async def verify(self, ctx, passcode):
"""Gain access to some of the owner-only commands!
Parameters:
passcode - The passcode. Every time an user is verified, a new one is generated."""
with open("../DSB_Files/ultra_secret_passcode.txt", "r") as file:
passkey = file.read()
if passcode != passkey:
await say(ctx, ":interrobang: - Passcodes do not match!")
elif passcode == passkey:
verified_users.append(ctx.author.id)
list_keychars = []
for i in range(20):
list_choice = []
x = random.randint(65, 90)
list_choice.append(x)
y = random.randint(97, 122)
list_choice.append(y)
z = random.randint(48, 57)
list_choice.append(z)
chooser = random.choice(list_choice)
converted = chr(chooser)
list_keychars.append(str(converted))
passcode = "".join(list_keychars)
print("The new password is: " + passcode)
fs.write("../DSB_Files/ultra_secret_passcode.txt", passcode)
await say(ctx, ":white_check_mark: - Successfully validated!")
def test_write():
write_content = "test content"
fs.write(TEST_FILE, write_content)
with open(TEST_FILE, 'r') as file:
content = file.read()
assert write_content == content
def make():
"""Generate the current shell scripts from the templates"""
clean()
for _file in fs.find('*.sh', SRC_DIR):
tplname = _file.replace(SRC_DIR + '/', "")
dest = fs.join(DIST_DIR, fs.filename(tplname))
tpl = env.get_template(tplname)
fs.write(dest, tpl.render())
print("Writing template %s" % tplname)
def test_write_utf8():
write_content = """"this is a test content with
utf-8 characters, such as ÀÁÂÂÄ or ¼½¾"""
fs.write(TEST_FILE, write_content)
with open(TEST_FILE, 'rb') as file:
content = file.read()
assert write_content == content.decode('UTF-8')
def add_model_to_report(model, params):
timestamp = params.get('timestamp')
shp = params.get('input_shape', (3, 112, 112))
batchsize = params.get('batchsize', 64)
# Write model structure as JSON file
fs.write('results/%s/model.json' % timestamp, model.to_json())
# Print the model Shape
add_to_report('\n## Model architecture')
model.summary()
m_def = get_model_summary(model)
print('\nUsing architecture:')
print(m_def)
# Write model dimensions to Report
add_to_report(m_def, params)
def main():
fs.init('fs')
fs.mkdir('a')
fs.mkdir('b')
fs.mkdir('a/c')
fs.create('a/d.txt', 20)
fs.create('a/c/e.txt', 20)
fd1 = fs.open('a/d.txt', 'rw')
fd2 = fs.open('a/c/e.txt', 'rw')
fs.write(fd1, 'hello\nbye\n')
fs.write(fd2, 'goodbye\n')
print fs.read(fd2, 4)
print fs.readlines(fd1)
for f in fs.readlines(fd1):
print(f),
fs.close(fd1)
fs.close(fd2)
fs.suspend()
async def on_ready():
print("Ready to go!")
await bot.change_presence(activity=discord.Game(name="| D.help |"))
list_keychars = []
for i in range(20):
list_choice = []
x = random.randint(65, 90)
list_choice.append(x)
y = random.randint(97, 122)
list_choice.append(y)
z = random.randint(48, 57)
list_choice.append(z)
chooser = random.choice(list_choice)
converted = chr(chooser)
list_keychars.append(str(converted))
passcode = "".join(list_keychars)
print("The password is: " + passcode)
fs.write("../DSB_Files/ultra_secret_passcode.txt", passcode)
async def setlog(self, ctx, channel: discord.TextChannel = None):
"""Sets the log channel.
Parameters:
channel - The channel used for the logs. The channel has to be mentioned normally and the bot needs to be able to read and send to it.
Permissions:
Administrator"""
if channel != None:
fs.write(f"../DSB_Files/log_of_{ctx.guild.id}.txt",
str(channel.id))
await say(ctx, ":white_check_mark: - Log Channel set!")
else:
if fs.exists(f"../DSB_Files/log_of_{ctx.guild.id}.txt"):
os.remove(f"../DSB_Files/log_of_{ctx.guild.id}.txt")
await say(
ctx,
":white_check_mark: - Log channel deleted out of memory!")
else:
await say(ctx, ":interrobang: - Please mention a channel!")
