-
-
Notifications
You must be signed in to change notification settings - Fork 2.8k
Expand file tree
/
Copy pathbasevalidators.py
More file actions
2711 lines (2315 loc) · 83.9 KB
/
basevalidators.py
File metadata and controls
2711 lines (2315 loc) · 83.9 KB
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
import base64
import numbers
import textwrap
import uuid
from importlib import import_module
import copy
import io
import re
import sys
import narwhals.stable.v1 as nw
from _plotly_utils.optional_imports import get_module
# back-port of fullmatch from Py3.4+
def fullmatch(regex, string, flags=0):
"""Emulate python-3.4 re.fullmatch()."""
if "pattern" in dir(regex):
regex_string = regex.pattern
else:
regex_string = regex
return re.match("(?:" + regex_string + r")\Z", string, flags=flags)
# Utility functions
# -----------------
def to_scalar_or_list(v):
# Handle the case where 'v' is a non-native scalar-like type,
# such as numpy.float32. Without this case, the object might be
# considered numpy-convertable and therefore promoted to a
# 0-dimensional array, but we instead want it converted to a
# Python native scalar type ('float' in the example above).
# We explicitly check if is has the 'item' method, which conventionally
# converts these types to native scalars.
np = get_module("numpy", should_load=False)
pd = get_module("pandas", should_load=False)
if np and np.isscalar(v) and hasattr(v, "item"):
return v.item()
if isinstance(v, (list, tuple)):
return [to_scalar_or_list(e) for e in v]
elif np and isinstance(v, np.ndarray):
if v.ndim == 0:
return v.item()
return [to_scalar_or_list(e) for e in v]
elif pd and isinstance(v, (pd.Series, pd.Index)):
return [to_scalar_or_list(e) for e in v]
elif is_numpy_convertable(v):
return to_scalar_or_list(np.array(v))
else:
return v
def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
"""
Convert an array-like value into a read-only numpy array
Parameters
----------
v : array like
Array like value (list, tuple, numpy array, pandas series, etc.)
kind : str or tuple of str
If specified, the numpy dtype kind (or kinds) that the array should
have, or be converted to if possible.
If not specified then let numpy infer the datatype
force_numeric : bool
If true, raise an exception if the resulting numpy array does not
have a numeric dtype (i.e. dtype.kind not in ['u', 'i', 'f'])
Returns
-------
np.ndarray
Numpy array with the 'WRITEABLE' flag set to False
"""
np = get_module("numpy")
assert np is not None
# ### Process kind ###
if not kind:
kind = ()
elif isinstance(kind, str):
kind = (kind,)
