/
LMR_proxy_pandas_rework.py
1071 lines (874 loc) · 38.5 KB
/
LMR_proxy_pandas_rework.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
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
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
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
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
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
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
"""
Module: LMR_proxy_pandas_rework.py
Purpose: Module containing various classes associated with proxy types to be
assimilated in the LMR, as well as numerous functionalities for
selection of proxy types/sites to be included in the reanalysis.
Rewritten by AndreP to incorporate features from the original
LMR_proxy code using OOP and Pandas. Is used by the driver but not by
verification scripts.
Originator: Andre Perkins, U. of Washington.
Revisions:
- Added capability to filter uploaded *NCDC proxies* according to the
database they are included in (PAGES1, PAGES2, or LMR_FM). This
information is found in the metadata, as extracted from the
NCDC-templated text files.
[ R. Tardif, U. Washington, March 2016 ]
- Added capability to filter out *NCDC proxies* listed in a blacklist.
This is mainly used to prevent the assimilation of chronologies known
to be duplicates.
[ R. Tardif, U. Washington, March 2016 ]
- Added capability to select proxies according to data availability over
the reconstruction period.
[ R. Tardif, U. Washington, October 2016 ]
- Added class for low-resolution marine proxies used for LGM & Holocene
reconstructions (NCDCdtda class).
[ R. Tardif, U. Washington, January 2017 ]
- Renamed the proxy databases to less-confusing convention.
'pages' renamed as 'PAGES2kv1' and 'NCDC' renamed as 'LMRdb'
[ R. Tardif, Univ. of Washington, Sept 2017 ]
"""
import LMR_psms
from load_data import load_data_frame
from LMR_utils import augment_docstr, class_docs_fixer
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from random import sample, seed
from copy import deepcopy
import ast
class ProxyManager:
"""
High-level manager to handle loading proxies from multiple sources and
randomly sampling the proxies.
Attributes
----------
all_proxies: list(BaseProxyObject like)
A list of all proxy objects loaded for current reconstruction
all_ids_by_group: dict{str: list(str)}
A dictionary holding list of proxy site ids for each proxy type loaded.
ind_assim: list(int)
List of indices (pretaining to all_proxies)to be assimilated during the
reconstruction.
ind_eval: list(int)
List of indices of proxies withheld for verification purposes.
Parameters
----------
config: LMR_config
Configuration module for current LMR run
data_range: list(int)
A two int list defining the beginning and ending time of the
reconstruction
"""
def __init__(self, config, data_range):
self.all_proxies = []
self.all_ids_by_group = defaultdict(list)
for proxy_class_key in config.proxies.use_from:
pclass = get_proxy_class(proxy_class_key)
# Load proxies for current class
ids_by_grp, proxies = pclass.load_all(config,
data_range,
None)
# Add to total lists
self.all_proxies += proxies
for k, v in ids_by_grp.items():
self.all_ids_by_group[k] += v
proxy_frac = config.proxies.proxy_frac
nsites = len(self.all_proxies)
# Sample subset from all proxies if specified
if proxy_frac < 1.0:
nsites_assim = int(nsites * proxy_frac)
seed(config.proxies.seed)
self.ind_assim = sample(list(range(nsites)), nsites_assim)
self.ind_assim.sort()
self.ind_eval = list(set(range(nsites)) - set(self.ind_assim))
self.ind_eval.sort()
# Make list of assimilated proxies by group
self.assim_ids_by_group = deepcopy(self.all_ids_by_group)
for idx in self.ind_eval:
pobj = self.all_proxies[idx]
grp = self.assim_ids_by_group[pobj.type]
if pobj.id in grp:
grp.remove(pobj.id)
else:
self.ind_assim = list(range(nsites))
self.ind_eval = None
self.assim_ids_by_group = self.all_ids_by_group
def proxy_obj_generator(self, indexes):
"""
Generator to iterate over proxy objects in list at specified indexes
Parameters
----------
indexes: list(int)
List of indices pertaining to self.all_proxies
Returns
-------
generator
A generator over all_proxies for each specified index
"""
for idx in indexes:
yield self.all_proxies[idx]
def sites_assim_proxy_objs(self):
"""
Generator over ind_assim indices.
