import numpy as np
from scipy import integrate
import sys
sys.path.append("/Users/ahagen/code")
from pymf import ctmfd as ahi
from pym import func as ahf
from pyg import twod as ahp
import glob
plot = ahp.ah2d()
fname = glob.glob('fem_sol/*.dat')
for i in range(len(fname)):
arr = np.loadtxt(fname[i])
z = arr[:, 0] * 1.0E4
C = arr[:, 1]
fem = ahf.curve(z, C, name=r'fem')
plot = fem.plot(linestyle='-', linecolor='#5C8727')
plot.lines_on()
plot.markers_off()
arr = np.loadtxt(fname[i].replace('fem', 'fdm'))
z = arr[:, 0] * 1.0E4
C = arr[:, 1]
fdm = ahf.curve(z, C, name=r'fdm')
fdm.plot(linestyle='-', linecolor='#2EAFA4', addto=plot)
plot.lines_on()
plot.markers_off()
import sys
import os
sys.path.append(os.environ['HOME'] + '/code')
from pyg import twod as pyg2
import numpy as np
# first lets make some cool data with trigonometric functions
x = np.linspace(0., 4. * np.pi, 500.)
y1 = np.sin(x)
y2 = np.power(np.sin(x), 2.)
# create three plot objects, two for one column, and another for two column
plot1 = pyg2.ah2d()
plot2 = pyg2.ah2d()
plot3 = pyg2.ah2d()
# add one line each to our single column plots
plot1.add_line(x, y1, linecolor='#285668', linestyle='-', name=r'$\sin$')
plot2.add_line(x, y2, linecolor='#285668', linestyle='-', name=r'$\sin^{2}$')
# add both of them to our double width object
plot3.add_line(x, y1, linecolor='#285668', linestyle='-', name=r'$\sin$')
plot3.add_line(x, y2, linecolor='#FC8D82', linestyle='--', name=r'$\sin^{2}$')
# make them pretty with labels and a legend
for plot in [plot1, plot2, plot3]:
plot.markers_off()
plot.lines_on()
plot.ylim(-1.1, 1.1)
plot.xlabel(r'$x$')
plot.ylabel(r'$y$')
# pwr = [np.mean(pwr_new[0:1]), np.mean(pwr_new[2]), np.mean(pwr_new[3]),
# np.mean(pwr_new[4:])]
# u_pwr = [np.std(pwr_new[0:1]), np.std(pwr_new[2]), np.std(pwr_new[3]),
# np.std(pwr_new[4:])]
# eff = np.array([np.mean(eff_new[0:1]), np.mean(eff_new[2]), np.mean(eff_new[3]),
# np.mean(eff_new[4:])]) / nps_dd
# u_eff = np.array([np.mean(u_eff[0:1]), np.mean(u_eff[2]), np.mean(u_eff[3]),
# np.mean(u_eff[4:])]) / nps_dd
# std_eff = np.array([np.std(eff_new[0:1]), np.std(eff_new[2]), np.std(eff_new[3]),
# np.std(eff_new[4:])]) / nps_dd
# u_eff = np.sqrt(np.power(u_eff, 2.0) + np.power(std_eff, 2.0))
pwr = pwr_new
eff = eff_new
dd_tena_eta = ahf.curve(pwr, 100. * eff, u_y=u_eff, name="dd_tena_setup")
plot = ahp.ah2d()
plot = dd_tena_eta.plot(linecolor='#746C66', linestyle='-')
plot = cf_tena_eta.plot(linecolor='#E3AE24', linestyle='-', addto=plot)
plot.lines_off()
plot.ax.set_yscale('log')
plot.add_data_pointer(3.5,
curve=cf_tena_eta,
string=r"$Cf$",
