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mode 100644 genindex.html create mode 100644 index.html create mode 100644 objects.inv create mode 100644 py-modindex.html create mode 100644 search.html create mode 100644 searchindex.js diff --git a/.buildinfo b/.buildinfo new file mode 100644 index 0000000..d66cf43 --- /dev/null +++ b/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: 2fb6e146442f9ad8a6880f891dfc83c8 +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/_autosummary/pyudv.attenuation.alpha_w.html b/_autosummary/pyudv.attenuation.alpha_w.html new file mode 100644 index 0000000..c093d61 --- /dev/null +++ b/_autosummary/pyudv.attenuation.alpha_w.html @@ -0,0 +1,582 @@ + + + + + + + + + + + pyudv.attenuation.alpha_w — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.alpha_w#

+
+
+alpha_w(f, T)[source]#
+

Calculate the fresh water attenuation in m-1.

+
+
Parameters:
+
    +

  • f (scalar, array) – Sound frequency in Hertz.

    • +

  • T (scalar, array) – Temperature in Celsius degrees.

    • +
+
+
Returns:
+

return the fresh water attenuation in m-1.

+
+
Return type:
+

scalar, array

+
+
+

Examples

+
>>> f = 2e6
+>>> T = 25
+>>> print(alpha_w(f, T))
+
+
+

References

+

[1] Fisher, F. H., & Simmons, V. P. (1977). Sound absorption in sea water. The Journal of the Acoustical Society of America, 62(3), 558-564.

+
+ +
+

Examples using pyudv.attenuation.alpha_w#

+
+

Concentration inference from amplitude signals

+
Concentration inference from amplitude signals
+
+
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
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+
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.correction_factor.html b/_autosummary/pyudv.attenuation.correction_factor.html new file mode 100644 index 0000000..935df28 --- /dev/null +++ b/_autosummary/pyudv.attenuation.correction_factor.html @@ -0,0 +1,571 @@ + + + + + + + + + + + pyudv.attenuation.correction_factor — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.correction_factor#

+
+
+correction_factor(r, alpha_w, Ks, Kt, psi)[source]#
+

Compute the part of the mean squared voltage independet of the sediment concentration: \(\frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}}}\)

+
+
Parameters:
+
    +

  • r (scalar, array) – radial coordinate

    • +

  • alpha_w (scalar, array) – water attenuation coefficient

    • +

  • Ks (scalar, array) – Sediment constant.

    • +

  • Kt (scalar, array) – Transducer constant.

    • +

  • psi (scalar, array) – near field function.

    • +
+
+
Returns:
+

return the part of the mean squared voltage that is independent of the sediment concentration.

+
+
Return type:
+

scalar, array

+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
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+ +
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+ +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.create_MSvoltage.html b/_autosummary/pyudv.attenuation.create_MSvoltage.html new file mode 100644 index 0000000..1193ec4 --- /dev/null +++ b/_autosummary/pyudv.attenuation.create_MSvoltage.html @@ -0,0 +1,581 @@ + + + + + + + + + + + pyudv.attenuation.create_MSvoltage — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

pyudv.attenuation.create_MSvoltage#

+
+
+create_MSvoltage(C, r, Xi, alpha_w, Ks, Kt, psi)[source]#
+

Compute the mean squared voltage from a concentration profile, given a set of acoustic coefficients: \(\langle V^{2} \rangle = C \frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}} - \int_{0}^{r}\xi C dr}\)

+
+
Parameters:
+
    +

  • C (scalar, array) – Sediment concentration.

    • +

  • r (scalar, array) – Radial coordinate.

    • +

  • Xi (scalar, array) – sediment attenuation constant

    • +

  • alpha_w (scalar, array) – water attenuation coefficient

    • +

  • Ks (scalar, array) – Sediment constant.

    • +

  • Kt (scalar, array) – Transducer constant.

    • +

  • psi (scalar, array) – near field function.

    • +
+
+
Returns:
+

    +

  • scalar, array – Mean squared voltage.

    • +

  • >>>

    • +
+

+
+
+
+ +
+

Examples using pyudv.attenuation.create_MSvoltage#

+
+

Concentration inference from amplitude signals

+
Concentration inference from amplitude signals
+
+
+ + +
+ + + + + +
+ + + +
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+
+ On this page +
+
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.cumulative_trapezoid.html b/_autosummary/pyudv.attenuation.cumulative_trapezoid.html new file mode 100644 index 0000000..e83682e --- /dev/null +++ b/_autosummary/pyudv.attenuation.cumulative_trapezoid.html @@ -0,0 +1,614 @@ + + + + + + + + + + + pyudv.attenuation.cumulative_trapezoid — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.cumulative_trapezoid#

+
+
+cumulative_trapezoid(y, x=None, dx=1.0, axis=-1, initial=None)[source]#
+

Cumulatively integrate y(x) using the composite trapezoidal rule.

+
+
Parameters:
+
    +

  • y (array_like) – Values to integrate.

    • +

  • x (array_like, optional) – The coordinate to integrate along. If None (default), use spacing dx +between consecutive elements in y.

    • +

  • dx (float, optional) – Spacing between elements of y. Only used if x is None.

    • +

  • axis (int, optional) – Specifies the axis to cumulate. Default is -1 (last axis).

    • +

  • initial (scalar, optional) – If given, insert this value at the beginning of the returned result. +Typically this value should be 0. Default is None, which means no +value at x[0] is returned and res has one element less than y +along the axis of integration.

    • +
+
+
Returns:
+

res – The result of cumulative integration of y along axis. +If initial is None, the shape is such that the axis of integration +has one less value than y. If initial is given, the shape is equal +to that of y.

+
+
Return type:
+

ndarray

+
+
+
+

See also

+

numpy.cumsum, numpy.cumprod

+
+
quad

adaptive quadrature using QUADPACK

+
+
romberg

adaptive Romberg quadrature

+
+
quadrature

adaptive Gaussian quadrature

+
+
fixed_quad

fixed-order Gaussian quadrature

+
+
dblquad

double integrals

+
+
tplquad

triple integrals

+
+
romb

integrators for sampled data

+
+
ode

ODE integrators

+
+
odeint

ODE integrators

+
+
+
+

Examples

+
>>> from scipy import integrate
+>>> import numpy as np
+>>> import matplotlib.pyplot as plt
+
+
+
>>> x = np.linspace(-2, 2, num=20)
+>>> y = x
+>>> y_int = integrate.cumulative_trapezoid(y, x, initial=0)
+>>> plt.plot(x, y_int, 'ro', x, y[0] + 0.5 * x**2, 'b-')
+>>> plt.show()
+
+
+
+ +
+ + +
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+ On this page +
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+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.direct_models.alpha_w.html b/_autosummary/pyudv.attenuation.direct_models.alpha_w.html new file mode 100644 index 0000000..8b63332 --- /dev/null +++ b/_autosummary/pyudv.attenuation.direct_models.alpha_w.html @@ -0,0 +1,580 @@ + + + + + + + + + + + pyudv.attenuation.direct_models.alpha_w — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.direct_models.alpha_w#

+
+
+alpha_w(f, T)[source]#
+

Calculate the fresh water attenuation in m-1.

+
+
Parameters:
+
    +

  • f (scalar, array) – Sound frequency in Hertz.

    • +

  • T (scalar, array) – Temperature in Celsius degrees.

    • +
+
+
Returns:
+

return the fresh water attenuation in m-1.

+
+
Return type:
+

scalar, array

+
+
+

Examples

+
>>> f = 2e6
+>>> T = 25
+>>> print(alpha_w(f, T))
+
+
+

References

+

[1] Fisher, F. H., & Simmons, V. P. (1977). Sound absorption in sea water. The Journal of the Acoustical Society of America, 62(3), 558-564.

+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.direct_models.correction_factor.html b/_autosummary/pyudv.attenuation.direct_models.correction_factor.html new file mode 100644 index 0000000..1ff43b7 --- /dev/null +++ b/_autosummary/pyudv.attenuation.direct_models.correction_factor.html @@ -0,0 +1,576 @@ + + + + + + + + + + + pyudv.attenuation.direct_models.correction_factor — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

pyudv.attenuation.direct_models.correction_factor#

+
+
+correction_factor(r, alpha_w, Ks, Kt, psi)[source]#
+

Compute the part of the mean squared voltage independet of the sediment concentration: \(\frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}}}\)

+
+
Parameters:
+
    +

  • r (scalar, array) – radial coordinate

    • +

  • alpha_w (scalar, array) – water attenuation coefficient

    • +

  • Ks (scalar, array) – Sediment constant.

    • +

  • Kt (scalar, array) – Transducer constant.

    • +

  • psi (scalar, array) – near field function.

    • +
+
+
Returns:
+

return the part of the mean squared voltage that is independent of the sediment concentration.

+
+
Return type:
+

scalar, array

+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.direct_models.create_MSvoltage.html b/_autosummary/pyudv.attenuation.direct_models.create_MSvoltage.html new file mode 100644 index 0000000..d9c0407 --- /dev/null +++ b/_autosummary/pyudv.attenuation.direct_models.create_MSvoltage.html @@ -0,0 +1,579 @@ + + + + + + + + + + + pyudv.attenuation.direct_models.create_MSvoltage — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + +
+ +
+

pyudv.attenuation.direct_models.create_MSvoltage#

+
+
+create_MSvoltage(C, r, Xi, alpha_w, Ks, Kt, psi)[source]#
+

Compute the mean squared voltage from a concentration profile, given a set of acoustic coefficients: \(\langle V^{2} \rangle = C \frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}} - \int_{0}^{r}\xi C dr}\)

+
+
Parameters:
+
    +

  • C (scalar, array) – Sediment concentration.

    • +

  • r (scalar, array) – Radial coordinate.

    • +

  • Xi (scalar, array) – sediment attenuation constant

    • +

  • alpha_w (scalar, array) – water attenuation coefficient

    • +

  • Ks (scalar, array) – Sediment constant.

    • +

  • Kt (scalar, array) – Transducer constant.

    • +

  • psi (scalar, array) – near field function.

    • +
+
+
Returns:
+

    +

  • scalar, array – Mean squared voltage.

    • +

  • >>>

    • +
+

+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.direct_models.cumulative_trapezoid.html b/_autosummary/pyudv.attenuation.direct_models.cumulative_trapezoid.html new file mode 100644 index 0000000..4512904 --- /dev/null +++ b/_autosummary/pyudv.attenuation.direct_models.cumulative_trapezoid.html @@ -0,0 +1,619 @@ + + + + + + + + + + + pyudv.attenuation.direct_models.cumulative_trapezoid — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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+

pyudv.attenuation.direct_models.cumulative_trapezoid#

+
+
+cumulative_trapezoid(y, x=None, dx=1.0, axis=-1, initial=None)[source]#
+

Cumulatively integrate y(x) using the composite trapezoidal rule.

+
+
Parameters:
+
    +

  • y (array_like) – Values to integrate.

    • +

  • x (array_like, optional) – The coordinate to integrate along. If None (default), use spacing dx +between consecutive elements in y.

    • +

  • dx (float, optional) – Spacing between elements of y. Only used if x is None.

    • +

  • axis (int, optional) – Specifies the axis to cumulate. Default is -1 (last axis).

    • +

  • initial (scalar, optional) – If given, insert this value at the beginning of the returned result. +Typically this value should be 0. Default is None, which means no +value at x[0] is returned and res has one element less than y +along the axis of integration.

    • +
+
+
Returns:
+

res – The result of cumulative integration of y along axis. +If initial is None, the shape is such that the axis of integration +has one less value than y. If initial is given, the shape is equal +to that of y.

+
+
Return type:
+

ndarray

+
+
+
+

See also

+

numpy.cumsum, numpy.cumprod

+
+
quad

adaptive quadrature using QUADPACK

+
+
romberg

adaptive Romberg quadrature

+
+
quadrature

adaptive Gaussian quadrature

+
+
fixed_quad

fixed-order Gaussian quadrature

+
+
dblquad

double integrals

+
+
tplquad

triple integrals

+
+
romb

integrators for sampled data

+
+
ode

ODE integrators

+
+
odeint

ODE integrators

+
+
+
+

Examples

+
>>> from scipy import integrate
+>>> import numpy as np
+>>> import matplotlib.pyplot as plt
+
+
+
>>> x = np.linspace(-2, 2, num=20)
+>>> y = x
+>>> y_int = integrate.cumulative_trapezoid(y, x, initial=0)
+>>> plt.plot(x, y_int, 'ro', x, y[0] + 0.5 * x**2, 'b-')
+>>> plt.show()
+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.direct_models.html b/_autosummary/pyudv.attenuation.direct_models.html new file mode 100644 index 0000000..5a9772d --- /dev/null +++ b/_autosummary/pyudv.attenuation.direct_models.html @@ -0,0 +1,553 @@ + + + + + + + + + + + pyudv.attenuation.direct_models — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

pyudv.attenuation.direct_models#

+

Functions

+ + + + + + + + + + + + + + + + + + + + + +

alpha_w

Calculate the fresh water attenuation in m-1.

correction_factor

Compute the part of the mean squared voltage independet of the sediment concentration: \(\frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}}}\)

create_MSvoltage

Compute the mean squared voltage from a concentration profile, given a set of acoustic coefficients: \(\langle V^{2} \rangle = C \frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}} - \int_{0}^{r}\xi C dr}\)

cumulative_trapezoid

Cumulatively integrate y(x) using the composite trapezoidal rule.

near_field_theoretical

Return the near field function.

sound_velocity

Compute sound velocity in fresh water in [m/s]

+
+ + +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.direct_models.near_field_theoretical.html b/_autosummary/pyudv.attenuation.direct_models.near_field_theoretical.html new file mode 100644 index 0000000..aa25f53 --- /dev/null +++ b/_autosummary/pyudv.attenuation.direct_models.near_field_theoretical.html @@ -0,0 +1,581 @@ + + + + + + + + + + + pyudv.attenuation.direct_models.near_field_theoretical — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

pyudv.attenuation.direct_models.near_field_theoretical#

+
+
+near_field_theoretical(r, rn)[source]#
+

Return the near field function.

+
+
Parameters:
+
    +

  • r (scalar, array) – Radial coordinate.

    • +

  • rn (scalar, array) – Near field distance

    • +
+
+
Returns:
+

return the near field function.

+
+
Return type:
+

scalar, array

+
+
+

Examples

+
>>> r = np.linspace(0.1, 1, 100)
+>>> rn = 0.2
+>>> Psi = near_field_theoretical(r, rn)
+
+
+

References

+

[1] Downing, A., Thorne, P. D., & Vincent, C. E. (1995). Backscattering from a suspension in the near field of a piston transducer. The Journal of the Acoustical Society of America, 97(3), 1614-1620. +[2] Pedocchi, F., & García, M. H. (2012). Acoustic measurement of suspended sediment concentration profiles in an oscillatory boundary layer. Continental Shelf Research, 46, 87-95.

+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.direct_models.sound_velocity.html b/_autosummary/pyudv.attenuation.direct_models.sound_velocity.html new file mode 100644 index 0000000..0916744 --- /dev/null +++ b/_autosummary/pyudv.attenuation.direct_models.sound_velocity.html @@ -0,0 +1,576 @@ + + + + + + + + + + + pyudv.attenuation.direct_models.sound_velocity — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + +
+ +
+

pyudv.attenuation.direct_models.sound_velocity#

+
+
+sound_velocity(T)[source]#
+

Compute sound velocity in fresh water in [m/s]

+
+
Parameters:
+

T (scalar, array) – Temperature in Celsius degrees.

+
+
Returns:
+

sound velocity in fresh water in [m/s]

+
+
Return type:
+

scalar, array

+
+
+

Examples

+
>>> T = 25
+>>> print(sound_velocity(T))
+
+
+

References

+

[1] Lubbers, J., & Graaff, R. (1998). A simple and accurate formula for the sound velocity in water. Ultrasound in medicine & biology, 24(7), 1065-1068.

+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.explicit_inversion.html b/_autosummary/pyudv.attenuation.explicit_inversion.html new file mode 100644 index 0000000..9fcdabf --- /dev/null +++ b/_autosummary/pyudv.attenuation.explicit_inversion.html @@ -0,0 +1,590 @@ + + + + + + + + + + + pyudv.attenuation.explicit_inversion — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.explicit_inversion#

+
+
+explicit_inversion(MSV, r, Xi, alpha_w, psi, C0, r0, delta_r=None, n_interp=1000, kind='cubic')[source]#
+

Use the following explicit solution to infer particle concentration from Mean Square Voltage (MSV, \(\langle V^{2} \rangle\)) data: +:rtype: tuple[ndarray, ndarray]

+
+\[C(r) = \frac{f}{B - 4\xi\displaystyle\int^{r}f},\]
+

where

+
+\[f(r) = \langle V^{2} \rangle \left(\frac{r\psi_{(r)}}{K_{\rm s} K_{\rm t}}\right) e^{4r\alpha_{\rm w}} = Ce^{-4\alpha_{\rm s}}.\]
+
+
Parameters:
+
    +

  • MSV (np.ndarray) – Input array containing \(\langle V^{2} \rangle\) data. If 1D, must be the same size as r. If 2D, its last dimension must match the size of r.

    • +

  • r (np.ndarray) – Radial distance to the probe array. Must be 1D, and match the last dimension of MSV.

    • +

  • Xi (float) – sediment asborption coefficient.

    • +

  • alpha_w (float) – water absorption coefficient.

    • +

  • psi (np.ndarray) – Near-field function. Must be 1D, and match the last dimension of MSV.

    • +

  • C0 (float | np.ndarray) – Reference concentration. Can be a float, or a 1D array matching the first dimension of MSV.

    • +

  • r0 (float) – Reference distance corresponding to C0.

    • +

  • delta_r (float, optional) – If not None, the reference concentration is then interpreted as an average between \(r-\delta r\) and \(r+\delta r\) (by default None).

    • +

  • n_interp (int, optional) – Number of point used in the interpolation of the input signal, by default 1000. It is advise to vary this parameter to find its lowest value above which the result becomes independant of it.

    • +

  • kind (str, optional) – Interpolation method used, by default “cubic”. Refer to the documentation of interp1d.

    • +
+
+
Returns:
+

    +

  • C (np.ndarray) – Inferred concentration.

    • +

  • f (np.ndarray) – Integrated array during the procedure. Returned to check the order of magnitudes over wich the integration is performed, and check possible numerical precision errors.

    • +
+

+
+
+
+ +
+

Examples using pyudv.attenuation.explicit_inversion#

+
+

Concentration inference from amplitude signals

+
Concentration inference from amplitude signals
+
+
+ + +
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+
+ On this page +
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pyudv.attenuation#

+

The amplitude model is currently under development, and yet undocumented.

+

Modules

+ + + + + + + + + + + + +

pyudv.attenuation.direct_models

pyudv.attenuation.inversion

pyudv.attenuation.sediment_acoustic_models

+

Functions

+ + + + + + + + + + + + + + + + + + + + + + + + +

alpha_w

Calculate the fresh water attenuation in m-1.

correction_factor

Compute the part of the mean squared voltage independet of the sediment concentration: \(\frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}}}\)

create_MSvoltage

Compute the mean squared voltage from a concentration profile, given a set of acoustic coefficients: \(\langle V^{2} \rangle = C \frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}} - \int_{0}^{r}\xi C dr}\)

cumulative_trapezoid

Cumulatively integrate y(x) using the composite trapezoidal rule.

explicit_inversion

Use the following explicit solution to infer particle concentration from Mean Square Voltage (MSV, \(\langle V^{2} \rangle\)) data: :rtype: tuple[ndarray, ndarray]

near_field_theoretical

Return the near field function.

sound_velocity

Compute sound velocity in fresh water in [m/s]

+
+ + +
+ + + + + +
+ + + + +
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+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.inversion.correction_factor.html b/_autosummary/pyudv.attenuation.inversion.correction_factor.html new file mode 100644 index 0000000..e0fa52f --- /dev/null +++ b/_autosummary/pyudv.attenuation.inversion.correction_factor.html @@ -0,0 +1,576 @@ + + + + + + + + + + + pyudv.attenuation.inversion.correction_factor — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.inversion.correction_factor#

+
+
+correction_factor(r, alpha_w, Ks, Kt, psi)[source]#
+

Compute the part of the mean squared voltage independet of the sediment concentration: \(\frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}}}\)

+
+
Parameters:
+
    +

  • r (scalar, array) – radial coordinate

    • +

  • alpha_w (scalar, array) – water attenuation coefficient

    • +

  • Ks (scalar, array) – Sediment constant.

    • +

  • Kt (scalar, array) – Transducer constant.

    • +

  • psi (scalar, array) – near field function.

    • +
+
+
Returns:
+

return the part of the mean squared voltage that is independent of the sediment concentration.

+
+
Return type:
+

scalar, array

+
+
+
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.inversion.explicit_inversion.html b/_autosummary/pyudv.attenuation.inversion.explicit_inversion.html new file mode 100644 index 0000000..2905fce --- /dev/null +++ b/_autosummary/pyudv.attenuation.inversion.explicit_inversion.html @@ -0,0 +1,588 @@ + + + + + + + + + + + pyudv.attenuation.inversion.explicit_inversion — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.inversion.explicit_inversion#

+
+
+explicit_inversion(MSV, r, Xi, alpha_w, psi, C0, r0, delta_r=None, n_interp=1000, kind='cubic')[source]#
+

Use the following explicit solution to infer particle concentration from Mean Square Voltage (MSV, \(\langle V^{2} \rangle\)) data: +:rtype: tuple[ndarray, ndarray]

+
+\[C(r) = \frac{f}{B - 4\xi\displaystyle\int^{r}f},\]
+

where

+
+\[f(r) = \langle V^{2} \rangle \left(\frac{r\psi_{(r)}}{K_{\rm s} K_{\rm t}}\right) e^{4r\alpha_{\rm w}} = Ce^{-4\alpha_{\rm s}}.\]
+
+
Parameters:
+
    +

  • MSV (np.ndarray) – Input array containing \(\langle V^{2} \rangle\) data. If 1D, must be the same size as r. If 2D, its last dimension must match the size of r.

    • +

  • r (np.ndarray) – Radial distance to the probe array. Must be 1D, and match the last dimension of MSV.

    • +

  • Xi (float) – sediment asborption coefficient.

    • +

  • alpha_w (float) – water absorption coefficient.

    • +

  • psi (np.ndarray) – Near-field function. Must be 1D, and match the last dimension of MSV.

    • +

  • C0 (float | np.ndarray) – Reference concentration. Can be a float, or a 1D array matching the first dimension of MSV.

    • +

  • r0 (float) – Reference distance corresponding to C0.

    • +

  • delta_r (float, optional) – If not None, the reference concentration is then interpreted as an average between \(r-\delta r\) and \(r+\delta r\) (by default None).

    • +

  • n_interp (int, optional) – Number of point used in the interpolation of the input signal, by default 1000. It is advise to vary this parameter to find its lowest value above which the result becomes independant of it.

    • +

  • kind (str, optional) – Interpolation method used, by default “cubic”. Refer to the documentation of interp1d.

    • +
+
+
Returns:
+

    +

  • C (np.ndarray) – Inferred concentration.

    • +

  • f (np.ndarray) – Integrated array during the procedure. Returned to check the order of magnitudes over wich the integration is performed, and check possible numerical precision errors.

    • +
+

+
+
+
+ +
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.inversion.html b/_autosummary/pyudv.attenuation.inversion.html new file mode 100644 index 0000000..40e8203 --- /dev/null +++ b/_autosummary/pyudv.attenuation.inversion.html @@ -0,0 +1,541 @@ + + + + + + + + + + + pyudv.attenuation.inversion — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.inversion#

+

Functions

+ + + + + + + + + +

correction_factor

Compute the part of the mean squared voltage independet of the sediment concentration: \(\frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}}}\)

explicit_inversion

Use the following explicit solution to infer particle concentration from Mean Square Voltage (MSV, \(\langle V^{2} \rangle\)) data: :rtype: tuple[ndarray, ndarray]

+
+ + +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.near_field_theoretical.html b/_autosummary/pyudv.attenuation.near_field_theoretical.html new file mode 100644 index 0000000..9b46721 --- /dev/null +++ b/_autosummary/pyudv.attenuation.near_field_theoretical.html @@ -0,0 +1,583 @@ + + + + + + + + + + + pyudv.attenuation.near_field_theoretical — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.near_field_theoretical#

+
+
+near_field_theoretical(r, rn)[source]#
+

Return the near field function.

+
+
Parameters:
+
    +

  • r (scalar, array) – Radial coordinate.

    • +

  • rn (scalar, array) – Near field distance

    • +
+
+
Returns:
+

return the near field function.

+
+
Return type:
+

scalar, array

+
+
+

Examples

+
>>> r = np.linspace(0.1, 1, 100)
+>>> rn = 0.2
+>>> Psi = near_field_theoretical(r, rn)
+
+
+

References

+

[1] Downing, A., Thorne, P. D., & Vincent, C. E. (1995). Backscattering from a suspension in the near field of a piston transducer. The Journal of the Acoustical Society of America, 97(3), 1614-1620. +[2] Pedocchi, F., & García, M. H. (2012). Acoustic measurement of suspended sediment concentration profiles in an oscillatory boundary layer. Continental Shelf Research, 46, 87-95.

+
+ +
+

Examples using pyudv.attenuation.near_field_theoretical#

+
+

Concentration inference from amplitude signals

+
Concentration inference from amplitude signals
+
+
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi.html new file mode 100644 index 0000000..23fe11c --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi.html @@ -0,0 +1,557 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi#

+
+
+Chi(k, Rp)[source]#
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi_i.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi_i.html new file mode 100644 index 0000000..998729e --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi_i.html @@ -0,0 +1,557 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi_i — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.sediment_acoustic_models.glass_sphere.Chi_i#

+
+
+Chi_i(k, Rp)[source]#
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
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+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Xi.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Xi.html new file mode 100644 index 0000000..ee27b13 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.Xi.html @@ -0,0 +1,557 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.glass_sphere.Xi — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.sediment_acoustic_models.glass_sphere.Xi#

+
+
+Xi(k, Rp)[source]#
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.html new file mode 100644 index 0000000..d83dcf9 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.html @@ -0,0 +1,551 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.glass_sphere — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.sediment_acoustic_models.glass_sphere#

+

Functions

+ + + + + + + + + + + + + + + +

Chi

Chi_i

Xi

trapezoid

Integrate along the given axis using the composite trapezoidal rule.

+
+ + +
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+ + + + +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.trapezoid.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.trapezoid.html new file mode 100644 index 0000000..b829ce0 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.glass_sphere.trapezoid.html @@ -0,0 +1,657 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.glass_sphere.trapezoid — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.sediment_acoustic_models.glass_sphere.trapezoid#

+
+
+trapezoid(y, x=None, dx=1.0, axis=-1)[source]#
+

Integrate along the given axis using the composite trapezoidal rule.

+

If x is provided, the integration happens in sequence along its +elements - they are not sorted.

+

Integrate y (x) along each 1d slice on the given axis, compute +\(\int y(x) dx\). +When x is specified, this integrates along the parametric curve, +computing \(\int_t y(t) dt = +\int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt\).

+
+
Parameters:
+
    +

  • y (array_like) – Input array to integrate.

    • +

  • x (array_like, optional) – The sample points corresponding to the y values. If x is None, +the sample points are assumed to be evenly spaced dx apart. The +default is None.

    • +

  • dx (scalar, optional) – The spacing between sample points when x is None. The default is 1.

    • +

  • axis (int, optional) – The axis along which to integrate.

    • +
+
+
Returns:
+

trapezoid – Definite integral of y = n-dimensional array as approximated along +a single axis by the trapezoidal rule. If y is a 1-dimensional array, +then the result is a float. If n is greater than 1, then the result +is an n-1 dimensional array.

+
+
Return type:
+

float or ndarray

+
+
+
+

See also

+

cumulative_trapezoid, simpson, romb

+
+

Notes

+

Image [2] illustrates trapezoidal rule – y-axis locations of points +will be taken from y array, by default x-axis distances between +points will be 1.0, alternatively they can be provided with x array +or with dx scalar. Return value will be equal to combined area under +the red lines.

+

References

+
+ + +
+

Examples

+

Use the trapezoidal rule on evenly spaced points:

+
>>> import numpy as np
+>>> from scipy import integrate
+>>> integrate.trapezoid([1, 2, 3])
+4.0
+
+
+

The spacing between sample points can be selected by either the +x or dx arguments:

+
>>> integrate.trapezoid([1, 2, 3], x=[4, 6, 8])
+8.0
+>>> integrate.trapezoid([1, 2, 3], dx=2)
+8.0
+
+
+

Using a decreasing x corresponds to integrating in reverse:

+
>>> integrate.trapezoid([1, 2, 3], x=[8, 6, 4])
+-8.0
+
+
+

More generally x is used to integrate along a parametric curve. We can +estimate the integral \(\int_0^1 x^2 = 1/3\) using:

+
>>> x = np.linspace(0, 1, num=50)
+>>> y = x**2
+>>> integrate.trapezoid(y, x)
+0.33340274885464394
+
+
+

Or estimate the area of a circle, noting we repeat the sample which closes +the curve:

+
>>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
+>>> integrate.trapezoid(np.cos(theta), x=np.sin(theta))
+3.141571941375841
+
+
+

trapezoid can be applied along a specified axis to do multiple +computations in one call:

+
>>> a = np.arange(6).reshape(2, 3)
+>>> a
+array([[0, 1, 2],
+       [3, 4, 5]])
+>>> integrate.trapezoid(a, axis=0)
+array([1.5, 2.5, 3.5])
+>>> integrate.trapezoid(a, axis=1)
+array([2.,  8.])
+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.html new file mode 100644 index 0000000..2e70255 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.html @@ -0,0 +1,540 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi.html new file mode 100644 index 0000000..626f438 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi.html @@ -0,0 +1,557 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi#

+
+
+Chi(k, Rp)[source]#
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi_i.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi_i.html new file mode 100644 index 0000000..7962c05 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi_i.html @@ -0,0 +1,557 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi_i — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.sediment_acoustic_models.quartz_sand.Chi_i#

+
+
+Chi_i(k, Rp)[source]#
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Xi.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Xi.html new file mode 100644 index 0000000..de6ca35 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.Xi.html @@ -0,0 +1,564 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.quartz_sand.Xi — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.sediment_acoustic_models.quartz_sand.Xi#

+
+
+Xi(k, Rp, rho=2650.0)[source]#
+
+ +
+

Examples using pyudv.attenuation.sediment_acoustic_models.quartz_sand.Xi#

+
+

Concentration inference from amplitude signals

+
Concentration inference from amplitude signals
+
+
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.html new file mode 100644 index 0000000..fb05185 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.html @@ -0,0 +1,551 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.quartz_sand — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.attenuation.sediment_acoustic_models.quartz_sand#

+

Functions

+ + + + + + + + + + + + + + + +

Chi

Chi_i

Xi

trapezoid

Integrate along the given axis using the composite trapezoidal rule.

+
+ + +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.trapezoid.html b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.trapezoid.html new file mode 100644 index 0000000..5f91fea --- /dev/null +++ b/_autosummary/pyudv.attenuation.sediment_acoustic_models.quartz_sand.trapezoid.html @@ -0,0 +1,657 @@ + + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.quartz_sand.trapezoid — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.attenuation.sediment_acoustic_models.quartz_sand.trapezoid#

+
+
+trapezoid(y, x=None, dx=1.0, axis=-1)[source]#
+

Integrate along the given axis using the composite trapezoidal rule.

+

If x is provided, the integration happens in sequence along its +elements - they are not sorted.

+

Integrate y (x) along each 1d slice on the given axis, compute +\(\int y(x) dx\). +When x is specified, this integrates along the parametric curve, +computing \(\int_t y(t) dt = +\int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt\).

+
+
Parameters:
+
    +

  • y (array_like) – Input array to integrate.

    • +

  • x (array_like, optional) – The sample points corresponding to the y values. If x is None, +the sample points are assumed to be evenly spaced dx apart. The +default is None.

    • +

  • dx (scalar, optional) – The spacing between sample points when x is None. The default is 1.

    • +

  • axis (int, optional) – The axis along which to integrate.

    • +
+
+
Returns:
+

trapezoid – Definite integral of y = n-dimensional array as approximated along +a single axis by the trapezoidal rule. If y is a 1-dimensional array, +then the result is a float. If n is greater than 1, then the result +is an n-1 dimensional array.

+
+
Return type:
+

float or ndarray

+
+
+
+

See also

+

cumulative_trapezoid, simpson, romb

+
+

Notes

+

Image [2] illustrates trapezoidal rule – y-axis locations of points +will be taken from y array, by default x-axis distances between +points will be 1.0, alternatively they can be provided with x array +or with dx scalar. Return value will be equal to combined area under +the red lines.

+

References

+
+ + +
+

Examples

+

Use the trapezoidal rule on evenly spaced points:

+
>>> import numpy as np
+>>> from scipy import integrate
+>>> integrate.trapezoid([1, 2, 3])
+4.0
+
+
+

The spacing between sample points can be selected by either the +x or dx arguments:

+
>>> integrate.trapezoid([1, 2, 3], x=[4, 6, 8])
+8.0
+>>> integrate.trapezoid([1, 2, 3], dx=2)
+8.0
+
+
+

Using a decreasing x corresponds to integrating in reverse:

+
>>> integrate.trapezoid([1, 2, 3], x=[8, 6, 4])
+-8.0
+
+
+

More generally x is used to integrate along a parametric curve. We can +estimate the integral \(\int_0^1 x^2 = 1/3\) using:

+
>>> x = np.linspace(0, 1, num=50)
+>>> y = x**2
+>>> integrate.trapezoid(y, x)
+0.33340274885464394
+
+
+

Or estimate the area of a circle, noting we repeat the sample which closes +the curve:

+
>>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
+>>> integrate.trapezoid(np.cos(theta), x=np.sin(theta))
+3.141571941375841
+
+
+

trapezoid can be applied along a specified axis to do multiple +computations in one call:

+
>>> a = np.arange(6).reshape(2, 3)
+>>> a
+array([[0, 1, 2],
+       [3, 4, 5]])
+>>> integrate.trapezoid(a, axis=0)
+array([1.5, 2.5, 3.5])
+>>> integrate.trapezoid(a, axis=1)
+array([2.,  8.])
+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.attenuation.sound_velocity.html b/_autosummary/pyudv.attenuation.sound_velocity.html new file mode 100644 index 0000000..f0861d5 --- /dev/null +++ b/_autosummary/pyudv.attenuation.sound_velocity.html @@ -0,0 +1,578 @@ + + + + + + + + + + + pyudv.attenuation.sound_velocity — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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+ + + Ctrl+K +
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+

pyudv.attenuation.sound_velocity#

+
+
+sound_velocity(T)[source]#
+

Compute sound velocity in fresh water in [m/s]

+
+
Parameters:
+

T (scalar, array) – Temperature in Celsius degrees.

+
+
Returns:
+

sound velocity in fresh water in [m/s]

+
+
Return type:
+

scalar, array

+
+
+

Examples

+
>>> T = 25
+>>> print(sound_velocity(T))
+
+
+

References

+

[1] Lubbers, J., & Graaff, R. (1998). A simple and accurate formula for the sound velocity in water. Ultrasound in medicine & biology, 24(7), 1065-1068.

+
+ +
+

Examples using pyudv.attenuation.sound_velocity#

+
+

Concentration inference from amplitude signals

+
Concentration inference from amplitude signals
+
+
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.E.html b/_autosummary/pyudv.geometry.Probe.E.html new file mode 100644 index 0000000..d6c8a5d --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.E.html @@ -0,0 +1,535 @@ + + + + + + + + + + + pyudv.geometry.Probe.E — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.geometry.Probe.E#

+

attribute

+
+ + +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.O.html b/_autosummary/pyudv.geometry.Probe.O.html new file mode 100644 index 0000000..0edb5e8 --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.O.html @@ -0,0 +1,535 @@ + + + + + + + + + + + pyudv.geometry.Probe.O — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.O#

+

attribute

+
+ + +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.Pref.html b/_autosummary/pyudv.geometry.Probe.Pref.html new file mode 100644 index 0000000..e973cdd --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.Pref.html @@ -0,0 +1,535 @@ + + + + + + + + + + + pyudv.geometry.Probe.Pref — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.geometry.Probe.Pref#

+

attribute

+
+ + +
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+ + + + +
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+ +
+
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.X_ref.html b/_autosummary/pyudv.geometry.Probe.X_ref.html new file mode 100644 index 0000000..83442ce --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.X_ref.html @@ -0,0 +1,535 @@ + + + + + + + + + + + pyudv.geometry.Probe.X_ref — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.X_ref#

+

attribute

+
+ + +
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+ + + + +
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+ +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.alpha.html b/_autosummary/pyudv.geometry.Probe.alpha.html new file mode 100644 index 0000000..e08c8b8 --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.alpha.html @@ -0,0 +1,535 @@ + + + + + + + + + + + pyudv.geometry.Probe.alpha — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.alpha#

+

attribute

+
+ + +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.html b/_autosummary/pyudv.geometry.Probe.html new file mode 100644 index 0000000..1bab6e5 --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.html @@ -0,0 +1,620 @@ + + + + + + + + + + + pyudv.geometry.Probe — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe#

+
+
+class Probe(r, alpha, Pref)[source]#
+
+

A class used to represent a single probe.

+
+
+
Parameters:
+
    +

  • r (ndarray, (N,)) – a numpy array containing the radial coordinates of the beam.

    • +

  • alpha (float) – orientation of the probe, in degree in the trigonometric referential.

    • +

  • Pref (list[float, ndarray]) – A list a size 2 defining a reference point by containing its radial coordinate, and a numpy array containing its coordinate in the “real” reference space.