Yields
------
BaseProxyObject like
Proxy object from the all_proxies list.
"""
return self.proxy_obj_generator(self.ind_assim)
def sites_eval_proxy_objs(self):
"""
Generator over ind_eval indices.
Yields
------
BaseProxyObject like
Proxy object from the all_proxies list.
"""
if self.ind_eval:
return self.proxy_obj_generator(self.ind_eval)
else:
return []
class BaseProxyObject(metaclass=ABCMeta):
"""
Class defining attributes and methods for descendant proxy objects.
Attributes
----------
id: str
Proxy site id
type: str
Proxy type
start_yr: int
Earliest year that data exists for this site
end_yr: int
Latest year that data exists for this site
values: pandas.DataFrame
Proxy record with time (in years) as the index
lat: float
Latitude of proxy site
lon: float
Longitude of proxy site
elev: float
Elevation/depth of proxy site
time: ndarray
List of times for which proxy contains valid data
psm_obj: BasePSM like
PSM for this proxy
psm: function
Exposed psm mapping function from the psm_obj
Parameters
----------
config: LMR_config
Configuration module for current LMR run
pid -> id
prox_type -> type
start_yr
end_yr
lat
lon
elev
values
time
Notes
-----
All proxy object classes should descend from the BaseProxyObject abstract
class.
"""
def __init__(self, config, pid, prox_type, start_yr, end_yr,
lat, lon, elev, seasonality, values, time):
if (values is None) or len(values) == 0:
raise ValueError('No proxy data given for object initialization')
assert len(values) == len(time), 'Time and value dimensions must match'
self.id = pid
self.type = prox_type
self.start_yr = start_yr
self.end_yr = end_yr
self.values = values
self.lat = lat
self.lon = fix_lon(lon)
self.elev = elev
self.time = time
self.seasonality = seasonality
try:
load_psmobj = config.core.load_psmobj # if attribute exists
except:
load_psmobj = True
if load_psmobj:
# Retrieve appropriate PSM object & associated attributes
psm_obj = self.get_psm_obj(config,prox_type)
self.psm_obj = psm_obj(config, self)
self.psm = self.psm_obj.psm
@staticmethod
@abstractmethod
def get_psm_obj():
""" Retrieves PSM object class to be attached to this proxy"""
pass
@classmethod
@abstractmethod
def load_site(cls, config, site, data_range=None, meta_src=None,
data_src=None):
"""
Load proxy object from single site.
Parameters
----------
config: LMR_config
Configuration for current LMR run
site: str
Key to identify which site to load from source data
meta_src: optional
Source for proxy metadata
data_src: optional
Source for proxy record data (might be same as meta_src)
data_range: iterable
Two-item container holding beginning and end date of reconstruction
Returns
-------
BaseProxyObject like
Proxy object instance at specified site
Notes
-----
If source data not specified, it should attempt to load data using
config file information.
"""
pass
@classmethod
@abstractmethod
def load_all(cls, config, data_range):
"""
Load proxy objects from all sites matching filter criterion.
Parameters
----------
config: LMR_config
Configuration for current LMR run
meta_src: optional
Source for proxy metadata
data_src: optional
Source for proxy record data (might be same as meta_src)
data_range: iterable
Two-item container holding beginning and end date of reconstruction
Returns
-------
dict
Dictionary of proxy types (keys) with associated site ids (values)
list
List of all proxy objects created
Notes
-----
If source data not specified, it should attempt to load data using
config file information.
