def friends_req_got(userData, friendshipData):
people = friendshipData.get_friendship_incoming_reqests(userData.Username)
msg ="Hello " + userData.Name +" \n" + "Please select users for accepting Freind Request"
title = "Friend req got"
choices = choice_maker(people)
user_resp = easygui.multchoicebox(msg, title, choices)
return user_resp
def other_users(userData, friendshipData):
people = friendshipData.get_other_users(userData.Username)
remove_current_user(people, userData.Username)
msg ="Hello " + userData.Name +" \n\n" + "Please select users for sending Freind Request"
title = "Other Users"
choices = choice_maker(people)
user_resp = easygui.multchoicebox(msg, title, choices)
return user_resp
def generate_easygui_deal(cardsinround):
areYouSure = False
while not areYouSure:
translated_choices = [translate_card_name(i) for i in all_cards_in_deck]
hand = easygui.multchoicebox(msg='Select ' + str(cardsinround) + ' cards', title='Dealing',
choices=translated_choices)
translated_hand = [translate_inv_card_name(i) for i in hand]
if len(translated_hand) == cardsinround:
areYouSure = True
return translated_hand
def join_on(headers, sql_msg):
""" Get 1 or more columns that contains the key to join on """
cols = eg.multchoicebox(msg='From the 1st file, which columns contain the common data fields between the two files?{0}'.format(sql_msg), choices=headers)
col_nums = []
for col in cols:
col_num = headers.index(col)
col_nums.append(col_num)
return col_nums
def getAndPrintFollowers(twitterHandle):
eg.msgbox("Please wait while all of your followers are loaded")
users = tweepy.Cursor(api.followers, screen_name=twitterHandle).items()
userList =[];
for user in users:
userList.append(user.screen_name)
question = "Who do you not want following you anymore? We will block and unblock them to remove the connection"
title = "Potential Unfollower"
choice = eg.multchoicebox(question, title, userList)
return choice
def processMiddleClick(self):
activeList = list()
nameList = list()
for drawable in self.drawList:
drawable.active = False
nameList.append(drawable.name)
import easygui
makeActiveList = easygui.multchoicebox(msg="Select which world objects you'd like to be visible",
title='View options:',
choices=(nameList))
for target in makeActiveList:
for drawable in self.drawList:
if target == drawable.name:
drawable.active = True
def gui_select_artifacts(self, task_name="", task_id=""):
"""
GUI: select the artifacts. (multiple)
@param task_name: the task name.
@param task_id: the task id.
@return: list of selections.
"""
try:
choices = self._get_latest_artifacts(task_id)
title = "Select Artifacts"
if task_name:
logger.info("Task Namespace: {}".format(task_name))
logger.info("Task ID: {}".format(task_id))
msg = textwrap.dedent(
"""\
Please select the artifacts you want to download.
- Task Name: [{}]
- Task ID: [{}]
* Tips: [↑][↓] Move, [Space] Select, [Enter] OK
"""
).format(task_name, task_id)
else:
logger.debug("Task ID: {}".format(task_id))
msg = textwrap.dedent(
"""\
Please select the artifacts you want to download.
- Task ID: [{}]
* Tips: [↑][↓] Move, [Space] Select, [Enter] OK, [Esc] Cancel
"""
).format(task_id)
return easygui.multchoicebox(msg, title, choices)
except Exception as e:
title = "Exception"
msg = textwrap.dedent(
"""\
Error: [{}] [{}]
Exception: {}
* Tips: [Enter] OK
"""
).format(task_name, task_id, e)
easygui.msgbox(msg, title)
return []
def app_gui():
main_button_choices = ("日常任务", "自动寻墨", "自动爬塔", "配置页面", "终止程序")
richang_button_choices = ("自动副本", "自动阵图", "自动璇玑", "自动分解")
#主界面
button_return = g.buttonbox('仅供学习使用，如造成损失，使用者责任自负！',
'蜀山传奇辅助脚本',
choices=("日常任务", "自动寻墨", "自动爬塔", "配置页面",
"终止程序"))
if button_return == main_button_choices[0]:
choice_return = g.multchoicebox('请选择自动打怪的类型，可多选',
'',
choices=("自动副本", "自动阵图", "自动璇玑",
"自动分解"))
elif button_return == main_button_choices[1]:
xunmo_run()
elif button_return == main_button_choices[3]:
config_gui()
def EngineDistrict():
value = g.enterbox(msg="爬虫引擎：", title='搜索')
text = value
#文本纠错
arg1 = "correct_query" #纠正后的text
arg2 = "correct_frag" #错误单词纠正
arg3 = "ori_frag" #原来错误片段
out = {}
try:
out = client.ecnet(text)
#print(client.ecnet(text))
if out["item"]["vec_fragment"]:
print(out["item"]["vec_fragment"][0][arg3])
print(out["item"]["vec_fragment"][0][arg2])
print(out["item"][arg1])
else:
print(out["item"][arg1])
request1 = out["item"][arg1]
#词法分析关键名词
out2 = client.lexerCustom(request1) #搜索关键词分词
fenci = ""
for i in out2["items"]:
fenci += i["item"] + "/"
print(i["item"])
start = time.time()
sousuo = getSearchByDesc(request1, content2http)
end = time.time()
index = g.indexbox(msg=fenci,
title="这是你的分词结果,共计用时" + str(end - start),
choices=["点击查看搜索结果", "不看了"])
if index == 0:
mult = g.multchoicebox(msg="以下为地址列表,共有" + str(len(sousuo)) + "条结果",
title="搜索结果",
choices=sousuo)
if mult != None:
for i in mult:
i_list = i.split(",")
#print(i_list[1][2:-1])
webbrowser.open(i_list[1][2:-1], new=0, autoraise=True)
SystemDistrict()
else:
SystemDistrict()
except BaseException:
sys.exit(0)
def main():
name = "LOLBind"
if sys.platform == "darwin":
Tk().destroy()
# Next we activate it
Popen(['osascript', '-e', 'tell application "Python" to activate'])
key_settings_path = "/Applications/League of Legends.app/Contents/LoL/Config/PersistedSettings.json"
elif sys.platform == "win32" or sys.platform == "win64":
key_settings_path = "C:\Riot Games\League of Legends\Config\PersistedSettings.json"
else:
easygui.msgbox("Unsupported operating system. LOLBind will now quit.", name)
sys.exit(0)
with open(key_settings_path, "r") as f:
jo = json.load(f)
ks = jo["files"][1]["sections"][0]["settings"]
i = 0
for k in ks:
if k["name"] == "evtChampMasteryDisplay":
cont = easygui.msgbox("Mastery Emote currently bound to %s.\nPress okay to continue." %
jo["files"][1]["sections"][0]["settings"][i]["value"], name)
if cont == "OK":
keys_to_bind = easygui.multchoicebox("Select keys you want to bind to Mastery Emote",
title=name, choices=["q", "w", "e", "r", "d", "f"])
mastery_key = "[Ctrl][6]"
if keys_to_bind is not None:
for key in keys_to_bind:
mastery_key = ("[%s]," % key) + mastery_key
jo["files"][1]["sections"][0]["settings"][i]["value"] = mastery_key
break
i += 1
if cont == "OK":
with open(key_settings_path, "w") as f:
json.dump(jo, f)
if easygui.msgbox("Sucessfully changed mastery keys to %s.\nTo change bound keys click okay." % mastery_key,
name) == "OK":
f.close()
main()
else:
sys.exit(0)
else:
sys.exit(0)
def choose_columns(headers1, headers2, sql_msg):
all_headers = map(lambda x: 'a.'+x, headers1) + map(lambda x: 'b.'+x, headers2)
columns_chosen = eg.multchoicebox(msg='Choose the columns you want to export{0}'.format(sql_msg), choices=all_headers)
sql_msg += ' '
sql_msg += ', '.join(columns_chosen)
indexes1 = []
indexes2 = []
for column in columns_chosen:
if column[0:1] == 'a':
indexes1.append(headers1.index(column[2:]))
elif column[0:1] == 'b':
indexes2.append(headers2.index(column[2:]))
else:
continue
return indexes1, indexes2, sql_msg
def createCfg(pathdir_mod, file_exts):
files = listFiles(pathdir_mod, file_exts)
cfg_files = eg.multchoicebox(msg='Pick as many items as you like.', title='Choose files', choices=files)
if cfg_files:
cfg_filename = input("Type a name for the CFG: ") + fileext_cfg
pathfile_cfg = os.path.join(pathdir_mod, 'MiniModManager', cfg_filename)
f = open(pathfile_cfg, 'w')
for line in cfg_files:
f.write(line + '\n')
f.close()
print("CFG created/edited.")
