This repository was archived by the owner on May 27, 2020. It is now read-only.
forked from AllenDowney/ModSimPy
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathmodsim.py
More file actions
1361 lines (997 loc) · 36.3 KB
/
modsim.py
File metadata and controls
1361 lines (997 loc) · 36.3 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
"""
Code from Modeling and Simulation in Python.
Copyright 2017 Allen Downey
License: https://creativecommons.org/licenses/by/4.0)
"""
import logging
logger = logging.getLogger(name='modsim.py')
#TODO: Make this Python 3.7 when conda is ready
# make sure we have Python 3.6 or better
import sys
if sys.version_info < (3, 6):
logger.warn('modsim.py depends on Python 3.6 features.')
import inspect
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy
import sympy
import seaborn as sns
sns.set(style='white', font_scale=1.2)
import pint
UNITS = pint.UnitRegistry()
Quantity = UNITS.Quantity
# expose some names so we can use them without dot notation
from copy import copy
from numpy import sqrt, log, exp, pi
from pandas import DataFrame, Series
from time import sleep
from scipy.interpolate import interp1d
from scipy.interpolate import InterpolatedUnivariateSpline
from scipy.integrate import odeint
from scipy.integrate import solve_ivp
from scipy.optimize import leastsq
from scipy.optimize import minimize_scalar
import scipy.optimize
def flip(p=0.5):
"""Flips a coin with the given probability.
p: float 0-1
returns: boolean (True or False)
"""
return np.random.random() < p
# For all the built-in Python functions that do math,
# let's use the NumPy version instead.
abs = np.abs
min = np.min
max = np.max
pow = np.power
sum = np.sum
round = np.round
def cart2pol(x, y, z=None):
"""Convert Cartesian coordinates to polar.
x: number or sequence
y: number or sequence
z: number or sequence (optional)
returns: theta, rho OR theta, rho, z
"""
x = np.asarray(x)
y = np.asarray(y)
# TODO: use hypot?
rho = np.sqrt(x**2 + y**2)
theta = np.arctan2(y, x)
if z is None:
return theta, rho
else:
return theta, rho, z
def pol2cart(theta, rho, z=None):
"""Convert polar coordinates to Cartesian.
theta: number or sequence
rho: number or sequence
z: number or sequence (optional)
returns: x, y OR x, y, z
"""
if hasattr(theta, 'units'):
if theta.units == UNITS.degree:
theta = theta.to(UNITS.radian)
if theta.units != UNITS.radian:
msg = """In pol2cart, theta must be either a number or
a Quantity in degrees or radians."""
raise ValueError(msg)
x = rho * np.cos(theta)
y = rho * np.sin(theta)
if z is None:
return x, y
else:
return x, y, z
def linspace(start, stop, num=50, **options):
"""Returns an array of evenly-spaced values in the interval [start, stop].
start: first value
stop: last value
num: number of values
Also accepts the same keyword arguments as np.linspace. See
https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html
returns: array or Quantity
"""
underride(options, dtype=np.float64)
# see if either of the arguments has units
units = getattr(start, 'units', None)
units = getattr(stop, 'units', units)
array = np.linspace(start, stop, num, **options)
if units:
array = array * units
return array
def linrange(start=0, stop=None, step=1, **options):
"""Returns an array of evenly-spaced values in the interval [start, stop].
This function works best if the space between start and stop
is divisible by step; otherwise the results might be surprising.
By default, the last value in the array is `stop-step`
(at least approximately).
If you provide the keyword argument `endpoint=True`,
the last value in the array is `stop`.
start: first value
stop: last value
step: space between values
Also accepts the same keyword arguments as np.linspace. See
https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html
returns: array or Quantity
"""
if stop is None:
stop = start
start = 0
# TODO: what breaks if we don't make the dtype float?
#underride(options, endpoint=True, dtype=np.float64)
underride(options, endpoint=False)
# see if any of the arguments has units
units = getattr(start, 'units', None)
units = getattr(stop, 'units', units)
units = getattr(step, 'units', units)
n = np.round((stop - start) / step)
if options['endpoint']:
n += 1
array = np.linspace(start, stop, int(n), **options)
if units:
array = array * units
return array
def magnitude(x):
"""Returns the magnitude of a Quantity or number.
x: Quantity or number
returns: number
"""
return x.magnitude if isinstance(x, Quantity) else x
def magnitudes(x):
"""Returns the magnitude of a Quantity or number, or sequence.
x: Quantity or number, or sequence
returns: number
"""
try:
return [magnitude(elt) for elt in x]
except TypeError: # not iterable
return magnitude(x)
def units(x):
"""Returns the units of a Quantity or number.
x: Quantity or number
returns: Unit object or 1
"""
return x.units if isinstance(x, Quantity) else 1
def remove_units(series):
"""Removes units from the values in a Series.
