#!/usr/bin/python3

r'''A simple non-automated test script

This script makes some plots, and tests the error detection. One could run this
script, and make sure all the plots come up. This is NOT an automated test. For
a demo of the capabilities of gnuplotlib, see the guide at

https://github.com/dkogan/gnuplotlib/blob/master/guide/guide.org

'''

import numpy as np
import numpysane as nps
import time
import sys

import gnuplotlib as gp


# some simple infrastructure
def print_red(x):
    """print the message in red"""
    sys.stdout.write("\x1b[31m" + x + "\x1b[0m\n")
def print_green(x):
    """Print the message in green"""
    sys.stdout.write("\x1b[32m" + x + "\x1b[0m\n")
def check_expected_error(what, f):
    sys.stderr.write(what + '\n')
    sys.stderr.write("=================================\n")
    try:
        f()
    except gp.GnuplotlibError as e:
        print_green("OK! Got err I was supposed to get:\n[[[[[[[\n{}\n]]]]]]]".format(e))
    except Exception as e:
        print_red("ERROR! Got some other error I was NOT supposed to get: {}".format(e))
    else:
        print_red("ERROR! An error was supposed to be reported but it was not")




# data I use for 2D testing
x = np.arange(21) - 10

# data I use for 3D testing
th   = np.linspace(0,        np.pi*2, 30)
ph   = np.linspace(-np.pi/2, np.pi*2, 30)[:,np.newaxis]

x_3d = (np.cos(ph) * np.cos(th))          .ravel()
y_3d = (np.cos(ph) * np.sin(th))          .ravel()
z_3d = (np.sin(ph) * np.ones( th.shape )) .ravel()

rho = np.linspace(0, 2*np.pi, 1000)  # dim=(  1000,)
a   = np.arange(-4,3)[:, np.newaxis] # dim=(7,1)



#################################
# Now the demos!
#################################

# first, some very basic stuff. Testing implicit domains, multiple curves in
# arguments, packed broadcastable data, etc
gp.plot(x**2, wait=1)
gp.plot(( np.transpose(nps.cat(x,x**2)),
          dict(_with='linespoints pt 4 ps 2'),
         ),
        ( 5,60,
          dict(tuplesize=2,
               _with='linespoints pt 5 ps 2'),
        ),
        ( np.array((3,40)),
          dict(_with='linespoints pt 6 ps 2'),
        ),
        tuplesize = -2,
        wait=1)
gp.plot(-x, x**3, wait=1)
gp.plot((x**2), wait=1)
gp.plot((-x, x**3, dict(_with = 'lines')), (x**2,), wait=1)
gp.plot( x, nps.cat(x**3, x**2) , wait=1)
gp.plot( nps.cat(-x**3, x**2), _with='lines' , wait=1)
gp.plot( (nps.cat(x**3, -x**2), dict(_with = 'points') ), wait=1)

gp.plot( (nps.cat(x**3, -x**2),
          dict(_with = 'points') ),
         y2 = np.array((0,1)),
         title = 'Broadcasting on y2',
         wait=1)
gp.plot( (nps.cat(x**3, -x**2),
          dict(_with = 'points') ),
         axes = np.array(('x1y1','x2y2')),
         title = 'Broadcasting on axes',
         wait=1)

# Make sure xrange settings don't get overridden. The label below should be out
# of bounds, and not visible
gp.plot( ( np.arange(10), ),
         ( np.array((5,),), np.array((2,),), np.array(("Seeing this is a bug!",),),
           dict(_with = 'labels',
                tuplesize = 3)),
         ( np.array((5,),), np.array((7,),), np.array(("This SHOULD be visible. Another label should be out-of-view, below the x-axis",),),
           dict(_with = 'labels',
                tuplesize = 3)),
         _set  = 'yrange [5:10]',
         unset = 'grid',
         wait  = True)

# # This should make no plot at all, with a warning that all the data is out of
# # bounds. I haven't written a test harness to look at stderr output yet, so I
# # disable this check
# gp.plot( np.arange(10),
#          _set = 'xrange [10:20]',
#          wait = True)

#################################
# some more varied plotting, using the object-oriented interface
plot1 = gp.gnuplotlib(_with = 'linespoints',
                      xmin  = -10,
                      title = 'Error bars and other things',
                      wait = 1)

plot1.plot( ( nps.cat(x, x*2, x*3), x**2 - 300,
              dict(_with  = 'lines lw 4',
                   y2     = True,
                   legend = 'parabolas')),

            (x**2 * 10, x**2/40, x**2/2, # implicit domain
             dict(_with      =      'xyerrorbars',
                  tuplesize = 4)),

            (x, nps.cat(x**3, x**3 - 100),
             dict(_with     = 'lines',
                  legend    = 'shifted cubics',
                  tuplesize = 2)))

