Spaces:
Runtime error
Runtime error
File size: 3,443 Bytes
e544607 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
import gradio as gr
from gradio.components import Markdown, Textbox, Button
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVR
from sklearn.pipeline import make_pipeline
from sunpy.net import Fido
from sunpy.net import attrs as a
from sunpy.timeseries import TimeSeries
def process_data():
# Define the time range for data retrieval
tstart = "2015-06-21 01:00"
tend = "2015-06-21 23:00"
# Query and fetch GOES XRS data
result_goes15 = Fido.search(a.Time(tstart, tend), a.Instrument("XRS"), a.goes.SatelliteNumber(15), a.Resolution("flx1s"))
files = Fido.fetch(result_goes15)
# Load the data into a TimeSeries
goes_15 = TimeSeries(files, concatenate=True)
# Extract X-ray flux and time data
flux_data = goes_15.quantity("xrsb").value
time_data = goes_15.time.datetime
# Create a feature matrix with time data (as numerical values)
X = np.array([(t - time_data[0]).total_seconds() for t in time_data]).reshape(-1, 1)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, flux_data, test_size=0.2, random_state=42)
# Train a linear regression model
linear_model = LinearRegression()
linear_model.fit(X_train, y_train)
# Train a quadratic regression model
quadratic_model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())
quadratic_model.fit(X_train, y_train)
# Train a cubic regression model
cubic_model = make_pipeline(PolynomialFeatures(degree=3), LinearRegression())
cubic_model.fit(X_train, y_train)
# Train a support vector regression (SVR) model
svr_model = SVR(kernel='linear')
svr_model.fit(X_train, y_train)
# Make predictions using all models
y_pred_linear = linear_model.predict(X_test)
y_pred_quadratic = quadratic_model.predict(X_test)
y_pred_cubic = cubic_model.predict(X_test)
y_pred_svr = svr_model.predict(X_test)
# Plot the actual and predicted data from all models
plt.figure(figsize=(12, 6))
plt.scatter(X_test, y_test, color='blue', label='Actual Data')
plt.plot(X_test, y_pred_linear, color='red', linewidth=2, label='Linear Prediction')
plt.plot(X_test, y_pred_quadratic, color='green', linewidth=2, label='Quadratic Prediction')
plt.plot(X_test, y_pred_cubic, color='orange', linewidth=2, label='Cubic Prediction')
plt.plot(X_test, y_pred_svr, color='purple', linewidth=2, label='SVR Prediction')
# Include solar flux data as an additional line in the plot
plt.plot(X, flux_data, color='cyan', linestyle='dashed', label='Solar Flux')
plt.title('GOES XRS Space Weather Forecast')
plt.xlabel('Time (seconds since start)')
plt.ylabel('X-ray Flux / Solar Flux')
plt.legend()
# Save the image
plt.savefig('space_weather_forecast.png')
# Display the plot
#plt.show()
fig = plt.figure()
process_data()
with gr.Blocks(title="Space Weather Forecast", analytics_enabled=False) as spaceml:
gr.Markdown("# Space Weather Forecast")
gr.Markdown("Welcome to the Space Weather Forecast!")
with gr.Row():
with gr.Column(scale=1):
gradio_plot = gr.Image('space_weather_forecast.png')
spaceml.queue().launch(show_api=True, share=True)
|