I have a Story model that inherits from ndb.Model, with an IntegerProperty wordCount. I'm trying to query Story objects that have a specific word count range, but the query seems to return the same results, regardless of the filter properties.
For this code:
q = Story.query()
q.filter(Story.wordCount > 900)
for s in q.fetch(5):
print s.title / s.wordCount
I get this result:
If only ... / 884
Timed release / 953
Grandfather paradox / 822
Harnessing the brane-deer / 1618
Quantum erat demonstrandum / 908
Here's the story declaration:
class Story(ndb.Model):
title = ndb.StringProperty(required=True)
wordCount = ndb.IntegerProperty('wc')
I would expect to only get stories that have 900 words exactly--or none. Inequalities and sorting are also broken. I tried deploying to GAE, and I'm seeing the same broken results.
Any ideas on what would be causing this?
NDB queries are immutable, and when you call q.filter(Story.wordCount > 900) you're creating a new query, and not assigning it to anything. Re-assigning to your q variable should work for you:
q = Story.query()
q = q.filter(Story.wordCount > 900)
for s in q.fetch(5):
print s.title / s.wordCount
Related
I am trying to solve the following coupled pde's in fipy. I tried the following
eq1 = (DiffusionTerm(coeff=1, var=f)-f*DiffusionTerm(coeff=1, var=phi)
+f-f**3 == 0)
eq2 = (2*DiffusionTerm(coeff=f, var=phi)+f*DiffusionTerm(coeff=1, var=phi)
== 0)
eq = eq1 & eq2
eq.solve()
but it does not like "f*DiffusionTerm(coeff=1, var=phi)" and I get the error.
"TermMultiplyError: Must multiply terms by int or float." Is there a way that I can implement a cell variable times a diffusion term?
Neither of the following will work in FiPy,
from fipy import CellVariable, DiffusionTerm, Grid1D
mesh = Grid1D(nx=10)
var = CellVariable(mesh=mesh)
# eqn = var * DiffusionTerm(coeff=1)
eqn = ImplicitSourceTerm(coeff=DiffusionTerm(coeff=1))
eqn.solve(var)
They both simply don't make sense with respect to the discretization in the finite volume method. Regardless, you can use the following identity to rewrite the term of interest
Basically, instead of using
var * DiffusionTerm(coeff=1)
you can use
DiffusionTerm(coeff=var) - var.grad.mag**2
Giving a regular diffusion term and an extra explicit source term.
am currently working on a project wherein am supposed to model public acceptance on pricing schemes.
The independent variables being used for model:- Age, gender,income etc... which are categorical in nature, so I converted them into factored variables using as.factor() function.
Age Gender Income
0 1 2
0 0 0
0 0 1
I have certain other variables like Transit satisfaction, Environment improvement etc... which are ordered factors on scale of 1 to 5 . 1 being extremely dissatisfied and 5 being very satisfied.
My model is as follows :-
mdl = oglmx( prcing ~Ann_In1+Edu+Env_imp+rs_imp,data=cpdat, link = "logit", constantMEAN = F, constantSD = F, delta = 0, threshparam = NULL)
summary(mdl)
Estimate Std. error t value Pr(>|t|)
Ann_In11 0.1605540 0.3021613 0.5314 0.5951749
Ann_In12 -0.9556992 0.4218504 -2.2655 0.0234824 *
Edu1 0.0710699 0.2678081 0.2654 0.7907196
Edu2 1.0732587 0.7112519 1.5090 0.1313061
Env_imp.L -0.8524288 0.4899275 -1.7399 0.0818752 .
Env_imp.Q 0.0784353 0.3936332 0.1993 0.8420595
Env_imp.C 0.4589036 0.4498676 1.0201 0.3076878
Env_imp^4 -0.2219108 0.4423486 -0.5017 0.6159032
rd_sft.L 2.6335035 0.7362206 3.5771 0.0003475 ***
rd_sft.Q -0.7064391 0.5773880 -1.2235 0.2211377
rd_sft.C 0.0130127 0.4408486 0.0295 0.9764519
rd_sft^4 -0.2886550 0.3582014 -0.8058 0.4203318
I obtained the results as below. Am unable to interpret the results. Any leads in this can be very helpful.
