LNC_plot_v1.py

def depol_plot(xdata, ydata, data):
    import matplotlib.pyplot as plt
    import matplotlib.colors as colors

    #set date(s) for plot title
    days = list(set(t.strftime('%B %d, %Y') for t in xdata))
    days.sort()
    sdays = list(set(t.strftime('%Y%m%d') for t in xdata))
    sdays.sort()
    
    if len(days) == 1:
        titledays = days[0]
        savedays = sdays[0]
    else:
        titledays = '%s - %s'%(days[0],days[-1])
        savedays = '%s-%s'%(sdays[0],sdays[-1])

    title = 'Depolarization Ratio: %s'%titledays
    savetitle = 'Depolrat_%s'%savedays
    
    #set colormap to be the same as 'jet' with the addition of white color for
    #depol ratios set to identiacally zero because they couldn't be calculated
    cdict = {'red': ((0,1,1),
                     (0.0001, 1, 0),
                     (0.35, 0, 0),
                     (0.66, 1, 1),
                     (0.89,1, 1),
                     (1, 0.5, 0.5)),
         'green': ((0,1,1),
                   (0.0001, 1, 0),
                   (0.125,0, 0),
                   (0.375,1, 1),
                   (0.64,1, 1),
                   (0.91,0,0),
                   (1, 0, 0)),
         'blue': ((0,1,1),
                  (0.0001,1,0.5),
                  (0.11, 1, 1),
                  (0.34, 1, 1),
                  (0.65,0, 0),
                  (1, 0, 0))}
    
    my_cmap = colors.LinearSegmentedColormap('my_colormap',cdict,1064)
    
    #create figure and plot image of depolarization ratios
    font = 21 #baseline font size
    ar = 2.0  #aspect ratio
    figheight = 10 #inches

    plt.rc('font', family='serif', size=font)
    
    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    
    im = ax1.imshow(data, vmin=0, vmax=0.5, cmap = my_cmap)
    forceAspect(ax1,ar)
    t = ax1.set_title(title, fontsize = font+10)
    t.set_y(1.1)
    plt.subplots_adjust(top = 0.86, bottom = 0.01, left = 0.09, right = 0.95)
    
    cbar = fig.colorbar(im, orientation = 'horizontal', pad = 0.15, aspect = 40)
    cbar.ax.tick_params(labelsize = font)

    #set axis ranges and tickmarks based on data ranges
    dateticks(ax1, xdata, font = font)
    ax1.set_xlabel('Time [PDT]',fontsize = font+4, labelpad = 15)
    fig.autofmt_xdate()

    for line in ax1.xaxis.get_ticklines():
        line.set_markersize(10)
        line.set_markeredgewidth(1)
        
    altticks(ax1, ydata, font = font)

    
    ax1.set_ylabel('Altitude [m]', fontsize = font+4, labelpad = 15)

    for line in ax1.yaxis.get_ticklines():
        line.set_markersize(10)
        line.set_markeredgewidth(1)
        
    ax1.axis('tight')
    fig.set_size_inches(figheight*ar,figheight)
    plt.savefig(savetitle,dpi = 100, edgecolor = 'b', bbox_inches = 'tight')
    plt.show()

    

def forceAspect(ax,aspect=1):
    im = ax.get_images()
    extent =  im[0].get_extent()
    ax.set_aspect(abs((extent[1]-extent[0])/(extent[3]-extent[2]))/aspect)

def dateticks(ax, axisdat,numticks = 10, font = 21):
    import matplotlib.pyplot as plt
    from time import strftime
    
    dold = axisdat[0].strftime('%d')
    tickmarks = []
    ticklabels = []
    fontsize = []
    n = 0
    l = len(axisdat)

    hours = ['06','12','18']
   # hours = ['03','06','09','12','15','18','21']
    
    for d in axisdat:
        dtemp = d.strftime('%d')
        if dtemp != dold:
            ticklabels.append(d.strftime('%H:%M \n %b %d'))
            tickmarks.append(n)
        else:
            htemp = d.strftime('%H')
            mtemp = d.strftime('%M')
            if htemp in hours and mtemp == '00':
                ticklabels.append(d.strftime('%H:%M'))
                tickmarks.append(n)

        dold = dtemp
        n += 1
    
    ax.set_xticks(tickmarks)
    ax.set_xticklabels(ticklabels)

def altticks(ax, axisdat, numticks = 10, font = 21):
    import matplotlib.pyplot as plt

    numpoints = len(axisdat)
    step = numpoints//numticks
    ticklabels = axisdat[::step]
    tickmarks = range(0,numpoints,step)
    ticklabels = [str(int(t)) for t in ticklabels]

    plt.yticks(tickmarks,ticklabels, fontsize = font)

def backscatter_plot(xdata, ydata, data):
    import matplotlib.pyplot as plt
    import matplotlib.colors as colors
    import numpy as np

    #set date(s) for plot title
    days = list(set(t.strftime('%B %d, %Y') for t in xdata))
    days.sort()
    sdays = list(set(t.strftime('%Y%m%d') for t in xdata))
    sdays.sort()
    
    if len(days) == 1:
        titledays = days[0]
        savedays = sdays[0]
    else:
        titledays = '%s - %s'%(days[0],days[-1])
        savedays = '%s-%s'%(sdays[0],sdays[-1])

    title = '1064nm Backscatter Ratio: %s'%titledays
    savetitle = 'Backrat_%s'%savedays
    
    #create figure and plot image of depolarization ratios
    font = 21 #baseline font size
    ar = 2.0  #aspect ratio
    figheight = 10 #inches

    plt.rc('font', family='serif', size=font)
    
    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    
    im = ax1.imshow(data, norm = colors.LogNorm(vmin = 1, vmax = 10),
                    cmap = plt.cm.jet)
    forceAspect(ax1,ar)
    t = ax1.set_title(title, fontsize = font+10)
    t.set_y(1.1)
    plt.subplots_adjust(top = 0.86, bottom = 0.01, left = 0.09, right = 0.95)
    
    cbar = fig.colorbar(im, orientation = 'horizontal', pad = 0.2, aspect = 40)
    cbar.ax.tick_params(labelsize = font)

    #set axis ranges and tickmarks based on data ranges
    dateticks(ax1, xdata, font = font)
    ax1.set_xlabel('Time [PDT]',fontsize = font+4, labelpad = 15)
    fig.autofmt_xdate()

    for line in ax1.xaxis.get_ticklines():
        line.set_markersize(10)
        line.set_markeredgewidth(1)
        
    altticks(ax1, ydata, font = font)

    
    ax1.set_ylabel('Altitude [m]', fontsize = font+4, labelpad = 15)

    for line in ax1.yaxis.get_ticklines():
        line.set_markersize(10)
        line.set_markeredgewidth(1)
        
    ax1.axis('tight')
    fig.set_size_inches(figheight*ar,figheight)
    plt.savefig(savetitle,dpi = 100, edgecolor = 'b', bbox_inches = 'tight')
    plt.show()
    
if __name__ == '__main__':
    import pandas as pan
    import os
    import LNC_tools_v2 as LNC

    os.chdir('F:\CORALNet\Data\ASCII Files')
    
    
    filename = LNC.get_files('Select file to be plotted', filetype = ('.pickle','*.pickle'))
    df = pan.load(filename[1:-1])
    datetime = df.index
    alt = df.columns

    backscatter_plot(datetime, alt[::-1], df.T[::-1])