Quick Start Pymrio Tutorial using WIOD

This notebook contains the interactive version of the quick start given in the Pymrio article (Stadler et al 2018 sub).

Pymrio requires a Python version >= 3.7. If you don't have Python installed, I recommend to use the Anaconda Scientific Python package.

Pymrio is available on

the Python Package Index PyPI

and on


Thus, two possibilities exist to install Pymrio and all required packages.

For using the version on PyPI use:

pip install pymrio --upgrade

To install from the Anaconda Cloud do:

conda install -c conda-forge pymrio

Further down in that notebood we will also use the country_converter package as well as seaborn and matplotlib for some plotting. You can install these packages with pip or conda analog to pymrio. Alternatively, you can also run this notebook in the cloud via binder following this link:

badge

You can than import the Pymrio package with

In [1]:
import pymrio

In this example here, we will use the WIOD MRIO database.

First, the Pymrio MRIO download function is used to get the WIOD MRIO database with:

In [2]:
raw_wiod_path = '/tmp/wiod/raw'
pymrio.download_wiod2013(storage_folder=raw_wiod_path,
                         years=[2008])
Out[2]:
Description: WIOD metadata file for pymrio
MRIO Name: WIOD
System: IxI
Version: data13
File: /tmp/wiod/raw/metadata.json
History:
20201120 14:24:19 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/water/wat_may12.zip to wat_may12.zip
20201120 14:24:19 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/materials/mat_may12.zip to mat_may12.zip
20201120 14:24:18 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/land/lan_may12.zip to lan_may12.zip
20201120 14:24:17 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/AIR/AIR_may12.zip to AIR_may12.zip
20201120 14:24:17 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/CO2/CO2_may12.zip to CO2_may12.zip
20201120 14:24:16 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/EM/EM_may12.zip to EM_may12.zip
20201120 14:24:15 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/EU/EU_may12.zip to EU_may12.zip
20201120 14:24:14 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/SEA/WIOD_SEA_July14.xlsx to WIOD_SEA_July14.xlsx
20201120 14:24:13 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/update_sep12/wiot/wiot08_row_sep12.xlsx to wiot08_row_sep12.xlsx

This downloads the 2008 MRIO table from WIOD. Omitting the year parameter would result getting all years. The function returns a Pymrio meta data object, which gives information about the WIOD version, system (in this case industry by industry) and records about from where the data was received.

To parse the database into a Pymrio object use:

In [3]:
wiod = pymrio.parse_wiod(raw_wiod_path, year=2008)

The available data can be explored by for example:

In [4]:
wiod.get_sectors()
Out[4]:
Index(['AtB', 'C', '15t16', '17t18', '19', '20', '21t22', '23', '24', '25',
       '26', '27t28', '29', '30t33', '34t35', '36t37', 'E', 'F', '50', '51',
       '52', 'H', '60', '61', '62', '63', '64', 'J', '70', '71t74', 'L', 'M',
       'N', 'O', 'P'],
      dtype='object', name='sector')

