def test_sliced_input(self):
# cython code chokes on non C contiguous arrays
xx = np.linspace(-1, 1, 100)
x = xx[::5]
y = xx[::5]
make_interp_spline(x, y, k=1)
def test_quadratic_deriv(self):
der = [(1, 8.)] # order, value: f'(x) = 8.
# derivative at right-hand edge
b = make_interp_spline(self.xx, self.yy, k=2, bc_type=(None, der))
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
assert_allclose(b(self.xx[-1], 1), der[0][1], atol=1e-14, rtol=1e-14)
# derivative at left-hand edge
b = make_interp_spline(self.xx, self.yy, k=2, bc_type=(der, None))
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
assert_allclose(b(self.xx[0], 1), der[0][1], atol=1e-14, rtol=1e-14)
def test_cubic_deriv(self):
k = 3
# first derivatives at left & right edges:
der_l, der_r = [(1, 3.)], [(1, 4.)]
b = make_interp_spline(self.xx, self.yy, k, bc_type=(der_l, der_r))
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
assert_allclose([b(self.xx[0], 1), b(self.xx[-1], 1)],
[der_l[0][1], der_r[0][1]], atol=1e-14, rtol=1e-14)
# 'natural' cubic spline, zero out 2nd derivatives at the boundaries
der_l, der_r = [(2, 0)], [(2, 0)]
b = make_interp_spline(self.xx, self.yy, k, bc_type=(der_l, der_r))
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
def test_shapes(self):
np.random.seed(1234)
k, n = 3, 22
x = np.sort(np.random.random(size=n))
y = np.random.random(size=(n, 5, 6, 7))
b = make_interp_spline(x, y, k)
assert_equal(b.c.shape, (n, 5, 6, 7))
# now throw in some derivatives
d_l = [(1, np.random.random((5, 6, 7)))]
d_r = [(1, np.random.random((5, 6, 7)))]
b = make_interp_spline(x, y, k, bc_type=(d_l, d_r))
assert_equal(b.c.shape, (n + k - 1, 5, 6, 7))
def test_complex(self):
k = 3
xx = self.xx
yy = self.yy + 1.j*self.yy
# first derivatives at left & right edges:
der_l, der_r = [(1, 3.j)], [(1, 4.+2.j)]
b = make_interp_spline(xx, yy, k, bc_type=(der_l, der_r))
assert_allclose(b(xx), yy, atol=1e-14, rtol=1e-14)
assert_allclose([b(xx[0], 1), b(xx[-1], 1)],
[der_l[0][1], der_r[0][1]], atol=1e-14, rtol=1e-14)
# also test zero and first order
for k in (0, 1):
b = make_interp_spline(xx, yy, k=k)
assert_allclose(b(xx), yy, atol=1e-14, rtol=1e-14)
def test_int_xy(self):
x = np.arange(10).astype(np.int_)
y = np.arange(10).astype(np.int_)
# cython chokes on "buffer type mismatch" (construction) or
# "no matching signature found" (evaluation)
for k in (0, 1, 2, 3):
b = make_interp_spline(x, y, k=k)
b(x)
def test_multiple_rhs(self):
yy = np.c_[np.sin(self.xx), np.cos(self.xx)]
der_l = [(1, [1., 2.])]
der_r = [(1, [3., 4.])]
b = make_interp_spline(self.xx, yy, k=3, bc_type=(der_l, der_r))
assert_allclose(b(self.xx), yy, atol=1e-14, rtol=1e-14)
assert_allclose(b(self.xx[0], 1), der_l[0][1], atol=1e-14, rtol=1e-14)
assert_allclose(b(self.xx[-1], 1), der_r[0][1], atol=1e-14, rtol=1e-14)
def test_integrate_ppoly(self):
# test .integrate method to be consistent with PPoly.integrate
x = [0, 1, 2, 3, 4]
b = make_interp_spline(x, x)
b.extrapolate = 'periodic'
p = PPoly.from_spline(b)
for x0, x1 in [(-5, 0.5), (0.5, 5), (-4, 13)]:
assert_allclose(b.integrate(x0, x1),
p.integrate(x0, x1))
def test_full_matrix(self):
np.random.seed(1234)
k, n = 3, 7
x = np.sort(np.random.random(size=n))
y = np.random.random(size=n)
t = _not_a_knot(x, k)
b = make_interp_spline(x, y, k, t)
cf = make_interp_full_matr(x, y, t, k)
assert_allclose(b.c, cf, atol=1e-14, rtol=1e-14)
def test_minimum_points_and_deriv(self):
# interpolation of f(x) = x**3 between 0 and 1. f'(x) = 3 * xx**2 and
# f'(0) = 0, f'(1) = 3.
k = 3
x = [0., 1.]
y = [0., 1.]
b = make_interp_spline(x, y, k, bc_type=([(1, 0.)], [(1, 3.)]))
xx = np.linspace(0., 1.)
