pyicon_tb.py 56.5 KB
Newer Older
1
print('sys')
2
import sys, glob, os
3
print('json')
4
5
import json
# --- calculations
6
print('numpy')
7
import numpy as np
8
print('scipy')
9
10
from scipy import interpolate
from scipy.spatial import cKDTree
11
# --- reading data 
12
print('netcdf datetime')
13
from netCDF4 import Dataset, num2date, date2num
14
import datetime
15
# --- plotting
16
print('matplotlib')
17
import matplotlib.pyplot as plt
18
import matplotlib
19
# --- debugging
20
print('mybreak')
21
#from ipdb import set_trace as mybreak  
22
23
print('pnadas')
import pandas as pd
24
print('xarray')
25
import xarray as xr
26
print('done xarray')
27

28
29
"""
pyicon
30
31
#  icon_to_regular_grid
#  icon_to_section
nbruegge's avatar
nbruegge committed
32
33
34
  apply_ckdtree
  ckdtree_hgrid
  ckdtree_section
35
  calc_ckdtree
nbruegge's avatar
nbruegge committed
36
37
  haversine_dist
  derive_section_points
38
39
40
41
42
  timing
  conv_gname
  identify_grid
  crop_tripolar_grid
  crop_regular_grid
nbruegge's avatar
nbruegge committed
43
44
45
  get_files_of_timeseries
  get_varnames
  get_timesteps
46
47
48
49
50
51
52
53
54

  ?load_data
  ?load_grid

  ?hplot
  ?update_hplot
  ?vplot
  ?update_vplot

nbruegge's avatar
nbruegge committed
55
  #IconDataFile
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79

  IconData
  IP_hor_sec_rect

  QuickPlotWebsite

  IDa: Icon data set (directory of files)
    - info about tsteps
    - info about vars
    - info about grid
    - IGr: Icon grid
    - IVa: Icon variable if loaded
  IIn: Icon interpolator class

  IPl: Icon plot class

IDa = pyic.IconData(fpath or path)
IDa.load_grid()
IDa.show()

IPl = pyic.hplot(IDa, 'var', iz, tstep, IIn)

"""

80
81
82
83
84
85
86
87
class pyicon_configure(object):
  def __init__(self, fpath_config):
    with open(fpath_config) as file_json:
      Dsettings = json.load(file_json)
    for key in Dsettings.keys():
      setattr(self, key, Dsettings[key])
    return

88
#def icon_to_regular_grid(data, shape, distances=None, \
89
#                  inds=None, radius_of_influence=1000e3):
90
91
92
93
94
95
96
97
#  """
#  """
#  data_interpolated = apply_ckdtree(data, distances=distances, inds=inds, 
#                                    radius_of_influence=radius_of_influence)
#  data_interpolated = data_interpolated.reshape(shape)
#  return data_interpolated
#
#def icon_to_section(data, distances=None, \
98
#                  inds=None, radius_of_influence=1000e3):
99
100
101
102
103
104
#  """
#  """
#  data_interpolated = apply_ckdtree(data, distances=distances, inds=inds, 
#                                    radius_of_influence=radius_of_influence)
#  return data_interpolated

Nils Brüggemann's avatar
Nils Brüggemann committed
105
106
107
"""
Routines to apply interpolation weights
"""
108
def apply_ckdtree_base(data, inds, distances, radius_of_influence=1000e3):
Nils Brüggemann's avatar
Nils Brüggemann committed
109
110
111
  if distances.ndim == 1:
    #distances_ma = np.ma.masked_greater(distances, radius_of_influence)
    if data.ndim==1:
112
113
114
115
      if isinstance(data, xr.core.dataarray.DataArray):
        data_interpolated = data.load()[inds]
      else:
        data_interpolated = data[inds]
Nils Brüggemann's avatar
Nils Brüggemann committed
116
117
      data_interpolated[distances>=radius_of_influence] = np.nan
    elif data.ndim==2:
118
119
120
121
      if isinstance(data, xr.core.dataarray.DataArray):
        data_interpolated = data.load()[:,inds]
      else:
        data_interpolated = data[:,inds]
Nils Brüggemann's avatar
Nils Brüggemann committed
122
123
124
125
126
127
128
129
130
131
132
      data_interpolated[:,distances>=radius_of_influence] = np.nan
  else:
    #raise ValueError("::: distances.ndim>1 is not properly supported yet. :::")
    #distances_ma = np.ma.masked_greater(distances, radius_of_influence)
    weights = 1.0 / distances**2
    if data.ndim==1:
      data_interpolated = np.ma.sum(weights * data[inds], axis=1) / np.ma.sum(weights, axis=1)
      #data_interpolated[distances>=radius_of_influence] = np.nan
    elif data.ndim==2:
      data_interpolated = np.ma.sum(weights[np.newaxis,:,:] * data[:,inds], axis=2) / np.ma.sum(weights[np.newaxis,:,:], axis=2)
      #data_interpolated[:,distances>=radius_of_influence] = np.nan
133
  data_interpolated = np.ma.masked_invalid(data_interpolated)
Nils Brüggemann's avatar
Nils Brüggemann committed
134
135
  return data_interpolated

136
def apply_ckdtree(data, fpath_ckdtree, mask=None, coordinates='clat clon', radius_of_influence=1000e3):
nbruegge's avatar
nbruegge committed
137
  """
138
  * credits
139
    function modified from pyfesom (Nikolay Koldunov)
140
  """
141
  ddnpz = np.load(fpath_ckdtree)
142
  #if coordinates=='clat clon':
143
  if ('clon' in coordinates) or (coordinates==''):
144
145
    distances = ddnpz['dckdtree_c']
    inds = ddnpz['ickdtree_c'] 
146
147
  #elif coordinates=='elat elon':
  elif 'elon' in coordinates:
148
149
    distances = ddnpz['dckdtree_e']
    inds = ddnpz['ickdtree_e'] 
150
151
  #elif coordinates=='vlat vlon':
  elif 'vlon' in coordinates:
152
153
154
155
156
    distances = ddnpz['dckdtree_v']
    inds = ddnpz['ickdtree_v'] 
  else:
    raise ValueError('::: Error: Unsupported coordinates: %s! ::: ' % (coordinates))

157
158
159
160
161
162
163
164
165
  if mask is not None:
    #if data.ndim==1:
    #  data = data[mask]
    #elif data.ndim==2:
    #  data = data[:,mask]
    if inds.ndim==1:
      inds = inds[mask]
      distances = distances[mask]
    elif inds.ndim==2:
166
      #raise ValueError('::: Warning: This was never checked! Please check carefully and remove this warning.:::')
167
168
      inds = inds[:,mask]
      distances = distances[:,mask]
169

