On Thu, Nov 2, 2017 at 6:56 AM, <josef.p...@gmail.com> wrote: > On Thu, Nov 2, 2017 at 8:46 AM, <josef.p...@gmail.com> wrote: > >> On Wed, Nov 1, 2017 at 6:55 PM, Nathan Goldbaum <nathan12...@gmail.com> >> wrote: >> >>> I think the biggest issues could be resolved if __array_concatenate__ >>> were finished. Unfortunately I don't feel like I can take that on right now. >>> >>> See Ryan May's talk at scipy about using an ndarray subclass for units >>> and the issues he's run into: >>> >>> https://www.youtube.com/watch?v=qCo9bkT9sow >>> >> >> >> Interesting talk, but I don't see how general library code should know >> what units the output has. >> for example if units are some flows per unit of time and we average, sum >> or integrate over time, then what are the new units? (e.g. pandas time >> aggregation) >> What are units of covariance or correlation between two variables with >> the same units, and what are they between variables with different units? >> >> How do you concatenate and operate arrays with different units? >> >> interpolation or prediction would work with using the existing units. >> >> partially related: >> statsmodels uses a wrapper for pandas Series and DataFrames and tries to >> preserve the index when possible and make up a new DataFrame or Series if >> the existing index doesn't apply. >> E.g. predicted values and residuals are in terms of the original provided >> index, and could also get original units assigned. That would also be >> possible with prediction confidence intervals. But for the rest, see above. >> > > using pint > > >>> x > <Quantity([0 1 2 3 4], 'meter')> > >>> x / x > <Quantity([ nan 1. 1. 1. 1.], 'dimensionless')> > > >>> x / (1 + x) > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > File "C:\...\python-3.4.4.amd64\lib\site-packages\pint\quantity.py", > line 669, in __add__ > raise DimensionalityError(self._units, 'dimensionless') > return self._add_sub(other, operator.add) > File "C:\...\python-3.4.4.amd64\lib\site-packages\pint\quantity.py", > line 580, in _add_sub > pint.errors.DimensionalityError: Cannot convert from 'meter' to > 'dimensionless' >
I'm not sure why you have a problem with that results. You tried to take a number in meters and add a dimensionless value to that--that's not a defined operation. That's like saying: "I have a distance of 12 meters and added 1 to it." 1 what? 1 meter? Great. 1 centimeter? I need to convert, but I can do that operation. 1 second? That makes no sense. If you add units to the 1 then it's a defined operation: >>> reg = pint.UnitRegistry() >>> x / (1 * ureg.meters + x) <Quantity([ 0. 0.5 0.66666667 0.75 0.8 ], 'dimensionless')> > np.exp(x) > raises > pint.errors.DimensionalityError: Cannot convert from 'meter' ([length]) > to 'dimensionless' (dimensionless) > Well, the Taylor series for exp (around a=0) is: exp(x) = 1 + x + x**2 / 2 + x**3 / 6 + ... so for that to properly add up, x needs to be dimensionless. It should be noted, though, that I've *never* seen a formula, theoretically derived or empirically fit, require directly taking exp(x) where x is a physical quantity with units. Instead, you have: f = a * exp(kx) Properly calculated values for a, k will have appropriate units attached to them that allows the calculation to proceed without error Ryan -- Ryan May
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