first_kind = kind[0] if kind else None
# u: unsigned int, i: signed int, f: float
numeric_kinds = {"u", "i", "f"}
kind_default_dtypes = {
"u": "uint32",
"i": "int32",
"f": "float64",
"O": "object",
}
# With `pass_through=True`, the original object will be returned if unable to convert
# to a Narwhals DataFrame or Series.
v = nw.from_native(v, allow_series=True, pass_through=True)
if isinstance(v, nw.Series):
if v.dtype == nw.Datetime and v.dtype.time_zone is not None:
# Remove time zone so that local time is displayed
v = v.dt.replace_time_zone(None).to_numpy()
else:
v = v.to_numpy()
elif isinstance(v, nw.DataFrame):
schema = v.schema
overrides = {}
for key, val in schema.items():
if val == nw.Datetime and val.time_zone is not None:
# Remove time zone so that local time is displayed
overrides[key] = nw.col(key).dt.replace_time_zone(None)
if overrides:
v = v.with_columns(**overrides)
v = v.to_numpy()
if not isinstance(v, np.ndarray):
# v has its own logic on how to convert itself into a numpy array
if is_numpy_convertable(v):
return copy_to_readonly_numpy_array(
np.array(v), kind=kind, force_numeric=force_numeric
)
else:
# v is not homogenous array
v_list = [to_scalar_or_list(e) for e in v]
# Lookup dtype for requested kind, if any
dtype = kind_default_dtypes.get(first_kind, None)
# construct new array from list
new_v = np.array(v_list, order="C", dtype=dtype)
elif v.dtype.kind in numeric_kinds:
# v is a homogenous numeric array
if kind and v.dtype.kind not in kind:
# Kind(s) were specified and this array doesn't match
# Convert to the default dtype for the first kind
dtype = kind_default_dtypes.get(first_kind, None)
new_v = np.ascontiguousarray(v.astype(dtype))
else:
# Either no kind was requested or requested kind is satisfied
new_v = np.ascontiguousarray(v.copy())
else:
# v is a non-numeric homogenous array
new_v = v.copy()
# Handle force numeric param
# --------------------------
if force_numeric and new_v.dtype.kind not in numeric_kinds:
raise ValueError(
"Input value is not numeric and force_numeric parameter set to True"
)
if "U" not in kind:
# Force non-numeric arrays to have object type
# --------------------------------------------
# Here we make sure that non-numeric arrays have the object
# datatype. This works around cases like np.array([1, 2, '3']) where
# numpy converts the integers to strings and returns array of dtype
# '<U21'
if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
new_v = np.array(v, dtype="object")
# Set new array to be read-only
# -----------------------------
new_v.flags["WRITEABLE"] = False
return new_v
def is_numpy_convertable(v):
"""
Return whether a value is meaningfully convertable to a numpy array
via 'numpy.array'
"""
return hasattr(v, "__array__") or hasattr(v, "__array_interface__")
def is_homogeneous_array(v):
"""
Return whether a value is considered to be a homogeneous array
"""
np = get_module("numpy", should_load=False)
pd = get_module("pandas", should_load=False)
if (
np
and isinstance(v, np.ndarray)
or (pd and isinstance(v, (pd.Series, pd.Index)))
or (isinstance(v, nw.Series))
):
return True
if is_numpy_convertable(v):
np = get_module("numpy", should_load=True)
if np:
v_numpy = np.array(v)
# v is essentially a scalar and so shouldn't count as an array
if v_numpy.shape == ():
return False
else:
return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
return False
def is_simple_array(v):
"""
Return whether a value is considered to be an simple array
"""
return isinstance(v, (list, tuple))
def is_array(v):
"""
Return whether a value is considered to be an array
"""
return is_simple_array(v) or is_homogeneous_array(v)
def type_str(v):
"""
Return a type string of the form module.name for the input value v
"""
if not isinstance(v, type):
v = type(v)
return "'{module}.{name}'".format(module=v.__module__, name=v.__name__)
def is_typed_array_spec(v):
"""
Return whether a value is considered to be a typed array spec for plotly.js
"""
return isinstance(v, dict) and "bdata" in v and "dtype" in v
def is_none_or_typed_array_spec(v):
return v is None or is_typed_array_spec(v)
# Validators
# ----------
class BaseValidator(object):
"""
Base class for all validator classes
"""
def __init__(self, plotly_name, parent_name, role=None, **_):
"""
Construct a validator instance
Parameters
----------
plotly_name : str
Name of the property being validated
parent_name : str
Names of all of the ancestors of this property joined on '.'
characters. e.g.
plotly_name == 'range' and parent_name == 'layout.xaxis'
role : str
The role string for the property as specified in
plot-schema.json
"""
self.parent_name = parent_name
self.plotly_name = plotly_name
self.role = role
self.array_ok = False
def description(self):
"""
Returns a string that describes the values that are acceptable
to the validator
Should start with:
The '{plotly_name}' property is a...
For consistancy, string should have leading 4-space indent
"""
raise NotImplementedError()
def raise_invalid_val(self, v, inds=None):
"""
Helper method to raise an informative exception when an invalid
value is passed to the validate_coerce method.
