File:Sweden ufo data sub year 1952-1968 2.png

Summary

Description
English: Ufo data of Sweden 1952-1968
Date
Source Own work
Author

ChatGPT 3 and 4o; Source of data:
K. Göstä Rehn
UFO! uusinta tietoa lentävistä lautasista
K Gösta Rehn 1969
Otava Helsinki 1970

p. 96-97, original sourfe FOA Archives and HÅkan Malmqvist


Python3 source code

PNG development

InfoField
 This plot was created with Matplotlib.
Category:PNG created with Matplotlib code#Sweden%20ufo%20data%20sub%20year%201952-1968%202.png

Source code

Python code

import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate

times1=np.array([1952.02196382429,
1952.08785529716,
1952.15374677003,
1952.26356589147,
1952.35142118863,
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1968.07751937985,
1968.20930232558,
1968.29715762274,
1968.34108527132,
1968.47286821705,
1968.97803617571])

cases1=np.array([
4.10810810810811,
2.05405405405405,
1.08108108108108,
2,
1.08108108108108,
9.13513513513514,
12.0540540540541,
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0.972972972972972,
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1.78378378378378,
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6.05405405405405,
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2.81081081081081,
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5.02702702702702,
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1.72972972972973,
0.648648648648646,
4.91891891891892,
2.86486486486486,
2.75675675675676,
-0.594594594594597,
1.67567567567567,
0.810810810810811,
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10.2162162162162,
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-0.486486486486488,
-0.378378378378382,
0.702702702702702,
-0.324324324324323,
7.02702702702703,
2.97297297297297,
4.91891891891892,
3.89189189189189,
2.81081081081081,
0.864864864864863,
3.94594594594594,
2.86486486486486,
1.78378378378378,
2.81081081081081,
-0.324324324324323,
-0.27027027027027])

cases1=np.where(cases1<0,0, cases1)
cases1=np.round(cases1, 0)

# Sort times1 and reorder cases1 accordingly to ensure times are in increasing order
sorted_indices = np.argsort(times1)
times1_sorted = times1[sorted_indices]
cases1_sorted = cases1[sorted_indices]

# Remove duplicate times and corresponding cases
unique_times, unique_indices = np.unique(times1_sorted, return_index=True)
unique_cases = cases1_sorted[unique_indices]

# Use smooth interpolation to create a continuous curve that passes through all points
# Using PCHIP (Piecewise Cubic Hermite Interpolating Polynomial) for smooth but precise interpolation
interpolator = interpolate.PchipInterpolator(unique_times, unique_cases)

# Create fine-grained time values for smoother plotting
times_fine = np.linspace(unique_times.min(), unique_times.max(), 1000)
cases_fine = interpolator(times_fine)

# Plot the data with labels and adjust the x-axis to show full years
plt.figure(figsize=(10, 6))
plt.plot(times_fine, cases_fine, label='Interpolated Cases', color='b', lw=3)
#plt.scatter(unique_times, unique_cases, color='r', zorder=5, label='Original Data')

# Set axis labels
plt.xlabel("Years")
plt.ylabel("Cases")

# Set x-axis ticks to show only integer years
plt.xticks(np.arange(int(unique_times.min()), int(unique_times.max()) + 1, 1))

# Set title
plt.title("Sweden FOA UFO Data")

# Add grid and legend
plt.grid(True)
plt.legend()

# Display the plot
plt.show()

Licensing

I, the copyright holder of this work, hereby publish it under the following license:
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Category:CC-Zero#Sweden%20ufo%20data%20sub%20year%201952-1968%202.pngCategory:Self-published work
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Category:PD-algorithm#Sweden%20ufo%20data%20sub%20year%201952-1968%202.png Category:Maps related to UFOs Category:AI-generated charts with matplotlib code
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