"""Simple examples of using the "frams" module to communicate directly with the Framsticks library (dll/so).

For an introduction to Framsticks, its usage and scripting, see https://www.youtube.com/playlist?list=PLkPlXm7pOPatTl3_Gecx8ZaCVGeH4UV1L
For a list of available classes, objects, methods and fields, see http://www.framsticks.com/files/classdoc/
For a number of examples of scripting, see the "scripts" directory in Framsticks distribution.
"""

import sys
import frams

frams.init(*(sys.argv[1:]))  # pass whatever args we have, init() is the right place to deal with different scenarios:
# frams.init() - should try to figure out everything (and might fail)
# frams.init('path/to/lib') - load the library from the specified directory and configure Framsticks path as "data" inside this directory
# frams.init('path/to/lib','-d/tmp/workdir/data') - as above, but set the working (writable) directory somewhere else (see also -D)
# frams.init('path/to/lib','-Lframs-objects-alt.dll') - use specified library location and non-default file name

print('Available objects:', dir(frams))
print()


def extValueDetails(v):
	"""A helper function to display basic information about a variable of type ExtValue."""
	return '\t"' + str(v) + '"    frams type=' + str(v._type()) + '    frams class=' + str(v._class()) + '    python type=' + str(type(v._value()))


dic_as_string = '[100,2.2,"abc",[null,[],{}],XYZ[9,8,7]]'
print("We have the following string:\n\t'%s'" % dic_as_string)
print("Looks like a serialized dictionary, let's ask Framsticks String.deserialize() to do its job.")
v = frams.String.deserialize(dic_as_string)
print("Framsticks String.deserialize() returned\n\t", type(v))
print("More specifically, it is:")
print(extValueDetails(v))
print("Even though it is ExtValue (Framsticks' Vector), it supports iteration like a python vector, so let's inspect its elements:")
for e in v:
	print(extValueDetails(e))

print("Now let's play with the Framsticks simulator. Let's create a Genotype object and set fields in its custom 'data' dictionary.")
g = frams.GenePools[0].add('X')
g.name = "Snakis Py"
g.data['custom'] = 123.456
g.data['a'] = 'b'  # implicit conversion, looks like python dictionary but still converts '3' and '4' to ExtValue
dic = frams.Dictionary.new()  # let's create a Dictionary object from Framsticks
dic.set('1', '2')  # calling set() from Framsticks Dictionary
dic['3'] = '4'  # implicit conversion, looks like python dictionary but still converts '3' and '4' to ExtValue
g.data['d'] = dic
print(extValueDetails(g))

print("Let's add a few mutants and display their data:")
for more in range(5):
	frams.GenePools[0].add(frams.GenMan.mutate(g.geno))

for g in frams.GenePools[0]:
	print("\t%d. name='%s'\tgenotype='%s'\tdata=%s" % (g.index._value(), str(g.name), str(g.genotype), str(g.data)))

print("Let's now change some property of the simulation. Current water level is", frams.World.wrldwat)
frams.World.wrldwat = 0.5
print("Now water level is", frams.World.wrldwat)
frams.World.wrldwat = frams.World.wrldwat._value() + 0.7
print("Now water level is", frams.World.wrldwat)

initial_genotype = 'X(X,RX(X[T],X[G]))'  # simple body with touch and gyroscope sensors
print("Let's perform a few simulation steps of the initial genotype:", initial_genotype)
frams.ExpProperties.initialgen = initial_genotype
frams.ExpProperties.p_mut = 0  # no mutation (the selection procedure will clone our initial genotype)
frams.ExpProperties.p_xov = 0  # no crossover (the selection procedure will clone our initial genotype)
frams.Populations[0].initial_nn_active = 1  # immediate simulation of neural network - no "waiting for stabilization" period
frams.World.wrldg = 5  # gravity=5x default, let it fall quickly

frams.Simulator.init()  # adds initial_genotype to gene pool (calls onInit() from standard.expdef)
frams.Simulator.start()  # this does not actually start the simulation, just sets the "Simulator.running" status variable
step = frams.Simulator.step  # cache reference to avoid repeated lookup in the loop (just for performance)
for s in range(15):
	step()  # first step performs selection and revives one genotype according to standard.expdef rules
	creature = frams.Populations[0][0]  # FramScript Creature object
	mechpart0 = creature.getMechPart(0)
	print('Step# = %d' % frams.Simulator.stepNumber._value(),
	      '\tSimulated_creatures =', frams.Populations[0].size._value(),
	      "\tpart0_xyz = (% .2f,% .2f,% .2f)" % (mechpart0.x._value(), mechpart0.y._value(), mechpart0.z._value()),
	      "\ttouch = % .3f\tgyro = % .3f" % (creature.getNeuro(0).state._value(), creature.getNeuro(1).state._value()))
frams.Simulator.stop()

# Note that implementing a complete expdef, especially a complex one, entirely in python may be inconvenient or impractical
# because you do not have access to "event handlers" like you have in FramScript - onStep(), onBorn(), onDied(), onCollision() etc.,
# so you would have to check various conditions in python in each simulation step to achieve the same effect.