flask_project/simple_plot.py at master · bcommerc/flask_project · GitHub

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import os
import glob
import numpy as np
import scipy
import matplotlib
import matplotlib.pylab as plt
import datetime
import time
import random
import astropy
from astropy.io import fits
from astropy import units as u
from astropy import table
#import bokeh.mpl
#import mpld3
plt.rcParams['figure.figsize'] = (12,8)

def plotData(NQuery, table, FigureStrBase, SurfMin=1e-1*u.M_sun/u.pc**2,
             SurfMax=1e5*u.M_sun/u.pc**2, VDispMin=1e-1*u.km/u.s,
             VDispMax=3e2*u.km/u.s, RadMin=1e-2*u.pc, RadMax=1e3*u.pc,):

    """
    This is where documentation needs to be added

    Parameters
    ----------
    NQuery
    FigureStrBase : str
        The start of the output filename, e.g. for "my_file.png" it would be
        my_file
    SurfMin
    SurfMax
    VDispMin
    VDispMax
    RadMin
    RadMax
    """

    figure = plt.figure(1)
    figure.clf()
    ax = figure.gca()

    # d = table.Table.read("merged_table.ipac", format='ascii.ipac')
    d = table
    Author = d['Names']
    Run = d['IDs']
    SurfDens = d['SurfaceDensity']
    VDisp = d['VelocityDispersion']
    Rad = d['Radius']
    if d['IsSimulated'].dtype == 'bool':
        IsSim = d['IsSimulated']
    else:
        IsSim = d['IsSimulated'] == 'True'

    UseSurf = (SurfDens > SurfMin) & (SurfDens < SurfMax)
    UseVDisp = (VDisp > VDispMin) & (VDisp < VDispMax)
    UseRad = (Rad > RadMin) & (Rad < RadMax)
    Use = UseSurf & UseVDisp & UseRad
    Obs = (~IsSim) & Use
    Sim = IsSim & Use

    UniqueAuthor = set(Author[Use])
    NUniqueAuthor = len(UniqueAuthor)

    #print d
    #print d[Use]
    #print 'Authors:', UniqueAuthor

    #colors = random.sample(matplotlib.colors.cnames, NUniqueAuthor)
    colors = list(plt.cm.jet(np.linspace(0,1,NUniqueAuthor)))
    random.shuffle(colors)

    ax.loglog()
    markers = ['o','s']
    for iAu,color in zip(UniqueAuthor,colors) :
        UsePlot = (Author == iAu) & Use
        ObsPlot = ((Author == iAu) & (~IsSim)) & Use
        SimPlot = ((Author == iAu) & (IsSim)) & Use
        if any(ObsPlot):
            ax.scatter(SurfDens[ObsPlot], VDisp[ObsPlot], marker=markers[0],
                        s=(np.log(np.array(Rad[ObsPlot]))-np.log(np.array(RadMin))+0.5)**3.,
                        color=color, alpha=0.5)
        if any(SimPlot):
            ax.scatter(SurfDens[SimPlot], VDisp[SimPlot], marker=markers[1],
                        s=(np.log(np.array(Rad[SimPlot]))-np.log(np.array(RadMin))+0.5)**3.,
                        color=color, alpha=0.5)
    if any(Obs):
        ax.scatter(SurfDens[Obs], VDisp[Obs], marker=markers[0],
                    s=(np.log(np.array(Rad[Obs]))-np.log(np.array(RadMin))+0.5)**3.,
                    facecolors='none', edgecolors='black',
                    alpha=0.5)
    if any(Sim):
        ax.scatter(SurfDens[Sim], VDisp[Sim], marker=markers[1],
                    s=(np.log(np.array(Rad[Sim]))-np.log(np.array(RadMin))+0.5)**3.,
                    facecolors='none', edgecolors='black',
                    alpha=0.5)
    ax.set_xlabel('$\Sigma$ [M$_{\odot}$ pc$^{-2}$]', fontsize=16)
    ax.set_ylabel('$\sigma$ [km s$^{-1}$]', fontsize=16)

    box = ax.get_position()
    ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])

    #html_bokeh = bokeh.mpl.to_bokeh(fig=figure, name="bokeh_"+FigureStrBase+NQuery)
    #html = mpld3.fig_to_html(figure)
    #with open("mpld3_"+FigureStrBase+NQuery+'.html','w') as f:
    #    f.write(html)

    ax.set_xlim((SurfMin.to(u.M_sun/u.pc**2).value,SurfMax.to(u.M_sun/u.pc**2).value))
    ax.set_ylim((VDispMin.to(u.km/u.s).value,VDispMax.to(u.km/u.s).value))

    # Put a legend to the right of the current axis
    ax.legend(UniqueAuthor, loc='center left', bbox_to_anchor=(1.0, 0.5), prop={'size':12}, markerscale = .7, scatterpoints = 1)

    plt.show()
    figure.savefig(FigureStrBase+NQuery+'.png',bbox_inches='tight',dpi=150)
    figure.savefig(FigureStrBase+NQuery+'.pdf',bbox_inches='tight',dpi=150)
    return FigureStrBase+NQuery+'.png'#, html, html_bokeh

def clearPlotOutput(FigureStrBase,TooOld) :

    for fl in glob.glob(FigureStrBase+"*.png") + glob.glob(FigureStrBase+"*.pdf"):
        now = time.time()
        if os.stat(fl).st_mtime < now - TooOld :
            os.remove(fl)

def timeString() :

    TimeString=datetime.datetime.now().strftime("%Y%m%d%H%M%S%f")
    return TimeString

# NQuery=timeString()
# FigureStrBase='Output_Sigma_sigma_r_'
# TooOld=300
#
# clearPlotOutput(FigureStrBase,TooOld)
#
# plotData(NQuery,FigureStrBase,SurfMin,SurfMax,VDispMin,VDispMax,RadMin,RadMax)
#
# #d.show_in_browser(jsviewer=True)
#
#