Only removes units from top-level values;
does not traverse nested values.
returns: new Series object
"""
res = copy(series)
print(type(res))
for label, value in res.iteritems():
res[label] = magnitude(value)
return res
def require_units(x, units):
"""Apply units to `x`, if necessary.
x: Quantity or number
units: Pint Units object
returns: Quantity
"""
if isinstance(x, Quantity):
return x.to(units)
else:
return Quantity(x, units)
def fit_leastsq(error_func, params, *args, **options):
"""Find the parameters that yield the best fit for the data.
`params` can be a sequence, array, or Series
Whatever arguments are provided are passed along to `error_func`
error_func: function that computes a sequence of errors
params: initial guess for the best parameters
data: the data to be fit; will be passed to min_fun
options: any other arguments are passed to leastsq
"""
# if any of the params are quantities, strip the units
x0 = [magnitude(x) for x in params]
# override `full_output` so we get a message if something goes wrong
options['full_output'] = True
# run leastsq
with units_off():
best_params, cov_x, infodict, mesg, ier = leastsq(error_func,
x0=x0, args=args, **options)
details = ModSimSeries(infodict)
details.set(cov_x=cov_x, mesg=mesg, ier=ier)
# if we got a Params object, we should return a Params object
if isinstance(params, Params):
best_params = Params(Series(best_params, params.index))
# return the best parameters and details
return best_params, details
def min_bounded(min_func, bounds, *args, **options):
"""Finds the input value that minimizes `min_func`.
Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
min_func: computes the function to be minimized
bounds: sequence of two values, lower and upper bounds of the
range to be searched
args: any additional positional arguments are passed to min_func
options: any keyword arguments are passed as options to minimize_scalar
returns: ModSimSeries object
"""
# try:
# print(bounds[0])
# min_func(bounds[0], *args)
# except Exception as e:
# msg = """Before running scipy.integrate.min_bounded, I tried
# running the slope function you provided with the
# initial conditions in system and t=0, and I got
# the following error:"""
# logger.error(msg)
# raise(e)
underride(options, xatol=1e-3)
# TODO: Do we need to remove units from bounds?
with units_off():
res = minimize_scalar(min_func,
bracket=bounds,
bounds=bounds,
args=args,
method='bounded',
options=options)
if not res.success:
msg = """scipy.optimize.minimize_scalar did not succeed.
The message it returned is %s""" % res.message
raise Exception(msg)
return ModSimSeries(res)
def max_bounded(max_func, bounds, *args, **options):
"""Finds the input value that maximizes `max_func`.
Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
min_func: computes the function to be maximized
bounds: sequence of two values, lower and upper bounds of the
range to be searched
args: any additional positional arguments are passed to max_func
options: any keyword arguments are passed as options to minimize_scalar
returns: ModSimSeries object
"""
def min_func(*args):
return -max_func(*args)
res = min_bounded(min_func, bounds, *args, **options)
# we have to negate the function value before returning res
res.fun = -res.fun
return res
def minimize(min_func, x0, *args, **options):
"""Finds the input value that minimizes `min_func`.
Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
min_func: computes the function to be minimized
x0: initial guess
args: any additional positional arguments are passed to min_func
options: any keyword arguments are passed as options to minimize_scalar
returns: ModSimSeries object
"""
underride(options, tol=1e-3)
with units_off():
res = scipy.optimize.minimize(min_func, x0, *args, **options)
return ModSimSeries(res)
def run_odeint(system, slope_func, **options):
"""Integrates an ordinary differential equation.
`system` should contain system parameters and `ts`, which
is an array or Series that specifies the time when the
solution will be computed.