or

In [5]:
wiod.get_regions()
Out[5]:
Index(['AUS', 'AUT', 'BEL', 'BGR', 'BRA', 'CAN', 'CHN', 'CYP', 'CZE', 'DEU',
       'DNK', 'ESP', 'EST', 'FIN', 'FRA', 'GBR', 'GRC', 'HUN', 'IDN', 'IND',
       'IRL', 'ITA', 'JPN', 'KOR', 'LTU', 'LUX', 'LVA', 'MEX', 'MLT', 'NLD',
       'POL', 'PRT', 'ROU', 'RUS', 'SVK', 'SVN', 'SWE', 'TUR', 'TWN', 'USA',
       'RoW'],
      dtype='object', name='region')
In [6]:
wiod.Z
Out[6]:
region AUS ... RoW
sector AtB C 15t16 17t18 19 20 21t22 23 24 25 ... 63 64 J 70 71t74 L M N O P
region sector
AUS AtB 4445.324330 41.919400 15625.681890 536.968630 154.395870 936.835140 273.018600 0.000000 215.708440 93.909230 ... 19.761917 0.001627 0.140044 0.043667 11.680006 3.113827 61.711687 9.898359 10.256983 0.000038
C 16.277934 3838.070873 189.934275 11.313686 3.253063 14.271582 58.136067 4424.333299 193.328895 20.968263 ... 0.211888 0.034300 0.005817 0.088101 14.418832 0.315809 0.182157 0.273387 0.493510 0.000861
15t16 1049.495726 100.611347 6754.110522 68.387761 19.663697 14.570366 49.431980 36.266290 835.587643 54.070009 ... 1.621756 1.588110 3.701685 2.743954 27.665274 14.865583 86.096798 46.736852 79.637133 0.006089
17t18 36.908420 43.779214 108.986668 355.675875 102.268333 18.335691 50.188234 15.538649 45.917943 45.612614 ... 0.401032 0.181367 1.492579 0.632427 1.492750 6.550554 0.878764 2.252624 2.428735 0.001057
19 9.518107 11.289978 28.105965 91.723266 26.373410 4.728489 12.942764 4.007176 11.841522 11.762783 ... 0.039708 0.017958 0.147786 0.062619 0.147803 0.648594 0.087010 0.223040 0.240478 0.000105
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
RoW L 0.547432 0.780406 1.176073 0.200903 0.057766 0.248589 0.636937 0.238198 1.078893 0.160527 ... 608.700335 709.004146 1595.531658 620.253937 1194.982705 4557.146576 518.488155 921.621306 901.624286 0.000000
M 1.319036 9.927575 11.742183 2.639874 0.759049 1.003295 4.474255 4.120725 4.517280 1.687657 ... 91.678620 463.402398 1802.804212 212.193133 1571.999239 7706.676330 5223.189539 1488.980938 527.910083 0.001030
N 7.894845 0.291041 11.603507 2.279403 0.655404 1.749095 6.793279 0.164896 18.550374 0.174962 ... 39.843405 106.735687 305.274672 70.364956 895.599603 1415.975389 840.530518 3513.234966 395.244668 0.000020
O 1.244926 3.686620 4.381357 0.155217 0.044633 0.562692 2.533730 0.758305 2.141030 0.484539 ... 5414.944076 4371.240663 5815.525102 2897.014578 14958.970521 15443.391745 7897.357003 8895.570576 16565.733127 44.703532
P 0.001018 0.000104 0.003666 0.000135 0.000039 0.000031 0.000096 0.000021 0.000116 0.000052 ... 1.324062 0.244611 1.104726 1.141934 50.552086 0.000000 1.089908 4.570108 41.517464 3.460032

1435 rows × 1435 columns

WIOD includes several satellite accounts, which are stored as child objects in Pymrio. For example, in order to see the AIR emissions provided by WIOD:

In [7]:
wiod.AIR.F
Out[7]:
region AUS ... RoW
sector AtB C 15t16 17t18 19 20 21t22 23 24 25 ... 63 64 J 70 71t74 L M N O P
stressor
CO2 6.471152e+03 2.331841e+04 3256.861259 392.819896 91.570641 147.075293 2100.167306 7928.850694 8832.607331 82.623337 ... 4.530961e+04 23843.716275 17594.617416 11774.769870 5.787274e+04 1.118612e+05 25382.470248 4.575128e+04 4.851286e+04 0.0
CH4 3.226169e+06 1.370016e+06 1221.450093 41.723574 6.112471 64.722688 189.787544 33785.867211 768.018325 22.631731 ... 2.031444e+04 2430.969769 3979.962245 5525.320218 1.617241e+04 8.865122e+04 5001.012041 1.394311e+04 1.360473e+07 0.0
N2O 6.527106e+04 1.243851e+02 527.652440 10.773378 1.335362 14.793543 111.798406 146.523698 10421.185919 6.723839 ... 7.185897e+02 320.774141 342.725185 215.478088 1.228492e+03 2.995831e+03 272.108324 7.680525e+03 9.170715e+04 0.0
NOX 2.000881e+05 1.709849e+05 70375.533177 3875.234721 964.709338 9146.373832 36269.747108 18894.321469 34546.808023 663.707765 ... 1.419601e+05 87411.639720 68809.569199 60385.601411 2.027940e+05 4.224802e+05 99579.016981 1.666314e+05 1.565510e+05 0.0
SOX 1.976645e+04 4.713841e+04 45815.675397 1068.354291 265.958435 2521.542160 26680.445075 150018.069958 91733.983039 182.976023 ... 6.973313e+04 42938.026115 33800.385037 29662.394375 9.961571e+04 2.075292e+05 48914.840693 8.185206e+04 7.690040e+04 0.0
CO 1.496859e+06 7.159254e+05 227663.413138 16225.875707 4039.304699 38296.499606 82187.571692 56833.653485 393632.686253 2778.990301 ... 1.385415e+06 853066.323392 671525.284261 589314.228014 1.979104e+06 4.123062e+06 971810.003503 1.626186e+06 1.527810e+06 0.0
NMVOC 3.824729e+05 2.409498e+05 141642.740887 5460.933412 1359.456610 12888.958231 28691.901753 65893.299291 105133.073315 935.288872 ... 3.377663e+05 207978.880902 163718.896513 143675.714632 4.825085e+05 1.005209e+06 236928.772636 3.964667e+05 3.724824e+05 0.0
NH3 4.049434e+05 4.575323e+02 112.157985 4.313657 0.449874 13.342974 48.333137 4.143371 366.328954 4.740067 ... 4.569925e+02 324.340516 229.110878 244.663450 1.643215e+03 1.357458e+03 113.829614 6.203676e+02 4.965187e+03 0.0

