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Invisible Axes Trisurf Plot Python


Invisible Axes Trisurf Plot Python

By : user2953088
Date : November 19 2020, 03:59 PM
it helps some times How can i make the x and y axes invisible? , Try this:
code :
ax.grid(False)
for a in (ax.w_xaxis, ax.w_yaxis):
    for t in a.get_ticklines()+a.get_ticklabels():
        t.set_visible(False)
    a.line.set_visible(False)
    a.pane.set_visible(False)


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make axes invisible or delete plot completely

make axes invisible or delete plot completely


By : raj malik
Date : March 29 2020, 07:55 AM
will help you I have a matlab gui that shall contain 4 plots. The first plot shall be updated if a different file is selected in a list. the other 3 shall only be visible (and be calculated) on request. , I now solved it with
code :
function z_removePlots(handles)

if (isfield(handles,'image') && isfield(handles.image,'hplot'))
    if ishandle(handles.image.hplot)
        delete(handles.image.hplot);
        delete(findall(gcf,'tag','Colorbar'));
        handles.image.hplot = 0;
        set(handles.axesImage, 'Visible', 'off');
    end
end
if (isfield(handles,'contour') && isfield(handles.contour,'hplot'))
    if ishandle(handles.contour.hplot)
        delete(handles.contour.hplot);
        handles.contour.hplot = 0;
        ClearLinesFromAxes(handles.axesContour)
        set(handles.axesContour, 'Visible', 'off');
    end
end
guidata(handles.output,handles);
function ClearLinesFromAxes(axisObj)
if ishandle(axisObj)
    handles2delete = get(axisObj,'Children');
    delete(handles2delete);
end
Python: 2D contour plot from 3 lists, axes not generated in plot

Python: 2D contour plot from 3 lists, axes not generated in plot


By : Jean Macena
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further As has been explained here, imshow by default uses an aspect ratio of 1. Which, in your case, leads to the badly scaled plot. Include a statement to adjust the aspect ratio in imshow - for example aspect='auto' - and you will get the plot you are expecting.
code :
plt.imshow(zi, vmin=z.min(), vmax=z.max(), origin='lower',
       extent=[x.min(), x.max(), y.min(), y.max()], aspect='auto') 
how to make plot invisible on axes of MATLAB GUI

how to make plot invisible on axes of MATLAB GUI


By : Lootii
Date : March 29 2020, 07:55 AM
I hope this helps . I have designed MATLAB GUI in which there are two axes for displaying images. Now, keeping image as background, i am plotting some points(or plot) on axes as foreground. I want to make the plot invisible(which is displayed as foreground over an image) dynamically through code. Please help me out. I tried with set(gcf,'visible','off'), but it makes the whole GUI invisible. Also, I tried set(gca,'visible','off'), but it doesn't work. , Try this:
code :
p = plot(something);
set(p,'visible','off');
  set(p,'visible','on');  
Python Numpy add hspace between 3D plot and 2D plot with shared axes

Python Numpy add hspace between 3D plot and 2D plot with shared axes


By : kaustubh khandelwal
Date : March 29 2020, 07:55 AM
may help you . In this case, it's best to use two separate GridSpec instances. That way you can have two separate hspace parameters. Alternatively, you can manually place the top axes.
As an example of the first option:
code :
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d

fig = plt.figure(figsize=(8, 10))
gs1 = plt.GridSpec(2, 1, hspace=0.2)
gs2 = plt.GridSpec(8, 1, hspace=0)

ax1 = fig.add_subplot(gs1[0], projection='3d')
ax1.plot(range(10), range(10), range(10))

ax = fig.add_subplot(gs2[4])
lower_axes = [ax]
for i in range(4, 8):
    if i > 4:
        ax = fig.add_subplot(gs2[i], sharex=lower_axes[0])
    ax.plot(range(10))
    ax.locator_params(axis='y', nbins=5, prune='both')
    lower_axes.append(ax)

for ax in lower_axes:
    ax.label_outer()

plt.show()
Draw a Double Box Plot Chart (2-Axes Box Plot; Box Plot Correlation Diagram) in Python

Draw a Double Box Plot Chart (2-Axes Box Plot; Box Plot Correlation Diagram) in Python