"""
pass
@abstractmethod
def error(self):
"""
error model for proxy data
"""
pass
@class_docs_fixer
class ProxyPAGES2kv1(BaseProxyObject):
@staticmethod
def get_psm_obj(config,proxy_type):
psm_key = config.proxies.PAGES2kv1.proxy_psm_type[proxy_type]
return LMR_psms.get_psm_class(psm_key)
@classmethod
@augment_docstr
def load_site(cls, config, site, data_range=None, meta_src=None,
data_src=None):
"""%%aug%%
Expects meta_src, data_src to be pickled pandas DataFrame objects.
"""
pages2kv1_cfg = config.proxies.PAGES2kv1
if meta_src is None:
meta_src = load_data_frame(pages2kv1_cfg.metafile_proxy)
if data_src is None:
data_src = load_data_frame(pages2kv1_cfg.datafile_proxy)
data_src = data_src.to_dense()
site_meta = meta_src[meta_src['Proxy ID'] == site]
pid = site_meta['Proxy ID'].iloc[0]
pmeasure = site_meta['Proxy measurement'].iloc[0]
pages2kv1_type = site_meta['Archive type'].iloc[0]
try:
proxy_type = pages2kv1_cfg.proxy_type_mapping[(pages2kv1_type, pmeasure)]
except (KeyError, ValueError) as e:
print('Proxy type/measurement not found in mapping: {}'.format(e))
raise ValueError(e)
start_yr = site_meta['Youngest (C.E.)'].iloc[0]
end_yr = site_meta['Oldest (C.E.)'].iloc[0]
lat = site_meta['Lat (N)'].iloc[0]
lon = site_meta['Lon (E)'].iloc[0]
elev = 0.0 # elev not info available in PAGES2kS1 data
seasonality = None # not defined in PAGES2kS1 metadata
site_data = data_src[site]
if data_range is not None:
start, finish = data_range
values = site_data[(site_data.index >= start) &
(site_data.index <= finish)]
else:
values = site_data
# Might need to remove following line
values = values[values.notnull()]
times = values.index.values
# transform in "anomalies" (time-mean removed) if option activated
if config.proxies.PAGES2kv1.proxy_timeseries_kind == 'anom':
values = values - values.mean()
if len(values) == 0:
raise ValueError('No observations in specified time range.')
return cls(config, pid, proxy_type, start_yr, end_yr, lat, lon, elev,
seasonality, values, times)
@classmethod
@augment_docstr
def load_all(cls, config, data_range, meta_src=None,
data_src=None):
"""%%aug%%
Expects meta_src, data_src to be pickled pandas DataFrame objects.
"""
# Load source data files
if meta_src is None:
meta_src = load_data_frame(config.proxies.PAGES2kv1.metafile_proxy)
if data_src is None:
data_src = load_data_frame(config.proxies.PAGES2kv1.datafile_proxy)
data_src = data_src.to_dense()
filters = config.proxies.PAGES2kv1.simple_filters
proxy_order = config.proxies.PAGES2kv1.proxy_order
ptype_filters = config.proxies.PAGES2kv1.proxy_assim2
availability_filter = config.proxies.PAGES2kv1.proxy_availability_filter
availability_fraction = config.proxies.PAGES2kv1.proxy_availability_fraction
# initial masks all true before filtering
useable = meta_src[meta_src.columns[0]] == 0
useable |= True
availability_mask = meta_src[meta_src.columns[0]] == 0
availability_mask |= True
# Find indices matching filter specifications
for colname, filt_list in filters.items():
simple_mask = meta_src[colname] == 0
simple_mask &= False
for value in filt_list:
simple_mask |= meta_src[colname] == value
useable &= simple_mask
# Filtering proxy records on conditions of availability during
# the reconstruction period (recon_period in configuration, or
# data_range here).
if availability_filter: # if not None
start, finish = data_range
# Checking proxy metadata's period of availability against
# reconstruction period.
availability_mask = ((meta_src['Oldest (C.E.)'] <= start) &
(meta_src['Youngest (C.E.)'] >= finish))
# Checking level of completeness of record within the reconstruction
# period (ignore record if fraction of available data is below user-defined
# threshold (proxy_availability_fraction in config).
maxnb = (finish - start) + 1
proxies_to_test = meta_src['Proxy ID'][availability_mask & useable].values
for prx in proxies_to_test.tolist():
values = data_src[prx][(data_src[:].index >= start) & (data_src[:].index <= finish)]
values = values[values.notnull()]
frac_available = float(len(values))/float(maxnb)
if frac_available < availability_fraction:
availability_mask[meta_src[meta_src['Proxy ID'] == prx].index] = False
# Create proxy id lists
proxy_id_by_type = {}
all_proxy_ids = []
type_col = 'Archive type'
measure_col = 'Proxy measurement'
for name in proxy_order:
type_mask = meta_src[type_col] == 0
type_mask |= True
# Filter to proxies of a certain type
ptype = name.split('_', 1)[0]
type_mask &= meta_src[type_col] == ptype
# Reduce to listed measures
measure_mask = meta_src[measure_col] == 0
measure_mask &= False
for measure in ptype_filters[name]:
measure_mask |= meta_src[measure_col] == measure
# Extract proxy ids using mask and append to lists
proxies = meta_src['Proxy ID'][measure_mask & type_mask &
availability_mask & useable].values
# If we have ids after filtering add them to the type list
if len(proxies) > 0:
proxy_id_by_type[name] = proxies.tolist()
all_proxy_ids += proxies.tolist()
# Create proxy objects list
all_proxies = []
for site in all_proxy_ids:
try:
pobj = cls.load_site(config, site, data_range,
meta_src=meta_src, data_src=data_src)
all_proxies.append(pobj)
except ValueError as e:
# Proxy had no obs or didn't meet psm r crit
for group in list(proxy_id_by_type.values()):
if site in group:
group.remove(site)
break # Should only be one instance
return proxy_id_by_type, all_proxies
@classmethod
def load_all_annual_no_filtering(cls, config, meta_src=None,
data_src=None):
"""
Method created to facilitate the loading of all possible proxy records
that can be calibrated with annual resolution.
Note: This is still subject to constraints from the PSM calibration (
i.e. if there is an r_crit or not enough calibration data the proxy
will not be loaded)
Returns
-------
proxy_objs: list(BaseProxyObject like)
"""
# Load source data files
if meta_src is None:
meta_src = load_data_frame(config.proxies.PAGES2kv1.metafile_proxy)
if data_src is None:
data_src = load_data_frame(config.proxies.PAGES2kv1.datafile_proxy)
data_src = data_src.to_dense()
useable = meta_src['Resolution (yr)'] == 1.0
proxy_ids = meta_src['Proxy ID'][useable].values
proxy_objs = []
for site in proxy_ids:
try:
pobj = cls.load_site(config, site,
meta_src=meta_src, data_src=data_src)
proxy_objs.append(pobj)
except ValueError as e:
print(e)
return proxy_objs
def error(self):
# Constant error for now
return 0.1
class ProxyLMRdb(BaseProxyObject):
@staticmethod
def get_psm_obj(config,proxy_type):
psm_key = config.proxies.LMRdb.proxy_psm_type[proxy_type]
return LMR_psms.get_psm_class(psm_key)
@classmethod
@augment_docstr
def load_site(cls, config, site, data_range=None, meta_src=None,
data_src=None):
"""%%aug%%
Expects meta_src, data_src to be pickled pandas DataFrame objects.
