### Install Visualization Packages

Source: https://github.com/meteoswiss/fast-barnes-py/blob/main/doc/ReproduceResults_Doc.md

Install additional packages required for visualizing figures, such as matplotlib, basemap, and Pillow.

```bash
pip install -r requirements.txt
```

--------------------------------

### Install fast-barnes-py

Source: https://github.com/meteoswiss/fast-barnes-py/blob/main/doc/ReproduceResults_Doc.md

Install the fast-barnes-py package and its core dependencies (numpy, scipy, numba).

```bash
pip install .
```

--------------------------------

### Execute Figure Script

Source: https://github.com/meteoswiss/fast-barnes-py/blob/main/doc/ReproduceResults_Doc.md

Navigate to the 'demo' directory and execute a Python script like 'figure1.py' to generate plots.

```bash
cd demo

python figure1.py
```

--------------------------------

### 2D Barnes Interpolation with Sample Data

Source: https://github.com/meteoswiss/fast-barnes-py/blob/main/doc/MinimalWorkingExamples_Doc.md

Demonstrates Barnes interpolation for 2D data using sample mean sea level pressure values. Requires numpy for data handling and fastbarnes for interpolation. The first execution may be slower due to JIT compilation.

```python
import numpy as np

# definition of 50 sample points with longitude, latitude and mean sea level pressure QFF
input_data = np.asarray([
    [-3.73,56.33,995.1], [2.64,47.05,1012.5], [-8.40,47.50,1011.3], [2.94,54.33,1006.0],
    [-2.90,49.90,1006.3], [-8.98,53.72,1002.1], [1.20,58.60,1002.6], [1.60,50.73,1009.1],
    [-7.38,57.36,997.7], [-1.25,53.01,1000.4], [-4.74,52.79,998.4], [-0.61,47.48,1013.0],
    [-6.10,50.10,1004.3], [-6.46,54.87,996.4], [-3.22,47.29,1012.8], [-1.60,55.42,996.6],
    [2.30,56.60,1004.5], [1.12,52.95,1003.6], [-0.90,57.80,999.9], [-7.90,51.40,1002.6],
    [-0.70,50.10,1007.5], [2.53,49.02,1010.8], [-5.06,48.47,1008.5], [-3.10,53.60,997.5],
    [-5.63,57.86,997.8], [-6.90,52.85,1000.9], [-4.15,51.09,1002.6], [-1.99,51.50,1002.7],
    [1.21,47.68,1011.7], [-5.70,56.30,995.5], [-1.98,53.13,998.5], [1.09,49.93,1009.0],
    [1.72,58.42,1002.9], [-6.30,52.30,999.4], [0.70,57.70,1001.9], [-3.50,53.60,995.9],
    [1.38,48.06,1011.6], [-4.37,51.71,1001.1], [-3.09,58.45,998.5], [2.00,56.40,1003.9],
    [1.90,57.00,1003.3], [0.45,51.90,1004.9], [-8.25,51.80,1002.5], [-1.87,53.81,997.4],
    [-2.38,55.71,995.1], [-4.01,54.80,992.1], [0.88,53.37,1002.6], [-1.69,51.86,1002.1],
    [-4.57,52.14,999.6], [-0.20,58.40,1001.1],
])

lon_lat_data = input_data[:, 0:2]
qff_values = input_data[:, 2]

```

```python
# definition of a 12° x 12° grid starting at 9°W / 47°N
resolution = 32.0
step = 1.0 / resolution
x0 = np.asarray([-9.0, 47.0], dtype=np.float64)
size = (int(12.0 / step), int(12.0 / step))

# calculate Barnes interpolation
from fastbarnes import interpolation
sigma = 1.0
field = interpolation.barnes(lon_lat_data, qff_values, sigma, x0, step, size)

```

```python
# draw graphic with labeled contours and scattered sample points
import matplotlib.pyplot as plt
plt.figure(figsize=(5, 5))
plt.margins(x=0, y=0)

gridX = np.arange(x0[0], x0[0]+size[1]*step, step)
gridY = np.arange(x0[1], x0[1]+size[0]*step, step)
levels = np.arange(976, 1026, 2)
cs = plt.contour(gridX, gridY, field, levels)
plt.clabel(cs, levels[::2], fmt='%d', fontsize=9)

plt.scatter(lon_lat_data[:, 0], lon_lat_data[:, 1], color='red', s=20, marker='.')

plt.show()