key = input('Do you want to use the just-created config? y/<n>: ').lower()
if key == 'y':
files_filter = readCfg(pathfile_cfg) # open cfg
renFiles(pathdir_mod, files, files_filter, 'X') # rename files with 'X' in the end
else:
print("No CFG was created/edited.")
def enter_borders(db, qry):
title = "Map Color"
state_set = set()
state_dict = dict()
for state in db:
# make a set of the state names
state_set.add(state.doc_id)
state_dict[state.doc_id] = state['name']
for state in db:
# make a set of the borders
border_set = set(state['borders'])
border_states_available = state_set.difference(border_set)
#print('borders available: ',border_states_available)
s_id = state.doc_id
s_name = state['name']
tlist = [
] # make a copy of the borders list, not an alias to the borders list
for b_id in border_states_available:
s_n = state_dict[b_id]
tlist.append(s_n)
tlist.remove(s_name) # don't enter a border with itself
msg = "Enter Borders for " + s_name
nlist = eg.multchoicebox(msg, title, tlist)
#print(nlist, 'nlist')
if nlist is not None:
dlist = []
for n in nlist:
s = db.get(qry.name == n)
dlist.append(s.doc_id)
# update the borders for this state
db.update({'borders': dlist}, doc_ids=[s_id])
# update the borders for this state
# update the states that this state borders
for d in dlist:
s = db.get(doc_id=d)
if s is not None:
border_set = set(s['borders'])
border_set.add(state.doc_id)
blist = list(border_set)
db.update({'borders': blist}, doc_ids=[d])
else:
print('state not found')
def chooseItems(foodOptionsList, newItem, carbsList, proteinsList, vegeList,
foodList):
if len(newItem) > 1:
newItem = newItem[0:1]
foodOptionsList = sorted(foodOptionsList)
if foodOptionsList[0] == "":
foodOptionsList = foodOptionsList[1:]
choices = eg.multchoicebox(msg="What do you want to eat?",
title="Choose Food",
choices=foodOptionsList)
newItem += choices
msg = "Are you sure you want:" + "\n"
for i in range(1, len(newItem)):
msg += "".join(newItem[i]) + ", "
msg = msg[:-2]
if eg.ccbox(msg):
score = calculate_grade(choices, carbsList, proteinsList, vegeList)
newItem.append(str(score))
# print newItem
if score == 1:
image = "resources/1fullapple.gif"
elif score == 2:
image = "resources/2fullapples.gif"
elif score == 3:
image = "resources/3fullapples.gif"
elif score == 4:
image = "resources/4fullapples.gif"
else:
image = "resources/5fullapples.gif"
reply = eg.buttonbox(image=image,
choices=[
"Oy Vey! I want to change that!",
"Great! I'll stick with that!"
])
if reply == "Oy Vey! I want to change that!":
chooseItems(foodOptionsList, newItem, carbsList, proteinsList,
vegeList, foodList)
else:
foodList.append(newItem)
welcomeWindow(proteinsList, vegeList, carbsList, foodOptionsList,
foodList)
else:
chooseItems(foodOptionsList, newItem, carbsList, proteinsList,
vegeList, foodList)
def get_data_choices():
options = ["Acceleration", "Gyro", "Temp"]
choices = gui.multchoicebox("Choose the data to be plotted.", "Sensors",
options)
if choices is None: #if there is no choice selected, stop running the program
return -1
if choices.count(
"Acceleration"
) == 1: #if Acceleration is checked parse the acceleration data
selections = get_axis_choices()
parse.plot_accel(selections)
if choices.count("Gyro") == 1: #if gyro is checked parse the gyro data
selections = get_axis_choices()
parse.plot_gyro(selections)
if choices.count("Temp") == 1: #if temp is checked parse the temp data
parse.plot_temp()
def exclude_players(df):
import pandas as pd
import numpy as np
import FF_scoring
import easygui as eg
players = pd.unique(df.Name.ravel()) #getting a list of teams for the week
question = "Select players to exclude"
title = "Select players"
listOfOptions = players
choice = eg.multchoicebox(question , title, listOfOptions)
print choice
cont = raw_input("Exclude these players (if none are showing, there are none selected)? (y/n)")
cont = str(cont)
if cont == 'y':
df = df[~df.Name.isin(choice)] #removing the teams
else:
df = FF_scoring.exclude_players(df)
return df
def spatially_combine_data(self, new_name,
lambdas,
column = None,
name_list=None):
""" Combine multiple spatial data sources with factors
Alphabetically order the weights!
Parameters
----------
new_name : str
Name for the new dataframe
lambdas : List or numpy.Array of floats
The weights
column : str (optional)
Which column to combine.
!! When None, it can cause errors when certain columns are
not float.
name_list : List
Which dataframes to combine. If None, use gui to select.
Returns
-------
new_df : pandas.DataFrame
The combined dataframe
"""
if name_list is None:
name_list = easygui.multchoicebox(msg="Pick the to combine dataframes",
choices=[item for item in self.data_dict])
name_list.sort()
if column is not None:
df_list = [self.data_dict[name_list[i]][column]*lambdas[i] for i in range(len(name_list))]
else:
df_list = [self.data_dict[name_list[i]]*lambdas[i] for i in range(len(name_list))]
new_df = sum(df_list)
self.data_dict[new_name] = new_df
return new_df
def remove_friends(self):
""" Removing Friends from Friends List """
img = "chat.gif"
msg = "Select Friends to Remove!"
un = self.username
val = json.loads(self.re.get(un))
# Users registered in DB
friends = eg.multchoicebox(msg, self.title, val[4], image=img)
if friends is not None:
fr = set(friends)
temp = set(val[4])
temp.difference_update(fr)
val[4] = list(temp)
val = json.dumps(val)
self.re.set(un, val)
self.re.bgsave()
return 1
else:
return 0
def spl_fit(band):
lc=np.loadtxt('lc2fit_'+band+'.dat', skiprows=8)
ph=lc[:,0]-tmax
plt.plot(ph, lc[:,1])
nsamp=100
a=eg.multchoicebox("choose from range:", "choices", ph)
t_arr=[]
cond=(ph>float(a[0])) & (ph<float(a[1]))
ph1=ph[cond]
print a
for k in range(nsamp):
real=np.random.normal(lc[:,1][cond], lc[:,2][cond])
ter=interp1d(ph[cond], real, kind='cubic')
l=np.linspace(ph1.min(), ph1.max())
kk=ter(l)
t=l[kk==min(kk)]
t_arr.append(t[0])
return np.array(t_arr)
def spl_fit(band):
lc = np.loadtxt('lc2fit_' + band + '.dat', skiprows=8)
ph = lc[:, 0] - tmax
plt.plot(ph, lc[:, 1])
nsamp = 100
a = eg.multchoicebox("choose from range:", "choices", ph)
t_arr = []
cond = (ph > float(a[0])) & (ph < float(a[1]))
ph1 = ph[cond]
print a
for k in range(nsamp):
real = np.random.normal(lc[:, 1][cond], lc[:, 2][cond])
ter = interp1d(ph[cond], real, kind='cubic')
l = np.linspace(ph1.min(), ph1.max())
kk = ter(l)
t = l[kk == min(kk)]
t_arr.append(t[0])
return np.array(t_arr)
def oldenter_borders(db, qry):
map = read_states_for_borders(db, qry)
title = "Map Color"
state_set = set()
state_dict = dict()
state_dict_name_id = dict()
state_dict_borders = dict()
for state in map:
# make a set of the state names to be used to build the
# borders to be checked against
state_set.add(state.id)
state_dict[state.id] = state.name
state_dict_name_id[state.name] = state.id
######## #this is to be used to update the borders list for selected states
#it puts co into nm when nm is selected as a border for co
#this does not work yet. The dictionary is not used yet.
state_dict_borders[state.name] = state.borders
for state in map:
# get available states to check for borders
border_states_available = state_set.difference(state.borders)
s_id = state.id
s_name = state.name
tlist = [] # make a list of border states available
for b_id in border_states_available:
s_n = state_dict[b_id]
tlist.append(s_n)
tlist.remove(s_name) # don't enter a border with itself
msg = "Enter Borders for " + s_name
nlist = eg.multchoicebox(msg, title, tlist)
if nlist is not None:
dlist = set()
for n in nlist:
"""create a set of borders for this state"""
s = state_dict_name_id[n]
dlist.add(s)
# update this states borders
state.borders = state.borders | dlist
# update the borders for this state
db.update({'borders': list(dlist)}, doc_ids=[s_id])
def RH_filter(self, name_list=None, inplace=True):
"""
"""
data_dict = self.data_dict
if name_list is None:
name_list = easygui.multchoicebox(
msg="Pick to filter dataframes",
choices=[item for item in self.data_dict])
for name in name_list:
data_dict[name].loc[data_dict[name]['RH'] < 1., 'RH'] = 0
new_df = data_dict[name] / 10.
if inplace:
self.data_dict[name] = new_df
else:
self.data_dict[name + 'F'] = new_df
def which_scores(df):
import pandas as pd
import numpy as np
import FF_scoring
import easygui as eg
scores = df.columns.values
players = pd.unique(df.Name.ravel()) #getting a list of teams for the week
question = "Select scores to include"
title = "Select scores"
listOfOptions = scores
choice = eg.multchoicebox(question , title, listOfOptions)
print choice
cont = raw_input("Include these scores (if none are showing, there are none selected)? (y/n)")
cont = str(cont)
if cont == 'y':
score_list = list(choice) #removing the teams
else:
df = FF_scoring.which_scores(df)
return score_list
def chat_window_gui(self):
""" Chat Window GUI """
img = "chat.gif"
msg = "Choose the Friends to talk!"