"""
LMRdb_cfg = config.proxies.LMRdb
if meta_src is None:
meta_src = load_data_frame(LMRdb_cfg.metafile_proxy)
if data_src is None:
data_src = load_data_frame(LMRdb_cfg.datafile_proxy)
data_src = data_src.to_dense()
site_meta = meta_src[meta_src['Proxy ID'] == site]
pid = site_meta['Proxy ID'].iloc[0]
pmeasure = site_meta['Proxy measurement'].iloc[0]
LMRdb_type = site_meta['Archive type'].iloc[0]
try:
proxy_type = LMRdb_cfg.proxy_type_mapping[(LMRdb_type,pmeasure)]
except (KeyError, ValueError) as e:
print('Proxy type/measurement not found in mapping: {}'.format(e))
raise ValueError(e)
start_yr = site_meta['Youngest (C.E.)'].iloc[0]
end_yr = site_meta['Oldest (C.E.)'].iloc[0]
lat = site_meta['Lat (N)'].iloc[0]
lon = site_meta['Lon (E)'].iloc[0]
elev = site_meta['Elev'].iloc[0]
site_data = data_src[site]
seasonality = site_meta['Seasonality'].iloc[0]
# make sure a list is returned
if type(seasonality) is not list: seasonality = ast.literal_eval(seasonality)
if data_range is not None:
start, finish = data_range
values = site_data[(site_data.index >= start) &
(site_data.index <= finish)]
else:
values = site_data
# Might need to remove following line
values = values[values.notnull()]
times = values.index.values
# transform in "anomalies" (time-mean removed) if option activated
if config.proxies.LMRdb.proxy_timeseries_kind == 'anom':
values = values - values.mean()
if len(values) == 0:
raise ValueError('No observations in specified time range.')
return cls(config, pid, proxy_type, start_yr, end_yr, lat, lon, elev,
seasonality, values, times)
@classmethod
@augment_docstr
def load_all(cls, config, data_range, meta_src=None,
data_src=None):
"""%%aug%%
Expects meta_src, data_src to be pickled pandas DataFrame objects.
"""
# Load source data files
if meta_src is None:
meta_src = load_data_frame(config.proxies.LMRdb.metafile_proxy)
if data_src is None:
data_src = load_data_frame(config.proxies.LMRdb.datafile_proxy)
data_src = data_src.to_dense()
filters = config.proxies.LMRdb.simple_filters
proxy_order = config.proxies.LMRdb.proxy_order
ptype_filters = config.proxies.LMRdb.proxy_assim2
dbase_filters = config.proxies.LMRdb.database_filter
proxy_blacklist = config.proxies.LMRdb.proxy_blacklist
availability_filter = config.proxies.LMRdb.proxy_availability_filter
availability_fraction = config.proxies.LMRdb.proxy_availability_fraction
# initial mask all true before filtering
useable = meta_src[meta_src.columns[0]] == 0
useable |= True
availability_mask = meta_src[meta_src.columns[0]] == 0
availability_mask |= True
# Find indices matching simple filter specifications
for colname, filt_list in filters.items():
simple_mask = meta_src[colname] == 0
simple_mask &= False
for value in filt_list:
simple_mask |= meta_src[colname] == value
useable &= simple_mask
# Filtering proxy records on conditions of availability during
# the reconstruction period (recon_period in configuration, or
# data_range here).
if availability_filter: # if not None
start, finish = data_range
# Checking proxy metadata's period of availability against
# reconstruction period.
availability_mask = ((meta_src['Oldest (C.E.)'] <= start) &
(meta_src['Youngest (C.E.)'] >= finish))