    • +
+
+
+

Methods

+ + + + + + + + + + + + + + + +

plot_probe(ax[, color])

Sketch the probe and its beam on a given matplotlib axe.

r_to_x(r)

Convert radial coordinates of the probe into x coordinates

r_to_z(r)

Convert radial coordinates of the probe into z coordinates

xz_to_r(xz[, axis])

Convert xz coordinates into probe radial coordinates

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

Probe.r

vector along beam axis

Probe.alpha

beam axis inclination, trigo referential

Probe.Pref

reference point [r_ref, (x_ref, y_ref)]

Probe.r_ref

reference point radial coordinats

Probe.X_ref

reference point real coordinates

Probe.unit_vec

unit vector defining the beam direction in real coordinates

Probe.O

coordinates of the origin of the beam

Probe.E

coordinates of the end of the beam

+
+ +
+

Examples using pyudv.geometry.Probe#

+
+

Sketch multiple probes

+
Sketch multiple probes
+
+

Probes and velocity reconstruction

+
Probes and velocity reconstruction
+
+
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.plot_probe.html b/_autosummary/pyudv.geometry.Probe.plot_probe.html new file mode 100644 index 0000000..7d28517 --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.plot_probe.html @@ -0,0 +1,573 @@ + + + + + + + + + + + pyudv.geometry.Probe.plot_probe — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.plot_probe#

+
+
+Probe.plot_probe(ax, color=None)[source]#
+

Sketch the probe and its beam on a given matplotlib axe.

+
+
Parameters:
+
    +

  • ax (mplaxes.Axes) – matplotlib axe on which the probe is sketched.

    • +

  • color (str, optional) – probe color, by default None

    • +
+
+
+
+ +
+

Examples using pyudv.geometry.Probe.plot_probe#

+
+

Sketch multiple probes

+
Sketch multiple probes
+
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.r.html b/_autosummary/pyudv.geometry.Probe.r.html new file mode 100644 index 0000000..3779d6e --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.r.html @@ -0,0 +1,535 @@ + + + + + + + + + + + pyudv.geometry.Probe.r — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.r#

+

attribute

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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.r_ref.html b/_autosummary/pyudv.geometry.Probe.r_ref.html new file mode 100644 index 0000000..ef2f357 --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.r_ref.html @@ -0,0 +1,535 @@ + + + + + + + + + + + pyudv.geometry.Probe.r_ref — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.r_ref#

+

attribute

+
+ + +
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+
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.r_to_x.html b/_autosummary/pyudv.geometry.Probe.r_to_x.html new file mode 100644 index 0000000..1217acc --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.r_to_x.html @@ -0,0 +1,572 @@ + + + + + + + + + + + pyudv.geometry.Probe.r_to_x — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.r_to_x#

+
+
+Probe.r_to_x(r)[source]#
+

Convert radial coordinates of the probe into x coordinates

+
+
Return type:
+

ndarray | float

+
+
Parameters:
+

r (np.ndarray | float) – corresponding x coordinate

+
+
Returns:
+

corresponding z coordinate

+
+
Return type:
+

np.ndarray | float

+
+
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.r_to_z.html b/_autosummary/pyudv.geometry.Probe.r_to_z.html new file mode 100644 index 0000000..5f0a804 --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.r_to_z.html @@ -0,0 +1,572 @@ + + + + + + + + + + + pyudv.geometry.Probe.r_to_z — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.r_to_z#

+
+
+Probe.r_to_z(r)[source]#
+

Convert radial coordinates of the probe into z coordinates

+
+
Return type:
+

ndarray | float

+
+
Parameters:
+

r (np.ndarray | float) – radial coordinate

+
+
Returns:
+

corresponding z coordinate

+
+
Return type:
+

np.ndarray | float

+
+
+
+ +
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.unit_vec.html b/_autosummary/pyudv.geometry.Probe.unit_vec.html new file mode 100644 index 0000000..1bbe132 --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.unit_vec.html @@ -0,0 +1,535 @@ + + + + + + + + + + + pyudv.geometry.Probe.unit_vec — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.unit_vec#

+

attribute

+
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.Probe.xz_to_r.html b/_autosummary/pyudv.geometry.Probe.xz_to_r.html new file mode 100644 index 0000000..0daa630 --- /dev/null +++ b/_autosummary/pyudv.geometry.Probe.xz_to_r.html @@ -0,0 +1,575 @@ + + + + + + + + + + + pyudv.geometry.Probe.xz_to_r — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.Probe.xz_to_r#

+
+
+Probe.xz_to_r(xz, axis=-1)[source]#
+

Convert xz coordinates into probe radial coordinates

+
+
Return type:
+

ndarray

+
+
Parameters:
+
    +

  • xz (np.ndarray) – xz coordinate

    • +

  • axis (int, optional) – axis along which the norm is calculated, by default -1

    • +
+
+
Returns:
+

radial coordinates

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.average_amplitude.html b/_autosummary/pyudv.geometry.average_amplitude.html new file mode 100644 index 0000000..4d77da5 --- /dev/null +++ b/_autosummary/pyudv.geometry.average_amplitude.html @@ -0,0 +1,576 @@ + + + + + + + + + + + pyudv.geometry.average_amplitude — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.average_amplitude#

+
+
+average_amplitude(a1, a2, probe1, probe2)[source]#
+

This function takes the amplitude measured by two probes and average them, assuming that it only depends on the vertical coordinate.

+
+
Return type:
+

tuple[ndarray, ndarray, ndarray, ndarray, ndarray, ndarray]

+
+
Parameters:
+
    +

  • a1 (npt.ArrayLike) – amplitude measured by first probe. The first dimension should be the same dimension as the radial coordinate contained in probe1.z.

    • +

  • a2 (npt.ArrayLike) – amplitude measured by second probe. The first dimension should be the same dimension as the radial coordinate contained in probe2.z.

    • +

  • probe1 (Probe) – First Probe object.

    • +

  • probe2 (Probe) – Second Probe object.

    • +
+
+
Returns:
+

    +

  • A (ndarray) – averaged amplitud in the coordinate system corresponding to the one of the reference points of the probes.

    • +

  • z_interp (ndarray) – coordinate vector corresponding to U

    • +

  • X (ndarray) – crossing point of the probes

    • +

  • dx_1 (ndarray) – horizontal distance between the first probe beam, and the vertical axis passing by the crossing point X

    • +

  • dx_2 (ndarray) – horizontal distance between the second probe beam, and the vertical axis passing by the crossing point X

    • +
+

+
+
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.compute_vertical_axis.html b/_autosummary/pyudv.geometry.compute_vertical_axis.html new file mode 100644 index 0000000..4801ac4 --- /dev/null +++ b/_autosummary/pyudv.geometry.compute_vertical_axis.html @@ -0,0 +1,569 @@ + + + + + + + + + + + pyudv.geometry.compute_vertical_axis — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.geometry.compute_vertical_axis#

+
+
+compute_vertical_axis(probe1, probe2)[source]#
+

compute the common vertical axis between two probe beams.

+
+
Parameters:
+
    +

  • probe1 (Probe) – First Probe object.

    • +

  • probe2 (Probe) – Second Probe object.

    • +
+
+
Returns:
+

    +

  • z_interp (ndarray) – numpy array containg the vertical coordinates of the common axis.

    • +

  • z_min (float) – minimal vertical coordinate

    • +

  • z_max (float) – maximal vertical coordinate

    • +
+

+
+
+
+ +
+ + +
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.html b/_autosummary/pyudv.geometry.html new file mode 100644 index 0000000..8b350cf --- /dev/null +++ b/_autosummary/pyudv.geometry.html @@ -0,0 +1,555 @@ + + + + + + + + + + + pyudv.geometry — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.geometry#

+

This module helps you deal with probe geometries.

+

Functions

+ + + + + + + + + + + + + + + + + + +

average_amplitude

This function takes the amplitude measured by two probes and average them, assuming that it only depends on the vertical coordinate.

compute_vertical_axis

compute the common vertical axis between two probe beams.

probe_crossing_point

calculate the crossing point of two probe beams.

reconstruct_velocity

This function takes the velocities measured by two probes, and reconstruct the velocity field, assuming that it only depends on the vertical coordinate.

sketch_probes

Sketch multiple probes, and optionaly their intersections, etc ...

+

Classes

+ + + + + + +

Probe

A class used to represent a single probe.

+
+ + +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.probe_crossing_point.html b/_autosummary/pyudv.geometry.probe_crossing_point.html new file mode 100644 index 0000000..8303b8c --- /dev/null +++ b/_autosummary/pyudv.geometry.probe_crossing_point.html @@ -0,0 +1,570 @@ + + + + + + + + + + + pyudv.geometry.probe_crossing_point — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.geometry.probe_crossing_point#

+
+
+probe_crossing_point(probe1, probe2)[source]#
+

calculate the crossing point of two probe beams.

+
+
Return type:
+

ndarray

+
+
Parameters:
+
    +

  • probe1 (Probe) – First Probe object.

    • +

  • probe2 (Probe) – Second Probe object.

    • +
+
+
Returns:
+

coordinates of the crossing point.

+
+
Return type:
+

ndarray, (2. )

+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.reconstruct_velocity.html b/_autosummary/pyudv.geometry.reconstruct_velocity.html new file mode 100644 index 0000000..c092658 --- /dev/null +++ b/_autosummary/pyudv.geometry.reconstruct_velocity.html @@ -0,0 +1,583 @@ + + + + + + + + + + + pyudv.geometry.reconstruct_velocity — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

pyudv.geometry.reconstruct_velocity#

+
+
+reconstruct_velocity(u1, u2, probe1, probe2)[source]#
+

This function takes the velocities measured by two probes, and reconstruct the velocity field, assuming that it only depends on the vertical coordinate.

+
+
Return type:
+

tuple[ndarray, ndarray, ndarray, ndarray, ndarray, ndarray]

+
+
Parameters:
+
    +

  • u1 (ArrayLike) – velocity vector measured by first probe. The first dimension should be the same dimension as the radial coordinate contained in probe1.z.

    • +

  • u2 (ArrayLike) – velocity vector measured by second probe. The first dimension should be the same dimension as the radial coordinate contained in probe2.z.

    • +

  • probe1 (Probe) – First Probe object.

    • +

  • probe2 (Probe) – Second Probe object.

    • +
+
+
Returns:
+

    +

  • U (ndarray) – reconstructed velocity components in the coordinate system corresponding to the one of the reference points of the probes.

    • +

  • z_interp (ndarray) – coordinate vector corresponding to U

    • +

  • X (ndarray) – crossing point of the probes

    • +

  • dx_1 (ndarray) – horizontal distance between the first probe beam, and the vertical axis passing by the crossing point X

    • +

  • dx_2 (ndarray) – horizontal distance between the second probe beam, and the vertical axis passing by the crossing point X

    • +
+

+
+
+
+ +
+

Examples using pyudv.geometry.reconstruct_velocity#

+
+

Probes and velocity reconstruction

+
Probes and velocity reconstruction
+
+
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.geometry.sketch_probes.html b/_autosummary/pyudv.geometry.sketch_probes.html new file mode 100644 index 0000000..dc5eeff --- /dev/null +++ b/_autosummary/pyudv.geometry.sketch_probes.html @@ -0,0 +1,574 @@ + + + + + + + + + + + pyudv.geometry.sketch_probes — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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pyudv.geometry.sketch_probes#

+
+
+sketch_probes(probes, combinations=None, ax=None, probe_colors=None, combination_colors=None)[source]#
+

Sketch multiple probes, and optionaly their intersections, etc …

+
+
Parameters:
+
    +

  • probes (list[Probe]) – list of Probe object to be sketched.

    • +

  • combinations (list[tuple[int, int]], optional) – list of tuple of size 2 containing indexes of probes whose intersection should be sketched.

    • +

  • ax (mplaxes.Axes, optional) – matplotlib axe on which the sketch is drawn. If None, a new figure is created (default is None).

    • +

  • probe_colors (list[str], optional) – list of colors for the probes, by default None. It should be the same size as probes.

    • +

  • combination_colors (list[str], optional) – list of colors for the intersetctions, by default None. It should be the same size as combinations.

    • +
+
+
+
+ +
+

Examples using pyudv.geometry.sketch_probes#

+
+

Sketch multiple probes

+
Sketch multiple probes
+
+

Probes and velocity reconstruction

+
Probes and velocity reconstruction
+
+
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.helpers.compute_common_part.html b/_autosummary/pyudv.helpers.compute_common_part.html new file mode 100644 index 0000000..28cb2d0 --- /dev/null +++ b/_autosummary/pyudv.helpers.compute_common_part.html @@ -0,0 +1,572 @@ + + + + + + + + + + + pyudv.helpers.compute_common_part — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.helpers.compute_common_part#

+
+
+compute_common_part(vec1, vec2)[source]#
+

Compute the common part between two vector, and return it. If it is not exactly colocated, it returns a interpolation with the same number of points betwen the bounds common to the two arrays.

+
+
Return type:
+

tuple[ndarray, float, float]

+
+
Parameters:
+
    +

  • vec1 (np.ndarray) – first 1D array.

    • +

  • vec2 (np.ndarray) – second 1D array.

    • +
+
+
Returns:
+

    +

  • z_interp (np.ndarray) – interpolated vector

    • +

  • z_min (float) – lower bound

    • +

  • z_max (float) – higher bound

    • +
+

+
+
+
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+ + + + + +
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.helpers.create_arboresence.html b/_autosummary/pyudv.helpers.create_arboresence.html new file mode 100644 index 0000000..3e3a669 --- /dev/null +++ b/_autosummary/pyudv.helpers.create_arboresence.html @@ -0,0 +1,552 @@ + + + + + + + + + + + pyudv.helpers.create_arboresence — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.helpers.create_arboresence#

+
+
+create_arboresence(path)[source]#
+
+ +
+ + +
+ + + + + +
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.helpers.moving_std.html b/_autosummary/pyudv.helpers.moving_std.html new file mode 100644 index 0000000..f7291bf --- /dev/null +++ b/_autosummary/pyudv.helpers.moving_std.html @@ -0,0 +1,568 @@ + + + + + + + + + + + pyudv.helpers.moving_std — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.helpers.moving_std#

+
+
+moving_std(arr, size, **kwargs)[source]#
+

Rolling standard deviation on n-dimensional arrays.

+
+
Parameters:
+
    +

  • arr (array) – Input array

    • +

  • size (int) – The sizes of the uniform filter are given for each axis as a sequence, or as a single number, in which case the size is equal for all axes.

    • +

  • **kwargs – Optional parameters passed to scipy.ndimage.uniform_filter

    • +
+
+
Returns:
+

Array of standard deviation

+
+
Return type:
+

array

+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.helpers.moving_std1d.html b/_autosummary/pyudv.helpers.moving_std1d.html new file mode 100644 index 0000000..66c33ab --- /dev/null +++ b/_autosummary/pyudv.helpers.moving_std1d.html @@ -0,0 +1,569 @@ + + + + + + + + + + + pyudv.helpers.moving_std1d — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.helpers.moving_std1d#

+
+
+moving_std1d(arr, windows, axis=-1, **kwargs)[source]#
+

Rolling standard deviation on 1 dimension of the input array.

+
+
Parameters:
+
    +

  • arr (array) – Input array

    • +

  • windows (int) – windows over wich the standard deviation is calculated.

    • +

  • axis (int) – axis along which the standard deviation is calculated. (the default is -1).

    • +

  • **kwargs – Optional parameters passed to scipy.ndimage.uniform_filter1d

    • +
+
+
Returns:
+

Array of standard deviation

+
+
Return type:
+

array

+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.helpers.uniform_filter.html b/_autosummary/pyudv.helpers.uniform_filter.html new file mode 100644 index 0000000..8e62cc1 --- /dev/null +++ b/_autosummary/pyudv.helpers.uniform_filter.html @@ -0,0 +1,638 @@ + + + + + + + + + + + pyudv.helpers.uniform_filter — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.helpers.uniform_filter#

+
+
+uniform_filter(input, size=3, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None)[source]#
+

Multidimensional uniform filter.

+
+
Parameters:
+
    +

  • input (array_like) – The input array.

    • +

  • size (int or sequence of ints, optional) – The sizes of the uniform filter are given for each axis as a +sequence, or as a single number, in which case the size is +equal for all axes.

    • +

  • output (array or dtype, optional) – The array in which to place the output, or the dtype of the +returned array. By default an array of the same dtype as input +will be created.

    • +

  • mode (str or sequence, optional) –

    The mode parameter determines how the input array is extended +when the filter overlaps a border. By passing a sequence of modes +with length equal to the number of dimensions of the input array, +different modes can be specified along each axis. Default value is +‘reflect’. The valid values and their behavior is as follows:

    +
    +
    ’reflect’ (d c b a | a b c d | d c b a)

    The input is extended by reflecting about the edge of the last +pixel. This mode is also sometimes referred to as half-sample +symmetric.

    +
    +
    ’constant’ (k k k k | a b c d | k k k k)

    The input is extended by filling all values beyond the edge with +the same constant value, defined by the cval parameter.

    +
    +
    ’nearest’ (a a a a | a b c d | d d d d)

    The input is extended by replicating the last pixel.

    +
    +
    ’mirror’ (d c b | a b c d | c b a)

    The input is extended by reflecting about the center of the last +pixel. This mode is also sometimes referred to as whole-sample +symmetric.

    +
    +
    ’wrap’ (a b c d | a b c d | a b c d)

    The input is extended by wrapping around to the opposite edge.

    +
    +
    +

    For consistency with the interpolation functions, the following mode +names can also be used:

    +
    +
    ’grid-constant’

    This is a synonym for ‘constant’.

    +
    +
    ’grid-mirror’

    This is a synonym for ‘reflect’.

    +
    +
    ’grid-wrap’

    This is a synonym for ‘wrap’.

    +
    +
    +

    • +

  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default +is 0.0.

    • +

  • origin (int or sequence, optional) – Controls the placement of the filter on the input array’s pixels. +A value of 0 (the default) centers the filter over the pixel, with +positive values shifting the filter to the left, and negative ones +to the right. By passing a sequence of origins with length equal to +the number of dimensions of the input array, different shifts can +be specified along each axis.

    • +

  • axes (tuple of int or None, optional) – If None, input is filtered along all axes. Otherwise, +input is filtered along the specified axes. When axes is +specified, any tuples used for size, origin, and/or mode +must match the length of axes. The ith entry in any of these tuples +corresponds to the ith entry in axes.

    • +
+
+
Returns:
+

uniform_filter – Filtered array. Has the same shape as input.

+
+
Return type:
+

ndarray

+
+
+

Notes

+

The multidimensional filter is implemented as a sequence of +1-D uniform filters. The intermediate arrays are stored +in the same data type as the output. Therefore, for output types +with a limited precision, the results may be imprecise because +intermediate results may be stored with insufficient precision.

+

Examples

+
>>> from scipy import ndimage, datasets
+>>> import matplotlib.pyplot as plt
+>>> fig = plt.figure()
+>>> plt.gray()  # show the filtered result in grayscale
+>>> ax1 = fig.add_subplot(121)  # left side
+>>> ax2 = fig.add_subplot(122)  # right side
+>>> ascent = datasets.ascent()
+>>> result = ndimage.uniform_filter(ascent, size=20)
+>>> ax1.imshow(ascent)
+>>> ax2.imshow(result)
+>>> plt.show()
+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.helpers.uniform_filter1d.html b/_autosummary/pyudv.helpers.uniform_filter1d.html new file mode 100644 index 0000000..c8ecc87 --- /dev/null +++ b/_autosummary/pyudv.helpers.uniform_filter1d.html @@ -0,0 +1,610 @@ + + + + + + + + + + + pyudv.helpers.uniform_filter1d — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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+

pyudv.helpers.uniform_filter1d#

+
+
+uniform_filter1d(input, size, axis=-1, output=None, mode='reflect', cval=0.0, origin=0)[source]#
+

Calculate a 1-D uniform filter along the given axis.

+

The lines of the array along the given axis are filtered with a +uniform filter of given size.

+
+
Parameters:
+
    +

  • input (array_like) – The input array.

    • +

  • size (int) – length of uniform filter

    • +

  • axis (int, optional) – The axis of input along which to calculate. Default is -1.

    • +

  • output (array or dtype, optional) – The array in which to place the output, or the dtype of the +returned array. By default an array of the same dtype as input +will be created.

    • +

  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) –

    The mode parameter determines how the input array is extended +beyond its boundaries. Default is ‘reflect’. Behavior for each valid +value is as follows:

    +
    +
    ’reflect’ (d c b a | a b c d | d c b a)

    The input is extended by reflecting about the edge of the last +pixel. This mode is also sometimes referred to as half-sample +symmetric.

    +
    +
    ’constant’ (k k k k | a b c d | k k k k)

    The input is extended by filling all values beyond the edge with +the same constant value, defined by the cval parameter.

    +
    +
    ’nearest’ (a a a a | a b c d | d d d d)

    The input is extended by replicating the last pixel.

    +
    +
    ’mirror’ (d c b | a b c d | c b a)

    The input is extended by reflecting about the center of the last +pixel. This mode is also sometimes referred to as whole-sample +symmetric.

    +
    +
    ’wrap’ (a b c d | a b c d | a b c d)

    The input is extended by wrapping around to the opposite edge.

    +
    +
    +

    For consistency with the interpolation functions, the following mode +names can also be used:

    +
    +
    ’grid-mirror’

    This is a synonym for ‘reflect’.

    +
    +
    ’grid-constant’

    This is a synonym for ‘constant’.

    +
    +
    ’grid-wrap’

    This is a synonym for ‘wrap’.

    +
    +
    +

    • +

  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default +is 0.0.

    • +

  • origin (int, optional) – Controls the placement of the filter on the input array’s pixels. +A value of 0 (the default) centers the filter over the pixel, with +positive values shifting the filter to the left, and negative ones +to the right.

    • +
+
+
+

Examples

+
>>> from scipy.ndimage import uniform_filter1d
+>>> uniform_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3)
+array([4, 3, 4, 1, 4, 6, 6, 3])
+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
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+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.html b/_autosummary/pyudv.html new file mode 100644 index 0000000..65d3569 --- /dev/null +++ b/_autosummary/pyudv.html @@ -0,0 +1,540 @@ + + + + + + + + + + + pyudv — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv#

+

Modules

+ + + + + + + + + + + + + + + +

pyudv.attenuation

The amplitude model is currently under development, and yet undocumented.

pyudv.geometry

This module helps you deal with probe geometries.

pyudv.helpers

pyudv.read_mfprof

This module allows you to read .mfprof files from Met-Flow UDVs, and extract the contained data.

+
+ + +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.read_mfprof.amplitude_from_UVPdata.html b/_autosummary/pyudv.read_mfprof.amplitude_from_UVPdata.html new file mode 100644 index 0000000..f9358c4 --- /dev/null +++ b/_autosummary/pyudv.read_mfprof.amplitude_from_UVPdata.html @@ -0,0 +1,573 @@ + + + + + + + + + + + pyudv.read_mfprof.amplitude_from_UVPdata — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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pyudv.read_mfprof.amplitude_from_UVPdata#

+
+
+amplitude_from_UVPdata(raw_data, z, GainStart, GainEnd, zend, zstart=0.00037, Nbytes=14, deltaV=5)[source]#
+

Correct the raw amplitude data as outut of the UVP-DUO.

+
+
Parameters:
+
    +

  • raw_data (scalar, array) – raw echo signal before demodulation outputted by the UVP.

    • +

  • z (scalar, array) – position corresponding to raw_data.

    • +

  • GainStart (scalar) – start absolute gain.

    • +

  • GainEnd (scalar) – end absolute gain.

    • +

  • zend (float) – Maximum measurable distance by the UVP.

    • +

  • zstart (float) – minimum measurable distance by the UVP (the default is 0.00037)).

    • +

  • Nbytes (int) – Number of bytes over which the raw data are coded (the default is 14).

    • +

  • deltaV (int) – Volt range corresponding to the number of bytes (the default is 5).

    • +
+
+
Returns:
+

Unamplified/corrected echo signal.

+
+
Return type:
+

scalar, array

+
+
+
+ +
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.read_mfprof.amplitude_from_mfprof_reading.html b/_autosummary/pyudv.read_mfprof.amplitude_from_mfprof_reading.html new file mode 100644 index 0000000..5e1ac17 --- /dev/null +++ b/_autosummary/pyudv.read_mfprof.amplitude_from_mfprof_reading.html @@ -0,0 +1,577 @@ + + + + + + + + + + + pyudv.read_mfprof.amplitude_from_mfprof_reading — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.read_mfprof.amplitude_from_mfprof_reading#

+
+
+amplitude_from_mfprof_reading(Data, Parameters, Nbytes=14, deltaV=5)[source]#
+

Calculate the unamplified/corrected echo signal from Data and Parameters dictionnaries as output of read_mfprof, +using the function amplitude_from_UVPdata.

+
+
Parameters:
+
    +

  • Data (dict) – Data dictionnary coming from read_mfprof.

    • +

  • Parameters (dict) – Parameters dictionnary coming from read_mfprof.

    • +

  • Nbytes (int) – Number of bytes over which the raw data are coded (the default is 14).

    • +

  • deltaV (int) – Volt range corresponding to the number of bytes (the default is 5).

    • +
+
+
Returns:
+

Unamplified/corrected echo signal.

+
+
Return type:
+

scalar, array

+
+
+
+ +
+

Examples using pyudv.read_mfprof.amplitude_from_mfprof_reading#

+
+

Read and plot .mfprof data

+
Read and plot .mfprof data
+
+
+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
+
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.read_mfprof.html b/_autosummary/pyudv.read_mfprof.html new file mode 100644 index 0000000..fe69972 --- /dev/null +++ b/_autosummary/pyudv.read_mfprof.html @@ -0,0 +1,553 @@ + + + + + + + + + + + pyudv.read_mfprof — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
+
+
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+ + + Ctrl+K +
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pyudv.read_mfprof#

+

This module allows you to read .mfprof files from Met-Flow UDVs, and extract the contained data.

+

Functions

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amplitude_from_UVPdata

Correct the raw amplitude data as outut of the UVP-DUO.

amplitude_from_mfprof_reading

Calculate the unamplified/corrected echo signal from Data and Parameters dictionnaries as output of read_mfprof, using the function amplitude_from_UVPdata.

mfprof_to_netcdf

Convert a .mfprof file to a netCDF file.

read_mfprof

Read .mfprof binary files of the Met-Flow UDV.

velocity_from_UVPdata

Calculate velocity from UDV raw data.

velocity_from_mfprof_reading

Calculate velocity from Data and Parameters dictionnaries as output of read_mfprof, using the function velocity_from_UVPdata.

write_dictionnary

Write parameter dictionnary to a .txt file.

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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.read_mfprof.mfprof_to_netcdf.html b/_autosummary/pyudv.read_mfprof.mfprof_to_netcdf.html new file mode 100644 index 0000000..ee1cf81 --- /dev/null +++ b/_autosummary/pyudv.read_mfprof.mfprof_to_netcdf.html @@ -0,0 +1,567 @@ + + + + + + + + + + + pyudv.read_mfprof.mfprof_to_netcdf — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.read_mfprof.mfprof_to_netcdf#

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+mfprof_to_netcdf(input_mfprof, output_netcdf, add_attr=None, cut_zeros=True)[source]#
+

Convert a .mfprof file to a netCDF file.

+
+
Parameters:
+
    +

  • input_mfprof (str) – input .mfprof file path

    • +

  • output_netcdf (str | object) – output netCDF file path, or netCDF4 object (Dataset, Group) in which the data will be stored.

    • +

  • add_attr (dict, optional) – dictionnary containing attributes to be added to the netCDF file, by default None. For example, add_attr = {name: ‘John Smith’, team: ‘the best’}

    • +

  • cut_zeros (bool, optional) – If True, cut the measurements data and remove all the trailing 0 added when the measurements are stopped bedore the attributed time, by default True

    • +
+
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Example

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>>> mfprof_to_netcdf('input.mfprof', 'output.nc', add_attr = {name: 'John Smith', team: 'the best'}, cut_zeros=True)
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.read_mfprof.read_mfprof.html b/_autosummary/pyudv.read_mfprof.read_mfprof.html new file mode 100644 index 0000000..2be67f2 --- /dev/null +++ b/_autosummary/pyudv.read_mfprof.read_mfprof.html @@ -0,0 +1,578 @@ + + + + + + + + + + + pyudv.read_mfprof.read_mfprof — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.read_mfprof.read_mfprof#

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+read_mfprof(fileName, SI_units=True, convert_time=True)[source]#
+

Read .mfprof binary files of the Met-Flow UDV. This is mostly a direct python translation of the matlab script given by Met-Flow.

+
+
Parameters:
+
    +

  • fileName (str) – Path to the .mfprof file.

    • +

  • SI_units (bool) – If True, convert units to the international system (the default is True).

    • +

  • convert_time (bool) – If True, convert the time vector into seconds (the default is True).

    • +
+
+
Returns:
+

    +

  • Data (dict) – Dictionnary with the data stored in the mprof files. Available keys are: ‘transducer’, ‘profileTime’, ‘DopplerData’, ‘AmplitudeData’, ‘DistanceAlongBeam’

    • +

  • Parameters (dict) – Dictionnary with the parameters used in the UVP software when sampling the data. See UVP documentation for detail. Keys are: ‘Frequency’, ‘StartChannel’, ‘ChannelDistance’, ‘ChannelWidth’, ‘MaximumDepth’, ‘SoundSpeed’, ‘Angle’, ‘GainStart’, ‘GainEnd’, ‘Voltage’, ‘Iterations’, ‘NoiseLevel’, ‘CyclesPerPulse’, ‘TriggerMode’, ‘TriggerModeName’, ‘ProfileLength’, ‘ProfilesPerBlock’, ‘Blocks’, ‘AmplitudeStored’, ‘DoNotStoreDoppler’, ‘RawDataMin’, ‘RawDataMax’, ‘RawDataRange’, ‘AmplDataMin’, ‘AmplDataMax’, ‘VelocityInterpretingMode’, ‘UserSampleTime’, ‘SampleTime’, ‘UseMultiplexer’, ‘FlowMapping’, ‘FirstValidChannel’, ‘LastValidChannel’, ‘FlowRateType’, ‘PeriodEnhOffset’, ‘PeriodEnhPeriod’, ‘PeriodEnhNCycles’, ‘Comment’, ‘MeasurementProtocol’, ‘NumberOfCycles’, ‘CycleDelay’, ‘Version’, ‘Table’

    • +

  • Infos (dict) – Dictionnary with some informations stored during sampling of the data. Keys are ‘Signum’, ‘measParamsOffset’, ‘nProfiles’, ‘reserved1’, ‘flags’, ‘recordSize’, ‘nChannels’, ‘reserved2’, ‘startTime’.

    • +

  • Units (dict) – Dictionnary with the units of the variables stored in the other dictionnaries. Keys are the name of the variables.

    • +
+

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Examples using pyudv.read_mfprof.read_mfprof#

+
+

Read and plot .mfprof data

+
Read and plot .mfprof data
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.read_mfprof.velocity_from_UVPdata.html b/_autosummary/pyudv.read_mfprof.velocity_from_UVPdata.html new file mode 100644 index 0000000..1215c17 --- /dev/null +++ b/_autosummary/pyudv.read_mfprof.velocity_from_UVPdata.html @@ -0,0 +1,571 @@ + + + + + + + + + + + pyudv.read_mfprof.velocity_from_UVPdata — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.read_mfprof.velocity_from_UVPdata#

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+velocity_from_UVPdata(raw_data, SoundSpeed, MaximumDepth, Angle, Frequency, Nbytes=8)[source]#
+

Calculate velocity from UDV raw data. Units must be checked outside of this function - no conversion is done here.

+
+
Parameters:
+
    +

  • raw_data (float, numpy array) – Raw data coming out of the UDV.

    • +

  • SoundSpeed (float) – Sound velocity.

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  • MaximumDepth (float) – Maximum depth measurement, as defined by the Pulse Repetition Frequency.

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  • Angle (float) – Angle between the probe and the flow direction.

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  • Frequency (float) – Frequency of the probe.

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  • Nbytes (int) – Number of bytes over which the raw data are coded (the default is 8).

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Returns:
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Velocity field

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Return type:
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float, numpy array

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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.read_mfprof.velocity_from_mfprof_reading.html b/_autosummary/pyudv.read_mfprof.velocity_from_mfprof_reading.html new file mode 100644 index 0000000..1844f19 --- /dev/null +++ b/_autosummary/pyudv.read_mfprof.velocity_from_mfprof_reading.html @@ -0,0 +1,576 @@ + + + + + + + + + + + pyudv.read_mfprof.velocity_from_mfprof_reading — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.read_mfprof.velocity_from_mfprof_reading#

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+velocity_from_mfprof_reading(Data, Parameters, Nbytes=8)[source]#
+

Calculate velocity from Data and Parameters dictionnaries as output of read_mfprof, +using the function velocity_from_UVPdata.

+
+
Parameters:
+
    +

  • Data (dict) – Data dictionnary coming from read_mfprof.

    • +

  • Parameters (dict) – Parameters dictionnary coming from read_mfprof.

    • +

  • Nbytes (int) – Number of bytes over which the raw data are coded (the default is 8).

    • +
+
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Returns:
+

Velocity field

+
+
Return type:
+

float, numpy array

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Examples using pyudv.read_mfprof.velocity_from_mfprof_reading#

+
+

Read and plot .mfprof data

+
Read and plot .mfprof data
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+ On this page +
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+ + + + + + + + \ No newline at end of file diff --git a/_autosummary/pyudv.read_mfprof.write_dictionnary.html b/_autosummary/pyudv.read_mfprof.write_dictionnary.html new file mode 100644 index 0000000..cef1f67 --- /dev/null +++ b/_autosummary/pyudv.read_mfprof.write_dictionnary.html @@ -0,0 +1,564 @@ + + + + + + + + + + + pyudv.read_mfprof.write_dictionnary — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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pyudv.read_mfprof.write_dictionnary#

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+write_dictionnary(dico, file)[source]#
+

Write parameter dictionnary to a .txt file. +Each line of this file is a dictionnary key, followed by the corresponding entry, directly converted using str.

+
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Parameters:
+
    +

  • dico (dict) – input dictionnary

    • +

  • file (str) – output txt file.

    • +
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Returns:
+

Nothing.

