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Changed quote style in tests to use double quotes at the outer level and backslash quotes at the inner level.
Remaining changes are fixes required by ruff.
1 parent 3f89d84 commit 90f8e05

21 files changed

+220
-86
lines changed

‎conftest.py

+8-2
Original file line numberDiff line numberDiff line change
@@ -152,10 +152,16 @@ def sample_ktensor_3way(): # noqa: D103
152152
def sample_ktensor_symmetric(): # noqa: D103
153153
weights = np.array([1.0, 1.0])
154154
fm0 = np.array(
155-
[[2.340431417384394, 4.951967353890655], [4.596069112758807, 8.012451489774961]]
155+
[
156+
[2.340431417384394, 4.951967353890655],
157+
[4.596069112758807, 8.012451489774961],
158+
]
156159
)
157160
fm1 = np.array(
158-
[[2.340431417384394, 4.951967353890655], [4.596069112758807, 8.012451489774961]]
161+
[
162+
[2.340431417384394, 4.951967353890655],
163+
[4.596069112758807, 8.012451489774961],
164+
]
159165
)
160166
factor_matrices = [fm0, fm1]
161167
data = {"weights": weights, "factor_matrices": factor_matrices}

‎docs/source/tutorial/algorithm_gcp_opt.ipynb

+19-9
Original file line numberDiff line numberDiff line change
@@ -212,7 +212,7 @@
212212
")\n",
213213
"\n",
214214
"print(\n",
215-
" f\"\\nFinal fit: {1 - np.linalg.norm((X-result_lbfgs.full()).double())/X.norm()} (for comparison to f(x) in CP-ALS)\\n\"\n",
215+
" f\"\\nFinal fit: {1 - np.linalg.norm((X - result_lbfgs.full()).double()) / X.norm()} (for comparison to f(x) in CP-ALS)\\n\"\n",
216216
")"
217217
]
218218
},
@@ -234,7 +234,7 @@
234234
" input_tensor=X, rank=rank, maxiters=2, init=initial_guess\n",
235235
")\n",
236236
"print(\n",
237-
" f\"\\nFinal fit: {1 - np.linalg.norm((X-result_als.full()).double())/X.norm()} (for comparison to f(x) in GCP-OPT)\\n\"\n",
237+
" f\"\\nFinal fit: {1 - np.linalg.norm((X - result_als.full()).double()) / X.norm()} (for comparison to f(x) in GCP-OPT)\\n\"\n",
238238
")"
239239
]
240240
},
@@ -269,7 +269,7 @@
269269
" printitn=1,\n",
270270
")\n",
271271
"print(\n",
272-
" f\"\\nFinal fit: {1 - np.linalg.norm((X-result_adam.full()).double())/X.norm()} (for comparison to f(x) in GCP-OPT & CP-ALS)\\n\"\n",
272+
" f\"\\nFinal fit: {1 - np.linalg.norm((X - result_adam.full()).double()) / X.norm()} (for comparison to f(x) in GCP-OPT & CP-ALS)\\n\"\n",
