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"""
Make X and Z Cuts:
- Density
- Velocity
- Pressure
- Temperature (Sound Speed)
"""

import matplotlib as mpl
from pymses import RamsesOutput
from pymses.filters import CellsToPoints
from pymses.utils import constants as C
from pymses.analysis.visualization import Camera, ScalarOperator, SliceMap, \
                                          FractionOperator
import matplotlib.pyplot as plt
import numpy as np
import sys

"""
Main Routine. Define Cameras. Take Slices. Plot.
"""
def main():

    # Defaults
    iout = 1

    # Parse Arguments
    if len(sys.argv) == 2:
    	iout = int(sys.argv[1])

    # Give Feedback
    print "Creating Cuts for Output %i." % iout
    print ""

    # Link AMR Data
    output = RamsesOutput(".", iout)
    source = output.amr_source(["rho", "vel", "P"])

    # Define Cameras
    camZ = Camera(center=[0.5, 0.5, 0.5], line_of_sight_axis='z',
    	region_size=[1., 1.], up_vector='y', map_max_size=512, log_sensitive=True)
    camX = Camera(center=[0.5, 0.5, 0.5], line_of_sight_axis='x',
    	region_size=[1., 1.], up_vector='y', map_max_size=512, log_sensitive=True)

    # Functions to Get Data
    func_rho = lambda dset: dset["rho"]
    func_vel = lambda dset: np.sqrt(np.sum(dset["vel"]**2, axis=1))
    # func_vel = lambda dset: dset["vel"][:,0]
    func_pre = lambda dset: dset["P"]

    # Operator to Get Data
    op_rho = ScalarOperator(func_rho)
    op_vel = ScalarOperator(func_vel)
    op_pre = ScalarOperator(func_pre)
    op_cs2 = FractionOperator(func_pre, func_rho)

    # Compute Slice Maps
    rhoZ = SliceMap(source, camZ, op_rho, z=0.0)
    rhoX = SliceMap(source, camX, op_rho, z=0.0)
    velZ = SliceMap(source, camZ, op_vel, z=0.0)
    velX = SliceMap(source, camX, op_vel, z=0.0)
    preZ = SliceMap(source, camZ, op_pre, z=0.0)
    preX = SliceMap(source, camX, op_pre, z=0.0)
    cs2Z = SliceMap(source, camZ, op_cs2, z=0.0)
    cs2X = SliceMap(source, camX, op_cs2, z=0.0)

    # Convert Human Mind Parsable Units Pt 1 - (CGS)
    rhoZ *= output.info["unit_density"].express(C.g_cc)
    rhoX *= output.info["unit_density"].express(C.g_cc)
    velZ *= output.info["unit_velocity"].express(C.cm / C.s)
    velX *= output.info["unit_velocity"].express(C.cm / C.s)
    preZ *= output.info["unit_pressure"].express(C.barye)
    preX *= output.info["unit_pressure"].express(C.barye)

    # Convert Human Mind Parsable Units Pt 2 - (km/s)
    # Sound Speed
    factor = output.info["unit_pressure"].express(C.barye) / \
             output.info["unit_density"].express(C.g_cc)
    cs2Z *= factor / 100.**2. / 1000.**2.
    cs2X *= factor / 100.**2. / 1000.**2.
    # Total Flow Speed
    velZ *= 1.0 / 100. / 1000.
    velX *= 1.0 / 100. / 1000.

    # Compute Temperature
    gamma = 1.4
    TX = (cs2X * 100.**2. * 1000.**2.) * 2. * C.mH.express(C.kg) / \
         gamma / C.kB.express(C.cm**2 * C.kg / C.s**2 / C.K)
    TZ = (cs2Z * 100.**2. * 1000.**2.) * 2. * C.mH.express(C.kg) / \
         gamma / C.kB.express(C.cm**2 * C.kg / C.s**2 / C.K)