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+ + + + + + + + \ No newline at end of file diff --git a/_downloads/041e21a5dcf4665a558ec24266dc2579/plot_reconstruct_velocity.py b/_downloads/041e21a5dcf4665a558ec24266dc2579/plot_reconstruct_velocity.py new file mode 100644 index 0000000..31a8de3 --- /dev/null +++ b/_downloads/041e21a5dcf4665a558ec24266dc2579/plot_reconstruct_velocity.py @@ -0,0 +1,67 @@ +""" +================================== +Probes and velocity reconstruction +================================== +""" + +import matplotlib.pyplot as plt +import numpy as np + +from pyudv.geometry import Probe, sketch_probes, reconstruct_velocity + + +def U(z): + u = 5 * (5 - z) ** 2 + v = u / 10 + U = np.array([u, v]) + return U + + +# %% +# Define probes and plot them +# =========================== + +# define probes +r = np.linspace(0, 5, 100) +alpha1, alpha2 = -120, -70 # deg +O1, O2 = np.array([1, 8]), np.array([-1, 7]) +probe1_pars = [r, alpha1, [0, O1]] +probe2_pars = [r, alpha2, [0, O2]] +# +probe1 = Probe(*probe1_pars) +probe2 = Probe(*probe2_pars) + +fig, ax = plt.subplots(1, 1, layout="constrained") +sketch_probes( + [probe1, probe2], + combinations=[[0, 1]], + combination_colors=["k"], + ax=ax, +) +ax.set_xlabel("x") +ax.set_ylabel("y") +plt.show() + +# %% +# Create fake signal +# ================== + +u1 = U(probe1.z).T @ probe1.unit_vec +u2 = U(probe2.z).T @ probe2.unit_vec + +# %% +# Velocity reconstruction +# ======================= + +U_rec, z_interp, X, dx_1, dx_2 = reconstruct_velocity(u1, u2, probe1, probe2) +U_th = U(z_interp) +# +fig, axarr = plt.subplots(1, 2, layout="constrained", sharey=True) +for ax, u_th, u_rec in zip(axarr, U_th, U_rec): + ax.plot(u_th, z_interp, ".", label="base") + ax.plot(u_rec, z_interp, ".", label="reconstructed") + ax.legend() +axarr[0].set_xlabel("u") +axarr[1].set_xlabel("v") +axarr[0].set_ylabel("z") +plt.show() diff --git a/_downloads/072ebe5500351177edd3cc97b2ba54e0/plot_direct_inversion.py b/_downloads/072ebe5500351177edd3cc97b2ba54e0/plot_direct_inversion.py new file mode 100644 index 0000000..f019e5d --- /dev/null +++ b/_downloads/072ebe5500351177edd3cc97b2ba54e0/plot_direct_inversion.py @@ -0,0 +1,188 @@ +""" +============================================== +Concentration inference from amplitude signals +============================================== +""" + +import matplotlib.pyplot as plt +import numpy as np + +import pyudv.attenuation.direct_models as DM +from pyudv.attenuation.inversion import explicit_inversion +from pyudv.attenuation.sediment_acoustic_models import quartz_sand as quartz + + +def C_to_phi(C, rho=2.65e3): + return C / rho + + +def phi_to_C(phi, rho=2.65e3): + return phi * rho + + +fig_width_small = 11.25 + +# %% +# Define the parameters for the direct model +# ========================================== + +d = 100e-6 # grain mean diameter [m] +rho = 2.65 # grain density [g/cm3] +rho = rho * 1e-3 / 1e-6 # grain density [kg/m3] + +F = 2e6 # frequency [Hz] +T = 20 # Temperature [Celsius degrees] +a_w = DM.alpha_w(F, T) # water absorption, [m-1] +k = 2 * np.pi * F / DM.sound_velocity(T) # wavenumber of the wave + +Ks, Kt = 1, 1 # sediment and transducer constants +Xi = quartz.Xi(k, d / 2) # sediment attenuation constant +rn = 0.05 # near field distance [cm] +r = np.linspace(0.001, 12, 330) # radial coordinates [cm] + +# Volumic fraction of grain +Phi = np.array([0.0001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1]) + + +# %% +# Influence of the imposed point in the integration +# ================================================= +# +# without near field function +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +psi = DM.near_field_theoretical(r, rn) * 0 + 1 # near field function +# defining a constant grain concentration profile +C = Phi[:, None] * rho * (r[None, :] * 0 + 1) +MSV = DM.create_MSvoltage(C, r, Xi, a_w, Ks, Kt, psi) +# +indexes = [2, 50, 150, 250, 320] + +color = [] +fig, ax = plt.subplots( + 1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True +) +for i, phi in enumerate(Phi): + if i == 0: + a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label="imposed") + else: + a = plt.plot(C[i, :], r, lw=4, alpha=0.3) + color.append(a[0].get_color()) +for j, index in enumerate(indexes): + C0 = C[:, index] + r0 = r[index] + C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi, C0, r0) + for i, phi in enumerate(Phi): + if i == 0: + plt.plot( + C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i] + ) + else: + plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i]) + ax.scatter(C0[i], r0, color=color[i]), +plt.xlabel("Concentration~[kg/m3]") +plt.ylabel("Distance from transducer~[m]") +ax.set_xscale("log") +plt.xlim([0.5 * C.min(), 2.5 * C.max()]) +plt.ylim([0, r.max()]) +ax.invert_yaxis() +secax = ax.secondary_xaxis("top", functions=(C_to_phi, phi_to_C)) +secax.set_xlabel("Volumic fraction") +plt.legend(title="Distance of imposed concentration [mm]") +plt.title("Without near field") + +plt.show() + +# %% +# with near field function +# ^^^^^^^^^^^^^^^^^^^^^^^^ + +psi = DM.near_field_theoretical(r, rn) # near field function +# defining a constant grain concentration profile +C = Phi[:, None] * rho * (r[None, :] * 0 + 1) +MSV = DM.create_MSvoltage(C, r[None, :], Xi, a_w, Ks, Kt, psi[None, :]) +# +color = [] +fig, ax = plt.subplots( + 1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True +) +for i, phi in enumerate(Phi): + if i == 0: + a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label="imposed") + else: + a = plt.plot(C[i, :], r, lw=4, alpha=0.3) + color.append(a[0].get_color()) +for j, index in enumerate(indexes): + C0 = C[:, index] + r0 = r[index] + C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi * 0 + 1, C0, r0) + for i, phi in enumerate(Phi): + if i == 0: + plt.plot( + C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i] + ) + else: + plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i]) +plt.xlabel("Concentration~[kg/m3]") +plt.ylabel("Distance from transducer~[m]") +ax.set_xscale("log") +plt.xlim([0.5 * C.min(), 2.5 * C.max()]) +plt.ylim([0, r.max()]) +ax.invert_yaxis() +secax = ax.secondary_xaxis("top", functions=(C_to_phi, phi_to_C)) +secax.set_xlabel("Volumic fraction") +plt.legend(title="Distance of imposed concentration [mm]") +plt.title("With near field") + +plt.show() + +# %% +# with real type concentration profiles +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +psi = DM.near_field_theoretical(r, rn) # near field function +# C = ( +# Phi[:, None] * rho * np.exp(-r[None, :] / 1) +# ) # defining an exponentially decreasing profile +C = ( + Phi[:, None] * rho * np.exp(r[None, :] / 10) +) # defining an exponentially increasing profile +# C = Phi[:, None] * rho * (r[None, :] * 0 + 1) # defining sedimentation-like profile +# C[..., :230] = 0 +# +MSV = DM.create_MSvoltage(C, r[None, :], Xi, a_w, Ks, Kt, psi[None, :]) +# +color = [] +fig, ax = plt.subplots( + 1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True +) +for i, phi in enumerate(Phi): + if i == 0: + a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label="imposed") + else: + a = plt.plot(C[i, :], r, lw=4, alpha=0.3) + color.append(a[0].get_color()) +for j, index in enumerate(indexes): + C0 = C[:, index] + r0 = r[index] + C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi * 0 + 1, C0, r0) + for i, phi in enumerate(Phi): + if i == 0: + plt.plot( + C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i] + ) + else: + plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i]) + # +plt.xlabel("Concentration~[kg/m3]") +plt.ylabel("Distance from transducer~[m]") +ax.set_xscale("log") +plt.xlim([0.2 * C[:, -1].min(), 2.5 * C.max()]) +plt.ylim([0, r.max()]) +ax.invert_yaxis() +secax = ax.secondary_xaxis("top", functions=(C_to_phi, phi_to_C)) +secax.set_xlabel("Volumic fraction") +plt.legend(title="Distance of imposed concentration [mm]") +plt.title("With exponential concentration profiles") + +plt.show() diff --git a/_downloads/25862d8e3051524fbbbd734856835c1b/plot_sketch_multiple_probes.ipynb b/_downloads/25862d8e3051524fbbbd734856835c1b/plot_sketch_multiple_probes.ipynb new file mode 100644 index 0000000..573aeac --- /dev/null +++ b/_downloads/25862d8e3051524fbbbd734856835c1b/plot_sketch_multiple_probes.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Sketch multiple probes\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom pyudv.geometry import Probe, sketch_probes\n\n# ## define probes\nr = np.linspace(1, 5, 100) # beam radial coordiantes, same for all probes\nalpha1, alpha2, alpha3, alpha4 = -120, -70, 70, -80 # probe inclinations in degree\n# probe origin points\nO1, O2, O3, O4 = (\n np.array([1, 8]),\n np.array([-1, 7]),\n np.array([2, 2]),\n np.array([3, 8.5]),\n)\n# probe parameters\nPROBE_PARS = [\n [r, alpha1, [0, O1]], # probe1\n [r, alpha2, [0, O2]], # probe2\n [r, alpha3, [0, O3]], # probe3\n [r, alpha4, [0, O4]], # probe4\n]\n#\nprobes = [Probe(*probe_pars) for probe_pars in PROBE_PARS]\nprobe_colors = [\"tab:blue\", \"tab:green\", \"tab:orange\", \"tab:red\"]\ncombinations = [(0, 1), (2, 3)]\ncombination_colors = [\"k\", \"grey\"]\n\nfig, ax = plt.subplots(1, 1, layout=\"constrained\")\nsketch_probes(\n probes,\n combinations=combinations,\n probe_colors=probe_colors,\n combination_colors=combination_colors,\n ax=ax,\n)\n\n# adding another probe afterwards\nprobe_add = Probe(r, 70, [0, np.array([4, 2])])\nprobe_add.plot_probe(ax, color=\"tab:pink\")\n\nax.set_xlabel(\"x\")\nax.set_ylabel(\"y\")\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { 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probe inclinations in degree +# probe origin points +O1, O2, O3, O4 = ( + np.array([1, 8]), + np.array([-1, 7]), + np.array([2, 2]), + np.array([3, 8.5]), +) +# probe parameters +PROBE_PARS = [ + [r, alpha1, [0, O1]], # probe1 + [r, alpha2, [0, O2]], # probe2 + [r, alpha3, [0, O3]], # probe3 + [r, alpha4, [0, O4]], # probe4 +] +# +probes = [Probe(*probe_pars) for probe_pars in PROBE_PARS] +probe_colors = ["tab:blue", "tab:green", "tab:orange", "tab:red"] +combinations = [(0, 1), (2, 3)] +combination_colors = ["k", "grey"] + +fig, ax = plt.subplots(1, 1, layout="constrained") +sketch_probes( + probes, + combinations=combinations, + probe_colors=probe_colors, + combination_colors=combination_colors, + ax=ax, +) + +# adding another probe afterwards +probe_add = Probe(r, 70, [0, np.array([4, 2])]) +probe_add.plot_probe(ax, color="tab:pink") + +ax.set_xlabel("x") +ax.set_ylabel("y") +plt.show() diff --git a/_downloads/6cec4194ce032359195b02a8876d221c/plot_direct_inversion.ipynb b/_downloads/6cec4194ce032359195b02a8876d221c/plot_direct_inversion.ipynb new file mode 100644 index 0000000..22d49a0 --- /dev/null +++ b/_downloads/6cec4194ce032359195b02a8876d221c/plot_direct_inversion.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Concentration inference from amplitude signals\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport pyudv.attenuation.direct_models as DM\nfrom pyudv.attenuation.inversion import explicit_inversion\nfrom pyudv.attenuation.sediment_acoustic_models import quartz_sand as quartz\n\n\ndef C_to_phi(C, rho=2.65e3):\n return C / rho\n\n\ndef phi_to_C(phi, rho=2.65e3):\n return phi * rho\n\n\nfig_width_small = 11.25" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the parameters for the direct model\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "d = 100e-6 # grain mean diameter [m]\nrho = 2.65 # grain density [g/cm3]\nrho = rho * 1e-3 / 1e-6 # grain density [kg/m3]\n\nF = 2e6 # frequency [Hz]\nT = 20 # Temperature [Celsius degrees]\na_w = DM.alpha_w(F, T) # water absorption, [m-1]\nk = 2 * np.pi * F / DM.sound_velocity(T) # wavenumber of the wave\n\nKs, Kt = 1, 1 # sediment and transducer constants\nXi = quartz.Xi(k, d / 2) # sediment attenuation constant\nrn = 0.05 # near field distance [cm]\nr = np.linspace(0.001, 12, 330) # radial coordinates [cm]\n\n# Volumic fraction of grain\nPhi = np.array([0.0001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Influence of the imposed point in the integration\n\n### without near field function\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "psi = DM.near_field_theoretical(r, rn) * 0 + 1 # near field function\n# defining a constant grain concentration profile\nC = Phi[:, None] * rho * (r[None, :] * 0 + 1)\nMSV = DM.create_MSvoltage(C, r, Xi, a_w, Ks, Kt, psi)\n#\nindexes = [2, 50, 150, 250, 320]\n\ncolor = []\nfig, ax = plt.subplots(\n 1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True\n)\nfor i, phi in enumerate(Phi):\n if i == 0:\n a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label=\"imposed\")\n else:\n a = plt.plot(C[i, :], r, lw=4, alpha=0.3)\n color.append(a[0].get_color())\nfor j, index in enumerate(indexes):\n C0 = C[:, index]\n r0 = r[index]\n C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi, C0, r0)\n for i, phi in enumerate(Phi):\n if i == 0:\n plt.plot(\n C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i]\n )\n else:\n plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i])\n ax.scatter(C0[i], r0, color=color[i]),\nplt.xlabel(\"Concentration~[kg/m3]\")\nplt.ylabel(\"Distance from transducer~[m]\")\nax.set_xscale(\"log\")\nplt.xlim([0.5 * C.min(), 2.5 * C.max()])\nplt.ylim([0, r.max()])\nax.invert_yaxis()\nsecax = ax.secondary_xaxis(\"top\", functions=(C_to_phi, phi_to_C))\nsecax.set_xlabel(\"Volumic fraction\")\nplt.legend(title=\"Distance of imposed concentration [mm]\")\nplt.title(\"Without near field\")\n\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### with near field function\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "psi = DM.near_field_theoretical(r, rn) # near field function\n# defining a constant grain concentration profile\nC = Phi[:, None] * rho * (r[None, :] * 0 + 1)\nMSV = DM.create_MSvoltage(C, r[None, :], Xi, a_w, Ks, Kt, psi[None, :])\n#\ncolor = []\nfig, ax = plt.subplots(\n 1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True\n)\nfor i, phi in enumerate(Phi):\n if i == 0:\n a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label=\"imposed\")\n else:\n a = plt.plot(C[i, :], r, lw=4, alpha=0.3)\n color.append(a[0].get_color())\nfor j, index in enumerate(indexes):\n C0 = C[:, index]\n r0 = r[index]\n C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi * 0 + 1, C0, r0)\n for i, phi in enumerate(Phi):\n if i == 0:\n plt.plot(\n C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i]\n )\n else:\n plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i])\nplt.xlabel(\"Concentration~[kg/m3]\")\nplt.ylabel(\"Distance from transducer~[m]\")\nax.set_xscale(\"log\")\nplt.xlim([0.5 * C.min(), 2.5 * C.max()])\nplt.ylim([0, r.max()])\nax.invert_yaxis()\nsecax = ax.secondary_xaxis(\"top\", functions=(C_to_phi, phi_to_C))\nsecax.set_xlabel(\"Volumic fraction\")\nplt.legend(title=\"Distance of imposed concentration [mm]\")\nplt.title(\"With near field\")\n\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### with real type concentration profiles\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "psi = DM.near_field_theoretical(r, rn) # near field function\n# C = (\n# Phi[:, None] * rho * np.exp(-r[None, :] / 1)\n# ) # defining an exponentially decreasing profile\nC = (\n Phi[:, None] * rho * np.exp(r[None, :] / 10)\n) # defining an exponentially increasing profile\n# C = Phi[:, None] * rho * (r[None, :] * 0 + 1) # defining sedimentation-like profile\n# C[..., :230] = 0\n#\nMSV = DM.create_MSvoltage(C, r[None, :], Xi, a_w, Ks, Kt, psi[None, :])\n#\ncolor = []\nfig, ax = plt.subplots(\n 1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True\n)\nfor i, phi in enumerate(Phi):\n if i == 0:\n a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label=\"imposed\")\n else:\n a = plt.plot(C[i, :], r, lw=4, alpha=0.3)\n color.append(a[0].get_color())\nfor j, index in enumerate(indexes):\n C0 = C[:, index]\n r0 = r[index]\n C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi * 0 + 1, C0, r0)\n for i, phi in enumerate(Phi):\n if i == 0:\n plt.plot(\n C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i]\n )\n else:\n plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i])\n #\nplt.xlabel(\"Concentration~[kg/m3]\")\nplt.ylabel(\"Distance from transducer~[m]\")\nax.set_xscale(\"log\")\nplt.xlim([0.2 * C[:, -1].min(), 2.5 * C.max()])\nplt.ylim([0, r.max()])\nax.invert_yaxis()\nsecax = ax.secondary_xaxis(\"top\", functions=(C_to_phi, phi_to_C))\nsecax.set_xlabel(\"Volumic fraction\")\nplt.legend(title=\"Distance of imposed concentration [mm]\")\nplt.title(\"With exponential concentration profiles\")\n\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/_downloads/a5c9e21f789d517bf8b16d2d54419403/plot_read_plot_mfprof_data.ipynb b/_downloads/a5c9e21f789d517bf8b16d2d54419403/plot_read_plot_mfprof_data.ipynb new file mode 100644 index 0000000..8c883a5 --- /dev/null +++ b/_downloads/a5c9e21f789d517bf8b16d2d54419403/plot_read_plot_mfprof_data.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Read and plot .mfprof data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.colors as colors\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom pyudv.read_mfprof import (\n amplitude_from_mfprof_reading,\n read_mfprof,\n velocity_from_mfprof_reading,\n)\n\n#\npath_data = \"src/data_sample.mfprof\"\n\n# #### Loading data\nData, Parameters, Info, Units = read_mfprof(path_data)\nAmplitude_data = amplitude_from_mfprof_reading(Data, Parameters)\nVelocity_data = velocity_from_mfprof_reading(Data, Parameters)\ntime = Data[\"profileTime\"]\nz_coordinates = Data[\"DistanceAlongBeam\"] * 1e2\nindmax_time = np.argwhere(Data[\"transducer\"] == 0)[1][0] - 1\nindmax_z = -1\nind_bottom = 572\n\n# #### plotting velocity\ndivnorm = colors.TwoSlopeNorm(vcenter=0, vmin=-0.2, vmax=0.2)\nfig, ax = plt.subplots(1, 1, constrained_layout=True)\nc = ax.pcolormesh(\n time[:indmax_time],\n z_coordinates[:indmax_z],\n Velocity_data[:indmax_z, :indmax_time],\n cmap=\"PuOr\",\n norm=divnorm,\n rasterized=True,\n shading=\"auto\",\n)\nax.axhline(y=z_coordinates[ind_bottom], color=\"k\", lw=\"0.5\", ls=\"--\")\nax.invert_yaxis()\nfig.colorbar(c, label=\"Velocity [m/s]\")\nax.set_xlabel(\"Time [s]\")\nax.set_ylabel(\"DistanceAlongBeam [cm]\")\n# fig.draw_without_rendering()\nplt.savefig(\"plots/Spatio_temporal_velocity.pdf\", dpi=600)\nplt.show()\n\n# #### plotting amplitude\ndivnorm = colors.TwoSlopeNorm(vcenter=0, vmin=-0.1, vmax=0.1)\nfig, ax = plt.subplots(1, 1, constrained_layout=True)\nc = ax.pcolormesh(\n time[:indmax_time],\n z_coordinates[:indmax_z],\n Amplitude_data[:indmax_z, :indmax_time],\n rasterized=True,\n shading=\"auto\",\n cmap=\"PuOr\",\n norm=divnorm,\n)\nax.axhline(y=z_coordinates[ind_bottom], color=\"k\", lw=\"0.5\", ls=\"--\")\nax.invert_yaxis()\nfig.colorbar(c, label=\"Amplitude [V]\")\nax.set_xlabel(\"Time [s]\")\nax.set_ylabel(\"DistanceAlongBeam [cm]\")\nplt.savefig(\"plots/Spatio_temporal_amplitude.pdf\", dpi=600)\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/_downloads/cd287cdd444dc329dfbb4e599fcea920/plot_reconstruct_velocity.ipynb b/_downloads/cd287cdd444dc329dfbb4e599fcea920/plot_reconstruct_velocity.ipynb new file mode 100644 index 0000000..7bae7be --- /dev/null +++ b/_downloads/cd287cdd444dc329dfbb4e599fcea920/plot_reconstruct_velocity.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Probes and velocity reconstruction\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom pyudv.geometry import Probe, sketch_probes, reconstruct_velocity\n\n\ndef U(z):\n u = 5 * (5 - z) ** 2\n v = u / 10\n U = np.array([u, v])\n return U" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define probes and plot them\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# define probes\nr = np.linspace(0, 5, 100)\nalpha1, alpha2 = -120, -70 # deg\nO1, O2 = np.array([1, 8]), np.array([-1, 7])\nprobe1_pars = [r, alpha1, [0, O1]]\nprobe2_pars = [r, alpha2, [0, O2]]\n#\nprobe1 = Probe(*probe1_pars)\nprobe2 = Probe(*probe2_pars)\n\nfig, ax = plt.subplots(1, 1, layout=\"constrained\")\nsketch_probes(\n [probe1, probe2],\n combinations=[[0, 1]],\n combination_colors=[\"k\"],\n ax=ax,\n)\nax.set_xlabel(\"x\")\nax.set_ylabel(\"y\")\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create fake signal\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "u1 = U(probe1.z).T @ probe1.unit_vec\nu2 = U(probe2.z).T @ probe2.unit_vec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Velocity reconstruction\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "U_rec, z_interp, X, dx_1, dx_2 = reconstruct_velocity(u1, u2, probe1, probe2)\nU_th = U(z_interp)\n#\nfig, axarr = plt.subplots(1, 2, layout=\"constrained\", sharey=True)\nfor ax, u_th, u_rec in zip(axarr, U_th, U_rec):\n ax.plot(u_th, z_interp, \".\", label=\"base\")\n ax.plot(u_rec, z_interp, \".\", label=\"reconstructed\")\n ax.legend()\naxarr[0].set_xlabel(\"u\")\naxarr[1].set_xlabel(\"v\")\naxarr[0].set_ylabel(\"z\")\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/_examples/index.html b/_examples/index.html new file mode 100644 index 0000000..1ae98b6 --- /dev/null +++ b/_examples/index.html @@ -0,0 +1,468 @@ + + + + + + + + + + + Examples — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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Examples#

+

Below is a gallery of examples demonstrating the use of several functions of pyudv.

+
+

Sketch multiple probes

+
Sketch multiple probes
+
+

Probes and velocity reconstruction

+
Probes and velocity reconstruction
+
+

Read and plot .mfprof data

+
Read and plot .mfprof data
+
+

Concentration inference from amplitude signals

+
Concentration inference from amplitude signals
+
+
+
+
+

Download all examples in Python source code: _examples_python.zip

+
+
+

Download all examples in Jupyter notebooks: _examples_jupyter.zip

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+
+

Gallery generated by Sphinx-Gallery

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+ + © Copyright 2023, Cyril Gadal. +
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+ Created using Sphinx 7.2.6. +
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+
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+ Built with the PyData Sphinx Theme 0.14.2. +

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+ + \ No newline at end of file diff --git a/_examples/plot_direct_inversion.html b/_examples/plot_direct_inversion.html new file mode 100644 index 0000000..3a8a3e5 --- /dev/null +++ b/_examples/plot_direct_inversion.html @@ -0,0 +1,669 @@ + + + + + + + + + + + Concentration inference from amplitude signals — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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Note

+

Go to the end +to download the full example code

+
+
+

Concentration inference from amplitude signals#

+
import matplotlib.pyplot as plt
+import numpy as np
+
+import pyudv.attenuation.direct_models as DM
+from pyudv.attenuation.inversion import explicit_inversion
+from pyudv.attenuation.sediment_acoustic_models import quartz_sand as quartz
+
+
+def C_to_phi(C, rho=2.65e3):
+    return C / rho
+
+
+def phi_to_C(phi, rho=2.65e3):
+    return phi * rho
+
+
+fig_width_small = 11.25
+
+
+
+

Define the parameters for the direct model#

+
d = 100e-6  # grain mean diameter [m]
+rho = 2.65  # grain density [g/cm3]
+rho = rho * 1e-3 / 1e-6  # grain density [kg/m3]
+
+F = 2e6  # frequency [Hz]
+T = 20  # Temperature [Celsius degrees]
+a_w = DM.alpha_w(F, T)  # water absorption, [m-1]
+k = 2 * np.pi * F / DM.sound_velocity(T)  # wavenumber of the wave
+
+Ks, Kt = 1, 1  # sediment and transducer constants
+Xi = quartz.Xi(k, d / 2)  # sediment attenuation constant
+rn = 0.05  # near field distance [cm]
+r = np.linspace(0.001, 12, 330)  # radial coordinates [cm]
+
+# Volumic fraction of grain
+Phi = np.array([0.0001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1])
+
+
+
+
+

Influence of the imposed point in the integration#

+
+

without near field function#

+
psi = DM.near_field_theoretical(r, rn) * 0 + 1  # near field function
+# defining a constant grain concentration profile
+C = Phi[:, None] * rho * (r[None, :] * 0 + 1)
+MSV = DM.create_MSvoltage(C, r, Xi, a_w, Ks, Kt, psi)
+#
+indexes = [2, 50, 150, 250, 320]
+
+color = []
+fig, ax = plt.subplots(
+    1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True
+)
+for i, phi in enumerate(Phi):
+    if i == 0:
+        a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label="imposed")
+    else:
+        a = plt.plot(C[i, :], r, lw=4, alpha=0.3)
+    color.append(a[0].get_color())
+for j, index in enumerate(indexes):
+    C0 = C[:, index]
+    r0 = r[index]
+    C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi, C0, r0)
+    for i, phi in enumerate(Phi):
+        if i == 0:
+            plt.plot(
+                C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i]
+            )
+        else:
+            plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i])
+        ax.scatter(C0[i], r0, color=color[i]),
+plt.xlabel("Concentration~[kg/m3]")
+plt.ylabel("Distance from transducer~[m]")
+ax.set_xscale("log")
+plt.xlim([0.5 * C.min(), 2.5 * C.max()])
+plt.ylim([0, r.max()])
+ax.invert_yaxis()
+secax = ax.secondary_xaxis("top", functions=(C_to_phi, phi_to_C))
+secax.set_xlabel("Volumic fraction")
+plt.legend(title="Distance of imposed concentration [mm]")
+plt.title("Without near field")
+
+plt.show()
+
+
+Without near field
+
+

with near field function#

+
psi = DM.near_field_theoretical(r, rn)  # near field function
+# defining a constant grain concentration profile
+C = Phi[:, None] * rho * (r[None, :] * 0 + 1)
+MSV = DM.create_MSvoltage(C, r[None, :], Xi, a_w, Ks, Kt, psi[None, :])
+#
+color = []
+fig, ax = plt.subplots(
+    1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True
+)
+for i, phi in enumerate(Phi):
+    if i == 0:
+        a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label="imposed")
+    else:
+        a = plt.plot(C[i, :], r, lw=4, alpha=0.3)
+    color.append(a[0].get_color())
+for j, index in enumerate(indexes):
+    C0 = C[:, index]
+    r0 = r[index]
+    C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi * 0 + 1, C0, r0)
+    for i, phi in enumerate(Phi):
+        if i == 0:
+            plt.plot(
+                C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i]
+            )
+        else:
+            plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i])
+plt.xlabel("Concentration~[kg/m3]")
+plt.ylabel("Distance from transducer~[m]")
+ax.set_xscale("log")
+plt.xlim([0.5 * C.min(), 2.5 * C.max()])
+plt.ylim([0, r.max()])
+ax.invert_yaxis()
+secax = ax.secondary_xaxis("top", functions=(C_to_phi, phi_to_C))
+secax.set_xlabel("Volumic fraction")
+plt.legend(title="Distance of imposed concentration [mm]")
+plt.title("With near field")
+
+plt.show()
+
+
+With near field
+
+

with real type concentration profiles#

+
psi = DM.near_field_theoretical(r, rn)  # near field function
+# C = (
+#     Phi[:, None] * rho * np.exp(-r[None, :] / 1)
+# )  # defining an exponentially decreasing profile
+C = (
+    Phi[:, None] * rho * np.exp(r[None, :] / 10)
+)  # defining an exponentially increasing profile
+# C = Phi[:, None] * rho * (r[None, :] * 0 + 1)  # defining sedimentation-like profile
+# C[..., :230] = 0
+#
+MSV = DM.create_MSvoltage(C, r[None, :], Xi, a_w, Ks, Kt, psi[None, :])
+#
+color = []
+fig, ax = plt.subplots(
+    1, 1, figsize=(fig_width_small, fig_width_small), constrained_layout=True
+)
+for i, phi in enumerate(Phi):
+    if i == 0:
+        a = plt.plot(C[i, :], r, lw=4, alpha=0.3, label="imposed")
+    else:
+        a = plt.plot(C[i, :], r, lw=4, alpha=0.3)
+    color.append(a[0].get_color())
+for j, index in enumerate(indexes):
+    C0 = C[:, index]
+    r0 = r[index]
+    C_inferred, *_ = explicit_inversion(MSV, r, Xi, a_w, psi * 0 + 1, C0, r0)
+    for i, phi in enumerate(Phi):
+        if i == 0:
+            plt.plot(
+                C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, label=str(r0), color=color[i]
+            )
+        else:
+            plt.plot(C_inferred[i, :], r, lw=2.5 - (j + 1) / 3, color=color[i])
+        #
+plt.xlabel("Concentration~[kg/m3]")
+plt.ylabel("Distance from transducer~[m]")
+ax.set_xscale("log")
+plt.xlim([0.2 * C[:, -1].min(), 2.5 * C.max()])
+plt.ylim([0, r.max()])
+ax.invert_yaxis()
+secax = ax.secondary_xaxis("top", functions=(C_to_phi, phi_to_C))
+secax.set_xlabel("Volumic fraction")
+plt.legend(title="Distance of imposed concentration [mm]")
+plt.title("With exponential concentration profiles")
+
+plt.show()
+
+
+With exponential concentration profiles

Total running time of the script: (0 minutes 3.286 seconds)

+ +

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+ On this page +
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+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

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+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
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+ + \ No newline at end of file diff --git a/_examples/plot_read_plot_mfprof_data.html b/_examples/plot_read_plot_mfprof_data.html new file mode 100644 index 0000000..63f6ccc --- /dev/null +++ b/_examples/plot_read_plot_mfprof_data.html @@ -0,0 +1,531 @@ + + + + + + + + + + + Read and plot .mfprof data — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Note

+

Go to the end +to download the full example code

+
+
+

Read and plot .mfprof data#

+
    +
  • plot read plot mfprof data
    • +
  • plot read plot mfprof data
    • +
+
import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import numpy as np
+
+from pyudv.read_mfprof import (
+    amplitude_from_mfprof_reading,
+    read_mfprof,
+    velocity_from_mfprof_reading,
+)
+
+#
+path_data = "src/data_sample.mfprof"
+
+# #### Loading data
+Data, Parameters, Info, Units = read_mfprof(path_data)
+Amplitude_data = amplitude_from_mfprof_reading(Data, Parameters)
+Velocity_data = velocity_from_mfprof_reading(Data, Parameters)
+time = Data["profileTime"]
+z_coordinates = Data["DistanceAlongBeam"] * 1e2
+indmax_time = np.argwhere(Data["transducer"] == 0)[1][0] - 1
+indmax_z = -1
+ind_bottom = 572
+
+# #### plotting velocity
+divnorm = colors.TwoSlopeNorm(vcenter=0, vmin=-0.2, vmax=0.2)
+fig, ax = plt.subplots(1, 1, constrained_layout=True)
+c = ax.pcolormesh(
+    time[:indmax_time],
+    z_coordinates[:indmax_z],
+    Velocity_data[:indmax_z, :indmax_time],
+    cmap="PuOr",
+    norm=divnorm,
+    rasterized=True,
+    shading="auto",
+)
+ax.axhline(y=z_coordinates[ind_bottom], color="k", lw="0.5", ls="--")
+ax.invert_yaxis()
+fig.colorbar(c, label="Velocity [m/s]")
+ax.set_xlabel("Time [s]")
+ax.set_ylabel("DistanceAlongBeam [cm]")
+# fig.draw_without_rendering()
+plt.savefig("plots/Spatio_temporal_velocity.pdf", dpi=600)
+plt.show()
+
+# #### plotting amplitude
+divnorm = colors.TwoSlopeNorm(vcenter=0, vmin=-0.1, vmax=0.1)
+fig, ax = plt.subplots(1, 1, constrained_layout=True)
+c = ax.pcolormesh(
+    time[:indmax_time],
+    z_coordinates[:indmax_z],
+    Amplitude_data[:indmax_z, :indmax_time],
+    rasterized=True,
+    shading="auto",
+    cmap="PuOr",
+    norm=divnorm,
+)
+ax.axhline(y=z_coordinates[ind_bottom], color="k", lw="0.5", ls="--")
+ax.invert_yaxis()
+fig.colorbar(c, label="Amplitude [V]")
+ax.set_xlabel("Time [s]")
+ax.set_ylabel("DistanceAlongBeam [cm]")
+plt.savefig("plots/Spatio_temporal_amplitude.pdf", dpi=600)
+plt.show()
+
+
+

Total running time of the script: (0 minutes 4.333 seconds)

+ +

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+
+ + +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
+ + + +
+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/_examples/plot_reconstruct_velocity.html b/_examples/plot_reconstruct_velocity.html new file mode 100644 index 0000000..327aa9d --- /dev/null +++ b/_examples/plot_reconstruct_velocity.html @@ -0,0 +1,544 @@ + + + + + + + + + + + Probes and velocity reconstruction — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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+ + + + + +
+ +
+

Note

+

Go to the end +to download the full example code

+
+
+

Probes and velocity reconstruction#

+
import matplotlib.pyplot as plt
+import numpy as np
+
+from pyudv.geometry import Probe, sketch_probes, reconstruct_velocity
+
+
+def U(z):
+    u = 5 * (5 - z) ** 2
+    v = u / 10
+    U = np.array([u, v])
+    return U
+
+
+
+

Define probes and plot them#

+
# define probes
+r = np.linspace(0, 5, 100)
+alpha1, alpha2 = -120, -70  # deg
+O1, O2 = np.array([1, 8]), np.array([-1, 7])
+probe1_pars = [r, alpha1, [0, O1]]
+probe2_pars = [r, alpha2, [0, O2]]
+#
+probe1 = Probe(*probe1_pars)
+probe2 = Probe(*probe2_pars)
+
+fig, ax = plt.subplots(1, 1, layout="constrained")
+sketch_probes(
+    [probe1, probe2],
+    combinations=[[0, 1]],
+    combination_colors=["k"],
+    ax=ax,
+)
+ax.set_xlabel("x")
+ax.set_ylabel("y")
+plt.show()
+
+
+plot reconstruct velocity
+
+

Create fake signal#

+
u1 = U(probe1.z).T @ probe1.unit_vec
+u2 = U(probe2.z).T @ probe2.unit_vec
+
+
+
+
+

Velocity reconstruction#

+
U_rec, z_interp, X, dx_1, dx_2 = reconstruct_velocity(u1, u2, probe1, probe2)
+U_th = U(z_interp)
+#
+fig, axarr = plt.subplots(1, 2, layout="constrained", sharey=True)
+for ax, u_th, u_rec in zip(axarr, U_th, U_rec):
+    ax.plot(u_th, z_interp, ".", label="base")
+    ax.plot(u_rec, z_interp, ".", label="reconstructed")
+    ax.legend()
+axarr[0].set_xlabel("u")
+axarr[1].set_xlabel("v")
+axarr[0].set_ylabel("z")
+plt.show()
+
+
+plot reconstruct velocity

Total running time of the script: (0 minutes 0.294 seconds)

+ +

Gallery generated by Sphinx-Gallery

+
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+ + +
+ + + + + +
+ + + +
+ +
+
+ On this page +
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+ + +
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+ +
+ +
+
+
+ + + + + +
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+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
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+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/_examples/plot_sketch_multiple_probes.html b/_examples/plot_sketch_multiple_probes.html new file mode 100644 index 0000000..758a5b5 --- /dev/null +++ b/_examples/plot_sketch_multiple_probes.html @@ -0,0 +1,508 @@ + + + + + + + + + + + Sketch multiple probes — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Note

+

Go to the end +to download the full example code

+
+
+

Sketch multiple probes#

+plot sketch multiple probes
import matplotlib.pyplot as plt
+import numpy as np
+
+from pyudv.geometry import Probe, sketch_probes
+
+# ## define probes
+r = np.linspace(1, 5, 100)  # beam radial coordiantes, same for all probes
+alpha1, alpha2, alpha3, alpha4 = -120, -70, 70, -80  # probe inclinations in degree
+# probe origin points
+O1, O2, O3, O4 = (
+    np.array([1, 8]),
+    np.array([-1, 7]),
+    np.array([2, 2]),
+    np.array([3, 8.5]),
+)
+# probe parameters
+PROBE_PARS = [
+    [r, alpha1, [0, O1]],  # probe1
+    [r, alpha2, [0, O2]],  # probe2
+    [r, alpha3, [0, O3]],  # probe3
+    [r, alpha4, [0, O4]],  # probe4
+]
+#
+probes = [Probe(*probe_pars) for probe_pars in PROBE_PARS]
+probe_colors = ["tab:blue", "tab:green", "tab:orange", "tab:red"]
+combinations = [(0, 1), (2, 3)]
+combination_colors = ["k", "grey"]
+
+fig, ax = plt.subplots(1, 1, layout="constrained")
+sketch_probes(
+    probes,
+    combinations=combinations,
+    probe_colors=probe_colors,
+    combination_colors=combination_colors,
+    ax=ax,
+)
+
+# adding another probe afterwards
+probe_add = Probe(r, 70, [0, np.array([4, 2])])
+probe_add.plot_probe(ax, color="tab:pink")
+
+ax.set_xlabel("x")
+ax.set_ylabel("y")
+plt.show()
+
+
+

Total running time of the script: (0 minutes 0.191 seconds)

+ +

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+
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+ + © Copyright 2023, Cyril Gadal. +
+ +

+
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+

+ Created using Sphinx 7.2.6. +
+

+
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+ Built with the PyData Sphinx Theme 0.14.2. +

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+ + \ No newline at end of file diff --git a/_examples/sg_execution_times.html b/_examples/sg_execution_times.html new file mode 100644 index 0000000..593a3be --- /dev/null +++ b/_examples/sg_execution_times.html @@ -0,0 +1,449 @@ + + + + + + + + + + + Computation times — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Created using Sphinx 7.2.6. +
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+ + \ No newline at end of file diff --git a/_images/sphx_glr_plot_direct_inversion_001.png b/_images/sphx_glr_plot_direct_inversion_001.png new file mode 100644 index 0000000000000000000000000000000000000000..a25dbae05dafbe412e3fa9fdcd50f0b6aabef67b GIT binary patch literal 88584 zcmbrmbzGBe^glifP(gSUkP=V<=@#jh?rtW+=#b8V1w4w>C~1(+5dxA^QAQ&<8j)_P z0i(Xx@cDdR-|xS_-(F0{w)?)$ed2xI=bY>It+uAp%^S2gAP~q+73C+o5Xco6{&|fM zyz|d$fF<}P>ZM@prRQerEPxn$RonT$92!%%j>zP z7%#8O|9*kT&BKmYsIsO6oaFj*WfM;bgv<*6Bq)?FaDWg%ASzE}_5IS;r+m}Qy_?Us z4@B*7&V99eZ(?HJ$jH(DW136+>Nh1t%tI<-1p;CZ1p>MePPX=%lPgbdU6FNoz^zRD zU={~(%(H)vTRgE1F$I`0>j{{bvJbHawJQoGYeFToD-T3dK-LC*y zAdnZO0#iH}uOLs^9>fDlz~ebVHp#_{7sS_q624dl&W2LiP={Ii&4x-Hz?ZYb30o@e z+b(n_h#k$x8JL@Mwesy9pw2CPXW(@ttv| zI{t*z&(3fdR3{&aMC)=)MMLj>Yh|#BxY?IoUCDg#O%0w==9Fp zC-_tBvrBX;P+54zP6uvDp8g_|{@HvmT{2c}*JyDY0vRQKBLIPr@GCLGKQXT?Btd)a z-CSKq5G-o*+kShUoJgC=TB}wG)EA7crWJCG=lT5WQcYc*)B0$oYtl6c1p7jp5c1-_?e~9g(Mksg 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+ + \ No newline at end of file diff --git a/_modules/pyudv/attenuation/direct_models.html b/_modules/pyudv/attenuation/direct_models.html new file mode 100644 index 0000000..eb7bd87 --- /dev/null +++ b/_modules/pyudv/attenuation/direct_models.html @@ -0,0 +1,598 @@ + + + + + + + + + + pyudv.attenuation.direct_models — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Source code for pyudv.attenuation.direct_models

