def get_colors(palette, num):
# palette = 'vaporwave'
# palette = 'cool'
colors = np.array([
colorsys.rgb_to_hsv(*tuple(int(c[i:i + 2], 16) for i in (1, 3, 5)))[0]
for c in vapeplot.palette(palette)
])
f = interp1d(np.arange(len(colors)), colors)
return f(np.arange(num) / (num - 1) * (len(colors) - 1))
"""
# import holoviews as hv
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import traces
import vapeplot
from bokeh.models import LinearAxis, Range1d
from bokeh.plotting import figure
from scipy import stats
# vapeplot.set_palette('sunset')
pal = sns.blend_palette(vapeplot.palette('sunset'))
# hv.extension('bokeh')
localData = np.load("localdata.npy")
globalData = np.load("globaldata.npy")
print globalData.shape, 'before'
g = 0
for i in xrange(globalData[:, 1].shape[0]):
# print globalData[138-i,-1]
if globalData[138 - i, -1] != 'nan':
g = globalData[138 - i, -1]
else:
globalData[138 - i, -1] = g
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import vapeplot
if __name__ == "__main__":
curdir = os.path.dirname(os.path.realpath(__file__))
pkl_path = os.path.join(curdir, "data-control-cloth.pkl")
df = pd.read_pickle(pkl_path)
pal = vapeplot.palette("vaporwave")
print(pal, len(pal))
gradsim_color = "#966bff"
physicsonly_color = "#ff6a8b"
# noisyphysics_color = "#20de8b"
random_color = "#94d0ff"
pal = [gradsim_color, physicsonly_color, random_color]
sns.lineplot(x="Epoch",
y="Position error",
hue="Approach",
data=df,
palette=pal)
plt.xlabel("Optimization iterations")
plt.ylabel("Position error (meters)")
# savepath = os.path.join(curdir, "plot-control-cloth.svg")
def recursivecell(
levels=3,
init=None,
tEnd=100,
dt=0.001,
couplcoeff=0.3,
drive=0.5,
plots=True,
plots2=True,
parallel=True, #for if we want the video paralellized or not
vidja=True,
scalefactor=0.8,
step=10,
width=1080,
height=1080,
dpi=100): #video parameters
#finding amount of cells
cellnr = cellamount(levels)
#random startvector
if np.any(init == None):
startvect = np.empty((cellnr, 1))
for i in range(cellnr):
startvect[i, 0] = random.random() * 2 * np.pi
else:
startvect = np.array(init)
startvect = np.reshape(startvect, (cellnr, 1))
initcon = np.array2string(np.reshape(startvect, (1, cellnr)))
#coupling matrices
totcoupl = n_matrixgen(levels,
0.25) #using function to generate coupling matrix
drivevect = np.ones(cellnr) * drive
#simulation setup
phasevect = startvect
lensarray = np.empty(1)
lensarray[0] = np.linalg.norm(phasevect) # array of lengths
t = 0 #+dt
print(totcoupl)
#phasearray = np.array([phasevect],) #total array of all the phases
phasearray = np.empty((cellnr, int(tEnd / dt + 1)))
phasearray[:, 0] = phasevect[:, 0]
bar1 = Bar('simulating', max=int(tEnd / dt))
stepcounter = 1 #stepcounter since int(t/dt) did strange things
#actually simulating
while stepcounter < tEnd / dt + 1:
dphase = couplcoeff * (
np.mod(-np.matmul(totcoupl, phasevect) + np.pi, np.pi * 2) -
np.pi) + drivevect #calculating derivative
#phasevect = RK4(phasevect,dt,totcoupl,couplcoeff,drivevect)
