def test_is_alphabetized(self): member_list = load_file('gryffindor.txt') self.assertFalse(is_alphabetized(member_list, order_first_name)) self.assertFalse(is_alphabetized(member_list, order_last_name)) member_list = load_file('sorted_first_name.txt') self.assertTrue(is_alphabetized(member_list, order_first_name)) member_list = load_file('sorted_last_name.txt') self.assertTrue(is_alphabetized(member_list, order_last_name))
def command_load_plugin(msg: twitchirc.ChannelMessage): argv = msg.text.split(' ') if len(argv) > 1: argv.pop(0) # Remove the command name try: pl = main.load_file(argv[0]) return f'Successfully loaded plugin: {pl.name}' except Exception as e: return f'An exception was encountered: {e!r}'
def test_load_file(mocked_gcs, test_file): event = { "bucket": "batch-import-slack-export-nonprod-1879", "name": "google-cloud/2020-06-24.json", "foo": "bar", } # when calling the GCS API, we expect data to come back in a certain way channel, data = main.load_file(**event) assert data is not None assert len(data) == 4 # messages that day
def on_file_select_OK_clicked(self, object, data=None): print "OK Clicked" sel = self.tree.get_selection() (model,rows)=sel.get_selected() self.filename="programs/"+model[rows][1] self.load_prog(load_file(self.filename)) self.liststore.clear() self.name[:]=[] self.date[:]=[] self.time[:]=[] self.file_select.hide()
# You should have received a copy of the GNU General Public License # along with this program. If not, see <https://www.gnu.org/licenses/>. import datetime import typing import aiohttp try: # noinspection PyPackageRequirements import main except ImportError: import util_bot as main exit() main.load_file('plugins/plugin_help.py') try: import plugin_plugin_help as plugin_help except ImportError: import plugins.plugin_help as plugin_help exit(1) try: import plugin_plugin_manager as plugin_manager except ImportError: import plugins.plugin_manager as plugin_manager try: import plugin_prometheus except ImportError: plugin_prometheus = None if typing.TYPE_CHECKING:
def display_graph(self): self.algorithm = self.combobox_algs.get() self.mode = self.combobox_types.get() print(self.mode) print(self.algorithm) if self.mode == "Google": self.file_path = "googl.us.txt" elif self.mode == "Apple": self.file_path = "aapl.us.txt" elif self.mode == "Amazon": self.file_path = "amzn.us.txt" elif self.mode == "Coca Cola": self.file_path = "ko.us.txt" # df = load_file(self.file_path) df = pd.read_csv(self.file_path) # if self.algorithm == "SVM": # print("Svm izabran") # # self.y_train, self.y_val, self.y_predict = svm_prediction(df) # # new_df = preprocess(load_file(self.file_path)) # x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) # self.y_predict = svm_prediction(df, x_train, y_train, x_valid, y_valid) # self.y_train = y_train # self.y_val = y_valid if self.algorithm == "Moving average": print("MA izabran") #NISAM TESTIRAO MA, TREBA MODIFIKOVATI new_df = preprocess(load_file(self.file_path)) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = predict_ma(df) self.y_train = y_train self.y_val = y_valid elif self.algorithm == "KNN": print("KNN izabran") df = load_file(self.file_path) new_df = preprocess(df) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = knn_predict(x_train, y_train, x_valid) self.y_train = y_train self.y_val = y_valid plt.plot(self.y_train) plt.plot(self.y_val) plt.plot(self.y_predict) plt.show() # plot_graph(self.train, self.valid, self.y_predict) elif self.algorithm == "Auto Arima": print("Auto Arima izabran") # UBACI OVDE POZIV AUTO ARIMA METODE df = load_file(self.file_path) new_df = preprocess(df) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = auto_arima_predict(df) self.y_train = y_train self.y_val = y_valid elif self.algorithm == "Linear Regression": print("Linear Regression izabran") # UBACI OVDE POZIV LINEAR REGRESSION df = load_file(self.file_path) new_df = preprocess(df) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = linearregression.run_regression(x_train, y_train, x_valid, y_valid) self.y_train = y_train self.y_val = y_valid plt.plot(self.y_train) plt.plot(self.y_val) plt.plot(self.y_predict) plt.show() elif self.algorithm == "Prophet": print("Prophet") df = load_file(self.file_path) new_df = preprocess(df) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = prophet_predict(self.train, self.valid) self.y_train = y_train self.y_val = y_valid plt.plot(self.y_train) plt.plot(self.y_val) plt.plot(self.y_predict) plt.show() print(self.y_predict) print("Zavrsio obucavanje i predikciju") p1 = figure(x_axis_type="datetime", title="Stock Closing Prices") p1.grid.grid_line_alpha = 0.3 p1.xaxis.axis_label = 'Date' p1.yaxis.axis_label = 'Price' plot_dates = df['Date'] print(self.valid) plot_dates = plot_dates[-len(self.y_predict):] p1.line(plot_dates, self.valid['Close'], color='#A6CEE3', legend=self.mode) p1.line(plot_dates, self.y_predict, color='#B2DF8A', legend="Predicted "+self.mode) output_file("stocks.html", title="Stocks prediction") show(gridplot([[p1]], plot_width=500, plot_height=500))
except ImportError: import util_bot as main exit() try: # noinspection PyPackageRequirements import plugin_plugin_manager as plugin_manager except ImportError: if typing.TYPE_CHECKING: import plugins.plugin_manager as plugin_manager else: raise main.load_file('plugins/plugin_help.py') try: import plugin_plugin_help as plugin_help except ImportError: if typing.TYPE_CHECKING: import plugins.plugin_help as plugin_help else: raise main.load_file('plugins/plugin_chat_cache.py') try: import plugin_chat_cache except ImportError: if typing.TYPE_CHECKING: from plugins.plugin_chat_cache import Plugin as PluginChatCache
song += '-' if icon_idx + 1 < len(instrument): song += ' ' if instr_idx + 1 < len(song_line): song += ' ' return song # ========== MAIN SCRIPT================ mycwd = os.getcwd() os.chdir("..") print('===== TRANSPOSITION TOOL IN THE CHROMATIC SCALE =====') first_line = input('Type or copy-paste notes, or enter file name (in ' + os.path.normpath(SONG_DIR_IN) + '/): ').strip() fp = load_file(SONG_DIR_IN, first_line) # loads file or asks for next line song_lines = read_lines(fp, first_line) try: note_shift = int(input('Transposition ? (-12 ; +12): ').strip()) except ValueError: note_shift = 0 skyparser = SongParser() skyparser.set_delimiters(ICON_DELIMITER, PAUSE, QUAVER_DELIMITER, COMMENT_DELIMITER, REPEAT_INDICATOR) possible_modes = skyparser.get_possible_modes(song_lines) if len(possible_modes) > 1: print('\nSeveral possible notations detected.') song_notation = ask_for_mode(possible_modes)
# along with this program. If not, see <https://www.gnu.org/licenses/>. import socket import threading import twitchirc try: # noinspection PyUnresolvedReferences import main except ImportError: # noinspection PyPackageRequirements import util_bot as main raise main.load_file('plugins/plugin_help.py') try: import plugin_plugin_help as plugin_help except ImportError: import plugins.plugin_help as plugin_help exit(1) try: import plugin_plugin_manager as plugin_manager except ImportError: import plugins.plugin_manager as plugin_manager exit(1) main.load_file('plugins/plugin_prefixes.py')
def test_alphabetize_by_last(self): self.maxDiff = None member_list = load_file('gryffindor.txt') solution = load_file('sorted_last_name.txt') (sorted_list, cost) = alphabetize(member_list, order_last_name) self.assertEqual(sorted_list, solution)
exit(1) try: # noinspection PyPackageRequirements import main except ImportError: import util_bot as main exit(1) try: import plugin_plugin_manager as plugin_manager except ImportError: import plugins.plugin_manager as plugin_manager main.load_file('plugins/plugin_hastebin.py') try: import plugin_hastebin as plugin_hastebin except ImportError: from plugins.plugin_hastebin import Plugin as PluginHastebin plugin_hastebin: PluginHastebin import random import twitchirc __meta_data__ = {'name': 'plugin_cancer', 'commands': []} log = main.make_log_function('cancerous_plugin') with open(__file__, 'r') as f:
async def audio_player_task(self): while True: self.next.clear() self.now = None if self.previous_message: await self.previous_message.delete() self.previous_message = None if self._loop: if self.current: await self.songs.put(self.current) try: async with timeout(5): self.current = await self.songs.get() except asyncio.TimeoutError: self.current = None if self._autoplay: if not self.voice: self.bot.loop.create_task(self.stop()) self.exists = False return INFO("Fetching autoplay List") if not self.autoplaylist: self.autoplaylist = list(load_file("autoplaylist.txt")) while self.autoplaylist: random_link = random.choice(self.autoplaylist) INFO(f"Trying {random_link} from autoplaylist") self.autoplaylist.remove(random_link) song_url, source_type, playlist = SourceDL.get_type( random_link) source_init = SourceDL.Source(self._ctx, source_type=source_type, loop=self.bot.loop) if playlist: playlist_info = await source_init.get_playlist_info( song_url) INFO( f"Adding {playlist_info.song_num} songs from {random_link}" ) try: sources = await source_init.get_playlist( song_url) except SourceDL.SourceError: continue else: if source_type == "GDrive": for num, each_source in enumerate(sources): sources[ num] = f"https://drive.google.com/file/d/{each_source}/view" self.autoplaylist = sources continue else: try: source = await source_init.create_source( song_url) except SourceDL.SourceError: pass else: song = Song(source) await self._ctx.voice_state.songs.put(song) self.current = await self.songs.get() if self.current: break continue if not self.current: self.bot.loop.create_task(self.stop()) self.exists = False return else: self.bot.loop.create_task(self.stop()) self.exists = False return for each_song in self.song_history: if self.current.source.data.title == each_song.source.data.title: self.song_history.remove(each_song) self.song_history.insert(0, self.current) self.current.source.volume = self._volume await self.current.source.ready_download() with open( f"audio_cache\\{self.current.source.data.expected_filename}", 'rb') as f: source = discord.FFmpegPCMAudio(f, pipe=True) self.voice.play(source, after=self.play_next_song) #await self.current.source.bot.change_presence(activity=discord.Game(f"{self.current.source.title}")) embed, thumbnail = self.current.create_embed() if thumbnail: self.previous_message = await self.current.source.channel.send( embed=embed, file=thumbnail) else: self.previous_message = await self.current.source.channel.send( embed=embed) await self.next.wait()
import main except ImportError: # noinspection PyUnreachableCode if False: import util_bot as main else: import main_stub # noinspection PyUnresolvedReferences import main # load the Fake from main_stub try: import plugin_plugin_manager as plugin_manager except ImportError: import plugins.plugin_manager as plugin_manager main.load_file('plugins/plugin_prefixes.py') try: import plugin_plugin_prefixes as plugin_prefixes except ImportError: import plugins.plugin_prefixes as plugin_prefixes __meta_data__ = {'name': 'plugin_help', 'commands': []} log = main.make_log_function('help') SECTION_LINKS = 0 SECTION_COMMANDS = 1 SECTION_ARGS = 2 SECTION_MISC = 7 all_help: Dict[int, Union[Dict[str, Tuple[int, str]], Dict[str, str]]] = { SECTION_LINKS: { # links # 'source': (1, 'target') <=> (section, target)
exit() import twitchirc __meta_data__ = {'name': 'auto_load', 'commands': []} log = main.make_log_function('auto_load') log('info', 'Plugin `auto_load` loaded') if 'plugins' in main.bot.storage.data: if main.bot.storage['plugins'] == 'auto': for i in os.listdir('plugins'): log('debug', f'Trying to load file: {i}') try: main.load_file(i) except Exception as e: log('err', f'Failed to load: {e}') for i in traceback.format_exc(30).split('\n'): log('err', i) else: for i in main.bot.storage['plugins']: log('debug', f'Trying to load file: {i}') try: main.load_file(i) except Exception as e: log('err', f'Failed to load: {e}') for i in traceback.format_exc(30).split('\n'): log('err', i) else: main.bot.storage['plugins'] = []
exit(1) try: # noinspection PyPackageRequirements import main except ImportError: import util_bot as main exit(1) try: import plugin_plugin_manager as plugin_manager except ImportError: import plugins.plugin_manager as plugin_manager main.load_file('plugins/plugin_hastebin.py') try: import plugin_hastebin as plugin_hastebin except ImportError: from plugins.plugin_hastebin import Plugin as PluginHastebin plugin_hastebin: PluginHastebin main.load_file('plugins/plugin_emotes.py') try: import plugin_emotes as plugin_emotes except ImportError: from plugins.plugin_emotes import Plugin as PluginEmotes plugin_emotes: PluginEmotes