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Merge pull request #465 from jhamman/update/month_mean_example
update monthly-means.rst using example data from xray-data repo
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‎doc/examples/monthly-means.rst

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Calculating Seasonal Averages from Timeseries of Monthly Means
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==============================================================
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Author: `Joe Hamman <http://www.hydro.washington.edu/~jhamman/>`_
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Author: `Joe Hamman <http://uw-hydro.github.io/current_member/joe_hamman/>`_
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The data for this example can be found in the `xray-data <https://github.com/xray/xray-data>`_ repository. This example is also available in an IPython Notebook that is available `here <https://github.com/xray/xray/tree/master/examples/xray_seasonal_means.ipynb>`_.
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Suppose we have a netCDF or xray Dataset of monthly mean data and we
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want to calculate the seasonal average. To do this properly, we need to
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.. code:: python
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%matplotlib inline
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import numpy as np
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import pandas as pd
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import xray
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.. parsed-literal::
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numpy version : 1.9.1
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pandas version : 0.15.2
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xray version : 0.4rc1-20-g52bbca3
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numpy version : 1.9.2
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pandas version : 0.16.2
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xray version : 0.5.1
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Some calendar information so we can support any netCDF calendar.
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'all_leap': [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
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'366_day': [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
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'360_day': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30]}
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A few calendar functions to determine the number of days in each month
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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if leap_year(year, calendar=calendar):
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month_length[i] += 1
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return month_length
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Open the ``Dataset``
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^^^^^^^^^^^^^^^^^^^^
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.. code:: python
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monthly_mean_file = '/raid2/jhamman/projects/RASM/data/processed/R1002RBRxaaa01a/lnd/monthly_mean_timeseries/R1002RBRxaaa01a.vic.hmm.197909-201212.nc'
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monthly_mean_file = 'RASM_example_data.nc'
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ds = xray.open_dataset(monthly_mean_file, decode_coords=False)
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ds.attrs['history'] = '' # get rid of the history attribute because its obnoxiously long
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print(ds)
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.. parsed-literal::
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<xray.Dataset>
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Dimensions: (depth: 3, time: 400, x: 275, y: 205)
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Dimensions: (time: 36, x: 275, y: 205)
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Coordinates:
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* time (time) datetime64[ns] 1979-09-16T12:00:00 1979-10-17 ...
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* depth (depth) int64 0 1 2
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* x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
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* y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
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* time (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...
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* x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
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* y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
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Data variables:
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Precipitation (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Evap (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Runoff (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Baseflow (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Soilw (time, depth, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
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Swq (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Swd (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Swnet (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Lwnet (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Lwin (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Netrad (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Swin (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Latht (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Senht (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Grdht (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Albedo (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Radt (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Surft (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Relhum (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Tair (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Tsoil (time, depth, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
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Wind (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
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Tair (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...
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Attributes:
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title: /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc
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institution: U.W.
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source: RACM R1002RBRxaaa01a
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output_frequency: daily
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output_mode: averaged
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convention: CF-1.4
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history:
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references: Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.
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comment: Output from the Variable Infiltration Capacity (VIC) model.
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nco_openmp_thread_number: 1
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NCO: 4.3.7
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history: history deleted for brevity
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Now for the heavy lifting:
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.. code:: python
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# Make a DataArray with the number of days in each month, size = len(time)
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month_length = xray.DataArray(get_dpm(ds.time.to_index(), calendar='noleap'),
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month_length = xray.DataArray(get_dpm(ds.time.to_index(),
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calendar='noleap'),
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coords=[ds.time], name='month_length')
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# Calculate the weights by grouping by 'time.season'
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weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()
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# Calculate the weighted average
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ds_weighted = (ds * weights).groupby('time.season').sum(dim='time')
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.. code:: python
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print(ds_weighted)
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.. parsed-literal::
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<xray.Dataset>
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Dimensions: (depth: 3, season: 4, x: 275, y: 205)
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Dimensions: (season: 4, x: 275, y: 205)
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Coordinates:
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* y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
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* x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
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* depth (depth) int64 0 1 2
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* season (season) object 'DJF' 'JJA' 'MAM' 'SON'
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* y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
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* x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
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* season (season) object 'DJF' 'JJA' 'MAM' 'SON'
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Data variables:
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Baseflow (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Tsoil (season, depth, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Wind (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Swin (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Swq (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Netrad (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Albedo (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Evap (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Swd (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Radt (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Lwin (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Relhum (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Soilw (season, depth, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Lwnet (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Senht (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Surft (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Latht (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Runoff (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Tair (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Grdht (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Swnet (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Precipitation (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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Tair (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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.. code:: python
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# only used for comparisons
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ds_unweighted = ds.groupby('time.season').mean('time')
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ds_diff = ds_weighted - ds_unweighted
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.. code:: python
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# Quick plot to show the results

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