#################################





# a way to control the point size
gp.plot( x**2, np.abs(x)/2, x*50,
         cbrange = '-600:600',
         _with   = 'points pointtype 7 pointsize variable palette',
         tuplesize = 4,
         wait = 1)

# labels
gp.plot(np.arange(5),np.arange(5)+1,
        np.array( ['{} {}'.format(x,x+1) for x in range(5)], dtype=str),
        _with='labels', tuplesize=3, ascii=1,
        wait = 1)

# Conchoids of de Sluze. Broadcasting example
gp.plot( rho,
         1./np.cos(rho) + a*np.cos(rho), # broadcasted. dim=(7,1000)

         _with  = 'lines',
         set    = 'polar',
         square = True,
         yrange = [-5,5],
         legend = a.ravel(),
         wait = 1)


################################
# some 3d stuff
################################

# gp.plot a sphere
gp.plot3d( x_3d, y_3d, z_3d,
           _with = 'points',
           title  = 'sphere',
           square = True,
           legend = 'sphere',
           wait = 1)

# sphere, ellipse together
gp.plot3d( (x_3d * nps.transpose(np.array([[1,2]])),
            y_3d * nps.transpose(np.array([[1,2]])),
            z_3d,
            dict( legend = np.array(('sphere', 'ellipse')))),

           title  = 'sphere, ellipse',
           square = True,
           _with  = 'points',
           wait = 1)


# similar, written to a png
gp.plot3d( (x_3d * nps.transpose(np.array([[1,2]])),
            y_3d * nps.transpose(np.array([[1,2]])),
            z_3d,
            dict( legend = np.array(('sphere', 'ellipse')))),

           title    = 'sphere, ellipse',
           square   = True,
           _with    = 'points',
           hardcopy = 'spheres.png',
           wait = 1)




# some paraboloids plotted on an implicit 2D domain
xx,yy = np.ogrid[-10:11, -10:11]
zz    = xx*xx + yy*yy
gp.plot3d( ( zz,  dict(legend = 'zplus')),
           (-zz,  dict(legend = 'zminus')),
           (zz*2, dict(legend = 'zplus2')),

           _with = 'points', title  = 'gridded paraboloids', ascii=True,
           wait = 1)

# 3d, variable color, variable pointsize
th2   = np.linspace(0, 6*np.pi, 200)
zz    = np.linspace(0, 5,       200)
size  = 0.5 + np.abs(np.cos(th2))
color = np.sin(2*th2)

gp.plot3d( ( np.cos(th2) * nps.transpose(np.array([[1,-1]])),
             np.sin(th2) * nps.transpose(np.array([[1,-1]])),
             zz, size, color, dict( legend = np.array(('spiral 1', 'spiral 2')))),

           title     = 'double helix',
           tuplesize = 5,
           _with = 'points pointsize variable pointtype 7 palette',
           wait = 1)


# implicit domain heat map
xx,yy = np.ogrid[-10:11, -10:11]
zz    = xx*xx + yy*yy
gp.plot3d(zz,
          title = 'Paraboloid heat map',
          set   = 'view map',
          _with = 'image',
          wait = 1)

# same, but as a 2d gp.plot, _with a curve drawn on top for good measure
xx,yy = np.ogrid[-10:11, -10:11]
zz    = xx*xx + yy*yy
xx    = np.linspace(0,20,100)
gp.plot( ( zz, dict(tuplesize = 3,
                    _with     = 'image')),
         (xx, 20*np.cos(xx/20 * np.pi/2),

          dict(tuplesize = 2,
               _with     = 'lines')),

         title  = 'Paraboloid heat map, 2D',
         xmin = 0,
         xmax = 20,
         ymin = 0,
         ymax = 20,
         wait = 1)



################################
# 2D implicit domain demos
################################
xx,yy = np.mgrid[-10:11, -10:11]
zz    = np.sqrt(xx*xx + yy*yy)

xx  = xx[:, 2:12]
zz  = zz[:, 2:12]

# single 3d matrix curve
gp.plot(zz,
        title     = 'Single 3D matrix plot. Binary.',
        square    = 1,
        tuplesize = 3,
        _with     = 'points palette pt 7',
        ascii     = False,
        wait      = 1)

# 4d matrix curve
gp.plot(zz, xx,
        title     = '4D matrix plot. Binary.',
        square    = 1,
        tuplesize = 4,
        _with     = 'points palette ps variable pt 7',
        ascii     = False,
        wait      = 1)

# Using broadcasting to plot each slice with a different style
gp.plot((nps.cat(xx,zz),
         dict(tuplesize = 3,
              _with     = np.array(('points palette pt 7','points ps variable pt 6')))),

        title  = 'Two 3D matrix plots. Binary.',
        square = 1,
        ascii  = False,
        wait   = 1)

