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#
#    http://www.apache.org/licenses/LICENSE-2.0
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# under the License.

"""
    simple_model_tstd.py

    Use OCW to download, evaluate against a OCW standard
    metrics (temporal STD) and plot the results (contour map).

    In this example:

    1. Download a netCDF files from a local site.
        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
    2. Load the local file into OCW dataset objects.
    3. Use the standard OCW metrics to build a metric against which to evaluation (temporal STD).
    4. Create an evaluation object of the dataset vs. the metric.
    5. Plot the results of the evaluation using a contour map.

    OCW modules demonstrated:

    1. datasource/local
    2. metrics
    3. evaluation
    4. plotter

"""
from __future__ import print_function

import sys
from os import path

import ocw.data_source.local as local
import ocw.evaluation as evaluation
import ocw.metrics as metrics
import ocw.plotter as plotter

if sys.version_info[0] >= 3:
    from urllib.request import urlretrieve
else:
    # Not Python 3 - today, it is most likely to be Python 2
    # But note that this might need an update when Python 4
    # might be around one day
    from urllib import urlretrieve

# File URL leader
FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"
# One Local Model Files
FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc"

# Filename for the output image/plot (without file extension)
OUTPUT_PLOT = "knmi_temporal_std"

# Download necessary NetCDF file if needed
if not path.exists(FILE_1):
    urlretrieve(FILE_LEADER + FILE_1, FILE_1)

# Step 1: Load Local NetCDF File into OCW Dataset Objects.
print("Loading %s into an OCW Dataset Object" % (FILE_1,))
# 'tasmax' is variable name of values
knmi_dataset = local.load_file(FILE_1, "tasmax")

print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,))

# Acessing latittudes and longitudes of netCDF file
lats = knmi_dataset.lats
lons = knmi_dataset.lons

# Step 2:  Build a Metric to use for Evaluation - Temporal STD for this example.
# You can build your own metrics, but OCW also ships with some common metrics
print("Setting up a Temporal STD metric to use for evaluation")
std = metrics.TemporalStdDev()

# Step 3: Create an Evaluation Object using Datasets and our Metric.
# The Evaluation Class Signature is:
# Evaluation(reference, targets, metrics, subregions=None)
# Evaluation can take in multiple targets and metrics, so we need to convert
# our examples into Python lists.  Evaluation will iterate over the lists
print("Making the Evaluation definition")
# Temporal STD Metric gets one target dataset then reference dataset
# should be None
std_evaluation = evaluation.Evaluation(None, [knmi_dataset], [std])
print("Executing the Evaluation using the object's run() method")
std_evaluation.run()

# Step 4: Make a Plot from the Evaluation.results.
# The Evaluation.results are a set of nested lists to support many different
# possible Evaluation scenarios.
#
# The Evaluation results docs say:
# The shape of results is (num_metrics, num_target_datasets) if no subregion
# Accessing the actual results when we have used 1 metric and 1 dataset is
# done this way:
print("Accessing the Results of the Evaluation run")
results = std_evaluation.unary_results[0][0]
print("The results are of type: %s" % type(results))

# From the temporal std output I want to make a Contour Map of the region
print("Generating a contour map using ocw.plotter.draw_contour_map()")

fname = OUTPUT_PLOT
gridshape = (4, 5)  # 20 Years worth of plots. 20 rows in 1 column
plot_title = "TASMAX Temporal Standard Deviation (1989 - 2008)"
sub_titles = range(1989, 2009, 1)

plotter.draw_contour_map(results, lats, lons, fname,
                         gridshape=gridshape, ptitle=plot_title,
                         subtitles=sub_titles)