Nils Brüggemann's avatar
Nils Brüggemann committed
170
  data_interpolated = apply_ckdtree_base(data, inds, distances, radius_of_influence)
171
172
  return data_interpolated

173
174
175
176
def interp_to_rectgrid(data, fpath_ckdtree, 
                       lon_reg=None, lat_reg=None,             # for new way of cropping
                       indx='all', indy='all', mask_reg=None,  # for old way of cropping
                       coordinates='clat clon'):
Nils Brüggemann's avatar
Nils Brüggemann committed
177
178
179
  ddnpz = np.load(fpath_ckdtree)
  lon = ddnpz['lon'] 
  lat = ddnpz['lat'] 
180
  # --- old way of cropping
181
182
183
  if not isinstance(indx, str):
    lon = lon[indx]
    lat = lat[indy]
184
185
186
187
188
189
190
191
192
193
  # --- prepare cropping the data to a region
  if lon_reg is not None:
    indx = np.where((lon>=lon_reg[0]) & (lon<lon_reg[1]))[0]
    indy = np.where((lat>=lat_reg[0]) & (lat<lat_reg[1]))[0]
    Lon, Lat = np.meshgrid(lon, lat) # full grid
    lon = lon[indx]
    lat = lat[indy]
    ind_reg = ((Lon>=lon_reg[0]) & (Lon<lon_reg[1]) & (Lat>=lat_reg[0]) & (Lat<lat_reg[1])).flatten()
    mask_reg = ind_reg
    Lon, Lat = np.meshgrid(lon, lat) # cropped grid
194
  datai = apply_ckdtree(data, fpath_ckdtree, mask=mask_reg, coordinates=coordinates)
195
  if datai.ndim==1:
196
    datai = datai.reshape(lat.size, lon.size)
197
198
199
200
201
  else:
    datai = datai.reshape([data.shape[0], lat.size, lon.size])
  datai[datai==0.] = np.ma.masked
  return lon, lat, datai

202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
def interp_to_rectgrid_xr(arr, fpath_ckdtree, 
                          lon_reg=None, lat_reg=None,
                          coordinates='clat clon',
                          radius_of_influence=1000e3,
                          compute=True,
                          mask_out_of_range=True,
                          mask_out_of_range_before=False,
                         ):

  # --- load interpolation indices
  ds_ckdt = xr.open_dataset(fpath_ckdtree)
  if ('clon' in coordinates) or (coordinates==''):
    inds = ds_ckdt.ickdtree_c
    dist = ds_ckdt.dckdtree_c
  elif 'elon' in coordinates:
    inds = ds_ckdt.ickdtree_e
    dist = ds_ckdt.dckdtree_e
  elif 'vlon' in coordinates:
    inds = ds_ckdt.ickdtree_v
    dist = ds_ckdt.dckdtree_v
  else:
    raise ValueError('::: Error: Unsupported coordinates: %s! ::: ' % (coordinates))
  dist = dist.compute()
  inds = inds.compute().data.flatten()
  lon = ds_ckdt.lon.compute().data
  lat = ds_ckdt.lat.compute().data

  # --- interpolate by nearest neighbor
  arr_interp = arr.isel(ncells=inds)

  # --- reshape
  arr_interp = arr_interp.assign_coords(ncells=pd.MultiIndex.from_product([lat, lon], names=("lat", "lon"))
                                ).unstack()

  # --- mask values where nearest neighbor is too far away
  # (doing this after compute seems to be faster) FIXME check that!
  if mask_out_of_range_before:
    arr_interp = arr_interp.where(dist<radius_of_influence)

  # --- compute data otherwise a lazy object is returned
  if compute:
    arr_interp = arr_interp.compute()

  # --- mask values where nearest neighbor is too far away
  # (doing this after compute seems to be faster) FIXME check that!
  if mask_out_of_range:
    arr_interp = arr_interp.where(dist<radius_of_influence)

  return  arr_interp

252
253
254
255
256
257
258
259
def interp_to_section(data, fpath_ckdtree, coordinates='clat clon'):
  ddnpz = np.load(fpath_ckdtree)
  lon_sec = ddnpz['lon_sec'] 
  lat_sec = ddnpz['lat_sec'] 
  dist_sec = ddnpz['dist_sec'] 
  datai = apply_ckdtree(data, fpath_ckdtree, coordinates=coordinates)
  datai[datai==0.] = np.ma.masked
  return lon_sec, lat_sec, dist_sec, datai
Nils Brüggemann's avatar
Nils Brüggemann committed
260

Nils Brüggemann's avatar
Nils Brüggemann committed
261
262
263
""" 
Routines for zonal averaging
"""
nbruegge's avatar
nbruegge committed
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
def zonal_average(fpath_data, var, basin='global', it=0, fpath_fx='', fpath_ckdtree=''):

  for fp in [fpath_data, fpath_fx, fpath_ckdtree]:
    if not os.path.exists(fp):
      raise ValueError('::: Error: Cannot find file %s! :::' % (fp))

  f = Dataset(fpath_fx, 'r')
  basin_c = f.variables['basin_c'][:]
  mask_basin = np.zeros(basin_c.shape, dtype=bool)
  if basin.lower()=='atlantic' or basin=='atl':
    mask_basin[basin_c==1] = True 
  elif basin.lower()=='pacific' or basin=='pac':
    mask_basin[basin_c==3] = True 
  elif basin.lower()=='southern ocean' or basin=='soc' or basin=='so':
    mask_basin[basin_c==6] = True 
  elif basin.lower()=='indian ocean' or basin=='ind' or basin=='io':
    mask_basin[basin_c==7] = True 
  elif basin.lower()=='global' or basin=='glob' or basin=='glo':
    mask_basin[basin_c!=0] = True 
  elif basin.lower()=='indopacific' or basin=='indopac':
    mask_basin[(basin_c==3) | (basin_c==7)] = True 
Nils Brüggemann's avatar
Nils Brüggemann committed
285
286
  elif basin.lower()=='indopacso':
    mask_basin[(basin_c==3) | (basin_c==7) | (basin_c==6)] = True 
nbruegge's avatar
nbruegge committed
287
288
289
290
291
292
293
294
295
296
  f.close()
  
  ddnpz = np.load(fpath_ckdtree)
  lon = ddnpz['lon'] 
  lat = ddnpz['lat'] 
  shape = [lat.size, lon.size]
  lat_sec = lat
  
  f = Dataset(fpath_data, 'r')
  nz = f.variables[var].shape[1]
297
  coordinates = f.variables[var].coordinates
nbruegge's avatar
nbruegge committed
298
299
  data_zave = np.ma.zeros((nz,lat_sec.size))
  for k in range(nz):
nbruegge's avatar
nbruegge committed
300
    #print('k = %d/%d'%(k,nz))
nbruegge's avatar
nbruegge committed
301
302
303
304
305
306
307
    # --- load data
    data = f.variables[var][it,k,:]
    # --- mask land points
    data[data==0] = np.ma.masked
    # --- mask not-this-basin points
    data[mask_basin==False] = np.ma.masked
    # --- go to normal np.array (not np.ma object)
308
309
    if isinstance(data, np.ma.core.MaskedArray):
      data = data.filled(0.)
nbruegge's avatar
nbruegge committed
310
    # --- interpolate to rectangular grid
311
312
    datai = apply_ckdtree(data, fpath_ckdtree, coordinates=coordinates)
    datai = datai.reshape(shape)
nbruegge's avatar
nbruegge committed
313
314
315
316
317
318
319
    # --- go back to masked array
    datai = np.ma.array(datai, mask=datai==0.)
    # --- do zonal average
    data_zave[k,:] = datai.mean(axis=1)
  f.close()
  return lat_sec, data_zave