273273
")"
274274
]
275275
},
@@ -352,7 +352,9 @@
352352
" data=X, rank=rank, objective=objective, optimizer=optimizer, printitn=1\n",
353353
")\n",
354354
"\n",
355-
"print(f\"\\nFinal fit: {1 - np.linalg.norm((X-result_lbfgs.full()).double())/X.norm()}\\n\")"
355+
"print(\n",
356+
" f\"\\nFinal fit: {1 - np.linalg.norm((X - result_lbfgs.full()).double()) / X.norm()}\\n\"\n",
357+
")"
356358
]
357359
},
358360
{
@@ -379,7 +381,9 @@
379381
" data=X, rank=rank, objective=objective, optimizer=optimizer, printitn=1\n",
380382
")\n",
381383
"\n",
382-
"print(f\"\\nFinal fit: {1 - np.linalg.norm((X-result_adam.full()).double())/X.norm()}\\n\")"
384+
"print(\n",
385+
" f\"\\nFinal fit: {1 - np.linalg.norm((X - result_adam.full()).double()) / X.norm()}\\n\"\n",
386+
")"
383387
]
384388
},
385389
{
@@ -453,7 +457,7 @@
453457
"# Convert to sparse tensor, real-valued 0/1 tensor since it was constructed\n",
454458
"# to be sparse\n",
455459
"X = Xfull.to_sptensor()\n",
456-
"print(f\"Proportion of nonzeros in X is {100*X.nnz / np.prod(shape):.2f}%\\n\");"
460+
"print(f\"Proportion of nonzeros in X is {100 * X.nnz / np.prod(shape):.2f}%\\n\");"
457461
]
458462
},
459463
{
@@ -499,7 +503,9 @@
499503
" data=Xfull, rank=rank, objective=objective, optimizer=optimizer\n",
500504
")\n",
501505
"\n",
502-
"print(f\"\\nFinal fit: {1 - np.linalg.norm((X-result_lbfgs.full()).double())/X.norm()}\\n\")"
506+
"print(\n",
507+
" f\"\\nFinal fit: {1 - np.linalg.norm((X - result_lbfgs.full()).double()) / X.norm()}\\n\"\n",
508+
")"
503509
]
504510
},
505511
{
@@ -527,7 +533,9 @@
527533
" data=X, rank=rank, objective=objective, optimizer=optimizer, printitn=1\n",
528534
")\n",
529535
"\n",
530-
"print(f\"\\nFinal fit: {1 - np.linalg.norm((X-result_adam.full()).double())/X.norm()}\\n\")"
536+
"print(\n",
537+
" f\"\\nFinal fit: {1 - np.linalg.norm((X - result_adam.full()).double()) / X.norm()}\\n\"\n",
538+
")"
531539
]
532540
},
533541
{
@@ -592,7 +600,9 @@
592600
" data=X, rank=rank, objective=objective, optimizer=optimizer, printitn=1\n",
593601
")\n",
594602
"\n",
595-
"print(f\"\\nFinal fit: {1 - np.linalg.norm((X-result_adam.full()).double())/X.norm()}\\n\")"
603+
"print(\n",
604+
" f\"\\nFinal fit: {1 - np.linalg.norm((X - result_adam.full()).double()) / X.norm()}\\n\"\n",
605+
")"
596606
]
597607
}
598608
],

