cpython/Lib/difflib.py at 3.5 · python/cpython · GitHub

Repository navigation

History

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 difflib -- helpers for computing deltas between objects.

Function get_close_matches(word, possibilities, n=3, cutoff=0.6):

Use SequenceMatcher to return list of the best "good enough" matches.

Function context_diff(a, b):

For two lists of strings, return a delta in context diff format.

Function ndiff(a, b):

Return a delta: the difference between `a` and `b` (lists of strings).

Function restore(delta, which):

Return one of the two sequences that generated an ndiff delta.

Function unified_diff(a, b):

For two lists of strings, return a delta in unified diff format.

Class SequenceMatcher:

A flexible class for comparing pairs of sequences of any type.

Class Differ:

For producing human-readable deltas from sequences of lines of text.

Class HtmlDiff:

For producing HTML side by side comparison with change highlights.

"""

__all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher',

'Differ','IS_CHARACTER_JUNK', 'IS_LINE_JUNK', 'context_diff',

'unified_diff', 'diff_bytes', 'HtmlDiff', 'Match']

from heapq import nlargest as _nlargest

from collections import namedtuple as _namedtuple

Match = _namedtuple('Match', 'a b size')

def _calculate_ratio(matches, length):

if length:

return 2.0 * matches / length

return 1.0

class SequenceMatcher:

"""

SequenceMatcher is a flexible class for comparing pairs of sequences of

any type, so long as the sequence elements are hashable. The basic

algorithm predates, and is a little fancier than, an algorithm

published in the late 1980's by Ratcliff and Obershelp under the

hyperbolic name "gestalt pattern matching". The basic idea is to find

the longest contiguous matching subsequence that contains no "junk"

elements (R-O doesn't address junk). The same idea is then applied

recursively to the pieces of the sequences to the left and to the right

of the matching subsequence. This does not yield minimal edit

sequences, but does tend to yield matches that "look right" to people.

SequenceMatcher tries to compute a "human-friendly diff" between two

sequences. Unlike e.g. UNIX(tm) diff, the fundamental notion is the

longest *contiguous* & junk-free matching subsequence. That's what

catches peoples' eyes. The Windows(tm) windiff has another interesting

notion, pairing up elements that appear uniquely in each sequence.

That, and the method here, appear to yield more intuitive difference

reports than does diff. This method appears to be the least vulnerable

to synching up on blocks of "junk lines", though (like blank lines in

ordinary text files, or maybe "<P>" lines in HTML files). That may be

because this is the only method of the 3 that has a *concept* of

"junk" <wink>.

Example, comparing two strings, and considering blanks to be "junk":

>>> s = SequenceMatcher(lambda x: x == " ",

... "private Thread currentThread;",

... "private volatile Thread currentThread;")

>>>

.ratio() returns a float in [0, 1], measuring the "similarity" of the

sequences. As a rule of thumb, a .ratio() value over 0.6 means the

sequences are close matches:

>>> print(round(s.ratio(), 3))

0.866

>>>

If you're only interested in where the sequences match,

.get_matching_blocks() is handy:

>>> for block in s.get_matching_blocks():

... print("a[%d] and b[%d] match for %d elements" % block)

a[0] and b[0] match for 8 elements

a[8] and b[17] match for 21 elements

a[29] and b[38] match for 0 elements

Note that the last tuple returned by .get_matching_blocks() is always a

dummy, (len(a), len(b), 0), and this is the only case in which the last

tuple element (number of elements matched) is 0.

If you want to know how to change the first sequence into the second,

use .get_opcodes():

>>> for opcode in s.get_opcodes():

... print("%6s a[%d:%d] b[%d:%d]" % opcode)

equal a[0:8] b[0:8]

insert a[8:8] b[8:17]

equal a[8:29] b[17:38]

See the Differ class for a fancy human-friendly file differencer, which

uses SequenceMatcher both to compare sequences of lines, and to compare

sequences of characters within similar (near-matching) lines.

See also function get_close_matches() in this module, which shows how

simple code building on SequenceMatcher can be used to do useful work.

