def start_puzzle_from(filename, index=None):
if filename.lower().endswith(".pgn"):
if filename.startswith("lichess_study"):
chessfile = PGNFile(protoopen(addDataPrefix("learn/puzzles/%s" % filename), encoding="utf-8"))
else:
chessfile = PGNFile(protoopen(addDataPrefix("learn/puzzles/%s" % filename)))
chessfile.limit = 1000
chessfile.init_tag_database()
elif filename.lower().endswith(".olv"):
chessfile = OLVFile(protoopen(addDataPrefix("learn/puzzles/%s" % filename), encoding="utf-8"))
records, plys = chessfile.get_records()
progress = puzzles_solving_progress.get(filename, [0] * chessfile.count)
if index is None:
index = progress.index(0)
rec = records[index]
timemodel = TimeModel(0, 0)
gamemodel = LearnModel(timemodel)
chessfile.loadToModel(rec, 0, gamemodel)
start_puzzle_game(gamemodel, filename, records, index, rec)
def row_activated(self, widget, path, col):
if path is None:
return
filename = addDataPrefix("lectures/%s" % LESSONS[path[0]][0])
chessfile = PGNFile(protoopen(filename))
self.importer = PgnImport(chessfile)
chessfile.init_tag_database(self.importer)
records, plys = chessfile.get_records()
rec = records[random.randrange(0, len(records))]
print(rec)
timemodel = TimeModel(0, 0)
gamemodel = GameModel(timemodel)
gamemodel.set_lesson_game()
chessfile.loadToModel(rec, -1, gamemodel)
name = conf.get("firstName", _("You"))
p0 = (LOCAL, Human, (WHITE, name), name)
name = "pychessbot"
p1 = (LOCAL, Human, (BLACK, name), name)
gamemodel.status = WAITING_TO_START
perspective = perspective_manager.get_perspective("games")
asyncio. async (perspective.generalStart(gamemodel, p0, p1))
def start_lesson_from(filename, index=None):
chessfile = PGNFile(protoopen(addDataPrefix("learn/lessons/%s" %
filename)))
chessfile.limit = 1000
chessfile.init_tag_database()
records, plys = chessfile.get_records()
progress = lessons_solving_progress.get(filename, [0] * chessfile.count)
if index is None:
index = progress.index(0)
rec = records[index]
timemodel = TimeModel(0, 0)
gamemodel = LearnModel(timemodel)
chessfile.loadToModel(rec, -1, gamemodel)
if len(gamemodel.moves) > 0:
start_lesson_game(gamemodel, filename, chessfile, records, index, rec)
else:
start_puzzle_game(gamemodel,
filename,
records,
index,
rec,
from_lesson=True)
def start_lesson_from(filename, index=None):
if filename.startswith("lichess_study"):
chessfile = PGNFile(
protoopen(addDataPrefix("learn/lessons/%s" % filename),
encoding="utf-8"))
else:
chessfile = PGNFile(
protoopen(addDataPrefix("learn/lessons/%s" % filename)))
chessfile.limit = 1000
chessfile.init_tag_database()
records, plys = chessfile.get_records()
progress = lessons_solving_progress.get(filename, [0] * chessfile.count)
if index is None:
index = progress.index(0)
rec = records[index]
timemodel = TimeModel(0, 0)
gamemodel = LearnModel(timemodel)
gamemodel.set_learn_data(LESSON, filename, index, len(records))
chessfile.loadToModel(rec, -1, gamemodel)
color = gamemodel.boards[0].color
player_name = conf.get("firstName", _("You"))
w_name = player_name if color == WHITE else "PyChess"
b_name = "PyChess" if color == WHITE else player_name
p0 = (LOCAL, Human, (WHITE, w_name), w_name)
p1 = (LOCAL, Human, (BLACK, b_name), b_name)
def learn_success(gamemodel):