# No. of users registered in DB
# nou = self.re.dbsize()
# Friends added to profile
fr = json.loads(self.re.get(self.username))
if fr[4] == []:
friends = ['']
else:
friends = fr[4]
fr = eg.multchoicebox(msg, self.title, friends, image=img)
if fr == ['']:
eg.msgbox("Add Friends to Start Chatting!", title=self.title)
return 0
else:
self.friends = fr
return self.gui()
def search_url():
f = open('list.txt', 'r', encoding='utf-8')
txt = []
line = f.readline()
while line:
string = re.search('(.*?)https', line)
txt.insert(0, string.group(1) + '\n')
line = f.readline()
f.close()
choices = t.multchoicebox(msg='选择需要下载的话', title='', choices=txt)
if choices is None:
return
f = open('list.txt', 'r', encoding='utf-8')
lst = f.read()
names_urls = {}
for choice in choices:
string = re.search(choice.replace("\n", "") + '.*?(ht.*?html)', lst)
names_urls.update({choice: string.group(1)})
f.close()
return names_urls
def add_friends(self):
""" Adding Friends to Personal List """
img = "chat.gif"
msg = "Select People to add as your Friends!"
un = self.username
val = json.loads(self.re.get(un))
# Users registered in DB
self.users = self.re.keys('*')
self.users.remove(self.username)
friends = eg.multchoicebox(msg, self.title, self.users, image=img)
if friends is not None:
fr = list(set(friends))
val[4].extend(fr)
#print val[4]
val[4] = list(set(val[4]))
val = json.dumps(val)
self.re.set(un, val)
self.re.bgsave()
return 1
else:
return 0
def gui_select_artifacts(self, task_name='', task_id=''):
"""
GUI: select the artifacts. (multiple)
@param task_name: the task name.
@param task_id: the task id.
@return: list of selections.
"""
try:
choices = self._get_latest_artifacts(task_id)
title = 'Select Artifacts'
if task_name:
msg = textwrap.dedent('''\
Please select the artifacts you want to download.
- Task Name: [{}]
- Task ID: [{}]
* Tips: [↑][↓] Move, [Space] Select, [Enter] OK
''').format(task_name, task_id)
else:
msg = textwrap.dedent('''\
Please select the artifacts you want to download.
- Task ID: [{}]
* Tips: [↑][↓] Move, [Space] Select, [Enter] OK, [Esc] Cancel
''').format(task_id)
return easygui.multchoicebox(msg, title, choices)
except Exception as e:
title = 'Exception'
msg = textwrap.dedent('''\
Error: [{}] [{}]
Exception: {}
* Tips: [Enter] OK
''').format(task_name, task_id, e)
easygui.msgbox(msg, title)
return []
def change_values(self, data, og_data, subjects):
IDs_for_change = eg.multchoicebox(
msg='Select the subject(s) for a change: ',
title='Select...',
choices=subjects)
columns = data.columns.tolist()
new_value_change = True
while new_value_change == True:
values_to_change = eg.choicebox(
msg='The following values are available for change: ',
title='Select...',
choices=columns)
set_value = eg.enterbox(
msg=f'Enter a new value for {values_to_change}: ',
title='New value...')
for values in IDs_for_change:
try:
data.at[values, values_to_change] = set_value
data.at[values,
'acceptedNameUsage'] = '{0} {1} {2}'.format(
data.at[values, 'genus'],
data.at[values, 'specificEpithet'],
data.at[values, 'nameAcordingTo'])
og_data.at[values, values_to_change] = set_value
og_data.at[values,
'acceptedNameUsage'] = '{0} {1} {2}'.format(
data.at[values, 'genus'],
data.at[values, 'specificEpithet'],
data.at[values, 'nameAcordingTo'])
except:
print('The changes can not be made')
pass
new_value_change = eg.ynbox(
msg='Do you want to change another values in this subjects?',
title='Value change')
return data
def main():
#will check for all files from script directory onwards
print(os.getcwd(), ":") #get dir address for current file
files_dir = list()
for root, dirs, files in os.walk('.'):
for file in files:
if '.xlsx' in file:
files_dir.append(file)
print("\t", file)
print("")
#dialog box to choose file
msg = "Please choose file to consolidate or Press <Cancel> to exit."
title = "Directory List of .xlsx Files"
choice_list = {}
choice_list = multchoicebox(msg, title, files_dir)
if choice_list is None:
sys.exit("No file was selected..")
sheetname = 'Part 6 Price Schedule'
data_file = 'output.xlsx'
writer = pd.ExcelWriter(data_file)
for filename in choice_list:
unmerged_filename = unmerge_file(filename, sheetname)
df = clean_compile(unmerged_filename, sheetname)
os.remove(unmerged_filename)
df.to_excel(writer, sheet_name=filename.split('.')[0])
writer.save()
writer.close()
combine_lists(data_file)
print("Compilation complete!")
def basic_filter(self, zval=1.5, name_list=None, inplace=True):
"""Do a basic, general filter over the given data.
Method is the Standard score:
https://en.wikipedia.org/wiki/Standard_score
(also called z-score)
When single column, removes rows.
When multiple columns, turns into NaN.
Does not return anything, but saves it to object.
Parameters
----------
zval : float (optional)
Default The maximum z-score of items. Higher than this value will
be removed
names : list of str (optional)
The items you want to filter
inplace : bool (optional)
Default value: True. When True, replaces old data.
Else, saves with new name; with 'F' appended to old name.
"""
data_dict = self.data_dict
if name_list is None:
name_list = easygui.multchoicebox(
msg="Pick to filter dataframes",
choices=[item for item in self.data_dict])
for name in name_list:
if len(data_dict[name].columns) == 1:
#print("test")
column = 'Value'
#print(data_dict[name][column])
#zscore = data_dict[name][column]
#print(~np.isnan(data_dict[name][column]))
#z_score[~np.isnan(data_dict[name][column])] = zscore(
# data_dict[name][column][~np.isnan(data_dict[name][column])])
z_score = zscore(data_dict[name][column], nan_policy='omit')
#print(z_score)
z_filter = np.where(
np.abs(z_score) < zval) # or z_filter, _ = ???
z_remove = np.where(
np.abs(z_score) > zval) # or z_filter, _ = ???
df_remove = data_dict[name][column].iloc[z_remove]
new_df = data_dict[name].iloc[z_filter]
else:
columns = data_dict[name].columns.values
for column in columns:
z_score = zscore(data_dict[name][column])
z_remove = np.where(
np.abs(z_score) > zval) # or z_filter, _ = ???