# Checking level of completeness of record within the reconstruction
# period (ignore record if fraction of available data is below user-defined
# threshold (proxy_availability_fraction in config).
maxnb = (finish - start) + 1
proxies_to_test = meta_src['Proxy ID'][availability_mask & useable].values
for prx in proxies_to_test.tolist():
values = data_src[prx][(data_src[:].index >= start) & (data_src[:].index <= finish)]
values = values[values.notnull()]
frac_available = float(len(values))/float(maxnb)
if frac_available < availability_fraction:
availability_mask[meta_src[meta_src['Proxy ID'] == prx].index] = False
# Find indices matching **database filter** specifications
database_col = 'Databases'
# dbase_filters not "None" or empty list (some selection on db has been activated)
if dbase_filters:
# define boolean array with right dimension & set all to False
dbase_mask = meta_src[database_col] == 0
# set mask to True for proxies matching all databases found in dbase_filters
for i in range(len(meta_src[database_col])):
if meta_src[database_col][i]:
#dbase_mask[i] = set(meta_src[database_col][i]).isdisjoint(dbase_filters) # oldold code
#dbase_mask[i] = set(dbase_filters).issubset(meta_src[database_col][i]) # old code
dbase_mask[i] = bool(set(meta_src[database_col][i]).intersection(set(dbase_filters)))
else:
dbase_mask[i] = False
else:
# selection on db has NOT been activated:
# define boolean array with right dimension & set all to True
dbase_mask = meta_src[database_col] != 0
# Define mask of proxies listed in a user-defined "blacklist"
# (see LMR_config).
# boolean array set with right dimension & all set to True
blacklist_mask = meta_src['Proxy ID'] != ' '
if proxy_blacklist:
# If site listed in blacklist, modify corresponding elements of
# boolean array to False
for pbl in proxy_blacklist:
tmp = meta_src['Proxy ID'].map(lambda x: x.startswith(pbl))
inds = meta_src['Proxy ID'][tmp].index
blacklist_mask[inds] = False
# Create proxy id lists
proxy_id_by_type = {}
all_proxy_ids = []
type_col = 'Archive type'
measure_col = 'Proxy measurement'
for name in proxy_order:
type_mask = meta_src[type_col] == 0
type_mask |= True
# Filter to proxies of a certain type
ptype = name.split('_', 1)[0]
type_mask &= meta_src[type_col] == ptype
# Reduce to listed measures
measure_mask = meta_src[measure_col] == 0
measure_mask &= False
for measure in ptype_filters[name]:
measure_mask |= meta_src[measure_col] == measure
# Extract proxy ids using mask and append to lists
proxies = meta_src['Proxy ID'][measure_mask & type_mask &
dbase_mask & blacklist_mask &
availability_mask & useable].values
# If we have ids after filtering add them to the type list
if len(proxies) > 0:
proxy_id_by_type[name] = proxies.tolist()
all_proxy_ids += proxies.tolist()
# Create proxy objects list
all_proxies = []
for site in all_proxy_ids:
try:
pobj = cls.load_site(config, site, data_range,
meta_src=meta_src, data_src=data_src)
all_proxies.append(pobj)
except ValueError as e:
# Proxy had no obs or didn't meet psm r crit
for group in list(proxy_id_by_type.values()):
if site in group:
group.remove(site)
break # Should only be one instance
return proxy_id_by_type, all_proxies
@classmethod
def load_all_annual_no_filtering(cls, config, meta_src=None,
data_src=None):
"""
Method created to facilitate the loading of all possible proxy records
that can be calibrated with annual resolution.
Note: This is still subject to constraints from the PSM calibration (
i.e. if there is an r_crit or not enough calibration data the proxy
will not be loaded)
Returns
-------
proxy_objs: list(BaseProxyObject like)
"""
# Load source data files
if meta_src is None:
meta_src = load_data_frame(config.proxies.LMRdb.metafile_proxy)
if data_src is None:
data_src = load_data_frame(config.proxies.LMRdb.datafile_proxy)
data_src = data_src.to_dense()
# TODO: For now hard coded to annual resolution - AP
useable = meta_src['Resolution (yr)'] == 1.0
proxy_ids = meta_src['Proxy ID'][useable].values
proxy_objs = []
for site in proxy_ids:
try:
pobj = cls.load_site(config, site,
meta_src=meta_src, data_src=data_src)
proxy_objs.append(pobj)
except ValueError as e:
print(e)
return proxy_objs
def error(self):
# Constant error for now
return 0.1
class ProxyNCDCdtda(BaseProxyObject):
@staticmethod
def get_psm_obj(config,proxy_type):
psm_key = config.proxies.NCDCdtda.proxy_psm_type[proxy_type]
return LMR_psms.get_psm_class(psm_key)
@classmethod
@augment_docstr
def load_site(cls, config, site, data_range=None, meta_src=None,
data_src=None):
"""%%aug%%
Expects meta_src, data_src to be pickled pandas DataFrame objects.