Timing: Basic R-O is cubic time worst case and quadratic time expected

case. SequenceMatcher is quadratic time for the worst case and has

expected-case behavior dependent in a complicated way on how many

elements the sequences have in common; best case time is linear.

Methods:

__init__(isjunk=None, a='', b='')

Construct a SequenceMatcher.

set_seqs(a, b)

Set the two sequences to be compared.

set_seq1(a)

Set the first sequence to be compared.

set_seq2(b)

Set the second sequence to be compared.

find_longest_match(alo, ahi, blo, bhi)

Find longest matching block in a[alo:ahi] and b[blo:bhi].

get_matching_blocks()

Return list of triples describing matching subsequences.

get_opcodes()

Return list of 5-tuples describing how to turn a into b.

ratio()

Return a measure of the sequences' similarity (float in [0,1]).

quick_ratio()

Return an upper bound on .ratio() relatively quickly.

real_quick_ratio()

Return an upper bound on ratio() very quickly.

"""

def __init__(self, isjunk=None, a='', b='', autojunk=True):

"""Construct a SequenceMatcher.

Optional arg isjunk is None (the default), or a one-argument

function that takes a sequence element and returns true iff the

element is junk. None is equivalent to passing "lambda x: 0", i.e.

no elements are considered to be junk. For example, pass

lambda x: x in " \\t"

if you're comparing lines as sequences of characters, and don't

want to synch up on blanks or hard tabs.

Optional arg a is the first of two sequences to be compared. By

default, an empty string. The elements of a must be hashable. See

also .set_seqs() and .set_seq1().

Optional arg b is the second of two sequences to be compared. By

default, an empty string. The elements of b must be hashable. See

also .set_seqs() and .set_seq2().

Optional arg autojunk should be set to False to disable the

"automatic junk heuristic" that treats popular elements as junk

(see module documentation for more information).

"""

# Members:

# a

# first sequence

# b

# second sequence; differences are computed as "what do

# we need to do to 'a' to change it into 'b'?"

# b2j

# for x in b, b2j[x] is a list of the indices (into b)

# at which x appears; junk and popular elements do not appear

# fullbcount

# for x in b, fullbcount[x] == the number of times x

# appears in b; only materialized if really needed (used

# only for computing quick_ratio())

# matching_blocks

# a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k];

# ascending & non-overlapping in i and in j; terminated by

# a dummy (len(a), len(b), 0) sentinel

# opcodes

# a list of (tag, i1, i2, j1, j2) tuples, where tag is

# one of

# 'replace' a[i1:i2] should be replaced by b[j1:j2]

# 'delete' a[i1:i2] should be deleted

# 'insert' b[j1:j2] should be inserted

# 'equal' a[i1:i2] == b[j1:j2]

# isjunk

# a user-supplied function taking a sequence element and

# returning true iff the element is "junk" -- this has

# subtle but helpful effects on the algorithm, which I'll

# get around to writing up someday <0.9 wink>.

# DON'T USE! Only __chain_b uses this. Use "in self.bjunk".

# bjunk

# the items in b for which isjunk is True.

# bpopular

# nonjunk items in b treated as junk by the heuristic (if used).

self.isjunk = isjunk

self.a = self.b = None

self.autojunk = autojunk

self.set_seqs(a, b)

def set_seqs(self, a, b):

"""Set the two sequences to be compared.

>>> s = SequenceMatcher()

>>> s.set_seqs("abcd", "bcde")

>>> s.ratio()

0.75

"""

self.set_seq1(a)

self.set_seq2(b)

def set_seq1(self, a):

"""Set the first sequence to be compared.

The second sequence to be compared is not changed.

>>> s = SequenceMatcher(None, "abcd", "bcde")

>>> s.ratio()

0.75

>>> s.set_seq1("bcde")

>>> s.ratio()

1.0

>>>

SequenceMatcher computes and caches detailed information about the

second sequence, so if you want to compare one sequence S against

many sequences, use .set_seq2(S) once and call .set_seq1(x)

repeatedly for each of the other sequences.

See also set_seqs() and set_seq1().

