Can someone please indicate how to solve operator OR is not supported for types AlignmentFlag? Below is my Qt python function for reference:
def data(self, index, role):
if role == Qt.BackgroundRole:
row = index.row()
color = self.calculateColorForRow(row)
return QBrush(color)
if role == Qt.DisplayRole:
# See below for the nested-list data structure.
# .row() indexes into the outer list,
# .column() indexes into the sub-list
return self._data[index.row()][index.column()]
elif role == Qt.TextAlignmentRole:
if index.column() == 0:
return int(Qt.AlignLeft | Qt.AlignVCenter)
else:
return int(Qt.AlignHCenter | Qt.AlignVCenter)
Thank you.
Related
I want to return a dataframe from this function, which can be used elsewhere (for plotly graph to be exact).
My idea is to use the dataframe I can create with points_sum(), save it as the team name, and then use that dataframe in my px.line(dataframe = team_name).
In essence, I want to use the men_points_df variable after I created it.
def points_sum(team):
points = 0
men_points = []
for index, row in menscore_df.iterrows():
if row['hometeam'] == team:
if row['homegoals'] > row['awaygoals']:
points += 2
elif row['homegoals'] == row['awaygoals']:
points += 1
elif row['homegoals'] < row['awaygoals']:
points == points
date = str(row['date'])
men_points.append([date, points])
if row['awayteam'] == team:
if row['homegoals'] < row['awaygoals']:
points += 2
elif row['homegoals'] == row['awaygoals']:
points += 1
elif row['homegoals'] > row['awaygoals']:
points == points
date = str(row['date'])
men_points.append([date, points])
men_points_df = pd.DataFrame(men_points, columns = ["Date", 'Points'])
return men_points_df
In plotly, I am trying to use my new dataframe (men_points_df), like below, but I get the error undefined name, even though I can print it (for example: test = points_sum("FIF") (FIF is one of the team names) and it shows the correct dataframe in the console (when I type test):
elif pathname == "/page-3":
return [html.H1('Seasonal performance',
style={'textAlign':'center'}),
html.Div(
children=[
html.H2('Select team',style={'textAlign':'center'}),
html.Br(),
html.Br(),
dcc.Dropdown(
id='team_dd',
options=[{'label': v, 'value': k} for k,v in teams_all.items()],
)]),
dcc.Graph(id="performance_graph")
]
Output(component_id="performance_graph", component_property="figure"),
Input(component_id="team_dd", component_property="value")
def update_graph(option_selected):
title = "none selected"
if option_selected:
title = option_selected
line_fig = px.line(
test, # <------------ THIS IS THE ISSUE
title = f"{title}",
x = "Date", y = "Points")
return line_fig
Just call points_sum in the update_graph function, before you use test:
def update_graph(option_selected):
title = "none selected"
if option_selected:
title = option_selected
# vvv Here vvv
test = points_sum("FIF")
line_fig = px.line(
test, #THIS IS THE ISSUE
title = f"{title}",
x = "Date", y = "Points")
return line_fig
I am having problem validating the user input (I am asking user if they wish to continue with the program of calculating factorial). The code is as follows: (User input validation is towards the end of the main function and I have not included the factorial function)
def main():
valid_inp = False
usr_continue = True
while usr_continue:
while valid_inp == False:
usr_inp = int(input('Please ENTER a number: '))
if usr_inp < 0:
print('ERROR, INVALID INPUT')
else:
valid_inp = True
continue
result = factorial(usr_inp)
print(str(result) + '\n')
con_inp = str(input('Would you like to continue ? '))
if con_inp == 'Y' or con_inp == 'y':
usr_continue
elif con_inp == 'N' or con_inp == 'n':
print('Goodbye...')
break
main()
Make a function that only returns on valid input. Use an exception handler to deal with bad integer input, then validate the integer is the range you want:
from math import factorial
def get_nonnegative_integer(prompt):
while True:
try:
val = int(input(prompt)) # bad input for int such as "abc" will raise ValueError
if val >= 0: # good input will be range-checked
return val
else:
print('enter a number >= 0')
except ValueError:
print('invalid input for integer')
def main():
while True:
usr_inp = get_nonnegative_integer('Please enter a number: ')
result = factorial(usr_inp)
print(result)
con_inp = input('Would you like to continue(Y/n)? ').upper() # default Yes
if con_inp.startswith('N'):
print('Goodbye...')
break
main()
I am a new Pyomo/Python user. Now I need to formulate one set of constraints with index 'n', where all of the 3 components are with different indices but correlate with index 'n'. I am just curious that how I can map the relationship between these sets.
