Update README.md

README.md

@@ -108,23 +108,90 @@ This curated dataset is continuously expanded as new [submissions](https://huggi
 ## How to Use
 
 ```bash
-pip install datasets
+# LOAD THE DATA SET
+# Install required library (run once)
+# pip install datasets pandas matplotlib scikit-learn
 
+from datasets import load_dataset
+import pandas as pd
 
+# Load dataset from Hugging Face Hub
+ds = load_dataset("OPheno/Weed-phenology")
 
-from datasets import load_dataset
+# Convert to pandas DataFrame
+df = ds["train"].to_pandas()
+
+print("Dataset loaded with", len(df), "rows")
+df.head(
+# Do you want to filter values?
+
+# Filter for a specific weed species
+echcg = df[df["weed"] == "ECHCG"]
+
+# Filter for emergence measurements only (raw counts)
+emergence = echcg[echcg["type_of_value"].isin([
+    "plant_density",
+    "newly_emerged_plants"
+])]
+
+emergence.head()
+
+print("Summary of selected values:")
+print(emergence[["date", "value", "type_of_value", "measurement_unit"]])
+
+# Plot the Emergence Curve
+
+import matplotlib.pyplot as plt
+
+# Ensure date is datetime type
+emergence["date"] = pd.to_datetime(emergence["date"])
+
+# Sort by date
+emergence = emergence.sort_values("date")
+
+plt.figure(figsize=(8, 4))
+plt.plot(emergence["date"], emergence["cum_emergence_percentage"], marker="o")
+
+plt.xlabel("Date")
+plt.ylabel("Cumulative emergence (%)")
+plt.title("Weed Emergence Curve")
+plt.xticks(rotation=45)
+plt.tight_layout()
+plt.show()
+
+# Create a model
+
+from sklearn.linear_model import LinearRegression
+import numpy as np
+
+# Prepare data
+X = emergence["days_after_start"].values.reshape(-1, 1)
+y = emergence["cum_emergence_percentage"].values.reshape(-1, 1)
+
+# Fit linear regression
+model = LinearRegression()
+model.fit(X, y)
+
+# Print model parameters
+print("Intercept:", model.intercept_[0])
+print("Slope:", model.coef_[0][0])
 
-# Load the dataset (replace with your actual dataset ID)
-ds = load_dataset("your-username/opheno")
+# Predict curve for plotting
+X_pred = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)
+y_pred = model.predict(X_pred)
 
-# Access the main data split
-# TODO: tba
+# Plot
+plt.figure(figsize=(8, 4))
+plt.scatter(X, y, label="Observed", color="blue")
+plt.plot(X_pred, y_pred, label="Linear model", color="red")
 
-# Example: Filter for BBCH observations and convert value to integer
-# TODO: tba
+plt.xlabel("Days after start")
+plt.ylabel("Cumulative emergence (%)")
+plt.title("Linear Model of Weed Emergence")
+plt.legend()
+plt.tight_layout()
+plt.show()
 
-# Example: Filter for Plant Density and convert value to float
-# TODO: tba
 ```
 For more details on exploring and manipulating data with the datasets library (e.g., advanced filtering, mapping, shuffling), please refer to the official Hugging Face Datasets documentation.