    # Log10?
    rhoZ = np.log10(rhoZ)
    rhoX = np.log10(rhoX)
    preZ = np.log10(preZ)
    preX = np.log10(preX)

    # Mark Arrays vs. NaN
    rhoZ = np.ma.masked_array(rhoZ, mask=np.isnan(rhoZ))
    rhoX = np.ma.masked_array(rhoX, mask=np.isnan(rhoX))
    velZ = np.ma.masked_array(velZ, mask=np.isnan(velZ))
    velX = np.ma.masked_array(velX, mask=np.isnan(velX))
    preZ = np.ma.masked_array(preZ, mask=np.isnan(preZ))
    preX = np.ma.masked_array(preX, mask=np.isnan(preX))
    
    # Set Masked Colormap
    # cmap = mpl.cm.hot
    cmap = mpl.cm.jet
    cmap.set_bad([0.5, 0.5, 0.5])

    # Colormaps:
    # http://matplotlib.org/examples/pylab_examples/show_colormaps.html
    # rho_cm = 'hot'
    # vel_cm = 'hot'
    # pre_cm = 'hot'
    rho_cm = cmap
    vel_cm = cmap
    pre_cm = cmap

    # Compute Image Extent
    ext = output.info["boxlen"] * np.array([-0.5, 0.5, -0.5, 0.5])

    # Plot Density
    f1 = plt.figure(1)
    plt.imshow(rhoZ, cmap=rho_cm, extent=ext, interpolation='none')
    plt.colorbar()
    plt.grid()
    plt.title('Log10 Density Cut [g/cc], t=%.2f' % output.info["time"])
    plt.xlabel('X')
    plt.ylabel('Y')

    f2 = plt.figure(2)
    plt.imshow(rhoX, cmap=rho_cm, extent=ext, interpolation='none')
    plt.colorbar()
    plt.grid()
    plt.title('Log10 Density Cut [g/cc], t=%.2f' % output.info["time"])
    plt.xlabel('Y')
    plt.ylabel('Z')

    # Plot Velocity
    f3 = plt.figure(3)
    plt.imshow(velZ, cmap=vel_cm, extent=ext)
    plt.colorbar()
    plt.grid()
    plt.title('Total Flow Speed Cut [km/s], t=%.2f' % output.info["time"])
    plt.xlabel('X')
    plt.ylabel('Y')

    f4 = plt.figure(4)
    plt.imshow(velX, cmap=vel_cm, extent=ext)
    plt.colorbar()
    plt.grid()
    plt.title('Total Flow Speed Cut [km/s], t=%.2f' % output.info["time"])
    plt.xlabel('Y')
    plt.ylabel('Z')

    # Plot Pressure
    f5 = plt.figure(5)
    plt.imshow(preZ, cmap=pre_cm, extent=ext, interpolation='none')
    plt.colorbar()
    plt.grid()
    plt.title('Log10 Pressure Cut [debye], t=%.2f' % output.info["time"])
    plt.xlabel('X')
    plt.ylabel('Y')

    f6 = plt.figure(6)
    plt.imshow(preX, cmap=pre_cm, extent=ext, interpolation='none')
    plt.colorbar()
    plt.grid()
    plt.title('Log10 Pressure Cut [debye], t=%.2f' % output.info["time"])
    plt.xlabel('Y')
    plt.ylabel('Z')

    # Plot Temperature
    f7 = plt.figure(7)
    plt.imshow(TZ, cmap=pre_cm, extent=ext, interpolation='none')
    plt.colorbar()
    plt.grid()
    plt.title('Temperature Cut [K], t=%.2f' % output.info["time"])
    plt.xlabel('X')
    plt.ylabel('Y')

    f8 = plt.figure(8)
    plt.imshow(TX, cmap=pre_cm, extent=ext, interpolation='none')
    plt.colorbar()
    plt.grid()
    plt.title('Temperature Cut [K], t=%.2f' % output.info["time"])
    plt.xlabel('Y')
    plt.ylabel('Z')

    #plt.close(f1)
    #plt.close(f2)
    # plt.close(f3)
    # plt.close(f4)
    #plt.close(f5)
    #plt.close(f6)
 
    plt.show()


"""
Jump into Main().
"""
if __name__ == "__main__":
    main()