How ninonarido/presana works
PRESANA is a collection of independent analysis scripts and notebooks, not a single application. Its user-to-result workflows are initiated by running a particular analysis: examples include simulating an M/M/1 queue, fitting a sales time-series model, solving an optimization problem, or analyzing a network. The graph preserves these distinct topics, including notebook-led capabilities indicated by the file names. Source excerpts establish the queue simulation, time-series, LP, and network workflows; relationships among unsampled notebooks are not inferred.
Main entry points: qmm1.py Queue Simulator · networkfun.py Network Analysis · TS.py Sales Forecasting · LP.py Linear Program
Components 20
Queue Simulation
- Queue Simulator
qmm1.py - Job Timing
qmm1.py - Queue System
qmm1.py - Delay Analysis
qmm1.py - Delay Plots
qmm1.py
Data Analysis
- Sales Forecasting
TS.py - Superstore Data
data/Superstore.xls - Network Analysis
networkfun.py - Network Nodes
data/nodes.tsv - Network Edges
data/edges.tsv
Optimization
- Linear Program
LP.py
Notebook Studies
- Decision Theory
DecisionTheory.ipynb - Integer Programming
ILP.ipynb - Network LP
NetworkLP.ipynb - Sensitivity Analysis
Sensitivity Analysis in Python.ipynb - Simplex Study
simplex.ipynb - Attention NMT
NMT_ATTN_1.ipynb - Fruit Classification
Fruit_Classification_TensorFlow_Model_for_Android_App.ipynb
Other components
- Analyst
- PuLP CBC Solver
Connections 12
- Analyst → Queue Simulator: runs
qmm1.py - Queue Simulator → Job Timing: creates
qmm1.py - Queue Simulator → Queue System: dispatches jobs
qmm1.py - Queue Simulator → Delay Analysis: returns jobs
qmm1.py - Delay Analysis → Delay Plots: plots delays
qmm1.py - Analyst → Sales Forecasting: runs
TS.py - Sales Forecasting → Superstore Data: reads
TS.py - Analyst → Network Analysis: runs
networkfun.py - Network Analysis → Network Nodes: reads
networkfun.py - Network Analysis → Network Edges: reads
networkfun.py - Analyst → Linear Program: runs
LP.py - Linear Program → PuLP CBC Solver: solves with
LP.py