imshow

I am a Python beginner. I have a list of X values x_list = [-1,2,10,3] and I have a list of Y values y_list = [3,-3,4,7] I then have a Z value for each couple. Schematically, this works like that: X Y Z -1 3 5 2 -3 1 10 4 2.5 3 7 4.5 and the Z values are stored in z_list = [5,1,2.5,4.5] . I need to get a 2D plot with the X values on the X axis, the Y values on the Y axis, and for each couple the Z value, represented by an intensity map. This is what I have tried, unsuccessfully: X, Y = np.meshgrid(x_list, y_list) fig, ax = plt.subplots() extent = [x_list.min(), x_list.max(), y_list.min(), y

I am using matplotlib In plot() or bar() , we can easily put legend, if we add labels to them. but what if it is a contourf() or imshow() I know there is a colorbar() which can present the color range, but it is not satisfied. I want such a legend which have names(labels). For what I can think of is that, add labels to each element in the matrix, then ,try legend(), to see if it works, but how to add label to the element, like a value?? in my case, the raw data is like: 1,2,3,3,4 2,3,4,4,5 1,1,1,2,2 for example, 1 represents 'grass', 2 represents 'sand', 3 represents 'hill'... and so on.

I have an image that is 6400 × 3200, while my screen is 1280 x 800. Therefore, the image needs to be resized for display only. I am using Python and OpenCV 2.4.9. According to OpenCV Documentation , If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow. That is what I am doing, but the image is not fitted to the screen, only a portion is shown because it's too big. I've also tried with cv2.resizeWindow, but it doesn't make any difference. import cv2 cv2.namedWindow("output", cv2.WINDOW_NORMAL) # Create

Let's suppose the example below import matplotlib.pyplot as plt import numpy as np v1 = -1 + 2*np.random.rand(50,150) fig = plt.figure() ax = fig.add_subplot(111) p = ax.imshow(v1,interpolation='nearest') cb = plt.colorbar(p,shrink=0.5) plt.xlabel('Day') plt.ylabel('Depth') cb.set_label('RWU') plt.show() I want to show the values below zero in a different colormap than the values above zero First of all, is it possible that you just want to use a diverging colormap, 'neutral' at zero, and diverging to two distinct colours? This is an example: import matplotlib.pyplot as plt import numpy as np

So I am new to programming with matplotlib. I have created a color plot using imshow() and an array. At first the axis were just the row and column number of my array. I used extent = (xmin,xmax,ymin,ymax) to get the x-axis in unix time and altitude, respectively. I want to change the x-axis from unix time (982376726,982377321) to UT(02:25:26, 02:35:21). I have created a list of the time range in HH:MM:SS. I am not sure how to replace my current x-axis with these new numbers, without changing the color plot (or making it disappear). I was looking at datetime.time but I got confused with it.

I have data in X, Y, Z format where all are 1D arrays, and Z is the amplitude of the measurement at coordinate (X,Y). I'd like to show this data as a contour or 'imshow' plot where the contours/color represent the the value Z (amplitude). The grid for measurements and X and Y look are irregularly spaced. Many thanks, len(X)=100 len(Y)=100 len(Z)=100 Does plt.tricontourf(x,y,z) satisfy your requirements? It will plot filled contours for irregularly spaced data (non-rectilinear grid). You might also want to look into plt.tripcolor() . import numpy as np import matplotlib.pyplot as plt x = np