How do we add text, shapes, and other annotations to Matplotlib plots? O By passing a list of strings to the corresponding function or method By passing the (x.y) coordinates to the corresponding function or method By passing an indicator variable indicating the quadrant of the graph for annotation to the corresponding function or method O By passing the axis labels for each object to the corresponding function or method

Using Java Create data-files/model/cube.off, an axis-aligned cube with unit edge lengths, centered at the origin.

Greetings I am trying to loop code over several FITS files to overlay several plots. At the moment I am only getting one graph (see pic) Ideally I would like to loop through several FITS that are in a folder on my Desktop. this is what I have so far.  Im thinking that I will need to define a function to be able to do this. import numpy as npimport matplotlib.pyplot as pltfrom astropy.io import fitsfrom astropy.wcs import WCSfrom mpl_interactions import ioff, panhandler, zoom_factory%matplotlib widget spec_list = ['~/Desktop/r.HARPN.2018-01-18T14:25:36.145_CCF_A.fits',            '~/Desktop/r.HARPN.2018-01-18T14:20:10.645_CCF_A.fits',            '~/Desktop/r.HARPN.2018-01-18T14:31:00.875_CCF_A.fits',            '~/Desktop/r.HARPN.2018-01-18T14:36:26.373_CCF_A.fits'] for i in spec_list:    hdul = fits.open(i)    data = hdul[1].data    h1 = hdul[1].header    flux = data[1]    w = WCS(h1, naxis=1, relax=False, fix=False)    lam = w.wcs_pix2world(np.arange(len(flux)), 0)[0]…

I would like to represent the "residuals" part of the code (see below) as a surface (meshplot), in essence a 3D plot of 2D data # Import librariesimport matplotlib.pyplot as pltimport globfrom astropy.io import fitsfrom astropy.wcs import WCSimport numpy as npimport seaborn as sns # Initial plotfig, ax = plt.subplots(figsize=(7, 7)) # Data from the FITSdata_dir = glob.glob('/Users/petrderuyter/Desktop/harpn_sun_release_package_ccf_2018/2018-04-02/*.fits') #'/Users/xxxxxxxxxxxx/Desktop/harpn_sun_release_package_ccf_2018/2018-01-18/*.fits' # Empty lists for storagelams = []fluxs = [] # Read the FITS files and extract datafor file_path in data_dir:    hdul = fits.open(file_path)    data = hdul[1].data    h1 = hdul[1].header    flux = data[1]    w = WCS(h1, naxis=1, relax=False, fix=False)    lam = w.wcs_pix2world(np.arange(len(flux)), 0)[0]    lams.append(lam)    fluxs.append(flux)    ax.plot(lam, flux) # Calculate mean fluxmean_ccf = np.mean(fluxs, axis=0) # New figure for the mean…