+import numpy as np
+from scipy.integrate import cumulative_trapezoid
+
+
+

+[docs]
+def alpha_w(f, T):
+    """Calculate the fresh water attenuation in m-1.
+
+    Parameters
+    ----------
+    f : scalar, array
+        Sound frequency in Hertz.
+    T : scalar, array
+        Temperature in Celsius degrees.
+
+    Returns
+    -------
+    scalar, array
+        return the fresh water attenuation in m-1.
+
+    Examples
+    --------
+    >>> f = 2e6
+    >>> T = 25
+    >>> print(alpha_w(f, T))
+
+    References
+    --------
+    [1] Fisher, F. H., & Simmons, V. P. (1977). Sound absorption in sea water. The Journal of the Acoustical Society of America, 62(3), 558-564.
+
+    """
+    return (
+        10 ** (-15)
+        * (55.9 - 2.37 * T + 4.77 * 10 ** (-2) * T**2 - 3.48 * 10 ** (-4) * T**3)
+        * f**2
+    )

+
+
+
+

+[docs]
+def sound_velocity(T):
+    """Compute sound velocity in fresh water in [m/s]
+
+    Parameters
+    ----------
+    T : scalar, array
+        Temperature in Celsius degrees.
+
+    Returns
+    -------
+    scalar, array
+        sound velocity in fresh water in [m/s]
+
+    Examples
+    --------
+    >>> T = 25
+    >>> print(sound_velocity(T))
+
+    References
+    --------
+    [1] Lubbers, J., & Graaff, R. (1998). A simple and accurate formula for the sound velocity in water. Ultrasound in medicine & biology, 24(7), 1065-1068.
+
+
+    """
+    return 1405.03 + 4.624 * T - 3.83 * 10 ** (-2) * T**2

+
+
+
+

+[docs]
+def near_field_theoretical(r, rn):
+    """Return the near field function.
+
+    Parameters
+    ----------
+    r : scalar, array
+        Radial coordinate.
+    rn : scalar, array
+        Near field distance
+
+    Returns
+    -------
+    scalar, array
+        return the near field function.
+
+    Examples
+    --------
+    >>> r = np.linspace(0.1, 1, 100)
+    >>> rn = 0.2
+    >>> Psi = near_field_theoretical(r, rn)
+
+    References
+    --------
+    [1] Downing, A., Thorne, P. D., & Vincent, C. E. (1995). Backscattering from a suspension in the near field of a piston transducer. The Journal of the Acoustical Society of America, 97(3), 1614-1620.
+    [2] Pedocchi, F., & García, M. H. (2012). Acoustic measurement of suspended sediment concentration profiles in an oscillatory boundary layer. Continental Shelf Research, 46, 87-95.
+
+
+    """
+    # return 1 + rn**3.2/(0.43*r*rn**2.2 + 0.48*r**3.2)
+    return 1 + rn**3.2 / (1.35 * r * rn**2.2 + (2.5 * r) ** 3.2)

+
+
+
+

+[docs]
+def correction_factor(r, alpha_w, Ks, Kt, psi):
+    r"""Compute the part of the mean squared voltage independet of the sediment concentration: :math:`\frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}}}`
+
+    Parameters
+    ----------
+    r : scalar, array
+        radial coordinate
+    alpha_w : scalar, array
+        water attenuation coefficient
+    Ks : scalar, array
+        Sediment constant.
+    Kt : scalar, array
+        Transducer constant.
+    psi : scalar, array
+        near field function.
+
+    Returns
+    -------
+    scalar, array
+        return the part of the mean squared voltage that is independent of the sediment concentration.
+
+    """
+    return np.exp(-4 * r * alpha_w) * ((Ks * Kt) / (r * psi)) ** 2

+
+
+
+

+[docs]
+def create_MSvoltage(C, r, Xi, alpha_w, Ks, Kt, psi):
+    r"""Compute the mean squared voltage from a concentration profile, given a set of acoustic coefficients: :math:`\langle V^{2} \rangle = C \frac{K_{\textup{s}} K_{\textup{t}}}{\psi r}^{2}e^{-4r\alpha_{\textup{w}} - \int_{0}^{r}\xi C dr}`
+
+    Parameters
+    ----------
+    C : scalar, array
+        Sediment concentration.
+    r : scalar, array
+        Radial coordinate.
+    Xi : scalar, array
+        sediment attenuation constant
+    alpha_w : scalar, array
+        water attenuation coefficient
+    Ks : scalar, array
+        Sediment constant.
+    Kt : scalar, array
+        Transducer constant.
+    psi : scalar, array
+        near field function.
+
+
+    Returns
+    -------
+    scalar, array
+        Mean squared voltage.
+    >>>
+
+    """
+    if Xi == 0:
+        integral = 0
+    else:
+        integral = cumulative_trapezoid(Xi * C + 0 * r, r + 0 * C, initial=0)
+    factor = correction_factor(r, alpha_w, Ks, Kt, psi)
+    return C * np.exp(-4 * integral) * factor

+
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+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
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+ Built with the PyData Sphinx Theme 0.14.2. +

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+ + \ No newline at end of file diff --git a/_modules/pyudv/attenuation/inversion.html b/_modules/pyudv/attenuation/inversion.html new file mode 100644 index 0000000..5b9d8d0 --- /dev/null +++ b/_modules/pyudv/attenuation/inversion.html @@ -0,0 +1,565 @@ + + + + + + + + + + pyudv.attenuation.inversion — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Source code for pyudv.attenuation.inversion

+import numpy as np
+import scipy.integrate as scipyint
+import scipy.interpolate as scipyinter
+
+from pyudv.attenuation.direct_models import correction_factor
+
+r"""
+Explicit inversion scheme --
+"""
+
+
+def _explicit_solution_form(f, B, Xi, r):
+    integral = scipyint.cumulative_trapezoid(f, r, initial=0)
+    return f / (B - 4 * Xi * integral), f
+
+
+

+[docs]
+def explicit_inversion(
+    MSV: np.ndarray,
+    r: np.ndarray,
+    Xi: float,
+    alpha_w: float,
+    psi: np.ndarray,
+    C0: float | np.ndarray,
+    r0: float,
+    delta_r: float = None,
+    n_interp: int = 1000,
+    kind: str = "cubic",
+) -> tuple[np.ndarray, np.ndarray]:
+    r"""
+    Use the following explicit solution to infer particle concentration from Mean Square Voltage (MSV, :math:`\langle V^{2} \rangle`) data:
+
+    .. math::
+
+        C(r) = \frac{f}{B - 4\xi\displaystyle\int^{r}f},
+
+    where
+
+    .. math::
+
+        f(r) = \langle V^{2} \rangle \left(\frac{r\psi_{(r)}}{K_{\rm s} K_{\rm t}}\right) e^{4r\alpha_{\rm w}} = Ce^{-4\alpha_{\rm s}}.
+
+    Parameters
+    ----------
+    MSV : np.ndarray
+        Input array containing :math:`\langle V^{2} \rangle` data. If 1D, must be the same size as `r`. If 2D, its last dimension must match the size of `r`.
+    r : np.ndarray
+        Radial distance to the probe array. Must be 1D, and match the last dimension of `MSV`.
+    Xi : float
+        sediment asborption coefficient.
+    alpha_w : float
+        water absorption coefficient.
+    psi : np.ndarray
+        Near-field function. Must be 1D, and match the last dimension of `MSV`.
+    C0 : float | np.ndarray
+        Reference concentration. Can be a float, or a 1D array matching the first dimension of `MSV`.
+    r0 : float
+        Reference distance corresponding to `C0`.
+    delta_r : float, optional
+        If not None, the reference concentration is then interpreted as an average between :math:`r-\delta r` and :math:`r+\delta r` (by default None).
+    n_interp : int, optional
+        Number of point used in the interpolation of the input signal, by default 1000. It is advise to vary this parameter to find its lowest value above which the result becomes independant of it.
+    kind : str, optional
+        Interpolation method used, by default "cubic". Refer to the documentation of :func:`interp1d <scipy.interpolate.interp1d>`.
+
+    Returns
+    -------
+    C : np.ndarray
+        Inferred concentration.
+    f : np.ndarray
+        Integrated array during the procedure. Returned to check the order of magnitudes over wich the integration is performed, and check possible numerical precision errors.
+    """
+
+    r_expanded = np.expand_dims(
+        np.squeeze(r), axis=tuple(np.arange(len(MSV.shape[:-1])))
+    )
+    psi_expanded = np.expand_dims(
+        np.squeeze(psi), axis=tuple(np.arange(len(MSV.shape[:-1])))
+    )
+    C0_expanded = C0 if np.isscalar(C0) else np.squeeze(C0)[..., None]
+    #
+    # ## calculating MSV reduced of factor
+    factor = correction_factor(r_expanded, alpha_w, 1, 1, psi_expanded)
+    f = MSV / factor
+    interp_f = scipyinter.interp1d(r, f, axis=-1, kind=kind)
+    # ## calculating adjustable constant with imposed point
+    r_new = np.linspace(r.min(), r0, n_interp)
+    if delta_r is None:  # if single fixed point
+        constant_int = scipyint.trapezoid(interp_f(r_new), r_new)[..., None]
+        constant_MSV = interp_f(r0)[..., None]
+        B = constant_MSV / C0_expanded + 4 * Xi * constant_int
+    else:  # if averaged within delta_r
+        r_new = np.linspace(r.min(), r0 + delta_r, n_interp)
+        constant_int_pos = scipyint.trapezoid(interp_f(r_new), r_new)[..., None]
+        #
+        r_new = np.linspace(r.min(), r0 - delta_r, n_interp)
+        constant_int_neg = scipyint.trapezoid(interp_f(r_new), r_new)[..., None]
+        #
+        B = (
+            4
+            * Xi
+            * (
+                constant_int_pos
+                - np.exp(-8 * delta_r * Xi * C0_expanded) * constant_int_neg
+            )
+            / (1 - np.exp(-8 * delta_r * Xi * C0_expanded))
+        )
+    return _explicit_solution_form(f, B, Xi, r_expanded)

+
+
+
+#
+# def explicit_inversion(MSV, r, Xi, alpha_w, psi, C0, r0):
+#     r_expanded = np.expand_dims(r, axis=tuple(np.arange(len(MSV.shape[:-1]))))
+#     psi_expanded = np.expand_dims(psi, axis=tuple(np.arange(len(MSV.shape[:-1]))))
+#     C0_expanded = np.expand_dims(C0, axis=-1)
+#     #
+#     factor = correction_factor(r_expanded, alpha_w, 1, 1, psi_expanded)
+#     MSV_reduced = MSV/factor
+#     #
+#     integral = cumulative_trapezoid(MSV_reduced, r_expanded, initial=0)
+#     if np.isscalar(r0):
+#         ind_r0 = np.arange(r.size)[r <= r0][-1]
+#         constant_int = trapezoid(MSV_reduced[..., :ind_r0], r_expanded[..., :ind_r0])
+#         return C0_expanded*MSV_reduced/(MSV_reduced[..., ind_r0:ind_r0 + 1] - C0_expanded*4*Xi*(integral - constant_int[..., None]))
+#     else:
+#         r_temp = np.broadcast_to(r_expanded, MSV_reduced.shape)
+#         MSV_zeroed = np.where(r_temp <= r0[..., None], MSV_reduced, 0)
+#         del r_temp
+#         constant_int = trapezoid(MSV_zeroed, r_expanded)
+#         del MSV_zeroed
+#         #
+#         ind_r0 = np.argmin(np.abs(r0[..., None] - r_expanded), axis=-1)
+#         constant_MSV = np.take_along_axis(MSV_reduced, ind_r0[..., None], axis=-1)
+#         del ind_r0
+#         return C0_expanded*MSV_reduced/(constant_MSV - C0_expanded*4*Xi*(integral - constant_int[..., None]))
+
+ +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
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+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
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+ + \ No newline at end of file diff --git a/_modules/pyudv/attenuation/sediment_acoustic_models/glass_sphere.html b/_modules/pyudv/attenuation/sediment_acoustic_models/glass_sphere.html new file mode 100644 index 0000000..a894d97 --- /dev/null +++ b/_modules/pyudv/attenuation/sediment_acoustic_models/glass_sphere.html @@ -0,0 +1,464 @@ + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.glass_sphere — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + +
+ +

Source code for pyudv.attenuation.sediment_acoustic_models.glass_sphere

+import numpy as np
+from scipy.integrate import trapezoid
+
+
+

+[docs]
+def Chi_i(k, Rp):
+    x = k * Rp
+    phi = 1 - 0.4 * np.exp(((x - 5.5) / 0.7) ** 2)
+    return (0.24 * phi * x**4) / (
+        0.7 + 0.3 * x + 2.1 * x**2 - 0.7 * x**3 + 0.3 * x**4
+    )

+
+
+
+

+[docs]
+def Chi(k, Rp):
+    if np.isscalar(Rp):
+        return Chi_i(k, Rp)
+    else:
+        trapezoid(Rp[0] * Rp[1], Rp[0]) * trapezoid(
+            Rp[0] ** 2 * Chi_i(k, Rp[0]) * Rp[1], Rp[0]
+        ) / trapezoid(Rp[0] ** 3 * Rp[1], Rp[0])

+
+
+
+

+[docs]
+def Xi(k, Rp):
+    if np.isscalar(Rp):
+        return 3 * Chi(k, Rp) / (4 * Rp)
+    else:
+        a0 = trapezoid(Rp[0] * Rp[1], Rp[0])  # mean value
+        return 3 * Chi(k, Rp) / (4 * a0)

+
+
+ +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
+ + + +
+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/_modules/pyudv/attenuation/sediment_acoustic_models/quartz_sand.html b/_modules/pyudv/attenuation/sediment_acoustic_models/quartz_sand.html new file mode 100644 index 0000000..9662041 --- /dev/null +++ b/_modules/pyudv/attenuation/sediment_acoustic_models/quartz_sand.html @@ -0,0 +1,467 @@ + + + + + + + + + + pyudv.attenuation.sediment_acoustic_models.quartz_sand — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + +
+ +

Source code for pyudv.attenuation.sediment_acoustic_models.quartz_sand

+import numpy as np
+from scipy.integrate import trapezoid
+
+# def Chi_i(k, Rp):
+#     x = k*Rp
+#     return (0.09*x**4/(1380 + 560*x**2 + 150*x**4))
+
+
+

+[docs]
+def Chi_i(k, Rp):
+    x = k * Rp
+    return (1.1 * (4 / 3) * 0.18 * x**4) / (
+        1 + 1.3 * x**2 + (4 / 3) * 0.18 * x**4
+    )

+
+
+
+

+[docs]
+def Chi(k, Rp):
+    if np.isscalar(Rp):
+        return Chi_i(k, Rp)
+    else:
+        trapezoid(Rp[0] * Rp[1], Rp[0]) * trapezoid(
+            Rp[0] ** 2 * Chi_i(k, Rp[0]) * Rp[1], Rp[0]
+        ) / trapezoid(Rp[0] ** 3 * Rp[1], Rp[0])

+
+
+
+

+[docs]
+def Xi(k, Rp, rho=2.65 * 1e3):
+    if np.isscalar(Rp):
+        return 3 * Chi(k, Rp) / (4 * Rp * rho)
+    else:
+        a0 = trapezoid(Rp[0] * Rp[1], Rp[0])  # mean value
+        return 3 * Chi(k, Rp) / (4 * a0 * rho)

+
+
+ +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
+ + + +
+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/_modules/pyudv/geometry.html b/_modules/pyudv/geometry.html new file mode 100644 index 0000000..e842154 --- /dev/null +++ b/_modules/pyudv/geometry.html @@ -0,0 +1,838 @@ + + + + + + + + + + pyudv.geometry — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
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+ +
+ + +
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+ + + + + +
+ +

Source code for pyudv.geometry

+"""
+This module helps you deal with probe geometries.
+"""
+
+import matplotlib.axes as mplaxes
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy.typing as npt
+import scipy.interpolate as scipyinterp
+
+import pyudv.helpers as ph
+
+
+

+[docs]
+class Probe:
+    """
+     A class used to represent a single probe.
+
+    Parameters
+    ----------
+    r : ndarray, (N,)
+        a numpy array containing the radial coordinates of the beam.
+    alpha : float
+        orientation of the probe, in degree in the trigonometric referential.
+    Pref : list[float, ndarray]
+        A list a size 2 defining a reference point by containing its radial coordinate, and a numpy array containing its coordinate in the "real" reference space.
+    """
+
+    def __init__(
+        self,
+        r: np.ndarray,
+        alpha: float,
+        Pref: list[float, np.ndarray],
+    ) -> None:
+        """
+        Parameters
+        ----------
+        r : ndarray
+            a numpy array containing the radial coordinates of the beam.
+        alpha : float
+            orientation of the probe, in degree in the trigonometric referential.
+        Pref : list[float, ndarray]
+            A list a size 2 defining a reference point by containing its radial coordinate, and a numpy array containing its coordinate in the "real" reference space.
+        """
+
+        self.r = r  #: vector along beam axis
+        self.alpha = alpha  #: beam axis inclination, trigo referential
+        self.Pref = Pref  #: reference point [r_ref, (x_ref, y_ref)]
+        #
+        self.r_ref = self.Pref[0]  #: reference point radial coordinats
+        self.X_ref = self.Pref[1]  #: reference point real coordinates
+        self.unit_vec = np.array([np.cos(np.radians(alpha)), np.sin(np.radians(alpha))
+                                  ])  #: unit vector defining the beam direction in real coordinates
+        self.O = (Pref[1] - Pref[0] * self.unit_vec)  #: coordinates of the origin of the beam
+        self.E = (self.X_ref + (r.max() - self.r_ref) * self.unit_vec)  #: coordinates of the end of the beam
+
+        # Beam coordinates
+        self.x = self.r_to_x(self.r)
+        self.z = self.r_to_z(self.r)
+
+

+[docs]
+    def r_to_z(self, r: np.ndarray | float) -> np.ndarray | float:
+        """
+        Convert radial coordinates of the probe into z coordinates
+
+        Parameters
+        ----------
+        r : np.ndarray | float
+            radial coordinate
+
+        Returns
+        -------
+        np.ndarray | float
+            corresponding z coordinate
+        """
+        return self.O[1] + r * self.unit_vec[1]

+
+
+

+[docs]
+    def r_to_x(self, r: np.ndarray | float) -> np.ndarray | float:
+        """
+        Convert radial coordinates of the probe into x coordinates
+
+        Parameters
+        ----------
+        r : np.ndarray | float
+            corresponding x coordinate
+
+        Returns
+        -------
+        np.ndarray | float
+            corresponding z coordinate
+        """
+        return self.O[0] + r * self.unit_vec[0]

+
+
+

+[docs]
+    def xz_to_r(self, xz: np.ndarray, axis=-1) -> np.ndarray:
+        """
+        Convert xz coordinates into probe radial coordinates
+
+        Parameters
+        ----------
+        xz : np.ndarray
+            xz coordinate
+        axis : int, optional
+            axis along which the norm is calculated, by default -1
+
+        Returns
+        -------
+        np.ndarray
+            radial coordinates
+        """
+        return np.linalg.norm(xz - self.O, axis=axis)

+
+
+

+[docs]
+    def plot_probe(self, ax: mplaxes.Axes, color: str = None):
+        """
+        Sketch the probe and its beam on a given matplotlib axe.
+
+        Parameters
+        ----------
+        ax : mplaxes.Axes
+            matplotlib axe on which the probe is sketched.
+        color : str, optional
+            probe color, by default None
+        """
+        a = ax.scatter(self.O[0], self.O[1], color=color)
+        # plot unused beam part
+        (a, ) = ax.plot([self.O[0], self.x[0]], [self.O[1], self.z[0]],
+                        color=a.get_facecolor(),
+                        ls="--",
+                        lw=1,
+                        alpha=0.3)
+        # plot used beam part
+        (a, ) = ax.plot(
+            [self.x[0], self.E[0]],
+            [self.z[0], self.E[1]],
+            color=a.get_color(),
+            ls="--",
+            lw=1,
+        )
+        ax.annotate(
+            "",
+            xytext=self.O,
+            xy=self.O + np.linalg.norm((self.E - self.O)) / 10 * self.unit_vec,
+            arrowprops=dict(arrowstyle="->", color=a.get_color()),
+        )

+

+
+
+
+

+[docs]
+def sketch_probes(
+    probes: list[Probe],
+    combinations: list[tuple[int, int]] = None,
+    ax: mplaxes.Axes = None,
+    probe_colors: list[str] = None,
+    combination_colors: list[str] = None,
+):
+    """
+    Sketch multiple probes, and optionaly their intersections, etc ...
+
+    Parameters
+    ----------
+    probes : list[Probe]
+        list of :class:`Probe <pyudv.probes.Probe>` object to be sketched.
+    combinations : list[tuple[int, int]], optional
+        list of tuple of size 2 containing indexes of `probes` whose intersection should be sketched.
+    ax : mplaxes.Axes, optional
+        matplotlib axe on which the sketch is drawn. If None, a new figure is created (default is None).
+    probe_colors : list[str], optional
+        list of colors for the probes, by default None. It should be the same size as `probes`.
+    combination_colors : list[str], optional
+        list of colors for the intersetctions, by default None. It should be the same size as `combinations`.
+    """
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, layout="constrained")
+    ax.set_aspect("equal")
+    #
+    if probe_colors is None:
+        probe_colors = [None for i in probes]
+    if (combinations is not None) and (combination_colors is None):
+        combination_colors = [None for i in combinations]
+    #
+    # plot probes
+    for probe, color in zip(probes, probe_colors):
+        probe.plot_probe(ax, color=color)
+    if combinations is not None:
+        for combination, combination_color in zip(combinations, combination_colors):
+            probe1, probe2 = probes[combination[0]], probes[combination[1]]
+            X = probe_crossing_point(probe1, probe2)
+            ax.scatter(X[0], X[1], color=combination_color)
+            #
+            z_interp, z_min, z_max = compute_vertical_axis(probe1, probe2)
+            ax.plot([X[0], X[0]], [z_min, z_max], color=combination_color)

+
+
+
+

+[docs]
+def probe_crossing_point(probe1: Probe, probe2: Probe) -> np.ndarray:
+    """
+    calculate the crossing point of two probe beams.
+
+    Parameters
+    ----------
+    probe1 : Probe
+        First :class:`Probe <pyudv.probes.Probe>` object.
+    probe2 : Probe
+        Second :class:`Probe <pyudv.probes.Probe>` object.
+
+    Returns
+    -------
+    ndarray, (2. )
+        coordinates of the crossing point.
+    """
+    M = np.array([-probe1.unit_vec, probe2.unit_vec]).T
+    S = probe1.O - probe2.O
+    T = np.linalg.inv(M) @ S
+    X = probe1.O + T[0] * probe1.unit_vec
+    return X

+
+
+
+

+[docs]
+def compute_vertical_axis(probe1: Probe, probe2: Probe):
+    """
+    compute the common vertical axis between two probe beams.
+
+    Parameters
+    ----------
+    probe1 : Probe
+        First :class:`Probe <pyudv.probes.Probe>` object.
+    probe2 : Probe
+        Second :class:`Probe <pyudv.probes.Probe>` object.
+
+    Returns
+    -------
+    z_interp : ndarray
+        numpy array containg the vertical coordinates of the common axis.
+    z_min : float
+        minimal vertical coordinate
+    z_max : float
+        maximal vertical coordinate
+    """
+    return ph.compute_common_part(probe1.z, probe2.z)

+
+
+
+

+[docs]
+def reconstruct_velocity(
+        u1: npt.ArrayLike, u2: npt.ArrayLike, probe1: Probe,
+        probe2: Probe) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """
+    This function takes the velocities measured by two probes, and reconstruct the velocity field, assuming that it only depends on the vertical coordinate.
+
+    Parameters
+    ----------
+    u1 : ArrayLike
+        velocity vector measured by first probe. The first dimension should be the same dimension as the radial coordinate contained in `probe1.z`.
+    u2 : ArrayLike
+        velocity vector measured by second probe. The first dimension should be the same dimension as the radial coordinate contained in `probe2.z`.
+    probe1 : Probe
+        First :class:`Probe <pyudv.probes.Probe>` object.
+    probe2 : Probe
+        Second :class:`Probe <pyudv.probes.Probe>` object.
+
+    Returns
+    -------
+    U : ndarray
+        reconstructed velocity components in the coordinate system corresponding to the one of the reference points of the probes.
+    z_interp : ndarray
+        coordinate vector corresponding to `U`
+    X : ndarray
+        crossing point of the probes
+    dx_1 : ndarray
+        horizontal distance between the first probe beam, and the vertical axis passing by the crossing point `X`
+    dx_2 : ndarray
+        horizontal distance between the second probe beam, and the vertical axis passing by the crossing point `X`
+
+    """
+    # # Build common vertical axis
+    z_interp, _, _ = compute_vertical_axis(probe1, probe2)
+    # # interpolating velocity
+    interp1 = scipyinterp.interp1d(probe1.z, u1, axis=0)
+    interp2 = scipyinterp.interp1d(probe2.z, u2, axis=0)
+    #
+    u1_interp = interp1(z_interp)
+    u2_interp = interp2(z_interp)
+    #
+    # velocity reconstruction
+    M = np.array([probe1.unit_vec, probe2.unit_vec])
+    # U = np.linalg.inv(M) @ np.array([u1_interp, u2_interp])
+    U = np.einsum("ij, jk... -> ik...", np.linalg.inv(M), np.array([u1_interp, u2_interp]))
+    #
+    # # other quantities
+    # crossing point
+    X = probe_crossing_point(probe1, probe2)
+    # horizontal distances to the vertical axis passing by the crossing point
+    dx_1 = np.tan(np.radians(probe1.alpha)) * (z_interp - X[1])
+    dx_2 = np.tan(np.radians(probe2.alpha)) * (z_interp - X[1])
+    return U, z_interp, X, dx_1, dx_2

+
+
+
+

+[docs]
+def average_amplitude(a1: npt.ArrayLike, a2: npt.ArrayLike, probe1: Probe,
+                      probe2: Probe) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """
+    This function takes the amplitude measured by two probes and average them, assuming that it only depends on the vertical coordinate.
+
+    Parameters
+    ----------
+    a1 : npt.ArrayLike
+        amplitude measured by first probe. The first dimension should be the same dimension as the radial coordinate contained in `probe1.z`.
+    a2 : npt.ArrayLike
+        amplitude measured by second probe. The first dimension should be the same dimension as the radial coordinate contained in `probe2.z`.
+    probe1 : Probe
+        First :class:`Probe <pyudv.probes.Probe>` object.
+    probe2 : Probe
+        Second :class:`Probe <pyudv.probes.Probe>` object.
+
+    Returns
+    -------
+    A : ndarray
+        averaged amplitud in the coordinate system corresponding to the one of the reference points of the probes.
+    z_interp : ndarray
+        coordinate vector corresponding to `U`
+    X : ndarray
+        crossing point of the probes
+    dx_1 : ndarray
+        horizontal distance between the first probe beam, and the vertical axis passing by the crossing point `X`
+    dx_2 : ndarray
+        horizontal distance between the second probe beam, and the vertical axis passing by the crossing point `X`
+
+    """
+    # # Build common vertical axis
+    z_interp, _, _ = compute_vertical_axis(probe1, probe2)
+    # # interpolating velocity
+    interp1 = scipyinterp.interp1d(probe1.z, a1, axis=0)
+    interp2 = scipyinterp.interp1d(probe2.z, a2, axis=0)
+    #
+    a1_interp = interp1(z_interp)
+    a2_interp = interp2(z_interp)
+    #
+    # # other quantities
+    # crossing point
+    X = probe_crossing_point(probe1, probe2)
+    # horizontal distances to the vertical axis passing by the crossing point
+    dx_1 = np.tan(np.radians(probe1.alpha)) * (z_interp - X[1])
+    dx_2 = np.tan(np.radians(probe2.alpha)) * (z_interp - X[1])
+    return (a1_interp+a2_interp) / 2, z_interp, X, dx_1, dx_2

+
+
+
+if __name__ == "__main__":
+    #
+    # # Test Probe object definition
+    r = np.linspace(0, 5, 100)
+    alpha1, alpha2 = -120, -70  # deg
+    O1, O2 = np.array([1, 8]), np.array([-1, 7])
+    probe1_pars = [r, alpha1, [0, O1]]
+    probe2_pars = [r, alpha2, [0, O2]]
+    #
+    probe1 = Probe(*probe1_pars)
+    probe2 = Probe(*probe2_pars)
+    #
+    sketch_probes(
+        [probe1, probe2],
+        combinations=[[0, 1]],
+        probe_colors=[None, None],
+        combination_colors=["k"],
+    )
+
+    # #### Test velocity reconstruction
+    def U(z):
+        u = 5 * (5 - z)**2
+        v = u / 10
+        U = np.array([u, v])
+        return U
+
+    #
+    # make fake signals
+    u1 = U(probe1.z).T @ probe1.unit_vec
+    u2 = U(probe2.z).T @ probe2.unit_vec
+    #
+    # #### reconstruction
+    U_rec, z_interp, X, dx_1, dx_2 = reconstruct_velocity(u1, u2, probe1, probe2)
+    U_th = U(z_interp)
+    #
+    fig, axarr = plt.subplots(1, 2, layout="constrained", sharey=True)
+    for ax, u_th, u_rec in zip(axarr, U_th, U_rec):
+        ax.plot(u_th, z_interp, ".", label="base")
+        ax.plot(u_rec, z_interp, ".", label="reconstructed")
+        ax.legend()
+    axarr[0].set_xlabel("u")
+    axarr[1].set_xlabel("v")
+    axarr[0].set_ylabel("z")
+    plt.show()
+
+ +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
+ + + +
+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/_modules/pyudv/helpers.html b/_modules/pyudv/helpers.html new file mode 100644 index 0000000..9279d98 --- /dev/null +++ b/_modules/pyudv/helpers.html @@ -0,0 +1,529 @@ + + + + + + + + + + pyudv.helpers — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
+
+
+
+ + + Ctrl+K +
+
+ + + +
+
+ +
+ + + +
+ + +
+ +
+ +
+ + + +
+ +
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+ +
+ +
+ +
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+ +
+ + +
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+ + + + + +
+ +

Source code for pyudv.helpers

+import os
+
+import numpy as np
+from scipy.ndimage import uniform_filter, uniform_filter1d
+
+
+

+[docs]
+def moving_std1d(arr, windows, axis=-1, **kwargs):
+    """Rolling standard deviation on 1 dimension of the input array.
+
+    Parameters
+    ----------
+    arr : array
+        Input array
+    windows : int
+        windows over wich the standard deviation is calculated.
+    axis : int
+        axis along which the standard deviation is calculated. (the default is -1).
+    **kwargs :
+        Optional parameters passed to `scipy.ndimage.uniform_filter1d`
+
+    Returns
+    -------
+    array
+        Array of standard deviation
+
+    """
+    # based on https://stackoverflow.com/a/18422519/9530017
+    c1 = uniform_filter1d(arr, windows, mode="constant", axis=axis, **kwargs)
+    c2 = uniform_filter1d(arr * arr, windows, mode="constant", axis=axis, **kwargs)
+    return (c2 - c1*c1)**0.5

+
+
+
+

+[docs]
+def moving_std(arr, size, **kwargs):
+    """Rolling standard deviation on n-dimensional arrays.
+
+    Parameters
+    ----------
+    arr : array
+        Input array
+    size : int
+        The sizes of the uniform filter are given for each axis as a sequence, or as a single number, in which case the size is equal for all axes.
+    **kwargs :
+        Optional parameters passed to `scipy.ndimage.uniform_filter`
+
+    Returns
+    -------
+    array
+        Array of standard deviation
+
+    """
+    # based on https://stackoverflow.com/a/18422519/9530017
+    c1 = uniform_filter(arr, size, mode="constant", **kwargs)
+    c2 = uniform_filter(arr * arr, size, mode="constant", **kwargs)
+    return (c2 - c1*c1)**0.5

+
+
+
+

+[docs]
+def compute_common_part(vec1: np.ndarray, vec2: np.ndarray) -> tuple[np.ndarray, float, float]:
+    """
+    Compute the common part between two vector, and return it. If it is not exactly colocated, it returns a interpolation with the same number of points betwen the bounds common to the two arrays.
+
+    Parameters
+    ----------
+    vec1 : np.ndarray
+        first 1D array.
+    vec2 : np.ndarray
+        second 1D array.
+
+    Returns
+    -------
+    z_interp: np.ndarray
+        interpolated vector
+    z_min: float
+        lower bound
+    z_max: float
+        higher bound
+    """
+    z_min = np.nanmax([np.nanmin(vec1), np.nanmin(vec2)])
+    z_max = np.min([np.nanmax(vec1), np.nanmax(vec2)])
+    n_pts = (((vec1 <= z_max) & (vec1 >= z_min)).sum() + ((vec2 <= z_max) & (vec2 >= z_min)).sum()) / 2
+    z_interp = np.linspace(z_min, z_max, int(n_pts))
+    return z_interp, z_min, z_max

+
+
+
+

+[docs]
+def create_arboresence(path):
+    # Create all arborescence present in path if directories does not exist. Otherwise leave them unaltered.
+    if not os.path.exists(path):
+        if path[-1] != os.sep:
+            path += os.sep
+        os.makedirs(path[:path.rindex(os.path.sep)], exist_ok=True)

+
+
+ +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
+ + + +
+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/_modules/pyudv/read_mfprof.html b/_modules/pyudv/read_mfprof.html new file mode 100644 index 0000000..a1f8438 --- /dev/null +++ b/_modules/pyudv/read_mfprof.html @@ -0,0 +1,1007 @@ + + + + + + + + + + pyudv.read_mfprof — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
+
+
+
+ + + Ctrl+K +
+
+ + + +
+
+ +
+ + + +
+ + +
+ +
+ +
+ + + +
+ +
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+ +
+ +
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+ + + + +
+ +
+ + +
+
+ + + + + +
+ +

Source code for pyudv.read_mfprof

+"""
+This module allows you to read `.mfprof` files from Met-Flow UDVs, and extract the contained data.
+
+"""
+
+import numpy as np
+import netCDF4
+
+Types = {
+    "Frequency": np.int32,
+    "StartChannel": np.float64,
+    "ChannelDistance": np.float64,
+    "ChannelWidth": np.float64,
+    "MaximumDepth": np.float64,
+    "SoundSpeed": np.int32,
+    "Angle": int,
+    "GainStart": np.int32,
+    "GainEnd": np.int32,
+    "Voltage": np.int32,
+    "Iterations": np.int32,
+    "NoiseLevel": np.int32,
+    "CyclesPerPulse": np.int32,
+    "TriggerMode": np.int32,
+    "TriggerModeName": str,
+    "ProfileLength": np.int32,
+    "ProfilesPerBlock": np.int32,
+    "Blocks": np.int32,
+    "AmplitudeStored": int,
+    "DoNotStoreDoppler": int,
+    "RawDataMin": int,
+    "RawDataMax": int,
+    "RawDataRange": np.int32,
+    "AmplDataMin": int,
+    "AmplDataMax": int,
+    "VelocityInterpretingMode": int,
+    "UserSampleTime": int,
+    "SampleTime": np.int32,
+    "UseMultiplexer": int,
+    "FlowMapping": int,
+    "FirstValidChannel": np.int32,
+    "LastValidChannel": np.int32,
+    "FlowRateType": int,
+    "PeriodEnhOffset": np.int32,
+    "PeriodEnhPeriod": np.int32,
+    "PeriodEnhNCycles": np.int32,
+    "Comment": str,
+    "MeasurementProtocol": str,
+    "NumberOfCycles": np.int32,
+    "CycleDelay": np.int32,
+    "Version": np.int32,
+    "Table": str,
+    "CalcOffsetPulsesCount": np.int32,
+    "MultiplexerUsingType": str,
+}
+
+Base_units = {
+    "Frequency": "Hz",
+    "StartChannel": "mm",
+    "ChannelDistance": "mm",
+    "ChannelWidth": "mm",
+    "MaximumDepth": "mm",
+    "SoundSpeed": "m/s",
+    "Angle": "int",
+    "GainStart": "int",
+    "GainEnd": "int",
+    "Voltage": "V",
+    "Iterations": "int",
+    "NoiseLevel": "int",
+    "CyclesPerPulse": "int",
+    "TriggerMode": "index",
+    "TriggerModeName": "text",
+    "ProfileLength": "nChannels",
+    "ProfilesPerBlock": "nSamples",
+    "Blocks": "int",
+    "AmplitudeStored": "bool",
+    "DoNotStoreDoppler": "-",
+    "RawDataMin": "int",
+    "RawDataMax": "int",
+    "RawDataRange": "int",
+    "AmplDataMin": "int",
+    "AmplDataMax": "int",
+    "VelocityInterpretingMode": "int",
+    "UserSampleTime": "bool",
+    # 'SampleTime': 'int',
+    "SampleTime": "ms",
+    "UseMultiplexer": "bool",
+    "FlowMapping": "bool",
+    "FirstValidChannel": "int",
+    "LastValidChannel": "int",
+    "FlowRateType": "bool",
+    "PeriodEnhOffset": "int",
+    "PeriodEnhPeriod": "int",
+    "PeriodEnhNCycles": "int",
+    "Comment": "text",
+    "MeasurementProtocol": "text",
+    "NumberOfCycles": "int",
+    "CycleDelay": "int",
+    "Version": "int",
+    "Table": "int",
+    # switched from np.int32 to str due to problem loading files with multiplexing
+    "CalcOffsetPulsesCount": "int",
+    "MultiplexerUsingType": "int",
+}
+
+Absolute_gains = {
+    0.5: {
+        3: 2.17,
+        4: 4.41,
+        5: 8.82,
+        6: 16.67,
+        7: 33.33,
+        8: 60.00,
+        9: 150.00
+    },
+    1: {
+        3: 2.17,
+        4: 4.41,
+        5: 8.82,
+        6: 16.67,
+        7: 33.33,
+        8: 60.00,
+        9: 150.00
+    },
+    2: {
+        3: 0.91,
+        4: 1.76,
+        5: 3.41,
+        6: 6.67,
+        7: 15.00,
+        8: 25.00,
+        9: 60.00
+    },
+    4: {
+        3: 0.91,
+        4: 1.76,
+        5: 3.41,
+        6: 6.67,
+        7: 15.00,
+        8: 25.00,
+        9: 60.00
+    },
+    8: {
+        3: 0.65,
+        4: 1.36,
+        5: 2.80,
+        6: 5.26,
+        7: 11.11,
+        8: 23.08,
+        9: 42.86
+    },
+}
+
+
+