320
def zonal_average_3d_data(data3d, basin='global', it=0, coordinates='clat clon', fpath_fx='', fpath_ckdtree=''):
nbruegge's avatar
nbruegge committed
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
  """ Like zonal_average but here data instead of path to data is given. This can only work if the whole data array fits into memory.
  """

  for fp in [fpath_fx, fpath_ckdtree]:
    if not os.path.exists(fp):
      raise ValueError('::: Error: Cannot find file %s! :::' % (fp))

  f = Dataset(fpath_fx, 'r')
  basin_c = f.variables['basin_c'][:]
  mask_basin = np.zeros(basin_c.shape, dtype=bool)
  if basin.lower()=='atlantic' or basin=='atl':
    mask_basin[basin_c==1] = True 
  elif basin.lower()=='pacific' or basin=='pac':
    mask_basin[basin_c==3] = True 
  elif basin.lower()=='southern ocean' or basin=='soc' or basin=='so':
    mask_basin[basin_c==6] = True 
  elif basin.lower()=='indian ocean' or basin=='ind' or basin=='io':
    mask_basin[basin_c==7] = True 
  elif basin.lower()=='global' or basin=='glob' or basin=='glo':
    mask_basin[basin_c!=0] = True 
  elif basin.lower()=='indopacific' or basin=='indopac':
    mask_basin[(basin_c==3) | (basin_c==7)] = True 
Nils Brüggemann's avatar
Nils Brüggemann committed
343
344
  elif basin.lower()=='indopacso':
    mask_basin[(basin_c==3) | (basin_c==7) | (basin_c==6)] = True 
nbruegge's avatar
nbruegge committed
345
346
347
  f.close()
  
  ddnpz = np.load(fpath_ckdtree)
348
349
  #dckdtree = ddnpz['dckdtree']
  #ickdtree = ddnpz['ickdtree'] 
nbruegge's avatar
nbruegge committed
350
351
352
353
354
355
356
357
  lon = ddnpz['lon'] 
  lat = ddnpz['lat'] 
  shape = [lat.size, lon.size]
  lat_sec = lat
  
  nz = data3d.shape[0]
  data_zave = np.ma.zeros((nz,lat_sec.size))
  for k in range(nz):
Nils Brüggemann's avatar
Nils Brüggemann committed
358
    data = 1.*data3d[k,:]
nbruegge's avatar
nbruegge committed
359
360
361
362
363
364
    #print('k = %d/%d'%(k,nz))
    # --- mask land points
    data[data==0] = np.ma.masked
    # --- mask not-this-basin points
    data[mask_basin==False] = np.ma.masked
    # --- go to normal np.array (not np.ma object)
365
366
    if isinstance(data, np.ma.core.MaskedArray):
      data = data.filled(0.)
nbruegge's avatar
nbruegge committed
367
    # --- interpolate to rectangular grid
368
369
    datai = apply_ckdtree(data, fpath_ckdtree, coordinates=coordinates)
    datai = datai.reshape(shape)
nbruegge's avatar
nbruegge committed
370
371
372
373
374
375
    # --- go back to masked array
    datai = np.ma.array(datai, mask=datai==0.)
    # --- do zonal average
    data_zave[k,:] = datai.mean(axis=1)
  return lat_sec, data_zave

376
def zonal_average_atmosphere(data3d, ind_lev, fac, fpath_ckdtree='', coordinates='clat clon',):
377
378
379
380
381
  icall = np.arange(data3d.shape[1],dtype=int)
  datavi = data3d[ind_lev,icall]*fac+data3d[ind_lev+1,icall]*(1.-fac)
  lon, lat, datavihi = interp_to_rectgrid(datavi, fpath_ckdtree, coordinates=coordinates)
  data_zave = datavihi.mean(axis=2)
  return lat, data_zave
382

383
def zonal_section_3d_data(data3d, fpath_ckdtree, coordinates):
384
385
386
387
388
389
390
391
  """
  (
   lon_sec, lat_sec, dist_sec, data_sec 
  ) = pyic.zonal_section_3d_data(tbias, 
    fpath_ckdtree=path_ckdtree+'sections/r2b4_nps100_30W80S_30W80N.npz')
  """
  # --- load ckdtree
  ddnpz = np.load(fpath_ckdtree)
392
393
  #dckdtree = ddnpz['dckdtree']
  #ickdtree = ddnpz['ickdtree'] 
394
395
396
397
398
399
400
  lon_sec = ddnpz['lon_sec'] 
  lat_sec = ddnpz['lat_sec'] 
  dist_sec = ddnpz['dist_sec'] 

  nz = data3d.shape[0]
  data_sec = np.ma.zeros((nz,dist_sec.size))
  for k in range(nz):
401
    data_sec[k,:] = apply_ckdtree(data3d[k,:], fpath_ckdtree, coordinates=coordinates)
402
403
  return lon_sec, lat_sec, dist_sec, data_sec

404
405
406
407
408
409
410
411
412
413
414
def lonlat2str(lon, lat):
  if lon<0:
    lon_s = '%gW'%(-lon)
  else:
    lon_s = '%gE'%(lon)
  if lat<0:
    lat_s = '%gS'%(-lat)
  else:
    lat_s = '%gN'%(lat)
  return lon_s, lat_s