data_dict[name][column].iloc[z_remove] = np.NaN
new_df = data_dict[name]
if inplace:
self.data_dict[name] = new_df
else:
self.data_dict[name + 'F'] = new_df
return df_remove
os.rmdir(pth)
print pth
except OSError:
pass
# Open a window to choose the location copying from
source_dir = eg.diropenbox(title='Choose Source Directory')
# allFileTypes is the list of all extensions in source_dir
# grabbed with function getFileExtList
allFileTypes = getFileExtList(source_dir)
# delete the blank file type
del allFileTypes[0]
# Open a window to choose the files to copy
copyList = eg.multchoicebox(msg='Select the file types to copy',
choices=(allFileTypes))
print "Extensions to copy: " + str(copyList)
# List comprehension adds the asterisk for glob
# while making an ignore list for copytree
ignoreList = ['*' + x for x in allFileTypes if x not in copyList]
# Open a window to choose the destination directory
root_dir = eg.diropenbox(title='Choose Destination Directory')
dest_dir = os.path.join(root_dir, os.path.basename(source_dir))
# copytree everything from the source_dir to the dest_dir ignoring
# all files types not chosen from the list
break
# Get the number of clusters in the chosen solution
num_clusters = easygui.multenterbox(msg = 'Which solution do you want to choose for electrode %i?' % electrode_num, fields = ['Number of clusters in the solution'])
num_clusters = int(num_clusters[0])
# Load data from the chosen electrode and solution
spike_waveforms = np.load('./spike_waveforms/electrode%i/spike_waveforms.npy' % electrode_num)
spike_times = np.load('./spike_times/electrode%i/spike_times.npy' % electrode_num)
pca_slices = np.load('./spike_waveforms/electrode%i/pca_waveforms.npy' % electrode_num)
energy = np.load('./spike_waveforms/electrode%i/energy.npy' % electrode_num)
amplitudes = np.load('./spike_waveforms/electrode%i/spike_amplitudes.npy' % electrode_num)
predictions = np.load('./clustering_results/electrode%i/clusters%i/predictions.npy' % (electrode_num, num_clusters))
# Get cluster choices from the chosen solution
clusters = easygui.multchoicebox(msg = 'Which clusters do you want to choose?', choices = tuple([str(i) for i in range(int(np.max(predictions) + 1))]))
# Check if the user wants to merge clusters if more than 1 cluster was chosen. Else ask if the user wants to split/re-cluster the chosen cluster
merge = False
re_cluster = False
if len(clusters) > 1:
merge = easygui.multchoicebox(msg = 'I want to merge these clusters into one unit (True = Yes, False = No)', choices = ('True', 'False'))
merge = ast.literal_eval(merge[0])
else:
re_cluster = easygui.multchoicebox(msg = 'I want to split this cluster (True = Yes, False = No)', choices = ('True', 'False'))
re_cluster = ast.literal_eval(re_cluster[0])
# If the user asked to split/re-cluster, ask them for the clustering parameters and perform clustering
split_predictions = []
chosen_split = 0
if re_cluster:
def _multchoicebox(msg='', choices=[], title=''):
return eg.multchoicebox(msg, title, choices)
num_clusters = easygui.multenterbox(
msg='Which solution do you want to choose for electrode %i?' %
electrode_num,
fields=['Number of clusters in the solution'])
num_clusters = int(num_clusters[0])
# Load data from the chosen electrode and solution
spike_waveforms = np.load('./spike_waveforms/electrode%i/spike_waveforms.npy' %
electrode_num)
predictions = np.load(
'./clustering_results/electrode%i/clusters%i/predictions.npy' %
(electrode_num, num_clusters))
# Get cluster choices from the chosen solution
clusters = easygui.multchoicebox(
msg='Which clusters do you want to choose?',
choices=tuple([str(i) for i in range(int(np.max(predictions) + 1))]))
for i in range(len(clusters)):
clusters[i] = int(clusters[i])
# Get the current plot data
plot_data = []
for cluster in clusters:
plot_data.append(np.where(predictions == cluster)[0])
plot_data = spike_waveforms[plot_data]
# Set up data
N = 0
x = np.arange(len(plot_data[N]) / 10)
y = spike_waveforms[0, ::10]
source = ColumnDataSource(data=dict(
hdf5_name = ''
for files in file_list:
if files[-2:] == 'h5':
hdf5_name = files
# Open the hdf5 file
hf5 = tables.open_file(hdf5_name, 'r+')
# Now ask the user to put in the identities of the digital inputs
trains_dig_in = hf5.list_nodes('/spike_trains')
identities = easygui.multenterbox(
msg='Put in the taste identities of the digital inputs',
fields=[train._v_name for train in trains_dig_in])
# Ask what taste to plot
plot_tastes = easygui.multchoicebox(msg='Which tastes do you want to plot? ',
choices=([taste for taste in identities]))
plot_tastes_dig_in = []
for taste in plot_tastes:
plot_tastes_dig_in.append(identities.index(taste))
# Ask the user for the pre stimulus duration used while making the spike arrays
pre_stim = easygui.multenterbox(
msg='What was the pre-stimulus duration pulled into the spike arrays?',
fields=['Pre stimulus (ms)'])
pre_stim = int(pre_stim[0])
# Get the psth paramaters from the user
params = easygui.multenterbox(msg='Enter the parameters for making the PSTHs',
fields=['Window size (ms)', 'Step size (ms)'])
for i in range(len(params)):
params[i] = int(params[i])
# coding:utf-8
# __author__ = 'Mark sinoberg'
# __date__ = '2016/5/25'
# __Desc__ = 一个多选的列表项.呵呵了，这个版本貌似有问题。我的多选并没有真正的实现
import easygui
msg = '选择此列表项中你喜欢的一个吧'
title = '必须选择一个哦'
choices = (1,2,3,4,5,6,7,8,9)
answer1 = easygui.multchoicebox(msg,title,choices)
for item in answer1:
print item
title="\t\tEscoger directorio", msg="\n" + "\tSeleccione el directorio con los archivos csv"
)
salida(directorio)
opciones = next(os.walk(directorio))[2]
salida(opciones)
# Seleccionamos solo las opciones .csv
xml = list()
for file in opciones:
if file[-4:] == ".xml":
xml.append(file)
archivos = e.multchoicebox(msg="Seleccione los archivos a transformar", title="Seleccion de archivos", choices=xml)
salida(archivos)
xlsx = list()
for file in opciones:
if file[-5:] == ".xlsx":
xlsx.append(file)
paraDict = e.choicebox(msg="Seleccionar libres", title="Seleccione el archivo de libres", choices=xlsx)
salida(paraDict)
wb = load_workbook((directorio + "%s" + paraDict) % "\\\\")
ws = wb.active
actual = 1
def setup():
msg = ("Welcome to the new employee notification tool.\n"
"The tool allows you to send customized automated emails "
"that contain information about all ActiveDirectory users "
"created within the last 24h.\n"
"You will be asked several questions and config filters. "
"Searches may take a few seconds to complete.\n"
)
msg2 = ("\nPlease create a new filter or load an existing filter "
"which you can then modify."
)
cfgs = os.listdir(cfgdir)
if len(cfgs)==0:
msg2 = ''
easygui.msgbox(msg,__app__)
pick='New Config'
else:
pick = easygui.buttonbox(msg+msg2, __app__, ['New Config','Load Existing',
'Cancel'])
if pick == 'Cancel':
return False
if pick != 'New Config':
config = easygui.choicebox("Select an existing config:",__app__,cfgs)
if not config:
return False
cfg = Settings(os.path.join(cfgdir,config))
cfg.restore
else:
config=''
while config == '':
config = easygui.enterbox('Saving this filter. Please enter a name for this notification.', __app__)
if not config:
sys.exit()
config=config.replace(' ','_')
if not config.endswith('.txt'):
config=config+'.txt'
cfg = Settings(os.path.join(cfgdir,config))
cfg.store() # persist the settings
msg = ("Please select employee information you would like to filter.\n"
"For example if you want to know about new staff in specific departments "
"please pick 'Departments'. If you want to know about new users "
"reporting to specific managers please pick 'Managers'. "
"If you'd like to check for new users in specific Divisions and optionally "
"filter by certain job titles please pick 'Divisions/Jobtitles'"
)
choi = [ "Departments", "Managers", "Divisions/Jobtitles" ]
choival = [ "department", "manager", "division" ]
pick = easygui.buttonbox(msg, __app__, choi)
if not pick:
return False
idx = choi.index(pick)
cfg.filter1=getUserAttrList (choival[idx])
cfg.filter1_attr=choival[idx]
cfg.filter1=easygui.multchoicebox(msg,"Select values from %s" % pick,cfg.filter1)
if cfg.filter1_attr == 'division':
alist=getUserAttrList ('title')
filter2_attr='title'
filter2=easygui.multchoicebox(msg,"Select values from 'Jobtitile'",alist)
else:
cfg.filter2_attr=''
cfg.filter2=''
msg = ("Please enter one or multiple email addresses (separated by comma) "
"to which you want to send a notification when new users arrive"
)
cfg.to = easygui.enterbox(msg, __app__,cfg.to)
msg = ("Please enter the email body you want to use for your notification "
"email. A list of new users will be added below this text."
)
cfg.mailbody = easygui.textbox(msg, __app__, cfg.mailbody, codebox=False)
msg = ("Optional: Please enter the command line of a script including full path "
"and command options. This script will be executed when new users arrive.\n"
"(Please leave this line empty to not execute anything.)"
)
cfg.run = easygui.enterbox(msg, __app__,cfg.run)
cfg.store() # persist the settings
testdate=''
out=''
while True:
if easygui.boolbox('Do you want to test this configuration (again) ?', __app__, ('Yes', 'No')):
msg = ("If you want to test this config, please enter the creation date "
"(format: YYYYMMDD) you would like to test."