+[docs]
+def read_mfprof(fileName: str, SI_units: bool = True, convert_time: bool = True):
+    """Read .mfprof binary files of the Met-Flow UDV. This is mostly a direct python translation of the matlab script given by Met-Flow.
+
+    Parameters
+    ----------
+    fileName : str
+        Path to the .mfprof file.
+    SI_units : bool
+        If `True`, convert units to the international system (the default is True).
+    convert_time : bool
+        If True, convert the time vector into seconds (the default is True).
+
+    Returns
+    -------
+    Data : dict
+        Dictionnary with the data stored in the mprof files. Available keys are: 'transducer', 'profileTime', 'DopplerData', 'AmplitudeData', 'DistanceAlongBeam'
+    Parameters : dict
+        Dictionnary with the parameters used in the UVP software when sampling the data. See UVP documentation for detail. Keys are: 'Frequency', 'StartChannel', 'ChannelDistance', 'ChannelWidth', 'MaximumDepth', 'SoundSpeed', 'Angle', 'GainStart', 'GainEnd', 'Voltage', 'Iterations', 'NoiseLevel', 'CyclesPerPulse', 'TriggerMode', 'TriggerModeName', 'ProfileLength', 'ProfilesPerBlock', 'Blocks', 'AmplitudeStored', 'DoNotStoreDoppler', 'RawDataMin', 'RawDataMax', 'RawDataRange', 'AmplDataMin', 'AmplDataMax', 'VelocityInterpretingMode', 'UserSampleTime', 'SampleTime', 'UseMultiplexer', 'FlowMapping', 'FirstValidChannel', 'LastValidChannel', 'FlowRateType', 'PeriodEnhOffset', 'PeriodEnhPeriod', 'PeriodEnhNCycles', 'Comment', 'MeasurementProtocol', 'NumberOfCycles', 'CycleDelay', 'Version', 'Table'
+    Infos: dict
+        Dictionnary with some informations stored during sampling of the data. Keys are 'Signum', 'measParamsOffset', 'nProfiles', 'reserved1', 'flags', 'recordSize', 'nChannels', 'reserved2', 'startTime'.
+    Units: dict
+        Dictionnary with the units of the variables stored in the other dictionnaries. Keys are the name of the variables.
+
+    """
+    Infos = {}
+    Parameters = {}
+    Data = {}
+    with open(fileName, mode="rb") as file:  # b is important -> binary
+        ############################################################################
+        # ######################## File HEADER #####################################
+        ############################################################################
+        # Met-Flow 64-symbol file type signature
+        Infos["Signum"] = np.fromfile(file, np.uint8, 64)  # char 64
+        # 64-bit number giving position of beginning of Measurement Parameters
+        Infos["measParamsOffset"] = np.fromfile(file, np.int64, 1)
+        # Number of profiles saved in the file
+        Infos["nProfiles"] = np.fromfile(file, np.int32, 1)
+        # Always 0 parameter
+        Infos["reserved1"] = np.fromfile(file, np.int32, 1)
+        # Flag. If this is set (=1) then Profiles also contain Amplitude Infos
+        Infos["flags"] = np.fromfile(file, np.int32, 1)
+        # Size of Infos in a single Profile
+        Infos["recordSize"] = np.fromfile(file, np.int32, 1)
+        # Number of Infos channels in a single Profile
+        Infos["nChannels"] = np.fromfile(file, np.int32, 1)
+        # Always 0 parameter
+        Infos["reserved2"] = np.fromfile(file, np.int32, 1)
+        # Time of measurement of the start(first profile) in the format FILETIME (Win32) structure.
+        Infos["startTime"] = np.fromfile(file, np.int64, 1)
+
+        ############################################################################
+        # ######### PROFILES - Read contents of PROFILES BLOCK #####################
+        ############################################################################
+
+        # Profile information (in 0 to 255, 8-bit)values follow immediately after
+        # Profile header. Profiles consists of nProfiles Doppler Infos and if flags = 1
+        # then profile also contain Amplitude Infos. Each block consists of:
+        # Profile Header, Doppler Infos and Amplitude Infos. This information must be
+        # read nProfiles times.
+
+        # First, we allocate memory space for faster file reading
+        Data["transducer"] = np.zeros((Infos["nProfiles"][0], ))
+        Data["profileTime"] = np.zeros((Infos["nProfiles"][0], ))
+        Data["DopplerData"] = np.zeros((Infos["nChannels"][0], Infos["nProfiles"][0]))
+        Data["AmplitudeData"] = np.zeros((Infos["nChannels"][0], Infos["nProfiles"][0]))
+
+        # READ CONTENTS FROM BLOCK nProfiles TIMES
+        for i in range(Infos["nProfiles"][0]):
+            # PROFILE HEADER BLOCK
+            # Always 0 not used
+            _ = np.fromfile(file, np.int32, 1)  # status
+            # Data['transducer'] number -reads which Data['transducer'] Infos comes from
+            Data["transducer"][i] = np.fromfile(file, np.int32, 1)
+            # Time of measurement of the profile in 100 ns increments
+            Data["profileTime"][i] = np.fromfile(file, np.int64, 1)
+            # DOPPLER Infos BLOCK
+            # Doppler Infos block contains nChannels Int16 Doppler values from UVP
+            Data["DopplerData"][:, i] = np.fromfile(file, np.int16, Infos["nChannels"][0])
+            # AMPLITUDE Infos BLOCK
+            # Amplitude Infos block contains nChannels Int16 Amplitude values from UVP
+            if Infos["flags"]:  # Read Infos if it exists
+                Data["AmplitudeData"][:, i] = np.fromfile(file, np.int16, Infos["nChannels"][0])
+
+        ############################################################################
+        # ##### UVP PARAMETER  - Read the UVP Parameters into a Matlab struct ######
+        ############################################################################
+
+        # UVP Measurement Parameters are stored at measParamsOffset position. Measurement
+        # Parameters are saved in text form. Values of parameters are saved in a form:
+        # parameter name=parameter value. Each parameter is given in one line. In case of
+        # text values including more lines, at the end of line (with the exception of the last
+        # line) a symbol '\' is added. E.g. comment including two lines:
+        # "An example of forwards-backwards oscillating 'piston' flow
+        # Piston action starts only after Profile 110" will be saved as:
+        # Comment=An example of forwards-backwards oscillating 'piston' flow.\
+        # Piston action starts only after Profile 110.
+        # Measurement Parameters consist of two blocks:
+        # 1. UVP Parameters, and 2. and Multiplexer Parameters if present
+
+        # READ CONTENTS FROM UVP PARAMETER BLOCK
+        # Find position of Measurement Parameters start location
+        file.seek(Infos["measParamsOffset"][0], 0)
+        _ = np.fromfile(file, np.uint8, 15)  # measParamsposition
+        Parameters = {}
+
+        # READ CONTENTS FROM UVP PARAMETER BLOCK LINE BY LINE
+        par = ""
+        while par != "Table":
+            line = file.readline().rstrip()  # Read new line
+            if (len(line) > 0) & (b"=" in line):  # skip empty lines
+                # print(line)
+                tp = line.decode("utf-8").split("=")[0]
+                par = line.decode("utf-8").split("=")[0]
+                if len(tp) > 1:
+                    Parameters[par] = line.decode("utf-8").split("=")[1]
+                else:
+                    Parameters[par] = []
+
+    # converting time in seconds - dt = 100 ns
+    if convert_time:
+        Data["profileTime"] = Data["profileTime"] * 100 * 1e-9
+    # Formatting parameters
+    Units = Base_units.copy()
+    for key in Parameters.keys():
+        # Assigning good type to parameters
+        Parameters[key] = Types[key](Parameters[key])
+        if SI_units & (Base_units[key] == "mm"):  # Converting mm to m
+            Parameters[key] = 1e-3 * Parameters[key]
+            Units[key] = "m"
+        if SI_units & (Base_units[key] == "ms"):  # Converting ms to s
+            Parameters[key] = 1e-3 * Parameters[key]
+            Units[key] = "s"
+
+    Data["DistanceAlongBeam"] = (np.arange(Data["AmplitudeData"].shape[0]) * Parameters["ChannelDistance"] +
+                                 Parameters["StartChannel"])
+    return Data, Parameters, Infos, Units

+
+
+
+# def filetime_to_dt(ft):
+#     us = int(ft) // 10
+#     return dt.datetime(1601, 1, 1) + dt.timedelta(microseconds=us)
+
+
+

+[docs]
+def velocity_from_UVPdata(raw_data, SoundSpeed, MaximumDepth, Angle: float, Frequency: float, Nbytes: int = 8):
+    """Calculate velocity from UDV raw data. Units must be checked outside of this function - no conversion is done here.
+
+    Parameters
+    ----------
+    raw_data : float, numpy array
+        Raw data coming out of the UDV.
+    SoundSpeed : float
+        Sound velocity.
+    MaximumDepth : float
+        Maximum depth measurement, as defined by the Pulse Repetition Frequency.
+    Angle : float
+        Angle between the probe and the flow direction.
+    Frequency : float
+        Frequency of the probe.
+    Nbytes : int
+        Number of bytes over which the raw data are coded (the default is 8).
+
+    Returns
+    -------
+    float, numpy array
+        Velocity field
+
+    """
+    PRF = SoundSpeed / (2*MaximumDepth)  # Pulse Repetiion Frequency
+    N_DU = 2**Nbytes  # Number of possible velocities coded on 'Nbytes'-bit
+    Doppler_coeff = PRF / N_DU  # Doppler coefficient
+    #
+    Fd = raw_data * Doppler_coeff  # Doppler frequencies
+    Angle_correction = 1 / np.sin(np.pi * Angle / 180)  # angle correction
+    #
+    # return radial velocity
+    return Fd * SoundSpeed * Angle_correction / (2*Frequency)

+
+
+
+

+[docs]
+def velocity_from_mfprof_reading(Data: dict, Parameters: dict, Nbytes: int = 8):
+    """Calculate velocity from `Data` and `Parameters` dictionnaries as output of :func:`read_mfprof <pyudv.read_mfprof.read_mfprof>`,
+    using the function :func:`velocity_from_UVPdata <pyudv.read_mfprof.velocity_from_UVPdata>`.
+
+    Parameters
+    ----------
+    Data : dict
+        Data dictionnary coming from :func:`read_mfprof <pyudv.read_mfprof.read_mfprof>`.
+    Parameters : dict
+        Parameters dictionnary coming from :func:`read_mfprof <pyudv.read_mfprof.read_mfprof>`.
+    Nbytes : int
+        Number of bytes over which the raw data are coded (the default is 8).
+
+    Returns
+    -------
+    float, numpy array
+        Velocity field
+
+    """
+    raw_data = Data["DopplerData"]
+    SoundSpeed, MaximumDepth, Angle, Frequency = (
+        Parameters["SoundSpeed"],
+        Parameters["MaximumDepth"],
+        Parameters["Angle"],
+        Parameters["Frequency"],
+    )
+    return velocity_from_UVPdata(raw_data, SoundSpeed, MaximumDepth, Angle, Frequency, Nbytes=Nbytes)

+
+
+
+

+[docs]
+def amplitude_from_UVPdata(
+    raw_data,
+    z,
+    GainStart,
+    GainEnd,
+    zend: float,
+    zstart: float = 0.37 * 1e-3,
+    Nbytes: int = 14,
+    deltaV: float = 5,
+):
+    """Correct the raw amplitude data as outut of the UVP-DUO.
+
+    Parameters
+    ----------
+    raw_data : scalar, array
+        raw echo signal before demodulation outputted by the UVP.
+    z : scalar, array
+        position corresponding to `raw_data`.
+    GainStart : scalar
+        start absolute gain.
+    GainEnd : scalar
+        end absolute gain.
+    zend : float
+        Maximum measurable distance by the UVP.
+    zstart : float
+        minimum measurable distance by the UVP (the default is 0.00037)).
+    Nbytes : int
+        Number of bytes over which the raw data are coded (the default is 14).
+    deltaV : int
+        Volt range corresponding to the number of bytes (the default is 5).
+
+    Returns
+    -------
+    scalar, array
+        Unamplified/corrected echo signal.
+
+    """
+    return (raw_data * (1/GainStart) * (GainStart / GainEnd)**((z-zstart) / (zend-zstart)) * deltaV / 2**Nbytes)

+
+
+
+

+[docs]
+def amplitude_from_mfprof_reading(Data: dict, Parameters: dict, Nbytes: int = 14, deltaV: float = 5):
+    """Calculate the unamplified/corrected echo signal from `Data` and `Parameters` dictionnaries as output of :func:`read_mfprof <pyudv.read_mfprof.read_mfprof>`,
+    using the function :func:`amplitude_from_UVPdata <pyudv.read_mfprof.amplitude_from_UVPdata>`.
+
+    Parameters
+    ----------
+    Data : dict
+        Data dictionnary coming from :func:`read_mfprof <pyudv.read_mfprof.read_mfprof>`.
+    Parameters : dict
+        Parameters dictionnary coming from :func:`read_mfprof <pyudv.read_mfprof.read_mfprof>`.
+    Nbytes : int
+        Number of bytes over which the raw data are coded (the default is 14).
+    deltaV : int
+        Volt range corresponding to the number of bytes (the default is 5).
+
+    Returns
+    -------
+    scalar, array
+        Unamplified/corrected echo signal.
+
+    """
+    raw_data, z = Data["AmplitudeData"], Data["DistanceAlongBeam"][:, None]
+    zend = Parameters["MaximumDepth"]
+    F0 = Parameters["Frequency"] / 1e6  # frequency in Mhz
+    GainStart, GainEnd = (
+        Absolute_gains[F0][Parameters["GainStart"]],
+        Absolute_gains[F0][Parameters["GainEnd"]],
+    )
+    return amplitude_from_UVPdata(raw_data, z, GainStart, GainEnd, zend, Nbytes=Nbytes, deltaV=deltaV)

+
+
+
+# #### Writing functions
+
+
+

+[docs]
+def write_dictionnary(dico: dict, file: str):
+    """Write parameter dictionnary to a .txt file.
+    Each line of this file is a dictionnary key, followed by the corresponding entry, directly converted using :func:`str <str>`.
+
+    Parameters
+    ----------
+    dico : dict
+        input dictionnary
+    file : str
+        output txt file.
+
+    Returns
+    -------
+
+        Nothing.
+
+    """
+    with open(file, "w") as f:
+        for key in sorted(dico.keys()):
+            line = key + ": " + str(dico[key]) + "\n"
+            f.write(line)

+
+
+
+def _create_variable(
+    netcdf_group,
+    name,
+    data,
+    dimensions=None,
+    std=None,
+    unit=None,
+    comments=None,
+    type="float64",
+):
+    if dimensions is not None:
+        var = netcdf_group.createVariable(name, type, (dimensions))
+    else:
+        var = netcdf_group.createVariable(name, type)
+    var[:] = data
+    if std is not None:
+        var.std = std
+    if unit is not None:
+        var.unit = unit
+    if comments is not None:
+        var.comments = comments
+
+
+

+[docs]
+def mfprof_to_netcdf(input_mfprof: str, output_netcdf: str | object, add_attr: dict = None, cut_zeros: bool = True):
+    """
+    Convert a .mfprof file to a netCDF file.
+
+    Parameters
+    ----------
+    input_mfprof : str
+        input .mfprof file path
+    output_netcdf : str | object
+        output netCDF file path, or netCDF4 object (Dataset, Group) in which the data will be stored.
+    add_attr : dict, optional
+        dictionnary containing attributes to be added to the netCDF file, by default None. For example, `add_attr = {name: 'John Smith', team: 'the best'}`
+    cut_zeros : bool, optional
+        If True, cut the measurements data and remove all the trailing 0 added when the measurements are stopped bedore the attributed time, by default True
+
+    Example
+    -------
+    >>> mfprof_to_netcdf('input.mfprof', 'output.nc', add_attr = {name: 'John Smith', team: 'the best'}, cut_zeros=True)
+    """
+    if isinstance(output_netcdf, str):
+        newfile = netCDF4.Dataset(output_netcdf, "w", format="NETCDF4")
+    else:
+        newfile = output_netcdf
+    #
+    Data, Parameters, Info, Units = read_mfprof(input_mfprof)
+    amplitude_data = amplitude_from_mfprof_reading(Data, Parameters)
+    velocity_data = velocity_from_mfprof_reading(Data, Parameters)
+    #
+    tmax = (np.argwhere(Data["transducer"] == 0).squeeze()[0] if ((Data["transducer"] == 0).any() & cut_zeros) else -1)
+    # ### fill Data
+    newfile.createDimension("time", Data["profileTime"][:tmax].size)
+    newfile.createDimension("z", Data["DistanceAlongBeam"].size)
+    #
+    for key, var in Data.items():
+        if key in ["DistanceAlongBeam"]:
+            dims = "z"
+        elif key in ["profileTime", "transducer"]:
+            dims = "time"
+        else:
+            dims = ("z", "time")
+        #
+        _create_variable(
+            newfile,
+            key,
+            var[..., :tmax] if key != "DistanceAlongBeam" else var,
+            dimensions=dims,
+            unit=Units[key] if key in Units.keys() else None,
+        )
+    _create_variable(
+        newfile,
+        "amplitude_data",
+        amplitude_data[..., :tmax],
+        dimensions=("z", "time"),
+        unit="V",
+    )
+    _create_variable(
+        newfile,
+        "velocity_data",
+        velocity_data[..., :tmax],
+        dimensions=("z", "time"),
+        unit="m/s",
+    )
+    # ### fill Parameters and Info
+    for key, var in Parameters.items():
+        setattr(newfile, key, var)
+    for key, var in Info.items():
+        setattr(newfile, key, var)
+    # ### fill additional attributes
+    if add_attr is not None:
+        for key, var in add_attr.items():
+            setattr(newfile, key, var)
+    #
+    if cut_zeros:
+        newfile.cut_zeros = "True. File has been shortened while converted to netcdf as there was trailing unused zeros."
+        newfile.NumberOfCyclesShort = Data["profileTime"][:tmax].size
+    #
+    if isinstance(output_netcdf, str):
+        newfile.close()

+
+
+ +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
+ + + +
+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/_modules/scipy/integrate/_quadrature.html b/_modules/scipy/integrate/_quadrature.html new file mode 100644 index 0000000..13496e9 --- /dev/null +++ b/_modules/scipy/integrate/_quadrature.html @@ -0,0 +1,1942 @@ + + + + + + + + + + scipy.integrate._quadrature — pyudv documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
+
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+
+ + + Ctrl+K +
+
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+ + +
+
+ + + + + +
+ +

Source code for scipy.integrate._quadrature

+from __future__ import annotations
+from typing import TYPE_CHECKING, Callable, Any, cast
+import numpy as np
+import math
+import warnings
+from collections import namedtuple
+
+from scipy.special import roots_legendre
+from scipy.special import gammaln, logsumexp
+from scipy._lib._util import _rng_spawn
+
+
+__all__ = ['fixed_quad', 'quadrature', 'romberg', 'romb',
+           'trapezoid', 'trapz', 'simps', 'simpson',
+           'cumulative_trapezoid', 'cumtrapz', 'newton_cotes',
+           'qmc_quad', 'AccuracyWarning']
+
+
+

+[docs]
+def trapezoid(y, x=None, dx=1.0, axis=-1):
+    r"""
+    Integrate along the given axis using the composite trapezoidal rule.
+
+    If `x` is provided, the integration happens in sequence along its
+    elements - they are not sorted.
+
+    Integrate `y` (`x`) along each 1d slice on the given axis, compute
+    :math:`\int y(x) dx`.
+    When `x` is specified, this integrates along the parametric curve,
+    computing :math:`\int_t y(t) dt =
+    \int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`.
+
+    Parameters
+    ----------
+    y : array_like
+        Input array to integrate.
+    x : array_like, optional
+        The sample points corresponding to the `y` values. If `x` is None,
+        the sample points are assumed to be evenly spaced `dx` apart. The
+        default is None.
+    dx : scalar, optional
+        The spacing between sample points when `x` is None. The default is 1.
+    axis : int, optional
+        The axis along which to integrate.
+
+    Returns
+    -------
+    trapezoid : float or ndarray
+        Definite integral of `y` = n-dimensional array as approximated along
+        a single axis by the trapezoidal rule. If `y` is a 1-dimensional array,
+        then the result is a float. If `n` is greater than 1, then the result
+        is an `n`-1 dimensional array.
+
+    See Also
+    --------
+    cumulative_trapezoid, simpson, romb
+
+    Notes
+    -----
+    Image [2]_ illustrates trapezoidal rule -- y-axis locations of points
+    will be taken from `y` array, by default x-axis distances between
+    points will be 1.0, alternatively they can be provided with `x` array
+    or with `dx` scalar.  Return value will be equal to combined area under
+    the red lines.
+
+    References
+    ----------
+    .. [1] Wikipedia page: https://en.wikipedia.org/wiki/Trapezoidal_rule
+
+    .. [2] Illustration image:
+           https://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
+
+    Examples
+    --------
+    Use the trapezoidal rule on evenly spaced points:
+
+    >>> import numpy as np
+    >>> from scipy import integrate
+    >>> integrate.trapezoid([1, 2, 3])
+    4.0
+
+    The spacing between sample points can be selected by either the
+    ``x`` or ``dx`` arguments:
+
+    >>> integrate.trapezoid([1, 2, 3], x=[4, 6, 8])
+    8.0
+    >>> integrate.trapezoid([1, 2, 3], dx=2)
+    8.0
+
+    Using a decreasing ``x`` corresponds to integrating in reverse:
+
+    >>> integrate.trapezoid([1, 2, 3], x=[8, 6, 4])
+    -8.0
+
+    More generally ``x`` is used to integrate along a parametric curve. We can
+    estimate the integral :math:`\int_0^1 x^2 = 1/3` using:
+
+    >>> x = np.linspace(0, 1, num=50)
+    >>> y = x**2
+    >>> integrate.trapezoid(y, x)
+    0.33340274885464394
+
+    Or estimate the area of a circle, noting we repeat the sample which closes
+    the curve:
+
+    >>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
+    >>> integrate.trapezoid(np.cos(theta), x=np.sin(theta))
+    3.141571941375841
+
+    ``trapezoid`` can be applied along a specified axis to do multiple
+    computations in one call:
+
+    >>> a = np.arange(6).reshape(2, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5]])
+    >>> integrate.trapezoid(a, axis=0)
+    array([1.5, 2.5, 3.5])
+    >>> integrate.trapezoid(a, axis=1)
+    array([2.,  8.])
+    """
+    # Future-proofing, in case NumPy moves from trapz to trapezoid for the same
+    # reasons as SciPy
+    if hasattr(np, 'trapezoid'):
+        return np.trapezoid(y, x=x, dx=dx, axis=axis)
+    else:
+        return np.trapz(y, x=x, dx=dx, axis=axis)

+
+
+
+# Note: alias kept for backwards compatibility. Rename was done
+# because trapz is a slur in colloquial English (see gh-12924).
+def trapz(y, x=None, dx=1.0, axis=-1):
+    """An alias of `trapezoid`.
+
+    `trapz` is kept for backwards compatibility. For new code, prefer
+    `trapezoid` instead.
+    """
+    return trapezoid(y, x=x, dx=dx, axis=axis)
+
+
+class AccuracyWarning(Warning):
+    pass
+
+
+if TYPE_CHECKING:
+    # workaround for mypy function attributes see:
+    # https://github.com/python/mypy/issues/2087#issuecomment-462726600
+    from typing import Protocol
+
+    class CacheAttributes(Protocol):
+        cache: dict[int, tuple[Any, Any]]
+else:
+    CacheAttributes = Callable
+
+
+def cache_decorator(func: Callable) -> CacheAttributes:
+    return cast(CacheAttributes, func)
+
+
+@cache_decorator
+def _cached_roots_legendre(n):
+    """
+    Cache roots_legendre results to speed up calls of the fixed_quad
+    function.
+    """
+    if n in _cached_roots_legendre.cache:
+        return _cached_roots_legendre.cache[n]
+
+    _cached_roots_legendre.cache[n] = roots_legendre(n)
+    return _cached_roots_legendre.cache[n]
+
+
+_cached_roots_legendre.cache = dict()
+
+
+def fixed_quad(func, a, b, args=(), n=5):
+    """
+    Compute a definite integral using fixed-order Gaussian quadrature.
+
+    Integrate `func` from `a` to `b` using Gaussian quadrature of
+    order `n`.
+
+    Parameters
+    ----------
+    func : callable
+        A Python function or method to integrate (must accept vector inputs).
+        If integrating a vector-valued function, the returned array must have
+        shape ``(..., len(x))``.
+    a : float
+        Lower limit of integration.
+    b : float
+        Upper limit of integration.
+    args : tuple, optional
+        Extra arguments to pass to function, if any.
+    n : int, optional
+        Order of quadrature integration. Default is 5.
+
+    Returns
+    -------
+    val : float
+        Gaussian quadrature approximation to the integral
+    none : None
+        Statically returned value of None
+
+    See Also
+    --------
+    quad : adaptive quadrature using QUADPACK
+    dblquad : double integrals
+    tplquad : triple integrals
+    romberg : adaptive Romberg quadrature
+    quadrature : adaptive Gaussian quadrature
+    romb : integrators for sampled data
+    simpson : integrators for sampled data
+    cumulative_trapezoid : cumulative integration for sampled data
+    ode : ODE integrator
+    odeint : ODE integrator
+
+    Examples
+    --------
+    >>> from scipy import integrate
+    >>> import numpy as np
+    >>> f = lambda x: x**8
+    >>> integrate.fixed_quad(f, 0.0, 1.0, n=4)
+    (0.1110884353741496, None)
+    >>> integrate.fixed_quad(f, 0.0, 1.0, n=5)
+    (0.11111111111111102, None)
+    >>> print(1/9.0)  # analytical result
+    0.1111111111111111
+
+    >>> integrate.fixed_quad(np.cos, 0.0, np.pi/2, n=4)
+    (0.9999999771971152, None)
+    >>> integrate.fixed_quad(np.cos, 0.0, np.pi/2, n=5)
+    (1.000000000039565, None)
+    >>> np.sin(np.pi/2)-np.sin(0)  # analytical result
+    1.0
+
+    """
+    x, w = _cached_roots_legendre(n)
+    x = np.real(x)
+    if np.isinf(a) or np.isinf(b):
+        raise ValueError("Gaussian quadrature is only available for "
+                         "finite limits.")
+    y = (b-a)*(x+1)/2.0 + a
+    return (b-a)/2.0 * np.sum(w*func(y, *args), axis=-1), None
+
+
+def vectorize1(func, args=(), vec_func=False):
+    """Vectorize the call to a function.
+
+    This is an internal utility function used by `romberg` and
+    `quadrature` to create a vectorized version of a function.
+
+    If `vec_func` is True, the function `func` is assumed to take vector
+    arguments.
+
+    Parameters
+    ----------
+    func : callable
+        User defined function.
+    args : tuple, optional
+        Extra arguments for the function.
+    vec_func : bool, optional
+        True if the function func takes vector arguments.
+
+    Returns
+    -------
+    vfunc : callable
+        A function that will take a vector argument and return the
+        result.
+
+    """
+    if vec_func:
+        def vfunc(x):
+            return func(x, *args)
+    else:
+        def vfunc(x):
+            if np.isscalar(x):
+                return func(x, *args)
+            x = np.asarray(x)
+            # call with first point to get output type
+            y0 = func(x[0], *args)
+            n = len(x)
+            dtype = getattr(y0, 'dtype', type(y0))
+            output = np.empty((n,), dtype=dtype)
+            output[0] = y0
+            for i in range(1, n):
+                output[i] = func(x[i], *args)
+            return output
+    return vfunc
+
+
+def quadrature(func, a, b, args=(), tol=1.49e-8, rtol=1.49e-8, maxiter=50,
+               vec_func=True, miniter=1):
+    """
+    Compute a definite integral using fixed-tolerance Gaussian quadrature.
+
+    Integrate `func` from `a` to `b` using Gaussian quadrature
+    with absolute tolerance `tol`.
+
+    Parameters
+    ----------
+    func : function
+        A Python function or method to integrate.
+    a : float
+        Lower limit of integration.
+    b : float
+        Upper limit of integration.
+    args : tuple, optional
+        Extra arguments to pass to function.
+    tol, rtol : float, optional
+        Iteration stops when error between last two iterates is less than
+        `tol` OR the relative change is less than `rtol`.
+    maxiter : int, optional
+        Maximum order of Gaussian quadrature.
+    vec_func : bool, optional
+        True or False if func handles arrays as arguments (is
+        a "vector" function). Default is True.
+    miniter : int, optional
+        Minimum order of Gaussian quadrature.
+
+    Returns
+    -------
+    val : float
+        Gaussian quadrature approximation (within tolerance) to integral.
+    err : float
+        Difference between last two estimates of the integral.
+
+    See Also
+    --------
+    romberg : adaptive Romberg quadrature
+    fixed_quad : fixed-order Gaussian quadrature
+    quad : adaptive quadrature using QUADPACK
+    dblquad : double integrals
+    tplquad : triple integrals
+    romb : integrator for sampled data
+    simpson : integrator for sampled data
+    cumulative_trapezoid : cumulative integration for sampled data
+    ode : ODE integrator
+    odeint : ODE integrator
+
+    Examples
+    --------
+    >>> from scipy import integrate
+    >>> import numpy as np
+    >>> f = lambda x: x**8
+    >>> integrate.quadrature(f, 0.0, 1.0)
+    (0.11111111111111106, 4.163336342344337e-17)
+    >>> print(1/9.0)  # analytical result
+    0.1111111111111111
+
+    >>> integrate.quadrature(np.cos, 0.0, np.pi/2)
+    (0.9999999999999536, 3.9611425250996035e-11)
+    >>> np.sin(np.pi/2)-np.sin(0)  # analytical result
+    1.0
+
+    """
+    if not isinstance(args, tuple):
+        args = (args,)
+    vfunc = vectorize1(func, args, vec_func=vec_func)
+    val = np.inf
+    err = np.inf
+    maxiter = max(miniter+1, maxiter)
+    for n in range(miniter, maxiter+1):
+        newval = fixed_quad(vfunc, a, b, (), n)[0]
+        err = abs(newval-val)
+        val = newval
+
+        if err < tol or err < rtol*abs(val):
+            break
+    else:
+        warnings.warn(
+            "maxiter (%d) exceeded. Latest difference = %e" % (maxiter, err),
+            AccuracyWarning)
+    return val, err
+
+
+def tupleset(t, i, value):
+    l = list(t)
+    l[i] = value
+    return tuple(l)
+
+
+# Note: alias kept for backwards compatibility. Rename was done
+# because cumtrapz is a slur in colloquial English (see gh-12924).
+def cumtrapz(y, x=None, dx=1.0, axis=-1, initial=None):
+    """An alias of `cumulative_trapezoid`.
+
+    `cumtrapz` is kept for backwards compatibility. For new code, prefer
+    `cumulative_trapezoid` instead.
+    """
+    return cumulative_trapezoid(y, x=x, dx=dx, axis=axis, initial=initial)
+
+
+

+[docs]
+def cumulative_trapezoid(y, x=None, dx=1.0, axis=-1, initial=None):
+    """
+    Cumulatively integrate y(x) using the composite trapezoidal rule.
+
+    Parameters
+    ----------
+    y : array_like
+        Values to integrate.
+    x : array_like, optional
+        The coordinate to integrate along. If None (default), use spacing `dx`
+        between consecutive elements in `y`.
+    dx : float, optional
+        Spacing between elements of `y`. Only used if `x` is None.
+    axis : int, optional
+        Specifies the axis to cumulate. Default is -1 (last axis).
+    initial : scalar, optional
+        If given, insert this value at the beginning of the returned result.
+        Typically this value should be 0. Default is None, which means no
+        value at ``x[0]`` is returned and `res` has one element less than `y`
+        along the axis of integration.
+
+    Returns
+    -------
+    res : ndarray
+        The result of cumulative integration of `y` along `axis`.
+        If `initial` is None, the shape is such that the axis of integration
+        has one less value than `y`. If `initial` is given, the shape is equal
+        to that of `y`.
+
+    See Also
+    --------
+    numpy.cumsum, numpy.cumprod
+    quad : adaptive quadrature using QUADPACK
+    romberg : adaptive Romberg quadrature
+    quadrature : adaptive Gaussian quadrature
+    fixed_quad : fixed-order Gaussian quadrature
+    dblquad : double integrals
+    tplquad : triple integrals
+    romb : integrators for sampled data
+    ode : ODE integrators
+    odeint : ODE integrators
+
+    Examples
+    --------
+    >>> from scipy import integrate
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+
+    >>> x = np.linspace(-2, 2, num=20)
+    >>> y = x
+    >>> y_int = integrate.cumulative_trapezoid(y, x, initial=0)
+    >>> plt.plot(x, y_int, 'ro', x, y[0] + 0.5 * x**2, 'b-')
+    >>> plt.show()
+
+    """
+    y = np.asarray(y)
+    if x is None:
+        d = dx
+    else:
+        x = np.asarray(x)
+        if x.ndim == 1:
+            d = np.diff(x)
+            # reshape to correct shape
+            shape = [1] * y.ndim
+            shape[axis] = -1
+            d = d.reshape(shape)
+        elif len(x.shape) != len(y.shape):
+            raise ValueError("If given, shape of x must be 1-D or the "
+                             "same as y.")
+        else:
+            d = np.diff(x, axis=axis)
+
+        if d.shape[axis] != y.shape[axis] - 1:
+            raise ValueError("If given, length of x along axis must be the "
+                             "same as y.")
+
+    nd = len(y.shape)
+    slice1 = tupleset((slice(None),)*nd, axis, slice(1, None))
+    slice2 = tupleset((slice(None),)*nd, axis, slice(None, -1))
+    res = np.cumsum(d * (y[slice1] + y[slice2]) / 2.0, axis=axis)
+
+    if initial is not None:
+        if not np.isscalar(initial):
+            raise ValueError("`initial` parameter should be a scalar.")
+
+        shape = list(res.shape)
+        shape[axis] = 1
+        res = np.concatenate([np.full(shape, initial, dtype=res.dtype), res],
+                             axis=axis)
+
+    return res