yy = xx**3
assert_allclose(b(xx), yy, atol=1e-14, rtol=1e-14)
def setup_method(self):
xx = np.linspace(0, 4.*np.pi, 41)
yy = np.cos(xx)
b = make_interp_spline(xx, yy)
self.tck = (b.t, b.c, b.k)
self.xx, self.yy, self.b = xx, yy, b
self.xnew = np.linspace(0, 4.*np.pi, 21)
c2 = np.c_[b.c, b.c, b.c]
self.c2 = np.dstack((c2, c2))
self.b2 = BSpline(b.t, self.c2, b.k)
def test_quintic_derivs(self):
k, n = 5, 7
x = np.arange(n).astype(np.float_)
y = np.sin(x)
der_l = [(1, -12.), (2, 1)]
der_r = [(1, 8.), (2, 3.)]
b = make_interp_spline(x, y, k=k, bc_type=(der_l, der_r))
assert_allclose(b(x), y, atol=1e-14, rtol=1e-14)
assert_allclose([b(x[0], 1), b(x[0], 2)],
[val for (nu, val) in der_l])
assert_allclose([b(x[-1], 1), b(x[-1], 2)],
[val for (nu, val) in der_r])
def test_deriv_spec(self):
# If one of the derivatives is omitted, the spline definition is
# incomplete.
x = y = [1.0, 2, 3, 4, 5, 6]
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=([(1, 0.)], None))
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=(1, 0.))
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=[(1, 0.)])
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=42)
# CubicSpline expects`bc_type=(left_pair, right_pair)`, while
# here we expect `bc_type=(iterable, iterable)`.
l, r = (1, 0.0), (1, 0.0)
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=(l, r))
def test_cubic_deriv_unstable(self):
# 1st and 2nd derivative at x[0], no derivative information at x[-1]
# The problem is not that it fails [who would use this anyway],
# the problem is that it fails *silently*, and I've no idea
# how to detect this sort of instability.
# In this particular case: it's OK for len(t) < 20, goes haywire
# at larger `len(t)`.
k = 3
t = _augknt(self.xx, k)
der_l = [(1, 3.), (2, 4.)]
b = make_interp_spline(self.xx, self.yy, k, t, bc_type=(der_l, None))
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
def test_deriv_spec(self):
# If one of the derivatives is omitted, the spline definition is
# incomplete.
x = y = [1.0, 2, 3, 4, 5, 6]
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=([(1, 0.)], None))
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=(1, 0.))
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=[(1, 0.)])
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=42)
# CubicSpline expects`bc_type=(left_pair, right_pair)`, while
# here we expect `bc_type=(iterable, iterable)`.
l, r = (1, 0.0), (1, 0.0)
with assert_raises(ValueError):
make_interp_spline(x, y, bc_type=(l, r))
def test_cubic_deriv_unstable(self):
# 1st and 2nd derivative at x[0], no derivative information at x[-1]
# The problem is not that it fails [who would use this anyway],
# the problem is that it fails *silently*, and I've no idea
# how to detect this sort of instability.
# In this particular case: it's OK for len(t) < 20, goes haywire
# at larger `len(t)`.
k = 3
t = _augknt(self.xx, k)
der_l = [(1, 3.), (2, 4.)]
b = make_interp_spline(self.xx, self.yy, k, t, bc_type=(der_l, None))
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
def apply_torques(self, system, time: np.float = 0.0):
# Check if RL algorithm changed the points we fit the spline at this time step
# if points_array changed create a new spline. Using this approach we don't create a
# spline every time step.
# Make sure that first and last point y values are zero. Because we cannot generate a
# torque at first and last nodes.
# print('torque',self.max_rate_of_change_of_activation)
if (
not np.array_equal(self.points_cached[1, 1:-1], self.points_array(time))
or self.initial_call_flag == 0
):
self.initial_call_flag = 1
# Apply filter to the activation signal, to prevent drastic changes in activation signal.
self.filter_activation(
self.points_cached[1, 1:-1],
np.array((self.points_array(time))),
self.max_rate_of_change_of_activation,
)
self.my_spline = make_interp_spline(
self.points_cached[0], self.points_cached[1]
)
cumulative_lengths = np.cumsum(system.lengths)
