identification.py

from enstools.feature.identification import IdentificationTechnique
import xarray as xr
import numpy as np
import os, sys

import metpy.calc as mpcalc
from .util import calc_adv
from matplotlib import pyplot as plt
import cartopy.crs as ccrs
from .filtering import keep_wavetrough
from .processing import populate_object, compute_cv
from skimage.draw import line_aa
from enstools.feature.util.enstools_utils import get_u_var, get_v_var, get_vertical_dim, get_longitude_dim, get_latitude_dim
import threading
from skimage.draw import line

class AEWIdentification(IdentificationTechnique):

    def __init__(self, wt_out_file=False, wt_traj_dir=None, cv='cv', year_summer=None, month=None, **kwargs):
        """
        Initialize the AEW Identification.

        Parameters (experimental)
        ----------
        kwargs
        wt_out_file: output the wavetroughs as new and only out-field in 0.5x0.5
        year_summer: if set, process AEW season (01.06.-31.10.) of given year
        
        """

        import enstools.feature.identification.african_easterly_waves.configuration as cfg
        self.config = cfg  # config
        self.config.out_traj_dir = wt_traj_dir
        self.config.cv_name = cv
        
        if year_summer is not None:
            if month is not None:
                m_str = str(month).zfill(2)
                self.config.start_date = str(year_summer) + '-' + m_str + '-01T00:00'
                self.config.end_date = str(year_summer) + '-' + m_str + '-30T00:00'
            else:
                self.config.start_date = str(year_summer) + '-06-01T00:00'
                self.config.end_date = str(year_summer) + '-10-31T00:00'
        
        self.config.out_wt = wt_out_file
        if wt_out_file:
            self.config.sum_over_all = True
        self.lock_ = threading.Lock()
        pass

    def precompute(self, dataset: xr.Dataset, **kwargs):
        print("Precompute for PV identification...")

        plt.switch_backend('agg')  # this is thread safe matplotlib but cant display.

        # --------------- CLIMATOLOGY
        lon_range = self.config.data_lon
        lat_range = self.config.data_lat
        clim_file = self.config.get_clim_file()

        level_str = get_vertical_dim(dataset)
        lat_str = get_latitude_dim(dataset)
        lon_str = get_longitude_dim(dataset)

        if os.path.isfile(clim_file):
            cv_clim = xr.open_dataset(clim_file)
            
        else:
            # generate: need all 40y of CV data.
            print("Climatology file not found. Computing climatology...")

            from .climatology import compute_climatology
            cv_clim = compute_climatology(self.config)
            cv_clim.to_netcdf(clim_file)
        
        cv_clim = cv_clim.sel(
            **{lat_str: slice(lat_range[0], lat_range[1])},
            **{lon_str: slice(lon_range[0], lon_range[1])})
        
        # --------------- SUBSET DATA ACCORDING TO CFG
        start_date_dt = np.datetime64(self.config.start_date) if self.config.start_date is not None else None
        end_date_dt = np.datetime64(self.config.end_date) if self.config.end_date is not None else None
        # get the data we want to investigate

        dataset = dataset.sel(
            **{lat_str: slice(lat_range[0], lat_range[1])},
            **{lon_str: slice(lon_range[0], lon_range[1])},
            time=slice(start_date_dt, end_date_dt))
        
        # dataset = dataset.expand_dims('level')
        # level_str = 'level'
        if level_str is not None:
            dataset = dataset.sel(**{level_str: self.config.levels})

        # rename cv_clim dimensions to be same as in data.
        cv_clim = cv_clim.rename({'lat': lat_str, 'lon': lon_str})
        if 'plev' in cv_clim.dims and 'plev' != level_str:
            print("plev from clim to level: div by 100.")
            cv_clim = cv_clim.rename({'plev': level_str})
            cv_clim = cv_clim.assign_coords({level_str: cv_clim[level_str] / 100})

        if self.config.cv_name not in dataset.data_vars:
            print("Curvature Vorticity not found, trying to compute it out of U and V...")
            u_name = get_u_var(dataset)
            v_name = get_v_var(dataset)

            dataset = compute_cv(dataset, u_name, v_name, self.config.cv_name)

