, scale)
p = ggplot() + geom_image(image_data=image_data, norm=norm)
p += options
ggbunch.add_plot(p, col * w_max, row * h_max, w, h)
return ggbunch
def image_matrix(image_data_array, *, norm: bool = None, scale=1) -> GGBunch:
"""
Display images in a grid.
The grid dimensions are determined by shape of the input 2D ndarray.
Elements of the input 2D array are images specified by ndarrays with shape
(n, m) or (n, m, 3) or (n, m, 4).
Parameters
----------
image_data_array : `ndarray`
2D `numpy.ndarray` containing images. Specifies dimensions of output grid.
norm : bool, default=True
False value disables default scaling of a luminance (grayscale) images to the (0, 255) range.
scale : float, default=1.0
Specifies magnification factor.
Returns
-------
`GGBunch`
Plot bunch object.
Examples
--------
.. jupyter-execute::
:linenos:
:emphasize-lines: 9
import numpy as np
from lets_plot import *
from lets_plot.bistro.im import *
LetsPlot.setup_html()
np.random.seed(42)
image = np.random.randint(256, size=(64, 64, 3))
matrix = np.empty((2, 3), dtype=object)
matrix.fill(image)
image_matrix(matrix)
|
.. jupyter-execute::
:linenos:
:emphasize-lines: 12
import numpy as np
from lets_plot import *
from lets_plot.bistro.im import *
LetsPlot.setup_html()
rows, cols = 3, 3
matrix = np.empty((rows, cols), dtype=object)
for r in range(rows):
for c in range(cols):
w, h = 32 + 16 * c, 32 + 16 * r
matrix[r][c] = 256 * np.linspace(np.linspace(0, .5, w), \\
np.linspace(.5, .5, w), h)
image_matrix(matrix, norm=False, scale=1.5)
"""
if not is_ndarray(image_data_array):
raise Exception(
"Invalid image_data_array: 2d ndarray is expacted but was {}".
format(type(image_data_array)))
if image_data_array.ndim != 2:
raise Exception(
"Invalid image_data_array: 2-dimentional ndarray is expacted but was {}-dimentional"
.format(image_data_array.ndim))
rows, cols = image_data_array.shape
if cols * rows <= 0:
return
w_max = 0
h_max = 0
for row in range(rows):
for col in range(cols):
image_data = image_data_array[row][col]
if image_data is None:
continue
_assert_image_data(image_data)
h, w = image_data.shape[0:2]
h, w = _expand_h_w(h, w, scale)
w_max = max(w_max, w)
h_max = max(h_max, h)
# no gaps between image and plot edges
options = scale_x_continuous(expand=[0, 0])
options += scale_y_continuous(expand=[0, 0])
# show no axis
options += theme(axis_line='blank',
axis_title='blank',
axis_ticks='blank',
axis_text='blank')
ggbunch = GGBunch()
for row in range(rows):
for col in range(cols):
image_data = image_data_array[row][col]
if image_data is None:
continue
h, w = image_data.shape[0:2]
h, w = _expand_h_w(h, w, scale)
p = ggplot() + geom_image(image_data=image_data, norm=norm)
p += options
ggbunch.add_plot(p, col * w_max, row * h_max, w, h)
return ggbunch