"""Example configuration file

This file illustrates how to define options and simulation parameter ranges.
It also defines the traversal order of the simulation space. As the file
extension suggests, configuration is pure python code, allowing complex
setups. Use at your own risk.

To use this example, run
   python3 study.py config_example

Otherwise copy it and modify as needed. The default traversal order defined
in the nested loop of nextShape() is good for most cases, but customizable
if needed.
"""

import logging
import itertools
import numpy as np
from DAS.shape import Shape

# Dump results into XML files
dumpXML = 1

# save progress and row/column distribution vectors to XML
saveProgress = 1

# plot progress for each run to PNG
plotProgress = 1

# Save row and column distributions
saveRCdist = 1

# Plot all figures
visualization = 1

# Verbosity level
logLevel = logging.INFO

# number of parallel workers. -1: all cores; 1: sequential
# for more details, see joblib.Parallel
numJobs = -1

# distribute rows/columns evenly between validators (True)
# or generate it using local randomness (False)
evenLineDistribution = True

# Number of simulation runs with the same parameters for statistical relevance
runs = range(3)

# Number of validators
numberNodes = range(128, 513, 128)

# select failure model between: "random, sequential, MEP, MEP+1, DEP, DEP+1, MREP, MREP-1"
failureModels = ["random"]

# Percentage of block not released by producer
failureRates = range(40, 81, 20)

# Block size in one dimension in segments. Block is blockSizes * blockSizes segments.
blockSizes = range(64, 113, 128)

# Per-topic mesh neighborhood size
netDegrees = range(8, 9, 2)

# number of rows and columns a validator is interested in
chis = range(2, 3, 2)

# ratio of class1 nodes (see below for parameters per class)
class1ratios = [0.8]

# Number of validators per beacon node
validatorsPerNode1 = [1]
validatorsPerNode2 = [500]

# Set uplink bandwidth in megabits/second
bwUplinksProd = [200]
bwUplinks1 = [10]
bwUplinks2 = [200]

# Step duration in miliseconds (Classic RTT is about 100ms)
stepDuration = 50

# Segment size in bytes (with proof)
segmentSize = 560

# Set to True if you want your run to be deterministic, False if not
deterministic = True

# If your run is deterministic you can decide the random seed. This is ignore otherwise.
randomSeed = "DAS"

# Number of steps without progress to stop simulation
steps4StopCondition = 7

# Number of validators ready to asume block is available
successCondition = 0.9

# If True, print diagnostics when the block is not available
diagnostics = False

# True to save git diff and git commit
saveGit = False

# --- DHT Parameters ---

# True to simulate the distribution of the BlockSegments over a simulated DHTNetwork
dhtSimulation = True

# Define the strategy used to seed or disseminate the Block to the DHT
# "builder-seeding-segments" -> The block builder is in charge of seeding the DHT with the block samples
dhtSeedings = ["builder-seeding-segments"]

# K replication factor, in how many DHT nodes are we going to store the block segments
# reused as how many DHT nodes will fit into each Kbucket to the routing table
ks = [20]

# Number of concurrent DHT nodes that will be contacted during a Lookup
alphas = [1]

# Number of steps without finding any closer DHT nodes to a Hash will the DHT lookup perform before finishing it
# Not using steps4StopCondition as 7 steps looks too much for the DHT (although it could be changed :))
nilStepsToStopLookup = 3

def nextShape():
    for run, fm, fr, class1ratio, chi, vpn1, vpn2, blockSize, nn, netDegree, bwUplinkProd, bwUplink1, bwUplink2, dhtSeeding, k, alpha in itertools.product(
        runs, failureModels, failureRates, class1ratios, chis, validatorsPerNode1, validatorsPerNode2, blockSizes, numberNodes, netDegrees, bwUplinksProd, bwUplinks1, bwUplinks2, dhtSeedings, ks, alphas):
        # Network Degree has to be an even number
        if netDegree % 2 == 0:
            shape = Shape(blockSize, nn, fm, fr, class1ratio, chi, vpn1, vpn2, netDegree, bwUplinkProd, bwUplink1, bwUplink2, dhtSeeding, k, alpha, run)
            yield shape