)
testdate = easygui.enterbox(msg, __app__,testdate)
if not testdate:
break
if testdate != '':
exe=getMyFile()
try:
out=subprocess.check_output(
'"%s" --debug --search-date %s %s' % (exe,testdate,config),
stderr=subprocess.STDOUT,
shell=True)
except :
print "likely subproces CalledProcessError.output "
easygui.codebox('Please see debugging output below:', __app__, out)
else:
break
cmdline = '%s %s' % (getMyFile(),config)
easygui.codebox('Please activate this command via cron:' , __app__,cmdline)
# Now ask the user to put in the identities of the digital inputs
identities = easygui.multenterbox(msg = 'Put in the identities of the digital inputs (only integers)', fields = [train._v_name for train in trains_dig_in])
for i in range(len(identities)):
identities[i] = int(identities[i])
# Get the palatability/identity calculation paramaters from the user
params = easygui.multenterbox(msg = 'Enter the parameters for palatability/identity calculation', fields = ['Window size (ms)', 'Step size (ms)'])
for i in range(len(params)):
params[i] = int(params[i])
# Get the pre-stimulus time from the user
pre_stim = easygui.multenterbox(msg = 'Enter the pre-stimulus time for the spike trains', fields = ['Pre stim (ms)'])
pre_stim = int(pre_stim[0])
# Ask the user about the type of units they want to do the calculations on (single or all units)
unit_type = easygui.multchoicebox(msg = 'Which type of units do you want to use?', choices = ('All units', 'Single units', 'Multi units', 'Custom choice'))
all_units = np.arange(trains_dig_in[0].spike_array.shape[1])
single_units = np.array([i for i in range(len(all_units)) if hf5.root.unit_descriptor[i]["single_unit"] == 1])
multi_units = np.array([i for i in range(len(all_units)) if hf5.root.unit_descriptor[i]["single_unit"] == 0])
chosen_units = []
if unit_type[0] == 'All units':
chosen_units = all_units
elif unit_type[0] == 'Single units':
chosen_units = single_units
elif unit_type[0] == 'Multi units':
chosen_units = multi_units
else:
chosen_units = easygui.multchoicebox(msg = 'Which units do you want to choose?', choices = ([i for i in all_units]))
for i in range(len(chosen_units)):
chosen_units[i] = int(chosen_units[i])
chosen_units = np.array(chosen_units)
hf5 = tables.open_file(hdf5_name, 'r+')
# Get electrode number from user
electrode_num = easygui.multenterbox(msg = 'Which electrode do you want to choose? Hit cancel to exit', fields = ['Electrode #'])
electrode_num = int(electrode_num[0])
# Get the number of clusters in the chosen solution
num_clusters = easygui.multenterbox(msg = 'Which solution do you want to choose for electrode %i?' % electrode_num, fields = ['Number of clusters in the solution'])
num_clusters = int(num_clusters[0])
# Load data from the chosen electrode and solution
spike_waveforms = np.load('./spike_waveforms/electrode%i/spike_waveforms.npy' % electrode_num)
predictions = np.load('./clustering_results/electrode%i/clusters%i/predictions.npy' % (electrode_num, num_clusters))
# Get cluster choices from the chosen solution
clusters = easygui.multchoicebox(msg = 'Which clusters do you want to choose?', choices = tuple([str(i) for i in range(int(np.max(predictions) + 1))]))
for i in range(len(clusters)):
clusters[i] = int(clusters[i])
# Get the current plot data
plot_data = []
for cluster in clusters:
plot_data.append(np.where(predictions == cluster)[0])
plot_data = spike_waveforms[plot_data]
# Set up data
N = 0
x = np.arange(len(plot_data[N])/10)
y = spike_waveforms[0, ::10]
source = ColumnDataSource(data=dict(xs=[x for i in range(50)], ys = [plot_data[N + i, ::10] for i in range(50)]))
# Look for the hdf5 file in the directory
file_list = os.listdir('./')
hdf5_name = ''
for files in file_list:
if files[-2:] == 'h5':
hdf5_name = files
# Open the hdf5 file
hf5 = tables.open_file(hdf5_name, 'r+')
# Now ask the user to put in the identities of the digital inputs
trains_dig_in = hf5.list_nodes('/spike_trains')
identities = easygui.multenterbox(msg = 'Put in the taste identities of the digital inputs', fields = [train._v_name for train in trains_dig_in])
# Ask what taste to plot
plot_tastes = easygui.multchoicebox(msg = 'Which tastes do you want to plot? ', choices = ([taste for taste in identities]))
plot_tastes_dig_in = []
for taste in plot_tastes:
plot_tastes_dig_in.append(identities.index(taste))
# Ask the user for the pre stimulus duration used while making the spike arrays
pre_stim = easygui.multenterbox(msg = 'What was the pre-stimulus duration pulled into the spike arrays?', fields = ['Pre stimulus (ms)'])
pre_stim = int(pre_stim[0])
# Get the psth paramaters from the user
params = easygui.multenterbox(msg = 'Enter the parameters for making the PSTHs', fields = ['Window size (ms)', 'Step size (ms)'])
for i in range(len(params)):
params[i] = int(params[i])
# Ask the user about the type of units they want to do the calculations on (single or all units)
chosen_units = []
starts_stops=[]
for starts in starts_of_down[0]:
i=starts
while filtered_signal[i]<0 and i<len(filtered_signal):
i+=1
stops=i
starts_stops.append([starts,stops])
starts_stops=np.array(starts_stops)
return starts_stops
directory=r'C:\Users\colorbox\Documents\benca3stuff'
file_list_opts=read_and_parse(directory)
msg ="Files to explore?"
title = "File exploration system"
choices = file_list_opts[0][1:]
choice = eg.multchoicebox(msg, title, choices)
for file in choice:
fs=20000
highcut=1000
lowcut=6
input_array2=np.genfromtxt(os.path.join(directory,file),delimiter=',')
filtered_signal=butter_bandpass_filter(input_array2,lowcut,highcut,fs,order=2)
seconds=len(filtered_signal)/fs
bins=np.round(seconds/10)
file_vars=1
threshold=0
while file_vars:
msg = "Fill out opts cancel goes to next file"
title = "Slice and thresh"
fieldNames = ["Slice number max = "+str(bins),"thresh"]
file_vars = ['',threshold] # we start with blanks for the values
def main():
"""The main function that makes the dialogs work."""
global package
di = {}
box = easygui.multenterbox(
msg='Fill in as many fields as possible. The first 3 are required.',
title='Setup.py Creation',
fields=choices)
none_check(box)
for i in range(len(choices)):
if i != 1:
di[choices[i]] = box[i]
version_string = 'version = \'{}\''.format(box[1])
classifiers_chosen = easygui.multchoicebox(
msg='Pick the classifiers you want to use',
title='Classifiers',
choices=clist)
none_check(classifiers_chosen)
classifiers_list = classifiers_chosen
classifiers_str = str(classifiers_chosen)
classifiers_string = 'classifiers = ' + classifiers_str
install = easygui.textbox(
msg=
'Enter in packages along with their version info. Place one per line.',
title='Packages')
none_check(install)
# noinspection PyTypeChecker
packages = install.split('\n')
package_list = packages
package_str = str(packages)
package = 'requirements = ' + package_str
write = """from setuptools import setup, find_packages
packages = find_packages()
{}
{}
{}
setup(""".format(classifiers_string, package, version_string)
for key in di:
if not di[key]:
pass
else:
write += '{}=\'{}\','.format(key, di[key])
if key == 'name':
write += 'version = version,'
write += 'packages = packages'
if not (len(package_list) == 1 and package_list[0] == ''):
write += ', requirements = requirements'
if not (len(classifiers_list) == 1 and classifiers_list[0] == ''):
write += ', classifiers = classifiers'
write += ')'
if os.path.exists('setup.py'):
with open('setup.py', 'r') as file:
with open('setup.py.old', 'w') as file2:
file2.write(file.read())
with open('setup.py', 'w') as file:
file.write(write)
try:
with open('.gitignore', 'r') as file:
pass
with open('gitignore', 'a+') as file:
if 'setup.py.old' in file.read():
pass
else:
file.write('\n#Old setup.py files\nsetup.py.old')
except FileNotFoundError:
pass
cmds = [
'build',
'build_py',
'build_ext',
'build_clib',
'build_scripts',
'clean',
'sdist',
'check',
'bdist_wheel',
'bdist_egg',
]
build = easygui.multchoicebox(msg='Choose commands to run',
title='Running setup.py commands',
choices=cmds)
none_check(build)
setupscripts = 'python setup.py '
for entry in build:
setupscripts += entry + ' '
import subprocess
subprocess.run(setupscripts)
up = easygui.multpasswordbox(
'Enter username and password to upload tp PyPi.',
title='Username & Password',
fields=['username', 'password'])
none_check(up)
username, password = up
cmd2 = 'python -m twine upload -u {} -p {} dist\\*'.format(
username, password)
subprocess.run(cmd2)
return 0
def ShowOptions(option_list, title, header):
choice = eg.multchoicebox(header, title, option_list)
return choice
} #这个是在fiddler中查看到的
raw_postdata["keyboard"] = easygui.enterbox("输入要搜索的动漫，注意字不能错哦",
default="进击的巨人")
postdata = urllib.parse.urlencode(raw_postdata).encode('utf-8')
req = urllib.request.Request(url, postdata)
req.add_header(
"User-Agent",
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.119 Safari/537.36"
)
data = urllib.request.urlopen(req).read().decode()
# print(data) #搜索时post之后返回的数据
search_data = re.compile('<h3><a href="/anime.*</a></h3>').findall(data)
search_data_name = []
search_data_url = []
j = 0
for i in search_data:
search_data_url.append("http://www.silisili.me/anime" +
search_data[j].split("anime")[1].split('.html')[0] +
".html")
search_data_name.append(search_data[j].split('">')[1].split('</a><')[0])
j += 1
name_choice = easygui.multchoicebox(
msg="选择你需要的结果，注意只能选择一个哦", title='搜索结果',
choices=search_data_name) #名字显示并选择，注意这里只能选择一个
# print(name_choice) #搜索结果中名字的列表
choice_location = search_data_name.index(
name_choice[0]) #查询选择的结果在名字列表中处于第几个，然后在url列表中调用该参数
# print(choice_location)
url_needed_first = search_data_url[choice_location]
# print(url_needed_first) #最终需要的url
feedoptions = []
feedorder = []
for feed in sorted(feeds):
feedoptions.append(feed.ljust(42) + ": " + feed_descriptions[feed])
feedorder.append(feed)
answer = g.choicebox(
'\n'.join([
'Choose a feed you would like to download data from.',
'Note: you have to be a subscriber of that.'
