line-plot

问题 This is a reference from a previous question two lines matplotib animation import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation x = np.arange(130, 190, 1) y = 97.928 * np.exp(- np.exp(- 0.1416 *( x - 146.1 ))) z = 96.9684 * np.exp(- np.exp(-0.1530*( x - 144.4))) fig, ax = plt.subplots() line1, = ax.plot(x, y, color = "r") line2, = ax.plot(x, z, color = "g") def update(num, x, y, z, line1, line2): line1.set_data(x[:num], y[:num]) line2.set_data(x[:num], z

问题 This is a reference from a previous question two lines matplotib animation import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation x = np.arange(130, 190, 1) y = 97.928 * np.exp(- np.exp(- 0.1416 *( x - 146.1 ))) z = 96.9684 * np.exp(- np.exp(-0.1530*( x - 144.4))) fig, ax = plt.subplots() line1, = ax.plot(x, y, color = "r") line2, = ax.plot(x, z, color = "g") def update(num, x, y, z, line1, line2): line1.set_data(x[:num], y[:num]) line2.set_data(x[:num], z

问题 I have a dataset which looks like this: A B C D AR 20201 1 200 AR 20202 1 300 AR 20203 1 1000 AR 20204 1 3000 AR 20205 1 700 AR 20206 1 800 AR 20207 1 900 AR 20201 2 1000 AR 20202 2 2000 AR 20203 2 3000 AR 20204 2 4000 AR 20205 2 5676 AR 20206 2 6000 AR 20207 2 1000 AR 20201 3 4500 AR 20202 3 4567 AR 20203 3 1000 AR 20204 3 900 AR 20205 3 600 AR 20206 3 200 AR 20207 3 100 ========================== BR 20201 1 BR 20202 1 BR 20203 1 BR 20204 1 BR 20205 1 BR 20206 1 BR 20207 1 BR 20201 2 BR

问题 Plotting a heatmap and a lineplot using Seaborn with shared x-axis, the ticks of the heatmap are placed in the middle of the heatmap bars. Consequently, the bottom lineplot will inherit heatmap ticks position and labels, not reflecting the true data as the lineplot ticks should start from zero. In other words, I need to either shift the ticks of both plots to start from the x-axis origin (optimal), or shift the lineplot toward the right by a half of a heatmap cell width, keeping the tick

来源： https://stackoverflow.com/questions/62894496/plots-obtained-via-matplotlib-for-each-row-of-a-2d-array-againt-their-indices-ar

来源： https://stackoverflow.com/questions/62894496/plots-obtained-via-matplotlib-for-each-row-of-a-2d-array-againt-their-indices-ar

问题 Is there any possibility to change the constraints of slices in a LinePlot display by script function? In order to detach all lines I want, I currenlty have to click each line one-by-one and select the option from the menu. I have not found such commands in the DM-script documentation. How can I do such a thing by script? 回答1: The commands you seek are possibly described in the section on "LinePlotDisplays": A quick example is: image spectrum := RealImage( "Test", 4, 512 ) spectrum = icol *

问题 I had been trying to make with some interactive plot using Altair on jupyter lab. I had reached this stage where the results is below. As you can see, the line doesnt pop to the front when its highlighted. How do I make it pop to the front? Attached is the code. import altair as alt source = df selection = alt.selection_multi(fields=['class'], on='click') color = alt.condition(selection, alt.Color('class:O', legend=None, scale=alt.Scale(scheme='category10')), alt.value('lightgray')) base =

问题 I'm making a multiple lines plot with errorbars. If I don't use the size argument, everything is fine: # sample data Response=runif(4) ResponseMin=Response-Response/5 ResponseMax=Response+Response/5 Cases=rep(c("Case1","Case2"),each=2) df=data.frame(x=1:2,Average=Response,Lower=ResponseMin,Upper=ResponseMax,Case=Cases) # let's plot library(ggplot2) ggplot(df,aes(x=x,y=Average,colour=Case)) + geom_line(aes(group=Case)) + geom_point() + geom_errorbar(aes(ymin=Lower,ymax=Upper,width=0.25)) +

问题 Is it possible to have different sized (i.e. thick) lines drawn with geom_line ? The size parameters is the same for all lines, irrespective of the group: bp <- ggplot(data=diamonds, aes(x=cut, y=depth)) + geom_line(aes(color=cut), size=1) However, I want the thickness of the lines to reflect their relative importance measured as number of observations: relative_size <- table(diamonds$cut)/nrow(diamonds) bp <- ggplot(data=diamonds, aes(x=cut, y=depth)) + geom_line(aes(color=cut), size=cut) bp