# MetPy¶

• Community toolkit for solving meteorology and atmospheric science problems
• Plotting
• File Formats
• Calculations

# Units?¶

• Handling of physical quantities (aka. "units") important in scientific applications

The MCO MIB has determined that the root cause for the loss of the MCO spacecraft was the failure to use metric units in the coding of a ground software file, “Small Forces,” used in trajectory models. Specifically, thruster performance data in English units instead of metric units was used in the software application code titled SM_FORCES (small forces).

• To avoid these problems, part of MetPy's core architecture is the use of a unit library, Pint
• Simplifies documentation
• Eliminates manual auditing of units in calculation functions
• Supports data files with arbitrary units (e.g. netCDF) more easily

# Pint?¶

• Pure Python unit-handling library
• Wraps Python scalars, lists, or NumPy arrays
• Supports units using offsets (i.e. temperature)
• Alternatives:
• astropy.units
• quantities
• cf_units
• Many others

## Example usage¶

In [2]:
import metpy.calc as mpcalc
from metpy.units import units

temperature = 75 * units.degF
relative_humidity = 70 * units.percent
mpcalc.dewpoint_rh(temperature, relative_humidity).to('degF')

Out[2]:
64.56301588685112 degF

## Without Pint¶

from scipy.constants import convert_temperature
temperature = convert_temperature(75, 'F', 'C')
relative_humidity = 0.7
convert_temperature(mpcalc.dewpoint_rh(temperature,
relative_humidity), 'C', 'F')


# The End¶

• Everything just works!
In [3]:
from metpy.constants import Rd
import numpy as np

t_diff = (np.random.randn(10) * units.degC -
np.random.randn(10) * units.degC)
np.trapz(t_diff)

Out[3]:
6.0560018454768532

# The End--of sanity¶

• Everything just works!
• The only thing worse than not using a unit library...
• ...is trying to use one with Python libraries expecting numpy arrays
• Everything I show broken here is also broken for astropy.units

# NumPy/SciPy¶

In [4]:
a = np.array([3.])
b = np.array([4.])
np.concatenate((a, b))

Out[4]:
array([ 3.,  4.])
• Now what about with units?
In [5]:
a = np.array([3.]) * units.m
b = np.array([4.]) * units.m
np.concatenate((a, b))

Out[5]:
array([ 3.,  4.])
• Units dropped because numpy creates a new array for storage
• No way for subclasses/array-like to declare how to make a new instance
• NumPy #4164

In [6]:
import numpy as np
a = np.arange(5)

Out[6]:
array([ 10.,  10.,  10.,  10.,  10.])

With units?

In [7]:
a = np.arange(5) * units.degC

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-6af9099bae25> in <module>()
1 a = np.arange(5) * units.degC
----> 2 np.gradient(a, 0.1 * units.m)

1692             if np.isscalar(distances):
1693                 continue
-> 1694             if len(distances) != f.shape[axes[i]]:
1695                 raise ValueError("distances must be either scalars or match "
1696                                  "the length of the corresponding dimension")

~/miniconda3/envs/py36/lib/python3.6/site-packages/pint/quantity.py in __len__(self)
1246
1247     def __len__(self):
-> 1248         return len(self._magnitude)
1249
1250     def __iter__(self):

TypeError: object of type 'float' has no len()
• np.isscalar() for a float with units attached returns False

## Interpolating: numpy.interp()¶

In [8]:
x = np.arange(5)
y = x
np.interp(1.5, x, y)

Out[8]:
1.5

With units?

In [9]:
x = np.arange(5) * units.m
y = x
np.interp(1.5 * units.m, x, y)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-03a96250bbae> in <module>()
1 x = np.arange(5) * units.m
2 y = x
----> 3 np.interp(1.5 * units.m, x, y)

~/miniconda3/envs/py36/lib/python3.6/site-packages/numpy/lib/function_base.py in interp(x, xp, fp, left, right, period)
2037             return interp_func([x], xp, fp, left, right).item()
2038         else:
-> 2039             return interp_func(x, xp, fp, left, right)
2040     else:
2041         if period == 0:

ValueError: object of too small depth for desired array
• There's no way the average user is going to figure that out
• Disappears into C code, so not sure where the problem lies...

