def graph(self, *args):
# load template
with open("template.json", "r") as f:
template = loadJSON(f)
item = self.scoutingDataLB.get(self.scoutingDataLB.curselection())
label = None
# item should be a question (possibly with data (INT))
# get JSONlabel
for i in template:
try:
if i['question'] in item:
label = i['jsonLabel']
except KeyError:
pass
if label is None:
return
data = []
for key_, value_ in self.currentTeam.JSONdata.items():
for key, value in value_.items():
if key == label:
data.append(value)
self.__graph__(data)
def version_info():
with open(path.join(path.dirname(argv[0]), 'printsrv', 'package.json'), 'rU') as package_file:
try:
local_package_json = loadJSON(package_file)
except ValueError:
print 'WARNING: local package.json is damaged.'
local_package_json = {'version': 'N/A'}
return local_package_json['version']
def refreshQuestions(self):
with open('template.json', 'r') as template:
for i in loadJSON(template):
if i['type'] == 'header' or i['type'] == 'space':
continue
self.questionsListBox.insert(END, i['question'])
def trainset(self,fname,tportion=0.25,epochs=50): if 'datasets/' not in fname: fname = 'datasets/' + fname if '.json' not in fname: fname = fname + '.json' f = open(fname) mlist = loadJSON(f) f.close() tportion = int(len(mlist)*max(0,min(1.0,tportion))) for k in range(epochs): print 'Training: epoch #' + str(k) for match in npchoice(mlist,tportion,replace=False): a,b = match['a'],match['b'] self.update(a,b,match['val'])
def trainset(self, fname, tportion=0.25, epochs=50):
if 'datasets/' not in fname:
fname = 'datasets/' + fname
if '.json' not in fname:
fname = fname + '.json'
f = open(fname)
mlist = loadJSON(f)
f.close()
tportion = int(len(mlist) * max(0, min(1.0, tportion)))
for k in range(epochs):
print 'Training: epoch #' + str(k)
for match in npchoice(mlist, tportion, replace=False):
a, b = match['a'], match['b']
self.update(a, b, match['val'])
def load(fname='NN-00',features=FTVEC): if 'datasets/' not in fname: fname = 'datasets/' + fname if '.json' not in fname: fname = fname + '.json' f = open(fname) data = loadJSON(f) f.close() NN = NeuralNet(features,hiddencount=data['hsize']) for i,hnode in enumerate(NN.hidden): hinfo = data['h'+str(i)] hnode.bias = hinfo['bias'] hnode.weights = hinfo['weights'] NN.output.bias = data['out']['bias'] NN.output.weights = data['out']['weights'] return NN
def load(fname='NN-00', features=FTVEC):
if 'datasets/' not in fname:
fname = 'datasets/' + fname
if '.json' not in fname:
fname = fname + '.json'
f = open(fname)
data = loadJSON(f)
f.close()
NN = NeuralNet(features, hiddencount=data['hsize'])
for i, hnode in enumerate(NN.hidden):
hinfo = data['h' + str(i)]
hnode.bias = hinfo['bias']
hnode.weights = hinfo['weights']
NN.output.bias = data['out']['bias']
NN.output.weights = data['out']['weights']
return NN
def update():
try:
with open('database.json', 'r') as f:
if time() - loadJSON(f)['last_update'] < 1000:
return getRet()
except (FileNotFoundError, JSONDecodeError, KeyError):
pass
api.sync()
od = dict()
with open("database.json", 'w') as f:
# Collaborators
od['users'] = list()
for collaborator in api.state['collaborators']:
od['users'].append(f"{collaborator['full_name']} "
f"<{collaborator['email']}> "
f"(Todoist User #{collaborator['id']})")
# Projects
od['projects'] = dict()
for project in api.state['projects']:
od['projects'][project['id']] = project['name']
# Tasks
od['items'] = list()
for item in api.state['items']:
od['items'].append(todoistitem2dict(item))
# Completed Tasks
for project_id in [project['id'] for project in api.state['projects']]:
for item in api.items.get_completed(project_id):
od['items'].append(todoistitem2dict(item))
od['items'] = list(
filter(lambda filter_item: filter_item is not None, od['items']))
od['meta'] = {
'users-count': len(od['users']),
'tasks-count': len(od['items'])
}
od['last_update'] = int(time())
dumpJSON(od, f)
return getRet()
def main():
with open('database.json', 'r') as f:
db = loadJSON(f)
start = request.args.get(
'after',
default=0,
type=int
)
perpage = request.args.get(
'perpage',
default=8,
type=int
)
end = min([
start + perpage - 1,
len(db['items'])
])
litems = db['items'] if perpage < 1 else db['items'][start:end + 1]
pid = request.args.get('project', type=int)
if pid is not None:
litems = filter(lambda item: item['parent-project'] == pid, litems)
return render_template(
"infoIndex.html",
currTime=lambda: strftime("%c"),
litems=litems,
pagination=None if perpage < 1 else {
'page': round(start / perpage) + 1,
