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