In my case, I read csv files in which their indices are related to 'n' to generate my set. For example: a1.n1, a2.n3, a3.n5 /// b1.n2, b2.n4, b3.n6, b4.n7 /// c1.n1, c2.n2, c3.n4, c4.n6 ///. The constraint expression of index n1 and n2 is the follows for example:
for n1: P(a1.n1) + L(c1.n1) == D(n1)
for n2: - F(b1.n2) + L(c2.n2) == D(n2)
Now let's go the coding. The set creating codes are as follow, they are within a class:
import pyomo
import pandas
import pyomo.opt
import pyomo.environ as pe
class MyModel:
def __init__(self, Afile, Bfile, Cfile):
self.A_data = pandas.read_csv(Afile)
self.A_data.set_index(['a'], inplace = True)
self.A_data.sort_index(inplace = True)
self.A_set = self.A_data.index.unique()
... ...
Then I tried to map the relationship in the constraint construction like follows:
def createModel(self):
self.m = pe.ConcreteModel()
self.m.A_set = pe.Set( initialize = self.A_set )
def obj_rule(m):
return ...
self.m.OBJ = pe.Objective(rule = obj_rule, sense = pe.minimize)
def constr(m, n)
As = self.A_data.reset_index()
Amap = As[ As['n'] == n ]['a']
Bs = self.B_data.reset_index()
Bmap = Bs[ Bs['n'] == n ]['b']
Cs = self.C_data.reset_index()
Cmap = Cs[ Cs['n'] == n ]['c']
return sum(m.P[(p,n)] for p in Amap) - sum(m.F[(s,n)] for s in Bmap) + sum(m.L[(r,n)] for r in Cmap) == self.D_data.ix[n, 'D']
self.m.cons = pe.Constraint(self.m.D_set, rule = constr)
def solve(self):
... ...
Finally, the error raises when I run this:
KeyError: "Index '(1, 1)' is not valid for indexed component 'P'"
I know it is the wrong way, so I am wondering if there is a good way to map their relationships. Thanks in advance!
Gabriel
I just forgot to post my answer to my own question when I solved this one week ago. The key thing towards this problem is setting up a map index.
Let me just modify the code in the question. Firstly, we need to modify the dataframe to include the information of the mapped indices. Then, the set for the mapped index can be constructed, taking 2 mapped indices as example:
self.m.A_set = pe.Set( initialize = self.A_set, dimen = 2 )
The names of the two mapped indices are 'alpha' and 'beta' respectively. Then the constraint can be formulated, based on the variables declared at the beginning:
def constr(m, n)
Amap = self.A_data[ self.A_data['alpha'] == n ]['beta']
Bmap = self.B_data[ self.B_data['alpha'] == n ]['beta']
return sum(m.P[(i,n)] for i in Amap) + sum(m.L[(r,n)] for r in Bmap) == D.loc[n, 'D']
m.TravelingBal = pe.Constraint(m.A_set, rule = constr)
The summation groups all associated B to A with a mapped index set.
I have a dataframe (maple) that, amongst others, has the columns 'THM', which is filled with float64 and 'Season_index', which is filled with int64. The 'THM' column has some missing values, and I want to fill them using the following function:
def fill_thm(cols):
THM = cols[0]
Season_index = cols[1]
if pd.isnull[THM]:
if Season_index == 1:
return 10
elif Season_index == 2:
return 20
elif Season_index == 3:
return 30
else:
return 40
else:
return THM
Then, to apply the function I used
maple['THM']= maple[['THM','Season_index']].apply(fill_thm,axis=1)
But I am getting the ("'function' object is not subscriptable", 'occurred at index 0') error. Anyone has any idea why? Thanks!