'totalPages': max(1, ceil(len(db['items']) / perpage)),
'perpage': perpage,
'first_page_url': '/?after=%i&perpage=%i' % (0, perpage),
'last_page_url': '/?after=%i&perpage=%i' % (len(db['items']) - len(db['items']) % perpage, perpage),
'prev_page_url': '/?after=%i&perpage=%i' % (max(0, start - perpage), perpage),
'next_page_url': '/?after=%i&perpage=%i' % (end + 1 if end + 1 < len(db['items']) else start, perpage)
},
timedelta2readable=lambda seconds: str(timedelta(seconds=seconds))
)
class FixedDropout(Dropout):
def _get_noise_shape(self, inputs):
if self.noise_shape is None:
return self.noise_shape
return tuple([
shape(inputs)[i] if sh is None else sh
for i, sh in enumerate(self.noise_shape)
])
customObjects = {'swish': nn.swish, 'FixedDropout': FixedDropout}
with open(join(dirname(realpath(__file__)), 'Settings.json'), 'r') as f:
data = loadJSON(f)
currentPath = join(str(data['CurrentPath']))
relativeData = join(str(data['RelativeData']))
classNames = list(array(data['ClassNames']).astype(str))
imageDimensions = list(array(data['ImageDimensions']).astype(int))
mode = str(data['Mode'])
checkpointsDir = join(currentPath, 'Checkpoints')
logDir = join(currentPath, 'Log')
if mode == 'classification':
def resize(imageDimensions):
return Sequential([
CenterPadToAspectRatio(float(imageDimensions[0]) /
float(imageDimensions[1]),
def getRet():
with open('database.json', 'r') as f:
return loadJSON(f)
def dictFromJSONFile(fName):
'''
Take a JSON file, return a dict with the information.
'''
with open(fName) as f:
return loadJSON(f)
def selectTeamFromList(self, teamNumber):
if not tba.isOnline():
return
# TODO: use team module instead
# do we? idk? this whole thing has gone to crap and i cant wait to nuke it
team = Team(teamNumber)
self.currentTeam = team
teamname = team.name
attendedEvents = "Attended events:\n"
for event, record in team.attendedEvents.items():
attendedEvents += "{}:{}".format(event, record)
attendedEvents += '\n'
for child in self.teamInfoFrame.winfo_children():
child.destroy()
header = Label(self.teamInfoFrame,
text="Team " + str(teamNumber) + " : " + teamname,
font=("Helvetica", 20, "bold"))
header.grid(row=0, column=0, columnspan=9999)
attendedEvents = Label(self.teamInfoFrame, text=attendedEvents)
attendedEvents.grid(row=1, column=0)
# setup team image
img = Image.open(team.image)
def onImageClick(event):
if system() == 'Darwin': # macOS
subCall(('open', team.image))
elif system() == 'Windows': # Windows
startfile(team.image)
else: # linux variants
subCall(('xdg-open', team.image))
# 231 x 231 is about the size of our defualt image, use that as our max size
img.thumbnail((231, 231), Image.ANTIALIAS)
self.photo = ImageTk.PhotoImage(img)
imgLabel = Label(self.teamInfoFrame, image=self.photo)
imgLabel.bind(
"<Button-1>", onImageClick
) # call onImageClick when mouse1 is pressed on the image
imgLabel.grid(row=1, column=1, padx=10, pady=10)
# display scouting data
scoutingDataFrame = Frame(self.teamInfoFrame)
scoutingDataFrame.grid(row=2, column=0, columnspan=2, sticky=NSEW)
# setup scroll bars and listbox to display the data
scrollx = Scrollbar(scoutingDataFrame, orient=HORIZONTAL)
scrolly = Scrollbar(scoutingDataFrame)
self.scoutingDataLB = Listbox(scoutingDataFrame,
xscrollcommand=scrollx.set,
yscrollcommand=scrolly.set)
self.scoutingDataLB.bind("<Double-Button-1>", self.graph)
scrollx.config(command=self.scoutingDataLB.xview)
scrolly.config(command=self.scoutingDataLB.yview)
scrollx.grid(row=1, column=0, sticky=EW)
scrolly.grid(row=0, column=1, sticky=NS)
self.scoutingDataLB.grid(row=0, column=0, sticky=NSEW)
scoutingDataFrame.columnconfigure(0, weight=1)
# setup seperate box for comments because massimo wants it
commentsScrollx = Scrollbar(scoutingDataFrame, orient=HORIZONTAL)
commentsScrolly = Scrollbar(scoutingDataFrame)
commentsLB = Listbox(scoutingDataFrame,
xscrollcommand=commentsScrollx.set,
yscrollcommand=commentsScrolly.set)
commentsScrollx.config(command=commentsLB.xview)
commentsScrolly.config(command=commentsLB.yview)
commentsScrollx.grid(row=3, column=0)
commentsScrolly.grid(row=2, column=1)
commentsLB.grid(row=2, column=0, sticky=NSEW)
commentsLB.insert(END, "Comments:")
# load template
with open("template.json", "r") as f:
template = loadJSON(f)
# loop though our scouting data, grab each question, and display it
# categorize our data
# vars for ints
totals = {}
increments = {}
# non-ints
nonInts = {
} # going to be like this {question0:[data from match 1, match 2, match 3], question1:[...