# Compute the muscle torque magnitude from the beta spline.
self.torque_magnitude_cache = self.muscle_torque_scale * self.my_spline(
cumulative_lengths
)
self.compute_muscle_torques(
self.torque_magnitude_cache, self.direction, system.external_torques,
)
if self.counter % self.step_skip == 0:
if self.torque_profile_recorder is not None:
self.torque_profile_recorder["time"].append(time)
self.torque_profile_recorder["torque_mag"].append(
self.torque_magnitude_cache.copy()
)
self.torque_profile_recorder["torque"].append(
system.external_torques.copy()
)
self.torque_profile_recorder["element_position"].append(
np.cumsum(system.lengths)
)
self.counter += 1
def test_pid(P = 0.2, I= 0.0, D = 0.0, L = 100):
""" test-PID
for i in range(1, END):
pid.update(feedback)
output = pid.output
if pid.Setpoint > 0:
feedback += (output - (1/i))
if i > 9:
pid.Setpoint = 1
time.sleep(0.02 )
L: end time
"""
pid = basic_pid.PID(P,I,D)
pid.SetPoint = 0.0
pid.setSampleTime(0.01)
END = L
feedback = 0
feedback_list = []
time_list = []
setpoint_list = []
for i in range(1, END):
pid.update(feedback)
output = pid.output
if pid.SetPoint > 0:
feedback += (output - 1/i)
if i > 9:
pid.SetPoint = 1
time.sleep(0.02)
feedback_list.append(feedback)
setpoint_list.append(pid.SetPoint)
time_list.append(i)
time_sm = np.array(time_list)
time_smooth = np.linspace(time_sm.min(), time_sm.max(), 300)
helper = make_interp_spline(time_list,feedback_list)
feedback_smooth = helper(time_smooth)
plt.plot(time_smooth, feedback_smooth)
plt.plot(time_list,setpoint_list)
plt.xlim((0,L))
# plt.ylim((min(feedback_list),max(feedback_list)))
plt.xlabel('time(s)')
plt.ylabel('PID(PV)')
plt.title('TEST PID')
plt.grid(True)
plt.show()
def create_graph(
data,
usercolor,
title=None,
dimensions=(6, 3),
draw=False,
background_color="#2f3136",
):
plt.rcParams["figure.figsize"] = [dimensions[0], dimensions[1]]
T = np.array(list(range(0, len(data))))
xnew = np.linspace(T.min(), T.max(), 240)
spl = make_interp_spline(T, data, k=3)
power_smooth = spl(xnew)
# remove under 0
power = []
for x in power_smooth:
if x < 0:
x = 0
power.append(x)
power_smooth = np.array(power)
fig = plt.figure()
fig.patch.set_facecolor(background_color)
if title is not None:
fig.suptitle(title, color="white")
plt.autoscale(tight=True)
plt.plot(xnew, power_smooth, color=usercolor)
ax = plt.gca()
loc = plticker.MultipleLocator(base=1.0)
ax.xaxis.set_major_locator(loc)
ax.set_facecolor(background_color)
ax.set_xlabel("Hour (UTC)")
# ax.set_ylabel('XP gain')
ax.spines["bottom"].set_color("white")
ax.spines["left"].set_color("white")
ax.xaxis.label.set_color("white")
ax.yaxis.label.set_color("white")
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.tick_params(axis="x", colors="white")
ax.tick_params(axis="y", colors="white")
plt.fill_between(xnew, power_smooth, color=usercolor, alpha=0.2)
if draw:
plt.show()
plt.savefig("downloads/graph.png",
facecolor=background_color,
bbox_inches="tight")
plt.close()
def scatter(cost, term, titel):
x = list(range(1, 10))
plt.scatter(x, cost)
plt.yscale('linear')
xnew = np.linspace(min(x), max(x), 300)
spl = make_interp_spline(x, cost, k=3) #BSpline object
power_smooth = spl(xnew)
plt.plot(xnew, power_smooth, color='orange')
plt.title('Distribution of cost per worker:searcher ants')
plt.xlabel('Amount searchers (total=10)')
plt.legend(labels=['Population ' + term]) #,'Population ' + term2])
plt.ylabel('Cost')
plt.savefig(titel)
plt.clf()
def cubic_interpolation(self, input_list, length, show_spline=False):
# get scale info from output list.
input_array = np.array(input_list)
scale = int(length / (len(input_list) - 1))
x = np.array([i * scale for i in range(len(input_list))])
b_n = interpolate.make_interp_spline(x, input_array)
output_list = []
for ii in range(length):
output_list.append(int(b_n(ii)))
if(show_spline==True):
plt.plot([i * scale for i in range(len(input_list))], input_list)
plt.plot(range(len(output_list)), output_list)
plt.show()
return output_list
def _reavg(X0, Y0, X1, k, axis):
"""cumsum, interpolate, and diff.