"""

if b is self.b:

return

self.b = b

self.matching_blocks = self.opcodes = None

self.fullbcount = None

self.__chain_b()

# For each element x in b, set b2j[x] to a list of the indices in

# b where x appears; the indices are in increasing order; note that

# the number of times x appears in b is len(b2j[x]) ...

# when self.isjunk is defined, junk elements don't show up in this

# map at all, which stops the central find_longest_match method

# from starting any matching block at a junk element ...

# b2j also does not contain entries for "popular" elements, meaning

# elements that account for more than 1 + 1% of the total elements, and

# when the sequence is reasonably large (>= 200 elements); this can

# be viewed as an adaptive notion of semi-junk, and yields an enormous

# speedup when, e.g., comparing program files with hundreds of

# instances of "return NULL;" ...

# note that this is only called when b changes; so for cross-product

# kinds of matches, it's best to call set_seq2 once, then set_seq1

# repeatedly

def __chain_b(self):

# Because isjunk is a user-defined (not C) function, and we test

# for junk a LOT, it's important to minimize the number of calls.

# Before the tricks described here, __chain_b was by far the most

# time-consuming routine in the whole module! If anyone sees

# Jim Roskind, thank him again for profile.py -- I never would

# have guessed that.

# The first trick is to build b2j ignoring the possibility

# of junk. I.e., we don't call isjunk at all yet. Throwing

# out the junk later is much cheaper than building b2j "right"

# from the start.

b = self.b

self.b2j = b2j = {}

for i, elt in enumerate(b):

indices = b2j.setdefault(elt, [])

indices.append(i)

# Purge junk elements

self.bjunk = junk = set()

isjunk = self.isjunk

if isjunk:

for elt in b2j.keys():

if isjunk(elt):

junk.add(elt)

for elt in junk: # separate loop avoids separate list of keys

del b2j[elt]

# Purge popular elements that are not junk

self.bpopular = popular = set()

n = len(b)

if self.autojunk and n >= 200:

ntest = n // 100 + 1

for elt, idxs in b2j.items():

if len(idxs) > ntest:

popular.add(elt)

for elt in popular: # ditto; as fast for 1% deletion

del b2j[elt]

def find_longest_match(self, alo, ahi, blo, bhi):

"""Find longest matching block in a[alo:ahi] and b[blo:bhi].

If isjunk is not defined:

Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where

alo <= i <= i+k <= ahi

blo <= j <= j+k <= bhi

and for all (i',j',k') meeting those conditions,

k >= k'

i <= i'

and if i == i', j <= j'

In other words, of all maximal matching blocks, return one that

starts earliest in a, and of all those maximal matching blocks that

start earliest in a, return the one that starts earliest in b.

>>> s = SequenceMatcher(None, " abcd", "abcd abcd")

>>> s.find_longest_match(0, 5, 0, 9)

Match(a=0, b=4, size=5)

If isjunk is defined, first the longest matching block is

determined as above, but with the additional restriction that no

junk element appears in the block. Then that block is extended as

far as possible by matching (only) junk elements on both sides. So

the resulting block never matches on junk except as identical junk

happens to be adjacent to an "interesting" match.

Here's the same example as before, but considering blanks to be

junk. That prevents " abcd" from matching the " abcd" at the tail

end of the second sequence directly. Instead only the "abcd" can

match, and matches the leftmost "abcd" in the second sequence:

>>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")

>>> s.find_longest_match(0, 5, 0, 9)

Match(a=1, b=0, size=4)

If no blocks match, return (alo, blo, 0).

>>> s = SequenceMatcher(None, "ab", "c")

>>> s.find_longest_match(0, 2, 0, 1)

Match(a=0, b=0, size=0)

"""

# CAUTION: stripping common prefix or suffix would be incorrect.

# E.g.,

# ab

# acab

# Longest matching block is "ab", but if common prefix is

# stripped, it's "a" (tied with "b"). UNIX(tm) diff does so

# strip, so ends up claiming that ab is changed to acab by

# inserting "ca" in the middle. That's minimal but unintuitive:

# "it's obvious" that someone inserted "ac" at the front.

# Windiff ends up at the same place as diff, but by pairing up

# the unique 'b's and then matching the first two 'a's.

a, b, b2j, isbjunk = self.a, self.b, self.b2j, self.bjunk.__contains__

besti, bestj, bestsize = alo, blo, 0

# find longest junk-free match

# during an iteration of the loop, j2len[j] = length of longest

# junk-free match ending with a[i-1] and b[j]

j2len = {}

nothing = []

for i in range(alo, ahi):