In case of rd_sft (road safety ) as rd_sft.L (linear) is signiicant than other levels, can we neglect the other levels i.e Q,C,^4 in model formation ??
please through some light on model formulation and its intepretation as i am new to R.
Please help I was trying to call watson assistant endpoint
https://gateway.watsonplatform.net/assistant/api/v1/workspaces/myworkspace/logs?version=2018-09-20 to get all the list of events
and filter by date range using this params
var param =
{ workspace_id: '{myworkspace}',
page_limit: 100000,
filter: 'response_timestamp%3C2018-17-12,response_timestamp%3E2019-01-01'}
apparently I got any empty response below.
{
"logs": [],
"pagination": {}
}
Couple of things to check.
1. You have 2018-17-12 which is a metric date. This translates to "12th day of the 17th month of 2018".
2. Assuming the date should be a valid one, your search says "Documents that are Before 17th Dec 2018 and after 1st Jan 2019". Which would return no documents.
3. Logs are only generated when you call the message() method through the API. So check your logging page in the tooling to see if you even have logs.
4. If you have a lite account logs are only stored for 7 days and then deleted. To keep logs longer you need to upgrade to a standard account.
Although not directly related to your issue, be aware that page_limit has an upper hard coded limit (IIRC 200-300?). So you may ask for 100,000 records, but it won't give it to you.
This is sample python code (unsupported) that is using pagination to read the logs:
from watson_developer_cloud import AssistantV1
username = '...'
password = '...'
workspace_id = '....'
url = '...'
version = '2018-09-20'
c = AssistantV1(url=url, version=version, username=username, password=password)
totalpages = 999
pagelimit = 200
logs = []
page_count = 1
cursor = None
count = 0
x = { 'pagination': 'DUMMY' }
while x['pagination']:
if page_count > totalpages:
break
print('Reading page {}. '.format(page_count), end='')
x = c.list_logs(workspace_id=workspace_id,cursor=cursor,page_limit=pagelimit)
if x is None: break
print('Status: {}'.format(x.get_status_code()))
x = x.get_result()
logs.append(x['logs'])
count = count + len(x['logs'])
page_count = page_count + 1
if 'pagination' in x and 'next_url' in x['pagination']:
p = x['pagination']['next_url']
u = urlparse(p)
query = parse_qs(u.query)
cursor = query['cursor'][0]
Your logs object should contain the logs.
I believe the limit is 500, and then we return a pagination URL so you can get the next 500. I dont think this is the issue but once you start getting logs back its good to know
I have this table named BondData which contains the following:
Settlement Maturity Price Coupon
8/27/2016 1/12/2017 106.901 9.250
8/27/2019 1/27/2017 104.79 7.000
8/28/2016 3/30/2017 106.144 7.500
8/28/2016 4/27/2017 105.847 7.000
8/29/2016 9/4/2017 110.779 9.125
For each day in this table, I am about to perform a certain task which is to assign several values to a variable and perform necessary computations. The logic is like:
do while Settlement is the same
m_settle=current_row_settlement_value
m_maturity=current_row_maturity_value
and so on...
my_computation_here...
end
It's like I wanted to loop through my settlement dates and perform task for as long as the date is the same.
