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

# # Intro

# This notebook demonstrates basic usage of the firefly_client API.
# 
# Note that it may be necessary to wait for some cells (like those displaying an image) to complete before executing later cells.

# ## Setup

# Imports for firefly_client:

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from firefly_client import FireflyClient
import astropy.utils.data
using_lab = False
#url = 'https://irsa.ipac.caltech.edu/irsaviewer'
url = 'http://127.0.0.1:8080/firefly'
#FireflyClient._debug= True


# The firefly_client needs to connect to a firefly server. In this example, we use the IRSA Viewer application for the server:

# Instantiate `FireflyClient` using the URL above:

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fc = FireflyClient.make_lab_client() if using_lab else FireflyClient.make_client(url)


# ## Download Sample Data

# For the following example, we download a sample image and table by using astropy (it's used only for convenience, firefly_client itself does not depend on astropy):

# Download a cutout of a WISE band as 1 FITS image:

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image_url = ('http://irsa.ipac.caltech.edu/ibe/data/wise/allsky/4band_p1bm_frm/6a/02206a' + 
             '/149/02206a149-w1-int-1b.fits?center=70,20&size=200pix')
filename = astropy.utils.data.download_file(image_url, cache=True, timeout=120)


# Download a 2MASS catalog using an IRSA VO Table Access Protocol ([TAP](https://irsa.ipac.caltech.edu/docs/program_interface/astropy_TAP.html)) search:

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table_url = ("http://irsa.ipac.caltech.edu/TAP/sync?FORMAT=IPAC_TABLE&" +
                                             "QUERY=SELECT+*+FROM+fp_psc+WHERE+CONTAINS(POINT('J2000',ra,dec)," +
                                             "CIRCLE('J2000',70.0,20.0,0.1))=1")
tablename = astropy.utils.data.download_file(table_url, timeout=120, cache=True)


# ## Display Tables and Catalogs

# Instantiating FireflyClient should open a browser to the firefly server in a new tab. You can also achieve this using the following method. The browser open only works when running the notebook locally, otherwise a link is displayed.

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# localbrowser, browser_url = fc.launch_browser()


# If it does not open automatically, try using the following command to display a web browser link to click on:

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fc.display_url()


# Alternatively, uncomment the lines below (while commenting out the 2 lines above) to display the browser application in the notebook:

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# from IPython.display import IFrame
# print('url: %s' % fc.get_firefly_url())
# IFrame(fc.get_firefly_url(), 1100, 1000)


# The current example involves 2 images in FITS format to be viewed through the chosen browser, both in different ways.
# For the 1st method, upload a local file to the server and then display it:

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imval = fc.upload_file(filename)
status = fc.show_fits(file_on_server=imval, plot_id="wise-cutout", title='WISE Cutout')


# For the 2nd method, pull an image directly from a URL:

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status = fc.show_fits(file_on_server=None, plot_id="wise-fullimage", 
                      URL='http://irsa.ipac.caltech.edu/ibe/data/wise/allsky/4band_p1bm_frm/6a/02206a' + 
             '/149/02206a149-w1-int-1b.fits')


# Upload a catalog table (with coordinates) to the server so it will overlay the image with markers by default. Note that markers can be visible on both "wise-cutout" and "wise-fullimage" since they would include the same field of view:

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file= fc.upload_file(tablename)
status = fc.show_table(file, tbl_id='tablemass', title='My 2MASS Catalog', page_size=50)


# Add an xy plot using the uploaded table data:

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status = fc.show_xyplot(tbl_id='tablemass', xCol='j_m', yCol='h_m-k_m')


# Alternatively, more generic method show_chart() can be used to create the same plot

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# trace0 = {'tbl_id': 'tablemass', 'x': "tables::j_m", 'y': "tables::h_m-k_m", 
#           'type' : 'scatter', 'mode': 'markers'}
# status = fc.show_chart(data=[trace0])


# Zoom into the full image by a factor of 2 (note the `plot_id` parameter we used earlier)

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status = fc.set_zoom('wise-fullimage', 2)


# Pan the full image to center on a given celestial coordinate:

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status = fc.set_pan('wise-fullimage', x=70, y=20, coord='J2000')


# Set the stretch for the full image based on IRAF zscale interval with a linear algorithm:

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status = fc.set_stretch('wise-fullimage', stype='zscale', algorithm='linear')


# Change color of the image:

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status = fc.set_color('wise-fullimage', colormap_id=6, bias=0.6, contrast=1.5)


# Add region data to the cutout image (2 areas to begin with):

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my_regions= ['image;polygon 125 25 160 195 150 150 #color=cyan',
            'icrs;circle 69.95d 20d 30i # color=orange text={region 5/7}']
status = fc.add_region_data(region_data=my_regions, region_layer_id='layer1',
                  plot_id='wise-cutout')


# Remove the second region whlie keeping the first one visible:

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fc.remove_region_data(region_data=my_regions[1], region_layer_id='layer1')


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