# look at all instances of a[i] in b; note that because

# b2j has no junk keys, the loop is skipped if a[i] is junk

j2lenget = j2len.get

newj2len = {}

for j in b2j.get(a[i], nothing):

# a[i] matches b[j]

if j < blo:

continue

if j >= bhi:

break

k = newj2len[j] = j2lenget(j-1, 0) + 1

if k > bestsize:

besti, bestj, bestsize = i-k+1, j-k+1, k

j2len = newj2len

# Extend the best by non-junk elements on each end. In particular,

# "popular" non-junk elements aren't in b2j, which greatly speeds

# the inner loop above, but also means "the best" match so far

# doesn't contain any junk *or* popular non-junk elements.

while besti > alo and bestj > blo and \

not isbjunk(b[bestj-1]) and \

a[besti-1] == b[bestj-1]:

besti, bestj, bestsize = besti-1, bestj-1, bestsize+1

while besti+bestsize < ahi and bestj+bestsize < bhi and \

not isbjunk(b[bestj+bestsize]) and \

a[besti+bestsize] == b[bestj+bestsize]:

bestsize += 1

# Now that we have a wholly interesting match (albeit possibly

# empty!), we may as well suck up the matching junk on each

# side of it too. Can't think of a good reason not to, and it

# saves post-processing the (possibly considerable) expense of

# figuring out what to do with it. In the case of an empty

# interesting match, this is clearly the right thing to do,

# because no other kind of match is possible in the regions.

while besti > alo and bestj > blo and \

isbjunk(b[bestj-1]) and \

a[besti-1] == b[bestj-1]:

besti, bestj, bestsize = besti-1, bestj-1, bestsize+1

while besti+bestsize < ahi and bestj+bestsize < bhi and \

isbjunk(b[bestj+bestsize]) and \

a[besti+bestsize] == b[bestj+bestsize]:

bestsize = bestsize + 1

return Match(besti, bestj, bestsize)

def get_matching_blocks(self):

"""Return list of triples describing matching subsequences.

Each triple is of the form (i, j, n), and means that

a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in

i and in j. New in Python 2.5, it's also guaranteed that if

(i, j, n) and (i', j', n') are adjacent triples in the list, and

the second is not the last triple in the list, then i+n != i' or

j+n != j'. IOW, adjacent triples never describe adjacent equal

blocks.

The last triple is a dummy, (len(a), len(b), 0), and is the only

triple with n==0.

>>> s = SequenceMatcher(None, "abxcd", "abcd")

>>> list(s.get_matching_blocks())

[Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)]

"""

if self.matching_blocks is not None:

return self.matching_blocks

la, lb = len(self.a), len(self.b)

# This is most naturally expressed as a recursive algorithm, but

# at least one user bumped into extreme use cases that exceeded

# the recursion limit on their box. So, now we maintain a list

# ('queue`) of blocks we still need to look at, and append partial

# results to `matching_blocks` in a loop; the matches are sorted

# at the end.

queue = [(0, la, 0, lb)]

matching_blocks = []

while queue:

alo, ahi, blo, bhi = queue.pop()

i, j, k = x = self.find_longest_match(alo, ahi, blo, bhi)

# a[alo:i] vs b[blo:j] unknown

# a[i:i+k] same as b[j:j+k]

# a[i+k:ahi] vs b[j+k:bhi] unknown

if k: # if k is 0, there was no matching block

matching_blocks.append(x)

if alo < i and blo < j:

queue.append((alo, i, blo, j))

if i+k < ahi and j+k < bhi:

queue.append((i+k, ahi, j+k, bhi))

matching_blocks.sort()

# It's possible that we have adjacent equal blocks in the

# matching_blocks list now. Starting with 2.5, this code was added

# to collapse them.

i1 = j1 = k1 = 0

non_adjacent = []

for i2, j2, k2 in matching_blocks:

# Is this block adjacent to i1, j1, k1?

if i1 + k1 == i2 and j1 + k1 == j2:

# Yes, so collapse them -- this just increases the length of

# the first block by the length of the second, and the first

# block so lengthened remains the block to compare against.

k1 += k2

else:

# Not adjacent. Remember the first block (k1==0 means it's

# the dummy we started with), and make the second block the

# new block to compare against.

if k1:

non_adjacent.append((i1, j1, k1))

i1, j1, k1 = i2, j2, k2

if k1:

non_adjacent.append((i1, j1, k1))

non_adjacent.append( (la, lb, 0) )

self.matching_blocks = list(map(Match._make, non_adjacent))

return self.matching_blocks

def get_opcodes(self):

"""Return list of 5-tuples describing how to turn a into b.

Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple

has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the

tuple preceding it, and likewise for j1 == the previous j2.

The tags are strings, with these meanings:

'replace': a[i1:i2] should be replaced by b[j1:j2]

'delete': a[i1:i2] should be deleted.

Note that j1==j2 in this case.

'insert': b[j1:j2] should be inserted at a[i1:i1].

Note that i1==i2 in this case.

'equal': a[i1:i2] == b[j1:j2]

>>> a = "qabxcd"

>>> b = "abycdf"

>>> s = SequenceMatcher(None, a, b)

>>> for tag, i1, i2, j1, j2 in s.get_opcodes():

... print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %

... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2])))

delete a[0:1] (q) b[0:0] ()

equal a[1:3] (ab) b[0:2] (ab)

replace a[3:4] (x) b[2:3] (y)

equal a[4:6] (cd) b[3:5] (cd)

insert a[6:6] () b[5:6] (f)

"""

if self.opcodes is not None:

return self.opcodes

i = j = 0

self.opcodes = answer = []

for ai, bj, size in self.get_matching_blocks():

# invariant: we've pumped out correct diffs to change

# a[:i] into b[:j], and the next matching block is

# a[ai:ai+size] == b[bj:bj+size]. So we need to pump

# out a diff to change a[i:ai] into b[j:bj], pump out

# the matching block, and move (i,j) beyond the match

tag = ''

if i < ai and j < bj:

tag = 'replace'

elif i < ai:

tag = 'delete'

elif j < bj:

tag = 'insert'

if tag:

answer.append( (tag, i, ai, j, bj) )

i, j = ai+size, bj+size

# the list of matching blocks is terminated by a

# sentinel with size 0

if size:

answer.append( ('equal', ai, i, bj, j) )

return answer

def get_grouped_opcodes(self, n=3):

""" Isolate change clusters by eliminating ranges with no changes.

Return a generator of groups with up to n lines of context.

Each group is in the same format as returned by get_opcodes().

>>> from pprint import pprint

>>> a = list(map(str, range(1,40)))

>>> b = a[:]

>>> b[8:8] = ['i'] # Make an insertion

>>> b[20] += 'x' # Make a replacement

>>> b[23:28] = [] # Make a deletion

>>> b[30] += 'y' # Make another replacement

>>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))

[[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],

[('equal', 16, 19, 17, 20),

('replace', 19, 20, 20, 21),

('equal', 20, 22, 21, 23),

('delete', 22, 27, 23, 23),

('equal', 27, 30, 23, 26)],

[('equal', 31, 34, 27, 30),

('replace', 34, 35, 30, 31),

('equal', 35, 38, 31, 34)]]

"""

codes = self.get_opcodes()

if not codes:

codes = [("equal", 0, 1, 0, 1)]

# Fixup leading and trailing groups if they show no changes.

if codes[0][0] == 'equal':

tag, i1, i2, j1, j2 = codes[0]

codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2

if codes[-1][0] == 'equal':

tag, i1, i2, j1, j2 = codes[-1]

codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n)

nn = n + n

group = []

for tag, i1, i2, j1, j2 in codes:

# End the current group and start a new one whenever

# there is a large range with no changes.

if tag == 'equal' and i2-i1 > nn:

group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n)))

yield group

group = []

i1, j1 = max(i1, i2-n), max(j1, j2-n)

group.append((tag, i1, i2, j1 ,j2))

if group and not (len(group)==1 and group[0][0] == 'equal'):

yield group

def ratio(self):

"""Return a measure of the sequences' similarity (float in [0,1]).