phasevect = phasevect + dt * dphase
lensarray = np.append(lensarray, np.linalg.norm(phasevect))
phasearray[:, stepcounter] = phasevect[:, 0]
stepcounter += 1
t = t + dt
bar1.next()
bar1.finish()
#finding time for filenames
now = datetime.now()
#transposing the array bc I didn't want to rewrite half the plots
phasearray = np.transpose(phasearray)
#dumping the whole thing to a csv for later use
np.savetxt(
'outputs/recursive/{now}drive{drive}coupl{coup}.csv'.format(
now=now, drive=drive, coup=couplcoeff), phasearray)
if plots:
#plot phase stuff
vp.set_palette('cool')
tarr = np.arange(0, tEnd + 1 * dt, dt)
plt.figure(figsize=(15, 10))
for i in range(cellnr):
plt.plot(tarr, np.mod(phasearray[:, i], 2 * np.pi))
plt.savefig('outputs/recursive/phase_{}.png'.format(now))
plt.close()
#plot sine
plt.figure(figsize=(15, 5))
for i in range(cellnr):
plt.plot(tarr, np.sin(phasearray[:, i]))
plt.savefig('outputs/recursive/sine_{}.png'.format(now))
plt.close()
#plots that are more useful for bigger sytems
if plots2:
cool = vp.palette('cool') #let's make em pretty
plotbar = Bar('plotting...', max=cellnr)
tarr = np.arange(0, tEnd + 1 * dt, dt)
fig = plt.figure(figsize=(15, 5 * math.ceil(cellnr / 3)))
for i in range(cellnr):
data = phasearray[:, i]
sins = np.sin(data)
diffs = centerdif(sins, dt)
#phasespace
ax = fig.add_subplot(int(math.ceil(cellnr / 3)), 3, i + 1)
ax.plot(sins, diffs, c=cool[np.mod(i, len(cool))])
ax.set_xlabel('x{}'.format(i + 1))
ax.set_ylabel("x'{}".format(i + 1))
ax.set_ylim(top=1, bottom=-1)
plotbar.next()
plt.savefig('outputs/recursive/{}phaseplane.png'.format(now))
plotbar.finish()
plt.close()
#sineplots
fig2 = plt.figure(figsize=(15, 5 * math.ceil(cellnr / 3)))
plotbar = Bar('plotting...', max=cellnr)
for i in range(cellnr):
data = phasearray[:, i]
sins = np.sin(data)
ax2 = fig2.add_subplot(cellnr, 1, i + 1)
ax2.plot(tarr, sins, c=cool[np.mod(i, len(cool))])
ax2.set_xlabel('t')
ax2.set_ylabel("x{}".format(i + 1))
plotbar.next()
plt.savefig('outputs/recursive/{}sins.png'.format(now))
plt.close()
plotbar.finish()
#Video stuff
if vidja == True:
if parallel == True:
parallelvideo(cellnr, levels, tEnd, dt, phasearray, totcoupl, now,
scalefactor, step, width, height, dpi)
else:
videomaker(cellnr, levels, tEnd, dt, phasearray, totcoupl, now,
scalefactor, step, width, height, dpi)
"""
from icecream import ic
import numpy as np
import pint
import vapeplot
from vapeplot import palette
import pandas as pd
import scipy.stats as st
import matplotlib.pyplot as plt
import seaborn as sns
ureg = pint.UnitRegistry()
ureg.auto_reduce_dimensions = True
Q_ = ureg.Quantity
pal = sns.blend_palette(vapeplot.palette("vaporwave"))
# radius of a 98A^2 surface "sphere"
# https://pubchem.ncbi.nlm.nih.gov/compound/439520#section=Top
r = (98e-20 / (4 * 3.14))**(1 / 2) * ureg.meter
k = 1.38e-23 * ureg.joule / ureg.K
mu = 0.001 * ureg.pascal * ureg.second
T = 320 * ureg.K
# data Lanzini, Bile 2003
r_BS_CH = 1e-9 * ureg.meter
r_BS_L_CH = 2.5e-9 * ureg.meter
r_BS_L_CH_vesicle = 35e-9 * ureg.meter