]), windowtitle, feedoptions)
if answer is None:
exit(6)
feedno = feedoptions.index(answer)
args['feed'] = feedorder[feedno]
if len(formatmatrix[args['feed']]) > 2:
answer = g.multchoicebox('Choose the preferred data format(s)',
windowtitle, formatmatrix[args['feed']])
if answer is None:
exit(6)
args['dataformats'] = answer
else:
args['dataformats'] = formatmatrix[args['feed']]
g.msgbox(
"The feed supports a single data format: %s.\nData will be downloaded in this format."
% args['dataformats'][0])
# Start initializing feed
feeds = []
for dataformat in args['dataformats']:
the_feed = wdf.WhoisDataFeed()
the_feed.set_no_resume(args['no_resume'])
the_feed.set_feed_type(FEEDCONFIGDIR, args['feed'], dataformat)
# Neo structures files in segments - we mostly have only 1 segment in our Plexon recordings. Read that segment
seg = reader.read_segment()
# Get the event names in this recording (corresponds to digital inputs) - ask the user which ones to use to splice the spike trains into trials
check = easygui.ynbox(msg = 'Digital input channels: ' + str([event.annotations['channel_name'] for event in seg.events]) + '\n' + 'No. of trials: ' + str([event.times.shape for event in seg.events]), title = 'Check and confirm the number of trials detected on digital input channels')
# Go ahead only if the user approves by saying yes
if check:
pass
else:
print("Well, if you don't agree, blech_clust can't do much!")
sys.exit()
# Ask the user which digital input channels should be used for getting spike train data, and convert the channel numbers into integers for pulling spikes out
dig_in_pathname = [event.annotations['channel_name'] for event in seg.events]
dig_in_channels = easygui.multchoicebox(msg = 'Which digital input channels should be used to produce spike train and emg data (if it exists) trial-wise?', choices = ([event.annotations['channel_name'] for event in seg.events]))
dig_in_channel_nums = []
for i in range(len(dig_in_pathname)):
if dig_in_pathname[i] in dig_in_channels:
dig_in_channel_nums.append(i)
# Ask the user for the pre and post stimulus durations to be pulled out, and convert to integers
durations = easygui.multenterbox(msg = 'What are the signal durations pre and post stimulus that you want to pull out', fields = ['Pre stimulus (ms)', 'Post stimulus (ms)'])
for i in range(len(durations)):
durations[i] = int(durations[i])
# Delete the spike_trains node in the hdf5 file if it exists, and then create it
try:
hf5.remove_node('/spike_trains', recursive = True)
except:
pass
first_file_df = pd.read_csv(first_file, sep="\t")
except UnicodeDecodeError:
first_file_df = pd.read_csv(first_file, sep="\t", encoding='cp1252')
except:
eg.msgbox(msg="Sorry, we're having trouble parsing %s" % first_file, title=title)
second_file = eg.fileopenbox (msg="Select your second file", title=title,default="*.txt")
try:
second_file_df = pd.read_csv(second_file, sep="\t")
except UnicodeDecodeError:
second_file_df = pd.read_csv(second_file, sep="\t", encoding='cp1252')
except:
eg.msgbox(msg="Sorry, we're having trouble parsing %s" % second_file, title=title)
cols = first_file_df.columns
text_cols = eg.multchoicebox(msg="Select the text fields", title=title, choices=cols)
number_cols = eg.multchoicebox(msg="Select the number fields", title=title, choices=cols)
on = eg.multchoicebox(msg="Select the unique field(s)", title=title, choices=cols)
xref = on[0]
missing_in_first_df = second_file_df[~second_file_df[xref].isin(first_file_df[xref])]
missing_in_first = second_file_df[~second_file_df[xref].isin(first_file_df[xref])][xref]
missing_in_second_df = first_file_df[~first_file_df[xref].isin(second_file_df[xref])]
missing_in_second = first_file_df[~first_file_df[xref].isin(second_file_df[xref])][xref]
universe_diffs = pd.concat([missing_in_first, missing_in_second])
first_file_root = first_file.split('\\')[-1][:-4]
second_file_root = second_file.split('\\')[-1][:-4]
changes.append(on_times[-1]) # append the last trial which will be missed by this method
except:
pass # Continue without appending anything if this port wasn't on at all
change_points.append(changes)
# Show the user the number of trials on each digital input channel, and ask them to confirm
check = easygui.ynbox(msg = 'Digital input channels: ' + str(dig_in_pathname) + '\n' + 'No. of trials: ' + str([len(changes) for changes in change_points]), title = 'Check and confirm the number of trials detected on digital input channels')
# Go ahead only if the user approves by saying yes
if check:
pass
else:
print("Well, if you don't agree, blech_clust can't do much!")
sys.exit()
# Ask the user which digital input channels should be used for slicing out EMG arrays, and convert the channel numbers into integers for pulling stuff out of change_points
dig_in_channels = easygui.multchoicebox(msg = 'Which digital input channels should be used to slice out EMG data trial-wise?', choices = ([path for path in dig_in_pathname]))
dig_in_channel_nums = []
for i in range(len(dig_in_pathname)):
if dig_in_pathname[i] in dig_in_channels:
dig_in_channel_nums.append(i)
# Ask the user for the pre and post stimulus durations to be pulled out, and convert to integers
durations = easygui.multenterbox(msg = 'What are the signal durations pre and post stimulus that you want to pull out', fields = ['Pre stimulus (ms)', 'Post stimulus (ms)'])
for i in range(len(durations)):
durations[i] = int(durations[i])
# Grab the names of the arrays containing emg recordings
emg_nodes = hf5.list_nodes('/raw_emg')
emg_pathname = []
for node in emg_nodes:
emg_pathname.append(node._v_pathname)
import easygui as g
ls = g.multchoicebox(msg="请选择购买的商品",
title="",
choices=('1.matebook13s:2000', '2.无线耳机:150', '3.蓝牙鼠标:30',
'4.鼠标垫:10', '5.双肩包:80'))
print(ls)
g.msgbox(ls)
def main():
reply = eg.boolbox(msg='Hello, do you have a pre-computed classifier?', choices=('Yes', 'No'))
if reply == 1:
filename = eg.fileopenbox(msg='Please specificy cached classifier file ending in .clfr')
model = utils.load_classifiers(filename)
reply = eg.boolbox(msg='Now that we have specified our classifier, we must now specify data to be classified. Are you ready to proceed?', choices=('Yes', 'No'))
if reply == 0:
sys.exit()
filename = eg.fileopenbox(msg='Please specify data to be classified.')