Parameters
----------
v :
Value that was input to validate_coerce and could not be coerced
inds: list of int or None (default)
Indexes to display after property name. e.g. if self.plotly_name
is 'prop' and inds=[2, 1] then the name in the validation error
message will be 'prop[2][1]`
Raises
-------
ValueError
"""
name = self.plotly_name
if inds:
for i in inds:
name += "[" + str(i) + "]"
raise ValueError(
"""
Invalid value of type {typ} received for the '{name}' property of {pname}
Received value: {v}
{valid_clr_desc}""".format(
name=name,
pname=self.parent_name,
typ=type_str(v),
v=repr(v),
valid_clr_desc=self.description(),
)
)
def raise_invalid_elements(self, invalid_els):
if invalid_els:
raise ValueError(
"""
Invalid element(s) received for the '{name}' property of {pname}
Invalid elements include: {invalid}
{valid_clr_desc}""".format(
name=self.plotly_name,
pname=self.parent_name,
invalid=invalid_els[:10],
valid_clr_desc=self.description(),
)
)
def validate_coerce(self, v):
"""
Validate whether an input value is compatible with this property,
and coerce the value to be compatible of possible.
Parameters
----------
v
The input value to be validated
Raises
------
ValueError
if `v` cannot be coerced into a compatible form
Returns
-------
The input `v` in a form that's compatible with this property
"""
raise NotImplementedError()
def present(self, v):
"""
Convert output value of a previous call to `validate_coerce` into a
form suitable to be returned to the user on upon property
access.
Note: The value returned by present must be either immutable or an
instance of BasePlotlyType, otherwise the value could be mutated by
the user and we wouldn't get notified about the change.
Parameters
----------
v
A value that was the ouput of a previous call the
`validate_coerce` method on the same object
Returns
-------
"""
if is_homogeneous_array(v):
# Note: numpy array was already coerced into read-only form so
# we don't need to copy it here.
return v
elif is_simple_array(v):
return tuple(v)
else:
return v
class DataArrayValidator(BaseValidator):
"""
"data_array": {
"description": "An {array} of data. The value MUST be an
{array}, or we ignore it.",
"requiredOpts": [],
"otherOpts": [
"dflt"
]
},
"""
def __init__(self, plotly_name, parent_name, **kwargs):
super(DataArrayValidator, self).__init__(
plotly_name=plotly_name, parent_name=parent_name, **kwargs
)
self.array_ok = True
def description(self):
return """\
The '{plotly_name}' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series""".format(plotly_name=self.plotly_name)
def validate_coerce(self, v):
if is_none_or_typed_array_spec(v):
pass
elif is_homogeneous_array(v):
v = copy_to_readonly_numpy_array(v)
elif is_simple_array(v):
v = to_scalar_or_list(v)
else:
self.raise_invalid_val(v)
return v
class EnumeratedValidator(BaseValidator):
"""
"enumerated": {
"description": "Enumerated value type. The available values are
listed in `values`.",
"requiredOpts": [
"values"
],
"otherOpts": [
"dflt",
"coerceNumber",
"arrayOk"
]
},
"""
def __init__(
self,
plotly_name,
parent_name,
values,
array_ok=False,
coerce_number=False,
**kwargs,
):
super(EnumeratedValidator, self).__init__(
plotly_name=plotly_name, parent_name=parent_name, **kwargs
)
# Save params
# -----------
self.values = values
self.array_ok = array_ok
# coerce_number is rarely used and not implemented
self.coerce_number = coerce_number
self.kwargs = kwargs
# Handle regular expressions
# --------------------------
# Compiled regexs
self.val_regexs = []
# regex replacements that run before the matching regex
# So far, this is only used to cast 'x1' -> 'x' for anchor-style
# enumeration properties
self.regex_replacements = []
# Loop over enumeration values
# ----------------------------
# Look for regular expressions
for v in self.values:
if v and isinstance(v, str) and v[0] == "/" and v[-1] == "/" and len(v) > 1:
# String is a regex with leading and trailing '/' character
regex_str = v[1:-1]
self.val_regexs.append(re.compile(regex_str))
self.regex_replacements.append(
EnumeratedValidator.build_regex_replacement(regex_str)
)
else:
self.val_regexs.append(None)
self.regex_replacements.append(None)
def __deepcopy__(self, memodict={}):
"""
A custom deepcopy method is needed here because compiled regex
objects don't support deepcopy
"""
cls = self.__class__
return cls(self.plotly_name, self.parent_name, values=self.values)
@staticmethod
def build_regex_replacement(regex_str):
# Example: regex_str == r"^y([2-9]|[1-9][0-9]+)?$"
#
# When we see a regular expression like the one above, we want to
# build regular expression replacement params that will remove a
# suffix of 1 from the input string ('y1' -> 'y' in this example)
#
# Why?: Regular expressions like this one are used in enumeration
# properties that refer to subplotids (e.g. layout.annotation.xref)
# The regular expressions forbid suffixes of 1, like 'x1'. But we
# want to accept 'x1' and coerce it into 'x'
#
# To be cautious, we only perform this conversion for enumerated
# values that match the anchor-style regex
match = re.match(
r"\^(\w)\(\[2\-9\]\|\[1\-9\]\[0\-9\]\+\)\?\( domain\)\?\$", regex_str
)
if match:
anchor_char = match.group(1)
return "^" + anchor_char + "1$", anchor_char
else:
return None
def perform_replacemenet(self, v):
"""
Return v with any applicable regex replacements applied
"""
if isinstance(v, str):
for repl_args in self.regex_replacements:
if repl_args:
v = re.sub(repl_args[0], repl_args[1], v)
return v
def description(self):
# Separate regular values from regular expressions
enum_vals = []
enum_regexs = []
for v, regex in zip(self.values, self.val_regexs):
if regex is not None:
enum_regexs.append(regex.pattern)
else:
enum_vals.append(v)
desc = """\
The '{name}' property is an enumeration that may be specified as:""".format(
name=self.plotly_name
)
if enum_vals:
enum_vals_str = "\n".join(
textwrap.wrap(
repr(enum_vals),
initial_indent=" " * 12,
subsequent_indent=" " * 12,
break_on_hyphens=False,
)
)
desc = (
desc
+ """
- One of the following enumeration values:
{enum_vals_str}""".format(enum_vals_str=enum_vals_str)
)
if enum_regexs:
enum_regexs_str = "\n".join(
textwrap.wrap(
repr(enum_regexs),
initial_indent=" " * 12,
subsequent_indent=" " * 12,
break_on_hyphens=False,
)
)
desc = (
desc
+ """
- A string that matches one of the following regular expressions:
{enum_regexs_str}""".format(enum_regexs_str=enum_regexs_str)
)
if self.array_ok:
desc = (
desc
+ """
- A tuple, list, or one-dimensional numpy array of the above"""
)
return desc
def in_values(self, e):
"""
Return whether a value matches one of the enumeration options
"""
is_str = isinstance(e, str)
for v, regex in zip(self.values, self.val_regexs):
if is_str and regex:
in_values = fullmatch(regex, e) is not None
# in_values = regex.fullmatch(e) is not None
else:
in_values = e == v
if in_values:
return True
return False
def validate_coerce(self, v):
if is_none_or_typed_array_spec(v):
pass
elif self.array_ok and is_array(v):
v_replaced = [self.perform_replacemenet(v_el) for v_el in v]
invalid_els = [e for e in v_replaced if (not self.in_values(e))]
if invalid_els:
self.raise_invalid_elements(invalid_els[:10])
if is_homogeneous_array(v):
v = copy_to_readonly_numpy_array(v)
else:
v = to_scalar_or_list(v)
else:
v = self.perform_replacemenet(v)
if not self.in_values(v):
self.raise_invalid_val(v)
return v
class BooleanValidator(BaseValidator):
"""
"boolean": {
"description": "A boolean (true/false) value.",
"requiredOpts": [],
