from autofit import conf
import os

# Setup the path to the workspace, using a relative directory name.
workspace_path = '{}/../'.format(os.path.dirname(os.path.realpath(__file__)))

# Use this path to explicitly set the config path and output path.
conf.instance = conf.Config(config_path=workspace_path + 'config', output_path=workspace_path + 'output')

from autofit.tools import path_util
from autocti.data import mask as msk
from autocti.charge_injection import ci_frame
from autocti.charge_injection import ci_pattern
from autocti.charge_injection import ci_data

from autocti.model import arctic_settings

from multiprocessing import Pool

# Welcome to the pipeline runner. This tool allows you to CTI calibration data on strong lenses, and pass it to
# pipelines for a PyAutoCTI analysis. To show you around, we'll load up some example data and run it through some of
# the example pipelines that come distributed with PyAutoCTI.

# The runner is supplied as both this Python script and a Juypter notebook. Its up to you which you use - I personally
# prefer the python script as provided you keep it relatively small, its quick and easy to comment out data-set names
# and pipelines to perform different analyses. However, notebooks are a tidier way to manage visualization - so
# feel free to use notebooks. Or, use both for a bit, and decide your favourite!

# The pipeline runner is fairly self explanatory. Make sure to checkout the pipelines in the
#  workspace/pipelines/examples/ folder - they come with detailed descriptions of what they do. I hope that you'll
# expand on them for your own personal scientific needs

# It is convenient to specify the image type and model used to simulate that image as strings, so that if the
# pipeline is applied to multiple images we don't have to change all of the path entries in the
# load_ci_data_from_fits function below.

ci_data_type = 'ci_images_uniform_cosmic_rays' # Charge injection data consisting of 2 images with uniform injections.
ci_data_model = 'parallel_x2' # Shows the data was creating using a parallel CTI model with one species.
ci_data_resolution = 'low_res' # The resolution of the image.

# Create the path where the data will be loaded from, which in this case is
# '/workspace/data/ci_images_uniform/serial_x3_species/high_res/'
ci_data_path = path_util.make_and_return_path_from_path_and_folder_names(
    path=workspace_path, folder_names=['data', ci_data_type, ci_data_model, ci_data_resolution])

# These tools loads the correct shape, frame geometry, normalizations and charge injection regions for parallel CTI.
shape = tools.parallel_shape_from_ci_data_resolution(ci_data_resolution=ci_data_resolution)
frame_geometry = tools.parallel_frame_geometry_from_ci_data_resolution(ci_data_resolution=ci_data_resolution)
normalizations = tools.normalization_from_ci_data_resolution(ci_data_resolution=ci_data_resolution)
normalization_tags = tools.normalization_tags_from_ci_data_resolution(ci_data_resolution=ci_data_resolution)
ci_regions = tools.parallel_ci_regions_from_ci_data_resolution(ci_data_resolution=ci_data_resolution)

# Create the charge injection pattern objects used for this pipeline.
patterns = ci_pattern.uniform_from_lists(normalizations=normalizations, regions=ci_regions)

# There are 8 images to load, which is what the normalizations list tells us. To load them, its easiest to create a
# 'data' list and then iterate over a for loop, to append each set of data to the list of data we pass to the pipeline
# when we run it.

datas = []

for index, normalization in enumerate(normalizations):

    datas.append(ci_data.ci_data_from_fits(
        frame_geometry=frame_geometry, ci_pattern=patterns[index],
        image_path=ci_data_path + 'image_' + str(int(normalization)) + '.fits',
        ci_pre_cti_path=ci_data_path +'ci_pre_cti_' + str(int(normalization)) + '.fits',
        cosmic_ray_image_path=ci_data_path + 'cosmic_ray_image_' + str(int(normalization)) + '.fits',
        cosmic_ray_image_hdu=0, noise_map_from_single_value=4.0))

# The CTI settings of arCTIc, which models the CCD read-out including CTI. For parallel ci data, we include 'charge
# injection mode' which accounts for the fact that every pixel is transferred over the full CCD.
parallel_cti_settings = arctic_settings.Settings(well_depth=84700, niter=1, express=2, n_levels=2000,
                                                 charge_injection_mode=True, readout_offset=0)
cti_settings = arctic_settings.ArcticSettings(parallel=parallel_cti_settings)

def mask_function_for_bug(shape, frame_geometry):
    return msk.Mask.from_masked_regions(shape=shape, frame_geometry=frame_geometry,
                                        masked_regions=[(0, 330, 0, shape[1])])

# Running a pipeline is easy, we simply import it from the pipelines folder and pass the ci data to its run function.
# Below, we'll' use a 2 phase example pipeline to fit the data with a one species parallel CTI model.
# Checkout workspace/pipelines/examples/parallel_x1_species.py' for a full description of the pipeline.

# The pool command tells our code to parallelize the analysis over 2 CPU's, where each CPU fits a different charge
# injection image

from workspace.pipelines import parallel_x2_species
pipeline = parallel_x2_species.make_pipeline(phase_folders=[ci_data_type, ci_data_model, ci_data_resolution],
                                             mask_function=mask_function_for_bug)
pipeline.run(ci_datas=datas, cti_settings=cti_settings, pool=Pool(processes=2))