place=(2.2, 6E-3))
plot.add_data_pointer(6.5,
curve=dd_tena_eta,
string=r"$DD$",
place=(7.5, 1E-5))
plot.xlabel(r'Power ($P$) [$W$]')
plot.ylabel(r'Efficiency ($\eta$) [$\%$]')
plot.export('../img/ef15_eff',
ode_solution = ahf.curve(x, sol[:, 0], name='ode')
t_0_25, y_0_25 = np.loadtxt('pendulum_0.250000.dat', usecols=(0, 1), unpack=True)
h_0_25_solution = ahf.curve(t_0_25, y_0_25, name=r'$h = 0.25 \mathrm{s}$')
t_0_125, y_0_125 = np.loadtxt('pendulum_0.125000.dat', usecols=(0, 1),
unpack=True)
h_0_125_solution = ahf.curve(t_0_125, y_0_125,
name=r'$h = 0.125 \mathrm{s}$')
t_0_00001, y_0_00001 = np.loadtxt('pendulum_0.000244.dat', usecols=(0, 1),
unpack=True)
h_0_00001_solution = ahf.curve(t_0_00001, y_0_00001,
name=r'$h = 0.000244 \mathrm{s}$')
plot = ahp.ah2d()
plot = h_0_25_solution.plot(linecolor='#7ED0E0', linestyle='-', addto=plot)
plot = h_0_125_solution.plot(linecolor='#7299C6', linestyle='-', addto=plot)
plot = h_0_00001_solution.plot(linecolor='#B63F97', linestyle='-', addto=plot)
plot = ode_solution.plot(linecolor='#2EAFA4', linestyle='--', addto=plot)
plot.lines_on()
plot.markers_off()
plot.ylabel(r'Position ($y$) [$\mathrm{cm}$]')
plot.xlabel(r'Time ($t$) [$\mathrm{s}$]')
plot.legend()
plot.export('full_result', formats=['pdf', 'pgf'], sizes=['2'])
plot.show()
h, y = np.loadtxt('convergence.dat', unpack=True)
unpack=True)
h_0_25_solution = ahf.curve(t_0_25, y_0_25, name=r'$h = 0.25 \mathrm{s}$')
t_0_125, y_0_125 = np.loadtxt('pendulum_0.125000.dat',
usecols=(0, 1),
unpack=True)
h_0_125_solution = ahf.curve(t_0_125, y_0_125, name=r'$h = 0.125 \mathrm{s}$')
t_0_00001, y_0_00001 = np.loadtxt('pendulum_0.000244.dat',
usecols=(0, 1),
unpack=True)
h_0_00001_solution = ahf.curve(t_0_00001,
y_0_00001,
name=r'$h = 0.000244 \mathrm{s}$')
plot = ahp.ah2d()
plot = h_0_25_solution.plot(linecolor='#7ED0E0', linestyle='-', addto=plot)
plot = h_0_125_solution.plot(linecolor='#7299C6', linestyle='-', addto=plot)
plot = h_0_00001_solution.plot(linecolor='#B63F97', linestyle='-', addto=plot)
plot = ode_solution.plot(linecolor='#2EAFA4', linestyle='--', addto=plot)
plot.lines_on()
plot.markers_off()
plot.ylabel(r'Position ($y$) [$\mathrm{cm}$]')
plot.xlabel(r'Time ($t$) [$\mathrm{s}$]')
plot.legend()
plot.export('full_result', formats=['pdf', 'pgf'], sizes=['2'])
plot.show()
h, y = np.loadtxt('convergence.dat', unpack=True)
import sys
import os
sys.path.append(os.environ['HOME'] + '/code')
from pyg import twod as pyg2
import numpy as np
# first lets make some data which relates to each other, but is way off scale
x = np.linspace(0., 4. * np.pi, 500.)