Nils Brüggemann's avatar
Nils Brüggemann committed
415
416
417
418
419
420
421
422
423
"""
Routines to calculate interpolation weights:

  | ckdtree_hgrid
  | ckdtree_section
  |-->| ckdtree_points
      |--> calc_ckdtree
"""

nbruegge's avatar
nbruegge committed
424
def ckdtree_hgrid(lon_reg, lat_reg, res, 
425
426
427
428
429
                 #fpath_grid_triangular='', 
                 fname_tgrid='',
                 path_tgrid='',
                 path_ckdtree='',
                 sname='',
Nils Brüggemann's avatar
Nils Brüggemann committed
430
                 gname='',
431
432
433
434
435
                 tgname='',
                 load_cgrid=True,
                 load_egrid=True,
                 load_vgrid=True,
                 n_nearest_neighbours=1,
436
                 n_jobs=1,
437
438
439
                 ):
  """
  """
440
441
442
  if tgname=='':
    Drgrid = identify_grid(path_tgrid, path_tgrid+fname_tgrid) 
    tgname = Drgrid['name']
443
444
445
  lon1str, lat1str = lonlat2str(lon_reg[0], lat_reg[0])
  lon2str, lat2str = lonlat2str(lon_reg[1], lat_reg[1])

446
447
448
449
  if n_nearest_neighbours==1:
    fname = '%s_res%3.2f_%s-%s_%s-%s.npz'%(tgname, res, lon1str, lon2str, lat1str, lat2str) 
  else:
    fname = '%s_res%3.2f_%dnn_%s-%s_%s-%s.npz'%(tgname, res, n_nearest_neighbours, lon1str, lon2str, lat1str, lat2str) 
450
  fpath_ckdtree = path_ckdtree+fname
Nils Brüggemann's avatar
Nils Brüggemann committed
451
  fpath_tgrid   = path_tgrid+fname_tgrid
452
453
454
455
456
457

  # --- make rectangular grid 
  lon = np.arange(lon_reg[0],lon_reg[1],res)
  lat = np.arange(lat_reg[0],lat_reg[1],res)
  Lon, Lat = np.meshgrid(lon, lat)

Nils Brüggemann's avatar
Nils Brüggemann committed
458
459
460
461
  lon_o = Lon.flatten()
  lat_o = Lat.flatten()
  
  # --- calculate ckdtree
462
463
  Dind_dist = ckdtree_points(fpath_tgrid, lon_o, lat_o, load_cgrid=load_cgrid, load_egrid=load_egrid, load_vgrid=load_vgrid,
                             n_nearest_neighbours=n_nearest_neighbours, n_jobs=n_jobs)
nbruegge's avatar
nbruegge committed
464
465
466
467
468
469

  # --- save grid
  print('Saving grid file: %s' % (fpath_ckdtree))
  np.savez(fpath_ckdtree,
            lon=lon,
            lat=lat,
470
            sname=sname,
Nils Brüggemann's avatar
Nils Brüggemann committed
471
            gname=gname,
472
            tgname='test',
Nils Brüggemann's avatar
Nils Brüggemann committed
473
            **Dind_dist,
nbruegge's avatar
nbruegge committed
474
475
476
477
           )
  return

def ckdtree_section(p1, p2, npoints=101, 
478
479
480
481
                 fname_tgrid='',
                 path_tgrid='',
                 path_ckdtree='',
                 sname='auto',
Nils Brüggemann's avatar
Nils Brüggemann committed
482
                 gname='',
483
                 tgname='',
Nils Brüggemann's avatar
Nils Brüggemann committed
484
                 n_nearest_neighbours=1,
485
                 n_jobs=1,
486
487
488
                 load_cgrid=True,
                 load_egrid=True,
                 load_vgrid=True,
nbruegge's avatar
nbruegge committed
489
490
491
                 ):
  """
  """
492
493
494
  if tgname=='':
    Drgrid = identify_grid(path_tgrid, path_tgrid+fname_tgrid) 
    tgname = Drgrid['name']
495
496
497
498
499
500
  lon1str, lat1str = lonlat2str(p1[0], p1[1])
  lon2str, lat2str = lonlat2str(p2[0], p2[1])

  if sname=='auto':
    sname = fpath_ckdtree.split('/')[-1][:-4]

Nils Brüggemann's avatar
Nils Brüggemann committed
501
502
503
504
  fname = '%s_nps%d_%s%s_%s%s.npz'%(tgname, npoints, lon1str, lat1str, lon2str, lat2str) 
  fpath_ckdtree = path_ckdtree+fname
  fpath_tgrid   = path_tgrid+fname_tgrid

nbruegge's avatar
nbruegge committed
505
506
  # --- derive section points
  lon_sec, lat_sec, dist_sec = derive_section_points(p1, p2, npoints)
Nils Brüggemann's avatar
Nils Brüggemann committed
507
508
  lon_o = lon_sec
  lat_o = lat_sec
nbruegge's avatar
nbruegge committed
509

Nils Brüggemann's avatar
Nils Brüggemann committed
510
  # --- calculate ckdtree
511
512
  Dind_dist = ckdtree_points(fpath_tgrid, lon_o, lat_o, load_cgrid=load_cgrid, load_egrid=load_egrid, load_vgrid=load_vgrid, n_nearest_neighbours=n_nearest_neighbours,
                             n_jobs=n_jobs)
nbruegge's avatar
nbruegge committed
513
514
515
516
517
518
519

  # --- save grid
  print('Saving grid file: %s' % (fpath_ckdtree))
  np.savez(fpath_ckdtree,
            lon_sec=lon_sec,
            lat_sec=lat_sec,
            dist_sec=dist_sec,
520
            sname=sname,
Nils Brüggemann's avatar
Nils Brüggemann committed
521
            gname=gname,
Nils Brüggemann's avatar
Nils Brüggemann committed
522
            **Dind_dist
nbruegge's avatar
nbruegge committed
523
           )
Nils Brüggemann's avatar
Nils Brüggemann committed
524
525
  return Dind_dist['dckdtree_c'], Dind_dist['ickdtree_c'], lon_sec, lat_sec, dist_sec

526
def ckdtree_points(fpath_tgrid, lon_o, lat_o, load_cgrid=True, load_egrid=True, load_vgrid=True, n_nearest_neighbours=1, n_jobs=1):
Nils Brüggemann's avatar
Nils Brüggemann committed
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
  """
  """
  # --- load triangular grid
  f = Dataset(fpath_tgrid, 'r')
  if load_cgrid:
    clon = f.variables['clon'][:] * 180./np.pi
    clat = f.variables['clat'][:] * 180./np.pi
  if load_egrid:
    elon = f.variables['elon'][:] * 180./np.pi
    elat = f.variables['elat'][:] * 180./np.pi
  if load_vgrid:
    vlon = f.variables['vlon'][:] * 180./np.pi
    vlat = f.variables['vlat'][:] * 180./np.pi
  f.close()