Where T is the total number of elements in both sequences, and

M is the number of matches, this is 2.0*M / T.

Note that this is 1 if the sequences are identical, and 0 if

they have nothing in common.

.ratio() is expensive to compute if you haven't already computed

.get_matching_blocks() or .get_opcodes(), in which case you may

want to try .quick_ratio() or .real_quick_ratio() first to get an

upper bound.

>>> s = SequenceMatcher(None, "abcd", "bcde")

>>> s.ratio()

0.75

>>> s.quick_ratio()

0.75

>>> s.real_quick_ratio()

1.0

"""

matches = sum(triple[-1] for triple in self.get_matching_blocks())

return _calculate_ratio(matches, len(self.a) + len(self.b))

def quick_ratio(self):

"""Return an upper bound on ratio() relatively quickly.

This isn't defined beyond that it is an upper bound on .ratio(), and

is faster to compute.

"""

# viewing a and b as multisets, set matches to the cardinality

# of their intersection; this counts the number of matches

# without regard to order, so is clearly an upper bound

if self.fullbcount is None:

self.fullbcount = fullbcount = {}

for elt in self.b:

fullbcount[elt] = fullbcount.get(elt, 0) + 1

fullbcount = self.fullbcount

# avail[x] is the number of times x appears in 'b' less the

# number of times we've seen it in 'a' so far ... kinda

avail = {}

availhas, matches = avail.__contains__, 0

for elt in self.a:

if availhas(elt):

numb = avail[elt]

else:

numb = fullbcount.get(elt, 0)

avail[elt] = numb - 1

if numb > 0:

matches = matches + 1

return _calculate_ratio(matches, len(self.a) + len(self.b))

def real_quick_ratio(self):

"""Return an upper bound on ratio() very quickly.

This isn't defined beyond that it is an upper bound on .ratio(), and

is faster to compute than either .ratio() or .quick_ratio().

"""

la, lb = len(self.a), len(self.b)

# can't have more matches than the number of elements in the

# shorter sequence

return _calculate_ratio(min(la, lb), la + lb)

def get_close_matches(word, possibilities, n=3, cutoff=0.6):

"""Use SequenceMatcher to return list of the best "good enough" matches.

word is a sequence for which close matches are desired (typically a

string).

possibilities is a list of sequences against which to match word

(typically a list of strings).

Optional arg n (default 3) is the maximum number of close matches to

return. n must be > 0.

Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities

that don't score at least that similar to word are ignored.

The best (no more than n) matches among the possibilities are returned

in a list, sorted by similarity score, most similar first.

>>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])

['apple', 'ape']

>>> import keyword as _keyword

>>> get_close_matches("wheel", _keyword.kwlist)

['while']

>>> get_close_matches("Apple", _keyword.kwlist)

[]

>>> get_close_matches("accept", _keyword.kwlist)

['except']

"""

if not n > 0:

raise ValueError("n must be > 0: %r" % (n,))

if not 0.0 <= cutoff <= 1.0:

raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,))

result = []

s = SequenceMatcher()

s.set_seq2(word)

for x in possibilities:

s.set_seq1(x)

if s.real_quick_ratio() >= cutoff and \

s.quick_ratio() >= cutoff and \

s.ratio() >= cutoff:

result.append((s.ratio(), x))

# Move the best scorers to head of list

result = _nlargest(n, result)

# Strip scores for the best n matches

return [x for score, x in result]

def _count_leading(line, ch):

"""

Return number of `ch` characters at the start of `line`.

Example:

>>> _count_leading(' abc', ' ')

"""

i, n = 0, len(line)

while i < n and line[i] == ch:

i += 1

return i

class Differ:

r"""

Differ is a class for comparing sequences of lines of text, and

producing human-readable differences or deltas. Differ uses

SequenceMatcher both to compare sequences of lines, and to compare

sequences of characters within similar (near-matching) lines.