"""
Y0_cum = Y0.cumsum(axis=axis)
if axis == -1: axis += Y0.ndim
_Y0_cum = numpy.zeros(Y0.shape[:axis] + (1,) + Y0.shape[axis+1:])
Y0_cum = numpy.concatenate((_Y0_cum, Y0_cum), axis=axis)
Y1_cum = make_interp_spline(X0, Y0_cum, k=k, axis=axis)(X1)
Y1 = numpy.diff(Y1_cum, axis=axis)
return Y1
def animate(i):
global U_prev, U_curr, F, surf, ax
for j in range(i * draw_every, (i + 1) * draw_every):
F = get_F(f, j * dt, points)
# Calculate the left hand side
B = get_rhs(dt, S, T, U_curr, U_prev, F)
# move one step
U_prev = U_curr.copy()
U_curr = spsolve(A, B)
# Update the plot
# Remove elements
surf.remove()
# cb.remove()
# # Get the new colormap levels
# mid = 0 #(U_curr.min() + U_curr.max())/2
# m = mid - max(abs(U_curr.min() - mid),abs(U_curr.max() - mid))
# M = mid + max(abs(U_curr.min() - mid),abs(U_curr.max() - mid))
# clev = np.linspace(m,M,49)
# Get the values along the z-plane
U = np.array([
np.mean(U_curr[mesh.simplices[mesh.find_simplex([r, z])]]) for r in RR
])
interp = interpolate.make_interp_spline(RR, U)
U = interp(RR)
Ff = lambda x, y: interp((x**2 + y**2)**0.5)
Z = Ff(X, Y)
# Plot the 3D version
surf = ax.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
cmap=cm.coolwarm,
norm=colors.CenteredNorm(),
edgecolor='none')
# surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,cmap=cm.coolwarm, edgecolor='none')
# cb = fig.colorbar(surf)
ax.set_title(r'Wave in $\phi$-slice t=%.2e' % (i * dt))
progress1.update(i)
return surf
def drawhourtrendgram(pivot_data, lblname):
plt.figure(figsize=(5, 2.5))
plt.subplots_adjust(0.11, 0.18, 0.98, 0.98, 0.2, 0.32)
x = pivot_data.iloc[:, 0]
x_new = np.linspace(x.min(), x.max(), 300)
for i in range(len(lblname)):
y = pivot_data.iloc[:, i + 1]
y_smooth = make_interp_spline(x, y)(x_new)
plt.plot(x_new, y_smooth, linewidth=1, label=lblname[i])
plt.xlabel('Hour', fontdict=fontdict)
plt.ylabel('Miles', fontdict=fontdict)
plt.xticks(fontproperties='Times New Roman', size=10)
plt.yticks(fontproperties='Times New Roman', size=10)
plt.legend(prop=fontdict, loc='upper right')
def test_pid(P=0.2, I=0.1, D=0.0, M=10, L=100):
pid = PID.PID(P, I, D, M)
pid.point = 0.0
pid.sample_time = 0.01
END = L
feedback = 0
feedback_list = []
time_list = []
setpoint_list = []
for i in range(1, END):
output = pid.update(feedback)
if not pid.point == 0:
feedback += simulator.func3(
output - (1 / i)) #+ random.uniform(-0.01, 0.01)
#print feedback
if i > 9:
pid.point = 1
time.sleep(0.02)
feedback_list.append(feedback)
setpoint_list.append(pid.point)
time_list.append(i)
time_sm = np.array(time_list)
time_smooth = np.linspace(time_sm.min(), time_sm.max(), 300)
# feedback_smooth = spline(time_list, feedback_list, time_smooth)
# Using make_interp_spline to create BSpline
helper_x3 = make_interp_spline(time_list, feedback_list)
feedback_smooth = helper_x3(time_smooth)
plt.plot(time_smooth, feedback_smooth)
plt.plot(time_list, setpoint_list)
plt.xlim((0, L))
plt.ylim((min(feedback_list) - 0.5, max(feedback_list) + 0.5))
plt.xlabel('time (s)')
plt.ylabel('PID (PV)')
plt.title('TEST PID')
plt.ylim((1 - 0.5, 1 + 0.5))
plt.grid(True)
plt.show()
def spline_it(data,n):
splx = np.array([])
for i in np.arange(n):
t = data[i][0]
IA = data[i][1]
U2 = data[i][2]
U2new = np.linspace(U2[0],U2[-1],100)
spl = make_interp_spline(U2,IA,k=5)
pair = np.array([spl,U2new])
splx = np.hstack((splx,pair))
return splx;
def savechart(df, sunrise, sunset):
myFmt = mdates.DateFormatter('%I %p')
x_min = east.localize(
datetime.combine(date.today(), time(sunrise.hour - 2, sunrise.minute)))
x_max = east.localize(
datetime.combine(date.today(), time(sunset.hour + 2, sunset.minute)))
y_max = (df['Pred(Ft)'].max())
y_min = (df['Pred(Ft)'].min())
fig = plt.figure() # an empty figure with no axes
fig.suptitle(
'No axes on this figure') # Add a title so we know which it is
fig, ax = plt.subplots(1, 1)
ax.set_xlim([x_min, x_max])
ax.set_ylim([y_min, y_max])
x = df['Timestamp']
y = df['Pred(Ft)']
xnew = np.linspace(
pd.Timestamp(x_min).value,
pd.Timestamp(x_max).value,
90) #300 represents number of points to make between T.min and T.max
xnew = pd.to_datetime(xnew)
spl = make_interp_spline(x, y, k=3) #BSpline object
x_wave = spl(xnew)
ax.plot(xnew, x_wave, markerfacecolor='dodgerblue', color='black')
#set axis labels to time
ax.xaxis.set_major_formatter(myFmt)
ax.set_facecolor('ivory')
ax.fill_between(xnew,
x_wave,
y2=y_min,
where=None,
interpolate=False,
facecolor='dodgerblue',
alpha=1,
zorder=10)
ax.axvspan(x_min, sunrise, alpha=0.2, color='blue', zorder=15)
ax.axvspan(x_max, sunset, alpha=0.2, color='blue', zorder=15)
ax.set_title("Tide and Daylight Forecast: ")
ax.set_yticklabels([])
ax.set_yticks([])
plt.savefig('tide_chart_new.png', bbox_inches='tight')
return ()
def hourtrendgram(pivot_data, lblname):
f = plt.figure(figsize=(2.6, 2.5))
ax = plt.axes()
plt.subplots_adjust(0.1, 0.17, 0.98, 0.98, 0.2, 0.2)
x = pivot_data.iloc[:, 0]
x_new = np.linspace(x.min(), x.max(), 300)
for i in range(len(lblname)):
y = pivot_data.iloc[:, i + 1]
y_smooth = make_interp_spline(x, y)(x_new)
plt.plot(x_new, y_smooth, color='k', linestyle=dict_linestyle[i], linewidth=1, label=lblname[i])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
seticksandlegend('Hour/h', 'Miles/km', loc='upper right', ncol=2, frameon=False)
savefig2tif(f, '小时趋势变化图.tif')
def figure3():
xs = [a for a, b in cum]
ys = [b / totalP for a, b in cum]
xs_new = np.linspace(min(xs), max(xs), 300)
ys_smooth = spy.make_interp_spline(xs, ys)
ys_new = ys_smooth(xs_new)
f = plt.figure(figsize=(5, 5))
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['top'].set_visible(False)
plt.plot(xs_new, ys_new, 'k')
f.savefig(pdffile3, bbox_inches='tight')
print("Scribeva:", pdffile3)
plt.show()
def test_knots_not_data_sites(self):