  # --- ckdtree for cells, edges and vertices
  if load_cgrid:
    dckdtree_c, ickdtree_c = calc_ckdtree(lon_i=clon, lat_i=clat,
                                          lon_o=lon_o, lat_o=lat_o,
                                          n_nearest_neighbours=n_nearest_neighbours,
547
                                          n_jobs=n_jobs,
Nils Brüggemann's avatar
Nils Brüggemann committed
548
549
550
551
552
                                          )
  if load_egrid:
    dckdtree_e, ickdtree_e = calc_ckdtree(lon_i=elon, lat_i=elat,
                                          lon_o=lon_o, lat_o=lat_o,
                                          n_nearest_neighbours=n_nearest_neighbours,
553
                                          n_jobs=n_jobs,
Nils Brüggemann's avatar
Nils Brüggemann committed
554
555
556
557
558
                                          )
  if load_vgrid:
    dckdtree_v, ickdtree_v = calc_ckdtree(lon_i=vlon, lat_i=vlat,
                                          lon_o=lon_o, lat_o=lat_o,
                                          n_nearest_neighbours=n_nearest_neighbours,
559
                                          n_jobs=n_jobs,
Nils Brüggemann's avatar
Nils Brüggemann committed
560
561
562
563
564
565
566
567
568
569
570
571
572
573
                                          )

  # --- save dict
  Dind_dist = dict()
  if load_cgrid: 
    Dind_dist['dckdtree_c'] = dckdtree_c
    Dind_dist['ickdtree_c'] = ickdtree_c
  if load_egrid: 
    Dind_dist['dckdtree_e'] = dckdtree_e
    Dind_dist['ickdtree_e'] = ickdtree_e
  if load_vgrid: 
    Dind_dist['dckdtree_v'] = dckdtree_v
    Dind_dist['ickdtree_v'] = ickdtree_v
  return Dind_dist
nbruegge's avatar
nbruegge committed
574

575
def calc_ckdtree(lon_i, lat_i, lon_o, lat_o, n_nearest_neighbours=1, n_jobs=1, use_npconcatenate=True):
nbruegge's avatar
nbruegge committed
576
577
  """
  """
578
  # --- do ckdtree
Nils Brüggemann's avatar
Nils Brüggemann committed
579
580
581
582
583
584
585
586
587
588
  if False:
    lzip_i = list(zip(lon_i, lat_i))
    tree = cKDTree(lzip_i)
    lzip_o = list(zip(lon_o, lat_o))
    dckdtree, ickdtree = tree.query(lzip_o , k=n_nearest_neighbours, n_jobs=1)
  else:
    #print('calc_ckdtree by cartesian distances')
    xi, yi, zi = spherical_to_cartesian(lon_i, lat_i)
    xo, yo, zo = spherical_to_cartesian(lon_o, lat_o)

589
590
591
592
593
594
595
    if not use_npconcatenate:
      lzip_i = list(zip(xi, yi, zi))
      lzip_o = list(zip(xo, yo, zo))
    else:
      # This option seems to be much faster but needs to be tested also for big grids
      lzip_i = np.concatenate((xi[:,np.newaxis],yi[:,np.newaxis],zi[:,np.newaxis]), axis=1)
      lzip_o = np.concatenate((xo[:,np.newaxis],yo[:,np.newaxis],zo[:,np.newaxis]), axis=1) 
Nils Brüggemann's avatar
Nils Brüggemann committed
596
    tree = cKDTree(lzip_i)
597
    dckdtree, ickdtree = tree.query(lzip_o , k=n_nearest_neighbours, n_jobs=n_jobs)
nbruegge's avatar
nbruegge committed
598
599
  return dckdtree, ickdtree

Nils Brüggemann's avatar
Nils Brüggemann committed
600
601
602
603
604
605
606
def calc_vertical_interp_weights(zdata, levs):
  """ Calculate vertical interpolation weights and indices.

Call example:
icall, ind_lev, fac = calc_vertical_interp_weights(zdata, levs)

Afterwards do interpolation like this:
607
datai = data[ind_lev,icall]*fac+data[ind_lev+1,icall]*(1.-fac)
Nils Brüggemann's avatar
Nils Brüggemann committed
608
609
610
611
612
613
614
615
616
617
618
619
620
  """
  nza = zdata.shape[0]
  # --- initializations
  ind_lev = np.zeros((levs.size,zdata.shape[1]),dtype=int)
  icall = np.arange(zdata.shape[1],dtype=int)
  icall = icall[np.newaxis,:]
  fac = np.ma.zeros((levs.size,zdata.shape[1]))
  for k, lev in enumerate(levs):
    #print(f'k = {k}')
    # --- find level below critical level
    ind_lev[k,:] = (zdata<levs[k]).sum(axis=0)-1
    ind_lev[k,ind_lev[k,:]==(nza-1)]=-1
    # --- zdata below and above lev 
621
622
623
624
    zd1 = zdata[ind_lev[k,:],icall]
    zd2 = zdata[ind_lev[k,:]+1,icall]
    # --- linear interpolation to get weight (fac=1 if lev=zd1)
    fac[k,:] = (0.-1.)/(zd2-zd1)*(levs[k]-zd1)+1.
Nils Brüggemann's avatar
Nils Brüggemann committed
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
  # --- mask values which are out of range
  fac[ind_lev==-1] = np.ma.masked 
  return icall, ind_lev, fac

"""
Routines to calculate grids and sections
"""

def derive_section_points(p1, p2, npoints=101,):
  # --- derive section points
  if p1[0]==p2[0]:
    lon_sec = p1[0]*np.ones((npoints)) 
    lat_sec = np.linspace(p1[1],p2[1],npoints)
  else:
    lon_sec = np.linspace(p1[0],p2[0],npoints)
    lat_sec = (p2[1]-p1[1])/(p2[0]-p1[0])*(lon_sec-p1[0])+p1[1]
  dist_sec = haversine_dist(lon_sec[0], lat_sec[0], lon_sec, lat_sec)
  return lon_sec, lat_sec, dist_sec

def calc_north_pole_interp_grid_points(lat_south=60., res=100e3):
  """
  Compute grid points optimized for plotting the North Pole area.

  Parameters:
  -----------
  lat_south : float
      Southern latitude of target grid.
  res : float
      resolution of target grid

  Returns:
  --------
  Lon_np, Lat_np: ndarray
      Longitude and latitude of target grid as 2d array.

