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Created August 28, 2023 14:07
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Concatenated OpenAI code for Adafruit's expected 1000 certification date
import pandas as pd
from sklearn.linear_model import LinearRegression
import numpy as np
import json
# Load the JSON data from the provided file
with open("/mnt/data/oshwa_data.json", "r") as file:
oshwa_data = json.load(file)
# Display the type of the root object and a brief summary of its content
root_type = type(oshwa_data)
summary = {}
if isinstance(oshwa_data, dict):
for key, value in oshwa_data.items():
summary[key] = type(value)
root_type, summary
# Extract the certification dates for Adafruit
adafruit_dates = [datetime.datetime.fromisoformat(entry['certificationDate'].split('T')[0])
for entry in oshwa_data
if entry['responsibleParty'] == 'Adafruit Industries, LLC' and 'certificationDate' in entry]
# Sort the dates and calculate the cumulative count
adafruit_cumulative_counts = np.arange(1, len(adafruit_dates) + 1)
# Convert dates to ordinal values for regression
adafruit_dates_ordinal = [date.toordinal() for date in adafruit_dates]
# Plot
plt.figure(figsize=(14, 8))
plt.plot(adafruit_dates, adafruit_cumulative_counts, label="Adafruit", marker='o')
plt.axhline(y=1000, color='r', linestyle='--', label="1,000 Certifications Target")
plt.ylabel("Cumulative Certifications")
plt.title("Cumulative Certifications Over Time (Adafruit)")
plt.grid(True, which='both', linestyle='--', linewidth=0.5)
# Predict certification counts for future dates
future_dates = pd.date_range(start=adafruit_dates[-1], periods=365*5, freq='D') # Predicting for the next 5 years
future_dates_ordinal = [date.toordinal() for date in future_dates]
predicted_counts = model.predict(np.array(future_dates_ordinal).reshape(-1, 1))
# Find the date when the predicted count reaches or exceeds 1,000
predicted_date_index = np.where(predicted_counts >= 1000)[0][0]
predicted_date = future_dates[predicted_date_index]
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