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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Excel Data Plotter with Pyodide</title>
<script src="https://cdn.jsdelivr.net/pyodide/v0.26.2/full/pyodide.js"></script>
<link href="https://bootswatch.com/5/morph/bootstrap.min.css" rel="stylesheet">
<script src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/js/bootstrap.bundle.min.js"></script>
</head>
<body>
<div class="container mt-5">
<h1 class="mb-4">Plotter for Patent</h1>
<div class="row">
<div class="col-md-6">
<div class="mb-3">
<textarea id="dataInput" class="form-control" rows="10" placeholder="Paste Excel data here"></textarea>
</div>
<button onclick="loadData()" class="btn btn-primary mb-3">Load Data</button>
</div>
<div class="col-md-6">
<div class="mb-3">
<label for="xSelect" class="form-label">Select X-axis:</label>
<select id="xSelect" class="form-select"></select>
</div>
<div class="mb-3">
<label for="ySelect" class="form-label">Select Y-axis:</label>
<select id="ySelect" class="form-select"></select>
</div>
<div class="mb-3">
<label for="errorSelect" class="form-label">Select Error column:</label>
<select id="errorSelect" class="form-select"></select>
</div>
</div>
</div>
<div class="row mt-3">
<div class="col-md-3 mb-3">
<label for="plotTitle" class="form-label">Plot Title:</label>
<input type="text" id="plotTitle" class="form-control" placeholder="Enter plot title">
</div>
<div class="col-md-3 mb-3">
<label for="xAxisLabel" class="form-label">X-axis Label:</label>
<input type="text" id="xAxisLabel" class="form-control" placeholder="Enter X-axis label">
</div>
<div class="col-md-3 mb-3">
<label for="yAxisLabel" class="form-label">Y-axis Label:</label>
<input type="text" id="yAxisLabel" class="form-control" placeholder="Enter Y-axis label">
</div>
<div class="col-md-3 mb-3">
<label for="dataSeriesLabel" class="form-label">Data Series Label:</label>
<input type="text" id="dataSeriesLabel" class="form-control" placeholder="Enter data series label">
</div>
</div>
<div class="row mt-3">
<div class="col-md-6">
<button onclick="plotScatterPlot()" class="btn btn-success me-2">Plot Scatter Plot</button>
<button onclick="plotBarPlot()" class="btn btn-info">Plot Bar Plot</button>
</div>
</div>
<div id="plotCanvas" class="mt-4"></div>
</div>
<script>
// Load Pyodide and initialize Python environment
async function initPyodide() {
const pyodide = await loadPyodide();
await pyodide.loadPackage('micropip');
await pyodide.runPythonAsync(`
import micropip
await micropip.install('matplotlib')
await micropip.install('pandas')
`);
window.pyodide = pyodide;
}
// Initialize Pyodide on page load
window.onload = async () => {
await initPyodide();
}
// Function to populate dropdowns based on CSV headers
function populateDropdowns(headers) {
const xSelect = document.getElementById('xSelect');
const ySelect = document.getElementById('ySelect');
const errorSelect = document.getElementById('errorSelect');
// Clear previous options
xSelect.innerHTML = '';
ySelect.innerHTML = '';
errorSelect.innerHTML = '';
headers.forEach(header => {
const option = document.createElement('option');
option.value = header;
option.textContent = header;
xSelect.appendChild(option.cloneNode(true));
ySelect.appendChild(option.cloneNode(true));
errorSelect.appendChild(option.cloneNode(true));
});
}
// Function to load and parse CSV data
function loadData() {
const data = document.getElementById('dataInput').value;
const rows = data.split('\n');
const headers = rows[0].split('\t');
populateDropdowns(headers);
return data;
}
async function plotScatterPlot() {
const data = document.getElementById('dataInput').value;
const xColumn = document.getElementById('xSelect').value;
const yColumn = document.getElementById('ySelect').value;
const errorColumn = document.getElementById('errorSelect').value;
const xLabel = document.getElementById('xAxisLabel').value;
const yLabel = document.getElementById('yAxisLabel').value;
const dataSeriesLabel = document.getElementById('dataSeriesLabel').value;
const plotTitle = document.getElementById('plotTitle').value;