  Examples:
  ---------
  Lon_np, Lat_np = calc_north_pole_interp_grid_points(lat_south=60., res=100e3)

  """
  R = 6371e3
  x1, y1, z1 = spherical_to_cartesian(  0., lat_south)
  x2, y2, z2 = spherical_to_cartesian( 90., lat_south)
  x3, y3, z3 = spherical_to_cartesian(180., lat_south)
  x4, y4, z4 = spherical_to_cartesian(270., lat_south)

  lon1, lat1 = cartesian_to_spherical(x1, y1, z1)
  lon2, lat2 = cartesian_to_spherical(x2, y2, z2)
  lon3, lat3 = cartesian_to_spherical(x3, y3, z3)
  lon4, lat4 = cartesian_to_spherical(x4, y4, z4)

  #x1 = R * np.cos(  0.*np.pi/180.) * np.cos(lat_south*np.pi/180.)
  #y1 = R * np.sin(  0.*np.pi/180.) * np.cos(lat_south*np.pi/180.)
  #z1 = R * np.sin(lat_south*np.pi/180.)
  #x2 = R * np.cos( 90.*np.pi/180.) * np.cos(lat_south*np.pi/180.)
  #y2 = R * np.sin( 90.*np.pi/180.) * np.cos(lat_south*np.pi/180.)
  #z2 = R * np.sin(lat_south*np.pi/180.)
  #x3 = R * np.cos(180.*np.pi/180.) * np.cos(lat_south*np.pi/180.)
  #y3 = R * np.sin(180.*np.pi/180.) * np.cos(lat_south*np.pi/180.)
  #z3 = R * np.sin(lat_south*np.pi/180.)
  #x4 = R * np.cos(270.*np.pi/180.) * np.cos(lat_south*np.pi/180.)
  #y4 = R * np.sin(270.*np.pi/180.) * np.cos(lat_south*np.pi/180.)
  #z4 = R * np.sin(lat_south*np.pi/180.)
  #
  #lat1 = np.arcsin(z1/np.sqrt(x1**2+y1**2+z1**2)) * 180./np.pi
  #lon1 = np.arctan2(y1,x1) * 180./np.pi
  #lat2 = np.arcsin(z2/np.sqrt(x2**2+y2**2+z2**2)) * 180./np.pi
  #lon2 = np.arctan2(y2,x2) * 180./np.pi
  #lat3 = np.arcsin(z3/np.sqrt(x3**2+y3**2+z3**2)) * 180./np.pi
  #lon3 = np.arctan2(y3,x3) * 180./np.pi
  #lat4 = np.arcsin(z4/np.sqrt(x4**2+y4**2+z4**2)) * 180./np.pi
  #lon4 = np.arctan2(y4,x4) * 180./np.pi
  
  xnp = np.arange(x3, x1+res, res)
  ynp = np.arange(y4, y2+res, res)
  
  Xnp, Ynp = np.meshgrid(xnp, ynp)
  Znp = R * np.sin(lat1*np.pi/180.) * np.ones((ynp.size,xnp.size))
  Lon_np = np.arctan2(Ynp,Xnp) * 180./np.pi
  Lat_np = np.arcsin(Znp/np.sqrt(Xnp**2+Ynp**2+Znp**2)) * 180./np.pi
  return Lon_np, Lat_np

"""
Routines related to spherical geometry
"""
nbruegge's avatar
nbruegge committed
710
711
712
713
714
715
716
717
718
719
720
721
722
723
def haversine_dist(lon_ref, lat_ref, lon_pts, lat_pts, degree=True):
  # for details see http://en.wikipedia.org/wiki/Haversine_formula
  r = 6378.e3
  if degree:
    lon_ref = lon_ref * np.pi/180.
    lat_ref = lat_ref * np.pi/180.
    lon_pts = lon_pts * np.pi/180.
    lat_pts = lat_pts * np.pi/180.
  arg = np.sqrt(   np.sin(0.5*(lat_pts-lat_ref))**2 
                 + np.sin(0.5*(lon_pts-lon_ref))**2
                 * np.cos(lat_ref)*np.cos(lat_pts) )
  dist = 2*r * np.arcsin(arg)
  return dist

Nils Brüggemann's avatar
Nils Brüggemann committed
724
725
726
727
728
729
730
731
732
733
734
def spherical_to_cartesian(lon, lat):
  earth_radius = 6371e3
  x = earth_radius * np.cos(lon*np.pi/180.) * np.cos(lat*np.pi/180.)
  y = earth_radius * np.sin(lon*np.pi/180.) * np.cos(lat*np.pi/180.)
  z = earth_radius * np.sin(lat*np.pi/180.)
  return x, y, z

def cartesian_to_spherical(x, y, z):
  lat = np.arcsin(z/np.sqrt(x**2+y**2+z**2)) * 180./np.pi
  lon = np.arctan2(y,x) * 180./np.pi
  return lon, lat
735

Nils Brüggemann's avatar
Nils Brüggemann committed
736
737
738
"""
Routines to load data
"""
739
def load_hsnap(fpath, var, it=0, iz=0, iw=None, fpath_ckdtree='', verbose=True):
740
  f = Dataset(fpath, 'r')
741
742
  if verbose:
    print("Loading %s from %s" % (var, fpath))
743
744
745
746
  if f.variables[var].ndim==2:
    data = f.variables[var][it,:]
  else:
    data = f.variables[var][it,iz,:]
747
748
  if iw is not None:
    data = np.concatenate((data[:,iw:],data[:,:iw]),axis=1)
749
750
751
752
753
  f.close()

  data[data==0.] = np.ma.masked
  return data

754
755
756
757
758
759
def datetime64_to_float(dates):
  years  = (dates.astype('datetime64[Y]').astype(int) + 1970).astype(int)
  months = (dates.astype('datetime64[M]').astype(int) % 12 + 1).astype(int)
  days   = (dates - dates.astype('datetime64[M]') + 1).astype(int)
  return years, months, days

760
def time_average(IcD, var, t1='none', t2='none', it_ave=[], iz='all', always_use_loop=False, verbose=False, use_xr=False, load_xr_data=False, dimension_from_file='first'):
Nils Brüggemann's avatar
Nils Brüggemann committed
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
  it_ave = np.array(it_ave)
  # --- if no it_ave is given use t1 and t2 to determine averaging indices it_ave
  if it_ave.size==0:
    # --- if t2=='none' set t2=t1 and no time average will be applied
    if isinstance(t2, str) and t2=='none':
      t2 = t1

    # --- convert to datetime64 objects if necessary
    if isinstance(t1, str):
      t1 = np.datetime64(t1)
    if isinstance(t2, str):
      t2 = np.datetime64(t2)

    # --- determine averaging interval
    it_ave = np.where( (IcD.times>=t1) & (IcD.times<=t2) )[0]
776
777
778
  else:
    t1 = IcD.times[it_ave[0]]
    t2 = IcD.times[it_ave[-1]]
779
780
781

  if it_ave.size==0:
    raise ValueError(f'::: Could not find any time steps in interval t1={t1} and t2={t2}! :::')
782
  