"otherOpts": [
"dflt"
]
},
"""
def __init__(self, plotly_name, parent_name, **kwargs):
super(BooleanValidator, self).__init__(
plotly_name=plotly_name, parent_name=parent_name, **kwargs
)
def description(self):
return """\
The '{plotly_name}' property must be specified as a bool
(either True, or False)""".format(plotly_name=self.plotly_name)
def validate_coerce(self, v):
if is_none_or_typed_array_spec(v):
pass
elif not isinstance(v, bool):
self.raise_invalid_val(v)
return v
class SrcValidator(BaseValidator):
def __init__(self, plotly_name, parent_name, **kwargs):
super(SrcValidator, self).__init__(
plotly_name=plotly_name, parent_name=parent_name, **kwargs
)
self.chart_studio = get_module("chart_studio")
def description(self):
return """\
The '{plotly_name}' property must be specified as a string or
as a plotly.grid_objs.Column object""".format(plotly_name=self.plotly_name)
def validate_coerce(self, v):
if is_none_or_typed_array_spec(v):
pass
elif isinstance(v, str):
pass
elif self.chart_studio and isinstance(v, self.chart_studio.grid_objs.Column):
# Convert to id string
v = v.id
else:
self.raise_invalid_val(v)
return v
class NumberValidator(BaseValidator):
"""
"number": {
"description": "A number or a numeric value (e.g. a number
inside a string). When applicable, values
greater (less) than `max` (`min`) are coerced to
the `dflt`.",
"requiredOpts": [],
"otherOpts": [
"dflt",
"min",
"max",
"arrayOk"
]
},
"""
def __init__(
self, plotly_name, parent_name, min=None, max=None, array_ok=False, **kwargs
):
super(NumberValidator, self).__init__(
plotly_name=plotly_name, parent_name=parent_name, **kwargs
)
# Handle min
if min is None and max is not None:
# Max was specified, so make min -inf
self.min_val = float("-inf")
else:
self.min_val = min
# Handle max
if max is None and min is not None:
# Min was specified, so make min inf
self.max_val = float("inf")
else:
self.max_val = max
if min is not None or max is not None:
self.has_min_max = True
else:
self.has_min_max = False
self.array_ok = array_ok
def description(self):
desc = """\
The '{plotly_name}' property is a number and may be specified as:""".format(
plotly_name=self.plotly_name
)
if not self.has_min_max:
desc = (
desc
+ """
- An int or float"""
)
else:
desc = (
desc
+ """
- An int or float in the interval [{min_val}, {max_val}]""".format(
min_val=self.min_val, max_val=self.max_val
)
)
if self.array_ok:
desc = (
desc
+ """
- A tuple, list, or one-dimensional numpy array of the above"""
)
return desc
def validate_coerce(self, v):
if is_none_or_typed_array_spec(v):
pass
elif self.array_ok and is_homogeneous_array(v):
np = get_module("numpy")
try:
v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
except (ValueError, TypeError, OverflowError):
self.raise_invalid_val(v)
# Check min/max
if self.has_min_max:
v_valid = np.logical_and(
self.min_val <= v_array, v_array <= self.max_val
)
if not np.all(v_valid):
# Grab up to the first 10 invalid values
v_invalid = np.logical_not(v_valid)
some_invalid_els = np.array(v, dtype="object")[v_invalid][
:10
].tolist()
self.raise_invalid_elements(some_invalid_els)
v = v_array # Always numeric numpy array
elif self.array_ok and is_simple_array(v):
# Check numeric
invalid_els = [e for e in v if not isinstance(e, numbers.Number)]
if invalid_els:
self.raise_invalid_elements(invalid_els[:10])
# Check min/max
if self.has_min_max:
invalid_els = [e for e in v if not (self.min_val <= e <= self.max_val)]
if invalid_els:
self.raise_invalid_elements(invalid_els[:10])
v = to_scalar_or_list(v)
else:
# Check numeric
if not isinstance(v, numbers.Number):
self.raise_invalid_val(v)
# Check min/max
if self.has_min_max:
if not (self.min_val <= v <= self.max_val):
self.raise_invalid_val(v)
return v