‎docs/source/tutorial/algorithm_hosvd.ipynb

+2-2
Original file line numberDiff line numberDiff line change
@@ -163,7 +163,7 @@
163163
" # Verbose output\n",
164164
" if verbose:\n",
165165
" print(\n",
166-
" f\"Created block with shape {tuple(bsz[i,:])} with norm ({block.norm()})^2 = {block.norm()**2}\"\n",
166+
" f\"Created block with shape {tuple(bsz[i, :])} with norm ({block.norm()})^2 = {block.norm() ** 2}\"\n",
167167
" )\n",
168168
"\n",
169169
" if dltnrmsqr > 0:\n",
@@ -174,7 +174,7 @@
174174
" G += block\n",
175175
" if verbose:\n",
176176
" print(\n",
177-
" f\"Created tensor with shape {tuple(gsz)} with off-block-diagonal norm ({block.norm()})^2 = {block.norm()**2}\"\n",
177+
" f\"Created tensor with shape {tuple(gsz)} with off-block-diagonal norm ({block.norm()})^2 = {block.norm() ** 2}\"\n",
178178
" )\n",
179179
"\n",
180180
" return G, bsz, bns"

‎docs/source/tutorial/class_ktensor.ipynb

+32-8
Original file line numberDiff line numberDiff line change
@@ -69,7 +69,11 @@
6969
"outputs": [],
7070
"source": [
7171
"Y = ttb.ktensor(\n",
72-
" [np.random.rand(4, 1), np.random.rand(2, 1), np.random.rand(3, 1)]\n",
72+
" [\n",
73+
" np.random.rand(4, 1),\n",
74+
" np.random.rand(2, 1),\n",
75+
" np.random.rand(3, 1),\n",
76+
" ]\n",
7377
") # Another ktensor.\n",
7478
"Y"
7579
]
@@ -436,10 +440,18 @@
436440
"source": [
437441
"np.random.seed(0)\n",
438442
"X = ttb.ktensor(\n",
439-
" [np.random.rand(4, 2), np.random.rand(2, 2), np.random.rand(3, 2)]\n",
443+
" [\n",
444+
" np.random.rand(4, 2),\n",
445+
" np.random.rand(2, 2),\n",
446+
" np.random.rand(3, 2),\n",
447+
" ]\n",
440448
") # Data.\n",
441449
"Y = ttb.ktensor(\n",
442-
" [np.random.rand(4, 2), np.random.rand(2, 2), np.random.rand(3, 2)]\n",
450+
" [\n",
451+
" np.random.rand(4, 2),\n",
452+
" np.random.rand(2, 2),\n",
453+
" np.random.rand(3, 2),\n",
454+
" ]\n",
443455
") # More data.\n",
444456
"X, Y"
445457
]
@@ -522,7 +534,11 @@
522534
"source": [
523535
"np.random.seed(0)\n",
524536
"X = ttb.ktensor(\n",
525-
" [np.random.rand(4, 2), np.random.rand(2, 2), np.random.rand(3, 2)]\n",
537+
" [\n",
538+
" np.random.rand(4, 2),\n",
539+
" np.random.rand(2, 2),\n",
540+
" np.random.rand(3, 2),\n",
541+
" ]\n",
526542
") # Data.\n",
527543
"X.permute(np.array((1, 2, 0))) # Reorders modes of X."
528544
]
@@ -543,7 +559,11 @@
543559
"source": [
544560
"np.random.seed(0)\n",
545561
"X = ttb.ktensor(\n",
546-
" [np.random.rand(3, 2), np.random.rand(4, 2), np.random.rand(2, 2)]\n",
562+
" [\n",
563+
" np.random.rand(3, 2),\n",
564+
" np.random.rand(4, 2),\n",
565+
" np.random.rand(2, 2),\n",
566+
" ]\n",
547567
") # Unit weights.\n",
548568
"X"
549569
]
@@ -574,7 +594,11 @@
574594
"source": [
575595
"np.random.seed(0)\n",
576596
"X = ttb.ktensor(\n",
577-
" [np.random.rand(4, 2), np.random.rand(2, 2), np.random.rand(3, 2)]\n",
597+
" [\n",
598+
" np.random.rand(4, 2),\n",
599+
" np.random.rand(2, 2),\n",
600+
" np.random.rand(3, 2),\n",
601+
" ]\n",
578602
") # Data.\n",
579603
"Y = X\n",
580604
"Y.factor_matrices[0][:, 0] = -Y.factor_matrices[0][\n",
@@ -629,10 +653,10 @@
629653
"metadata": {},
630654
"outputs": [],
631655
"source": [
632-
"print(f\"U*S*Vh:\\n{U@np.diag(S)@Vh}\")\n",
656+
"print(f\"U*S*Vh:\\n{U @ np.diag(S) @ Vh}\")\n",
633657
"print(\n",
634658
" f\"\\nX.factor_matrices[0]@np.diag(X.weights)@(X.factor_matrices[1].transpose()):\\n\\\n",
635-
"{X.factor_matrices[0]@np.diag(X.weights)@(X.factor_matrices[1].transpose())}\"\n",
659+
"{X.factor_matrices[0] @ np.diag(X.weights) @ (X.factor_matrices[1].transpose())}\"\n",
636660
")\n",
637661
"print(f\"\\nX.full():\\n{X.full()}\") # Reassemble the original matrix."
638662
]

‎docs/source/tutorial/class_sptenmat.ipynb

+1-1
Original file line numberDiff line numberDiff line change
@@ -363,7 +363,7 @@
363363
"metadata": {},
364364
"outputs": [],
365365
"source": [
366-
"print(f\"Matrix shape: {A.shape}\\n\" f\"Original tensor shape: {A.tshape}\")"
366+
"print(f\"Matrix shape: {A.shape}\\nOriginal tensor shape: {A.tshape}\")"
367367
]
368368
},
369369
{