783
784
785
786
787
788
789
790
  ## --- decide whether the file consists of monthly or yearly averages (or something else)
  #dt1 = (IcD.times[it_ave][1]-IcD.times[it_ave][0]).astype(float)/(86400)
  #if dt1==365 or dt1==366:
  #  ave_mode = 'yearly'
  #elif dt1==28 or dt1==29 or dt1==30 or dt1==31:
  #  ave_mode = 'monthly'
  #else:
  #  ave_mode = 'unknown'
791
792
793
794
       
  dt64type = IcD.times[0].dtype
  time_bnds = IcD.times[it_ave]
  yy, mm, dd = datetime64_to_float(time_bnds[0])
795
796
797
798
799
800
801
802
803
804
805
806
807
808
  if t1!=t2:
    if IcD.output_freq=='yearly':
      time_bnds = np.concatenate(([np.datetime64(f'{yy-1:04d}-{mm:02d}-{dd:02d}').astype(dt64type)],time_bnds))
    elif IcD.output_freq=='monthly':
      if mm==1:
        yy += -1
        mm = 13
      time_bnds = np.concatenate(([np.datetime64(f'{yy:04d}-{mm-1:02d}-{dd:02d}').astype(dt64type)],time_bnds))
    elif IcD.output_freq=='unknown':
      time_bnds = np.concatenate(([time_bnds[0]-(time_bnds[1]-time_bnds[0])], time_bnds))
    dt = np.diff(time_bnds).astype(IcD.dtype)
  else:
    # load single time instance
    dt = np.array([1])
809
810
  #dt = np.ones((it_ave.size), dtype=IcD.dtype)
  #print('Warning dt set to ones!!!')
811
812

  # --- get dimensions to allocate data
813
814
815
816
817
  if dimension_from_file=='first':
    dimension_from_file = IcD.flist_ts[0]
  elif dimension_from_file=='last':
    dimension_from_file = IcD.flist_ts[-1]
  f = Dataset(dimension_from_file, 'r')
818
  # FIXME: If == ('time', 'lat', 'lon') works well use it everywhere
819
820
821
  load_hfl_type = False
  load_moc_type = False
  if f.variables[var].dimensions == ('time', 'lat', 'lon'): # e.g. for heat fluxes
822
823
    nt, nc, nx = f.variables[var].shape
    nz = 0
824
    load_hfl_type = True
825
  elif f.variables[var].dimensions == ('time', 'depth', 'lat', 'lon'): # e.g. for MOC 
826
827
    nt, nz, nc, ndummy = f.variables[var].shape 
    load_moc_type = True
828
829
  elif f.variables[var].ndim==3:
    nt, nz, nc = f.variables[var].shape
830
  elif f.variables[var].ndim==2: # e.g. for 2D variables like zos and mld
831
832
833
834
835
836
837
838
839
840
841
842
    nt, nc = f.variables[var].shape
    nz = 0
  f.close()

  # --- set iz to all levels
  if isinstance(iz,str) and iz=='all':
    iz = np.arange(nz)
  #else:
  #  iz = np.array([iz])

  # --- if all data is coming from one file take faster approach
  fpaths = np.unique(IcD.flist_ts[it_ave])
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
  if use_xr:
    #print(dt)
    if load_hfl_type:
      data_ave = (IcD.ds[var][it_ave,:,0]*dt[:,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
    elif load_moc_type:
      data_ave = (IcD.ds[var][it_ave,:,:,0]*dt[:,np.newaxis,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
    elif nz>0 and isinstance(iz,(int,np.integer)): # data has no depth dim afterwards
      #data_ave = (IcD.ds[var][it_ave,iz,:]*dt[:,np.newaxis]).sum(axis=0)/dt.sum()
      data_ave = (IcD.ds[var][it_ave,iz,:]*dt[:,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
    elif nz>0 and not isinstance(iz,(int,np.integer)): # data has depth dim afterwards
      data_ave = (IcD.ds[var][it_ave,iz,:]*dt[:,np.newaxis,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
    else:
      data_ave = (IcD.ds[var][it_ave,:]*dt[:,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
    #dataxr = dsxr[var][it_ave,:,:].mean(axis=0)
    if load_xr_data:
      data_ave = data_ave.load().data
  elif (fpaths.size==1) and not always_use_loop:
860
    f = Dataset(fpaths[0], 'r')
861
    if load_hfl_type:
862
      data_ave = (f.variables[var][IcD.its[it_ave],:,0]*dt[:,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
863
    elif load_moc_type:
864
      data_ave = (f.variables[var][IcD.its[it_ave],:,:,0]*dt[:,np.newaxis,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
865
    elif nz>0 and isinstance(iz,(int,np.integer)): # data has no depth dim afterwards
866
      data_ave = (f.variables[var][IcD.its[it_ave],iz,:]*dt[:,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
867
    elif nz>0 and not isinstance(iz,(int,np.integer)): # data has depth dim afterwards
868
      data_ave = (f.variables[var][IcD.its[it_ave],iz,:]*dt[:,np.newaxis,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
869
    else:
870
      data_ave = (f.variables[var][IcD.its[it_ave],:]*dt[:,np.newaxis]).sum(axis=0, dtype='float64')/dt.sum()
871
872
873
874
    f.close()
  # --- otherwise loop ovar all files is needed
  else:
    # --- allocate data
875
    if isinstance(iz,(int,np.integer)) or nz==0:
876
      data_ave = np.ma.zeros((nc), dtype=IcD.dtype)
877
    else:
878
      data_ave = np.ma.zeros((iz.size,nc), dtype=IcD.dtype)
879
880

    # --- average by looping over all files and time steps
881
    for ll, it in enumerate(it_ave):
882
      f = Dataset(IcD.flist_ts[it], 'r')
883
      if load_hfl_type:
884
        data_ave += f.variables[var][IcD.its[it],:,0]*dt[ll]/dt.sum()
885
      elif load_moc_type:
886
        data_ave += f.variables[var][IcD.its[it],:,:,0]*dt[ll]/dt.sum()
887
      elif nz>0:
888
        data_ave += f.variables[var][IcD.its[it],iz,:]*dt[ll]/dt.sum()
889
      else:
890
        data_ave += f.variables[var][IcD.its[it],:]*dt[ll]/dt.sum()
891
      f.close()
892
  data_ave = data_ave.astype(IcD.dtype)
893
894
895
  if verbose:
    #print(f'pyicon.time_average: var={var}: it_ave={it_ave}')
    print(f'pyicon.time_average: var={var}: it_ave={IcD.times[it_ave]}')
896
897
  return data_ave, it_ave

898
def timing(ts, string='', verbose=True):
899
900
901
902
  if ts[0]==0:
    ts = np.array([datetime.datetime.now()])
  else:
    ts = np.append(ts, [datetime.datetime.now()])
903
904
    if verbose:
      print(ts[-1]-ts[-2], ' ', (ts[-1]-ts[0]), ' '+string)
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
  return ts

def conv_gname(gname):
  gname = gname[:-4]

  ogrid = gname.split('_')[0]
  res = float(gname.split('_')[1][1:])

  lo1 = gname.split('_')[2]
  if lo1[-1]=='w':
    lo1 = -float(lo1[:-1])
  else:
    lo1 = float(lo1[:-1])
  lo2 = gname.split('_')[3]
  if lo2[-1]=='w':
    lo2 = -float(lo2[:-1])
  else:
    lo2 = float(lo2[:-1])

  la1 = gname.split('_')[4]
  if la1[-1]=='s':
    la1 = -float(la1[:-1])
  else:
    la1 = float(la1[:-1])
  la2 = gname.split('_')[5]
  if la2[-1]=='s':
    la2 = -float(la2[:-1])
  else:
    la2 = float(la2[:-1])

  lon_reg = [lo1, lo2]
  lat_reg = [la1, la2]
  return ogrid, res, lon_reg, lat_reg

Nils Brüggemann's avatar
Nils Brüggemann committed
939
940
941
"""
Grid related functions
"""
942
943
944
def identify_grid(path_grid, fpath_data):
  """ Identifies ICON grid in depending on clon.size in fpath_data.
  