```

--------------------------------

### Prepare Input Data for Barnes Interpolation

Source: https://github.com/meteoswiss/fast-barnes-py/blob/main/doc/MinimalWorkingExamples_Doc.md

Converts time strings to seconds and prepares input data for Barnes interpolation. Requires numpy.

```python
import numpy as np

# airport "EDDF" from 25.08.2024
input_data = [
    [ "03:15:00", 15.4, 799.0 ], [ "03:35:00", 12.8, 793.0 ], [ "04:51:00", 11.5, 807.0 ],
    [ "04:58:00", 10.7, 805.0 ], [ "05:03:00", 11.9, 807.0 ], [ "05:05:00", 11.3, 805.0 ],
    [ "05:12:00", 12.7, 807.0 ], [ "05:14:00", 11.8, 806.0 ], [ "05:23:00", 12.6, 816.0 ],
    [ "05:24:00", 11.3, 813.0 ], [ "05:32:04", 11.6, 818.0 ], [ "05:33:00", 12.9, 818.0 ],
    [ "05:36:00", 11.8, 815.0 ], [ "05:45:00", 10.9, 822.0 ], [ "05:47:00", 11.5, 823.0 ],
    [ "05:53:00", 10.2, 820.0 ], [ "06:05:37", 10.9, 823.0 ], [ "06:12:00", 10.3, 820.0 ],
    [ "06:19:00",  9.8, 819.0 ], [ "06:20:00", 10.3, 821.0 ], [ "06:21:39", 11.6, 825.0 ],
    [ "06:30:00", 10.9, 823.0 ], [ "06:33:35", 10.7, 824.0 ], [ "06:59:00", 10.8, 829.0 ],
    [ "07:09:00", 10.2, 829.0 ], [ "07:11:00", 10.0, 830.0 ], [ "07:21:00", 10.6, 831.0 ],
    [ "07:29:00",  9.6, 830.0 ], [ "08:18:00", 10.3, 839.0 ], [ "08:20:00", 10.5, 840.0 ],
    [ "08:22:00", 10.6, 839.0 ], [ "08:36:00", 10.4, 838.0 ], [ "08:39:00", 11.2, 842.0 ],
    [ "08:52:00", 11.1, 840.0 ], [ "09:00:16", 11.7, 842.0 ], [ "09:14:00", 10.4, 841.0 ],
    [ "09:24:00", 12.6, 845.0 ], [ "09:39:00", 10.8, 839.0 ], [ "09:57:00", 11.3, 841.0 ],
    [ "10:02:00", 11.4, 841.0 ], [ "10:09:00", 11.0, 840.0 ], [ "10:19:00", 11.7, 841.0 ],
    [ "10:33:00", 11.2, 840.0 ], [ "10:52:00", 11.9, 843.0 ], [ "10:53:00", 12.5, 845.0 ],
    [ "11:01:00", 12.7, 845.0 ], [ "11:03:00", 11.6, 843.0 ], [ "11:33:04", 11.8, 843.0 ],
    [ "11:35:00", 12.3, 844.0 ], [ "11:36:00", 13.1, 845.0 ], [ "12:13:00", 12.3, 844.0 ],
    [ "12:17:00", 13.4, 846.0 ], [ "12:49:00", 12.8, 846.0 ], [ "12:54:00", 12.6, 844.0 ],
    [ "13:08:00", 13.5, 847.0 ], [ "13:22:00", 14.6, 849.0 ], [ "13:30:00", 12.8, 846.0 ],
    [ "13:35:00", 14.1, 848.0 ], [ "13:44:02", 13.8, 848.0 ], [ "13:54:47", 14.1, 850.0 ],
    [ "14:05:00", 13.7, 848.0 ], [ "14:11:00", 13.8, 849.0 ], [ "14:17:00", 13.3, 848.0 ],
    [ "14:24:00", 13.6, 849.0 ], [ "14:31:00", 13.9, 849.0 ], [ "14:39:00", 15.1, 851.0 ],
    [ "14:54:00", 13.5, 847.0 ], [ "15:20:00", 13.9, 848.0 ], [ "15:25:00", 14.9, 850.0 ],
    [ "15:26:00", 14.0, 848.0 ], [ "15:28:00", 14.9, 851.0 ], [ "15:29:00", 14.6, 849.0 ],
    [ "15:44:00", 13.8, 849.0 ], [ "15:46:00", 14.2, 849.0 ], [ "16:19:06", 14.8, 851.0 ],
    [ "16:36:00", 13.9, 847.0 ], [ "16:39:00", 13.9, 848.0 ], [ "16:46:00", 14.2, 849.0 ],
    [ "16:48:00", 13.6, 847.0 ], [ "17:08:00", 14.1, 847.0 ], [ "17:29:00", 14.2, 848.0 ],
    [ "17:51:26", 15.8, 853.0 ], [ "18:04:00", 13.9, 847.0 ], [ "18:15:05", 15.2, 851.0 ],
    [ "18:19:00", 13.9, 847.0 ], [ "18:25:00", 15.4, 851.0 ], [ "18:34:00", 15.1, 849.0 ],
    [ "18:54:00", 13.5, 846.0 ], [ "18:58:00", 15.1, 850.0 ], [ "18:58:23", 13.9, 847.0 ],
    [ "19:16:00", 14.1, 847.0 ], [ "19:32:00", 14.0, 846.0 ], [ "20:14:00", 14.5, 856.0 ],
    [ "20:44:00", 13.3, 853.0 ],
]

def seconds_of_day(timestr):
    """ Translates a time String 'HH:mm:ss' into the equivalent number of seconds since 0:00. """
    arr = timestr.split(':')
    return (int(arr[0])*60 + int(arr[1]))*60 + int(arr[2])

# read data
input_data = [ [seconds_of_day(item[0]), item[1] ] for item in input_data]
input_data = np.asarray(input_data)

# extract time and temperature arrays
time_values = input_data[:, 0]
temp_values = input_data[:, 1]
```