        # make dataset to 2.5 (or same as cv_clim)
        dataset = dataset.interp({lat_str: cv_clim.coords[lat_str], lon_str: cv_clim.coords[lon_str]})


        # make sure that lat and lon are last two dimensions
        if lat_str not in dataset[self.config.cv_name].coords.dims[-2:] or lon_str not in dataset[
                                                                                              self.config.cv_name].coords.dims[
                                                                                          -2:]:
            print("Reordering dimensions so lat and lon at back. Required for metpy.calc.")
            dataset = dataset.transpose(..., lat_str, lon_str)

        # --------------- DO NUMPY PARALLELIZED STUFF: CREATE TROUGH MASKS

        u = dataset.u if 'u' in dataset.data_vars else dataset.U
        v = dataset.v if 'v' in dataset.data_vars else dataset.V
        cv = dataset[self.config.cv_name]
        # smooth CV with kernel

        cv = mpcalc.smooth_n_point(cv, n=9, passes=2).metpy.dequantify()


        # create hourofyear to get anomalies
        cv = cv.assign_coords(hourofyear=cv.time.dt.strftime("%m-%d %H"))
        cv_anom = cv.groupby('hourofyear') - cv_clim.cv

        # compute advection of cv: first and second derivative
        adv1, adv2 = calc_adv(cv_anom, u, v)

        # xr.where() anomaly data exceeds the percentile from the hourofyear climatology:
        # replace data time with hourofyear -> compare with climatology percentile -> back to real time
        cv_anom_h = cv_anom.swap_dims(dims_dict={'time': 'hourofyear'})
        perc_mask_h = cv_anom_h.where(
            cv_anom_h > cv_clim.cva_quantile_hoy.sel(dict(hourofyear=cv_anom.hourofyear.data)))
        perc_mask = perc_mask_h.swap_dims(dims_dict={'hourofyear': 'time'})

        cv_perc_thresh = np.nanpercentile(cv, self.config.cv_percentile)  # 66th percentile of cv anomalies
        # print(cv_perc_thresh)

        print('Locating wave troughs...')
        # filter the advection field given our conditions:
        trough_mask = adv1.where(np.logical_and(
            ~np.isnan(perc_mask),  # percentile of anomaly over threshold from climatology
            adv2.values > self.config.second_advection_min_thr,
            # second time derivative > 0: dont detect local minima over the percentile
            u.values < self.config.max_u_thresh))  # threshold for propagation speed -> keep only westward

        dataset['trough_mask'] = trough_mask

        # create 0.5x0.5 dataarray for wavetroughs
        min_lat = dataset[lat_str].data.min()
        max_lat = dataset[lat_str].data.max()
        min_lon = dataset[lon_str].data.min()
        max_lon = dataset[lon_str].data.max()

        lat05 = np.linspace(min_lat, max_lat, int((max_lat - min_lat) * 2) + 1)
        lon05 = np.linspace(min_lon, max_lon, int((max_lon - min_lon) * 2) + 1)

        # 0.5x0.5 for wavetroughs
        wt = xr.zeros_like(dataset['trough_mask'], dtype=float)
        wt = wt.isel(**{lat_str: 0}).drop(lat_str).isel(**{lon_str: 0}).drop(lon_str)
        wt = wt.expand_dims(lon05=lon05).expand_dims(lat05=lat05)
        wt = wt.transpose(..., 'lat05', 'lon05')