Nils Brüggemann's avatar
Nils Brüggemann committed
945
946
947
948
949
950
  r2b4:  160km:    15117: OceanOnly_Icos_0158km_etopo40.nc
  r2b4a: 160km:    20480: /pool/data/ICON/grids/public/mpim/0013/icon_grid_0013_R02B04_G.nc
  r2b6:   40km:   327680: OCEANINP_pre04_LndnoLak_039km_editSLOHH2017_G.nc
  r2b8:   10km:  3729001: OceanOnly_Global_IcosSymmetric_0010km_rotatedZ37d_modified_srtm30_1min.nc
  r2b9:    5km: 14886338: OceanOnly_IcosSymmetric_4932m_rotatedZ37d_modified_srtm30_1min.nc
  r2b9a:   5km: 20971520: /pool/data/ICON/grids/public/mpim/0015/icon_grid_0015_R02B09_G.nc
951
952
953
  """
  
  Dgrid_list = dict()
954
955
956
957
958
959
960
961

  grid_name = 'r2b4_oce_r0003'; Dgrid_list[grid_name] = dict()
  Dgrid_list[grid_name]['name'] = grid_name
  Dgrid_list[grid_name]['res'] = '160km'
  Dgrid_list[grid_name]['long_name'] = 'OceanOnly_Icos_0158km_etopo40'
  Dgrid_list[grid_name]['size'] = 15117
  #Dgrid_list[grid_name]['fpath_grid'] = path_grid + Dgrid_list[grid_name]['long_name'] + '/' + Dgrid_list[grid_name]['long_name'] + '.nc'
  Dgrid_list[grid_name]['fpath_grid'] = f'{path_grid}/{grid_name}/{grid_name}_tgrid.nc'
962
  
963
  grid_name = 'r2b4_oce_r0004'; Dgrid_list[grid_name] = dict()
964
965
966
  Dgrid_list[grid_name]['name'] = grid_name
  Dgrid_list[grid_name]['res'] = '160km'
  Dgrid_list[grid_name]['long_name'] = 'OceanOnly_Icos_0158km_etopo40'
967
968
969
  Dgrid_list[grid_name]['size'] = 15105
  #Dgrid_list[grid_name]['fpath_grid'] = path_grid + Dgrid_list[grid_name]['long_name'] + '/' + Dgrid_list[grid_name]['long_name'] + '.nc'
  Dgrid_list[grid_name]['fpath_grid'] = f'{path_grid}/{grid_name}/{grid_name}_tgrid.nc'
Nils Brüggemann's avatar
Nils Brüggemann committed
970
 
971
  grid_name = 'r2b4_atm_r0013'; Dgrid_list[grid_name] = dict()
Nils Brüggemann's avatar
Nils Brüggemann committed
972
973
974
975
  Dgrid_list[grid_name]['name'] = grid_name
  Dgrid_list[grid_name]['res'] = '160km'
  Dgrid_list[grid_name]['long_name'] = 'icon_grid_0013_R02B04_G'
  Dgrid_list[grid_name]['size'] = 20480
976
977
  #Dgrid_list[grid_name]['fpath_grid'] = path_grid + Dgrid_list[grid_name]['long_name'] + '/' + Dgrid_list[grid_name]['long_name'] + '.nc'
  Dgrid_list[grid_name]['fpath_grid'] = f'{path_grid}/{grid_name}/{grid_name}_tgrid.nc'
Nils Brüggemann's avatar
Nils Brüggemann committed
978

Nils Brüggemann's avatar
Nils Brüggemann committed
979
  grid_name = 'r2b6old'; Dgrid_list[grid_name] = dict()
980
981
982
983
  Dgrid_list[grid_name]['name'] = grid_name
  Dgrid_list[grid_name]['res'] = '40km'
  Dgrid_list[grid_name]['long_name'] = 'OCEANINP_pre04_LndnoLak_039km_editSLOHH2017_G'
  Dgrid_list[grid_name]['size'] = 327680
984
985
  #Dgrid_list[grid_name]['fpath_grid'] = path_grid + Dgrid_list[grid_name]['long_name'] + '/' + Dgrid_list[grid_name]['long_name'] + '.nc'
  Dgrid_list[grid_name]['fpath_grid'] = f'{path_grid}/{grid_name}/{grid_name}_tgrid.nc'
986
  
987
  grid_name = 'r2b6_oce_r0004'; Dgrid_list[grid_name] = dict()
988
989
990
991
  Dgrid_list[grid_name]['name'] = grid_name
  Dgrid_list[grid_name]['res'] = '40km'
  Dgrid_list[grid_name]['long_name'] = 'OceanOnly_Global_IcosSymmetric_0039km_rotatedZ37d_BlackSea_Greenland_modified_srtm30_1min'
  Dgrid_list[grid_name]['size'] = 235403 
992
993
  #Dgrid_list[grid_name]['fpath_grid'] = path_grid + Dgrid_list[grid_name]['long_name'] + '/' + Dgrid_list[grid_name]['long_name'] + '.nc'
  Dgrid_list[grid_name]['fpath_grid'] = f'{path_grid}/{grid_name}/{grid_name}_tgrid.nc'
994

995
  grid_name = 'r2b8_oce_r0004'; Dgrid_list[grid_name] = dict()
996
997
998
999
  Dgrid_list[grid_name]['name'] = grid_name
  Dgrid_list[grid_name]['res'] = '10km'
  Dgrid_list[grid_name]['long_name'] = 'OceanOnly_Global_IcosSymmetric_0010km_rotatedZ37d_modified_srtm30_1min'
  Dgrid_list[grid_name]['size'] = 3729001
1000
  #Dgrid_list[grid_name]['fpath_grid'] = path_grid + Dgrid_list[grid_name]['long_name'] + '/' + Dgrid_list[grid_name]['long_name'] + '.nc'
For faster browsing, not all history is shown. View entire blame