8 rows × 1435 columns

WIOD, however, does neither provide any normalized data (A-matrix, satellite account coefficient data) nor any consumption based accounts (footprints).

In order to calculate them, one could go through all the missing data and compute each account. Pymrio provides the required function, for example to calculate the A-matrix:

In [8]:
x = pymrio.calc_x(Z=wiod.Z, Y=wiod.Y)
A = pymrio.calc_A(Z=wiod.Z, x=x)
In [9]:
A.head()
Out[9]:
region AUS ... RoW
sector AtB C 15t16 17t18 19 20 21t22 23 24 25 ... 63 64 J 70 71t74 L M N O P
region sector
AUS AtB 0.095452 0.000346 0.220811 0.086780 0.096757 0.093637 0.009559 0.000000 0.008643 0.008967 ... 1.143737e-04 5.153717e-09 2.285107e-07 7.786856e-08 1.517644e-05 4.463792e-06 1.377724e-04 2.552552e-05 2.604967e-05 6.252650e-09
C 0.000350 0.031718 0.002684 0.001828 0.002039 0.001426 0.002035 0.220910 0.007746 0.002002 ... 1.226316e-06 1.086802e-07 9.492337e-09 1.571046e-07 1.873514e-05 4.527246e-07 4.066696e-07 7.050016e-07 1.253368e-06 1.432423e-07
15t16 0.022535 0.000831 0.095444 0.011052 0.012323 0.001456 0.001731 0.001811 0.033481 0.005163 ... 9.386042e-06 5.031958e-06 6.040074e-06 4.893121e-06 3.594692e-05 2.131039e-05 1.922126e-04 1.205233e-04 2.022545e-04 1.013494e-06
17t18 0.000793 0.000362 0.001540 0.057481 0.064090 0.001833 0.001757 0.000776 0.001840 0.004355 ... 2.321002e-06 5.746660e-07 2.435455e-06 1.127767e-06 1.939607e-06 9.390472e-06 1.961856e-06 5.808983e-06 6.168260e-06 1.759452e-07
19 0.000204 0.000093 0.000397 0.014824 0.016528 0.000473 0.000453 0.000200 0.000474 0.001123 ... 2.298108e-07 5.689976e-08 2.411432e-07 1.116643e-07 1.920475e-07 9.297846e-07 1.942504e-07 5.751684e-07 6.107418e-07 1.742097e-08

5 rows × 1435 columns

Alternatively, Pymrio provides a function which iterates through all missing accounts and calculates them:

In [10]:
wiod.calc_all()
Out[10]:
<pymrio.core.mriosystem.IOSystem at 0x7fb60b1900d0>

At this point, a basic EE MRIO analysis is accomplished. For example, the regional consumption based accounts of the AIR emissions are now given by:

In [11]:
wiod.AIR.D_cba_reg
Out[11]:
region AUS AUT BEL BGR BRA CAN CHN CYP CZE DEU ... PRT ROU RUS SVK SVN SWE TUR TWN USA RoW
stressor
CO2 4.404070e+05 1.022100e+05 1.586176e+05 42924.986975 4.059629e+05 5.659664e+05 5.031700e+06 13943.187686 108758.745642 1.054136e+06 ... 7.658922e+04 1.173831e+05 1.311461e+06 40459.233377 24251.728341 9.434506e+04 3.494179e+05 2.246294e+05 6.210161e+06 5.620778e+06
CH4 4.275465e+06 7.599975e+05 1.030354e+06 464018.748607 1.352464e+07 4.068558e+06 5.433871e+07 157009.091900 780222.089424 6.668537e+06 ... 8.948877e+05 1.344168e+06 1.532052e+07 411099.098085 182504.004554 7.352664e+05 3.652537e+06 1.104729e+06 3.917121e+07 7.560548e+07
N2O 9.588178e+04 3.086814e+04 4.609171e+04 13203.713081 5.899229e+05 1.634371e+05 1.831795e+06 3200.309665 27441.728571 2.914646e+05 ... 3.091511e+04 5.163863e+04 4.422776e+05 13658.674182 7011.741463 3.576881e+04 9.538255e+04 3.551477e+04 1.182906e+06 3.590470e+06
NOX 2.359815e+06 3.324339e+05 4.508892e+05 142917.818720 2.786076e+06 1.904551e+06 1.925370e+07 35972.513098 292246.717821 2.701648e+06 ... 3.025544e+05 3.380263e+05 4.444685e+06 125333.624730 73379.182597 3.524920e+05 1.797639e+06 8.632669e+05 1.845556e+07 3.504645e+07
SOX 2.399335e+06 1.983047e+05 3.702525e+05 400357.951750 1.699074e+06 2.088103e+06 3.245490e+07 43500.967386 225907.785277 1.951840e+06 ... 1.918064e+05 4.979996e+05 1.398364e+06 103186.610826 48180.228365 2.078760e+05 1.548830e+06 1.075249e+06 1.523013e+07 2.860410e+07
CO 2.173900e+07 1.371366e+06 2.167114e+06 703172.284772 2.681292e+07 7.525147e+06 9.904520e+07 144686.477852 829881.093571 1.099191e+07 ... 1.194956e+06 2.055640e+06 2.165403e+07 455232.910264 580264.681074 2.048666e+06 4.860666e+06 4.927789e+06 1.005814e+08 3.566157e+08
NMVOC 3.101630e+06 3.582680e+05 5.920832e+05 190582.650539 5.323333e+06 2.131757e+06 2.016103e+07 43518.270216 273912.655126 2.923060e+06 ... 4.003833e+05 6.065405e+05 4.179851e+06 138654.031320 142881.841642 5.666544e+05 1.738178e+06 1.095519e+06 2.095710e+07 4.208942e+07
NH3 3.851776e+05 9.254548e+04 1.245648e+05 45897.394639 1.345046e+06 4.204562e+05 6.415339e+06 8931.594745 67224.140441 8.505438e+05 ... 8.987684e+04 1.911781e+05 7.889417e+05 35515.968170 23476.633339 8.488958e+04 5.979386e+05 1.032068e+05 3.090159e+06 8.572543e+06

8 rows × 41 columns

In [12]:
wiod.AIR.unit
Out[12]:
unit unit
stressor
CO2 Gg
CH4 t
N2O t
NOX Unnamed: 0
SOX Unnamed: 0
CO t
NMVOC t
NH3 t

Pymrio can be linked with the country converter coco to ease the aggregation of MRIO and results into different classifications. Using the country converter, WIOD can be aggregated into EU and non-EU countries with singling out Germany by:

In [13]:
import country_converter as coco
wiod.aggregate(region_agg = coco.agg_conc(original_countries='WIOD',
                                          aggregates=[{'DEU': 'DEU', 'GBR':'GBR'}, 'EU'],
                                          missing_countries='Other',
                                          merge_multiple_string=None))
Out[13]:
<pymrio.core.mriosystem.IOSystem at 0x7fb60b1900d0>

We rename the EU account to reflect that is does not include Germany:

In [14]:
wiod.rename_regions({'EU':'Rest of EU'})
Out[14]:
<pymrio.core.mriosystem.IOSystem at 0x7fb60b1900d0>

The regional footprint account are now:

In [15]:
wiod.AIR.D_cba_reg
Out[15]:
region Other Rest of EU DEU GBR
stressor
CO2 2.436179e+07 3.472823e+06 1.054136e+06 7.397044e+05
CH4 2.540661e+08 2.711250e+07 6.668537e+06 5.235498e+06
N2O 9.705186e+06 1.128531e+06 2.914646e+05 2.118832e+05
NOX 1.043111e+08 1.093267e+07 2.701648e+06 2.164933e+06
SOX 1.037493e+08 8.344435e+06 1.951840e+06 1.421854e+06
CO 7.661455e+08 5.466639e+07 1.099191e+07 1.068169e+07
NMVOC 1.280392e+08 1.577316e+07 2.923060e+06 2.986943e+06
NH3 2.672782e+07 3.493227e+06 8.505438e+05 5.900984e+05