# Knots need not coincide with the data sites.
# use a quadratic spline, knots are at data averages,
# two additional constraints are zero 2nd derivs at edges
k = 2
t = np.r_[(self.xx[0],)*(k+1),
(self.xx[1:] + self.xx[:-1]) / 2.,
(self.xx[-1],)*(k+1)]
b = make_interp_spline(self.xx, self.yy, k, t,
bc_type=([(2, 0)], [(2, 0)]))
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
assert_allclose([b(self.xx[0], 2), b(self.xx[-1], 2)], [0., 0.],
atol=1e-14)
def test_periodic_axis(self):
n = self.xx.shape[0]
np.random.seed(1234)
x = np.random.random_sample(n) * 2 * np.pi
x = np.sort(x)
x[0] = 0.
x[-1] = 2 * np.pi
y = np.zeros((2, n))
y[0] = np.sin(x)
y[1] = np.cos(x)
b = make_interp_spline(x, y, k=5, bc_type='periodic', axis=1)
for i in range(n):
assert_allclose(b(x[i]), y[:, i], atol=1e-14)
assert_allclose(b(x[0]), b(x[-1]), atol=1e-14)
def plot():
x = ln(forward, backward)[0]
y = ln(forward, backward)[1]
plt.scatter(x, y, s=30)
z = sorted(x.flatten())
x_new = np.linspace(min(z), max(z), 15)
a_BSpline = make_interp_spline(z, y, k=len(x) - 1)
y_new = a_BSpline(x_new)
plt.grid(True)
plt.title("Water consumption")
plt.xlabel("x")
plt.ylabel("y")
plt.plot(x, y)
plt.show()
def test_knots_not_data_sites(self):
# Knots need not coincide with the data sites.
# use a quadratic spline, knots are at data averages,
# two additional constraints are zero 2nd derivatives at edges
k = 2
t = np.r_[(self.xx[0],)*(k+1),
(self.xx[1:] + self.xx[:-1]) / 2.,
(self.xx[-1],)*(k+1)]
b = make_interp_spline(self.xx, self.yy, k, t,
bc_type=([(2, 0)], [(2, 0)]))
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
assert_allclose([b(self.xx[0], 2), b(self.xx[-1], 2)], [0., 0.],
atol=1e-14)
def plot_confidence_interval(*args,
label=None,
q_inf=0.1,
q_sup=0.9,
alpha=.3,
smoothing=None,
dots=False):
y_data = np.asarray(args[-1])
if len(args) == 1:
# We only have the data. We create x-axis values.
x_data = np.arange(y_data.shape[0])
else:
x_data = np.asarray(args[0])
avg = np.mean(y_data, axis=0)
q10 = np.quantile(y_data, q_inf, axis=0)
q90 = np.quantile(y_data, q_sup, axis=0)
if smoothing is not None:
x_plot = np.linspace(x_data.min(), x_data.max(),
x_data.shape[0] * smoothing)
avg_plot = make_interp_spline(x_data, avg, k=2)(x_plot)
q10_plot = make_interp_spline(x_data, q10, k=2)(x_plot)
q90_plot = make_interp_spline(x_data, q90, k=2)(x_plot)
else:
x_plot = x_data
avg_plot = avg
q10_plot = q10
q90_plot = q90
line = plt.plot(x_plot, avg_plot, label=label)
color = line[0].get_c()
if dots:
plt.scatter(x_data, avg, c=color)
plt.fill_between(x_plot, q90_plot, q10_plot, color=color, alpha=alpha)
def test_pid(P=0.2, I=0.0, D=0.0, L=100):
pid = PID(P, I, D)
pid.SetPoint = 0.0
pid.setSampleTime(0.01)
END = L
feedback = 0
feedback_list = []
time_list = []
setpoint_list = []
for i in range(1, END):
pid.update(feedback)
output = pid.output
# print(output)
if pid.SetPoint > 0:
feedback += output # (output - (1/i))控制系统的函数
if 9 <= i <= 40:
pid.SetPoint = 1
elif i > 40:
pid.SetPoint = 0.5
time.sleep(0.01)
feedback_list.append(feedback)
setpoint_list.append(pid.SetPoint)
time_list.append(i)
time_sm = np.array(time_list)
time_smooth = np.linspace(time_sm.min(), time_sm.max(), 300)
feedback_smooth = make_interp_spline(time_list, feedback_list)(time_smooth)
plt.figure(figsize=(8, 8 * 0.618))
plt.plot(time_smooth, feedback_smooth)
plt.plot(time_list, setpoint_list)
plt.xlabel("Time(s)")
plt.ylabel("PID(PV)")
plt.title("TEST-PID Kp={} Ki={} Kd={}".format(P, I, D))
plt.grid(True)
plt.savefig("PID-Kp={},Ki={},Kd={}.jpg".format(P, I, D))
plt.savefig("PID-Kp={},Ki={},Kd={}.pdf".format(P, I, D))
plt.close()
return 0
def plot_func(plot_name, func):
"""
It plots a single variable function.