‎docs/source/tutorial/class_sptensor.ipynb

+8-1
Original file line numberDiff line numberDiff line change
@@ -66,7 +66,14 @@
6666
"outputs": [],
6767
"source": [
6868
"subs = np.array(\n",
69-
" [[0, 0, 0], [0, 0, 2], [2, 2, 2], [3, 3, 3], [0, 0, 0], [0, 0, 0]]\n",
69+
" [\n",
70+
" [0, 0, 0],\n",
71+
" [0, 0, 2],\n",
72+
" [2, 2, 2],\n",
73+
" [3, 3, 3],\n",
74+
" [0, 0, 0],\n",
75+
" [0, 0, 0],\n",
76+
" ]\n",
7077
") # (1,1,1) is repeated.\n",
7178
"vals = np.array([2, 2, 2, 2, 2, 2])[:, None] # Vals is a column vector.\n",
7279
"X = ttb.sptensor.from_aggregator(subs, vals)\n",

‎docs/source/tutorial/class_sumtensor.ipynb

+4-4
Original file line numberDiff line numberDiff line change
@@ -160,7 +160,7 @@
160160
"metadata": {},
161161
"outputs": [],
162162
"source": [
163-
"print(f\"Ndims: {T.ndims}\\n\" f\"Shape: {T.shape}\")"
163+
"print(f\"Ndims: {T.ndims}\\nShape: {T.shape}\")"
164164
]
165165
},
166166
{
@@ -316,8 +316,8 @@
316316
"outputs": [],
317317
"source": [
318318
"# Equivalent additions despite the order\n",
319-
"print(f\"T+S:\\n{T+S}\\n\")\n",
320-
"print(f\"S+T:\\n{S+T}\")"
319+
"print(f\"T+S:\\n{T + S}\\n\")\n",
320+
"print(f\"S+T:\\n{S + T}\")"
321321
]
322322
},
323323
{
@@ -336,7 +336,7 @@
336336
"metadata": {},
337337
"outputs": [],
338338
"source": [
339-
"print(f\"Part 0:\\n{T.parts[0]}\\n\\n\" f\"Part 1:\\n{T.parts[1]}\")"
339+
"print(f\"Part 0:\\n{T.parts[0]}\\n\\nPart 1:\\n{T.parts[1]}\")"
340340
]
341341
}
342342
],

‎docs/source/tutorial/class_ttensor.ipynb

+3-3
Original file line numberDiff line numberDiff line change
@@ -538,7 +538,7 @@
538538
"# since the random generation process below wasn't expected\n",
539539
"# to return a low-rank approximation\n",
540540
"print(\n",
541-
" f\"Compression: {X.data.nbytes/(T.core.data.nbytes + np.sum([i.nbytes for i in T.factor_matrices]))} x\"\n",
541+
" f\"Compression: {X.data.nbytes / (T.core.data.nbytes + np.sum([i.nbytes for i in T.factor_matrices]))} x\"\n",
542542
")"
543543
]
544544
},
@@ -584,7 +584,7 @@
584584
"metadata": {},
585585
"outputs": [],
586586
"source": [
587-
"print(f\"Compression: {Xf.data.nbytes/Xslice.data.nbytes} x\")"
587+
"print(f\"Compression: {Xf.data.nbytes / Xslice.data.nbytes} x\")"
588588
]
589589
},
590590
{
@@ -622,7 +622,7 @@
622622
"metadata": {},
623623
"outputs": [],
624624
"source": [
625-
"print(f\"Compression: {Xf.data.nbytes/Xds.data.nbytes} x\")"
625+
"print(f\"Compression: {Xf.data.nbytes / Xds.data.nbytes} x\")"
626626
]
627627
},
628628
{