To visualize for example the CH4 accounts:

In [16]:
import matplotlib.pyplot as plt
with plt.style.context('ggplot'):
    wiod.AIR.plot_account('CH4', figsize=(8,5))
    plt.savefig('/tmp/wiod/airch4.png', dpi=300)
    plt.show()

To calculate the source (in terms of regions and sectors) of a certain stressor or impact driven by consumption, one needs to diagonalize this stressor/impact. This can be done with Pymrio by:

In [17]:
diag_CH4 = wiod.AIR.diag_stressor('CH4')

and be reassigned to the aggregated WIOD system:

In [18]:
wiod.CH4_source = diag_CH4

In the next step the automatic calculation routine of Pymrio is called again to compute the missing accounts in this new extension: and be reassigned to the aggregated WIOD system:

In [19]:
wiod.calc_all()
Out[19]:
<pymrio.core.mriosystem.IOSystem at 0x7fb60b1900d0>

The diagonalized CH4 data now shows the source and destination of the specified stressor (CH4):

In [20]:
wiod.CH4_source.D_cba.head()
Out[20]:
region Other ... GBR
sector AtB C 15t16 17t18 19 20 21t22 23 24 25 ... 63 64 J 70 71t74 L M N O P
region sector
Other AtB 6.120041e+07 8.455234e+04 3.658411e+07 2.988418e+06 867172.684194 230646.602537 441913.627992 1.766666e+05 6.045454e+05 197135.975631 ... 669.903691 3406.973739 8805.678036 6656.233633 4123.121986 19984.727842 9398.054711 44359.672517 12305.895420 3.055303
C 1.008359e+06 6.714292e+06 2.047332e+06 6.420307e+05 117665.005135 33160.714629 281538.575874 5.643346e+06 1.564758e+06 231675.430540 ... 1405.758158 9357.742729 20287.540899 18680.219527 8586.783042 49408.298618 15853.401016 93562.146555 23512.720979 4.041194
15t16 3.968202e+03 6.218228e+01 8.736127e+04 5.178453e+02 639.802755 24.298048 128.945944 1.099366e+02 4.216024e+02 83.091425 ... 0.460965 3.215184 8.818689 5.805941 3.871073 18.671491 8.695184 44.370659 11.707402 0.001748
17t18 9.369869e+01 2.287802e+01 2.464623e+02 1.185856e+04 200.126039 4.500219 68.017013 3.330082e+01 7.862589e+01 59.680321 ... 0.093075 0.848278 1.738428 1.444981 0.919367 6.608830 2.031215 12.658218 3.765588 0.001256
19 5.587156e+00 1.040420e+00 1.268140e+01 1.252185e+02 1231.987687 0.344568 4.205279 1.580128e+00 4.194052e+00 3.043624 ... 0.008632 0.117312 0.225215 0.171093 0.106037 1.104701 0.343349 1.430827 0.646012 0.000075

5 rows × 140 columns

In this square footprint matrix, every column represents the amount of stressor occurring in each region - sector driven by the consumption stated in the column header. Conversly, each row states where the stressor impacts occurring in the row are distributed due (from where they are driven).

In [21]:
CH4_source_reg = wiod.CH4_source.D_cba.groupby(
    level='region', axis=0).sum().groupby(
    level='region', axis=1).sum()
In [22]:
CH4_source_reg
Out[22]:
region DEU GBR Other Rest of EU
region
DEU 1.485343e+06 4.634238e+04 2.892830e+05 3.819713e+05
GBR 5.139252e+04 1.833541e+06 2.112405e+05 1.879226e+05
Other 3.696832e+06 2.711860e+06 2.457410e+08 1.317725e+07
Rest of EU 7.402886e+05 4.186665e+05 1.756700e+06 1.128755e+07
In [23]:
import seaborn as sns
CH4_source_reg.columns.name = 'Receiving region'
CH4_source_reg.index.name = 'Souce region'
sns.heatmap(CH4_source_reg, vmax=5E6, 
            annot=True, cmap='YlOrRd', linewidths=0.1,
            cbar_kws={'label': 'CH4 emissions ({})'.format(wiod.CH4_source.unit.unit[0])})
plt.savefig('/tmp/wiod/airch4_source_reg.png', dpi=300)
plt.show()