By : Eideh Titanilla
Date : March 29 2020, 07:55 AM
I hope this helps . Here's a rough attempt at solving the problem using numpys percentile method and Rectangles and Line2Ds for the actual plotting:
code :
from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.lines import Line2D

import numpy as np

def boxplot_2d(x,y, ax, whis=1.5):
    xlimits = [np.percentile(x, q) for q in (25, 50, 75)]
    ylimits = [np.percentile(y, q) for q in (25, 50, 75)]

    ##the box
    box = Rectangle(
        (xlimits[0],ylimits[0]),
        (xlimits[2]-xlimits[0]),
        (ylimits[2]-ylimits[0]),
        ec = 'k',
        zorder=0
    )
    ax.add_patch(box)

    ##the x median
    vline = Line2D(
        [xlimits[1],xlimits[1]],[ylimits[0],ylimits[2]],
        color='k',
        zorder=1
    )
    ax.add_line(vline)

    ##the y median
    hline = Line2D(
        [xlimits[0],xlimits[2]],[ylimits[1],ylimits[1]],
        color='k',
        zorder=1
    )
    ax.add_line(hline)

    ##the central point
    ax.plot([xlimits[1]],[ylimits[1]], color='k', marker='o')

    ##the x-whisker
    ##defined as in matplotlib boxplot:
    ##As a float, determines the reach of the whiskers to the beyond the
    ##first and third quartiles. In other words, where IQR is the
    ##interquartile range (Q3-Q1), the upper whisker will extend to
    ##last datum less than Q3 + whis*IQR). Similarly, the lower whisker
    ####will extend to the first datum greater than Q1 - whis*IQR. Beyond
    ##the whiskers, data are considered outliers and are plotted as
    ##individual points. Set this to an unreasonably high value to force
    ##the whiskers to show the min and max values. Alternatively, set this
    ##to an ascending sequence of percentile (e.g., [5, 95]) to set the
    ##whiskers at specific percentiles of the data. Finally, whis can
    ##be the string 'range' to force the whiskers to the min and max of
    ##the data.
    iqr = xlimits[2]-xlimits[0]

    ##left
    left = np.min(x[x > xlimits[0]-whis*iqr])
    whisker_line = Line2D(
        [left, xlimits[0]], [ylimits[1],ylimits[1]],
        color = 'k',
        zorder = 1
    )
    ax.add_line(whisker_line)
    whisker_bar = Line2D(
        [left, left], [ylimits[0],ylimits[2]],
        color = 'k',
        zorder = 1
    )
    ax.add_line(whisker_bar)

    ##right
    right = np.max(x[x < xlimits[2]+whis*iqr])
    whisker_line = Line2D(
        [right, xlimits[2]], [ylimits[1],ylimits[1]],
        color = 'k',
        zorder = 1
    )
    ax.add_line(whisker_line)
    whisker_bar = Line2D(
        [right, right], [ylimits[0],ylimits[2]],
        color = 'k',
        zorder = 1
    )
    ax.add_line(whisker_bar)

    ##the y-whisker
    iqr = ylimits[2]-ylimits[0]

    ##bottom
    bottom = np.min(y[y > ylimits[0]-whis*iqr])
    whisker_line = Line2D(
        [xlimits[1],xlimits[1]], [bottom, ylimits[0]], 
        color = 'k',
        zorder = 1
    )
    ax.add_line(whisker_line)
    whisker_bar = Line2D(
        [xlimits[0],xlimits[2]], [bottom, bottom], 
        color = 'k',
        zorder = 1
    )
    ax.add_line(whisker_bar)

    ##top
    top = np.max(y[y < ylimits[2]+whis*iqr])
    whisker_line = Line2D(
        [xlimits[1],xlimits[1]], [top, ylimits[2]], 
        color = 'k',
        zorder = 1
    )
    ax.add_line(whisker_line)
    whisker_bar = Line2D(
        [xlimits[0],xlimits[2]], [top, top], 
        color = 'k',
        zorder = 1
    )
    ax.add_line(whisker_bar)

    ##outliers
    mask = (x<left)|(x>right)|(y<bottom)|(y>top)
    ax.scatter(
        x[mask],y[mask],
        facecolors='none', edgecolors='k'
    )

#the figure and axes
fig,(ax1,ax2) = plt.subplots(ncols=2)

#some fake data
x = np.random.rand(1000)**2
y = np.sqrt(np.random.rand(1000))
#x = np.random.rand(1000)
#y = np.random.rand(1000)

#plotting the original data
ax1.scatter(x,y,c='r', s=1)

#doing the box plot
boxplot_2d(x,y,ax=ax2, whis=1)

plt.show()
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