chessfile.loadToModel(rec, -1, gamemodel)
progress = lessons_solving_progress[gamemodel.source]
progress[gamemodel.current_index] = 1
lessons_solving_progress[gamemodel.source] = progress
asyncio. async (gamemodel.restart_analyzer(HINT))
gamemodel.connect("learn_success", learn_success)
def on_game_started(gamemodel):
perspective.activate_panel("annotationPanel")
asyncio. async (gamemodel.start_analyzer(
HINT, force_engine=discoverer.getEngineLearn()))
gamemodel.connect("game_started", on_game_started)
gamemodel.status = WAITING_TO_START
perspective = perspective_manager.get_perspective("games")
asyncio. async (perspective.generalStart(gamemodel, p0, p1))
def on_enter_game_clipboard_activate(self, widget):
ok = False
clipboard = Gtk.Clipboard.get(Gdk.SELECTION_CLIPBOARD)
text = clipboard.wait_for_text()
if text not in [None, '']:
try:
pgn = PGNFile(StringIO(text))
pgn.loadToModel(None)
newGameDialog.loadPgnAndRun(text)
ok = True
except Exception:
pass
if not ok:
dialog = Gtk.MessageDialog(mainwindow(), type=Gtk.MessageType.ERROR, buttons=Gtk.ButtonsType.OK)
dialog.set_markup(_('The clipboard contains no relevant chess data.'))
dialog.run()
dialog.destroy()
def loadToModel(self, gameno, position=-1, model=None):
self.comments = []
self.comment_idx = 0
model = PGNFile.loadToModel(self,
gameno,
position=position,
model=model)
model.game_id = self.games[gameno]["Id"]
return model
def start_lesson_from(filename, index=None):
chessfile = PGNFile(protoopen(addDataPrefix("learn/lessons/%s" % filename)))
chessfile.limit = 1000
chessfile.init_tag_database()
records, plys = chessfile.get_records()
progress = lessons_solving_progress.get(filename, [0] * chessfile.count)
if index is None:
index = progress.index(0)
rec = records[index]
timemodel = TimeModel(0, 0)
gamemodel = LearnModel(timemodel)
gamemodel.set_learn_data(LESSON, filename, index, len(records))
chessfile.loadToModel(rec, -1, gamemodel)
color = gamemodel.boards[0].color
player_name = conf.get("firstName", _("You"))
w_name = player_name if color == WHITE else "PyChess"
b_name = "PyChess" if color == WHITE else player_name
p0 = (LOCAL, Human, (WHITE, w_name), w_name)
p1 = (LOCAL, Human, (BLACK, b_name), b_name)
def learn_success(gamemodel):
progress = lessons_solving_progress[gamemodel.source]
progress[gamemodel.current_index] = 1
lessons_solving_progress[gamemodel.source] = progress
asyncio.async(gamemodel.restart_analyzer(HINT))
gamemodel.connect("learn_success", learn_success)
def start_analyzer(gamemodel):
asyncio.async(gamemodel.start_analyzer(HINT, force_engine=discoverer.getEngineLearn()))
gamemodel.connect("game_started", start_analyzer)
gamemodel.status = WAITING_TO_START
perspective = perspective_manager.get_perspective("games")
asyncio.async(perspective.generalStart(gamemodel, p0, p1))
def start_puzzle_from(filename):
chessfile = PGNFile(protoopen(addDataPrefix("lectures/%s" % filename)))
chessfile.limit = 1000
importer = PgnImport(chessfile)
chessfile.init_tag_database(importer)
records, plys = chessfile.get_records()
rec = records[random.randrange(0, len(records))]
timemodel = TimeModel(0, 0)
gamemodel = GameModel(timemodel)
gamemodel.set_practice_game()
gamemodel.practice = ("puzzle", filename)
chessfile.loadToModel(rec, 0, gamemodel)