class IntegerValidator(BaseValidator):
"""
"integer": {
"description": "An integer or an integer inside a string. When
applicable, values greater (less) than `max`
(`min`) are coerced to the `dflt`.",
"requiredOpts": [],
"otherOpts": [
"dflt",
"min",
"max",
"extras",
"arrayOk"
]
},
"""
def __init__(
self,
plotly_name,
parent_name,
min=None,
max=None,
extras=None,
array_ok=False,
**kwargs,
):
super(IntegerValidator, self).__init__(
plotly_name=plotly_name, parent_name=parent_name, **kwargs
)
# Handle min
if min is None and max is not None:
# Max was specified, so make min -inf
self.min_val = -sys.maxsize - 1
else:
self.min_val = min
# Handle max
if max is None and min is not None:
# Min was specified, so make min inf
self.max_val = sys.maxsize
else:
self.max_val = max
if min is not None or max is not None:
self.has_min_max = True
else:
self.has_min_max = False
self.extras = extras if extras is not None else []
self.array_ok = array_ok
def description(self):
desc = """\
The '{plotly_name}' property is a integer and may be specified as:""".format(
plotly_name=self.plotly_name
)
if not self.has_min_max:
desc = (
desc
+ """
- An int (or float that will be cast to an int)"""
)
else:
desc = desc + (
"""
- An int (or float that will be cast to an int)
in the interval [{min_val}, {max_val}]""".format(
min_val=self.min_val, max_val=self.max_val
)
)
# Extras
if self.extras:
desc = desc + (
"""
OR exactly one of {extras} (e.g. '{eg_extra}')"""
).format(extras=self.extras, eg_extra=self.extras[-1])
if self.array_ok:
desc = (
desc
+ """
- A tuple, list, or one-dimensional numpy array of the above"""
)
return desc
def validate_coerce(self, v):
if is_none_or_typed_array_spec(v):
pass
elif v in self.extras:
return v
elif self.array_ok and is_homogeneous_array(v):
np = get_module("numpy")
v_array = copy_to_readonly_numpy_array(
v, kind=("i", "u"), force_numeric=True
)
if v_array.dtype.kind not in ["i", "u"]:
self.raise_invalid_val(v)
# Check min/max
if self.has_min_max:
v_valid = np.logical_and(
self.min_val <= v_array, v_array <= self.max_val
)
if not np.all(v_valid):
# Grab up to the first 10 invalid values
v_invalid = np.logical_not(v_valid)
some_invalid_els = np.array(v, dtype="object")[v_invalid][
:10
].tolist()
self.raise_invalid_elements(some_invalid_els)
v = v_array
elif self.array_ok and is_simple_array(v):
# Check integer type
invalid_els = [
e for e in v if not isinstance(e, int) and e not in self.extras
]
if invalid_els:
self.raise_invalid_elements(invalid_els[:10])
# Check min/max
if self.has_min_max:
invalid_els = [
e
for e in v
if not (isinstance(e, int) and self.min_val <= e <= self.max_val)
and e not in self.extras
]
if invalid_els:
self.raise_invalid_elements(invalid_els[:10])
v = to_scalar_or_list(v)
else:
# Check int
if not isinstance(v, int):
# don't let int() cast strings to ints
self.raise_invalid_val(v)
# Check min/max
if self.has_min_max:
if not (self.min_val <= v <= self.max_val):
self.raise_invalid_val(v)
return v
class StringValidator(BaseValidator):
"""
"string": {
"description": "A string value. Numbers are converted to strings
except for attributes with `strict` set to true.",
"requiredOpts": [],
"otherOpts": [
"dflt",
"noBlank",
"strict",
"arrayOk",
"values"
]
},
"""
def __init__(
self,
plotly_name,
parent_name,
no_blank=False,
strict=False,
array_ok=False,
values=None,
**kwargs,
):
super(StringValidator, self).__init__(
plotly_name=plotly_name, parent_name=parent_name, **kwargs
)
self.no_blank = no_blank
self.strict = strict
self.array_ok = array_ok
self.values = values
@staticmethod
def to_str_or_unicode_or_none(v):
"""
Convert a value to a string if it's not None, a string,
or a unicode (on Python 2).
"""
if v is None or isinstance(v, str):
return v
else:
return str(v)