‎pyttb/gcp/optimizers.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -234,7 +234,7 @@ def solve( # noqa: PLR0913
234234
"End Main Loop\n"
235235
f"Final f-est: {f_est: 10.4e}\n"
236236
f"Main loop time: {main_time: .2f}\n"
237-
f"Total iterations: {n_epoch*self._epoch_iters}"
237+
f"Total iterations: {n_epoch * self._epoch_iters}"
238238
)
239239
logging.info(msg)
240240

‎pyttb/gcp_opt.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -111,7 +111,7 @@ def gcp_opt( # noqa: PLR0912,PLR0913
111111
)
112112
if nmissing > 0:
113113
welcome_msg += (
114-
f"Missing entries: {nmissing} ({100*nmissing/tensor_size:.2g}%)"
114+
f"Missing entries: {nmissing} ({100 * nmissing / tensor_size:.2g}%)"
115115
)
116116
logging.info(welcome_msg)
117117

‎pyttb/hosvd.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -143,7 +143,7 @@ def hosvd( # noqa: PLR0912,PLR0913,PLR0915
143143
relnorm = np.sqrt(diffnormsqr / normxsqr)
144144
print(f"Shape of core: {G.shape}")
145145
if relnorm <= tol:
146-
print(f"||X-T||/||X|| = {relnorm: g} <=" f"{tol: f} (tol)")
146+
print(f"||X-T||/||X|| = {relnorm: g} <={tol: f} (tol)")
147147
else:
148148
print(
149149
"Tolerance not satisfied!! "

‎pyttb/pyttb_utils.py

+7-5
Original file line numberDiff line numberDiff line change
@@ -70,7 +70,10 @@ def tt_union_rows(MatrixA: np.ndarray, MatrixB: np.ndarray) -> np.ndarray:
7070
MatrixBUnique[np.argsort(idxB)], MatrixAUnique[np.argsort(idxA)]
7171
)
7272
union = np.vstack(
73-
(MatrixB[np.sort(idxB[np.where(location < 0)])], MatrixA[np.sort(idxA)])
73+
(
74+
MatrixB[np.sort(idxB[np.where(location < 0)])],
75+
MatrixA[np.sort(idxA)],
76+
)
7477
)
7578
return union
7679

@@ -861,10 +864,9 @@ def gather_wrap_dims(
861864
+ [i for i in range(ndims - 1, rdims[0], -1)]
862865
)
863866
else:
864-
assert False, (
865-
"Unrecognized value for cdims_cyclic pattern, "
866-
'must be "fc" or "bc".'
867-
)
867+
assert (
868+
False
869+
), 'Unrecognized value for cdims_cyclic pattern, must be "fc" or "bc".'
868870
else:
869871
# Multiple row mapping
870872
cdims = np.setdiff1d(alldims, rdims)

‎pyttb/sptensor.py

+5-2
Original file line numberDiff line numberDiff line change
@@ -667,7 +667,7 @@ def extract(self, searchsubs: np.ndarray) -> np.ndarray:
667667
badsubs = searchsubs[badloc, :]
668668
for i in np.arange(0, badloc[0].size):
669669
error_msg += f"\tsubscript = {np.array2string(badsubs[i, :])} \n"
670-
assert False, f"{error_msg}" "Invalid subscripts"
670+
assert False, f"{error_msg}Invalid subscripts"
671671

672672
# Set the default answer to zero
673673
a = np.zeros(shape=(p, 1), dtype=self.vals.dtype, order=self.order)
@@ -2578,7 +2578,10 @@ def _set_subtensor(self, key, value): # noqa: PLR0912, PLR0915
25782578
if self.subs.size > 0:
25792579
self.subs = np.vstack((self.subs, addsubs.astype(int)))
25802580
self.vals = np.vstack(
2581-
(self.vals, value * np.ones((addsubs.shape[0], 1)))
2581+
(
2582+
self.vals,
2583+
value * np.ones((addsubs.shape[0], 1)),
2584+
)
25822585
)
25832586
else:
25842587
self.subs = addsubs.astype(int)

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