y1 = np.sin(x)
y2 = np.exp(np.sin(x))
# create three plot objects, two for one column, and another for two column
plot = pyg2.ah2d()
# add one line each to our single column plots
plot.add_line(x, y1, linecolor='#285668', linestyle='-', name=r'$\sin$')
plot.fill_between(x, np.zeros_like(y1), y1, fc='#ccccff', name=r'$\int y dx$')
plot.add_line_yy(x, y2, linecolor='#FC8D82', linestyle='-',
name=r'$\exp \left( \sin \right)$')
plot.fill_between(x, np.zeros_like(y2), y2, fc='#ffcccc', name=r'$\int y dx$',
axes=plot.ax2)
# make them pretty with labels and a legend
plot.markers_off()
plot.lines_on()
plot.xlim(0., 4. * np.pi)
plot.ylim(-1.5, 1.5)
plot.ylim(0.0, 4.0, axes=plot.ax2)
plot.xlabel(r'$x$')
plot.ylabel(r'$y = \sin$')
plot.ylabel(r'$y = \exp \left( \sin \right)$', axes=plot.ax2)
def cluster_power(self, fnames, nps, u_nps):
t_pwr = []
pwr = []
u_pwr = []
t_det = []
det = []
levels = []
# plot the average power
plot = ahp.ah2d()
colors = ['#E3AE24', '#B63F97', '#5C6F7B', '#5C8727', '#7E543A', '#A3792C', '#7299C6', '#B8B308']
for fname in fnames:
# check if an alex fileset or a brian file
if "pwr" in fname:
# open power file
t_pwr_new, pwr_new, u_pwr_new = \
np.loadtxt(expanduser("~") + '/data/tena/' + fname + '.dat',
delimiter=",", dtype=object,
converters={0: strpdate2num("%d-%b-%Y %H:%M:%S"),
1: float, 2: float}, unpack=True)
t_pwr_new = np.array(t_pwr_new) * 3600. * 24.
t_det_new, det_new, _ = \
np.loadtxt(expanduser("~") + "/data/tena/" + fname.replace("pwr", "det") + '.dat',
delimiter=",", dtype=object,
converters={0: strpdate2num("%d-%b-%Y %H:%M:%S"),
1: float, 2: float}, unpack=True)
t_det_new = np.array(t_det_new) * 3600. * 24.
eps = 0.1
min_samples = 10
else:
# get the date from the filename
# find string matching %i_min_%2i_%2i_%2i[_%i] at end of filename
# get the count interval from the filename
# find string matching %i_min at end of string
pattern = \
".*_([0-9]*)_min_([0-9]*)_([0-9]*)_([0-9]*)(?:_([0-9]*))?$"
result = re.split(pattern, fname)
mm = result[2]
dd = result[3]
yy = result[4]
t_int = float(result[1]) * 60.
epoch = datetime.utcfromtimestamp(0)
t_start = (datetime.strptime(
mm + "/" + dd + "/" + yy + " 01:00:00",
"%m/%d/%y %H:%M:%S") - epoch).total_seconds()
pwr_new, eff_new, u_eff_new = \
np.loadtxt(expanduser("~") + '/data/tena/' + fname + '.dat',
delimiter=",", dtype=object,
converters={0: float, 1: float, 2: float},
unpack=True)
u_pwr_new = 0.2 * np.ones_like(pwr_new)
det_new = nps * np.array(eff_new)
u_eff_new = u_nps * np.array(u_eff_new)
t = t_start
t_pwr_new = []
for i in range(len(pwr_new)):
t_pwr_new = np.append(t_pwr_new, t)
t += t_int
t_det_new = t_pwr_new.copy()
eps = 0.24
min_samples = 2
# put the data in row form
pwr_data = \
[[float(t_pwr_new[i]), float(pwr_new[i])] for i in range(len(pwr_new))]
# use the dbscan clustering algorithm
X = StandardScaler().fit_transform(pwr_data)
db = DBSCAN(eps=eps, min_samples=min_samples).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
marked = np.zeros_like(t_pwr_new)
for i in range(len(t_pwr_new))[::-1]:
label = labels[i]
other_labels = [x for x in np.unique(labels) if x != label]
for other in other_labels:
if (other >= 0) and inrange(t_pwr_new[i], t_pwr_new[labels == other]):
marked[i] = 1
labels[marked == 1] = -1
for i in [x for x in np.unique(labels) if x != -1]:
if len(pwr_new[labels == i]) >= min_samples:
level = power_level(t_pwr_new[labels == i], pwr_new[labels == i],
t_det_new, det_new)
levels = np.append(levels, level)
t_det = np.append(t_det, t_det_new)
t_pwr = np.append(t_pwr, t_pwr_new)
pwr = np.append(pwr, pwr_new)
u_pwr = np.append(u_pwr, u_pwr_new)
det = np.append(det, det_new)
return levels
