Skip to content

Device

Params

name

A human readable description of the object.

carbon_footprint_fabrication

Carbon footprint fabrication of device on which the user journey is made in kilogram.

power

Power of device on which the user journey is made in watt.

lifespan

Lifespan of device on which the user journey is made in year.

fraction_of_usage_time

Device on which the user journey is made fraction of usage time in hour / day.

Calculated attributes

energy_footprint_per_usage_pattern

Dictionary with UsagePattern as keys and Device on which the user journey is made usage footprint for usage pattern as values, in kilogram.

Example value: {
UsagePattern usage pattern (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in g:
first 10 vals [2.83, 11.3, 12.8, 11.3, 4.25, 1.42, 9.92, 8.5, 12.8, 2.83],
last 10 vals [4.25, 11.3, 12.8, 2.83, 11.3, 8.5, 7.08, 2.83, 8.5, 4.25],
}

Depends directly on:

through the following calculations:

You can also visit the link to device on which the user journey is made usage footprint for usage pattern’s full calculation graph.

energy_footprint

Devices energy footprint of device on which the user journey is made in kilogram.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in g:
first 10 vals [2.83, 11.3, 12.8, 11.3, 4.25, 1.42, 9.92, 8.5, 12.8, 2.83],
last 10 vals [4.25, 11.3, 12.8, 2.83, 11.3, 8.5, 7.08, 2.83, 8.5, 4.25]

Depends directly on:

through the following calculations:

You can also visit the link to Devices energy footprint of device on which the user journey is made’s full calculation graph.

instances_fabrication_footprint

Devices fabrication footprint of device on which the user journey is made in kilogram.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in g:
first 10 vals [6.78, 27.1, 30.5, 27.1, 10.2, 3.39, 23.7, 20.3, 30.5, 6.78],
last 10 vals [10.2, 27.1, 30.5, 6.78, 27.1, 20.3, 16.9, 6.78, 20.3, 10.2]

Depends directly on:

through the following calculations:

You can also visit the link to Devices fabrication footprint of device on which the user journey is made’s full calculation graph.

fabrication_impact_repartition_weights

Dictionary with UsageJourneyStep as keys and 20 min streaming fabrication weight in device on which the user journey is made impact repartition as values, in concurrent * minute.

Example value: {
UsageJourneyStep 20 min streaming (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in ·min:
first 10 vals [13.3, 53.3, 60, 53.3, 20, 6.67, 46.7, 40, 60, 13.3],
last 10 vals [20, 53.3, 60, 13.3, 53.3, 40, 33.3, 13.3, 40, 20],
}

Depends directly on:

through the following calculations:

You can also visit the link to 20 min streaming fabrication weight in device on which the user journey is made impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of device on which the user journey is made fabrication impact repartition weights in concurrent * minute.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in ·min:
first 10 vals [13.3, 53.3, 60, 53.3, 20, 6.67, 46.7, 40, 60, 13.3],
last 10 vals [20, 53.3, 60, 13.3, 53.3, 40, 33.3, 13.3, 40, 20]

Depends directly on:

through the following calculations:

You can also visit the link to Sum of device on which the user journey is made fabrication impact repartition weights’s full calculation graph.

fabrication_impact_repartition

Dictionary with UsageJourneyStep as keys and Device on which the user journey is made fabrication impact attribution to 20 min streaming as values, in concurrent.

Example value: {
UsageJourneyStep 20 min streaming (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
}

Depends directly on:

through the following calculations:

You can also visit the link to device on which the user journey is made fabrication impact attribution to 20 min streaming’s full calculation graph.

usage_impact_repartition_weights

Dictionary with UsageJourneyStep as keys and 20 min streaming usage weight in device on which the user journey is made impact repartition as values, in kilogram * minute.

Example value: {
UsageJourneyStep 20 min streaming (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in kg·min:
first 10 vals [0.0567, 0.227, 0.255, 0.227, 0.085, 0.0283, 0.198, 0.17, 0.255, 0.0567],
last 10 vals [0.085, 0.227, 0.255, 0.0567, 0.227, 0.17, 0.142, 0.0567, 0.17, 0.085],
}

Depends directly on:

through the following calculations:

You can also visit the link to 20 min streaming usage weight in device on which the user journey is made impact repartition’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of device on which the user journey is made usage impact repartition weights in kilogram * minute.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in kg·min:
first 10 vals [0.0567, 0.227, 0.255, 0.227, 0.085, 0.0283, 0.198, 0.17, 0.255, 0.0567],
last 10 vals [0.085, 0.227, 0.255, 0.0567, 0.227, 0.17, 0.142, 0.0567, 0.17, 0.085]

Depends directly on:

through the following calculations:

You can also visit the link to Sum of device on which the user journey is made usage impact repartition weights’s full calculation graph.

usage_impact_repartition

Dictionary with UsageJourneyStep as keys and Device on which the user journey is made usage impact attribution to 20 min streaming as values, in concurrent.

Example value: {
UsageJourneyStep 20 min streaming (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
}

Depends directly on:

through the following calculations:

You can also visit the link to device on which the user journey is made usage impact attribution to 20 min streaming’s full calculation graph.