system: System object
slope_func: function that computes slopes
returns: TimeFrame
"""
# make sure `system` contains `ts`
if not hasattr(system, 'ts'):
msg = """It looks like `system` does not contain `ts`
as a system variable. `ts` should be an array
or Series that specifies the times when the
solution will be computed:"""
raise ValueError(msg)
# make sure `system` contains `ts`
if not hasattr(system, 'init'):
msg = """It looks like `system` does not contain `init`
as a system variable. `init` should be a State
object that specifies the initial condition:"""
raise ValueError(msg)
# make the system parameters available as globals
unpack(system)
# try running the slope function with the initial conditions
try:
slope_func(init, ts[0], system)
except Exception as e:
msg = """Before running scipy.integrate.odeint, I tried
running the slope function you provided with the
initial conditions in system and t=0, and I got
the following error:"""
logger.error(msg)
raise(e)
# when odeint calls slope_func, it should pass `system` as
# the third argument. To make that work, we have to make a
# tuple with a single element and pass the tuple to odeint as `args`
args = (system,)
# now we're ready to run `odeint` with `init` and `ts` from `system`
with units_off():
array = odeint(slope_func, list(init), ts, args, **options)
# the return value from odeint is an array, so let's pack it into
# a TimeFrame with appropriate columns and index
frame = TimeFrame(array, columns=init.index, index=ts, dtype=np.float64)
return frame
def run_ode_solver(system, slope_func, **options):
"""Computes a numerical solution to a differential equation.
`system` must contain `init` with initial conditions,
`t_0` with the start time, and `t_end` with the end time.
It can contain any other parameters required by the slope function.
`options` can be any legal options of `scipy.integrate.solve_ivp`
system: System object
slope_func: function that computes slopes
returns: TimeFrame
"""
# make sure `system` contains `init`
if not hasattr(system, 'init'):
msg = """It looks like `system` does not contain `init`
as a system variable. `init` should be a State
object that specifies the initial condition:"""
raise ValueError(msg)
# make sure `system` contains `t_end`
if not hasattr(system, 't_end'):
msg = """It looks like `system` does not contain `t_end`
as a system variable. `t_end` should be the
final time:"""
raise ValueError(msg)
# make the system parameters available as globals
unpack(system)
# the default value for t_0 is 0
t_0 = getattr(system, 't_0', 0)
# try running the slope function with the initial conditions
# try:
# slope_func(init, t_0, system)
# except Exception as e:
# msg = """Before running scipy.integrate.solve_ivp, I tried
# running the slope function you provided with the
# initial conditions in `system` and `t=t_0` and I got
# the following error:"""
# logger.error(msg)
# raise(e)
# wrap the slope function to reverse the arguments and add `system`
f = lambda t, y: slope_func(y, t, system)
def wrap_event(event):
"""Wrap the event functions.
Make events terminal by default.
"""
wrapped = lambda t, y: event(y, t, system)
wrapped.terminal = getattr(event, 'terminal', True)
wrapped.direction = getattr(event, 'direction', 0)
return wrapped
# wrap the event functions so they take the right arguments
events = options.pop('events', [])
try:
events = [wrap_event(event) for event in events]
except TypeError:
events = wrap_event(events)
# remove dimensions from the initial conditions.
# we need this because otherwise `init` gets copied into the
# results array along with its units
y_0 = [magnitude(x) for x in init]
# run the solver
with units_off():
bunch = solve_ivp(f, [t_0, t_end], y_0, events=events, **options)
# separate the results from the details
y = bunch.pop('y')
t = bunch.pop('t')
details = ModSimSeries(bunch)
# pack the results into a TimeFrame
results = TimeFrame(np.transpose(y), index=t, columns=init.index)
return results, details
def fsolve(func, x0, *args, **options):
"""Return the roots of the (non-linear) equations
defined by func(x) = 0 given a starting estimate.
Uses scipy.optimize.fsolve, with extra error-checking.
func: function to find the roots of
x0: scalar or array, initial guess
args: additional positional arguments are passed along to fsolve,
which passes them along to func
returns: solution as an array
"""
# make sure we can run the given function with x0
try:
func(x0, *args)
except Exception as e:
msg = """Before running scipy.optimize.fsolve, I tried
running the error function you provided with the x0
you provided, and I got the following error:"""
logger.error(msg)
raise(e)
# make the tolerance more forgiving than the default
underride(options, xtol=1e-6)
x0 = magnitude(x0)
# run fsolve
with units_off():
result = scipy.optimize.fsolve(func, x0, args=args, **options)
return result
def crossings(series, value):
"""Find the labels where the series passes through value.
