Added missing file

pycif/plugins/obsvects/standard/init_background.py

+from builtins import str
+import pandas as pd
+import sys
+import os
+import shutil
+import numpy as np
+from datetime import timedelta
+from pycif.utils.datastores.dump import dump_datastore, read_datastore
+from pycif.plugins.obsvects.standard.utils.tstep import tstep
+from pycif.plugins.obsvects.standard.utils.crop_monitor import crop_monitor
+
+from pycif.utils.check import verbose
+from pycif.utils import path
+from pycif.utils.check.errclass import PluginError
+
+
+def init_background(obsvect, **kwargs):
+    """Initializes the background
+       Reads and regrids concentration fields
+       Reads sensitivity to initial conditions fields
+       Calculates contribution to observations from initial concentrations
+    
+    Args:
+        obsvect (Plugin): the observation vector with all its attributes
+    
+    Returns:
+        obsvect, datastore updated with contribution to observations from initial 
+        concentrations
+    
+    """
+
+    background = obsvect.background
+    model = obsvect.model
+
+    # Read global concentration fields. Regrid to model grid and
+    # filter by start and end date of simulation
+    conc_ini = background.read_conc(model, background.dir_initconc,
+                                   background.file_initconc, **kwargs)
+
+
+    n_x = model.domain.nlon_glob
+    n_y = model.domain.nlat_glob
+    n_z = len(model.domain.heights)
+
+    # cini is dimension number of observations x zones to optimize
+    # Stored in monitor as cini_1, cini_2 etc.
+
+    obsvect.n_cini = len(background.cini_lat)
+    obsvect.cini_names = ["cini_{}".format(i) for i in range(obsvect.n_cini)]
+    cini = np.empty((len(obsvect.datastore), obsvect.n_cini), order='C'); cini[:,:]=np.nan
+
+    # Read FLEXPART header to access trajdays
+    fp_header = None
+        
+    # Loop over all observations and calculate background contribution
+    for obs_i, row in enumerate(obsvect.datastore.itertuples()):
+
+        # Subdirectory for FLEXPART grid_initial files
+        subdir = row.Index.strftime("%Y%m")
+        station = row.station
+        runsubdir_glob = os.path.join(
+            model.run_dir_glob, station.upper(), subdir)
+        file_date = row.Index.strftime('%Y%m%d%H%M%S')
+        file_name = 'grid_initial_' + file_date + '_001'
+        
+        if not os.path.isfile(os.path.join(runsubdir_glob, file_name)):
+            continue
+
+        # Read grid_initial file
+        gridini = model.utils.read.read_init(runsubdir_glob, file_name,
+                                             n_x, n_y, n_z)
+
+        # Assume all trajdays are the same so read header once only
+        if fp_header is None:
+            fp_header = model.utils.flexpart_header.Flexpartheader()
+            fp_header.read_header(os.path.join(runsubdir_glob, 'header'))
+            trajdays = fp_header.trajdays
+
+        
+        # Interpolate background concentrations to observation date minus trajdays
+        time_interpolate = row.Index - timedelta(days=trajdays)
+        conc = np.array(conc_ini.interp(coords={'time' : time_interpolate}))
+        
+        for iz in range(len(model.domain.heights)):
+            print("background, iz:", iz) 
+            for iy in range(model.domain.nlat_glob):
+                n = np.min(np.where(model.domain.lat_glob[iy] < background.cini_lat))
+                for ix in range(model.domain.nlon_glob):
+                    cini[obs_i, n] += gridini[ix, iy, iz] * conc[ix, iy, iz]
+
+        # Add initial concentrations to datastore
+        for i, name in enumerate(obsvect.cini_names):
+            obsvect.datastore[name] = cini[:, i]
+            
+
+    
+    return obsvect