Each line of a Differ delta begins with a two-letter code:

'- ' line unique to sequence 1

'+ ' line unique to sequence 2

' ' line common to both sequences

'? ' line not present in either input sequence

Lines beginning with '? ' attempt to guide the eye to intraline

differences, and were not present in either input sequence. These lines

can be confusing if the sequences contain tab characters.

Note that Differ makes no claim to produce a *minimal* diff. To the

contrary, minimal diffs are often counter-intuitive, because they synch

up anywhere possible, sometimes accidental matches 100 pages apart.

Restricting synch points to contiguous matches preserves some notion of

locality, at the occasional cost of producing a longer diff.

Example: Comparing two texts.

First we set up the texts, sequences of individual single-line strings

ending with newlines (such sequences can also be obtained from the

`readlines()` method of file-like objects):

>>> text1 = ''' 1. Beautiful is better than ugly.

... 2. Explicit is better than implicit.

... 3. Simple is better than complex.

... 4. Complex is better than complicated.

... '''.splitlines(keepends=True)

>>> len(text1)

>>> text1[0][-1]

'\n'

>>> text2 = ''' 1. Beautiful is better than ugly.

... 3. Simple is better than complex.

... 4. Complicated is better than complex.

... 5. Flat is better than nested.

... '''.splitlines(keepends=True)

Next we instantiate a Differ object:

>>> d = Differ()

Note that when instantiating a Differ object we may pass functions to

filter out line and character 'junk'. See Differ.__init__ for details.

Finally, we compare the two:

>>> result = list(d.compare(text1, text2))

'result' is a list of strings, so let's pretty-print it:

>>> from pprint import pprint as _pprint

>>> _pprint(result)

[' 1. Beautiful is better than ugly.\n',

'- 2. Explicit is better than implicit.\n',

'- 3. Simple is better than complex.\n',

'+ 3. Simple is better than complex.\n',

'? ++\n',

'- 4. Complex is better than complicated.\n',

'? ^ ---- ^\n',

'+ 4. Complicated is better than complex.\n',

'? ++++ ^ ^\n',

'+ 5. Flat is better than nested.\n']

As a single multi-line string it looks like this:

>>> print(''.join(result), end="")

1. Beautiful is better than ugly.

- 2. Explicit is better than implicit.

- 3. Simple is better than complex.

+ 3. Simple is better than complex.

? ++

- 4. Complex is better than complicated.

? ^ ---- ^

+ 4. Complicated is better than complex.

? ++++ ^ ^

+ 5. Flat is better than nested.

Methods:

__init__(linejunk=None, charjunk=None)

Construct a text differencer, with optional filters.

compare(a, b)

Compare two sequences of lines; generate the resulting delta.

"""

def __init__(self, linejunk=None, charjunk=None):

"""

Construct a text differencer, with optional filters.

The two optional keyword parameters are for filter functions:

- `linejunk`: A function that should accept a single string argument,

and return true iff the string is junk. The module-level function

`IS_LINE_JUNK` may be used to filter out lines without visible

characters, except for at most one splat ('#'). It is recommended

to leave linejunk None; the underlying SequenceMatcher class has

an adaptive notion of "noise" lines that's better than any static

definition the author has ever been able to craft.

- `charjunk`: A function that should accept a string of length 1. The

module-level function `IS_CHARACTER_JUNK` may be used to filter out

whitespace characters (a blank or tab; **note**: bad idea to include

newline in this!). Use of IS_CHARACTER_JUNK is recommended.

"""

self.linejunk = linejunk

self.charjunk = charjunk

def compare(self, a, b):

r"""

Compare two sequences of lines; generate the resulting delta.

Each sequence must contain individual single-line strings ending with

newlines. Such sequences can be obtained from the `readlines()` method

of file-like objects. The delta generated also consists of newline-

terminated strings, ready to be printed as-is via the writeline()

method of a file-like object.