The labels in series must be increasing numerical values.
series: Series
value: number
returns: sequence of labels
"""
interp = InterpolatedUnivariateSpline(series.index, series-value)
return interp.roots()
def interpolate(series, **options):
"""Creates an interpolation function.
series: Series object
options: any legal options to scipy.interpolate.interp1d
returns: function that maps from the index of the series to values
"""
# TODO: add error checking for nonmonotonicity
if sum(series.index.isnull()):
msg = """The Series you passed to interpolate contains
NaN values in the index, which would result in
undefined behavior. So I'm putting a stop to that."""
raise ValueError(msg)
# make the interpolate function extrapolate past the ends of
# the range, unless `options` already specifies a value for `fill_value`
underride(options, fill_value='extrapolate')
# call interp1d, which returns a new function object
interp_func = interp1d(series.index, series.values, **options)
units = getattr(series, 'units', None)
if units:
return lambda x: Quantity(interp_func(x), units)
else:
return interp_func
def interp_inverse(series, **options):
"""Interpolate the inverse function of a Series.
series: Series object, represents a mapping from `a` to `b`
kind: string, which kind of iterpolation
options: keyword arguments passed to interpolate
returns: interpolation object, can be used as a function
from `b` to `a`
"""
inverse = Series(series.index, index=series.values)
T = interpolate(inverse, **options)
return T
def unpack(series):
"""Make the names in `series` available as globals.
series: Series with variables names in the index
"""
# TODO: Make this a context manager, so the syntax is
# with series:
# and maybe even add an __exit__ that copies changes back
frame = inspect.currentframe()
caller = frame.f_back
caller.f_globals.update(series)
def source_code(obj):
"""Prints the source code for a given object.
obj: function or method object
"""
print(inspect.getsource(obj))
def underride(d, **options):
"""Add key-value pairs to d only if key is not in d.
If d is None, create a new dictionary.
d: dictionary
options: keyword args to add to d
"""
if d is None:
d = {}
for key, val in options.items():
d.setdefault(key, val)
return d
def plot(*args, **options):
"""Makes line plots.
args can be:
plot(y)
plot(y, style_string)
plot(x, y)
plot(x, y, style_string)
options are the same as for pyplot.plot
"""
# TODO: add lines to REPLOT_CACHE
x, y, style = parse_plot_args(*args, **options)
if isinstance(x, pd.DataFrame) or isinstance(y, pd.DataFrame):
raise ValueError("modsimpy.plot can't handle DataFrames.")
if x is None:
if isinstance(y, np.ndarray):
x = np.arange(len(y))
if isinstance(y, pd.Series):
x = y.index
y = y.values
x = magnitudes(x)
y = magnitudes(y)
underride(options, linewidth=3, alpha=0.6)
if style is not None:
lines = plt.plot(x, y, style, **options)
else:
lines = plt.plot(x, y, **options)
return lines
REPLOT_CACHE = {}
def replot(*args, **options):
"""
"""
try:
label = options['label']
except KeyError:
raise ValueError('To use replot, you must provide a label argument.')
axes = plt.gca()
key = (axes, label)
if key not in REPLOT_CACHE:
lines = plot(*args, **options)
if len(lines) != 1:
raise ValueError('Replot only works with a single plotted element.')
REPLOT_CACHE[key] = lines[0]
return lines
line = REPLOT_CACHE[key]
x, y, style = parse_plot_args(*args, **options)
line.set_xdata(x)
line.set_ydata(y)
def parse_plot_args(*args, **options):
"""Parse the args the same way plt.plot does."""
x = None
y = None
style = None
if len(args) == 1:
y = args[0]
elif len(args) == 2:
if isinstance(args[1], str):
y, style = args
else:
x, y = args
elif len(args) == 3:
x, y, style = args
return x, y, style
def contour(df, **options):
"""Makes a contour plot from a DataFrame.
Note: columns and index must be numerical
df: DataFrame
"""
x = results.columns
y = results.index
X, Y = np.meshgrid(x, y)
cs = plt.contour(X, Y, results, **options)
plt.clabel(cs, inline=1, fontsize=10)
def savefig(filename, **options):
"""Save the current figure.
Keyword arguments are passed along to plt.savefig
https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
filename: string
"""
print('Saving figure to file', filename)
plt.savefig(filename, **options)
def decorate(**options):
"""Decorate the current axes.
Call decorate with keyword arguments like
decorate(title='Title',
xlabel='x',
ylabel='y')
The keyword arguments can be any of the axis properties
https://matplotlib.org/api/axes_api.html
In addition, you can use `legend=False` to suppress the legend.