# # Gnuplot doesn't support this
# gp.plot(z, x,
#         title     = '4D matrix plot. Binary.',
#         square    = 1,
#         tuplesize = 4,
#         _with     = 'points palette ps variable pt 7',
#         ascii     = True,
#         wait      = 1)
#
# 2 3d matrix curves
gp.plot((nps.cat(xx,zz),
         dict(tuplesize = 3,
              _with     = np.array(('points palette pt 7','points ps variable pt 6')))),

        title  = 'Two 3D matrix plots. Binary.',
        square = 1,
        ascii  = True,
        wait   = 1)

###################################
# fancy contours just because I can
###################################
yy,xx = np.mgrid[0:61,0:61]
xx -= 30
yy -= 30
zz = np.sin(xx / 4.0) * yy

# single 3d matrix curve. Two plots: the image and the contours together.
# Broadcasting the styles
gp.plot3d( (zz, dict(tuplesize = 3,
                     _with     = np.array(('image','lines')))),

           title = 'matrix plot with contours',
           _set  = [ 'contours base',
                     'cntrparam bspline',
                     'cntrparam levels 15',
                     'view 0,0'],
           unset  = 'grid',
           _unset = 'surface',
           square = 1,
           wait = 1)

################################
# multiplot
################################

# basics
gp.plot( th, nps.cat( np.cos(th), np.sin(th)),
         title = 'broadcasting sin, cos',
         _xrange = [0,2.*np.pi],
         _yrange = [-1,1],
         wait = 1)

gp.plot( (th, np.cos(th)),
         (th, np.sin(th)),
         title = 'separate plots for sin, cos',
         _xrange = [0,2.*np.pi],
         _yrange = [-1,1],
         wait = 1)

gp.plot( (th, np.cos(th), dict(title="cos",
                               _xrange = [0,2.*np.pi],
                               _yrange = [-1,1],)),
         (th, np.sin(th), dict(title="sin",
                               _xrange = [0,2.*np.pi],
                               _yrange = [-1,1])),
         multiplot='title "multiplot sin,cos" layout 2,1',
         wait = 1)

gp.plot( (x**2,),
         (-x, x**3),
         ( rho,
           1./np.cos(rho) + a*np.cos(rho), # broadcasted. dim=(7,1000)

           dict( _with  = 'lines',
                 set    = 'polar',
                 square = True,
                 yrange = [-5,5],
                 legend = a.ravel())),
         (x_3d, y_3d, z_3d,
          dict( _with = 'points',
                title  = 'sphere',
                square = True,
                legend = 'sphere',
                _3d    = True)),
         wait=1,
         multiplot='title "basic multiplot" layout 2,2', )

# fancy contours stacked on top of one another. Using multiplot to render
# several plots directly onto one another
xx,yy = np.meshgrid(np.linspace(-5,5,100),
                    np.linspace(-5,5,100))
zz0 = np.sin(xx) + yy*yy/8.
zz1 = np.sin(xx) + yy*yy/10.
zz2 = np.sin(xx) + yy*yy/12.

commonset = ( 'origin 0,0',
              'size 1,1',
              'view 60,20,1,1',
              'xrange [0:100]',
              'yrange [0:100]',
              'zrange [0:150]',
              'contour base' )
for hardcopy in (None, "stacked-contours.png", "stacked-contours.gp",):

    gp.plot3d( (zz0, dict(_set = commonset + ('xyplane at 10',))),
               (zz1, dict(_set = commonset + ('xyplane at 80',  'border 15'), unset=('ztics',))),
               (zz2, dict(_set = commonset + ('xyplane at 150', 'border 15'), unset=('ztics',))),

               tuplesize=3,
               _with = np.array(('lines nosurface',
                                 'labels boxed nosurface')),
               square=1,
               wait=True,
               hardcopy=hardcopy,
               multiplot=True)


################################
# testing some error detection
################################

sys.stderr.write("\n\n\n")
sys.stderr.write("==== Testing error detection ====\n")

check_expected_error('I should complain about an invalid "with"',
                     lambda: gp.plot(np.arange(5), _with = 'bogusstyle'))

check_expected_error('Error detection in multiplots',
                     lambda: gp.plot( (x**2,),
                                      (-x, x**3),
                                      ( rho,
                                        1./np.cos(rho) + a*np.cos(rho), # broadcasted. dim=(7,1000)

                                        dict( _with  = 'lines',
                                              set    = 'poflar',
                                              square = True,
                                              yrange = [-5,5],
                                              legend = a.ravel())),
                                      (x_3d, y_3d, z_3d,
                                       dict( _with = 'points',
                                             title  = 'sphere',
                                             square = True,
                                             legend = 'sphere',
                                             _3d    = True)),
                                      wait=1,
                                      multiplot='title "basic multiplot" layout 2,2', ) )

check_expected_error('gnuplotlib can detect I/O hangs. Here I ask for a delay, so I should detect this and quit after a few seconds...',
                     lambda: gp.plot( np.arange(5), cmds = 'pause 20' ))