+
+
+
+def _basic_simpson(y, start, stop, x, dx, axis):
+    nd = len(y.shape)
+    if start is None:
+        start = 0
+    step = 2
+    slice_all = (slice(None),)*nd
+    slice0 = tupleset(slice_all, axis, slice(start, stop, step))
+    slice1 = tupleset(slice_all, axis, slice(start+1, stop+1, step))
+    slice2 = tupleset(slice_all, axis, slice(start+2, stop+2, step))
+
+    if x is None:  # Even-spaced Simpson's rule.
+        result = np.sum(y[slice0] + 4.0*y[slice1] + y[slice2], axis=axis)
+        result *= dx / 3.0
+    else:
+        # Account for possibly different spacings.
+        #    Simpson's rule changes a bit.
+        h = np.diff(x, axis=axis)
+        sl0 = tupleset(slice_all, axis, slice(start, stop, step))
+        sl1 = tupleset(slice_all, axis, slice(start+1, stop+1, step))
+        h0 = h[sl0].astype(float, copy=False)
+        h1 = h[sl1].astype(float, copy=False)
+        hsum = h0 + h1
+        hprod = h0 * h1
+        h0divh1 = np.true_divide(h0, h1, out=np.zeros_like(h0), where=h1 != 0)
+        tmp = hsum/6.0 * (y[slice0] *
+                          (2.0 - np.true_divide(1.0, h0divh1,
+                                                out=np.zeros_like(h0divh1),
+                                                where=h0divh1 != 0)) +
+                          y[slice1] * (hsum *
+                                       np.true_divide(hsum, hprod,
+                                                      out=np.zeros_like(hsum),
+                                                      where=hprod != 0)) +
+                          y[slice2] * (2.0 - h0divh1))
+        result = np.sum(tmp, axis=axis)
+    return result
+
+
+# Note: alias kept for backwards compatibility. simps was renamed to simpson
+# because the former is a slur in colloquial English (see gh-12924).
+def simps(y, x=None, dx=1.0, axis=-1, even=None):
+    """An alias of `simpson`.
+
+    `simps` is kept for backwards compatibility. For new code, prefer
+    `simpson` instead.
+    """
+    return simpson(y, x=x, dx=dx, axis=axis, even=even)
+
+
+def simpson(y, x=None, dx=1.0, axis=-1, even=None):
+    """
+    Integrate y(x) using samples along the given axis and the composite
+    Simpson's rule. If x is None, spacing of dx is assumed.
+
+    If there are an even number of samples, N, then there are an odd
+    number of intervals (N-1), but Simpson's rule requires an even number
+    of intervals. The parameter 'even' controls how this is handled.
+
+    Parameters
+    ----------
+    y : array_like
+        Array to be integrated.
+    x : array_like, optional
+        If given, the points at which `y` is sampled.
+    dx : float, optional
+        Spacing of integration points along axis of `x`. Only used when
+        `x` is None. Default is 1.
+    axis : int, optional
+        Axis along which to integrate. Default is the last axis.
+    even : {None, 'simpson', 'avg', 'first', 'last'}, optional
+        'avg' : Average two results:
+            1) use the first N-2 intervals with
+               a trapezoidal rule on the last interval and
+            2) use the last
+               N-2 intervals with a trapezoidal rule on the first interval.
+
+        'first' : Use Simpson's rule for the first N-2 intervals with
+                a trapezoidal rule on the last interval.
+
+        'last' : Use Simpson's rule for the last N-2 intervals with a
+               trapezoidal rule on the first interval.
+
+        None : equivalent to 'simpson' (default)
+
+        'simpson' : Use Simpson's rule for the first N-2 intervals with the
+                  addition of a 3-point parabolic segment for the last
+                  interval using equations outlined by Cartwright [1]_.
+                  If the axis to be integrated over only has two points then
+                  the integration falls back to a trapezoidal integration.
+
+                  .. versionadded:: 1.11.0
+
+        .. versionchanged:: 1.11.0
+            The newly added 'simpson' option is now the default as it is more
+            accurate in most situations.
+
+        .. deprecated:: 1.11.0
+            Parameter `even` is deprecated and will be removed in SciPy
+            1.13.0. After this time the behaviour for an even number of
+            points will follow that of `even='simpson'`.
+
+    Returns
+    -------
+    float
+        The estimated integral computed with the composite Simpson's rule.
+
+    See Also
+    --------
+    quad : adaptive quadrature using QUADPACK
+    romberg : adaptive Romberg quadrature
+    quadrature : adaptive Gaussian quadrature
+    fixed_quad : fixed-order Gaussian quadrature
+    dblquad : double integrals
+    tplquad : triple integrals
+    romb : integrators for sampled data
+    cumulative_trapezoid : cumulative integration for sampled data
+    ode : ODE integrators
+    odeint : ODE integrators
+
+    Notes
+    -----
+    For an odd number of samples that are equally spaced the result is
+    exact if the function is a polynomial of order 3 or less. If
+    the samples are not equally spaced, then the result is exact only
+    if the function is a polynomial of order 2 or less.
+
+    References
+    ----------
+    .. [1] Cartwright, Kenneth V. Simpson's Rule Cumulative Integration with
+           MS Excel and Irregularly-spaced Data. Journal of Mathematical
+           Sciences and Mathematics Education. 12 (2): 1-9
+
+    Examples
+    --------
+    >>> from scipy import integrate
+    >>> import numpy as np
+    >>> x = np.arange(0, 10)
+    >>> y = np.arange(0, 10)
+
+    >>> integrate.simpson(y, x)
+    40.5
+
+    >>> y = np.power(x, 3)
+    >>> integrate.simpson(y, x)
+    1640.5
+    >>> integrate.quad(lambda x: x**3, 0, 9)[0]
+    1640.25
+
+    >>> integrate.simpson(y, x, even='first')
+    1644.5
+
+    """
+    y = np.asarray(y)
+    nd = len(y.shape)
+    N = y.shape[axis]
+    last_dx = dx
+    first_dx = dx
+    returnshape = 0
+    if x is not None:
+        x = np.asarray(x)
+        if len(x.shape) == 1:
+            shapex = [1] * nd
+            shapex[axis] = x.shape[0]
+            saveshape = x.shape
+            returnshape = 1
+            x = x.reshape(tuple(shapex))
+        elif len(x.shape) != len(y.shape):
+            raise ValueError("If given, shape of x must be 1-D or the "
+                             "same as y.")
+        if x.shape[axis] != N:
+            raise ValueError("If given, length of x along axis must be the "
+                             "same as y.")
+
+    # even keyword parameter is deprecated
+    if even is not None:
+        warnings.warn(
+            "The 'even' keyword is deprecated as of SciPy 1.11.0 and will be "
+            "removed in SciPy 1.13.0",
+            DeprecationWarning, stacklevel=2
+        )
+
+    if N % 2 == 0:
+        val = 0.0
+        result = 0.0
+        slice_all = (slice(None),) * nd
+
+        # default is 'simpson'
+        even = even if even is not None else "simpson"
+
+        if even not in ['avg', 'last', 'first', 'simpson']:
+            raise ValueError(
+                "Parameter 'even' must be 'simpson', "
+                "'avg', 'last', or 'first'."
+            )
+
+        if N == 2:
+            # need at least 3 points in integration axis to form parabolic
+            # segment. If there are two points then any of 'avg', 'first',
+            # 'last' should give the same result.
+            slice1 = tupleset(slice_all, axis, -1)
+            slice2 = tupleset(slice_all, axis, -2)
+            if x is not None:
+                last_dx = x[slice1] - x[slice2]
+            val += 0.5 * last_dx * (y[slice1] + y[slice2])
+
+            # calculation is finished. Set `even` to None to skip other
+            # scenarios
+            even = None
+
+        if even == 'simpson':
+            # use Simpson's rule on first intervals
+            result = _basic_simpson(y, 0, N-3, x, dx, axis)
+
+            slice1 = tupleset(slice_all, axis, -1)
+            slice2 = tupleset(slice_all, axis, -2)
+            slice3 = tupleset(slice_all, axis, -3)
+
+            h = np.asfarray([dx, dx])
+            if x is not None:
+                # grab the last two spacings from the appropriate axis
+                hm2 = tupleset(slice_all, axis, slice(-2, -1, 1))
+                hm1 = tupleset(slice_all, axis, slice(-1, None, 1))
+
+                diffs = np.float64(np.diff(x, axis=axis))
+                h = [np.squeeze(diffs[hm2], axis=axis),
+                     np.squeeze(diffs[hm1], axis=axis)]
+
+            # This is the correction for the last interval according to
+            # Cartwright.
+            # However, I used the equations given at
+            # https://en.wikipedia.org/wiki/Simpson%27s_rule#Composite_Simpson's_rule_for_irregularly_spaced_data
+            # A footnote on Wikipedia says:
+            # Cartwright 2017, Equation 8. The equation in Cartwright is
+            # calculating the first interval whereas the equations in the
+            # Wikipedia article are adjusting for the last integral. If the
+            # proper algebraic substitutions are made, the equation results in
+            # the values shown.
+            num = 2 * h[1] ** 2 + 3 * h[0] * h[1]
+            den = 6 * (h[1] + h[0])
+            alpha = np.true_divide(
+                num,
+                den,
+                out=np.zeros_like(den),
+                where=den != 0
+            )
+
+            num = h[1] ** 2 + 3.0 * h[0] * h[1]
+            den = 6 * h[0]
+            beta = np.true_divide(
+                num,
+                den,
+                out=np.zeros_like(den),
+                where=den != 0
+            )
+
+            num = 1 * h[1] ** 3
+            den = 6 * h[0] * (h[0] + h[1])
+            eta = np.true_divide(
+                num,
+                den,
+                out=np.zeros_like(den),
+                where=den != 0
+            )
+
+            result += alpha*y[slice1] + beta*y[slice2] - eta*y[slice3]
+
+        # The following code (down to result=result+val) can be removed
+        # once the 'even' keyword is removed.
+
+        # Compute using Simpson's rule on first intervals
+        if even in ['avg', 'first']:
+            slice1 = tupleset(slice_all, axis, -1)
+            slice2 = tupleset(slice_all, axis, -2)
+            if x is not None:
+                last_dx = x[slice1] - x[slice2]
+            val += 0.5*last_dx*(y[slice1]+y[slice2])
+            result = _basic_simpson(y, 0, N-3, x, dx, axis)
+        # Compute using Simpson's rule on last set of intervals
+        if even in ['avg', 'last']:
+            slice1 = tupleset(slice_all, axis, 0)
+            slice2 = tupleset(slice_all, axis, 1)
+            if x is not None:
+                first_dx = x[tuple(slice2)] - x[tuple(slice1)]
+            val += 0.5*first_dx*(y[slice2]+y[slice1])
+            result += _basic_simpson(y, 1, N-2, x, dx, axis)
+        if even == 'avg':
+            val /= 2.0
+            result /= 2.0
+        result = result + val
+    else:
+        result = _basic_simpson(y, 0, N-2, x, dx, axis)
+    if returnshape:
+        x = x.reshape(saveshape)
+    return result
+
+
+def romb(y, dx=1.0, axis=-1, show=False):
+    """
+    Romberg integration using samples of a function.
+
+    Parameters
+    ----------
+    y : array_like
+        A vector of ``2**k + 1`` equally-spaced samples of a function.
+    dx : float, optional
+        The sample spacing. Default is 1.
+    axis : int, optional
+        The axis along which to integrate. Default is -1 (last axis).
+    show : bool, optional
+        When `y` is a single 1-D array, then if this argument is True
+        print the table showing Richardson extrapolation from the
+        samples. Default is False.
+
+    Returns
+    -------
+    romb : ndarray
+        The integrated result for `axis`.
+
+    See Also
+    --------
+    quad : adaptive quadrature using QUADPACK
+    romberg : adaptive Romberg quadrature
+    quadrature : adaptive Gaussian quadrature
+    fixed_quad : fixed-order Gaussian quadrature
+    dblquad : double integrals
+    tplquad : triple integrals
+    simpson : integrators for sampled data
+    cumulative_trapezoid : cumulative integration for sampled data
+    ode : ODE integrators
+    odeint : ODE integrators
+
+    Examples
+    --------
+    >>> from scipy import integrate
+    >>> import numpy as np
+    >>> x = np.arange(10, 14.25, 0.25)
+    >>> y = np.arange(3, 12)
+
+    >>> integrate.romb(y)
+    56.0
+
+    >>> y = np.sin(np.power(x, 2.5))
+    >>> integrate.romb(y)
+    -0.742561336672229
+
+    >>> integrate.romb(y, show=True)
+    Richardson Extrapolation Table for Romberg Integration
+    ======================================================
+    -0.81576
+     4.63862  6.45674
+    -1.10581 -3.02062 -3.65245
+    -2.57379 -3.06311 -3.06595 -3.05664
+    -1.34093 -0.92997 -0.78776 -0.75160 -0.74256
+    ======================================================
+    -0.742561336672229  # may vary
+
+    """
+    y = np.asarray(y)
+    nd = len(y.shape)
+    Nsamps = y.shape[axis]
+    Ninterv = Nsamps-1
+    n = 1
+    k = 0
+    while n < Ninterv:
+        n <<= 1
+        k += 1
+    if n != Ninterv:
+        raise ValueError("Number of samples must be one plus a "
+                         "non-negative power of 2.")
+
+    R = {}
+    slice_all = (slice(None),) * nd
+    slice0 = tupleset(slice_all, axis, 0)
+    slicem1 = tupleset(slice_all, axis, -1)
+    h = Ninterv * np.asarray(dx, dtype=float)
+    R[(0, 0)] = (y[slice0] + y[slicem1])/2.0*h
+    slice_R = slice_all
+    start = stop = step = Ninterv
+    for i in range(1, k+1):
+        start >>= 1
+        slice_R = tupleset(slice_R, axis, slice(start, stop, step))
+        step >>= 1
+        R[(i, 0)] = 0.5*(R[(i-1, 0)] + h*y[slice_R].sum(axis=axis))
+        for j in range(1, i+1):
+            prev = R[(i, j-1)]
+            R[(i, j)] = prev + (prev-R[(i-1, j-1)]) / ((1 << (2*j))-1)
+        h /= 2.0
+
+    if show:
+        if not np.isscalar(R[(0, 0)]):
+            print("*** Printing table only supported for integrals" +
+                  " of a single data set.")
+        else:
+            try:
+                precis = show[0]
+            except (TypeError, IndexError):
+                precis = 5
+            try:
+                width = show[1]
+            except (TypeError, IndexError):
+                width = 8
+            formstr = "%%%d.%df" % (width, precis)
+
+            title = "Richardson Extrapolation Table for Romberg Integration"
+            print(title, "=" * len(title), sep="\n", end="\n")
+            for i in range(k+1):
+                for j in range(i+1):
+                    print(formstr % R[(i, j)], end=" ")
+                print()
+            print("=" * len(title))
+
+    return R[(k, k)]
+
+# Romberg quadratures for numeric integration.
+#
+# Written by Scott M. Ransom <ransom@cfa.harvard.edu>
+# last revision: 14 Nov 98
+#
+# Cosmetic changes by Konrad Hinsen <hinsen@cnrs-orleans.fr>
+# last revision: 1999-7-21
+#
+# Adapted to SciPy by Travis Oliphant <oliphant.travis@ieee.org>
+# last revision: Dec 2001
+
+
+def _difftrap(function, interval, numtraps):
+    """
+    Perform part of the trapezoidal rule to integrate a function.
+    Assume that we had called difftrap with all lower powers-of-2
+    starting with 1. Calling difftrap only returns the summation
+    of the new ordinates. It does _not_ multiply by the width
+    of the trapezoids. This must be performed by the caller.
+        'function' is the function to evaluate (must accept vector arguments).
+        'interval' is a sequence with lower and upper limits
+                   of integration.
+        'numtraps' is the number of trapezoids to use (must be a
+                   power-of-2).
+    """
+    if numtraps <= 0:
+        raise ValueError("numtraps must be > 0 in difftrap().")
+    elif numtraps == 1:
+        return 0.5*(function(interval[0])+function(interval[1]))
+    else:
+        numtosum = numtraps/2
+        h = float(interval[1]-interval[0])/numtosum
+        lox = interval[0] + 0.5 * h
+        points = lox + h * np.arange(numtosum)
+        s = np.sum(function(points), axis=0)
+        return s
+
+
+def _romberg_diff(b, c, k):
+    """
+    Compute the differences for the Romberg quadrature corrections.
+    See Forman Acton's "Real Computing Made Real," p 143.
+    """
+    tmp = 4.0**k
+    return (tmp * c - b)/(tmp - 1.0)
+
+
+def _printresmat(function, interval, resmat):
+    # Print the Romberg result matrix.
+    i = j = 0
+    print('Romberg integration of', repr(function), end=' ')
+    print('from', interval)
+    print('')
+    print('%6s %9s %9s' % ('Steps', 'StepSize', 'Results'))
+    for i in range(len(resmat)):
+        print('%6d %9f' % (2**i, (interval[1]-interval[0])/(2.**i)), end=' ')
+        for j in range(i+1):
+            print('%9f' % (resmat[i][j]), end=' ')
+        print('')
+    print('')
+    print('The final result is', resmat[i][j], end=' ')
+    print('after', 2**(len(resmat)-1)+1, 'function evaluations.')
+
+
+def romberg(function, a, b, args=(), tol=1.48e-8, rtol=1.48e-8, show=False,
+            divmax=10, vec_func=False):
+    """
+    Romberg integration of a callable function or method.
+
+    Returns the integral of `function` (a function of one variable)
+    over the interval (`a`, `b`).
+
+    If `show` is 1, the triangular array of the intermediate results
+    will be printed. If `vec_func` is True (default is False), then
+    `function` is assumed to support vector arguments.
+
+    Parameters
+    ----------
+    function : callable
+        Function to be integrated.
+    a : float
+        Lower limit of integration.
+    b : float
+        Upper limit of integration.
+
+    Returns
+    -------
+    results : float
+        Result of the integration.
+
+    Other Parameters
+    ----------------
+    args : tuple, optional
+        Extra arguments to pass to function. Each element of `args` will
+        be passed as a single argument to `func`. Default is to pass no
+        extra arguments.
+    tol, rtol : float, optional
+        The desired absolute and relative tolerances. Defaults are 1.48e-8.
+    show : bool, optional
+        Whether to print the results. Default is False.
+    divmax : int, optional
+        Maximum order of extrapolation. Default is 10.
+    vec_func : bool, optional
+        Whether `func` handles arrays as arguments (i.e., whether it is a
+        "vector" function). Default is False.
+
+    See Also
+    --------
+    fixed_quad : Fixed-order Gaussian quadrature.
+    quad : Adaptive quadrature using QUADPACK.
+    dblquad : Double integrals.
+    tplquad : Triple integrals.
+    romb : Integrators for sampled data.
+    simpson : Integrators for sampled data.
+    cumulative_trapezoid : Cumulative integration for sampled data.
+    ode : ODE integrator.
+    odeint : ODE integrator.
+
+    References
+    ----------
+    .. [1] 'Romberg's method' https://en.wikipedia.org/wiki/Romberg%27s_method
+
+    Examples
+    --------
+    Integrate a gaussian from 0 to 1 and compare to the error function.
+
+    >>> from scipy import integrate
+    >>> from scipy.special import erf
+    >>> import numpy as np
+    >>> gaussian = lambda x: 1/np.sqrt(np.pi) * np.exp(-x**2)
+    >>> result = integrate.romberg(gaussian, 0, 1, show=True)
+    Romberg integration of <function vfunc at ...> from [0, 1]
+
+    ::
+
+       Steps  StepSize  Results
+           1  1.000000  0.385872
+           2  0.500000  0.412631  0.421551
+           4  0.250000  0.419184  0.421368  0.421356
+           8  0.125000  0.420810  0.421352  0.421350  0.421350
+          16  0.062500  0.421215  0.421350  0.421350  0.421350  0.421350
+          32  0.031250  0.421317  0.421350  0.421350  0.421350  0.421350  0.421350
+
+    The final result is 0.421350396475 after 33 function evaluations.
+
+    >>> print("%g %g" % (2*result, erf(1)))
+    0.842701 0.842701
+
+    """
+    if np.isinf(a) or np.isinf(b):
+        raise ValueError("Romberg integration only available "
+                         "for finite limits.")
+    vfunc = vectorize1(function, args, vec_func=vec_func)
+    n = 1
+    interval = [a, b]
+    intrange = b - a
+    ordsum = _difftrap(vfunc, interval, n)
+    result = intrange * ordsum
+    resmat = [[result]]
+    err = np.inf
+    last_row = resmat[0]
+    for i in range(1, divmax+1):
+        n *= 2
+        ordsum += _difftrap(vfunc, interval, n)
+        row = [intrange * ordsum / n]
+        for k in range(i):
+            row.append(_romberg_diff(last_row[k], row[k], k+1))
+        result = row[i]
+        lastresult = last_row[i-1]
+        if show:
+            resmat.append(row)
+        err = abs(result - lastresult)
+        if err < tol or err < rtol * abs(result):
+            break
+        last_row = row
+    else:
+        warnings.warn(
+            "divmax (%d) exceeded. Latest difference = %e" % (divmax, err),
+            AccuracyWarning)
+
+    if show:
+        _printresmat(vfunc, interval, resmat)
+    return result
+
+
+# Coefficients for Newton-Cotes quadrature
+#
+# These are the points being used
+#  to construct the local interpolating polynomial
+#  a are the weights for Newton-Cotes integration
+#  B is the error coefficient.
+#  error in these coefficients grows as N gets larger.
+#  or as samples are closer and closer together
+
+# You can use maxima to find these rational coefficients
+#  for equally spaced data using the commands
+#  a(i,N) := integrate(product(r-j,j,0,i-1) * product(r-j,j,i+1,N),r,0,N) / ((N-i)! * i!) * (-1)^(N-i);
+#  Be(N) := N^(N+2)/(N+2)! * (N/(N+3) - sum((i/N)^(N+2)*a(i,N),i,0,N));
+#  Bo(N) := N^(N+1)/(N+1)! * (N/(N+2) - sum((i/N)^(N+1)*a(i,N),i,0,N));
+#  B(N) := (if (mod(N,2)=0) then Be(N) else Bo(N));
+#
+# pre-computed for equally-spaced weights
+#
+# num_a, den_a, int_a, num_B, den_B = _builtincoeffs[N]
+#
+#  a = num_a*array(int_a)/den_a
+#  B = num_B*1.0 / den_B
+#
+#  integrate(f(x),x,x_0,x_N) = dx*sum(a*f(x_i)) + B*(dx)^(2k+3) f^(2k+2)(x*)
+#    where k = N // 2
+#
+_builtincoeffs = {
+    1: (1,2,[1,1],-1,12),
+    2: (1,3,[1,4,1],-1,90),
+    3: (3,8,[1,3,3,1],-3,80),
+    4: (2,45,[7,32,12,32,7],-8,945),
+    5: (5,288,[19,75,50,50,75,19],-275,12096),
+    6: (1,140,[41,216,27,272,27,216,41],-9,1400),
+    7: (7,17280,[751,3577,1323,2989,2989,1323,3577,751],-8183,518400),
+    8: (4,14175,[989,5888,-928,10496,-4540,10496,-928,5888,989],
+        -2368,467775),
+    9: (9,89600,[2857,15741,1080,19344,5778,5778,19344,1080,
+                 15741,2857], -4671, 394240),
+    10: (5,299376,[16067,106300,-48525,272400,-260550,427368,
+                   -260550,272400,-48525,106300,16067],
+         -673175, 163459296),
+    11: (11,87091200,[2171465,13486539,-3237113, 25226685,-9595542,
+                      15493566,15493566,-9595542,25226685,-3237113,
+                      13486539,2171465], -2224234463, 237758976000),
+    12: (1, 5255250, [1364651,9903168,-7587864,35725120,-51491295,
+                      87516288,-87797136,87516288,-51491295,35725120,
+                      -7587864,9903168,1364651], -3012, 875875),
+    13: (13, 402361344000,[8181904909, 56280729661, -31268252574,
+                           156074417954,-151659573325,206683437987,
+                           -43111992612,-43111992612,206683437987,
+                           -151659573325,156074417954,-31268252574,
+                           56280729661,8181904909], -2639651053,
+         344881152000),
+    14: (7, 2501928000, [90241897,710986864,-770720657,3501442784,
+                         -6625093363,12630121616,-16802270373,19534438464,
+                         -16802270373,12630121616,-6625093363,3501442784,
+                         -770720657,710986864,90241897], -3740727473,
+         1275983280000)
+    }
+
+
+def newton_cotes(rn, equal=0):
+    r"""
+    Return weights and error coefficient for Newton-Cotes integration.
+
+    Suppose we have (N+1) samples of f at the positions
+    x_0, x_1, ..., x_N. Then an N-point Newton-Cotes formula for the
+    integral between x_0 and x_N is:
+
+    :math:`\int_{x_0}^{x_N} f(x)dx = \Delta x \sum_{i=0}^{N} a_i f(x_i)
+    + B_N (\Delta x)^{N+2} f^{N+1} (\xi)`
+
+    where :math:`\xi \in [x_0,x_N]`
+    and :math:`\Delta x = \frac{x_N-x_0}{N}` is the average samples spacing.
+
+    If the samples are equally-spaced and N is even, then the error
+    term is :math:`B_N (\Delta x)^{N+3} f^{N+2}(\xi)`.
+
+    Parameters
+    ----------
+    rn : int
+        The integer order for equally-spaced data or the relative positions of
+        the samples with the first sample at 0 and the last at N, where N+1 is
+        the length of `rn`. N is the order of the Newton-Cotes integration.
+    equal : int, optional
+        Set to 1 to enforce equally spaced data.
+
+    Returns
+    -------
+    an : ndarray
+        1-D array of weights to apply to the function at the provided sample
+        positions.
+    B : float
+        Error coefficient.
+
+    Notes
+    -----
+    Normally, the Newton-Cotes rules are used on smaller integration
+    regions and a composite rule is used to return the total integral.
+
+    Examples
+    --------
+    Compute the integral of sin(x) in [0, :math:`\pi`]:
+
+    >>> from scipy.integrate import newton_cotes
+    >>> import numpy as np
+    >>> def f(x):
+    ...     return np.sin(x)
+    >>> a = 0
+    >>> b = np.pi
+    >>> exact = 2
+    >>> for N in [2, 4, 6, 8, 10]:
+    ...     x = np.linspace(a, b, N + 1)
+    ...     an, B = newton_cotes(N, 1)
+    ...     dx = (b - a) / N
+    ...     quad = dx * np.sum(an * f(x))
+    ...     error = abs(quad - exact)
+    ...     print('{:2d}  {:10.9f}  {:.5e}'.format(N, quad, error))
+    ...
+     2   2.094395102   9.43951e-02
+     4   1.998570732   1.42927e-03
+     6   2.000017814   1.78136e-05
+     8   1.999999835   1.64725e-07
+    10   2.000000001   1.14677e-09
+
+    """
+    try:
+        N = len(rn)-1
+        if equal:
+            rn = np.arange(N+1)
+        elif np.all(np.diff(rn) == 1):
+            equal = 1
+    except Exception:
+        N = rn
+        rn = np.arange(N+1)
+        equal = 1
+
+    if equal and N in _builtincoeffs:
+        na, da, vi, nb, db = _builtincoeffs[N]
+        an = na * np.array(vi, dtype=float) / da
+        return an, float(nb)/db
+
+    if (rn[0] != 0) or (rn[-1] != N):
+        raise ValueError("The sample positions must start at 0"
+                         " and end at N")
+    yi = rn / float(N)
+    ti = 2 * yi - 1
+    nvec = np.arange(N+1)
+    C = ti ** nvec[:, np.newaxis]
+    Cinv = np.linalg.inv(C)
+    # improve precision of result
+    for i in range(2):
+        Cinv = 2*Cinv - Cinv.dot(C).dot(Cinv)
+    vec = 2.0 / (nvec[::2]+1)
+    ai = Cinv[:, ::2].dot(vec) * (N / 2.)
+
+    if (N % 2 == 0) and equal:
+        BN = N/(N+3.)
+        power = N+2
+    else:
+        BN = N/(N+2.)
+        power = N+1
+
+    BN = BN - np.dot(yi**power, ai)
+    p1 = power+1
+    fac = power*math.log(N) - gammaln(p1)
+    fac = math.exp(fac)
+    return ai, BN*fac
+
+
+def _qmc_quad_iv(func, a, b, n_points, n_estimates, qrng, log):
+
+    # lazy import to avoid issues with partially-initialized submodule
+    if not hasattr(qmc_quad, 'qmc'):
+        from scipy import stats
+        qmc_quad.stats = stats
+    else:
+        stats = qmc_quad.stats
+
+    if not callable(func):
+        message = "`func` must be callable."
+        raise TypeError(message)
+
+    # a, b will be modified, so copy. Oh well if it's copied twice.
+    a = np.atleast_1d(a).copy()
+    b = np.atleast_1d(b).copy()
+    a, b = np.broadcast_arrays(a, b)
+    dim = a.shape[0]
+
+    try:
+        func((a + b) / 2)
+    except Exception as e:
+        message = ("`func` must evaluate the integrand at points within "
+                   "the integration range; e.g. `func( (a + b) / 2)` "
+                   "must return the integrand at the centroid of the "
+                   "integration volume.")
+        raise ValueError(message) from e
+
+    try:
+        func(np.array([a, b]).T)
+        vfunc = func
+    except Exception as e:
+        message = ("Exception encountered when attempting vectorized call to "
+                   f"`func`: {e}. For better performance, `func` should "
+                   "accept two-dimensional array `x` with shape `(len(a), "
+                   "n_points)` and return an array of the integrand value at "
+                   "each of the `n_points.")
+        warnings.warn(message, stacklevel=3)
+
+        def vfunc(x):
+            return np.apply_along_axis(func, axis=-1, arr=x)
+
+    n_points_int = np.int64(n_points)
+    if n_points != n_points_int:
+        message = "`n_points` must be an integer."
+        raise TypeError(message)
+
+    n_estimates_int = np.int64(n_estimates)
+    if n_estimates != n_estimates_int:
+        message = "`n_estimates` must be an integer."
+        raise TypeError(message)
+
+    if qrng is None:
+        qrng = stats.qmc.Halton(dim)
+    elif not isinstance(qrng, stats.qmc.QMCEngine):
+        message = "`qrng` must be an instance of scipy.stats.qmc.QMCEngine."
+        raise TypeError(message)
+
+    if qrng.d != a.shape[0]:
+        message = ("`qrng` must be initialized with dimensionality equal to "
+                   "the number of variables in `a`, i.e., "
+                   "`qrng.random().shape[-1]` must equal `a.shape[0]`.")
+        raise ValueError(message)
+
+    rng_seed = getattr(qrng, 'rng_seed', None)
+    rng = stats._qmc.check_random_state(rng_seed)
+
+    if log not in {True, False}:
+        message = "`log` must be boolean (`True` or `False`)."
+        raise TypeError(message)
+
+    return (vfunc, a, b, n_points_int, n_estimates_int, qrng, rng, log, stats)
+
+
+QMCQuadResult = namedtuple('QMCQuadResult', ['integral', 'standard_error'])
+
+
+def qmc_quad(func, a, b, *, n_estimates=8, n_points=1024, qrng=None,
+             log=False):
+    """
+    Compute an integral in N-dimensions using Quasi-Monte Carlo quadrature.
+
+    Parameters
+    ----------
+    func : callable
+        The integrand. Must accept a single argument ``x``, an array which
+        specifies the point(s) at which to evaluate the scalar-valued
+        integrand, and return the value(s) of the integrand.
+        For efficiency, the function should be vectorized to accept an array of
+        shape ``(d, n_points)``, where ``d`` is the number of variables (i.e.
+        the dimensionality of the function domain) and `n_points` is the number
+        of quadrature points, and return an array of shape ``(n_points,)``,
+        the integrand at each quadrature point.
+    a, b : array-like
+        One-dimensional arrays specifying the lower and upper integration
+        limits, respectively, of each of the ``d`` variables.
+    n_estimates, n_points : int, optional
+        `n_estimates` (default: 8) statistically independent QMC samples, each
+        of `n_points` (default: 1024) points, will be generated by `qrng`.
+        The total number of points at which the integrand `func` will be
+        evaluated is ``n_points * n_estimates``. See Notes for details.
+    qrng : `~scipy.stats.qmc.QMCEngine`, optional
+        An instance of the QMCEngine from which to sample QMC points.
+        The QMCEngine must be initialized to a number of dimensions ``d``
+        corresponding with the number of variables ``x1, ..., xd`` passed to
+        `func`.
+        The provided QMCEngine is used to produce the first integral estimate.
+        If `n_estimates` is greater than one, additional QMCEngines are
+        spawned from the first (with scrambling enabled, if it is an option.)
+        If a QMCEngine is not provided, the default `scipy.stats.qmc.Halton`
+        will be initialized with the number of dimensions determine from
+        the length of `a`.
+    log : boolean, default: False
+        When set to True, `func` returns the log of the integrand, and
+        the result object contains the log of the integral.
+
+    Returns
+    -------
+    result : object
+        A result object with attributes:
+
+        integral : float
+            The estimate of the integral.
+        standard_error :
+            The error estimate. See Notes for interpretation.
+
+    Notes
+    -----
+    Values of the integrand at each of the `n_points` points of a QMC sample
+    are used to produce an estimate of the integral. This estimate is drawn
+    from a population of possible estimates of the integral, the value of
+    which we obtain depends on the particular points at which the integral
+    was evaluated. We perform this process `n_estimates` times, each time
+    evaluating the integrand at different scrambled QMC points, effectively
+    drawing i.i.d. random samples from the population of integral estimates.
+    The sample mean :math:`m` of these integral estimates is an
+    unbiased estimator of the true value of the integral, and the standard
+    error of the mean :math:`s` of these estimates may be used to generate
+    confidence intervals using the t distribution with ``n_estimates - 1``
+    degrees of freedom. Perhaps counter-intuitively, increasing `n_points`
+    while keeping the total number of function evaluation points
+    ``n_points * n_estimates`` fixed tends to reduce the actual error, whereas
+    increasing `n_estimates` tends to decrease the error estimate.
+
+    Examples
+    --------
+    QMC quadrature is particularly useful for computing integrals in higher
+    dimensions. An example integrand is the probability density function
+    of a multivariate normal distribution.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> dim = 8
+    >>> mean = np.zeros(dim)
+    >>> cov = np.eye(dim)
+    >>> def func(x):
+    ...     # `multivariate_normal` expects the _last_ axis to correspond with
+    ...     # the dimensionality of the space, so `x` must be transposed
+    ...     return stats.multivariate_normal.pdf(x.T, mean, cov)
+
+    To compute the integral over the unit hypercube:
+
+    >>> from scipy.integrate import qmc_quad
+    >>> a = np.zeros(dim)
+    >>> b = np.ones(dim)
+    >>> rng = np.random.default_rng()
+    >>> qrng = stats.qmc.Halton(d=dim, seed=rng)
+    >>> n_estimates = 8
+    >>> res = qmc_quad(func, a, b, n_estimates=n_estimates, qrng=qrng)
+    >>> res.integral, res.standard_error
+    (0.00018429555666024108, 1.0389431116001344e-07)
+
+    A two-sided, 99% confidence interval for the integral may be estimated
+    as:
+
+    >>> t = stats.t(df=n_estimates-1, loc=res.integral,
+    ...             scale=res.standard_error)
+    >>> t.interval(0.99)
+    (0.0001839319802536469, 0.00018465913306683527)
+
+    Indeed, the value reported by `scipy.stats.multivariate_normal` is
+    within this range.
+
+    >>> stats.multivariate_normal.cdf(b, mean, cov, lower_limit=a)
+    0.00018430867675187443
+
+    """
+    args = _qmc_quad_iv(func, a, b, n_points, n_estimates, qrng, log)
+    func, a, b, n_points, n_estimates, qrng, rng, log, stats = args
+
+    def sum_product(integrands, dA, log=False):
+        if log:
+            return logsumexp(integrands) + np.log(dA)
+        else:
+            return np.sum(integrands * dA)
+
+    def mean(estimates, log=False):
+        if log:
+            return logsumexp(estimates) - np.log(n_estimates)
+        else:
+            return np.mean(estimates)
+
+    def std(estimates, m=None, ddof=0, log=False):
+        m = m or mean(estimates, log)
+        if log:
+            estimates, m = np.broadcast_arrays(estimates, m)
+            temp = np.vstack((estimates, m + np.pi * 1j))
+            diff = logsumexp(temp, axis=0)
+            return np.real(0.5 * (logsumexp(2 * diff)
+                                  - np.log(n_estimates - ddof)))
+        else:
+            return np.std(estimates, ddof=ddof)
+
+    def sem(estimates, m=None, s=None, log=False):
+        m = m or mean(estimates, log)
+        s = s or std(estimates, m, ddof=1, log=log)
+        if log:
+            return s - 0.5*np.log(n_estimates)
+        else:
+            return s / np.sqrt(n_estimates)
+
+    # The sign of the integral depends on the order of the limits. Fix this by
+    # ensuring that lower bounds are indeed lower and setting sign of resulting
+    # integral manually
+    if np.any(a == b):
+        message = ("A lower limit was equal to an upper limit, so the value "
+                   "of the integral is zero by definition.")
+        warnings.warn(message, stacklevel=2)
+        return QMCQuadResult(-np.inf if log else 0, 0)
+
+    i_swap = b < a
+    sign = (-1)**(i_swap.sum(axis=-1))  # odd # of swaps -> negative
+    a[i_swap], b[i_swap] = b[i_swap], a[i_swap]
+
+    A = np.prod(b - a)
+    dA = A / n_points
+
+    estimates = np.zeros(n_estimates)
+    rngs = _rng_spawn(qrng.rng, n_estimates)
+    for i in range(n_estimates):
+        # Generate integral estimate
+        sample = qrng.random(n_points)
+        # The rationale for transposing is that this allows users to easily
+        # unpack `x` into separate variables, if desired. This is consistent
+        # with the `xx` array passed into the `scipy.integrate.nquad` `func`.
+        x = stats.qmc.scale(sample, a, b).T  # (n_dim, n_points)
+        integrands = func(x)
+        estimates[i] = sum_product(integrands, dA, log)
+
+        # Get a new, independently-scrambled QRNG for next time
+        qrng = type(qrng)(seed=rngs[i], **qrng._init_quad)
+
+    integral = mean(estimates, log)
+    standard_error = sem(estimates, m=integral, log=log)
+    integral = integral + np.pi*1j if (log and sign < 0) else integral*sign
+    return QMCQuadResult(integral, standard_error)
+
+ +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

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Source code for scipy.ndimage._filters