"""
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# Eliminate upper and right axes
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
x = [-2, -1.5, -1, 0, 1, 1.5, 2]
y = [func(i) for i in x]
# Creating smooth curve
xnew = np.linspace(min(x), max(x), 300)
spl = make_interp_spline(x, y, k=3)
smooth_y = spl(xnew)
plt.plot(xnew, smooth_y, color='black')
# Plot constraint
#plt.axvline(2, color='k', linestyle='dashed')
# plot min point
#plt.plot([0], [0], marker='o', color='k')
# range of x and y axis
x_range = ax.get_xlim()
y_range = ax.get_ylim()
plt.xticks(np.arange(int(x_range[0]), int(x_range[1]) + 1, step=1))
plt.yticks(np.arange(0, int(y_range[1]) + 1, step=1))
# axis major lines
ax.xaxis.grid(which='major', linestyle='--')
ax.yaxis.grid(which='major', linestyle='--')
plt.ylabel(r'$f(x)$')
plt.xlabel(r'$x$')
# Set margin to zero to make y-axis connected to x-axis
#plt.margins(0)
plt.tight_layout() # To fix axis labels not shown completely in the fig
plt.show()
fig.savefig(join('./figs/', plot_name + '.png'), format='png', dpi=500)
def getRSSIGraphs():
for x in range(11):
if x % 2 == 0:
# RSSI Values for Boxes + No Boxes
noBoxRSSI = getRSSI('NoBoxData/noBox' + str(x) + '.csv')
boxRSSI = getRSSI('BoxData/box' + str(x) + '.csv')
# Scan values for Boxes + No Boxes
noBoxScans = getIteration('NoBoxData/noBox' + str(x) + '.csv')
boxScans = getIteration('BoxData/box' + str(x) + '.csv')
# Range Limit
plt.ylim(min(noBoxRSSI) - 20, max(noBoxRSSI) + 20)
# Get first 50 scans
plt.xlim(0, 51)
# X scale
plt.xticks(np.arange(0, 51, 2.0))
# Axes Labels
plt.xlabel('Scan Number')
plt.ylabel('RSSI Value (dBm)')
# Title
plt.title(str(x) + " Meters RSSI Values")
# Smoothen Graph
xNoBoxNew = np.linspace(min(noBoxScans), max(noBoxScans), 600)
xBoxNew = np.linspace(min(boxScans), max(boxScans), 600)
spl = make_interp_spline(noBoxScans, noBoxRSSI, k=3)
spl2 = make_interp_spline(boxScans, boxRSSI, k=3)
newNoBox = spl(xNoBoxNew)
newBox = spl2(xBoxNew)
# Plot Graph
plt.plot(xNoBoxNew, newNoBox, "-b", label="No BoxData")
plt.plot(xBoxNew, newBox, "-r", label="Boxed")
# Legend
plt.legend(loc="upper right")
# Save + close plot
plt.savefig('RSSIGraphs/' + str(x) + "Meters")
plt.close()
def __init__(self):
self.f = open('./media/test2.txt').read()
self.f = open('./media/test2.txt').read()
self.data = self.f[self.f.find(' -'):self.f.find('\n\n\n')].split('\n')[3:]
self.all_data = []
for line in self.data:
if 'threshold' not in line:
line = line.strip()
self.all_data.append(list(map(float, re.split(r'\s+', line)[0:2])))
else:
break
with open('./media/all_data.csv', 'wb') as f:
np.savetxt(f, self.all_data, fmt='%.2e %.2f', delimiter=',')
self.read_data = np.genfromtxt('./media/all_data.csv')
x = list(x for x in range(self.read_data.shape[0]))
y1 = [np.log10(x) for x in self.read_data[:, 0]]
y2 = self.read_data[:, 1]
self.xnew = np.linspace(min(x), max(x), 30)
spl1 = make_interp_spline(x, y1, k=3)
spl2 = make_interp_spline(x, y2, k=3)
self.y1_new = spl1(self.xnew)
self.y2_new = spl2(self.xnew)
def plotHoughTransform(data, t, smooth=True):
_, ax = plt.subplots()
for datum in data:
x, y, *th = datum
if smooth:
new_t = linspace(t[0], t[-1], 100)
a_spline = interpolate.make_interp_spline(t, th)
new_th = a_spline(new_t)
plt.plot(new_t, new_th, label="(" + str(x) + "," + str(y) + ")")
else:
plt.plot(t, th, label="(" + str(x) + "," + str(y) + ")")
ax.set(xlabel="theta", ylabel="r value", title="HOUGH LINE CURVE")
plt.legend(bbox_to_anchor=(1, 1), loc='upper left', borderaxespad=0.)