Storing the MRIO database can be done with

In [24]:
storage_path = '/tmp/wiod/aly'
wiod.save_all(storage_path)
Out[24]:
<pymrio.core.mriosystem.IOSystem at 0x7fb60b1900d0>

From where it can be received subsequently by:

In [25]:
wiod = pymrio.load_all(storage_path)

The meta attribute of Pymrio mentioned at the beginning kept track of all modifications of the system. This can be shown with:

In [26]:
wiod.meta
Out[26]:
Description: WIOD metadata file for pymrio
MRIO Name: WIOD
System: industry-by-industry
Version: data13
File: /tmp/wiod/aly/metadata.json
History:
20201125 14:17:05 - FILEIO -  Added satellite account from /tmp/wiod/aly/factor_inputs
20201125 14:17:05 - FILEIO -  Added satellite account from /tmp/wiod/aly/SEA
20201125 14:17:05 - FILEIO -  Added satellite account from /tmp/wiod/aly/AIR
20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/CO2
20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/EM
20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/EU
20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/lan
20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/mat
20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/wat
20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/CH4_source
 ... (more lines in history)

Custom notes can be added to the meta with:

In [27]:
wiod.meta.note("Custom note")

The history of the meta data can be filtered for specific entries like:

In [28]:
wiod.meta.file_io_history
Out[28]:
['20201125 14:17:05 - FILEIO -  Added satellite account from /tmp/wiod/aly/factor_inputs',
 '20201125 14:17:05 - FILEIO -  Added satellite account from /tmp/wiod/aly/SEA',
 '20201125 14:17:05 - FILEIO -  Added satellite account from /tmp/wiod/aly/AIR',
 '20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/CO2',
 '20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/EM',
 '20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/EU',
 '20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/lan',
 '20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/mat',
 '20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/wat',
 '20201125 14:17:04 - FILEIO -  Added satellite account from /tmp/wiod/aly/CH4_source',
 '20201125 14:17:04 - FILEIO -  Loaded IO system from /tmp/wiod/aly',
 '20201125 14:17:03 - FILEIO -  Saved WIOD to /tmp/wiod/aly',
 '20201125 14:17:00 - FILEIO -  Extension wat parsed from /tmp/wiod/raw',
 '20201125 14:16:59 - FILEIO -  Extension mat parsed from /tmp/wiod/raw',
 '20201125 14:16:58 - FILEIO -  Extension lan parsed from /tmp/wiod/raw',
 '20201125 14:16:57 - FILEIO -  Extension EU parsed from /tmp/wiod/raw',
 '20201125 14:16:55 - FILEIO -  Extension EM parsed from /tmp/wiod/raw',
 '20201125 14:16:53 - FILEIO -  Extension CO2 parsed from /tmp/wiod/raw',
 '20201125 14:16:51 - FILEIO -  Extension AIR parsed from /tmp/wiod/raw',
 '20201125 14:16:50 - FILEIO -  SEA file extension parsed from /tmp/wiod/raw',
 '20201125 14:16:39 - FILEIO -  WIOD data parsed from /tmp/wiod/raw/wiot08_row_sep12.xlsx',
 '20201120 14:24:19 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/water/wat_may12.zip to wat_may12.zip',
 '20201120 14:24:19 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/materials/mat_may12.zip to mat_may12.zip',
 '20201120 14:24:18 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/land/lan_may12.zip to lan_may12.zip',
 '20201120 14:24:17 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/AIR/AIR_may12.zip to AIR_may12.zip',
 '20201120 14:24:17 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/CO2/CO2_may12.zip to CO2_may12.zip',
 '20201120 14:24:16 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/EM/EM_may12.zip to EM_may12.zip',
 '20201120 14:24:15 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/EU/EU_may12.zip to EU_may12.zip',
 '20201120 14:24:14 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/SEA/WIOD_SEA_July14.xlsx to WIOD_SEA_July14.xlsx',
 '20201120 14:24:13 - FILEIO -  Downloaded http://www.wiod.org/protected3/data13/update_sep12/wiot/wiot08_row_sep12.xlsx to wiot08_row_sep12.xlsx']

This tutorial gave a short overview about the basic functionality of Pymrio. For more information about the capabilities of pymrio check the online documentation.

CC-BY4.0 licence

Licences of underlying dataset and software apply.