from sys import argv
import pandas as pd
import numpy as np
from os import path
import MoNeT_MGDrivE as monet
import matplotlib.pyplot as plt
from deap import base, creator, algorithms, tools
import pickle as pkl
import TRP_gaFun as ga
import TRP_aux as aux
import TRP_fun as fun
from PIL import Image
if monet.isNotebook():
(EXP_FNAME, TRAPS_NUM) = ('SQR_01-150-HOM', 1)
(PT_DTA, PT_GA, PT_IMG) = aux.selectPaths('lab')
else:
(EXP_FNAME, TRAPS_NUM) = (argv[1], int(argv[2]))
(PT_DTA, PT_GA, PT_IMG) = aux.selectPaths(argv[3])
(kPars, GENS) = (aux.KPARS, 1000)
print('* Optimizing: {} (traps={})'.format(EXP_FNAME, TRAPS_NUM))
bgImg = '{}_BF.png'.format(EXP_FNAME)
###############################################################################
# Read migration matrix and pop sites
###############################################################################
pthBase = path.join(PT_DTA, EXP_FNAME)
migMat = np.genfromtxt(pthBase+'_MX.csv', delimiter=',')
sites = np.genfromtxt(pthBase+'_XY.csv', delimiter=',')
# Sites and landscape shapes --------------------------------------------------
sitesNum = sites.shape[0]
if sites.shape[1] > 2:
from os import path
from sys import argv
import networkx as nx
# import cdlib.algorithms as cd
import MoNeT_MGDrivE as monet
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import normalize
from sklearn.cluster import AgglomerativeClustering
import TRP_aux as aux
import TRP_fun as fun
from scipy.interpolate import interp1d
if monet.isNotebook():
(EXP_FNAME, TRAPS_NUM) = ('UNIF_MD-200-HET', 1)
(PT_DTA, PT_IMG) = aux.selectPaths('lab')
else:
(EXP_FNAME, TRAPS_NUM) = (argv[1], 1)
(PT_DTA, PT_IMG) = aux.selectPaths(argv[2])
kPars = aux.KPARS
(LAY_TRAP, STEPS, delta) = (False, 120, 0.01)
###############################################################################
# Read migration matrix and pop sites
###############################################################################
pth = path.join(PT_DTA, EXP_FNAME)
migMat = np.genfromtxt(pth + '_MX.csv', delimiter=',')
sites = np.genfromtxt(pth + '_XY.csv', delimiter=',')
sitesNum = sites.shape[0]
pTypes = None
if sites.shape[1] > 2:
pTypes = sites[:, 2]
from os import path
from sys import argv
import networkx as nx
# import cdlib.algorithms as cd
import MoNeT_MGDrivE as monet
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import normalize
from sklearn.cluster import AgglomerativeClustering
import TRP_aux as aux
import TRP_fun as fun
from scipy.interpolate import interp1d
if monet.isNotebook():
(EXP_FNAME, TRAPS_NUM) = ('LRG_01-350-HOM', 1)
(PT_DTA, PT_GA, PT_IMG) = aux.selectPaths('lab')
else:
(EXP_FNAME, TRAPS_NUM) = (argv[1], 1)
(PT_DTA, PT_GA, PT_IMG) = aux.selectPaths(argv[2])
kPars = aux.KPARS
(LAY_TRAP, STEPS) = (False, 120)
print('* Generating base plot: {}'.format(EXP_FNAME))
###############################################################################
# Read migration matrix and pop sites
###############################################################################
pth = path.join(PT_DTA, EXP_FNAME)
migMat = np.genfromtxt(pth + '_MX.csv', delimiter=',')
sites = np.genfromtxt(pth + '_XY.csv', delimiter=',')
sitesNum = sites.shape[0]
pTypes = None
if sites.shape[1] > 2:
import math
import random
import numpy as np
from os import path
import pandas as pd
import numpy.random as rand
import MoNeT_MGDrivE as monet
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
import TRP_aux as aux
(LND, MOD) = ('Donut', 'HET')
if monet.isNotebook():
(POINTS, EXP_FNAME) = (400, 'MOV_01')
(PT_DTA, PT_GA, PT_IMG) = aux.selectPaths('lab')
else:
POINTS = argv[1]
(PT_DTA, PT_GA, PT_IMG) = aux.selectPaths(argv[2])
###############################################################################
# Constants
###############################################################################
sca = 10
(xRan, yRan) = ((-1280 / sca, 1280 / sca), (-720 / sca, 720 / sca))
# (xRan, yRan) = ((-10, 10), (-10, 10))
if MOD == 'HOM':
PTS_TMAT = np.asarray([[1 / 3, 1 / 3, 1 / 3], [1 / 3, 1 / 3, 1 / 3],
[1 / 3, 1 / 3, 1 / 3]])
else:
PTS_TMAT = np.asarray([[0.005, 0.975, 0.020], [0.020, 0.005, 0.975],
[0.975, 0.020, 0.005]])
PTYPE_PROB = [.1, .6, .3]
(fig, ax) = plt.subplots(figsize=(15, 15))
plt.imshow(tauN, vmax=1e-1, cmap='Purples', interpolation='nearest')
fig.savefig(path.join(PT_IMG, EXP_FNAME + '_trapsMatrix.png'),
dpi=500,
bbox_inches='tight')
###############################################################################
# Plot landscape
###############################################################################
BBN = tauN[:sitesNum, :sitesNum]
BQN = tauN[:sitesNum, sitesNum:]
(LW, ALPHA, SCA) = (.125, .2, 50)
(fig, ax) = plt.subplots(figsize=(15, 15))
(fig, ax) = aux.plotNetwork(fig,
ax,
BQN * SCA,
traps,
sites, [0],
c='#f72585',
lw=LW * 2.5,
alpha=.75)
(fig, ax) = aux.plotNetwork(fig,
ax,
BBN * SCA,
sites,
sites, [0],
c='#2B62F7',
lw=LW,
alpha=ALPHA)
plt.scatter(sites.T[0],
sites.T[1],
marker='^',
color='#03045eDB',
from sys import argv
import pandas as pd
import numpy as np
from os import path
import MoNeT_MGDrivE as monet
import matplotlib.pyplot as plt
from deap import base, creator, algorithms, tools
import pickle as pkl
import TRP_gaFun as ga
import TRP_aux as aux
import TRP_fun as fun
from PIL import Image
if monet.isNotebook():
(EXP_FNAME, TRAPS_NUM) = ('MOV_01-400-HOM', 10)
(PT_DTA, PT_GA, PT_IMG) = aux.selectPaths('lab')
else:
(EXP_FNAME, TRAPS_NUM) = (argv[1], int(argv[2]))
(PT_DTA, PT_GA, PT_IMG) = aux.selectPaths(argv[3])
kPars = aux.KPARS
print('* Plotting: {} (traps={})'.format(EXP_FNAME, TRAPS_NUM))
bgImg = '{}_BF.png'.format(EXP_FNAME)
pklPath = path.join(PT_GA, '{}_{}_GA'.format(EXP_FNAME,
str(TRAPS_NUM).zfill(2)))
###############################################################################
# Read migration matrix and pop sites
###############################################################################
pthBase = path.join(PT_DTA, EXP_FNAME)
migMat = np.genfromtxt(pthBase + '_MX.csv', delimiter=',')
sites = np.genfromtxt(pthBase + '_XY.csv', delimiter=',')
# Sites and landscape shapes --------------------------------------------------