Save your final figure as po2 yieldPoint.jpg. 2) Create an m-file called po2_yieldPoint.m and solve the following inside it. 5.5 Adjust the plot created in Problem 5.4 so that the xaxis goes from -6 to +6. In general, markers are included only on plots of measured data, not for calculated values. Do not include markers on any of the graphs. Recall that the appropriate MATLAB® syntax for 2x is 2* x.) 5.4 Adjust the plot created in Problem 5.3 so that: 5.3 Plot the following functions on the same graph for x values from - to selecting spacing to create a smooth plot: 1 = sin(x) » = sin(2x) Ys = sin (3x) (Hint. Save your final figure as p01_sinewaves.jpg. 1) Create an m-file called p01_sinewaves.m and solve the following problem inside it. Don't forget to include your name, the date, etc. Each of your m-files should include appropriate comments to identify the problem and to describe your calculation process. Point MATLAB/Freemat to this folder and create a separate m-file for each problem below. # add a colorbar to the bottom of the imageĬax = div.append_axes('bottom', size='5%', pad=0.4)Ĭbar = plt.Create a folder called assignment4. Im = ax.imshow(z, interpolation='nearest', cmap=cmap, clim=(0,5000)) # plot data, apply colormap, set limit such that our interpretation is correct # some data to plot: distance to point at (50,50) Lower = np.linspace(1, upper, lower.shape)Ĭmap = (cmap, name='m圜olorMap', N=cmap.shape) # range linearly between white (1,1,1) and the first color of the upper colormap # - modify the first three columns (RGB): # - initialize all entries to 1 to make sure that the alpha channel (4th column) is 1 # - 1/5 : custom colormap, ranging from white to the first color of the colormap single- or multi-color colormaps etc.įrom mpl_toolkits.axes_grid1 import make_axes_locatable For example, you can choose different types of interpolation: linear, exponential. The colormaps and their sizes depend on your problem. The choices depend fully on what you want to show. For any distance lower than some critical value, the colors will linearly go from white to the first color of the previously mentioned map.For any distance higher than some critical value, the colors will be taken from a standard colormap.To do this one has to create an RGBA-matrix: a matrix with on each row the amount (between 0 and 1) of Red, Green, Blue, and Alpha (transparency 0 means that the pixel does not have any coverage information and is transparent).Īs an example the distance to some point is plotted in two dimensions. The answer to get the result smooth lies in constructing your own colormap. #speed = np.ma.masked_where(speed < 0.4, speed)Ĭs = map.contourf(x,y,speed,levels, cmap='jet')Ĭbar = plt.colorbar(cs, orientation='horizontal', cmap='jet', spacing='proportional',ticks=ticks)Ĭbar.set_label('850 mb Vector Wind Anomalies (m/s)') Result with set_bad (problem: no smooth transition to white):Ĭode so far: from netCDF4 import Dataset as NetCDFFile Result with continuous colormap (problem: no white): I tried masking those values and using set_bad but I ended up with a real blocky appearance, losing the nice smooth contours seen in the original image. I'm close but can't quite figure out how to modify a matplotlib colormap to make values <0.4 go to white. I'm trying to produce a similar version of this image using Python:
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