Example:

>>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True),

... 'ore\ntree\nemu\n'.splitlines(True))),

... end="")

- one

? ^

+ ore

? ^

- two

- three

? -

+ tree

+ emu

"""

cruncher = SequenceMatcher(self.linejunk, a, b)

for tag, alo, ahi, blo, bhi in cruncher.get_opcodes():

if tag == 'replace':

g = self._fancy_replace(a, alo, ahi, b, blo, bhi)

elif tag == 'delete':

g = self._dump('-', a, alo, ahi)

elif tag == 'insert':

g = self._dump('+', b, blo, bhi)

elif tag == 'equal':

g = self._dump(' ', a, alo, ahi)

else:

raise ValueError('unknown tag %r' % (tag,))

yield from g

def _dump(self, tag, x, lo, hi):

"""Generate comparison results for a same-tagged range."""

for i in range(lo, hi):

yield '%s %s' % (tag, x[i])

def _plain_replace(self, a, alo, ahi, b, blo, bhi):

assert alo < ahi and blo < bhi

# dump the shorter block first -- reduces the burden on short-term

# memory if the blocks are of very different sizes

if bhi - blo < ahi - alo:

first = self._dump('+', b, blo, bhi)

second = self._dump('-', a, alo, ahi)

else:

first = self._dump('-', a, alo, ahi)

second = self._dump('+', b, blo, bhi)

for g in first, second:

yield from g

def _fancy_replace(self, a, alo, ahi, b, blo, bhi):

r"""

When replacing one block of lines with another, search the blocks

for *similar* lines; the best-matching pair (if any) is used as a

synch point, and intraline difference marking is done on the

similar pair. Lots of work, but often worth it.

Example:

>>> d = Differ()

>>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1,

... ['abcdefGhijkl\n'], 0, 1)

>>> print(''.join(results), end="")

- abcDefghiJkl

? ^ ^ ^

+ abcdefGhijkl

? ^ ^ ^

"""

# don't synch up unless the lines have a similarity score of at

# least cutoff; best_ratio tracks the best score seen so far

best_ratio, cutoff = 0.74, 0.75

cruncher = SequenceMatcher(self.charjunk)

eqi, eqj = None, None # 1st indices of equal lines (if any)

# search for the pair that matches best without being identical

# (identical lines must be junk lines, & we don't want to synch up

# on junk -- unless we have to)

for j in range(blo, bhi):

bj = b[j]

cruncher.set_seq2(bj)

for i in range(alo, ahi):

ai = a[i]

if ai == bj:

if eqi is None:

eqi, eqj = i, j

continue

cruncher.set_seq1(ai)

# computing similarity is expensive, so use the quick

# upper bounds first -- have seen this speed up messy

# compares by a factor of 3.

# note that ratio() is only expensive to compute the first

# time it's called on a sequence pair; the expensive part

# of the computation is cached by cruncher

if cruncher.real_quick_ratio() > best_ratio and \

cruncher.quick_ratio() > best_ratio and \

cruncher.ratio() > best_ratio:

best_ratio, best_i, best_j = cruncher.ratio(), i, j

if best_ratio < cutoff:

# no non-identical "pretty close" pair

if eqi is None:

# no identical pair either -- treat it as a straight replace

yield from self._plain_replace(a, alo, ahi, b, blo, bhi)

return

# no close pair, but an identical pair -- synch up on that

best_i, best_j, best_ratio = eqi, eqj, 1.0

else:

# there's a close pair, so forget the identical pair (if any)

eqi = None

# a[best_i] very similar to b[best_j]; eqi is None iff they're not

# identical

# pump out diffs from before the synch point

yield from self._fancy_helper(a, alo, best_i, b, blo, best_j)

# do intraline marking on the synch pair

aelt, belt = a[best_i], b[best_j]

if eqi is None:

# pump out a '-', '?', '+', '?' quad for the synched lines

atags = btags = ""

cruncher.set_seqs(aelt, belt)

for tag, ai1, ai2, bj1, bj2 in cruncher.get_opcodes():

View remainder of file in raw view

cpython/Lib/difflib.py at 3.5 · python/cpython · GitHub

Repository navigation

Expand file tree

Breadcrumbs

difflib.py

Latest commit

History

Breadcrumbs

difflib.py

File metadata and controls