#!/usr/bin/env python
# coding: utf-8

# Tutorial taken from http://www.ster.kuleuven.be/~pieterd/python/html/plotting/aplpy.html
# 
# Start off by downloading this tar file: http://www.ster.kuleuven.be/~pieterd/python/html/_downloads/ic348_wise.tar

# In[9]:


#import urllib, tarfile
#url = 'http://python4astronomers.github.com/_downloads/ic348_wise.tar'
#tarfile.open(fileobj=urllib.request.urlopen(url), mode='r|').extractall()


# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')

import matplotlib.pyplot as plt
import aplpy


# create a new figure to plot the FITS file with:

# In[2]:


# Launch APLpy figure of image
img = aplpy.FITSFigure('w1.fits')

# Apply grayscale mapping of image
img.show_grayscale()

# Or apply a different stretch to the image
img.show_grayscale(stretch='arcsinh')

# Specifically specify lower and upper limit
img.show_grayscale(stretch='arcsinh', vmin=1, vmax=290)

# Display a grid and tweak the properties
img.show_grid()

# Modify the tick labels for precision and format
img.tick_labels.set_xformat('hhmm')
img.tick_labels.set_yformat('ddmm')

# Move the tick labels
img.tick_labels.set_xposition('top')
img.tick_labels.set_yposition('right')
img.save('ic348_basic.eps')


# # Color Images
# Now let’s do something a bit more advanced where we’ll make a color image from the fits files and plot some extra features on top of it.
# conda install -c https://conda.anaconda.org/astropy montage-wrapper
# If you cannot get Montage and python-montage installed, then you can use the already produced files to follow along with the exercise:

# In[3]:


import montage_wrapper as montage


# In[4]:


import aplpy


# Make RGB cube - images do not need not to have the same WCS
aplpy.make_rgb_cube(['w3.fits','w2.fits','w1.fits'], 'cube.fits')

# Make RGB image - you can specify color parameters here
aplpy.make_rgb_image('cube.fits','rgb_image_linear.png')

# Customize it
aplpy.make_rgb_image('cube.fits','rgb_image_arcsinh.png',
                      stretch_r='arcsinh', stretch_g='arcsinh',
                      stretch_b='arcsinh')

# Launch APLpy figure of 2D cube
img = aplpy.FITSFigure('cube_2d.fits')
img.show_rgb('rgb_image_arcsinh.png')

# Maybe we would like the arcsinh stretched image more?
#img.show_rgb('ic348_color_arcsinh.png')

# Modify the tick labels for precision and format
img.tick_labels.set_xformat('hhmmss')
img.tick_labels.set_yformat('ddmm')

# Let's add a scalebar to it
img.add_scalebar(5/60.)
img.scalebar.set_label('5 arcmin')
img.scalebar.set_color('white')

# We may want to lengthen the scalebar, move it to the top left,
# and apply a physical scale
img.scalebar.set_corner('top left')
img.scalebar.set_length(17/60.)
img.scalebar.set_label('1 parsec')
img.save('ic348_color_arcsinh.pdf')


# # Publication Quality Images
# We’re ready at this point to start making science-grade plots with APLpy with contours and markers:
# conda install -c https://conda.anaconda.org/auto atpy

# In[16]:


import aplpy, atpy

# Launch APLpy figure of image
img = aplpy.FITSFigure('w2.fits')

# Apply grayscale mapping of image
img.show_grayscale()

# Or apply a different stretch to the image
img.show_grayscale(stretch='arcsinh')

# Specifically specify lower and upper limit
img.show_grayscale(stretch='arcsinh', vmin=10, vmax=180)

# Modify the tick labels for precision and format
img.tick_labels.set_xformat('hhmmss')
img.tick_labels.set_yformat('ddmm')

# ATpy to pull in sources
# If you want to see what columns are available - type t.describe()
t = atpy.Table('per_ysoc_c2d.fits')

# Now, let's display the young stellar objects (YSOs) in the region
# using c2d Spitzer data
img.show_markers(t.ra, t.dec)

# But let's make the markers bigger and triangles instead
# Before we can do that we need to list the layers
img.list_layers()
img.remove_layer('marker_set_1')

# Now we add the new markers
img.show_markers(t.ra, t.dec, marker='^', s=100)


# Let's add a contour from Av map of the region from COMPLETE Survey
#img.show_contour('per_extn2mass.fits', levels=[8,7,6,5,4], colors='orange')
img.show_contour('per_av_j2000.fits', levels=4, colors='orange')

img.save('ic348_marker_contour.png')


# In[6]:


import aplpy
import atpy

# Exercise

img = aplpy.FITSFigure('per_av_j2000.fits')

# Identical to show_grayscale
img.show_colorscale()

# Recenter the image with a defined width of 5 degrees
img.recenter(54.51, 31.39, radius=2.5)

# Show the grid
img.show_grid()

# Define tick label style
img.tick_labels.set_xformat('hhmm')
img.tick_labels.set_yformat('ddmm')

img.show_colorbar()

# Read in table using atpy
t = atpy.Table('per_ysoc_c2d.fits')

# Plot markers
img.show_markers(t.ra, t.dec, facecolor='black', edgecolor='none', s=20)

img.save('ic348_av_markers.png')


# # Subplots and DS9 Region Files
# With APLpy we’re able to make subplots within a figure just like Matplotlib and we can pull in a DS9 region files. Unfortunately, there is a bug in the official pyregion release, so it’s suggested that you download the latest development version here http://github.com/leejjoon/pyregion/zipball/master. Use pip to install the package like the following.:

# pip install pyregion/ --user --upgrade
# 
# conda install -c https://conda.anaconda.org/astropy pyregion
# 
# 

# Now, let’s work on an example that shows both capabilities below:

# In[7]:


import aplpy
from astropy.io import fits
import matplotlib.pyplot as mpl

fig = mpl.figure()

# Initiating first subplot
f1 = aplpy.FITSFigure('w4.fits', figure=fig)
f1.show_grayscale()

# Load a regions file into APLpy plot
f1.show_regions('bowshock.reg')

# Modify the tick labels for precision and format
f1.tick_labels.set_xformat('hhmmss')
f1.tick_labels.set_yformat('ddmm')

"""
# Another way of adding a subplot to the figure
d2 = fits.getdata('w4_bw1.fits')
f2 = fig.add_axes([0.3,0.3,0.11,0.15])
f2.imshow(d2, origin='lower left', cmap='jet')
#f2.imshow(d,cmap='jet')
f2.axes.get_xaxis().set_visible(False)
f2.axes.get_yaxis().set_visible(False)

"""
# Initiating second subplot
f2 = aplpy.FITSFigure('w4_bw1.fits', figure=fig,
                       subplot=[0.3,0.3,0.10,0.15])
f2.show_colorscale(cmap = 'jet')

# Hiding the ticks, tick labels (numbers) and axis labels (e.g. RA & Dec.)
f2.ticks.hide()
f2.tick_labels.hide()
f2.axis_labels.hide()
#"""


# Another way of adding a subplot to the figure
d3 = fits.getdata('w4_bw2.fits')
f3 = fig.add_axes([0.25,0.7,0.15,0.15])
f3.imshow(d3, origin='lower left', cmap='jet')
#f3.imshow(d,cmap='jet')
f3.axes.get_xaxis().set_visible(False)
f3.axes.get_yaxis().set_visible(False)

fig.canvas.draw()

fig.savefig('subplots.pdf', bbox_inches='tight')