Plot a piecewise-defined function
$begingroup$
I would like to plot the following function:
$$
begin{align}
Lambda(deltatau) &equiv chi(deltatau, 0)
= frac{1}{T_i} int_0^{T_i} a(t_0+t')a(t_0+t'+deltatau) dt' \
&= begin{cases}
1 - frac{left|deltatauright|}{tau_c}, &left|deltatauright| le tau_c(1+frac{tau_c}{T_i}) \
-frac{tau_c}{T_i}, &left|deltatauright| gt tau_c(1+frac{tau_c}{T_i}) \
end{cases}
end{align}
$$
which represents a simple triangular shape.
There is a conditional statement. My implementation uses a for loop as follows:
def waf_delay(delay_increment):
for d in delay_increment:
if np.abs(d) <= delay_chip*(1+delay_chip/integration_time):
yield 1 - np.abs(d)/delay_chip
else:
yield -delay_chip/integration_time;
integration_time = 1e-3 # seconds
delay_chip = 1/1.023e6 # seconds
x=np.arange(-5.0, 5.0, 0.1)
y=list(waf_delay(x))
plt.plot(x, y)
plt.show()
Is there a more correct way to transform an array based on a condition rather than just looping through it? Instead of having something like this:
def f(x_array):
for x in x_array:
if np.abs(x) <= 3:
yield 1 - x/3
else:
yield 0
x=np.arange(-5.0, 5.0, 0.1)
y=list(f(x))
plt.plot(x, y)
plt.show()
I would like to write something like this:
def f(x):
if np.abs(x) <= 3:
yield 1 - x/3
else:
yield 0
x=np.arange(-5.0, 5.0, 0.1)
plt.plot(x, f(x))
plt.show()
that could take an array.
python numpy matplotlib
edited 8 hours ago
200_success
129k15152415
asked 8 hours ago
WooWapDaBugWooWapDaBug
365314
$endgroup$
add a comment |
$begingroup$
I would like to plot the following function:
$$
begin{align}
Lambda(deltatau) &equiv chi(deltatau, 0)
= frac{1}{T_i} int_0^{T_i} a(t_0+t')a(t_0+t'+deltatau) dt' \
&= begin{cases}
1 - frac{left|deltatauright|}{tau_c}, &left|deltatauright| le tau_c(1+frac{tau_c}{T_i}) \
-frac{tau_c}{T_i}, &left|deltatauright| gt tau_c(1+frac{tau_c}{T_i}) \
end{cases}
end{align}
$$
which represents a simple triangular shape.
There is a conditional statement. My implementation uses a for loop as follows:
def waf_delay(delay_increment):
for d in delay_increment:
if np.abs(d) <= delay_chip*(1+delay_chip/integration_time):
yield 1 - np.abs(d)/delay_chip
else:
yield -delay_chip/integration_time;
integration_time = 1e-3 # seconds
delay_chip = 1/1.023e6 # seconds
x=np.arange(-5.0, 5.0, 0.1)
y=list(waf_delay(x))
plt.plot(x, y)
plt.show()
Is there a more correct way to transform an array based on a condition rather than just looping through it? Instead of having something like this:
def f(x_array):
for x in x_array:
if np.abs(x) <= 3:
yield 1 - x/3
else:
yield 0
x=np.arange(-5.0, 5.0, 0.1)
y=list(f(x))
plt.plot(x, y)
plt.show()
I would like to write something like this:
def f(x):
if np.abs(x) <= 3:
yield 1 - x/3
else:
yield 0
x=np.arange(-5.0, 5.0, 0.1)
plt.plot(x, f(x))
plt.show()
that could take an array.
python numpy matplotlib
edited 8 hours ago
200_success
129k15152415
asked 8 hours ago
WooWapDaBugWooWapDaBug
365314
$endgroup$
add a comment |
$begingroup$
I would like to plot the following function:
$$
begin{align}
Lambda(deltatau) &equiv chi(deltatau, 0)
= frac{1}{T_i} int_0^{T_i} a(t_0+t')a(t_0+t'+deltatau) dt' \
&= begin{cases}
1 - frac{left|deltatauright|}{tau_c}, &left|deltatauright| le tau_c(1+frac{tau_c}{T_i}) \
-frac{tau_c}{T_i}, &left|deltatauright| gt tau_c(1+frac{tau_c}{T_i}) \
end{cases}
end{align}
$$
which represents a simple triangular shape.
