WARNING: This repository will be superseded by the cyan effort and is no longer actively developed.

This repository provides a location of analysis and data visualization tools for Cyclus output.

Overview

The tool suite is nominally broken into 4 modules.

• Utility - provides access tools to the Cyclus database
• Tables - provides table descriptions of the Cyclus database
• Queries - provides an interface for standard queries of the Cyclus database
• Helpers - provides helper functions to aggregate data and time series

Usage

Connection to the Database

The analysis tools use SQLAlchemy's Object Relational Mapping (ORM) interface to assist in making querying the Cyclus database easier. Querying the database requires an active session, which can be accessed via:

from utility import loadSession
dbPath = 'some_relative_path_to_the_database'
session = loadSession(dbPath)

Querying the Database

Any query can be made on the database through the session, obtained above. A number of standard queries, however, are supported in the queries module. Some examples are provided below.

Simulation Time Information

Cyclus simulations run using a month-based time step. The queries module provides easy access to information about the beginning and ending time steps.

import queries as q

# the month index from which the simulation begins
startMonth = q.startMonth(session)

# the month index on which the simulation ends
endMonth = q.endMonth(session)

# the number of months in the simulation
# equivalent to endMonth - startMonth + 1
nMonths = q.nMonths(session)

The Cyclus database can store multiple simulation runs. Accordingly, the above method signatures can take an optional parameter (simID) which is 1 by default.

Facility Deployment

Another normal query to be made about Cyclus simulation output is the facility deployment at each time step. Let's say you have a facility type named hwr_reactor in your simulation. In order to determine the deployment of hwr_reactors at a given timestep, one can perform the following:

facility_type = "hwr_reactor"
timestep = startMonth
deployment = q.nFacs(session, facility_type, timestep)

Material Flows

Another metric important to most fuel cycle analyses is the amount of material flowing into or out of certain types of facilities. In Cyclus each "type" of material is denoted by its commodity name. Let's return to the hwr_reactor example and assume that you've named your input fuel "hwr_fuel" and your output fuel "hwr_used_fuel". You can get the amount of material entering or leaving all hwr_reactors in the following manner:

# fuel flowing into reactors
commodity_type = "hwr_fuel"
direction = "in"
flow = q.materialFlow(session, facility_type, commodity_type, timestep, direction = direction)

# fuel flowing out of reactors
commodity_type = "hwr_used_fuel"
direction = "out"
flow = q.materialFlow(session, facility_type, commodity_type, timestep, direction = direction)

If it's preferable to aggregate material flow over some time period, one can add the optional endTime parameter to sum the flows over the range of the starting time and ending time.

# the sum of all fuel flowing into reactors over the life of the simulation
commodity_type = "hwr_fuel"
direction = "in"
startTime = startMonth
endTime = endMonth
flow = q.materialFlow(session, facility_type, commodity_type, startTime, endTime = endTime, direction = direction)

Enrichment Parameters

Other standard queries include the amount of SWUs and natural uranium used by enrichment facilities in a simulation. These queries follow the same normal form used above.

# the amount of SWUs used during a simulation by enrichment facilities
swu_used = q.SWU(session, startMonth, endTime = endMonth)

# the amount of natural uranium used during a simulation by enrichment facilities
natl_u_used = q.natlU(session, startMonth, endTime = endMonth)

Producing Fuel Cycle Metrics

The most general case of fuel cycle analytics use is to develop graphs of fuel cycle metrics. The curves module provides a number of methods to develop such metrics. The helpers module also provides some quality-of-life methods to easily get year-based indices.

Let's say you want want to graph the deployment curve, fuel use, and used fuel production for hwr_reactors in your simulation year-by-year and plot the result. You could perform such an operation by:

import matplotlib.pyplot as plt
import helpers as h
import curves as c

# get yearly values
inYears = True

# get year indicies based on month indicies
year_indicies = h.yearIndicies(startMonth, endMonth)

# get facility deployment and plot the result
facility_type = "hwr_reactor"
deployment = c.growthCurve(session, facility_type, startMonth, endMonth, inYears = inYears)
plt.plot(year_indicies, deployment)
plt.show()

# get input fuel flow and plot the result
commodity_type = "hwr_fuel"
direction = "in"
input = c.materialFlowCurve(session, facility_type, commodity_type, startMonth, endMonth, direction = direction, inYears = inYears)
plt.plot(year_indicies, input)
plt.show()

# get output fuel flow and plot the result
commodity_type = "hwr_used_fuel"
direction = "out"
output = c.materialFlowCurve(session, facility_type, commodity_type, startMonth, endMonth, direction = direction, inYears = inYears)
plt.plot(year_indicies, output)
plt.show()