5 frames

/usr/local/lib/python3.10/dist-packages/matplotlib/contour.py in _check_xyz(self, x, y, z, kwargs)
**1494**         elif x.ndim == 2:
**1495**             if x.shape != z.shape:
-> 1496                 raise TypeError(
**1497**                     f"Shapes of x {x.shape} and z {z.shape} do not match")
**1498**             if y.shape != z.shape:

TypeError: Shapes of x (200, 200) and z (650, 650) do not match

why is that?

The error message you're encountering is due to a mismatch in the shapes (or dimensions) of the `x` and `z` arrays when trying to create a contour plot using `matplotlib`.

### Explanation:

In a contour plot, `x`, `y`, and `z` should all have compatible dimensions:
- `x` and `y` represent the grid points (coordinates), and `z` represents the values of the function you're plotting at those grid points.
- Typically, `x`, `y`, and `z` need to have the same shape because they define the grid on which the contour levels are drawn.

In your case:
- `x` has a shape of `(200, 200)`.
- `z` has a shape of `(650, 650)`.

This mismatch means that `x` and `z` define grids of different sizes. Since `x` defines the grid of points for plotting the contours and `z` defines the function values at those points, they need to correspond in size. If they don’t, `matplotlib` cannot match the grid points to the function values, hence the error.

### Possible Reasons for This Issue:
1. **Misaligned Data Arrays**: You may have generated the `x`, `y`, and `z` arrays from different sources or using different resolutions, which led to the mismatch.
2. **Improper Resizing**: At some point, you might have reshaped or resized one of the arrays (`x`, `y`, or `z`) incorrectly, causing them to no longer correspond.

### How to Fix It:
- Ensure that `x`, `y`, and `z` have the same shape or are compatible. If you are generating a mesh grid using `numpy.meshgrid`, verify that the shapes match.
- If your `z` data is on a finer or coarser grid than `x` and `y`, you may need to interpolate the data to the correct shape.

Here’s an example of ensuring the shapes match:

```python
import numpy as np
import matplotlib.pyplot as plt

# Create a meshgrid
x = np.linspace(-5, 5, 200)
y = np.linspace(-5, 5, 200)
X, Y = np.meshgrid(x, y)

# Create z based on the meshgrid (must have the same shape as X and Y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Now the shapes should match: X.shape == Y.shape == Z.shape
plt.contour(X, Y, Z)
plt.show()
```

If the shape mismatch persists, check the data preparation stage to ensure that your `x`, `y`, and `z` arrays are correctly aligned.

Do you want more details, or do you have any questions?

Here are 5 related questions you might explore:
1. How can you interpolate or resize a grid of data in Python to match dimensions?
2. What are alternative ways to visualize mismatched grid data?
3. What is the impact of different grid resolutions on contour plot quality?
4. How can `numpy.meshgrid` help ensure consistent grid shapes?
5. Are there other types of plots that can handle mismatched grids?

**Tip**: When working with contour plots, always verify the shapes of your data arrays before plotting to avoid runtime errors.

Learn about fixing shape mismatches in contour plots using Matplotlib. This detailed guide explains the causes of errors like 'Shapes of x (200, 200) and z (650, 650) do not match' and provides solutions using Python. Ideal for data scientists and Python programmers.

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