There is a conditional statement. My implementation uses a for loop as follows:
def waf_delay(delay_increment):
for d in delay_increment:
if np.abs(d) <= delay_chip*(1+delay_chip/integration_time):
yield 1 - np.abs(d)/delay_chip
else:
yield -delay_chip/integration_time;
integration_time = 1e-3 # seconds
delay_chip = 1/1.023e6 # seconds
x=np.arange(-5.0, 5.0, 0.1)
y=list(waf_delay(x))
plt.plot(x, y)
plt.show()
Is there a more correct way to transform an array based on a condition rather than just looping through it? Instead of having something like this:
def f(x_array):
for x in x_array:
if np.abs(x) <= 3:
yield 1 - x/3
else:
yield 0
x=np.arange(-5.0, 5.0, 0.1)
y=list(f(x))
plt.plot(x, y)
plt.show()
I would like to write something like this:
def f(x):
if np.abs(x) <= 3:
yield 1 - x/3
else:
yield 0
x=np.arange(-5.0, 5.0, 0.1)
plt.plot(x, f(x))
plt.show()
that could take an array.
python numpy matplotlib
edited 8 hours ago
200_success
129k15152415
asked 8 hours ago
WooWapDaBugWooWapDaBug
365314
$endgroup$
I would like to plot the following function:
$$
begin{align}
Lambda(deltatau) &equiv chi(deltatau, 0)
= frac{1}{T_i} int_0^{T_i} a(t_0+t')a(t_0+t'+deltatau) dt' \
&= begin{cases}
1 - frac{left|deltatauright|}{tau_c}, &left|deltatauright| le tau_c(1+frac{tau_c}{T_i}) \
-frac{tau_c}{T_i}, &left|deltatauright| gt tau_c(1+frac{tau_c}{T_i}) \
end{cases}
end{align}
$$
which represents a simple triangular shape.
There is a conditional statement. My implementation uses a for loop as follows:
def waf_delay(delay_increment):
for d in delay_increment:
if np.abs(d) <= delay_chip*(1+delay_chip/integration_time):
yield 1 - np.abs(d)/delay_chip
else:
yield -delay_chip/integration_time;
integration_time = 1e-3 # seconds
delay_chip = 1/1.023e6 # seconds
x=np.arange(-5.0, 5.0, 0.1)
y=list(waf_delay(x))
plt.plot(x, y)
plt.show()
Is there a more correct way to transform an array based on a condition rather than just looping through it? Instead of having something like this:
def f(x_array):
for x in x_array:
if np.abs(x) <= 3:
yield 1 - x/3
else:
yield 0
x=np.arange(-5.0, 5.0, 0.1)
y=list(f(x))
plt.plot(x, y)
plt.show()
I would like to write something like this:
def f(x):
if np.abs(x) <= 3:
yield 1 - x/3
else:
yield 0
x=np.arange(-5.0, 5.0, 0.1)
plt.plot(x, f(x))
plt.show()
that could take an array.
python numpy matplotlib
python numpy matplotlib
edited 8 hours ago
200_success
129k15152415
asked 8 hours ago
WooWapDaBugWooWapDaBug
365314
edited 8 hours ago
200_success
129k15152415
asked 8 hours ago
WooWapDaBugWooWapDaBug
365314
edited 8 hours ago
200_success
129k15152415
edited 8 hours ago
200_success
129k15152415
edited 8 hours ago
200_success
129k15152415
129k15152415
asked 8 hours ago
WooWapDaBugWooWapDaBug
365314
asked 8 hours ago
WooWapDaBugWooWapDaBug
365314
asked 8 hours ago
WooWapDaBugWooWapDaBug
365314
365314
add a comment |
add a comment |
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