plt.show()
def plot_quadratic(plot_name):
"""
It plots a quadratic function
"""
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# move left y-axis to center
ax.spines['left'].set_position('center')
#ax.spines['bottom'].set_position('center')
# Eliminate upper and right axes
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
x = [-3, -2, -1, 0, 1, 2, 3]
y = [i**2 for i in x]
# Creating smooth curve
xnew = np.linspace(min(x), max(x), 300)
spl = make_interp_spline(x, y, k=3)
smooth_y = spl(xnew)
plt.plot(xnew, smooth_y, color='black')
# Plot constraint
#plt.axvline(2, color='k', linestyle='dashed')
# plot min point
#plt.plot([0], [0], marker='o', color='k')
# range of x and y axis
x_range = ax.get_xlim()
y_range = ax.get_ylim()
plt.xticks(np.arange(int(x_range[0]), int(x_range[1]) + 1, step=1))
plt.yticks(np.arange(int(y_range[0]) + 1, int(y_range[1]) + 1, step=1))
# axis major lines
ax.xaxis.grid(which='major', linestyle='--')
ax.yaxis.grid(which='major', linestyle='--')
# Set margin to zero to make y-axis connected to x-axis
plt.margins(0)
plt.show()
fig.savefig(join('./figs/', plot_name + '.png'), format='png', dpi=500)
def paint4(list1, list2, title, filename):
length = len(list1)
length2 = len(list2)
x = np.arange(0, length, 1)
x2 = np.arange(0, length2, 1)
# x2 *= int(max(length, length2) / length2)
x2 *= 10
y1 = list1
y2 = list2
x_new = np.linspace(x.min(), x.max(), 300)
# x2_new = np.linspace(x2.min(), x2.max(), 300)
y_smooth1 = make_interp_spline(x, y1)(x_new)
# y_smooth2 = make_interp_spline(x2, y2)(x2_new)
plt.title(title, fontsize='14')
plt.xlabel("Time (h)", fontsize='14')
plt.ylabel("Rate (numbers/min)", fontsize='14')
plt.plot(x_new,
y_smooth1,
c='burlywood',
label='Instantaneous rate',
linewidth='2')
plt.plot(x2,
y2,
c='green',
label='Average rate',
linewidth='2',
marker="o",
markersize=3)
plt.xticks([
0,
int(length / 8),
int(length / 4),
int(3 * length / 8),
int(2 * length / 4),
int(5 * length / 8),
int(3 * length / 4),
int(7 * length / 8), length
], ["0:00", '', "6:00", '', "12:00", '', "18:00", '', "0:00"])
plt.grid(axis='y') # 添加网格
# sns.despine()
plt.tick_params(labelsize=12) # 刻度字体大小13
plt.legend(fontsize="13")
plt.savefig(filename)
plt.show()
def fit_plot(dataTrain, dataTest, K): # get w w, p_train, p_test, err_train, err_test = train_w(dataTrain, dataTest, K) # plot graph plt.scatter(dataTrain[0],dataTrain[1],20) x_new = np.linspace(np.amin(dataTrain[0]), np.amax(dataTrain[0]),1000) a_BSpline = interpolate.make_interp_spline(dataTrain[0,dataTrain[0,:].argsort()],p_train[dataTrain[0,:].argsort()]) y_new = a_BSpline(x_new) plt.plot(x_new,y_new,color="red") plt.ylim(np.amin(dataTrain[1])-5, np.amax(dataTrain[1])+5) plt.show() return w, err_train, err_test
def test_minimum_points_and_deriv(self):
# interpolation of f(x) = x**3 between 0 and 1. f'(x) = 3 * xx**2 and
# f'(0) = 0, f'(1) = 3.
k = 3
x = [0., 1.]
y = [0., 1.]
b = make_interp_spline(x, y, k, bc_type=([(1, 0.)], [(1, 3.)]))
xx = np.linspace(0., 1.)