"""
NCDCdtda_cfg = config.proxies.NCDCdtda
if meta_src is None:
meta_src = load_data_frame(NCDCdtda_cfg.metafile_proxy)
if data_src is None:
data_src = load_data_frame(NCDCdtda_cfg.datafile_proxy)
site_meta = meta_src[meta_src['Proxy ID'] == site]
pid = site_meta['Proxy ID'].iloc[0]
pmeasure = site_meta['Proxy measurement'].iloc[0]
NCDCdtda_type = site_meta['Archive type'].iloc[0]
try:
proxy_type = NCDCdtda_cfg.proxy_type_mapping[(NCDCdtda_type,pmeasure)]
except (KeyError, ValueError) as e:
print('Proxy type/measurement not found in mapping: {}'.format(e))
raise ValueError(e)
start_yr = site_meta['Youngest (C.E.)'].iloc[0]
end_yr = site_meta['Oldest (C.E.)'].iloc[0]
lat = site_meta['Lat (N)'].iloc[0]
lon = site_meta['Lon (E)'].iloc[0]
elev = site_meta['Elev'].iloc[0]
site_data = data_src[site]
seasonality = site_meta['Seasonality'].iloc[0]
# if field exists, make sure a list is returned for seasonality
if seasonality:
if type(seasonality) is not list: seasonality = ast.literal_eval(seasonality)
else:
seasonality = None
if data_range is not None:
start, finish = data_range
values = site_data[(site_data.index >= start) &
(site_data.index <= finish)]
else:
values = site_data
# Might need to remove following line
values = values[values.notnull()]
times = values.index.values
# transform in "anomalies" (time-mean removed) if option activated
if config.proxies.NCDCdtda.proxy_timeseries_kind == 'anom':
values = values - values.mean()
if len(values) == 0:
raise ValueError('No observations in specified time range.')
return cls(config, pid, proxy_type, start_yr, end_yr, lat, lon, elev,
seasonality, values, times)
@classmethod
@augment_docstr
def load_all(cls, config, data_range, meta_src=None,
data_src=None):
"""%%aug%%
Expects meta_src, data_src to be pickled pandas DataFrame objects.
"""
# Load source data files
if meta_src is None:
meta_src = load_data_frame(config.proxies.NCDCdtda.metafile_proxy)
if data_src is None:
data_src = load_data_frame(config.proxies.NCDCdtda.datafile_proxy)
filters = config.proxies.NCDCdtda.simple_filters
proxy_order = config.proxies.NCDCdtda.proxy_order
ptype_filters = config.proxies.NCDCdtda.proxy_assim2
dbase_filters = config.proxies.NCDCdtda.database_filter
proxy_blacklist = config.proxies.NCDCdtda.proxy_blacklist
availability_filter = config.proxies.NCDCdtda.proxy_availability_filter
availability_fraction = config.proxies.NCDCdtda.proxy_availability_fraction
# initial mask all true before filtering
useable = meta_src[meta_src.columns[0]] == 0
useable |= True
availability_mask = meta_src[meta_src.columns[0]] == 0
availability_mask |= True
# Find indices matching simple filter specifications
for colname, filt_list in filters.items():
simple_mask = meta_src[colname] == 0
simple_mask &= False
for value in filt_list:
if colname == 'Resolution (yr)' and type(value) is tuple:
for i in range(len(meta_src[colname].index)):
simple_mask[i] |= (value[0] <= meta_src[colname][i] <= value[1])
else:
simple_mask |= meta_src[colname] == value
useable &= simple_mask
# Filtering proxy records on conditions of availability during
# the reconstruction period (recon_period in configuration, or
# data_range here).
if availability_filter: # if not None
start, finish = data_range
# Checking proxy metadata's period of availability against
# reconstruction period.
availability_mask = ((meta_src['Oldest (C.E.)'] <= start) &
(meta_src['Youngest (C.E.)'] >= finish))
# Checking level of completeness of record within the reconstruction
# period (ignore record if fraction of available data is below user-defined
# threshold (proxy_availability_fraction in config).
maxnb = (finish - start) + 1
proxies_to_test = meta_src['Proxy ID'][availability_mask & useable].values
for prx in proxies_to_test.tolist():
values = data_src[prx][(data_src[:].index >= start) & (data_src[:].index <= finish)]
values = values[values.notnull()]
frac_available = float(len(values))/float(maxnb)
if frac_available < availability_fraction:
availability_mask[meta_src[meta_src['Proxy ID'] == prx].index] = False
# Find indices matching **database filter** specifications
database_col = 'Databases'
# dbase_filters not "None" or empty list (some selection on db has been activated)
if dbase_filters:
# define boolean array with right dimension & set all to False
dbase_mask = meta_src[database_col] == 0
# set mask to True for proxies matching all databases found in dbase_filters
for i in range(len(meta_src[database_col])):
if meta_src[database_col][i]:
#dbase_mask[i] = set(meta_src[database_col][i]).isdisjoint(dbase_filters) # oldold code
#dbase_mask[i] = set(dbase_filters).issubset(meta_src[database_col][i]) # old code
dbase_mask[i] = bool(set(meta_src[database_col][i]).intersection(set(dbase_filters)))
else:
dbase_mask[i] = False
else:
# selection on db has NOT been activated:
# define boolean array with right dimension & set all to True
dbase_mask = meta_src[database_col] != 0
# Define mask of proxies listed in a user-defined "blacklist"
# (see LMR_config).
# boolean array set with right dimension & all set to True
blacklist_mask = meta_src['Proxy ID'] != ' '
if proxy_blacklist:
# If site listed in blacklist, modify corresponding elements of
# boolean array to False
for pbl in proxy_blacklist:
tmp = meta_src['Proxy ID'].map(lambda x: x.startswith(pbl))
inds = meta_src['Proxy ID'][tmp].index
blacklist_mask[inds] = False
# Create proxy id lists
proxy_id_by_type = {}
all_proxy_ids = []
type_col = 'Archive type'
measure_col = 'Proxy measurement'
for name in proxy_order:
type_mask = meta_src[type_col] == 0
type_mask |= True
# Filter to proxies of a certain type
ptype = name.split('_', 1)[0]
type_mask &= meta_src[type_col] == ptype
# Reduce to listed measures
measure_mask = meta_src[measure_col] == 0
measure_mask &= False
for measure in ptype_filters[name]:
measure_mask |= meta_src[measure_col] == measure
# Extract proxy ids using mask and append to lists
proxies = meta_src['Proxy ID'][measure_mask & type_mask &
dbase_mask & blacklist_mask &
availability_mask & useable].values
# If we have ids after filtering add them to the type list
if len(proxies) > 0:
proxy_id_by_type[name] = proxies.tolist()
all_proxy_ids += proxies.tolist()
# Create proxy objects list
all_proxies = []
for site in all_proxy_ids:
try:
pobj = cls.load_site(config, site, data_range,
meta_src=meta_src, data_src=data_src)
all_proxies.append(pobj)
except ValueError as e:
# Proxy had no obs or didn't meet psm r crit
for group in list(proxy_id_by_type.values()):
if site in group:
group.remove(site)
break # Should only be one instance
return proxy_id_by_type, all_proxies
@classmethod
def load_all_annual_no_filtering(cls, config, meta_src=None,
data_src=None):
"""
Method created to facilitate the loading of all possible proxy records