Removed wigner, Qd and TwoMode

Examples for removed material are already in visualization-exposition.md

Author: MathiB123

tutorials-v5/visualization/distributions.md

@@ -18,89 +18,25 @@ Author: Mathis Beaudoin (2025)
 
 ### Introduction
 
-This notebook shows how to use the different types of probability distributions inside qutip.
-
-We begin by importing the necessary packages.
+This notebook shows how to use probability distributions inside QuTiP. We begin by importing the necessary packages.
 
 ```python
-from qutip import *
-from qutip.distributions import *
+from qutip import fock
+from qutip.distributions import HarmonicOscillatorWaveFunction, HarmonicOscillatorProbabilityFunction
 import matplotlib.pyplot as plt
 ```
-    
-Then we define some quantum state.
-
-```python
-N = 20
-alpha = 2.0 + 2j
-rho = (coherent(N, alpha) + coherent(N, -alpha)).unit()
-```
-
-### Wigner Distribution
-
-To use the Wigner distribution, simply use the `WignerDistribution` class and pass a quantum state to the constructor. Optionally, define a range of values for each coordinate with the parameter called `extent`. Also, define a number of data points inside the given range with the optional parameter called `steps`. From this information, the distribution is generated and can be visualized with the `visualize` method.
-
-```python
-#Generate the wigner distribution
-wigner = WignerDistribution(rho, extent=[[-10, 10], [-10, 10]], steps=300)
-wigner.visualize()
-```
-
-It is also possible to calculate the marginal and projection distribution along a given dimension. These methods are available for the other distributions below as well.
-
-```python
-alpha = 2.0
-N = 20
-rho = (coherent(N, alpha) + coherent(N, -alpha)).unit()
-wigner = WignerDistribution(rho)
-wigner.visualize()
-
-#x axis
-wigner_x = wigner.marginal(dim=0)
-wigner_x.visualize()
-
-#y axis
-wigner_y = wigner.marginal(dim=1)
-wigner_y.visualize()
-
-#projection
-proj = wigner.project(dim=1)
-proj.visualize()
-```
 
-### Husimi-Q Distribution
-
-To use the Husimi-Q distribution, simply use the `QDistribution` class and pass it a quantum state. Again, `extent` and `steps` are optional parameters for this distribution.
-
-```python
-hq = QDistribution(rho, extent=[[-10, 10], [-10, 10]], steps=300)
-hq.visualize()
-```
-
-### Two-mode quadrature correlations
-We start with a new quantum state.
-
-```python
-alpha = 1.0 
-psi = (tensor(coherent(N, alpha), basis(N, 0)) + tensor(basis(N, 0), coherent(N, -alpha))).unit() 
-rho = (ket2dm(tensor(coherent(N, alpha), basis(N, 0))) + ket2dm(tensor(basis(N, 0), coherent(N, -alpha)))).unit() 
-```
-
-In this case, we use the `TwoModeQuadratureCorrelation` class.
+### Harmonic Oscillator Wave Function
 
-```python
-two_mode_psi = TwoModeQuadratureCorrelation(psi)
-two_mode_psi.visualize()
+Here, we display the spatial distribution of the wave function for the harmonic oscillator (n=0 to n=7) with the `HarmonicOscillatorWaveFunction()` class.
 
-two_mode_rho = TwoModeQuadratureCorrelation(rho)
-two_mode_rho.visualize()
-```
+Optionally, define a range of values for each coordinate with the parameter called `extent`. Also, define a number of data points inside the given range with the optional parameter called `steps`. From this information, the distribution is generated and can be visualized with the `.visualize()` method.
 
-### Harmonic Oscillator Wave Function
-Here, we display the spatial distribution of the wave function for the harmonic oscillator (n=0 to n=7) with the `HarmonicOscillatorWaveFunction` class.
+It is also possible to calculate, along a given axis, the marginal distribution with `.marginal()` or the projection distribution with `.project()`.
 
 ```python
 M=8
+N=20
 
 fig, ax = plt.subplots(M, 1, figsize=(10, 12), sharex=True)
 
@@ -112,10 +48,11 @@ for n in range(M):
 
 ### Harmonic Oscillator Probability Function
 
-The class `HarmonicOscillatorProbabilityFunction` is the squared magnitude of the data that would normally be in `HarmonicOscillatorWaveFunction`. We use the same example as before.
+The class `HarmonicOscillatorProbabilityFunction()` is the squared magnitude of the data that would normally be in `HarmonicOscillatorWaveFunction()`. We use the same example as before.
 
 ```python
 M=8
+N=20
 
 fig, ax = plt.subplots(M, 1, figsize=(10, 12), sharex=True)