--------------------------------

### Perform Barnes Interpolation

Source: https://github.com/meteoswiss/fast-barnes-py/blob/main/doc/MinimalWorkingExamples_Doc.md

Applies Barnes interpolation to time and temperature data. Requires fastbarnes library.

```python
# definition of a time grid (with unit seconds) starting at 00:00 until 24:00 hPa with
# steps of 30 seconds
step = 30.0
x0 = 0.0
size = int(86400 / step + 1)

# calculate Barnes interpolation
from fastbarnes import interpolation
sigma = 2400.0
temp_arr = interpolation.barnes(time_values, temp_values, sigma, x0, step, size)
```

--------------------------------

### Plot Temperature Interpolation and Scattered Data

Source: https://github.com/meteoswiss/fast-barnes-py/blob/main/doc/MinimalWorkingExamples_Doc.md

This snippet plots interpolated temperature data against time, overlaying scattered input temperature values. It requires matplotlib and numpy. Ensure data arrays (temp_arr, time_values, temp_values) and grid parameters (x0, size, step) are defined before execution.

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

# Assume x0, size, step, temp_arr, time_values, temp_values are defined
# Example placeholder values:
x0 = 7200.0
size = 72000.0
step = 10.0
temp_arr = np.linspace(10, 15, 7200)
time_values = np.linspace(10800, 75600, 50)
temp_values = np.random.uniform(9.5, 15.5, 50)

plt.figure(figsize=(12, 3.5), dpi=150)
plt.grid(visible=True)
plt.xlim(7200.0, 79200.0)
plt.xticks([10800, 21600, 32400, 43200, 54000, 64800, 75600],
    ['03:00', '06:00', '09:00', '12:00', '15:00', '18:00', '21:00'])
plt.ylim(9.3, 15.7)

plt.title("Frankfurt Airport AMDAR Reports from 25.08.2024")
plt.xlabel("Day Time [UTC]")
plt.ylabel("Temperature [°C]\non 925hPa level")

grid = np.arange(x0, x0+size*step, step)
# print(temp_arr) # Uncomment to print temp_arr
# print(grid)     # Uncomment to print grid
plt.plot(grid, temp_arr)
plt.scatter(time_values, temp_values, color='red', s=20, marker='.')
# print(time_values) # Uncomment to print time_values
# print(temp_values) # Uncomment to print temp_values

plt.tight_layout()
plt.show()
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.