Try this:
def fill_thm(THM, S_i):
if pd.isnull[THM]:
if S_i == 1:
return 10
elif S_i == 2:
return 20
elif S_i == 3:
return 30
else:
return 40
else:
return THM
And apply with:
maple.loc[:,'THM'] = maple[['THM','Season_index']].apply(lambda row: pd.Series((fill_thm(row['THM'], row['Season_index']))), axis=1)
Try this code:
def fill(cols):
Age = cols[0]
Pclass=cols[1]
if pd.isnull['Age']:
if Pclass==1:
return 37
elif Pclass==2:
return 30
else:
return 28
else:
return Age
train[:,'Age'] = train[['Age','Pclass']].apply(fill,axis=1)
first of all, when you use apply on a specific column, you need not to specify axis=1.
second, if you are using pandas 0.22, just upgrade to 0.24. It solves all the issues with apply on Dataframes.
In Grinder we'd like to add customized statistics
grinder.statistics.registerSummaryExpression("connTimeout", "userLong0")
grinder.statistics.forCurrentTest.addLong("userLong0", 1)
And it seems to be successful as we can get the customized field in Grinder out file
The problem is that the value of that statistics is always 0
Here is the complete script implemented by Jython
# -*- coding: utf-8 -*-
from net.grinder.script.Grinder import grinder
from net.grinder.script import Test
from com.netease.cloud.ndir.performance import Query
from com.netease.cloud.ndir.performance import QueryReturnCode
def writeToFile(text):
filename = "response.log"
file = open(filename, "a")
file.write(str(text) + "\n")
file.close()
ndir_client = grinder.getProperties().getProperty("ndirClient")
query_file = grinder.getProperties().getProperty("queryFile")
request = Query("grinder.properties", query_file)
grinder.statistics.registerSummaryExpression("connTimeout", "userLong0")
grinder.statistics.registerSummaryExpression("readTimeout", "userLong1")
grinder.statistics.registerSummaryExpression("code!=200", "userLong2")
grinder.statistics.registerSummaryExpression("docs=[]", "userLong3")
grinder.statistics.registerSummaryExpression("unknown", "userLong4")
class TestRunner:
def __init__(self):
grinder.statistics.delayReports=True
def initialSleep(self):
sleepTime = grinder.threadNumber * 20 # per thread
grinder.sleep(sleepTime, 0)
def query(self):
if ndir_client == "true":
query = request.randomQueryByNdirClient
else:
query = request.randomQueryByHttpGet
try:
result = query()
except:
writeToFile("exception")
grinder.statistics.forCurrentTest.addLong("userLong4", 1)
grinder.getStatistics().getForCurrentTest().setSuccess(False)
return
if result == 0:
grinder.getStatistics().getForCurrentTest().setSuccess(True)
return
elif result == 1:
grinder.statistics.forCurrentTest.addLong("userLong0", 1)
grinder.getStatistics().getForCurrentTest().setSuccess(False)
return
elif result == 2:
grinder.statistics.forCurrentTest.addLong("userLong1", 1)
grinder.getStatistics().getForCurrentTest().setSuccess(False)
return
elif result == 3:
grinder.statistics.forCurrentTest.addLong("userLong2", 1)
grinder.getStatistics().getForCurrentTest().setSuccess(False)
return
elif result == 4:
grinder.statistics.forCurrentTest.addLong("userLong3", 1)
grinder.getStatistics().getForCurrentTest().setSuccess(True)
return
else:
grinder.statistics.forCurrentTest.addLong("userLong4", 1)
grinder.getStatistics().getForCurrentTest().setSuccess(False)
return
request = Test(120, 'query').wrap(query)
def __call__(self):
if grinder.runNumber == 0:
self.initialSleep()
self.request(self)
I suspect the problem is that you are marking tests as failed, but expecting the statistics to appear in the summary. Only successful tests are accumulated into the summary statistics.
Try registering data log expressions as well
grinder.statistics.registerDataLogExpression("connTimeout", "userLong0")
grinder.statistics.registerDataLogExpression("readTimeout", "userLong1")
grinder.statistics.registerDataLogExpression("code!=200", "userLong2")
grinder.statistics.registerDataLogExpression("docs=[]", "userLong3")
grinder.statistics.registerDataLogExpression("unknown", "userLong4")
Then you'll at least see the values in the data log file of the worker process.