+# Copyright (C) 2003-2005 Peter J. Verveer
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#
+# 3. The name of the author may not be used to endorse or promote
+#    products derived from this software without specific prior
+#    written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from collections.abc import Iterable
+import numbers
+import warnings
+import numpy
+import operator
+from numpy.core.multiarray import normalize_axis_index
+from . import _ni_support
+from . import _nd_image
+from . import _ni_docstrings
+
+__all__ = ['correlate1d', 'convolve1d', 'gaussian_filter1d', 'gaussian_filter',
+           'prewitt', 'sobel', 'generic_laplace', 'laplace',
+           'gaussian_laplace', 'generic_gradient_magnitude',
+           'gaussian_gradient_magnitude', 'correlate', 'convolve',
+           'uniform_filter1d', 'uniform_filter', 'minimum_filter1d',
+           'maximum_filter1d', 'minimum_filter', 'maximum_filter',
+           'rank_filter', 'median_filter', 'percentile_filter',
+           'generic_filter1d', 'generic_filter']
+
+
+def _invalid_origin(origin, lenw):
+    return (origin < -(lenw // 2)) or (origin > (lenw - 1) // 2)
+
+
+def _complex_via_real_components(func, input, weights, output, cval, **kwargs):
+    """Complex convolution via a linear combination of real convolutions."""
+    complex_input = input.dtype.kind == 'c'
+    complex_weights = weights.dtype.kind == 'c'
+    if complex_input and complex_weights:
+        # real component of the output
+        func(input.real, weights.real, output=output.real,
+             cval=numpy.real(cval), **kwargs)
+        output.real -= func(input.imag, weights.imag, output=None,
+                            cval=numpy.imag(cval), **kwargs)
+        # imaginary component of the output
+        func(input.real, weights.imag, output=output.imag,
+             cval=numpy.real(cval), **kwargs)
+        output.imag += func(input.imag, weights.real, output=None,
+                            cval=numpy.imag(cval), **kwargs)
+    elif complex_input:
+        func(input.real, weights, output=output.real, cval=numpy.real(cval),
+             **kwargs)
+        func(input.imag, weights, output=output.imag, cval=numpy.imag(cval),
+             **kwargs)
+    else:
+        if numpy.iscomplexobj(cval):
+            raise ValueError("Cannot provide a complex-valued cval when the "
+                             "input is real.")
+        func(input, weights.real, output=output.real, cval=cval, **kwargs)
+        func(input, weights.imag, output=output.imag, cval=cval, **kwargs)
+    return output
+
+
+@_ni_docstrings.docfiller
+def correlate1d(input, weights, axis=-1, output=None, mode="reflect",
+                cval=0.0, origin=0):
+    """Calculate a 1-D correlation along the given axis.
+
+    The lines of the array along the given axis are correlated with the
+    given weights.
+
+    Parameters
+    ----------
+    %(input)s
+    weights : array
+        1-D sequence of numbers.
+    %(axis)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin)s
+
+    Examples
+    --------
+    >>> from scipy.ndimage import correlate1d
+    >>> correlate1d([2, 8, 0, 4, 1, 9, 9, 0], weights=[1, 3])
+    array([ 8, 26,  8, 12,  7, 28, 36,  9])
+    """
+    input = numpy.asarray(input)
+    weights = numpy.asarray(weights)
+    complex_input = input.dtype.kind == 'c'
+    complex_weights = weights.dtype.kind == 'c'
+    if complex_input or complex_weights:
+        if complex_weights:
+            weights = weights.conj()
+            weights = weights.astype(numpy.complex128, copy=False)
+        kwargs = dict(axis=axis, mode=mode, origin=origin)
+        output = _ni_support._get_output(output, input, complex_output=True)
+        return _complex_via_real_components(correlate1d, input, weights,
+                                            output, cval, **kwargs)
+
+    output = _ni_support._get_output(output, input)
+    weights = numpy.asarray(weights, dtype=numpy.float64)
+    if weights.ndim != 1 or weights.shape[0] < 1:
+        raise RuntimeError('no filter weights given')
+    if not weights.flags.contiguous:
+        weights = weights.copy()
+    axis = normalize_axis_index(axis, input.ndim)
+    if _invalid_origin(origin, len(weights)):
+        raise ValueError('Invalid origin; origin must satisfy '
+                         '-(len(weights) // 2) <= origin <= '
+                         '(len(weights)-1) // 2')
+    mode = _ni_support._extend_mode_to_code(mode)
+    _nd_image.correlate1d(input, weights, axis, output, mode, cval,
+                          origin)
+    return output
+
+
+@_ni_docstrings.docfiller
+def convolve1d(input, weights, axis=-1, output=None, mode="reflect",
+               cval=0.0, origin=0):
+    """Calculate a 1-D convolution along the given axis.
+
+    The lines of the array along the given axis are convolved with the
+    given weights.
+
+    Parameters
+    ----------
+    %(input)s
+    weights : ndarray
+        1-D sequence of numbers.
+    %(axis)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin)s
+
+    Returns
+    -------
+    convolve1d : ndarray
+        Convolved array with same shape as input
+
+    Examples
+    --------
+    >>> from scipy.ndimage import convolve1d
+    >>> convolve1d([2, 8, 0, 4, 1, 9, 9, 0], weights=[1, 3])
+    array([14, 24,  4, 13, 12, 36, 27,  0])
+    """
+    weights = weights[::-1]
+    origin = -origin
+    if not len(weights) & 1:
+        origin -= 1
+    weights = numpy.asarray(weights)
+    if weights.dtype.kind == 'c':
+        # pre-conjugate here to counteract the conjugation in correlate1d
+        weights = weights.conj()
+    return correlate1d(input, weights, axis, output, mode, cval, origin)
+
+
+def _gaussian_kernel1d(sigma, order, radius):
+    """
+    Computes a 1-D Gaussian convolution kernel.
+    """
+    if order < 0:
+        raise ValueError('order must be non-negative')
+    exponent_range = numpy.arange(order + 1)
+    sigma2 = sigma * sigma
+    x = numpy.arange(-radius, radius+1)
+    phi_x = numpy.exp(-0.5 / sigma2 * x ** 2)
+    phi_x = phi_x / phi_x.sum()
+
+    if order == 0:
+        return phi_x
+    else:
+        # f(x) = q(x) * phi(x) = q(x) * exp(p(x))
+        # f'(x) = (q'(x) + q(x) * p'(x)) * phi(x)
+        # p'(x) = -1 / sigma ** 2
+        # Implement q'(x) + q(x) * p'(x) as a matrix operator and apply to the
+        # coefficients of q(x)
+        q = numpy.zeros(order + 1)
+        q[0] = 1
+        D = numpy.diag(exponent_range[1:], 1)  # D @ q(x) = q'(x)
+        P = numpy.diag(numpy.ones(order)/-sigma2, -1)  # P @ q(x) = q(x) * p'(x)
+        Q_deriv = D + P
+        for _ in range(order):
+            q = Q_deriv.dot(q)
+        q = (x[:, None] ** exponent_range).dot(q)
+        return q * phi_x
+
+
+@_ni_docstrings.docfiller
+def gaussian_filter1d(input, sigma, axis=-1, order=0, output=None,
+                      mode="reflect", cval=0.0, truncate=4.0, *, radius=None):
+    """1-D Gaussian filter.
+
+    Parameters
+    ----------
+    %(input)s
+    sigma : scalar
+        standard deviation for Gaussian kernel
+    %(axis)s
+    order : int, optional
+        An order of 0 corresponds to convolution with a Gaussian
+        kernel. A positive order corresponds to convolution with
+        that derivative of a Gaussian.
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    truncate : float, optional
+        Truncate the filter at this many standard deviations.
+        Default is 4.0.
+    radius : None or int, optional
+        Radius of the Gaussian kernel. If specified, the size of
+        the kernel will be ``2*radius + 1``, and `truncate` is ignored.
+        Default is None.
+
+    Returns
+    -------
+    gaussian_filter1d : ndarray
+
+    Notes
+    -----
+    The Gaussian kernel will have size ``2*radius + 1`` along each axis. If
+    `radius` is None, a default ``radius = round(truncate * sigma)`` will be
+    used.
+
+    Examples
+    --------
+    >>> from scipy.ndimage import gaussian_filter1d
+    >>> import numpy as np
+    >>> gaussian_filter1d([1.0, 2.0, 3.0, 4.0, 5.0], 1)
+    array([ 1.42704095,  2.06782203,  3.        ,  3.93217797,  4.57295905])
+    >>> gaussian_filter1d([1.0, 2.0, 3.0, 4.0, 5.0], 4)
+    array([ 2.91948343,  2.95023502,  3.        ,  3.04976498,  3.08051657])
+    >>> import matplotlib.pyplot as plt
+    >>> rng = np.random.default_rng()
+    >>> x = rng.standard_normal(101).cumsum()
+    >>> y3 = gaussian_filter1d(x, 3)
+    >>> y6 = gaussian_filter1d(x, 6)
+    >>> plt.plot(x, 'k', label='original data')
+    >>> plt.plot(y3, '--', label='filtered, sigma=3')
+    >>> plt.plot(y6, ':', label='filtered, sigma=6')
+    >>> plt.legend()
+    >>> plt.grid()
+    >>> plt.show()
+
+    """
+    sd = float(sigma)
+    # make the radius of the filter equal to truncate standard deviations
+    lw = int(truncate * sd + 0.5)
+    if radius is not None:
+        lw = radius
+    if not isinstance(lw, numbers.Integral) or lw < 0:
+        raise ValueError('Radius must be a nonnegative integer.')
+    # Since we are calling correlate, not convolve, revert the kernel
+    weights = _gaussian_kernel1d(sigma, order, lw)[::-1]
+    return correlate1d(input, weights, axis, output, mode, cval, 0)
+
+
+@_ni_docstrings.docfiller
+def gaussian_filter(input, sigma, order=0, output=None,
+                    mode="reflect", cval=0.0, truncate=4.0, *, radius=None,
+                    axes=None):
+    """Multidimensional Gaussian filter.
+
+    Parameters
+    ----------
+    %(input)s
+    sigma : scalar or sequence of scalars
+        Standard deviation for Gaussian kernel. The standard
+        deviations of the Gaussian filter are given for each axis as a
+        sequence, or as a single number, in which case it is equal for
+        all axes.
+    order : int or sequence of ints, optional
+        The order of the filter along each axis is given as a sequence
+        of integers, or as a single number. An order of 0 corresponds
+        to convolution with a Gaussian kernel. A positive order
+        corresponds to convolution with that derivative of a Gaussian.
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+    truncate : float, optional
+        Truncate the filter at this many standard deviations.
+        Default is 4.0.
+    radius : None or int or sequence of ints, optional
+        Radius of the Gaussian kernel. The radius are given for each axis
+        as a sequence, or as a single number, in which case it is equal
+        for all axes. If specified, the size of the kernel along each axis
+        will be ``2*radius + 1``, and `truncate` is ignored.
+        Default is None.
+    axes : tuple of int or None, optional
+        If None, `input` is filtered along all axes. Otherwise,
+        `input` is filtered along the specified axes. When `axes` is
+        specified, any tuples used for `sigma`, `order`, `mode` and/or `radius`
+        must match the length of `axes`. The ith entry in any of these tuples
+        corresponds to the ith entry in `axes`.
+
+    Returns
+    -------
+    gaussian_filter : ndarray
+        Returned array of same shape as `input`.
+
+    Notes
+    -----
+    The multidimensional filter is implemented as a sequence of
+    1-D convolution filters. The intermediate arrays are
+    stored in the same data type as the output. Therefore, for output
+    types with a limited precision, the results may be imprecise
+    because intermediate results may be stored with insufficient
+    precision.
+
+    The Gaussian kernel will have size ``2*radius + 1`` along each axis. If
+    `radius` is None, the default ``radius = round(truncate * sigma)`` will be
+    used.
+
+    Examples
+    --------
+    >>> from scipy.ndimage import gaussian_filter
+    >>> import numpy as np
+    >>> a = np.arange(50, step=2).reshape((5,5))
+    >>> a
+    array([[ 0,  2,  4,  6,  8],
+           [10, 12, 14, 16, 18],
+           [20, 22, 24, 26, 28],
+           [30, 32, 34, 36, 38],
+           [40, 42, 44, 46, 48]])
+    >>> gaussian_filter(a, sigma=1)
+    array([[ 4,  6,  8,  9, 11],
+           [10, 12, 14, 15, 17],
+           [20, 22, 24, 25, 27],
+           [29, 31, 33, 34, 36],
+           [35, 37, 39, 40, 42]])
+
+    >>> from scipy import datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = gaussian_filter(ascent, sigma=5)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    input = numpy.asarray(input)
+    output = _ni_support._get_output(output, input)
+
+    axes = _ni_support._check_axes(axes, input.ndim)
+    num_axes = len(axes)
+    orders = _ni_support._normalize_sequence(order, num_axes)
+    sigmas = _ni_support._normalize_sequence(sigma, num_axes)
+    modes = _ni_support._normalize_sequence(mode, num_axes)
+    radiuses = _ni_support._normalize_sequence(radius, num_axes)
+    axes = [(axes[ii], sigmas[ii], orders[ii], modes[ii], radiuses[ii])
+            for ii in range(num_axes) if sigmas[ii] > 1e-15]
+    if len(axes) > 0:
+        for axis, sigma, order, mode, radius in axes:
+            gaussian_filter1d(input, sigma, axis, order, output,
+                              mode, cval, truncate, radius=radius)
+            input = output
+    else:
+        output[...] = input[...]
+    return output
+
+
+@_ni_docstrings.docfiller
+def prewitt(input, axis=-1, output=None, mode="reflect", cval=0.0):
+    """Calculate a Prewitt filter.
+
+    Parameters
+    ----------
+    %(input)s
+    %(axis)s
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.prewitt(ascent)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    input = numpy.asarray(input)
+    axis = normalize_axis_index(axis, input.ndim)
+    output = _ni_support._get_output(output, input)
+    modes = _ni_support._normalize_sequence(mode, input.ndim)
+    correlate1d(input, [-1, 0, 1], axis, output, modes[axis], cval, 0)
+    axes = [ii for ii in range(input.ndim) if ii != axis]
+    for ii in axes:
+        correlate1d(output, [1, 1, 1], ii, output, modes[ii], cval, 0,)
+    return output
+
+
+@_ni_docstrings.docfiller
+def sobel(input, axis=-1, output=None, mode="reflect", cval=0.0):
+    """Calculate a Sobel filter.
+
+    Parameters
+    ----------
+    %(input)s
+    %(axis)s
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+
+    Notes
+    -----
+    This function computes the axis-specific Sobel gradient.
+    The horizontal edges can emphasised with the horizontal trasform (axis=0),
+    the vertical edges with the vertical transform (axis=1) and so on for higher
+    dimensions. These can be combined to give the magnitude.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> import numpy as np
+    >>> ascent = datasets.ascent().astype('int32')
+    >>> sobel_h = ndimage.sobel(ascent, 0)  # horizontal gradient
+    >>> sobel_v = ndimage.sobel(ascent, 1)  # vertical gradient
+    >>> magnitude = np.sqrt(sobel_h**2 + sobel_v**2)
+    >>> magnitude *= 255.0 / np.max(magnitude)  # normalization
+    >>> fig, axs = plt.subplots(2, 2, figsize=(8, 8))
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> axs[0, 0].imshow(ascent)
+    >>> axs[0, 1].imshow(sobel_h)
+    >>> axs[1, 0].imshow(sobel_v)
+    >>> axs[1, 1].imshow(magnitude)
+    >>> titles = ["original", "horizontal", "vertical", "magnitude"]
+    >>> for i, ax in enumerate(axs.ravel()):
+    ...     ax.set_title(titles[i])
+    ...     ax.axis("off")
+    >>> plt.show()
+
+    """
+    input = numpy.asarray(input)
+    axis = normalize_axis_index(axis, input.ndim)
+    output = _ni_support._get_output(output, input)
+    modes = _ni_support._normalize_sequence(mode, input.ndim)
+    correlate1d(input, [-1, 0, 1], axis, output, modes[axis], cval, 0)
+    axes = [ii for ii in range(input.ndim) if ii != axis]
+    for ii in axes:
+        correlate1d(output, [1, 2, 1], ii, output, modes[ii], cval, 0)
+    return output
+
+
+@_ni_docstrings.docfiller
+def generic_laplace(input, derivative2, output=None, mode="reflect",
+                    cval=0.0,
+                    extra_arguments=(),
+                    extra_keywords=None):
+    """
+    N-D Laplace filter using a provided second derivative function.
+
+    Parameters
+    ----------
+    %(input)s
+    derivative2 : callable
+        Callable with the following signature::
+
+            derivative2(input, axis, output, mode, cval,
+                        *extra_arguments, **extra_keywords)
+
+        See `extra_arguments`, `extra_keywords` below.
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+    %(extra_keywords)s
+    %(extra_arguments)s
+    """
+    if extra_keywords is None:
+        extra_keywords = {}
+    input = numpy.asarray(input)
+    output = _ni_support._get_output(output, input)
+    axes = list(range(input.ndim))
+    if len(axes) > 0:
+        modes = _ni_support._normalize_sequence(mode, len(axes))
+        derivative2(input, axes[0], output, modes[0], cval,
+                    *extra_arguments, **extra_keywords)
+        for ii in range(1, len(axes)):
+            tmp = derivative2(input, axes[ii], output.dtype, modes[ii], cval,
+                              *extra_arguments, **extra_keywords)
+            output += tmp
+    else:
+        output[...] = input[...]
+    return output
+
+
+@_ni_docstrings.docfiller
+def laplace(input, output=None, mode="reflect", cval=0.0):
+    """N-D Laplace filter based on approximate second derivatives.
+
+    Parameters
+    ----------
+    %(input)s
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.laplace(ascent)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    def derivative2(input, axis, output, mode, cval):
+        return correlate1d(input, [1, -2, 1], axis, output, mode, cval, 0)
+    return generic_laplace(input, derivative2, output, mode, cval)
+
+
+@_ni_docstrings.docfiller
+def gaussian_laplace(input, sigma, output=None, mode="reflect",
+                     cval=0.0, **kwargs):
+    """Multidimensional Laplace filter using Gaussian second derivatives.
+
+    Parameters
+    ----------
+    %(input)s
+    sigma : scalar or sequence of scalars
+        The standard deviations of the Gaussian filter are given for
+        each axis as a sequence, or as a single number, in which case
+        it is equal for all axes.
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+    Extra keyword arguments will be passed to gaussian_filter().
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> ascent = datasets.ascent()
+
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+
+    >>> result = ndimage.gaussian_laplace(ascent, sigma=1)
+    >>> ax1.imshow(result)
+
+    >>> result = ndimage.gaussian_laplace(ascent, sigma=3)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    input = numpy.asarray(input)
+
+    def derivative2(input, axis, output, mode, cval, sigma, **kwargs):
+        order = [0] * input.ndim
+        order[axis] = 2
+        return gaussian_filter(input, sigma, order, output, mode, cval,
+                               **kwargs)
+
+    return generic_laplace(input, derivative2, output, mode, cval,
+                           extra_arguments=(sigma,),
+                           extra_keywords=kwargs)
+
+
+@_ni_docstrings.docfiller
+def generic_gradient_magnitude(input, derivative, output=None,
+                               mode="reflect", cval=0.0,
+                               extra_arguments=(), extra_keywords=None):
+    """Gradient magnitude using a provided gradient function.
+
+    Parameters
+    ----------
+    %(input)s
+    derivative : callable
+        Callable with the following signature::
+
+            derivative(input, axis, output, mode, cval,
+                       *extra_arguments, **extra_keywords)
+
+        See `extra_arguments`, `extra_keywords` below.
+        `derivative` can assume that `input` and `output` are ndarrays.
+        Note that the output from `derivative` is modified inplace;
+        be careful to copy important inputs before returning them.
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+    %(extra_keywords)s
+    %(extra_arguments)s
+    """
+    if extra_keywords is None:
+        extra_keywords = {}
+    input = numpy.asarray(input)
+    output = _ni_support._get_output(output, input)
+    axes = list(range(input.ndim))
+    if len(axes) > 0:
+        modes = _ni_support._normalize_sequence(mode, len(axes))
+        derivative(input, axes[0], output, modes[0], cval,
+                   *extra_arguments, **extra_keywords)
+        numpy.multiply(output, output, output)
+        for ii in range(1, len(axes)):
+            tmp = derivative(input, axes[ii], output.dtype, modes[ii], cval,
+                             *extra_arguments, **extra_keywords)
+            numpy.multiply(tmp, tmp, tmp)
+            output += tmp
+        # This allows the sqrt to work with a different default casting
+        numpy.sqrt(output, output, casting='unsafe')
+    else:
+        output[...] = input[...]
+    return output
+
+
+@_ni_docstrings.docfiller
+def gaussian_gradient_magnitude(input, sigma, output=None,
+                                mode="reflect", cval=0.0, **kwargs):
+    """Multidimensional gradient magnitude using Gaussian derivatives.
+
+    Parameters
+    ----------
+    %(input)s
+    sigma : scalar or sequence of scalars
+        The standard deviations of the Gaussian filter are given for
+        each axis as a sequence, or as a single number, in which case
+        it is equal for all axes.
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+    Extra keyword arguments will be passed to gaussian_filter().
+
+    Returns
+    -------
+    gaussian_gradient_magnitude : ndarray
+        Filtered array. Has the same shape as `input`.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.gaussian_gradient_magnitude(ascent, sigma=5)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    input = numpy.asarray(input)
+
+    def derivative(input, axis, output, mode, cval, sigma, **kwargs):
+        order = [0] * input.ndim
+        order[axis] = 1
+        return gaussian_filter(input, sigma, order, output, mode,
+                               cval, **kwargs)
+
+    return generic_gradient_magnitude(input, derivative, output, mode,
+                                      cval, extra_arguments=(sigma,),
+                                      extra_keywords=kwargs)
+
+
+def _correlate_or_convolve(input, weights, output, mode, cval, origin,
+                           convolution):
+    input = numpy.asarray(input)
+    weights = numpy.asarray(weights)
+    complex_input = input.dtype.kind == 'c'
+    complex_weights = weights.dtype.kind == 'c'
+    if complex_input or complex_weights:
+        if complex_weights and not convolution:
+            # As for numpy.correlate, conjugate weights rather than input.
+            weights = weights.conj()
+        kwargs = dict(
+            mode=mode, origin=origin, convolution=convolution
+        )
+        output = _ni_support._get_output(output, input, complex_output=True)
+
+        return _complex_via_real_components(_correlate_or_convolve, input,
+                                            weights, output, cval, **kwargs)
+
+    origins = _ni_support._normalize_sequence(origin, input.ndim)
+    weights = numpy.asarray(weights, dtype=numpy.float64)
+    wshape = [ii for ii in weights.shape if ii > 0]
+    if len(wshape) != input.ndim:
+        raise RuntimeError('filter weights array has incorrect shape.')
+    if convolution:
+        weights = weights[tuple([slice(None, None, -1)] * weights.ndim)]
+        for ii in range(len(origins)):
+            origins[ii] = -origins[ii]
+            if not weights.shape[ii] & 1:
+                origins[ii] -= 1
+    for origin, lenw in zip(origins, wshape):
+        if _invalid_origin(origin, lenw):
+            raise ValueError('Invalid origin; origin must satisfy '
+                             '-(weights.shape[k] // 2) <= origin[k] <= '
+                             '(weights.shape[k]-1) // 2')
+
+    if not weights.flags.contiguous:
+        weights = weights.copy()
+    output = _ni_support._get_output(output, input)
+    temp_needed = numpy.may_share_memory(input, output)
+    if temp_needed:
+        # input and output arrays cannot share memory
+        temp = output
+        output = _ni_support._get_output(output.dtype, input)
+    if not isinstance(mode, str) and isinstance(mode, Iterable):
+        raise RuntimeError("A sequence of modes is not supported")
+    mode = _ni_support._extend_mode_to_code(mode)
+    _nd_image.correlate(input, weights, output, mode, cval, origins)
+    if temp_needed:
+        temp[...] = output
+        output = temp
+    return output
+
+
+@_ni_docstrings.docfiller
+def correlate(input, weights, output=None, mode='reflect', cval=0.0,
+              origin=0):
+    """
+    Multidimensional correlation.
+
+    The array is correlated with the given kernel.
+
+    Parameters
+    ----------
+    %(input)s
+    weights : ndarray
+        array of weights, same number of dimensions as input
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin_multiple)s
+
+    Returns
+    -------
+    result : ndarray
+        The result of correlation of `input` with `weights`.
+
+    See Also
+    --------
+    convolve : Convolve an image with a kernel.
+
+    Examples
+    --------
+    Correlation is the process of moving a filter mask often referred to
+    as kernel over the image and computing the sum of products at each location.
+
+    >>> from scipy.ndimage import correlate
+    >>> import numpy as np
+    >>> input_img = np.arange(25).reshape(5,5)
+    >>> print(input_img)
+    [[ 0  1  2  3  4]
+    [ 5  6  7  8  9]
+    [10 11 12 13 14]
+    [15 16 17 18 19]
+    [20 21 22 23 24]]
+
+    Define a kernel (weights) for correlation. In this example, it is for sum of
+    center and up, down, left and right next elements.
+
+    >>> weights = [[0, 1, 0],
+    ...            [1, 1, 1],
+    ...            [0, 1, 0]]
+
+    We can calculate a correlation result:
+    For example, element ``[2,2]`` is ``7 + 11 + 12 + 13 + 17 = 60``.
+
+    >>> correlate(input_img, weights)
+    array([[  6,  10,  15,  20,  24],
+        [ 26,  30,  35,  40,  44],
+        [ 51,  55,  60,  65,  69],
+        [ 76,  80,  85,  90,  94],
+        [ 96, 100, 105, 110, 114]])
+
+    """
+    return _correlate_or_convolve(input, weights, output, mode, cval,
+                                  origin, False)
+
+
+@_ni_docstrings.docfiller
+def convolve(input, weights, output=None, mode='reflect', cval=0.0,
+             origin=0):
+    """
+    Multidimensional convolution.
+
+    The array is convolved with the given kernel.
+
+    Parameters
+    ----------
+    %(input)s
+    weights : array_like
+        Array of weights, same number of dimensions as input
+    %(output)s
+    %(mode_reflect)s
+    cval : scalar, optional
+        Value to fill past edges of input if `mode` is 'constant'. Default
+        is 0.0
+    origin : int, optional
+        Controls the origin of the input signal, which is where the
+        filter is centered to produce the first element of the output.
+        Positive values shift the filter to the right, and negative values
+        shift the filter to the left. Default is 0.
+
+    Returns
+    -------
+    result : ndarray
+        The result of convolution of `input` with `weights`.
+
+    See Also
+    --------
+    correlate : Correlate an image with a kernel.
+
+    Notes
+    -----
+    Each value in result is :math:`C_i = \\sum_j{I_{i+k-j} W_j}`, where
+    W is the `weights` kernel,
+    j is the N-D spatial index over :math:`W`,
+    I is the `input` and k is the coordinate of the center of
+    W, specified by `origin` in the input parameters.
+
+    Examples
+    --------
+    Perhaps the simplest case to understand is ``mode='constant', cval=0.0``,
+    because in this case borders (i.e., where the `weights` kernel, centered
+    on any one value, extends beyond an edge of `input`) are treated as zeros.
+
+    >>> import numpy as np
+    >>> a = np.array([[1, 2, 0, 0],
+    ...               [5, 3, 0, 4],
+    ...               [0, 0, 0, 7],
+    ...               [9, 3, 0, 0]])
+    >>> k = np.array([[1,1,1],[1,1,0],[1,0,0]])
+    >>> from scipy import ndimage
+    >>> ndimage.convolve(a, k, mode='constant', cval=0.0)
+    array([[11, 10,  7,  4],
+           [10,  3, 11, 11],
+           [15, 12, 14,  7],
+           [12,  3,  7,  0]])
+
+    Setting ``cval=1.0`` is equivalent to padding the outer edge of `input`
+    with 1.0's (and then extracting only the original region of the result).
+
+    >>> ndimage.convolve(a, k, mode='constant', cval=1.0)
+    array([[13, 11,  8,  7],
+           [11,  3, 11, 14],
+           [16, 12, 14, 10],
+           [15,  6, 10,  5]])
+
+    With ``mode='reflect'`` (the default), outer values are reflected at the
+    edge of `input` to fill in missing values.
+
+    >>> b = np.array([[2, 0, 0],
+    ...               [1, 0, 0],
+    ...               [0, 0, 0]])
+    >>> k = np.array([[0,1,0], [0,1,0], [0,1,0]])
+    >>> ndimage.convolve(b, k, mode='reflect')
+    array([[5, 0, 0],
+           [3, 0, 0],
+           [1, 0, 0]])
+
+    This includes diagonally at the corners.
+
+    >>> k = np.array([[1,0,0],[0,1,0],[0,0,1]])
+    >>> ndimage.convolve(b, k)
+    array([[4, 2, 0],
+           [3, 2, 0],
+           [1, 1, 0]])
+
+    With ``mode='nearest'``, the single nearest value in to an edge in
+    `input` is repeated as many times as needed to match the overlapping
+    `weights`.
+
+    >>> c = np.array([[2, 0, 1],
+    ...               [1, 0, 0],
+    ...               [0, 0, 0]])
+    >>> k = np.array([[0, 1, 0],
+    ...               [0, 1, 0],
+    ...               [0, 1, 0],
+    ...               [0, 1, 0],
+    ...               [0, 1, 0]])
+    >>> ndimage.convolve(c, k, mode='nearest')
+    array([[7, 0, 3],
+           [5, 0, 2],
+           [3, 0, 1]])
+
+    """
+    return _correlate_or_convolve(input, weights, output, mode, cval,
+                                  origin, True)
+
+
+

+[docs]
+@_ni_docstrings.docfiller
+def uniform_filter1d(input, size, axis=-1, output=None,
+                     mode="reflect", cval=0.0, origin=0):
+    """Calculate a 1-D uniform filter along the given axis.
+
+    The lines of the array along the given axis are filtered with a
+    uniform filter of given size.
+
+    Parameters
+    ----------
+    %(input)s
+    size : int
+        length of uniform filter
+    %(axis)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin)s
+
+    Examples
+    --------
+    >>> from scipy.ndimage import uniform_filter1d
+    >>> uniform_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3)
+    array([4, 3, 4, 1, 4, 6, 6, 3])
+    """
+    input = numpy.asarray(input)
+    axis = normalize_axis_index(axis, input.ndim)
+    if size < 1:
+        raise RuntimeError('incorrect filter size')
+    complex_output = input.dtype.kind == 'c'
+    output = _ni_support._get_output(output, input,
+                                     complex_output=complex_output)
+    if (size // 2 + origin < 0) or (size // 2 + origin >= size):
+        raise ValueError('invalid origin')
+    mode = _ni_support._extend_mode_to_code(mode)
+    if not complex_output:
+        _nd_image.uniform_filter1d(input, size, axis, output, mode, cval,
+                                   origin)
+    else:
+        _nd_image.uniform_filter1d(input.real, size, axis, output.real, mode,
+                                   numpy.real(cval), origin)
+        _nd_image.uniform_filter1d(input.imag, size, axis, output.imag, mode,
+                                   numpy.imag(cval), origin)
+    return output

+
+
+
+

+[docs]
+@_ni_docstrings.docfiller
+def uniform_filter(input, size=3, output=None, mode="reflect",
+                   cval=0.0, origin=0, *, axes=None):
+    """Multidimensional uniform filter.
+
+    Parameters
+    ----------
+    %(input)s
+    size : int or sequence of ints, optional
+        The sizes of the uniform filter are given for each axis as a
+        sequence, or as a single number, in which case the size is
+        equal for all axes.
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+    %(origin_multiple)s
+    axes : tuple of int or None, optional
+        If None, `input` is filtered along all axes. Otherwise,
+        `input` is filtered along the specified axes. When `axes` is
+        specified, any tuples used for `size`, `origin`, and/or `mode`
+        must match the length of `axes`. The ith entry in any of these tuples
+        corresponds to the ith entry in `axes`.
+
+    Returns
+    -------
+    uniform_filter : ndarray
+        Filtered array. Has the same shape as `input`.
+
+    Notes
+    -----
+    The multidimensional filter is implemented as a sequence of
+    1-D uniform filters. The intermediate arrays are stored
+    in the same data type as the output. Therefore, for output types
+    with a limited precision, the results may be imprecise because
+    intermediate results may be stored with insufficient precision.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.uniform_filter(ascent, size=20)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    input = numpy.asarray(input)
+    output = _ni_support._get_output(output, input,
+                                     complex_output=input.dtype.kind == 'c')
+    axes = _ni_support._check_axes(axes, input.ndim)
+    num_axes = len(axes)
+    sizes = _ni_support._normalize_sequence(size, num_axes)
+    origins = _ni_support._normalize_sequence(origin, num_axes)
+    modes = _ni_support._normalize_sequence(mode, num_axes)
+    axes = [(axes[ii], sizes[ii], origins[ii], modes[ii])
+            for ii in range(num_axes) if sizes[ii] > 1]
+    if len(axes) > 0:
+        for axis, size, origin, mode in axes:
+            uniform_filter1d(input, int(size), axis, output, mode,
+                             cval, origin)
+            input = output
+    else:
+        output[...] = input[...]
+    return output