        dataset['wavetroughs'] = wt
        dataset['wavetroughs'].attrs['units'] = 'prob'
        dataset['wavetroughs'].attrs['standard_name'] = 'wavetroughs'
        dataset['wavetroughs'].attrs['long_name'] = 'position_of_wavetrough'
        dataset['lat05'].attrs['long_name'] = 'latitude'
        dataset['lat05'].attrs['standard_name'] = 'latitude'
        dataset['lon05'].attrs['long_name'] = 'longitude'
        dataset['lon05'].attrs['standard_name'] = 'longitude'
        dataset['lat05'].attrs['units'] = 'degrees_north'
        dataset['lon05'].attrs['units'] = 'degrees_east'

        return dataset

    def identify(self, data_chunk: xr.Dataset, **kwargs):
        objs = []
        trough_mask_cur = data_chunk.trough_mask

        def clip(tup, mint, maxt):
            return np.clip(tup, mint, maxt)

        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 15), subplot_kw={'projection': ccrs.PlateCarree()})
        # generate zero-contours with matplotlib core
        c = trough_mask_cur.plot.contour(transform=ccrs.PlateCarree(), colors='blue', levels=[0.0],
                                         subplot_kws={'projection': ccrs.PlateCarree()})

        paths = c.collections[0].get_paths()

        wt = data_chunk.wavetroughs

        min_lat = wt.lat05.data.min()
        max_lat = wt.lat05.data.max()
        min_lon = wt.lon05.data.min()
        max_lon = wt.lon05.data.max()
        lons = len(wt.lon05.data)
        lats = len(wt.lat05.data)

        id_ = 1
        for path in paths:
            # get new object, set id
            o = self.get_new_object()
            o.id = id_
            # populate it
            populate_object(o.properties, path)

            # add to objects if keep
            if keep_wavetrough(o.properties, self.config):
                objs.append(o)
                id_ += 1
                if not self.config.out_wt:
                    continue

                for v_idx in range(len(path.vertices) - 1):
                    start_lonlat = path.vertices[v_idx][0], path.vertices[v_idx][1]
                    end_lonlat = path.vertices[v_idx + 1][0], path.vertices[v_idx + 1][1]

                    start_idx = ((start_lonlat[0] - min_lon) / (max_lon - min_lon) * lons,
                                 (start_lonlat[1] - min_lat) / (max_lat - min_lat) * lats)
                    # start_idx = clip(start_idx, (0, 0), (lons, lats))

                    end_idx = ((end_lonlat[0] - min_lon) / (max_lon - min_lon) * lons,
                               (end_lonlat[1] - min_lat) / (max_lat - min_lat) * lats)
                    # end_idx = clip(end_idx, (0, 0), (lons, lats))

                    rr, cc, val = line_aa(int(start_idx[0]), int(start_idx[1]), int(end_idx[0]), int(end_idx[1]))
                    rr = clip(rr, 0, lons - 1)
                    cc = clip(cc, 0, lats - 1)

                    wt.data[cc, rr] = np.where(np.greater(val, wt.data[cc, rr]), val, wt.data[cc, rr])

        return data_chunk, objs

    def postprocess(self, dataset: xr.Dataset, data_desc, **kwargs):

        lat_str = get_latitude_dim(dataset)
        lon_str = get_longitude_dim(dataset)

        data_desc = self.make_ids_unique(data_desc)

        # drop everything, only keep WTs as 0.5x0.5
        if self.config.out_wt:
            for var in dataset.data_vars:
                if var not in ['wavetroughs']:
                    dataset = dataset.drop_vars([var])
            # wavetroughs are 0.5x0.5 in lat05,lon05 field. remove other stuff
            for dim in dataset.dims:
                if dim in [lat_str, lon_str, 'hourofyear', 'quantile']:
                    dataset = dataset.drop_vars([dim])

            dataset = dataset.rename({'lat05': lat_str, 'lon05': lon_str})

            level_str = get_vertical_dim(dataset)
            if level_str is not None:
                dataset = dataset.squeeze(drop=True)
                
            if self.config.sum_over_all:
                dataset['wavetroughs'] = dataset.wavetroughs.sum(dim='time')