for key_, value_ in team.JSONdata.items():
for key, value in value_.items():
if key == "robot" or key == "match":
continue
if type(value) is int:
try:
totals[key] += value
increments[key] += 1
except KeyError:
totals[key] = value
increments[key] = 1
else:
if type(value) == bool:
value = "Yes" if value else "No"
try:
nonInts[key].append(value)
except KeyError:
nonInts[key] = [value]
for key, value in totals.items():
# find the question
question = ""
for i in template:
try:
if i['jsonLabel'] == key:
question = i['question']
except KeyError:
pass
# add this question to our data output
self.scoutingDataLB.insert(
END, question + " : " +
str(round((float(value) / increments[key]), 2)))
for key, value in nonInts.items():
# find the question
# this is pretty similar to the previous loop and could probably be combined but at this point idk
if key != "comments":
question = ""
for i in template:
try:
if i['jsonLabel'] == key:
question = i['question']
except KeyError:
pass
# add it to our box/output
self.scoutingDataLB.insert(END, question + ":")
for i in value:
if i.strip() != '':
if key == "comments":
commentsLB.insert(END, " " + i)
else:
self.scoutingDataLB.insert(END, " " + i)
raise ValueError('"info" must be either "fiscal", "tickets" or "update" in {0}.'.format(PLP_FILENAME))
if plp_json_data['info'] == 'fiscal':
if not 'operation' in plp_json_data:
raise IndexError('Missing "operation" field in plp file {0}.'.format(PLP_FILENAME))
if plp_json_data['operation'] not in ('sale', 'refund', 'startshift', 'endshift'):
raise ValueError('"operation" must be one of "sale", "refund", "startshift", "endshift" in {0}.'.format(PLP_FILENAME))
if plp_json_data['info'] == 'update':
if not 'version' in plp_json_data:
print('Note: Missing "version" field in plp file {0}.'.format(PLP_FILENAME))
# Detect plp file type.
# If valid JSON, read file type from 'info' field, otherwise assume it's for tickets
try:
with open(PLP_FILENAME, 'rU') as plp_data_file:
PLP_JSON_DATA = loadJSON(plp_data_file)
PLP_JSON_FILENAME = PLP_FILENAME
chmod(PLP_JSON_FILENAME, S_IWRITE)
except ValueError:
PLP_JSON_DATA = read_plp_file(PLP_FILENAME)
# Backward compatible
PLP_JSON_DATA['info'] = 'tickets'
PLP_JSON_FILENAME = '{0}.json'.format(PLP_FILENAME)
else:
pass
PLP_CONTENT_TYPE = PLP_JSON_DATA['info']
PLP_PRINTER_TYPE = (PLP_JSON_DATA['printerType'] if 'printerType' in PLP_JSON_DATA else PLP_CONTENT_TYPE)
if PLP_PRINTER_TYPE == 'fiscal':
with ioOpen(PLP_JSON_FILENAME, 'w', encoding='utf-8') as outfile:
from json import load as loadJSON
from os import path
from sys import argv
BASEDIR = path.realpath(path.dirname(argv[0]))
PACKAGE_FILE_NAME = path.join(BASEDIR, 'package.json')
with open(PACKAGE_FILE_NAME, 'r') as package_json_file:
VERSION = loadJSON(package_json_file)['version']
def updateSort(self, question, listBox, reversed=False):
listBox.delete(0, END)
data = {}
jsonLabel = ""
isRdo = False
with open('template.json') as t:
template = loadJSON(t)
for i in template:
if i["type"] == "header" or i["type"] == "space":
continue
if i['question'] == question:
isRdo = i['type'] == 'rdoBtn'
jsonLabel = i['jsonLabel']
for file in listdir(GUI.filedir):
if file.lower().endswith(".json"):
with open(GUI.filedir + '\\' + file) as f:
fileJson = loadJSON(f)
for i in fileJson:
try:
robot = i['robot']
toAdd = i[jsonLabel]
if isRdo:
if toAdd == '':
continue
toAdd = int(toAdd)
if robot in data:
data[robot].append(toAdd)
else:
data[robot] = [toAdd]
except KeyError: # that question isn't in this data
continue
# now we have to do some operations depending on what datatype we're working with
# get our datatype
datatype = type(data[list(data.keys())[0]][0])
newData = {}
if datatype == int:
# turn everything into averages
for team, value in data.items():
total = 0.0
for i in value:
total += i
total /= len(value)
newData[team] = total
elif datatype == bool:
# calculate our average (false = 0, true = 1, >=.5 is true)
for team, value in data.items():
total = 0.0
for i in value:
total += 1 if i else 0
total /= len(value)
newData[team] = total * 100
sortedData = sorted(newData.items(),
key=itemgetter(1),
reverse=reversed)
for i in sortedData:
listBox.insert(END, i)
async def getMeme(self, interaction: discord.Interaction):
meme = loadJSON(request('https://meme-api.herokuapp.com/gimme').text)
embed = discord.Embed(title=meme['title'])
embed.set_image(url=meme['url'])
await interaction.response.send_message(embed=embed)