EDIT: Just to clarify my issue, I am implementing Yield Curve fitting using Nelson-Siegel and Svensson models.Here are my codes so far:
function NS_SV_Models()
load bondsdata
BondData=table(Settlement,Maturity,Price,Coupon);
BondData.Settlement = categorical(BondData.Settlement);
Settlements = categories(BondData.Settlement); % get all unique Settlement
for k = 1:numel(Settlements)
rows = BondData.Settlement==Settlements(k);
Bonds.Settle = Settlements(k); % current_row_settlement_value
Bonds.Maturity = BondData.Maturity(rows); % current_row_maturity_value
Bonds.Prices=BondData.Price(rows);
Bonds.Coupon=BondData.Coupon(rows);
Settle = Bonds.Settle;
Maturity = Bonds.Maturity;
CleanPrice = Bonds.Prices;
CouponRate = Bonds.Coupon;
Instruments = [Settle Maturity CleanPrice CouponRate];
Yield = bndyield(CleanPrice,CouponRate,Settle,Maturity);
NSModel = IRFunctionCurve.fitNelsonSiegel('Zero',Settlements(k),Instruments);
SVModel = IRFunctionCurve.fitSvensson('Zero',Settlements(k),Instruments);
NSModel.Parameters
SVModel.Parameters
end
end
Again, my main objective is to get each model's parameters (beta0, beta1, beta2, etc.) on a per day basis. I am getting an error in Instruments = [Settle Maturity CleanPrice CouponRate]; because Settle contains only one record (8/27/2016), it's suppose to have two since there are two rows for this date. Also, I noticed that Maturity, CleanPrice and CouponRate contains all records. They should only contain respective data for each day.
Hope I made my issue clearer now. By the way, I am using MATLAB R2015a.
Use categorical array. Here is your function (without its' headline, and all rows I can't run are commented):
BondData = table(datetime(Settlement),datetime(Maturity),Price,Coupon,...
'VariableNames',{'Settlement','Maturity','Price','Coupon'});
BondData.Settlement = categorical(BondData.Settlement);
Settlements = categories(BondData.Settlement); % get all unique Settlement
for k = 1:numel(Settlements)
rows = BondData.Settlement==Settlements(k);
Settle = BondData.Settlement(rows); % current_row_settlement_value
Mature = BondData.Maturity(rows); % current_row_maturity_value
CleanPrice = BondData.Price(rows);
CouponRate = BondData.Coupon(rows);
Instruments = [datenum(char(Settle)) datenum(char(Mature))...
CleanPrice CouponRate];
% Yield = bndyield(CleanPrice,CouponRate,Settle,Mature);
%
% NSModel = IRFunctionCurve.fitNelsonSiegel('Zero',Settlements(k),Instruments);
% SVModel = IRFunctionCurve.fitSvensson('Zero',Settlements(k),Instruments);
%
% NSModel.Parameters
% SVModel.Parameters
end
Keep in mind the following:
You cannot concat different types of variables as you try to do in: Instruments = [Settle Maturity CleanPrice CouponRate];
There is no need in the structure Bond, you don't use it (e.g. Settle = Bonds.Settle;).
Use the relevant functions to convert between a datetime object and string or numbers. For instance, in the code above: datenum(char(Settle)). I don't know what kind of input you need to pass to the following functions.
I perform an app engine query to get a cursor (wrec), and the code shows the number of records correctly by iterating. But then "for rec in wrec" does not run (no logging.info inside this loop).
There's also a GQL SELECT of the same table, with another cursor (wikiCursor) that jinja2 renders properly. Here's the part that doesn't work:
wrec = Wiki.all().ancestor(wiki_key()).filter('pagename >=', findPage).filter('pagename <', findPage + u'\ufffd').run()
foundRecs = sum(1 for _ in wrec)
logging.info("Class WikiPage: foundRecs is %s", foundRecs)
aFoundRecs = []
if foundRecs > 0:
for rec in wrec:
logging.info("Class WikiPage: value is %s", wrec.pagename)
aFoundRecs.append(rec.pagename)
self.render("permalink.html", userRec=self.userRec, wikipage=pagename,
wikiCursor=wikiCursor, wrec=wrec, foundRecs=foundRecs)
else:
errorText = "Could not find anything for your entry."
self.render("permalink.html", userRec=self.userRec, wikipage=pagename, wikiCursor=wikiCursor, error=errorText)
Here is a portion of the log, showing the first logging.info statement, but not the second:
INFO 2014-09-15 19:35:29,525 main.py:410] Class WikiPage: foundRecs is 3
INFO 2014-09-15 19:35:29,581 module.py:652] default: "POST / HTTP/1.1" 200 3058
Why is the wrec for loop not running?
When you use sum to count, the query already iterates until the end. That is expected behavior that if you try to iterate over it again, it won't work (because it already at the ends)