try {
// Send the data to Pyodide for processing
await pyodide.runPythonAsync(`
import pandas as pd
from io import StringIO
import matplotlib.pyplot as plt
import base64
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
csv_data = '''${data}'''
df = pd.read_csv(StringIO(csv_data), delimiter='\t')
df = df.groupby('${xColumn}').agg({ '${yColumn}': 'mean', '${errorColumn}': 'std' }).reset_index()
x = df['${xColumn}']
y = df['${yColumn}']
yerr = df['${errorColumn}'] if '${errorColumn}' in df.columns else None
# Sort data by x to ensure correct plotting order
df = df.sort_values('${xColumn}')
x = df['${xColumn}']
y = df['${yColumn}']
yerr = df['${errorColumn}'] if '${errorColumn}' in df.columns else None
fig = Figure(figsize=(10, 6))
ax = fig.add_subplot(111)
# Plot data with error bars
ax.errorbar(x, y, yerr=yerr, fmt='o-', capsize=5, capthick=2, color='grey', ecolor='black', elinewidth=2, markerfacecolor='black', markersize=10, label='${dataSeriesLabel}')
# Add a line connecting the data points
#ax.plot(x, y, color='black', linestyle='-', marker='o')
ax.set_title('${plotTitle}')
ax.set_xlabel('${xLabel}')
ax.set_ylabel('${yLabel}')
ax.grid(False)
ax.legend()
# Save plot to PNG
canvas = FigureCanvasAgg(fig)
import io
buf = io.BytesIO()
canvas.print_png(buf)
buf.seek(0)
img_data = base64.b64encode(buf.getvalue()).decode('utf-8')`);
// Retrieve and display the plot
const imgData = await pyodide.globals.get('img_data');
document.getElementById('plotCanvas').innerHTML = `<img src="data:image/png;base64,${imgData}" />`;
} catch (error) {
console.error('Error in plotScatterPlot:', error);
alert('An error occurred while plotting the data. Please check the console for details.');
}
}
async function plotBarPlot() {
const data = document.getElementById('dataInput').value;
const xColumn = document.getElementById('xSelect').value;
const yColumn = document.getElementById('ySelect').value;
const errorColumn = document.getElementById('errorSelect').value;
const xLabel = document.getElementById('xAxisLabel').value;
const yLabel = document.getElementById('yAxisLabel').value;
const dataSeriesLabel = document.getElementById('dataSeriesLabel').value;
const plotTitle = document.getElementById('plotTitle').value;
try {
// Send the data to Pyodide for processing
await pyodide.runPythonAsync(`
import pandas as pd
from io import StringIO
import matplotlib.pyplot as plt
import base64
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
csv_data = '''${data}'''
df = pd.read_csv(StringIO(csv_data), delimiter='\t')
df = df.groupby('${xColumn}').agg({ '${yColumn}': 'mean', '${errorColumn}': 'std' }).reset_index()
x = df['${xColumn}']
y = df['${yColumn}']
yerr = df['${errorColumn}'] if '${errorColumn}' in df.columns else None
# Sort data by x to ensure correct plotting order
df = df.sort_values('${xColumn}')
x = df['${xColumn}'].astype(str)
y = df['${yColumn}']
yerr = df['${errorColumn}'] if '${errorColumn}' in df.columns else None
fig = Figure(figsize=(10, 6))
ax = fig.add_subplot(111)
# Plot data with error bars
bar_positions = range(len(x))
ax.bar(x, y, yerr=yerr,capsize=10, color='gray', alpha=0.5, error_kw={'elinewidth': 2, 'capsize':5,'capthick':2}, label='${dataSeriesLabel}')
ax.set_xticks(bar_positions)
ax.set_xticklabels(x)
ax.set_title('${plotTitle}')
ax.set_xlabel('${xLabel}')
ax.set_ylabel('${yLabel}')
ax.grid(False)
ax.legend()
ax.tick_params(axis='x', rotation=0)
fig.tight_layout()
# Save plot to PNG
canvas = FigureCanvasAgg(fig)
import io
buf = io.BytesIO()
canvas.print_png(buf)
buf.seek(0)
img_data = base64.b64encode(buf.getvalue()).decode('utf-8')`);
// Retrieve and display the plot
const imgData = await pyodide.globals.get('img_data');
document.getElementById('plotCanvas').innerHTML = `<img src="data:image/png;base64,${imgData}" />`;
} catch (error) {
console.error('Error in plotScatterPlot:', error);
alert('An error occurred while plotting the data. Please check the console for details.');
}
}
</script>
</script>
</body>
</html>
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