yy = xx**3
assert_allclose(b(xx), yy, atol=1e-14, rtol=1e-14)
# If one of the derivatives is omitted, the spline definition is
# incomplete:
assert_raises(ValueError, make_interp_spline, x, y, k,
**dict(bc_type=([(1, 0.)], None)))
def __init__(self):
self.nmass = 3 # number of mass eigen states
self.nnu = const.nnu_standard
self.mass = np.zeros(self.nmass)
self.lammax = 1e3 # max of am/T
self.lammin = 1e-3 # min of am/T
nlam = 1000
lnlam = np.linspace(np.log(self.lammin),np.log(self.lammax),nlam)
lnrho = np.zeros(nlam)
for i in range(nlam):
lam = np.exp(lnlam[i])
lnrho[i] = integrate.quad(lambda x:x**2*np.sqrt(x**2+lam**2)/(np.exp(x)+1),0,100)[0]
lnrho = np.log(lnrho[:]*const.nu_energy) # ratio of massive to massless
self.spl_lnrho = interpolate.make_interp_spline(lnlam,lnrho)
def oscillatorPlot(A,T):
p=np.array(T)
q=np.array(A)/2
plt.title('Oscillator-plot : Amplitude vs Time')
x=np.linspace(p.min(),p.max(),100)
a_BSpline=ie.make_interp_spline(p,q)
y=a_BSpline(x)
plt.plot(x,y,label="Generated Graph")
plt.plot(p,q,marker='o',label="Actual Graph")
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
plt.grid(True,which='both')
plt.axhline(y=0, color='k')
plt.xlabel('Time in ms')
plt.ylabel('Amplitude in volts')
plt.show()
def test_minimum_points_and_deriv(self):
# interpolation of f(x) = x**3 between 0 and 1. f'(x) = 3 * xx**2 and
# f'(0) = 0, f'(1) = 3.
k = 3
x = [0., 1.]
y = [0., 1.]
b = make_interp_spline(x, y, k, bc_type=([(1, 0.)], [(1, 3.)]))
xx = np.linspace(0., 1.)
yy = xx**3
assert_allclose(b(xx), yy, atol=1e-14, rtol=1e-14)
# If one of the derivatives is omitted, the spline definition is
# incomplete:
assert_raises(ValueError, make_interp_spline, x, y, k,
**dict(bc_type=([(1, 0.)], None)))
def test_linear(self):
b = make_interp_spline(self.xx, self.yy, k=1)
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
def test_list_input(self, k):
# regression test for gh-8714: TypeError for x, y being lists and k=2
x = list(range(10))
y = [a**2 for a in x]
make_interp_spline(x, y, k=k)
def test_string_aliases(self):
yy = np.sin(self.xx)
# a single string is duplicated
b1 = make_interp_spline(self.xx, yy, k=3, bc_type='natural')
b2 = make_interp_spline(self.xx, yy, k=3, bc_type=([(2, 0)], [(2, 0)]))
assert_allclose(b1.c, b2.c, atol=1e-15)
# two strings are handled
b1 = make_interp_spline(self.xx, yy, k=3,
bc_type=('natural', 'clamped'))
b2 = make_interp_spline(self.xx, yy, k=3,
bc_type=([(2, 0)], [(1, 0)]))
assert_allclose(b1.c, b2.c, atol=1e-15)
# one-sided BCs are OK
b1 = make_interp_spline(self.xx, yy, k=2, bc_type=(None, 'clamped'))
b2 = make_interp_spline(self.xx, yy, k=2, bc_type=(None, [(1, 0.0)]))
assert_allclose(b1.c, b2.c, atol=1e-15)
# 'not-a-knot' is equivalent to None
b1 = make_interp_spline(self.xx, yy, k=3, bc_type='not-a-knot')
b2 = make_interp_spline(self.xx, yy, k=3, bc_type=None)
assert_allclose(b1.c, b2.c, atol=1e-15)
# unknown strings do not pass
with assert_raises(ValueError):
make_interp_spline(self.xx, yy, k=3, bc_type='typo')
# string aliases are handled for 2D values
yy = np.c_[np.sin(self.xx), np.cos(self.xx)]
der_l = [(1, [0., 0.])]
der_r = [(2, [0., 0.])]
b2 = make_interp_spline(self.xx, yy, k=3, bc_type=(der_l, der_r))
b1 = make_interp_spline(self.xx, yy, k=3,
bc_type=('clamped', 'natural'))
assert_allclose(b1.c, b2.c, atol=1e-15)
# ... and for n-D values:
np.random.seed(1234)
k, n = 3, 22
x = np.sort(np.random.random(size=n))
y = np.random.random(size=(n, 5, 6, 7))
# now throw in some derivatives
d_l = [(1, np.zeros((5, 6, 7)))]
d_r = [(1, np.zeros((5, 6, 7)))]
b1 = make_interp_spline(x, y, k, bc_type=(d_l, d_r))
b2 = make_interp_spline(x, y, k, bc_type='clamped')
assert_allclose(b1.c, b2.c, atol=1e-15)
def test_int_xy(self):
x = np.arange(10).astype(np.int_)
y = np.arange(10).astype(np.int_)
# cython chokes on "buffer type mismatch"
make_interp_spline(x, y, k=1)
def test_non_int_order(self):
with assert_raises(TypeError):
make_interp_spline(self.xx, self.yy, k=2.5)
def spl_interp(x, y, axis):
return make_interp_spline(x, y, axis=axis)
def test_order_0(self):
b = make_interp_spline(self.xx, self.yy, k=0)
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
def test_not_a_knot(self):
for k in [3, 5]:
b = make_interp_spline(self.xx, self.yy, k)
assert_allclose(b(self.xx), self.yy, atol=1e-14, rtol=1e-14)
def bspl_antideriv(x, y, axis=0):
return make_interp_spline(x, y, axis=axis).antiderivative()