D = utils.parse_data(filename)
outfilename = ''.join(filename.split('.')[:-1]) + '.lbls'
print 'Classifying data...'
with open(outfilename, 'w') as f:
for d in D:
f.write(str(model.classify_vector(d)))
f.write('\n')
print 'Done!'
else:
filename = eg.fileopenbox(msg='Please specify training data for your classifier')
D = utils.parse_data(filename)
reply = eg.boolbox(msg='Would you like to engage in manual or automatic construction of your classifier?', choices=('Manual', 'Automatic'))
#manual selection
if reply == 1:
algs = eg.multchoicebox(msg='Please choose at least one algorithm:', choices=('k-Nearest Neighbors', 'Support Vector Machine', 'Naive Bayes'))
alg_params = {alg : 'auto' for alg in algs}
#storage for set of classifiers
C = []
for alg in algs:
reply = eg.boolbox(msg='Would you like to engage in manual or automatic parameter tuning for your ' + alg + ' algorithm?', choices=('Manual', 'Automatic'))
if reply == 1:
if alg[0] == 'k':
params = eg.multenterbox(msg='Please select the following parameters for your ' + alg + ' algorithm:', fields=('k'), values=['1'])
print 'Building ' + alg + ' classifier...'
C.append(cl.kNN(D, k=int(params[0])))
print 'Done!\n'
if alg[0] == 'S':
reply = eg.boolbox(msg='What type of kernel would you like to use for your Support Vector Machine?', choices=('Radial Basis Function', 'Linear'))
if reply == 1:
params = eg.multenterbox(msg='Please select the following parameters for your RBF Support Vector Machine:', fields=('margin', 'gamma'), values=['1.0', '1.0'])
print 'Building ' + alg + ' classifier...'
C.append(cl.SVM(D, kernel_type='RBF', margin=float(params[0]), gamma=float(params[1])))
print 'Done!\n'
else:
params = eg.enterbox(msg='Please select the margin parameter for your Linear Support Vector Machine:', default='1.0')
print 'Building ' + alg + ' classifier...'
C.append(cl.SVM(D, kernel_type='Linear', margin=float(params[0])))
print 'Done!\n'
if alg[0] == 'N':
params = eg.enterbox(msg='Please select the binning threshold parameter for your Naive Bayes algorithm:', default='.01')
print 'Building ' + alg + ' classifier...'
C.append(cl.NB(D))
print 'Done!\n'
else:
if alg[0] == 'k':
print 'Building ' + alg + ' classifier...'
C.append(cl.kNN(D))
print 'Done!\n'
if alg[0] == 'S':
print 'Building ' + alg + ' classifier...'
C.append(cl.SVM(D))
print 'Done!\n'
if alg[0] == 'N':
print 'Building ' + alg + ' classifier...'
C.append(cl.NB(D))
print 'Done!\n'
model = mcl.AdaBoost(C)
#automatic selection
else:
print 'Constructing classifiers...'
model = mcl.AdaBoost([cl.kNN(D), cl.SVM(D), cl.NB(D)])
print 'Done!\n'
reply = eg.boolbox(msg='Now that we have specified our classifier, we must now specify data to be classified. Are you ready to proceed?', choices=('Yes', 'No'))
if reply == 0:
sys.exit()
filename = eg.fileopenbox(msg='Please specify data to be classified.')
D = utils.parse_data(filename)
outfilename = ''.join(filename.split('.')[:-1])
print 'Classifying data...'
with open(outfilename + '.lbls', 'w') as f:
for d in D:
f.write(str(model.classify_vector(d)))
f.write('\n')
print 'Done!'
#cache our classifier
utils.store_classifiers(outfilename + '.clfr', model)
#give user some information on classifiers used
open(outfilename + '.info', 'w').write(model.get_info())
def askCollectionSelection(collectionsList): #Ask for the collections to conduct operations on msg = "Select the collections to conduct operations on:" selColList = eg.multchoicebox(msg,"Collections",collectionsList) if selColList is None: quit() return selColList
import random
import easygui as g
import itchat as c
login=g.boolbox("欢迎使用狼人杀发牌助手\n继续进程需要登录您的微信网页版\n确认登录微信吗？")
if login==True:
c.login()
player=['filehelper']
playersname=['Andy周勉','Leo','Andy Liu','Esther','Jack','Hagrid周文哲','Tania','刘嘉鹏']
#allfriends=c.get_friends(update=True)[0:]
#playersname=[]
#for i in range(len(allfriends)):
#playersname.append(allfriends[1][Name])
msg="Choose the players"
title="Setting"
playerlist=g.multchoicebox(msg=msg,title=title,choices=playersname)
for i in range(len(playerlist)):
user = c.search_friends(name=playerlist[i])
uname = user[0]['UserName']
player.append(uname)
msg="设置各身份人数：(除上帝外共"+str(len(playerlist))+"人)"
title="设置"
fields=["设置村民数量：","设置女巫数量：","设置猎人数量：","设置预言家数量：","设置守卫数量：","设置白痴数量：","设置狼人数量：","设置白狼王数量"]
cardnumber=[0,0,0,0,0,0,0,0]
while sum(cardnumber)!=len(playerlist):
cardnumberp=g.multenterbox(msg=msg,title=title,fields=fields)
for i in range(len(cardnumberp)):
cardnumber[i]=int(cardnumberp[i])
msg+="\n输入身份数须与人数相符！"
villagernumber = cardnumber[0]
sorceressnumber = cardnumber[1]
hunternumber = cardnumber[2]
self.spots_diameter[i] = full_data[row][25]
self.spots_quality[i] = full_data[row][27]
DATABASE_DIR = 'O:\\protons\\Work in Progress\\Callum\\AccessDatabase\\AssetsDatabase_beCG.accdb'
pswrd = ''
conn = pypyodbc.connect('Driver={Microsoft Access Driver (*.mdb, *.accdb)};'
'DBQ=' + DATABASE_DIR + ';' + pswrd)
cursor = conn.cursor()
# Produce list of energies for multchoicebox - could be linked to Database?
energy_options = [x for x in list(range(70, 245, 5)) + [244]]
# Select acquired energies
energies = eg.multchoicebox(
'Select Energies', 'Please select the energies that '
'have spot pattern image files', energy_options)
energies = sorted([int(i) for i in energies], reverse=True)
print(f'Energies acquired: {energies}\n')
# Select Operator from list in Database
cursor.execute('select * from [Operators]')
operators = [row[2] for row in cursor.fetchall()]
operator = eg.choicebox('Who performed the measurements?', 'Operator',
operators)
if not operator:
eg.msgbox('Please re-run the code and select an Operator')
raise SystemExit
print(f'Operator = {operator}\n')
# Select Gantry from list in Database
def _multpasswordbox(msg='', title=''):
return eg.multchoicebox(msg, title)
def search_index():
#============================================================
# Define constants
#============================================================
mJup = 1.89813e30
AU = 1.49597871e13
#============================================================
# Read in the index file (data) as (to) a python table
#============================================================
path_to_index_file = '/Volumes/Data/Work/Research/BodenheimerCode/Code_for_Kozai/outputs/SIMULATION_INDEX.txt'
#index_data = atpy.Table(path_to_index_file,type='ascii',masked=True)
index_data = atpy.Table(path_to_index_file,type='ascii')
dates = pd.to_datetime(index_data.DateCreated)
#============================================================
# Get a list of all the parameter/column names available to sort on
#============================================================
param_list = index_data.columns.keys
kozai_indices = (0,47,48,49,50,51,52,54,55,56,57)
kozai_params = map(param_list.__getitem__,kozai_indices)
kozai_params.append('MinNumResults')
other_var_indices = []
for i in range(len(param_list)):
if i not in kozai_indices:
other_var_indices.append(i)
other_params = map(param_list.__getitem__,other_var_indices)
#============================================================
## Load the index data into a data frame (for database querying/searching later on)
#============================================================
full_data = pd.DataFrame(index_data[::],index=dates)
full_data = full_data.sort()
