EdgeUsageJourney
Params
name
A human readable description of the object.
edge_functions
A list of EdgeFunctions.
usage_span
Usage span of edge usage journey in year.
Backwards links
Calculated attributes
nb_edge_usage_journeys_in_parallel_per_edge_usage_pattern
Dictionary with EdgeUsagePattern as keys and Edge usage journey hourly nb of edge usage journeys in parallel as values, in concurrent.
Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997],
}
Depends directly on:
through the following calculations:
You can also visit the link to edge usage journey hourly nb of edge usage journeys in parallel’s full calculation graph.
fabrication_impact_repartition_weights
Dictionary with EdgeUsagePattern as keys and Default edge usage pattern fabrication weight in edge usage journey impact repartition as values, in concurrent.
Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997],
}
Depends directly on:
through the following calculations:
You can also visit the link to Default edge usage pattern fabrication weight in edge usage journey impact repartition’s full calculation graph.
fabrication_impact_repartition_weight_sum
Sum of edge usage journey fabrication impact repartition weights in concurrent.
Example value: 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997]
Depends directly on:
through the following calculations:
You can also visit the link to Sum of edge usage journey fabrication impact repartition weights’s full calculation graph.
fabrication_impact_repartition
Dictionary with EdgeUsagePattern as keys and Edge usage journey fabrication impact attribution to default edge usage pattern as values, in concurrent.
Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+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:
- Default edge usage pattern fabrication weight in edge usage journey impact repartition
- Sum of edge usage journey fabrication impact repartition weights
through the following calculations:
You can also visit the link to edge usage journey fabrication impact attribution to Default edge usage pattern’s full calculation graph.
usage_impact_repartition_weights
Dictionary with EdgeUsagePattern as keys and Default edge usage pattern usage weight in edge usage journey impact repartition as values, in concurrent * gram / kilowatt_hour.
Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in ·g/kWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 84900, 170000],
last 10 vals [425000, 425000, 425000, 425000, 425000, 425000, 340000, 255000, 170000, 84700],
}
Depends directly on:
- edge usage journey hourly nb of edge usage journeys in parallel
- Average carbon intensity of devices country
through the following calculations:
You can also visit the link to Default edge usage pattern usage weight in edge usage journey impact repartition’s full calculation graph.
usage_impact_repartition_weight_sum
Sum of edge usage journey usage impact repartition weights in concurrent * gram / kilowatt_hour.
Example value: 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in ·g/kWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 84900, 170000],
last 10 vals [425000, 425000, 425000, 425000, 425000, 425000, 340000, 255000, 170000, 84700]
Depends directly on:
through the following calculations:
You can also visit the link to Sum of edge usage journey usage impact repartition weights’s full calculation graph.
usage_impact_repartition
Dictionary with EdgeUsagePattern as keys and Edge usage journey usage impact attribution to default edge usage pattern as values, in concurrent.
Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+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:
- Default edge usage pattern usage weight in edge usage journey impact repartition
- Sum of edge usage journey usage impact repartition weights
through the following calculations:
You can also visit the link to edge usage journey usage impact attribution to Default edge usage pattern’s full calculation graph.