## What about using scipy optimization?¶

• Fixed point iteration scipy.optimize.fixed_point
• Uses other calculations that need units
from scipy.optimize import fixed_point
def step(p, p0, w, t):
td = dewpoint(vapor_pressure(p, w))
return (p0 * (td / t) ** (1. / kappa))

fp = so.fixed_point(step, pressure,
args=(pressure, w, temperature))


Nope, that doesn't work...

from scipy.optimize import fixed_point
def step(p, p0, w, t):
td = dewpoint(vapor_pressure(units.Quantity(p, pressure.units), w))
return (p0 * (td / t) ** (1. / kappa)).magnitude

fp = so.fixed_point(step, pressure.magnitude,
args=(pressure.magnitude, w, temperature))

• Need to drop units when going into scipy code...
• ...and reattach before going into MetPy code.

### Mitigation¶

• Drop units as necessary when calling functions...
• ...and reattach correct units on the way out
• Essentially subverting all of the unit-handling
• Not so bad if we're hiding inside calculation functions

# Matplotlib¶

## Plot()¶

• Plotting with masked arrays in matplotlib
• Breaks up line nicely around missing points

First with units:

In [11]:
import matplotlib.pyplot as plt
temp_data = np.random.randn(50)
temp_units = temp_data * units.degC
t = np.linspace(0, 5, 50)
plt.plot(t, temp_units)

Out[11]:
[<matplotlib.lines.Line2D at 0x10f2f35f8>]
• Looks good

In [12]:
temp_masked = np.ma.masked_array(temp_data, mask=temp_data<0)

Out[12]:
[<matplotlib.lines.Line2D at 0x10f40df60>]
• Ok, that's fine

In [13]:
temp_units_masked = temp_masked * units.degC

Out[13]:
[<matplotlib.lines.Line2D at 0x10f501cc0>]

• I have no idea what's going on here
• The filling is *not* the original values

## axhline()/axvline()¶

• Horizontal/Vertical line spanning the entire axis
• Matplotlib has built-in support for unit conversions
• In theory, allows plotting data with one unit but request axes in another
• Let's see how this works
plt.axhline(0 * units.degC)

ValueError: Cannot compare Quantity and <class 'numpy.float64'>

• Problem is comparison between plotted value and plot limits
• Enabling Matplotlib's unit-conversion behavior doesn't help
• There is literally no code path through that allows the proper comparison with unit-ed values

## Mitigation¶

• Register with matplotlib's unit-handling...
• ...but use that to automatically drop units for every plotting function

# Xarray¶

• Xarray provides labeled axes for nd-grids
• Sort of works with units?
In [14]:
import xarray as xr
data = xr.DataArray([1, 2, 3], dims=('x',), coords={'x':[1, 2, 3]})
units.m * data

Out[14]:
array([1, 2, 3])Coordinates: * x (x) int64 1 2 3 meter
• Ok, but I'm not wild about meters being next to the coordinate values
• Putting units to the right of the data looks more reasonable (to me):
In [15]:
data * units.m

Out[15]:
<xarray.DataArray (x: 3)>
array([1, 2, 3])
Coordinates:
* x        (x) int64 1 2 3
• Oh come on.

• One way uses pint.Quantity.__add__
• The other xarray.DataArray.__add__
• I will not teach users that the order of multiplication matters

# The end of the story¶

• This is the current state of things
• There is no happy ending here--I don't have any implemented solutions
• Core problems:
• np.(as)array == "Please silently discard my units"
• Uses:
• Convert lists/floats to array
• Make sure something supports math and slicing
• Enforce compatibility with numpy C-API
• Things that should be considered arrays aren't
• Other array-managing libraries need to know about your need for units
• np.asanyarray only helps for subclasses

## Possible solutions¶

• NumPy's new __array_ufunc__ support
• Available in numpy 1.13
• Improves overriding behavior for ufuncs
• Not how this plays out for operand order problem (like xarray example)
• NumPy Abstract Base Class(es) (ABCs)
• Allow other array-like classes to declare they implement certain interface
• Create separate ABCs for portions of numpy interface
• numpy.asarray can perform isinstance check with (one) ABC
• Will require downstream libraries to migrate away from asarray for other ABCs
• Not sure how this plays with composition between array-like libraries
• Custom NumPy dtypes
• Modify NumPy C-level code to allow custom datatypes
• Encode units as part of datatype--then units don't drop
• Would probably solve all of the problems
• Most technically challenging

# Concluding¶

• The user experience when trying to substitute for numpy arrays with a unit-ed array is pretty frustrating
• There has to be a way to make this work better
• I'm open to suggestions (besides removing our unit support)
• I'm not here to rag on anyone's work, this comes from a place of love...
• ...and immense frustration