always be very careful when mixing floating point types, but
should numpy prevent (or warn) the user from doing so in this case?
On Wed, Jul 2, 2014 at 10:07 AM, Mark Szepieniec wrote:
> Hi Catherine,
>
> I can't reproduce your issue with bins_list vs. bins_arange, but passing
&g
Hi Catherine,
I can't reproduce your issue with bins_list vs. bins_arange, but passing
both range and number of bins to np.histogram does give the same strange
behavior for me:
In [16]: data = np.array([ 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. ,
0. , 0.05, -0.05])
I
On Sun, Sep 22, 2013 at 1:24 PM, wrote:
> On Sat, Sep 21, 2013 at 1:55 PM, Jeremy Hetzel wrote:
> > I've added a trapezoidal distribution to numpy.random for consideration,
> > pull request 3770:
> > https://github.com/numpy/numpy/pull/3770
> >
> > Similar to the triangular distribution, the tra