+
+
+
+@_ni_docstrings.docfiller
+def minimum_filter1d(input, size, axis=-1, output=None,
+                     mode="reflect", cval=0.0, origin=0):
+    """Calculate a 1-D minimum filter along the given axis.
+
+    The lines of the array along the given axis are filtered with a
+    minimum filter of given size.
+
+    Parameters
+    ----------
+    %(input)s
+    size : int
+        length along which to calculate 1D minimum
+    %(axis)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin)s
+
+    Notes
+    -----
+    This function implements the MINLIST algorithm [1]_, as described by
+    Richard Harter [2]_, and has a guaranteed O(n) performance, `n` being
+    the `input` length, regardless of filter size.
+
+    References
+    ----------
+    .. [1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.2777
+    .. [2] http://www.richardhartersworld.com/cri/2001/slidingmin.html
+
+
+    Examples
+    --------
+    >>> from scipy.ndimage import minimum_filter1d
+    >>> minimum_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3)
+    array([2, 0, 0, 0, 1, 1, 0, 0])
+    """
+    input = numpy.asarray(input)
+    if numpy.iscomplexobj(input):
+        raise TypeError('Complex type not supported')
+    axis = normalize_axis_index(axis, input.ndim)
+    if size < 1:
+        raise RuntimeError('incorrect filter size')
+    output = _ni_support._get_output(output, input)
+    if (size // 2 + origin < 0) or (size // 2 + origin >= size):
+        raise ValueError('invalid origin')
+    mode = _ni_support._extend_mode_to_code(mode)
+    _nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
+                                  origin, 1)
+    return output
+
+
+@_ni_docstrings.docfiller
+def maximum_filter1d(input, size, axis=-1, output=None,
+                     mode="reflect", cval=0.0, origin=0):
+    """Calculate a 1-D maximum filter along the given axis.
+
+    The lines of the array along the given axis are filtered with a
+    maximum filter of given size.
+
+    Parameters
+    ----------
+    %(input)s
+    size : int
+        Length along which to calculate the 1-D maximum.
+    %(axis)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin)s
+
+    Returns
+    -------
+    maximum1d : ndarray, None
+        Maximum-filtered array with same shape as input.
+        None if `output` is not None
+
+    Notes
+    -----
+    This function implements the MAXLIST algorithm [1]_, as described by
+    Richard Harter [2]_, and has a guaranteed O(n) performance, `n` being
+    the `input` length, regardless of filter size.
+
+    References
+    ----------
+    .. [1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.2777
+    .. [2] http://www.richardhartersworld.com/cri/2001/slidingmin.html
+
+    Examples
+    --------
+    >>> from scipy.ndimage import maximum_filter1d
+    >>> maximum_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3)
+    array([8, 8, 8, 4, 9, 9, 9, 9])
+    """
+    input = numpy.asarray(input)
+    if numpy.iscomplexobj(input):
+        raise TypeError('Complex type not supported')
+    axis = normalize_axis_index(axis, input.ndim)
+    if size < 1:
+        raise RuntimeError('incorrect filter size')
+    output = _ni_support._get_output(output, input)
+    if (size // 2 + origin < 0) or (size // 2 + origin >= size):
+        raise ValueError('invalid origin')
+    mode = _ni_support._extend_mode_to_code(mode)
+    _nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
+                                  origin, 0)
+    return output
+
+
+def _min_or_max_filter(input, size, footprint, structure, output, mode,
+                       cval, origin, minimum, axes=None):
+    if (size is not None) and (footprint is not None):
+        warnings.warn("ignoring size because footprint is set", UserWarning, stacklevel=3)
+    if structure is None:
+        if footprint is None:
+            if size is None:
+                raise RuntimeError("no footprint provided")
+            separable = True
+        else:
+            footprint = numpy.asarray(footprint, dtype=bool)
+            if not footprint.any():
+                raise ValueError("All-zero footprint is not supported.")
+            if footprint.all():
+                size = footprint.shape
+                footprint = None
+                separable = True
+            else:
+                separable = False
+    else:
+        structure = numpy.asarray(structure, dtype=numpy.float64)
+        separable = False
+        if footprint is None:
+            footprint = numpy.ones(structure.shape, bool)
+        else:
+            footprint = numpy.asarray(footprint, dtype=bool)
+    input = numpy.asarray(input)
+    if numpy.iscomplexobj(input):
+        raise TypeError('Complex type not supported')
+    output = _ni_support._get_output(output, input)
+    temp_needed = numpy.may_share_memory(input, output)
+    if temp_needed:
+        # input and output arrays cannot share memory
+        temp = output
+        output = _ni_support._get_output(output.dtype, input)
+    axes = _ni_support._check_axes(axes, input.ndim)
+    num_axes = len(axes)
+    if separable:
+        origins = _ni_support._normalize_sequence(origin, num_axes)
+        sizes = _ni_support._normalize_sequence(size, num_axes)
+        modes = _ni_support._normalize_sequence(mode, num_axes)
+        axes = [(axes[ii], sizes[ii], origins[ii], modes[ii])
+                for ii in range(len(axes)) if sizes[ii] > 1]
+        if minimum:
+            filter_ = minimum_filter1d
+        else:
+            filter_ = maximum_filter1d
+        if len(axes) > 0:
+            for axis, size, origin, mode in axes:
+                filter_(input, int(size), axis, output, mode, cval, origin)
+                input = output
+        else:
+            output[...] = input[...]
+    else:
+        origins = _ni_support._normalize_sequence(origin, input.ndim)
+        if num_axes < input.ndim:
+            if footprint.ndim != num_axes:
+                raise RuntimeError("footprint array has incorrect shape")
+            footprint = numpy.expand_dims(
+                footprint,
+                tuple(ax for ax in range(input.ndim) if ax not in axes)
+            )
+        fshape = [ii for ii in footprint.shape if ii > 0]
+        if len(fshape) != input.ndim:
+            raise RuntimeError('footprint array has incorrect shape.')
+        for origin, lenf in zip(origins, fshape):
+            if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
+                raise ValueError('invalid origin')
+        if not footprint.flags.contiguous:
+            footprint = footprint.copy()
+        if structure is not None:
+            if len(structure.shape) != input.ndim:
+                raise RuntimeError('structure array has incorrect shape')
+            if num_axes != structure.ndim:
+                structure = numpy.expand_dims(
+                    structure,
+                    tuple(ax for ax in range(structure.ndim) if ax not in axes)
+                )
+            if not structure.flags.contiguous:
+                structure = structure.copy()
+        if not isinstance(mode, str) and isinstance(mode, Iterable):
+            raise RuntimeError(
+                "A sequence of modes is not supported for non-separable "
+                "footprints")
+        mode = _ni_support._extend_mode_to_code(mode)
+        _nd_image.min_or_max_filter(input, footprint, structure, output,
+                                    mode, cval, origins, minimum)
+    if temp_needed:
+        temp[...] = output
+        output = temp
+    return output
+
+
+@_ni_docstrings.docfiller
+def minimum_filter(input, size=None, footprint=None, output=None,
+                   mode="reflect", cval=0.0, origin=0, *, axes=None):
+    """Calculate a multidimensional minimum filter.
+
+    Parameters
+    ----------
+    %(input)s
+    %(size_foot)s
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+    %(origin_multiple)s
+    axes : tuple of int or None, optional
+        If None, `input` is filtered along all axes. Otherwise,
+        `input` is filtered along the specified axes. When `axes` is
+        specified, any tuples used for `size`, `origin`, and/or `mode`
+        must match the length of `axes`. The ith entry in any of these tuples
+        corresponds to the ith entry in `axes`.
+
+    Returns
+    -------
+    minimum_filter : ndarray
+        Filtered array. Has the same shape as `input`.
+
+    Notes
+    -----
+    A sequence of modes (one per axis) is only supported when the footprint is
+    separable. Otherwise, a single mode string must be provided.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.minimum_filter(ascent, size=20)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    return _min_or_max_filter(input, size, footprint, None, output, mode,
+                              cval, origin, 1, axes)
+
+
+@_ni_docstrings.docfiller
+def maximum_filter(input, size=None, footprint=None, output=None,
+                   mode="reflect", cval=0.0, origin=0, *, axes=None):
+    """Calculate a multidimensional maximum filter.
+
+    Parameters
+    ----------
+    %(input)s
+    %(size_foot)s
+    %(output)s
+    %(mode_multiple)s
+    %(cval)s
+    %(origin_multiple)s
+    axes : tuple of int or None, optional
+        If None, `input` is filtered along all axes. Otherwise,
+        `input` is filtered along the specified axes. When `axes` is
+        specified, any tuples used for `size`, `origin`, and/or `mode`
+        must match the length of `axes`. The ith entry in any of these tuples
+        corresponds to the ith entry in `axes`.
+
+    Returns
+    -------
+    maximum_filter : ndarray
+        Filtered array. Has the same shape as `input`.
+
+    Notes
+    -----
+    A sequence of modes (one per axis) is only supported when the footprint is
+    separable. Otherwise, a single mode string must be provided.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.maximum_filter(ascent, size=20)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    return _min_or_max_filter(input, size, footprint, None, output, mode,
+                              cval, origin, 0, axes)
+
+
+@_ni_docstrings.docfiller
+def _rank_filter(input, rank, size=None, footprint=None, output=None,
+                 mode="reflect", cval=0.0, origin=0, operation='rank',
+                 axes=None):
+    if (size is not None) and (footprint is not None):
+        warnings.warn("ignoring size because footprint is set", UserWarning, stacklevel=3)
+    input = numpy.asarray(input)
+    if numpy.iscomplexobj(input):
+        raise TypeError('Complex type not supported')
+    axes = _ni_support._check_axes(axes, input.ndim)
+    num_axes = len(axes)
+    origins = _ni_support._normalize_sequence(origin, num_axes)
+    if footprint is None:
+        if size is None:
+            raise RuntimeError("no footprint or filter size provided")
+        sizes = _ni_support._normalize_sequence(size, num_axes)
+        footprint = numpy.ones(sizes, dtype=bool)
+    else:
+        footprint = numpy.asarray(footprint, dtype=bool)
+    if num_axes < input.ndim:
+        # set origin = 0 for any axes not being filtered
+        origins_temp = [0,] * input.ndim
+        for o, ax in zip(origins, axes):
+            origins_temp[ax] = o
+        origins = origins_temp
+
+        if not isinstance(mode, str) and isinstance(mode, Iterable):
+            # set mode = 'constant' for any axes not being filtered
+            modes = _ni_support._normalize_sequence(mode, num_axes)
+            modes_temp = ['constant'] * input.ndim
+            for m, ax in zip(modes, axes):
+                modes_temp[ax] = m
+            mode = modes_temp
+
+        # insert singleton dimension along any non-filtered axes
+        if footprint.ndim != num_axes:
+            raise RuntimeError("footprint array has incorrect shape")
+        footprint = numpy.expand_dims(
+            footprint,
+            tuple(ax for ax in range(input.ndim) if ax not in axes)
+        )
+    fshape = [ii for ii in footprint.shape if ii > 0]
+    if len(fshape) != input.ndim:
+        raise RuntimeError('footprint array has incorrect shape.')
+    for origin, lenf in zip(origins, fshape):
+        if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
+            raise ValueError('invalid origin')
+    if not footprint.flags.contiguous:
+        footprint = footprint.copy()
+    filter_size = numpy.where(footprint, 1, 0).sum()
+    if operation == 'median':
+        rank = filter_size // 2
+    elif operation == 'percentile':
+        percentile = rank
+        if percentile < 0.0:
+            percentile += 100.0
+        if percentile < 0 or percentile > 100:
+            raise RuntimeError('invalid percentile')
+        if percentile == 100.0:
+            rank = filter_size - 1
+        else:
+            rank = int(float(filter_size) * percentile / 100.0)
+    if rank < 0:
+        rank += filter_size
+    if rank < 0 or rank >= filter_size:
+        raise RuntimeError('rank not within filter footprint size')
+    if rank == 0:
+        return minimum_filter(input, None, footprint, output, mode, cval,
+                              origins, axes=None)
+    elif rank == filter_size - 1:
+        return maximum_filter(input, None, footprint, output, mode, cval,
+                              origins, axes=None)
+    else:
+        output = _ni_support._get_output(output, input)
+        temp_needed = numpy.may_share_memory(input, output)
+        if temp_needed:
+            # input and output arrays cannot share memory
+            temp = output
+            output = _ni_support._get_output(output.dtype, input)
+        if not isinstance(mode, str) and isinstance(mode, Iterable):
+            raise RuntimeError(
+                "A sequence of modes is not supported by non-separable rank "
+                "filters")
+        mode = _ni_support._extend_mode_to_code(mode)
+        _nd_image.rank_filter(input, rank, footprint, output, mode, cval,
+                              origins)
+        if temp_needed:
+            temp[...] = output
+            output = temp
+        return output
+
+
+@_ni_docstrings.docfiller
+def rank_filter(input, rank, size=None, footprint=None, output=None,
+                mode="reflect", cval=0.0, origin=0, *, axes=None):
+    """Calculate a multidimensional rank filter.
+
+    Parameters
+    ----------
+    %(input)s
+    rank : int
+        The rank parameter may be less than zero, i.e., rank = -1
+        indicates the largest element.
+    %(size_foot)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin_multiple)s
+    axes : tuple of int or None, optional
+        If None, `input` is filtered along all axes. Otherwise,
+        `input` is filtered along the specified axes.
+
+    Returns
+    -------
+    rank_filter : ndarray
+        Filtered array. Has the same shape as `input`.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.rank_filter(ascent, rank=42, size=20)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    rank = operator.index(rank)
+    return _rank_filter(input, rank, size, footprint, output, mode, cval,
+                        origin, 'rank', axes=axes)
+
+
+@_ni_docstrings.docfiller
+def median_filter(input, size=None, footprint=None, output=None,
+                  mode="reflect", cval=0.0, origin=0, *, axes=None):
+    """
+    Calculate a multidimensional median filter.
+
+    Parameters
+    ----------
+    %(input)s
+    %(size_foot)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin_multiple)s
+    axes : tuple of int or None, optional
+        If None, `input` is filtered along all axes. Otherwise,
+        `input` is filtered along the specified axes.
+
+    Returns
+    -------
+    median_filter : ndarray
+        Filtered array. Has the same shape as `input`.
+
+    See Also
+    --------
+    scipy.signal.medfilt2d
+
+    Notes
+    -----
+    For 2-dimensional images with ``uint8``, ``float32`` or ``float64`` dtypes
+    the specialised function `scipy.signal.medfilt2d` may be faster. It is
+    however limited to constant mode with ``cval=0``.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.median_filter(ascent, size=20)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    return _rank_filter(input, 0, size, footprint, output, mode, cval,
+                        origin, 'median', axes=axes)
+
+
+@_ni_docstrings.docfiller
+def percentile_filter(input, percentile, size=None, footprint=None,
+                      output=None, mode="reflect", cval=0.0, origin=0, *,
+                      axes=None):
+    """Calculate a multidimensional percentile filter.
+
+    Parameters
+    ----------
+    %(input)s
+    percentile : scalar
+        The percentile parameter may be less than zero, i.e.,
+        percentile = -20 equals percentile = 80
+    %(size_foot)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin_multiple)s
+    axes : tuple of int or None, optional
+        If None, `input` is filtered along all axes. Otherwise,
+        `input` is filtered along the specified axes.
+
+    Returns
+    -------
+    percentile_filter : ndarray
+        Filtered array. Has the same shape as `input`.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ax1 = fig.add_subplot(121)  # left side
+    >>> ax2 = fig.add_subplot(122)  # right side
+    >>> ascent = datasets.ascent()
+    >>> result = ndimage.percentile_filter(ascent, percentile=20, size=20)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result)
+    >>> plt.show()
+    """
+    return _rank_filter(input, percentile, size, footprint, output, mode,
+                        cval, origin, 'percentile', axes=axes)
+
+
+@_ni_docstrings.docfiller
+def generic_filter1d(input, function, filter_size, axis=-1,
+                     output=None, mode="reflect", cval=0.0, origin=0,
+                     extra_arguments=(), extra_keywords=None):
+    """Calculate a 1-D filter along the given axis.
+
+    `generic_filter1d` iterates over the lines of the array, calling the
+    given function at each line. The arguments of the line are the
+    input line, and the output line. The input and output lines are 1-D
+    double arrays. The input line is extended appropriately according
+    to the filter size and origin. The output line must be modified
+    in-place with the result.
+
+    Parameters
+    ----------
+    %(input)s
+    function : {callable, scipy.LowLevelCallable}
+        Function to apply along given axis.
+    filter_size : scalar
+        Length of the filter.
+    %(axis)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin)s
+    %(extra_arguments)s
+    %(extra_keywords)s
+
+    Notes
+    -----
+    This function also accepts low-level callback functions with one of
+    the following signatures and wrapped in `scipy.LowLevelCallable`:
+
+    .. code:: c
+
+       int function(double *input_line, npy_intp input_length,
+                    double *output_line, npy_intp output_length,
+                    void *user_data)
+       int function(double *input_line, intptr_t input_length,
+                    double *output_line, intptr_t output_length,
+                    void *user_data)
+
+    The calling function iterates over the lines of the input and output
+    arrays, calling the callback function at each line. The current line
+    is extended according to the border conditions set by the calling
+    function, and the result is copied into the array that is passed
+    through ``input_line``. The length of the input line (after extension)
+    is passed through ``input_length``. The callback function should apply
+    the filter and store the result in the array passed through
+    ``output_line``. The length of the output line is passed through
+    ``output_length``. ``user_data`` is the data pointer provided
+    to `scipy.LowLevelCallable` as-is.
+
+    The callback function must return an integer error status that is zero
+    if something went wrong and one otherwise. If an error occurs, you should
+    normally set the python error status with an informative message
+    before returning, otherwise a default error message is set by the
+    calling function.
+
+    In addition, some other low-level function pointer specifications
+    are accepted, but these are for backward compatibility only and should
+    not be used in new code.
+
+    """
+    if extra_keywords is None:
+        extra_keywords = {}
+    input = numpy.asarray(input)
+    if numpy.iscomplexobj(input):
+        raise TypeError('Complex type not supported')
+    output = _ni_support._get_output(output, input)
+    if filter_size < 1:
+        raise RuntimeError('invalid filter size')
+    axis = normalize_axis_index(axis, input.ndim)
+    if (filter_size // 2 + origin < 0) or (filter_size // 2 + origin >=
+                                           filter_size):
+        raise ValueError('invalid origin')
+    mode = _ni_support._extend_mode_to_code(mode)
+    _nd_image.generic_filter1d(input, function, filter_size, axis, output,
+                               mode, cval, origin, extra_arguments,
+                               extra_keywords)
+    return output
+
+
+@_ni_docstrings.docfiller
+def generic_filter(input, function, size=None, footprint=None,
+                   output=None, mode="reflect", cval=0.0, origin=0,
+                   extra_arguments=(), extra_keywords=None):
+    """Calculate a multidimensional filter using the given function.
+
+    At each element the provided function is called. The input values
+    within the filter footprint at that element are passed to the function
+    as a 1-D array of double values.
+
+    Parameters
+    ----------
+    %(input)s
+    function : {callable, scipy.LowLevelCallable}
+        Function to apply at each element.
+    %(size_foot)s
+    %(output)s
+    %(mode_reflect)s
+    %(cval)s
+    %(origin_multiple)s
+    %(extra_arguments)s
+    %(extra_keywords)s
+
+    Notes
+    -----
+    This function also accepts low-level callback functions with one of
+    the following signatures and wrapped in `scipy.LowLevelCallable`:
+
+    .. code:: c
+
+       int callback(double *buffer, npy_intp filter_size,
+                    double *return_value, void *user_data)
+       int callback(double *buffer, intptr_t filter_size,
+                    double *return_value, void *user_data)
+
+    The calling function iterates over the elements of the input and
+    output arrays, calling the callback function at each element. The
+    elements within the footprint of the filter at the current element are
+    passed through the ``buffer`` parameter, and the number of elements
+    within the footprint through ``filter_size``. The calculated value is
+    returned in ``return_value``. ``user_data`` is the data pointer provided
+    to `scipy.LowLevelCallable` as-is.
+
+    The callback function must return an integer error status that is zero
+    if something went wrong and one otherwise. If an error occurs, you should
+    normally set the python error status with an informative message
+    before returning, otherwise a default error message is set by the
+    calling function.
+
+    In addition, some other low-level function pointer specifications
+    are accepted, but these are for backward compatibility only and should
+    not be used in new code.
+
+    Examples
+    --------
+    Import the necessary modules and load the example image used for
+    filtering.
+
+    >>> import numpy as np
+    >>> from scipy import datasets
+    >>> from scipy.ndimage import generic_filter
+    >>> import matplotlib.pyplot as plt
+    >>> ascent = datasets.ascent()
+
+    Compute a maximum filter with kernel size 10 by passing a simple NumPy
+    aggregation function as argument to `function`.
+
+    >>> maximum_filter_result = generic_filter(ascent, np.amax, [10, 10])
+
+    While a maximmum filter could also directly be obtained using
+    `maximum_filter`, `generic_filter` allows generic Python function or
+    `scipy.LowLevelCallable` to be used as a filter. Here, we compute the
+    range between maximum and minimum value as an example for a kernel size
+    of 5.
+
+    >>> def custom_filter(image):
+    ...     return np.amax(image) - np.amin(image)
+    >>> custom_filter_result = generic_filter(ascent, custom_filter, [5, 5])
+
+    Plot the original and filtered images.
+
+    >>> fig, axes = plt.subplots(3, 1, figsize=(4, 12))
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> top, middle, bottom = axes
+    >>> for ax in axes:
+    ...     ax.set_axis_off()  # remove coordinate system
+    >>> top.imshow(ascent)
+    >>> top.set_title("Original image")
+    >>> middle.imshow(maximum_filter_result)
+    >>> middle.set_title("Maximum filter, Kernel: 10x10")
+    >>> bottom.imshow(custom_filter_result)
+    >>> bottom.set_title("Custom filter, Kernel: 5x5")
+    >>> fig.tight_layout()
+
+    """
+    if (size is not None) and (footprint is not None):
+        warnings.warn("ignoring size because footprint is set", UserWarning, stacklevel=2)
+    if extra_keywords is None:
+        extra_keywords = {}
+    input = numpy.asarray(input)
+    if numpy.iscomplexobj(input):
+        raise TypeError('Complex type not supported')
+    origins = _ni_support._normalize_sequence(origin, input.ndim)
+    if footprint is None:
+        if size is None:
+            raise RuntimeError("no footprint or filter size provided")
+        sizes = _ni_support._normalize_sequence(size, input.ndim)
+        footprint = numpy.ones(sizes, dtype=bool)
+    else:
+        footprint = numpy.asarray(footprint, dtype=bool)
+    fshape = [ii for ii in footprint.shape if ii > 0]
+    if len(fshape) != input.ndim:
+        raise RuntimeError('filter footprint array has incorrect shape.')
+    for origin, lenf in zip(origins, fshape):
+        if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
+            raise ValueError('invalid origin')
+    if not footprint.flags.contiguous:
+        footprint = footprint.copy()
+    output = _ni_support._get_output(output, input)
+    mode = _ni_support._extend_mode_to_code(mode)
+    _nd_image.generic_filter(input, function, footprint, output, mode,
+                             cval, origins, extra_arguments, extra_keywords)
+    return output
+
+ +
+ + + + + +
+ + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2023, Cyril Gadal. +
+ +

+
+ +
+

+ Created using Sphinx 7.2.6. +
+

+
+ +
+ + + +
+ +

+ Built with the PyData Sphinx Theme 0.14.2. +

+ +
+ +
+ +
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display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures 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font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + 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0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000..1823c1b --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: false, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3 GIT binary patch literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( literal 0 HcmV?d00001 diff --git a/_static/jupyterlite_badge_logo.svg b/_static/jupyterlite_badge_logo.svg new file mode 100644 index 0000000..5de36d7 --- /dev/null +++ b/_static/jupyterlite_badge_logo.svg @@ -0,0 +1,3 @@ + + +launchlaunchlitelite \ No newline at end of file diff --git a/_static/language_data.js b/_static/language_data.js new file mode 100644 index 0000000..250f566 --- /dev/null +++ b/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/minus.png b/_static/minus.png new file 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NAME(){return"swipe"}dispose(){ue.off(this._element,ke)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Qt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Qt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(ue.on(this._element,$e,(t=>this._start(t))),ue.on(this._element,Ie,(t=>this._end(t))),this._element.classList.add("pointer-event")):(ue.on(this._element,Le,(t=>this._start(t))),ue.on(this._element,Se,(t=>this._move(t))),ue.on(this._element,De,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static 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NAME(){return"carousel"}next(){this._slide(He)}nextWhenVisible(){!document.hidden&&Ht(this._element)&&this.next()}prev(){this._slide(Be)}pause(){this._isSliding&&Mt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?ue.one(this._element,qe,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void ue.one(this._element,qe,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?He:Be;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&ue.on(this._element,Ve,(t=>this._keydown(t))),"hover"===this._config.pause&&(ue.on(this._element,Ye,(()=>this.pause())),ue.on(this._element,Ke,(()=>this._maybeEnableCycle()))),this._config.touch&&Me.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of ye.find(".carousel-item img",this._element))ue.on(t,Qe,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(We)),rightCallback:()=>this._slide(this._directionToOrder(ze)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new Me(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ii[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ye.findOne(Ze,this._indicatorsElement);e.classList.remove(Je),e.removeAttribute("aria-current");const i=ye.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Je),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===He,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>ue.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),Rt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Je),i.classList.remove(Je,c,l),this._isSliding=!1,r(qe)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ye.findOne(ei,this._element)}_getItems(){return ye.find(ti,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Yt()?t===We?Be:He:t===We?He:Be}_orderToDirection(t){return Yt()?t===Be?We:ze:t===Be?ze:We}static jQueryInterface(t){return this.each((function(){const e=oi.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}ue.on(document,Ue,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=ye.getElementFromSelector(this);if(!e||!e.classList.contains(Ge))return;t.preventDefault();const i=oi.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===ge.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),ue.on(window,Xe,(()=>{const t=ye.find('[data-bs-ride="carousel"]');for(const e of t)oi.getOrCreateInstance(e)})),Kt(oi);const ri=".bs.collapse",ai=`show${ri}`,li=`shown${ri}`,ci=`hide${ri}`,hi=`hidden${ri}`,di=`click${ri}.data-api`,ui="show",fi="collapse",pi="collapsing",mi=`:scope .${fi} .${fi}`,gi='[data-bs-toggle="collapse"]',_i={parent:null,toggle:!0},bi={parent:"(null|element)",toggle:"boolean"};class vi extends be{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ye.find(gi);for(const t of i){const e=ye.getSelectorFromElement(t),i=ye.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return _i}static get DefaultType(){return bi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>vi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(ue.trigger(this._element,ai).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(fi),this._element.classList.add(pi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(pi),this._element.classList.add(fi,ui),this._element.style[e]="",ue.trigger(this._element,li)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(ue.trigger(this._element,ci).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,Rt(this._element),this._element.classList.add(pi),this._element.classList.remove(fi,ui);for(const t of this._triggerArray){const e=ye.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(pi),this._element.classList.add(fi),ue.trigger(this._element,hi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(ui)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ft(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(gi);for(const e of t){const t=ye.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ye.find(mi,this._config.parent);return ye.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=vi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}ue.on(document,di,gi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of ye.getMultipleElementsFromSelector(this))vi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Kt(vi);const yi="dropdown",wi=".bs.dropdown",Ei=".data-api",Ai="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ei}`,Si=`keydown${wi}${Ei}`,Di=`keyup${wi}${Ei}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=Yt()?"top-end":"top-start",ji=Yt()?"top-start":"top-end",Fi=Yt()?"bottom-end":"bottom-start",Hi=Yt()?"bottom-start":"bottom-end",Bi=Yt()?"left-start":"right-start",Wi=Yt()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends be{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ye.next(this._element,Pi)[0]||ye.prev(this._element,Pi)[0]||ye.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Bt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!ue.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),ue.trigger(this._element,ki,t)}}hide(){if(Bt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!ue.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),ge.removeDataAttribute(this._menu,"popper"),ue.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!jt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:jt(this._config.reference)?t=Ft(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=St(t,this._menu,i)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return Wi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(ge.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Qt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=ye.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Ht(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ye.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ai,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:ye.prev(this,Ii)[0]||ye.next(this,Ii)[0]||ye.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}ue.on(document,Si,Ii,qi.dataApiKeydownHandler),ue.on(document,Si,Pi,qi.dataApiKeydownHandler),ue.on(document,Li,qi.clearMenus),ue.on(document,Di,qi.clearMenus),ue.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),Kt(qi);const Vi="backdrop",Yi="show",Ki=`mousedown.bs.${Vi}`,Qi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Xi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends _e{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Qi}static get DefaultType(){return Xi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void Qt(t);this._append();const e=this._getElement();this._config.isAnimated&&Rt(e),e.classList.add(Yi),this._emulateAnimation((()=>{Qt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Yi),this._emulateAnimation((()=>{this.dispose(),Qt(t)}))):Qt(t)}dispose(){this._isAppended&&(ue.off(this._element,Ki),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ft(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),ue.on(t,Ki,(()=>{Qt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Xt(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends _e{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),ue.off(document,Gi),ue.on(document,Ji,(t=>this._handleFocusin(t))),ue.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,ue.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ye.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&ge.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=ge.getDataAttribute(t,e);null!==i?(ge.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(jt(t))e(t);else for(const i of ye.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",En="show",An="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends be{constructor(t,e){super(t,e),this._dialog=ye.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||ue.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(ue.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){ue.off(window,hn),ue.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ye.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),Rt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,ue.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){ue.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),ue.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),ue.on(this._element,bn,(t=>{ue.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),ue.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(ue.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(An)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(An),this._queueCallback((()=>{this._element.classList.remove(An),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Yt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Yt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}ue.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=ye.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),ue.one(e,pn,(t=>{t.defaultPrevented||ue.one(e,fn,(()=>{Ht(this)&&this.focus()}))}));const i=ye.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),we(On),Kt(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Bn=`click${xn}${kn}`,Wn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends be{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||ue.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),ue.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(ue.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),ue.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():ue.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){ue.on(this._element,Wn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():ue.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}ue.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=ye.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Bt(this))return;ue.one(e,Fn,(()=>{Ht(this)&&this.focus()}));const i=ye.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),ue.on(window,Ln,(()=>{for(const t of ye.find(In))qn.getOrCreateInstance(t).show()})),ue.on(window,Hn,(()=>{for(const t of ye.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),we(qn),Kt(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Yn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Kn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Qn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Yn.has(i)||Boolean(Kn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Xn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends _e{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Xn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=ye.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?jt(e)?this._putElementInTemplate(Ft(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Qn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Qt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:Yt()?"left":"right",BOTTOM:"bottom",LEFT:Yt()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends be{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),ue.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=ue.trigger(this._element,this.constructor.eventName("show")),e=(Wt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),ue.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._queueCallback((()=>{ue.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!ue.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),ue.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=Qt(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return St(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Qt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Qt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)ue.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");ue.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),ue.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},ue.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=ge.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ft(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Es={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class As extends be{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return Es}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ft(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(ue.off(this._config.target,ms),ue.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ye.find(bs,this._config.target);for(const e of t){if(!e.hash||Bt(e))continue;const t=ye.findOne(decodeURI(e.hash),this._element);Ht(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),ue.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ye.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of ye.parents(t,".nav, .list-group"))for(const t of ye.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=ye.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=As.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(window,gs,(()=>{for(const t of ye.find('[data-bs-spy="scroll"]'))As.getOrCreateInstance(t)})),Kt(As);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Bs="show",Ws=".dropdown-toggle",zs=`:not(${Ws})`,Rs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${zs}, .list-group-item${zs}, [role="tab"]${zs}, ${Rs}`,Vs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Ys extends be{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),ue.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?ue.trigger(e,Cs,{relatedTarget:t}):null;ue.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(ye.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),ue.trigger(t,ks,{relatedTarget:e})):t.classList.add(Bs)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(ye.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),ue.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Bs)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Bt(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=Ut(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Ys.getOrCreateInstance(i).show())}_getChildren(){return ye.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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b/_static/scripts/bootstrap.js.LICENSE.txt @@ -0,0 +1,5 @@ +/*! + * Bootstrap v5.3.2 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors) + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */ diff --git a/_static/scripts/bootstrap.js.map b/_static/scripts/bootstrap.js.map new file mode 100644 index 0000000..e5bc157 --- /dev/null +++ b/_static/scripts/bootstrap.js.map @@ -0,0 +1 @@ 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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.3.2 (https://getbootstrap.com/)\n * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n const instanceMap = elementMap.get(element);\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n instanceMap.set(key, instance);\n },\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n return null;\n },\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key);\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n if (selector && window.CSS && window.CSS.escape) {\n // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n }\n return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n }\n\n // Get transition-duration of the element\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n // Return 0 if element or transition duration is not found\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n }\n\n // If multiple durations are defined, take the first\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object));\n }\n return null;\n};\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])');\n if (!closedDetails) {\n return elementIsVisible;\n }\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n if (summary === null) {\n return false;\n }\n }\n return elementIsVisible;\n};\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n if (element.classList.contains('disabled')) {\n return true;\n }\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n if (element instanceof ShadowRoot) {\n return element;\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null;\n }\n return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement);\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n index += shouldGetNext ? 1 : -1;\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n return fn.apply(element, [event]);\n };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n hydrateObj(event, {\n delegateTarget: target\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n return fn.apply(target, [event]);\n }\n }\n };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string';\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n callable = wrapFunction(callable);\n }\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n if (!fn) {\n return;\n }\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n const evt = hydrateObj(new Event(event, {\n bubbles,\n cancelable: true\n }), args);\n if (defaultPrevented) {\n evt.preventDefault();\n }\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n return evt;\n }\n};\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value;\n }\n });\n }\n }\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n if (value === 'false') {\n return false;\n }\n if (value === Number(value).toString()) {\n return Number(value);\n }\n if (value === '' || value === 'null') {\n return null;\n }\n if (typeof value !== 'string') {\n return value;\n }\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n return attributes;\n },\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n static get DefaultType() {\n return {};\n }\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n return config;\n }\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.2';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n if (!element) {\n return;\n }\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n static get VERSION() {\n return VERSION;\n }\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href');\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n selector = hrefAttribute && hrefAttribute !== '#' ? parseSelector(hrefAttribute.trim()) : null;\n }\n return selector;\n};\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n return parents;\n },\n prev(element, selector) {\n let previous = element.previousElementSibling;\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n previous = previous.previousElementSibling;\n }\n return [];\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n next = next.nextElementSibling;\n }\n return [];\n },\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n },\n getSelectorFromElement(element) {\n const selector = getSelector(element);\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null;\n }\n return null;\n },\n getElementFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.findOne(selector) : null;\n },\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.find(selector) : [];\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target);\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n if (closeEvent.defaultPrevented) {\n return;\n }\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n }\n\n // Private\n _destroyElement() {\n this._element.remove();\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n if (!element || !Swipe.isSupported()) {\n return;\n }\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n this._initEvents();\n }\n\n // Getters\n static get Default() {\n return Default$c;\n }\n static get DefaultType() {\n return DefaultType$c;\n }\n static get NAME() {\n return NAME$d;\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n this._handleSwipe();\n execute(this._config.endCallback);\n }\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n if (!direction) {\n return;\n }\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n this._addEventListeners();\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$b;\n }\n static get DefaultType() {\n return DefaultType$b;\n }\n static get NAME() {\n return NAME$c;\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT);\n }\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n prev() {\n this._slide(ORDER_PREV);\n }\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n this._clearInterval();\n }\n cycle() {\n this._clearInterval();\n this._updateInterval();\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n this.cycle();\n }\n to(index) {\n const items = this._getItems();\n if (index > items.length - 1 || index < 0) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n const activeIndex = this._getItemIndex(this._getActive());\n if (activeIndex === index) {\n return;\n }\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n this._slide(order, items[index]);\n }\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause();\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n const direction = KEY_TO_DIRECTION[event.key];\n if (direction) {\n event.preventDefault();\n this._slide(this._directionToOrder(direction));\n }\n }\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n if (!element) {\n return;\n }\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n const activeElement = this._getActive();\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n if (nextElement === activeElement) {\n return;\n }\n const nextElementIndex = this._getItemIndex(nextElement);\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n const slideEvent = triggerEvent(EVENT_SLIDE);\n if (slideEvent.defaultPrevented) {\n return;\n }\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return;\n }\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n this._setActiveIndicatorElement(nextElementIndex);\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n if (isCycling) {\n this.cycle();\n }\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n if (slideIndex) {\n carousel.to(slideIndex);\n carousel._maybeEnableCycle();\n return;\n }\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n carousel._maybeEnableCycle();\n return;\n }\n carousel.prev();\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n this._initializeChildren();\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n if (this._config.toggle) {\n this.toggle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$a;\n }\n static get DefaultType() {\n return DefaultType$a;\n }\n static get NAME() {\n return NAME$b;\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n let activeChildren = [];\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n const dimension = this._getDimension();\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.style[dimension] = 0;\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n this._queueCallback(complete, this._element, true);\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n const dimension = this._getDimension();\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger);\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n this._element.style[dimension] = '';\n this._queueCallback(complete, this._element, true);\n }\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n config.parent = getElement(config.parent);\n return config;\n }\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element);\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {};\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n }\n\n // Getters\n static get Default() {\n return Default$9;\n }\n static get DefaultType() {\n return DefaultType$9;\n }\n static get NAME() {\n return NAME$a;\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n if (showEvent.defaultPrevented) {\n return;\n }\n this._createPopper();\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n this._element.focus();\n this._element.setAttribute('aria-expanded', true);\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n this._element.classList.add(CLASS_NAME_SHOW$6);\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n this._completeHide(relatedTarget);\n }\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n super.dispose();\n }\n update() {\n this._inNavbar = this._detectNavbar();\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n if (this._popper) {\n this._popper.destroy();\n }\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n this._element.setAttribute('aria-expanded', 'false');\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n _getConfig(config) {\n config = super._getConfig(config);\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n return config;\n }\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n let referenceElement = this._element;\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n const popperConfig = this._getPopperConfig();\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n _getPlacement() {\n const parentDropdown = this._parent;\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n };\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n if (!items.length) {\n return;\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n if (!context || context._config.autoClose === false) {\n continue;\n }\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n const relatedTarget = {\n relatedTarget: context._element\n };\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n context._completeHide(relatedTarget);\n }\n }\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n if (isInput && !isEscapeEvent) {\n return;\n }\n event.preventDefault();\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n instance._selectMenuItem(event);\n return;\n }\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n};\n\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n }\n\n // Getters\n static get Default() {\n return Default$8;\n }\n static get DefaultType() {\n return DefaultType$8;\n }\n static get NAME() {\n return NAME$9;\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._append();\n const element = this._getElement();\n if (this._config.isAnimated) {\n reflow(element);\n }\n element.classList.add(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n dispose() {\n if (!this._isAppended) {\n return;\n }\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n this._element.remove();\n this._isAppended = false;\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n this._element = backdrop;\n }\n return this._element;\n }\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n _append() {\n if (this._isAppended) {\n return;\n }\n const element = this._getElement();\n this._config.rootElement.append(element);\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n};\n\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n }\n\n // Getters\n static get Default() {\n return Default$7;\n }\n static get DefaultType() {\n return DefaultType$7;\n }\n static get NAME() {\n return NAME$8;\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return;\n }\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n deactivate() {\n if (!this._isActive) {\n return;\n }\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n }\n\n // Private\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n const elements = SelectorEngine.focusableChildren(trapElement);\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n hide() {\n const width = this.getWidth();\n this._disableOverFlow();\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n isOverflowing() {\n return this.getWidth() > 0;\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n this._element.style.overflow = 'hidden';\n }\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n this._saveInitialAttribute(element, styleProperty);\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty);\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$6;\n }\n static get DefaultType() {\n return DefaultType$6;\n }\n static get NAME() {\n return NAME$7;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._isTransitioning = true;\n this._scrollBar.hide();\n document.body.classList.add(CLASS_NAME_OPEN);\n this._adjustDialog();\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._isShown = false;\n this._isTransitioning = true;\n this._focustrap.deactivate();\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n dispose() {\n EventHandler.off(window, EVENT_KEY$4);\n EventHandler.off(this._dialog, EVENT_KEY$4);\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n handleUpdate() {\n this._adjustDialog();\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n this._element.style.display = 'block';\n this._element.removeAttribute('aria-hidden');\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_SHOW$4);\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n return;\n }\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n _hideModal() {\n this._element.style.display = 'none';\n this._element.setAttribute('aria-hidden', true);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n this._isTransitioning = false;\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n this._resetAdjustments();\n this._scrollBar.reset();\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n if (hideEvent.defaultPrevented) {\n return;\n }\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY;\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n this._element.classList.add(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n this._element.focus();\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const scrollbarWidth = this._scrollBar.getWidth();\n const isBodyOverflowing = scrollbarWidth > 0;\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](relatedTarget);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n });\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$5;\n }\n static get DefaultType() {\n return DefaultType$5;\n }\n static get NAME() {\n return NAME$6;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._backdrop.show();\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n this._element.classList.add(CLASS_NAME_SHOW$3);\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n this._queueCallback(completeCallBack, this._element, true);\n }\n hide() {\n if (!this._isShown) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._focustrap.deactivate();\n this._element.blur();\n this._isShown = false;\n this._element.classList.add(CLASS_NAME_HIDING);\n this._backdrop.hide();\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n this._queueCallback(completeCallback, this._element, true);\n }\n dispose() {\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n this.hide();\n };\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n });\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n });\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n }\n return true;\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n }\n\n // Getters\n static get Default() {\n return Default$4;\n }\n static get DefaultType() {\n return DefaultType$4;\n }\n static get NAME() {\n return NAME$5;\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n hasContent() {\n return this.getContent().length > 0;\n }\n changeContent(content) {\n this._checkContent(content);\n this._config.content = {\n ...this._config.content,\n ...content\n };\n return this;\n }\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n const template = templateWrapper.children[0];\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n return template;\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n this._checkContent(config.content);\n }\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n if (!templateElement) {\n return;\n }\n content = this._resolvePossibleFunction(content);\n if (!content) {\n templateElement.remove();\n return;\n }\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n return;\n }\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n templateElement.textContent = content;\n }\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this]);\n }\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n templateElement.textContent = element.textContent;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n super(element, config);\n\n // Private\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null;\n\n // Protected\n this.tip = null;\n this._setListeners();\n if (!this._config.selector) {\n this._fixTitle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$3;\n }\n static get DefaultType() {\n return DefaultType$3;\n }\n static get NAME() {\n return NAME$4;\n }\n\n // Public\n enable() {\n this._isEnabled = true;\n }\n disable() {\n this._isEnabled = false;\n }\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n this._activeTrigger.click = !this._activeTrigger.click;\n if (this._isShown()) {\n this._leave();\n return;\n }\n this._enter();\n }\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n this._disposePopper();\n super.dispose();\n }\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper();\n const tip = this._getTipElement();\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n const {\n container\n } = this._config;\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n if (this._isHovered === false) {\n this._leave();\n }\n this._isHovered = false;\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n hide() {\n if (!this._isShown()) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n if (hideEvent.defaultPrevented) {\n return;\n }\n const tip = this._getTipElement();\n tip.classList.remove(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n if (!this._isHovered) {\n this._disposePopper();\n }\n this._element.removeAttribute('aria-describedby');\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n update() {\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n return this.tip;\n }\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml();\n\n // TODO: remove this check in v6\n if (!tip) {\n return null;\n }\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n return tip;\n }\n setContent(content) {\n this._newContent = content;\n if (this._isShown()) {\n this._disposePopper();\n this.show();\n }\n }\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n return this._templateFactory;\n }\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element]);\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element]);\n }\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n context._leave();\n });\n }\n }\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n _fixTitle() {\n const title = this._element.getAttribute('title');\n if (!title) {\n return;\n }\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title');\n }\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n this._isHovered = true;\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n this._isHovered = false;\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n return config;\n }\n _getDelegateConfig() {\n const config = {};\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value;\n }\n }\n config.selector = false;\n config.trigger = 'manual';\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config;\n }\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n this._popper = null;\n }\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n static get DefaultType() {\n return DefaultType$2;\n }\n static get NAME() {\n return NAME$3;\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent();\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n }\n\n // Getters\n static get Default() {\n return Default$1;\n }\n static get DefaultType() {\n return DefaultType$1;\n }\n static get NAME() {\n return NAME$2;\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables();\n this._maybeEnableSmoothScroll();\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n dispose() {\n this._observer.disconnect();\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body;\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n return config;\n }\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height;\n }\n });\n }\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n this._process(targetElement(entry));\n };\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n this._clearActiveClass(targetElement(entry));\n continue;\n }\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry);\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return;\n }\n continue;\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor);\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n this._clearActiveClass(this._config.target);\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n this._activateParents(target);\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both

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