# TODO: change colors according to FEN!
name = rec["White"]
p0 = (LOCAL, Human, (WHITE, name), name)
engine = discoverer.getEngineByName(stockfish_name)
name = rec["Black"]
ponder_off = True
p1 = (ARTIFICIAL, discoverer.initPlayerEngine,
(engine, BLACK, 20, variants[NORMALCHESS], 60, 0, 0,
ponder_off), name)
def fix_name(gamemodel, name):
asyncio. async (gamemodel.start_analyzer(HINT,
force_engine=stockfish_name))
gamemodel.players[1].name = name
gamemodel.emit("players_changed")
gamemodel.connect("game_started", fix_name, name)
gamemodel.variant.need_initial_board = True
gamemodel.status = WAITING_TO_START
perspective = perspective_manager.get_perspective("games")
asyncio. async (perspective.generalStart(gamemodel, p0, p1))
def start_lesson_from(filename, index=None):
chessfile = PGNFile(protoopen(addDataPrefix("learn/lessons/%s" % filename)))
chessfile.limit = 1000
chessfile.init_tag_database()
records, plys = chessfile.get_records()
progress = lessons_solving_progress.get(filename, [0] * chessfile.count)
if index is None:
index = progress.index(0)
rec = records[index]
timemodel = TimeModel(0, 0)
gamemodel = LearnModel(timemodel)
chessfile.loadToModel(rec, -1, gamemodel)
if len(gamemodel.moves) > 0:
start_lesson_game(gamemodel, filename, chessfile, records, index, rec)
else:
start_puzzle_game(gamemodel, filename, records, index, rec, from_lesson=True)
def start_puzzle_from(filename, index=None):
if filename.lower().endswith(".pgn"):
chessfile = PGNFile(protoopen(addDataPrefix("learn/puzzles/%s" % filename)))
chessfile.limit = 1000
chessfile.init_tag_database()
elif filename.lower().endswith(".olv"):
chessfile = OLVFile(protoopen(addDataPrefix("learn/puzzles/%s" % filename), encoding="utf-8"))
records, plys = chessfile.get_records()
progress = puzzles_solving_progress.get(filename, [0] * chessfile.count)
if index is None:
index = progress.index(0)
rec = records[index]
timemodel = TimeModel(0, 0)
gamemodel = LearnModel(timemodel)
chessfile.loadToModel(rec, 0, gamemodel)
start_puzzle_game(gamemodel, filename, records, index, rec)
def start_puzzle_from(filename, index=None):
if filename.lower().endswith(".pgn"):
if filename.startswith("lichess_study"):
chessfile = PGNFile(
protoopen(addDataPrefix("learn/puzzles/%s" % filename),
encoding="utf-8"))
else:
chessfile = PGNFile(
protoopen(addDataPrefix("learn/puzzles/%s" % filename)))
chessfile.limit = 1000
chessfile.init_tag_database()
elif filename.lower().endswith(".olv"):
chessfile = OLVFile(
protoopen(addDataPrefix("learn/puzzles/%s" % filename),
encoding="utf-8"))
records, plys = chessfile.get_records()
progress = puzzles_solving_progress.get(filename, [0] * chessfile.count)
if index is None:
index = progress.index(0)
rec = records[index]
timemodel = TimeModel(0, 0)
gamemodel = LearnModel(timemodel)
gamemodel.set_learn_data(PUZZLE, filename, index, len(records))
chessfile.loadToModel(rec, 0, gamemodel)
engine = discoverer.getEngineByName(discoverer.getEngineLearn())
ponder_off = True
color = gamemodel.boards[0].color
w_name = "" if rec["White"] is None else rec["White"]
b_name = "" if rec["Black"] is None else rec["Black"]
player_name = conf.get("firstName", _("You"))
engine_name = discoverer.getName(engine)
if rec["Event"].startswith("Lichess Practice"):
w_name = player_name if color == WHITE else engine_name
b_name = engine_name if color == WHITE else player_name
opp_name = engine_name if rec["Event"].startswith(
"Lichess Practice") else b_name
if color == WHITE:
p0 = (LOCAL, Human, (WHITE, w_name), w_name)
p1 = (ARTIFICIAL, discoverer.initPlayerEngine,
(engine, BLACK, 20, variants[NORMALCHESS], 20, 0, 0,
ponder_off), b_name)
else:
p0 = (ARTIFICIAL, discoverer.initPlayerEngine,
(engine, WHITE, 20, variants[NORMALCHESS], 20, 0, 0,
ponder_off), w_name)
p1 = (LOCAL, Human, (BLACK, b_name), b_name)
def on_game_started(gamemodel, name, color):
perspective.activate_panel("annotationPanel")
asyncio. async (gamemodel.start_analyzer(
HINT, force_engine=discoverer.getEngineLearn()))
gamemodel.players[1 - color].name = name
gamemodel.emit("players_changed")
gamemodel.connect("game_started", on_game_started, opp_name, color)
def goal_checked(gamemodle):
if gamemodel.reason == PRACTICE_GOAL_REACHED:
progress = puzzles_solving_progress[gamemodel.source]
progress[gamemodel.current_index] = 1
puzzles_solving_progress[gamemodel.source] = progress
gamemodel.connect("goal_checked", goal_checked)
gamemodel.variant.need_initial_board = True
gamemodel.status = WAITING_TO_START
perspective = perspective_manager.get_perspective("games")
asyncio. async (perspective.generalStart(gamemodel, p0, p1))
def start_puzzle_from(filename, index=None):
if filename.lower().endswith(".pgn"):
chessfile = PGNFile(protoopen(addDataPrefix("learn/puzzles/%s" % filename)))
chessfile.limit = 1000
chessfile.init_tag_database()
elif filename.lower().endswith(".olv"):
chessfile = OLVFile(protoopen(addDataPrefix("learn/puzzles/%s" % filename), encoding="utf-8"))
records, plys = chessfile.get_records()
progress = puzzles_solving_progress.get(filename, [0] * chessfile.count)
if index is None:
index = progress.index(0)
rec = records[index]
timemodel = TimeModel(0, 0)
gamemodel = LearnModel(timemodel)
chessfile.loadToModel(rec, 0, gamemodel)
gamemodel.set_learn_data(PUZZLE, filename, index, len(records))
engine = discoverer.getEngineByName(discoverer.getEngineLearn())
ponder_off = True
color = gamemodel.boards[0].color
w_name = "" if rec["White"] is None else rec["White"]
b_name = "" if rec["Black"] is None else rec["Black"]
player_name = conf.get("firstName", _("You"))
engine_name = discoverer.getName(engine)
if rec["Event"].startswith("Lichess Practice"):
w_name = player_name if color == WHITE else engine_name
b_name = engine_name if color == WHITE else player_name
opp_name = engine_name if rec["Event"].startswith("Lichess Practice") else b_name
if color == WHITE:
p0 = (LOCAL, Human, (WHITE, w_name), w_name)
p1 = (ARTIFICIAL, discoverer.initPlayerEngine,
(engine, BLACK, 20, variants[NORMALCHESS], 20, 0, 0, ponder_off), b_name)
else:
p0 = (ARTIFICIAL, discoverer.initPlayerEngine,
(engine, WHITE, 20, variants[NORMALCHESS], 20, 0, 0, ponder_off), w_name)
p1 = (LOCAL, Human, (BLACK, b_name), b_name)
def start_analyzer(gamemodel, name, color):
asyncio.async(gamemodel.start_analyzer(HINT, force_engine=discoverer.getEngineLearn()))
gamemodel.players[1 - color].name = name
gamemodel.emit("players_changed")
gamemodel.connect("game_started", start_analyzer, opp_name, color)
def goal_checked(gamemodle):
if gamemodel.reason == PRACTICE_GOAL_REACHED:
progress = puzzles_solving_progress[gamemodel.source]
progress[gamemodel.current_index] = 1
puzzles_solving_progress[gamemodel.source] = progress
gamemodel.connect("goal_checked", goal_checked)
gamemodel.variant.need_initial_board = True
gamemodel.status = WAITING_TO_START
perspective = perspective_manager.get_perspective("games")
asyncio.async(perspective.generalStart(gamemodel, p0, p1))