        # create met3d like trajectories TODO not really working right now...
        if self.config.out_traj_dir:
            if not os.path.exists(self.config.out_traj_dir):
                os.makedirs(self.config.out_traj_dir)

            assert(len(data_desc.sets) == 1) # TODO assert one set. maybe expand at some point
            desc_set = data_desc.sets[0]
            desc_times = desc_set.timesteps

            for idx, ts in enumerate(desc_times):
                # need to make separate dataset for each init-time
                # because number of trajs (WTs) are different from time to time
                dataset_wt = xr.Dataset()

                lon_list = []
                lat_list = []
                pres_list = []
                max_pts_in_wt = -1 # TODO what if no wts
                for o in ts.objects: # get lons and lats
                    pt_list = o.properties.line_pts
                    lon_list.append(np.array([pt.lon for pt in pt_list]))
                    lat_list.append(np.array([pt.lat for pt in pt_list]))
                    pres_list.append(np.array([850.0 for pt in pt_list]))
                    max_pts_in_wt = max(max_pts_in_wt, len(lon_list[-1]))
                # go again and fill with NaNs at end
                for i in range(len(lon_list)): # get lons and lats
                    lon_list[i] = np.pad(lon_list[i], (0, max_pts_in_wt - len(lon_list[i])), mode='constant',
                                         constant_values=np.nan)
                    lat_list[i] = np.pad(lat_list[i], (0, max_pts_in_wt - len(lat_list[i])), mode='constant',
                                         constant_values=np.nan)
                    pres_list[i] = np.pad(pres_list[i], (0, max_pts_in_wt - len(pres_list[i])), mode='constant',
                                        constant_values=np.nan)

                dataset_wt = dataset_wt.expand_dims(time=np.arange(0, max_pts_in_wt).astype(dtype=float)) # fake traj time
                dataset_wt = dataset_wt.expand_dims(ensemble=[0])
                dataset_wt = dataset_wt.expand_dims(trajectory=np.arange(1, len(ts.objects) + 1))

                lons = xr.DataArray(np.zeros((1, len(ts.objects), max_pts_in_wt)), dims=("ensemble", "trajectory", "time"))
                lons.attrs['standard_name'] = "longitude"
                lons.attrs['long_name'] = "longitude"
                lons.attrs['units'] = "degrees_east"

                lats = xr.zeros_like(lons)
                lats.attrs['standard_name'] = "latitude"
                lats.attrs['long_name'] = "latitude"
                lats.attrs['units'] = "degrees_north"

                pres = xr.zeros_like(lons)
                pres.attrs['standard_name'] = "air_pressure"
                pres.attrs['long_name'] = "pressure"
                pres.attrs['units'] = "hPa"
                pres.attrs['positive'] = "down"
                pres.attrs['axis'] = "Z"

                dataset_wt['lon'] = lons
                dataset_wt['lat'] = lats
                dataset_wt['pressure'] = pres
                # TODO auxiliary smth?
                lon_list_np = np.array(lon_list)
                lat_list_np = np.array(lat_list)
                pres_list_np = np.array(pres_list)

                dataset_wt['lon'].data[0] = lon_list_np
                dataset_wt['lat'].data[0] = lat_list_np
                dataset_wt['pressure'].data[0] = pres_list_np

                dataset_wt['time'].attrs['standard_name'] = "time"
                dataset_wt['time'].attrs['long_name'] = "time"
                dataset_wt['time'].attrs['units'] = "hours since " + ts.valid_time.replace('T', ' ')
                dataset_wt['time'].attrs['trajectory_starttime'] = ts.valid_time.replace('T', ' ')
                dataset_wt['time'].attrs['forecast_inittime'] = ts.valid_time.replace('T', ' ') # '2006-09-01 12:00:00' # TODO ts.valid_time.replace('T', ' ')
                
                out_path = self.config.out_traj_dir + ts.valid_time.replace(':','_') + '.nc'
                dataset_wt.to_netcdf(out_path)

        return dataset, data_desc