#============================================================
# Create the 'selected data' copy of the full data dataframe.
#============================================================
selected_data = full_data
#============================================================
# Open a GUI window that displays all the params available to sort on
#============================================================
sort_params1 = []
sort_params2=[]
sort_params1 = eg.multchoicebox(msg='Pick the KOZAI params to sort on',choices = kozai_params)
#sort_params2 = eg.multchoicebox(msg='Pick the other params to sort on',choices = other_params)
sort_params = sort_params1 + sort_params2
#============================================================
# Ask the user for min/max or 'contains string' criteria on all the selected fields
#============================================================
for item in range(len(sort_params)):
if sort_params[item] == 'Path':
# thing = raw_input('File path contains: ')
thing = eg.enterbox('File path contains: ')
selected_data = selected_data[selected_data.Path.str.contains(thing)]
elif sort_params[item] == 'DateCreated':
minDate = eg.enterbox(msg='Enter the earliest date (yyyy-mm-dd):')
maxDate = eg.enterbox(msg='Enter latest date (yyyy-mm-dd):',default=minDate)
selected_data = selected_data[minDate:maxDate]
print 'minDate: ',minDate,' maxDate: ',maxDate
elif sort_params[item] == 'Zmass':
foo = sort(selected_data.drop_duplicates('Zmass').Zmass.values)
min = eg.indexbox(msg = 'Minimum mass value, in units of Mjup',choices = foo/mJup)
max = min + eg.indexbox(msg = 'Maximum mass value, in units of Mjup',choices = foo[min::]/mJup)
min = foo[min]
max = foo[max]
selected_data = selected_data[(selected_data[sort_params[item]] <= max) & (selected_data[sort_params[item]] >= min)]
print min," <= Zmass <= ",max
elif sort_params[item] == 'SEMI':
foo = sort(selected_data.drop_duplicates('SEMI').SEMI.values)
min = eg.indexbox(msg='Minimum semi-major axis value, in AU',choices=foo/AU)
max = eg.indexbox(msg='Maximum semi-major axis value, in AU',choices=foo/AU)
min = foo[min]
max = foo[max]
selected_data = selected_data[(selected_data[sort_params[item]] <= max) & (selected_data[sort_params[item]] >= min)]
print min," <= SEMI <= ",max
elif sort_params[item] == 'MinNumResults':
min = eg.enterbox(msg='Enter the minimum number of results each simulation should have',default=1)
values = map(check_num_sims_in_dir,selected_data.Path.values, [1*int(min)]*int(len(selected_data)) )
selected_data = selected_data[values]
print 'Minimum number of converged models required: ',min
else:
foo = sort(selected_data.drop_duplicates(sort_params[item])[sort_params[item]].values)
min = eg.indexbox(msg='Minimum '+sort_params[item]+' value',choices=foo)
max = eg.indexbox(msg='Maximum '+sort_params[item]+' value',choices=foo)
min = foo[min]
max = foo[max]
selected_data = selected_data[(selected_data[sort_params[item]] <= max) & (selected_data[sort_params[item]] >= min)]
print min," <= ",sort_params[item]," <= ",max
result = []
# print '\n+++++++++++++++++++++++++++'
if len(selected_data) < 1:
print 'No simulations match your requirements'
print '+++++++++++++++++++++++++++\n'
else:
print str(len(selected_data))+ ' simulations meet these requirements'
# print 'Enter \'print result\' to see them!'
# print '+++++++++++++++++++++++++++'
# result = selected_data.Path.values
# result = result.tolist()
return selected_data
goodgame = g.buttonbox('which game do you think is the best one?','choose',('game1','game2','game3'))
if str(goodgame) == 'game1':
g.msgbox('you choose the game1 is the best one!')
if str(goodgame) == 'game2':
g.msgbox('you choose the game2 is the best one!')
if str(goodgame) == 'game3':
g.msgbox('you choose the game3 is the best one!')
index1 = g.indexbox('indexboxtest','indexibox',('yes','no'))
if index1 == 0:
g.msgbox('indexbox choose yes')
if index1 == 1:
g.msgbox('indexbox choose no')
a = g.multchoicebox('multchoicebox test','multchoicebox',['game1','game2','game3','game4'])
if a == None:
print(a)
a = ['meiyou']
print(type(a))
try:
b = list(g.multchoicebox('multchoicebox test','multchoicebox',a))
except:
g.exceptionbox()
if g.ccbox('continue the game?','please choose'):
pass
else:
sys.exit(0)
from moodleFunction import *
import urllib, urllib2, cookielib
from bs4 import BeautifulSoup
import easygui as eg
import os
import pickle
url="http://moodle.iitb.ac.in"
opener = moodleLogin()
courseList = getCourseList(opener,url)
try:
f = open('course','r')
choice = f.read().splitlines()
except IOError:
choice =eg.multchoicebox("Courses to Sync","MoodleSync",courseList.keys())
f = open('course','w')
f.write("\n".join(choice))
f.close()
cList ={}
for course in choice:
cList[course] = courseList[course]
for course in cList.items():
print(course[0])
dirName='MyCourses/'+ str(course[0])
openHistory(dirName)
if not os.path.exists(dirName):
def get_list_of_desired_messages(list_of_message_format_names):
question = 'Please select the data to convert.'
title = 'Convert .bin.log or .tlog to .csv.'
choice = eg.multchoicebox(question, title, list_of_message_format_names)
return choice
def __init__(self,height,width,lvl): #temp
print "Load game? " + str(consts.loadedGame)
if consts.loadedGame==False: ##new game ##change true to 'consts.loadedGame==False'
##(cell,angle,carnivore,eggwithdraw,food,foodWithdraw,ID,lifeTime,location,speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength=0):
self.screenheight=height
self.screenwidth=width
"""
self.myCell = classes.baseCell(None, angle, carnivor, eggwithdraw, p_food, foodWithdraw, 0, p_lifetime,
classes.Location(400, 400), speed, rad, lifewithdraw, timeToLay, AI,
vision, eggHatchTime, strength)
"""
if lvl==1:#easy
self.myCell = classes.baseCell(None, 0, 1, consts.framerate, 40, consts.framerate, 0, 40,
classes.Location(400, 400), 3, 10, consts.framerate, 15, 2,
200, 3, 10)
if lvl==2:#lil harder
self.myCell = classes.baseCell(None, 0, 0, consts.framerate, 40, consts.framerate, 0, 40,
classes.Location(400, 400), 2, 10, consts.framerate, 20, 2,
200, 3, 10)
if lvl==3:#normal
self.myCell = classes.baseCell(None, 0, 0, consts.framerate, 30, consts.framerate, 0, 40,
classes.Location(400, 400), 1, 10, consts.framerate, 20, 2,
200, 3, 5)
if lvl==4:#hard
self.myCell = classes.baseCell(None, 0, 0, consts.framerate, 20, consts.framerate, 0, 30,
classes.Location(400, 400), 1, 10, consts.framerate, 25, 2,
200, 3, 1)
angle = 4
lifewithdraw = consts.framerate
carnivore = 0
eggwithdraw = consts.framerate
food = 30
foodWithdraw = consts.framerate
rad = 10
AI = 2
vision = 200
lifeTime = 40
p_lifetime = lifeTime
p_food = food
speed = 1
timeToLay = 20
eggHatchTime = 3
strength = 5
# cell,angle,carnivore,eggwithdraw,foodleft,foodwithdraw,ID,Lifetime,location,speed,rad,lifewithdeaw,timetolay,AI,vision,eggHatchtime,strngth
#self.myCell=classes.baseCell(None,angle,1,eggwithdraw,p_food,foodWithdraw,0,p_lifetime,classes.Location(400,400),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength)
self.myCell.base90, self.myCell.base45 = pygame.image.load('src/IMG/HeadD.png'),pygame.image.load('src/IMG/HeadUL.png')
self.myCell.image=self.myCell.base90
self.cellList.append(classes.baseCell(None, angle, carnivore, eggwithdraw, food, foodWithdraw, 1, lifeTime,classes.Location(random.randint(0, self.screenwidth),random.randint(0, self.screenheight)), speed, rad,lifewithdraw, timeToLay, AI, vision, eggHatchTime, strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,2,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,3,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,4,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,5,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,6,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,7,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,8,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,9,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
self.cellList.append(classes.baseCell(None,angle,carnivore,eggwithdraw,food,foodWithdraw,10,lifeTime,classes.Location(random.randint(0,self.screenwidth),random.randint(0,self.screenheight)),speed,rad,lifewithdraw,timeToLay,AI,vision,eggHatchTime,strength))
##self.cellEggs[0].mixPlayerCells(self.myCell,self.myCell)
#self.cellEggs.append(classes.Egg((100,100),self.cellList[0],None,6,0))
self.putFood()
else: ##later code goes here
onlyfiles = [f for f in listdir("src/SAV") if isfile(join("src/SAV", f))]
choice=easygui.multchoicebox("Pick savefile:","",onlyfiles)
file_path = path.relpath("src/SAV/"+choice)
file = open(file_path,"r")
sp=file.read()
spp=base64.urlsafe_b64decode(sp).split('\n')
self.load(spp)
file.close()
self.myCell.base90, self.myCell.base45 = pygame.image.load('src/IMG/HeadD.png'),pygame.image.load('src/IMG/HeadUL.png')
self.myCell.image=self.myCell.base90
self.p2spaceLimiter=classes.spaceLimited(consts.screenwidth/2,0.05)
