#grabs the player's discord id
player_name = player_data[target_member.id][0]
#sends the id to the champion function, which will return the champion name
champ_name = Get_Champion(player_name)
#sets the nickname
await target_member.edit(nick=champ_name)
player_data[target_member.id][2] = int(time.time())
print('timer passed')
run_timer_thread(target_member)
#'handles' the error
except KeyError:
pass
print('timer exceded')
#now that the rest of the code is out of the way, it's time to start the bot
client.run(token)
#handles anything that might interrupt the code
#it will back up player_data to its storage file before closing
except:
data = open('data.py', 'w')
data.truncate()
data.write("player_data={}".format(player_data))
data.close()
def __del__(self):
"""
Tanca la comunicació.
"""
data.close()
def event(user_id, event_name):
sql = "INSERT INTO events (user_id, timestamp, event_value_id) VALUES (%s, %s, %s)"
cursor = get_cursor()
cursor.execute(sql, (user_id, time.time(), event_value_ids[event_name]))
commit()
close()
def fit_ESR(self, name, datapath = '', fit_data = True, save = True, f_dip = 2.828E9):
if datapath == '':
datapath = os.path.join(r'D:\measuring\data',
self.get_latest_data(name))
else:
datapath = datapath
###########################################
######## MEASUREMENT SPECS ################
###########################################
files = os.listdir(datapath)
for k in files:
if (name in k) and ('.npz' in k):
data_file = k
data = np.load(os.path.join(datapath,data_file))
mw_freq = data['freq']
counts = data['counts']
data.close()
f_dip_guess = f_dip
offset_guess = counts.max()
dip_depth_guess = offset_guess - counts.min()
width_guess = 5e-3
if fit_data:
fit_result=fit.fit1d(mw_freq/1E9, counts, common.fit_gauss,
offset_guess, dip_depth_guess, f_dip_guess/1E9,width_guess,
do_plot = False, do_print = False, newfig = False,ret=True)
x0 = fit_result['params_dict']['x0']
a = fit_result['params_dict']['a']
A = fit_result['params_dict']['A']
sigma = fit_result['params_dict']['sigma']
#s0 = fit_result[0]['params_dict']['s0']
x = np.linspace(mw_freq.min(), mw_freq.max(), 501)
fit_curve = np.zeros(len(x))
fit_curve = np.exp(-(((x/1E9)-x0)/sigma)**2)
fit_curve = a*np.ones(len(x)) + A*fit_curve
plot1 = qt.Plot2D(mw_freq/1E9, counts, '-ok', x/1E9, fit_curve, '-r',name='ESR',clear=True)
plot1.set_xlabel('MW frequency (GHz)')
plot1.set_ylabel('Integrated counts')
plot1.set_plottitle('MW frequency sweep')
if save:
plot1.save_png(datapath+'\\histogram_integrated.png')
data.close()
#plot1.clear()
#plot1.quit()
if save:
#Save a dat file for use in e.g. Origin with the dark esr data.
curr_date = '#'+time.ctime()+'\n'
col_names = '#Col0: MW freq (GHz)\tCol1: Integrated counts\n'
col_vals = str()
for k in range(len(counts)):
col_vals += self.num2str(mw_freq[k]/1E9,10)+'\t'+self.num2str(counts[k],0)+'\n'
fo = open(datapath+'\\mw_f_calibration_integrated_histogram.dat', "w")
for item in [curr_date, col_names, col_vals]:
fo.writelines(item)
fo.close()
return x0*1E9