And you can use `loc` to indicate the location of the legend
(the default value is 'best')
"""
loc = options.pop('loc', 'best')
if options.pop('legend', True):
legend(loc=loc)
plt.gca().set(**options)
plt.tight_layout()
def legend(**options):
"""Draws a legend only if there is at least one labeled item.
options are passed to plt.legend()
https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html
"""
underride(options, loc='best')
ax = plt.gca()
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, **options)
def remove_from_legend(bad_labels):
"""Removes some labels from the legend.
bad_labels: sequence of strings
"""
ax = plt.gca()
handles, labels = ax.get_legend_handles_labels()
handle_list, label_list = [], []
for handle, label in zip(handles, labels):
if label not in bad_labels:
handle_list.append(handle)
label_list.append(label)
ax.legend(handle_list, label_list)
# TODO: Either finish SubPlots or remove it
class SubPlots:
def __init__(self, fig, axes_seq):
self.fig = fig
self.axes_seq = axes_seq
self.current_axes_index = 0
def current_axes():
return self.axes_seq(self.current_axes_index)
# TODO: consider making SubPlots iterable
def next_axes(self):
self.current_axes_index += 1
return current_axes()
def subplots(*args, **options):
fig, axes_seq = plt.subplots(*args, **options)
return SubPlots(fig, axes_seq)
def subplot(nrows, ncols, plot_number, **options):
figsize = {(2, 1): (8, 8),
(3, 1): (8, 10)}
key = nrows, ncols
default = (8, 5.5)
width, height = figsize.get(key, default)
plt.subplot(nrows, ncols, plot_number, **options)
fig = plt.gcf()
fig.set_figwidth(width)
fig.set_figheight(height)
class ModSimSeries(pd.Series):
"""Modified version of a Pandas Series,
with a few changes to make it more suited to our purpose.
In particular:
1. I provide a more consistent __init__ method.
2. Series provides two special variables called
`dt` and `T` that cause problems if we try to use those names
as variables. I override them so they can be used variable names.
3. Series doesn't provide a good _repr_html, so it doesn't look
good in Jupyter notebooks.
4. ModSimSeries provides a set() method that takes keyword arguments.
"""
def __init__(self, *args, **kwargs):
"""Initialize a Series.
Note: this cleans up a weird Series behavior, which is
that Series() and Series([]) yield different results.
See: https://github.com/pandas-dev/pandas/issues/16737
"""
if args or kwargs:
underride(kwargs, copy=True)
super().__init__(*args, **kwargs)
else:
super().__init__([], dtype=np.float64)
def _repr_html_(self):
"""Returns an HTML representation of the series.
Mostly used for Jupyter notebooks.
"""
df = pd.DataFrame(self.values, index=self.index, columns=['values'])
return df._repr_html_()
def __copy__(self, deep=True):
series = super().copy(deep=deep)
return self.__class__(series)
copy = __copy__
def set(self, **kwargs):
"""Uses keyword arguments to update the Series in place.
Example: series.set(a=1, b=2)
"""
for name, value in kwargs.items():
self[name] = value
@property
def dt(self):
"""Intercept the Series accessor object so we can use `dt`
as a row label and access it using dot notation.
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dt.html
"""
return self.loc['dt']
@property
def T(self):
"""Intercept the Series accessor object so we can use `T`
as a row label and access it using dot notation.
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.T.html
"""
return self.loc['T']
def get_first_label(series):
"""Returns the label of the first element."""
return series.index[0]
def get_last_label(series):
"""Returns the label of the first element."""
return series.index[-1]
def get_index_label(series, i):
"""Returns the ith label in the index."""
return series.index[i]
def get_first_value(series):
"""Returns the value of the first element."""
return series.values[0]
def get_last_value(series):
"""Returns the value of the first element."""
return series.values[-1]
def gradient(series):
"""Computes the numerical derivative of a series."""
a = np.gradient(series, series.index)
return TimeSeries(a, series.index)
class TimeSeries(ModSimSeries):
"""Represents a mapping from times to values."""
pass
class SweepSeries(ModSimSeries):
"""Represents a mapping from parameter values to metrics."""
pass
class System(ModSimSeries):
"""Contains system variables and their values.
Takes keyword arguments and stores them as rows.
"""
def __init__(self, *args, **kwargs):
"""Initialize the series.
If there are no positional arguments, use kwargs.
If there is one positional argument, copy it and add
in the kwargs.
More than one positional argument is an error.
"""
if len(args) == 0:
super().__init__(list(kwargs.values()), index=kwargs)
elif len(args) == 1:
super().__init__(*args, copy=True)
self.set(**kwargs)
else:
msg = '__init__() takes at most one positional argument'
raise TypeError(msg)
class State(System):
"""Contains state variables and their values.
Takes keyword arguments and stores them as rows.
"""
pass
class Condition(System):
"""Represents the condition of a system.
Condition objects are often used to construct a System object.
"""
pass
class Params(System):
"""Represents a set of parameters.
"""
pass
def compute_abs_diff(seq):
xs = np.asarray(seq)
diff = np.ediff1d(xs, np.nan)
if isinstance(seq, Series):
return Series(diff, seq.index)
else:
return diff