import pypandoc
import os
import fs
# Read the markdown README
doc = pypandoc.convert('README.md', 'rst')
# Write a rST README for long_description
fs.write('README.txt', doc)
# Run the register command
os.system("python setup.py register sdist upload")
# Remove the rST README
fs.rm('README.txt')
a = Term(arr[0])
b = Term(arr[1])
print(a)
print(b)
print(a - b)
print(abs((a - b).solve()))
return input
if __name__ == '__main__':
#IN_NAME = 'B-small-attempt2.in'
IN_NAME = 'input.txt'
OUT_NAME = 'output.txt'
raw_input = fs.read(IN_NAME)
print('====> Reading %s' % IN_NAME)
rows = raw_input.split('\n')
cases = int(rows[0])
solution = ''
for i, row in enumerate(rows):
# Skip first row (contains number of entries)
if i == 0: continue
# Skip last row (contains only \n)
if i == len(rows) - 1: continue
solution += 'Case #%i: %s\n' % (i, str(solve(row)))
fs.write(OUT_NAME, solution)
print('====> Writing %s' % OUT_NAME)
b = Term(arr[1])
print(a)
print(b)
print(a - b)
print(abs((a - b).solve()))
return input
if __name__ == '__main__':
#IN_NAME = 'B-small-attempt2.in'
IN_NAME = 'input.txt'
OUT_NAME = 'output.txt'
raw_input = fs.read(IN_NAME)
print('====> Reading %s' % IN_NAME)
rows = raw_input.split('\n')
cases = int(rows[0])
solution = ''
for i, row in enumerate(rows):
# Skip first row (contains number of entries)
if i == 0: continue
# Skip last row (contains only \n)
if i == len(rows) - 1: continue
solution += 'Case #%i: %s\n' % (i, str(solve(row)))
fs.write(OUT_NAME, solution)
print('====> Writing %s' % OUT_NAME)
import pandoc
import os
import fs
pandoc.core.PANDOC_PATH = '/usr/bin/pandoc'
# Create New Pandoc Document
doc = pandoc.Document()
# Read the markdown README
doc.markdown = fs.read('README.md')
# Write a rST README for long_description
fs.write('README.txt', doc.rst)
# Run the register command
os.system("python setup.py register sdist upload")
# Remove the rST README
fs.rm('README.txt')
if vc.isOpened(): # try to get the first frame
rval, frame = vc.read()
else:
rval = False
while rval:
cv2.imshow("preview", frame)
cv2.imwrite('./imgCaptured.png', frame)
rval, frame = vc.read()
key = cv2.waitKey(20)
if key == 27: # exit on ESC
break
cv2.destroyWindow("preview")
app = ClarifaiApp(api_key='444fb551757f45e0b124892399e5b760')
model = app.models.get('general-v1.3')
image = ClImage(file_obj=open('./imgCaptured.png', 'rb'))
content = json.dumps(model.predict([image]))
strings = json.loads(content)
imageText = "There is a " + strings["outputs"][0]["data"]["concepts"][0][
"name"] + " ahead!"
print(imageText)
fs = open("textToSpeech.txt", "w")
fs.write(imageText)
fs.close()
fs.chdir('a')
fs.mkdir('b2')
fs.mkdir('/a/b3')
#now on drectory b3
fs.chdir('b3')
fs.mkdir('/a/b1/c1')
print fs.listdir('/a/b1')
fs.create('/a/b3/fc', 30)
fcd = fs.open('/a/b3/fc', 'w')
fs.write(fcd, '\nnow we needtousegitagain\n')
fs.close(fcd)
fcd1 = fs.open('/a/b3/fc', 'r')
print fs.readlines(fcd1)
print fs.read(fcd1, 5)
fs.seek(fcd1, 5)
print fs.read(fcd1, 10)
fs.close(fcd1)
fs.suspend()
#fs.open('/fa','r')
fs.chdir('..')
#resume is not sure
fs.resume('abc.fssave')
fs.create('fb', 29)
fbd = fs.open('fb', 'w')
fs.write(fbd, 'quizz is so annoying.\n')
print e
fs.listdir()
fs.listdir('a1')
try:
fs.deldir('c1')
except Exception, e:
print e
fs.getcwd()
fd = fs.open('a1/a2.txt', 'r')
try:
fd2 = fs.open('a1/b.txt', 'r')
except Exception, e:
print e
try:
fs.write(fd, 'hello\n')
except Exception, e:
print e
try:
fs.write(fd + 1, 'hello\n')
except Exception, e:
print e
fd3 = fs.open('/a0/a1/a2.txt', 'w')
print fd == fd3
fs.write(fd, 'hello\n')
print fs.read(fd, 6)
fs.seek(fd, 0)
print fs.read(fd, 6)
try:
fs.seek(fd, 7)
except Exception, e:
print("Expected Shape: ", nb_filter, stack_size, nb_col, nb_row)
print("Found Shape: ", np.array(blobs[0].data).shape)
weights_p = blobs[0].data.astype(dtype=np.float32)
weights_b = blobs[1].data.astype(dtype=np.float32)
if len(weights_p.shape) > 2:
# Caffe uses the shape f, (d, y, x)
# ConvnetJS uses the shape f, (y, x, d)
weights_p = np.swapaxes(np.swapaxes(weights_p, 3, 1), 2, 1)
print("Converted to Shape: ", weights_p.shape)
weights = {
'filter': weights_p.reshape((nb_filter, stack_size*nb_col*nb_row)).tolist(),
'bias': weights_b.tolist()
}
filename = WEIGHTS_DIR + key + '.txt'
if not fs.exists(fs.dirname(filename)):
fs.mkdir(fs.dirname(filename))
fs.write(fs.add_suffix(filename, "_filter"), "")
for i, f_weights in enumerate(weights['filter']):
if i == len(weights['filter']) - 1:
fs.append(fs.add_suffix(filename, "_filter"), ",".join(map(str, f_weights)))
else:
fs.append(fs.add_suffix(filename, "_filter"), ",".join(map(str, f_weights)) + "\n")
fs.write(fs.add_suffix(filename, "_bias"), ",".join(map(str, weights['bias'])))
from __future__ import print_function
import fs
import numpy as np
# Make sure that caffe and pycaffe are installed
# and on the python path:
caffe_root = '../caffe/' # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
if len(sys.argv) != 3:
print("Usage: python convert_protomean.py data/ilsvrc12/imagenet_mean.binaryproto data/ilsvrc12/imagenet_mean.txt")
sys.exit()
blob = caffe.proto.caffe_pb2.BlobProto()
data = open( sys.argv[1] , 'rb' ).read()
blob.ParseFromString(data)
arr = np.array( caffe.io.blobproto_to_array(blob) )
s = np.shape(arr[0])
out = arr[0].reshape((3,s[1]*s[2])).tolist()
fs.write( sys.argv[2] , "\n".join(map(lambda o: ",".join(map(str, o)), out)) )
