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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 (tab-delimited) 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>
<!-- Plot configuration -->
<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>
<!-- Trendline options -->
<div class="row mt-3">
<div class="col-md-3">
<div class="form-check mb-3">
<input class="form-check-input" type="checkbox" value="" id="addTrendline" />
<label class="form-check-label" for="addTrendline">Add Trendline</label>
</div>
</div>
<div class="col-md-3">
<div class="mb-3">
<label for="trendlineSelect" class="form-label">Trendline Type:</label>
<select id="trendlineSelect" class="form-select">
<option value="none" selected>None</option>
<option value="linear">Linear</option>
<option value="exponential">Exponential</option>
<option value="logarithmic">Logarithmic</option>
<option value="polynomial">Polynomial</option>
<option value="power">Power</option>
</select>
</div>
</div>
<div class="col-md-3">
<div class="mb-3" id="polynomialOrderContainer" style="display: none;">
<label for="polynomialOrder" class="form-label">Polynomial Order:</label>
<input type="number" id="polynomialOrder" class="form-control" value="2" min="1"/>
</div>
</div>
</div>
<!-- Action buttons -->
<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>
<!-- Plot output -->
<div id="plotCanvas" class="mt-4"></div>
</div>
<script>
// Initialize Pyodide on page load
window.onload = async () => {
await initPyodide();
// Event listener to show/hide polynomial order
document.getElementById('trendlineSelect').addEventListener('change', function () {
const trendlineType = this.value;
const orderContainer = document.getElementById('polynomialOrderContainer');
if (trendlineType === 'polynomial') {
orderContainer.style.display = 'block';
} else {
orderContainer.style.display = 'none';
}
});
};
// Load Pyodide and initialize Python environment
async function initPyodide() {
const pyodide = await loadPyodide();
await pyodide.loadPackage('micropip');
await pyodide.runPythonAsync(`
import micropip
# Install the needed packages
await micropip.install('matplotlib')
await micropip.install('pandas')
await micropip.install('numpy')
`);
window.pyodide = pyodide;
}
// Function to populate dropdowns based on header row
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 data from textarea, parse headers
function loadData() {
const data = document.getElementById('dataInput').value;
const rows = data.split('\n');
const headers = rows[0].split('\t');
populateDropdowns(headers);
return data;
}
// Helper function that returns a Python code snippet for fitting a trendline
// based on user’s selection in JavaScript.
function getTrendlineCode(xColumn, yColumn, trendlineType, polynomialOrder) {
// Return Python code as a string that performs the chosen regression
// and plots a trendline on the active subplot (`ax`).
//
// We'll assume x and y are numeric. You may want to handle exceptions
// for non-positive X when using log/power, or handle missing data, etc.
// This snippet is appended directly inside the Python code block.
return `
import numpy as np
# If user wants a trendline, perform the chosen regression:
if "${trendlineType}" != "none":
# Extract numeric arrays for fitting
x_vals = df["${xColumn}"].to_numpy(dtype=float)
y_vals = df["${yColumn}"].to_numpy(dtype=float)
# Sort by x for a cleaner line plot
sort_idx = np.argsort(x_vals)
x_sorted = x_vals[sort_idx]
y_sorted = y_vals[sort_idx]
if "${trendlineType}" == "linear":
# y = a*x + b
p = np.polyfit(x_sorted, y_sorted, 1)
y_fit = np.polyval(p, x_sorted)
elif "${trendlineType}" == "polynomial":
# y = a_0 + a_1*x + a_2*x^2 + ...
order = int("${polynomialOrder}")
p = np.polyfit(x_sorted, y_sorted, order)
y_fit = np.polyval(p, x_sorted)
elif "${trendlineType}" == "exponential":
# y = a * e^(b*x)
# ln(y) = ln(a) + b*x => do linear fit
log_y = np.log(y_sorted)
p = np.polyfit(x_sorted, log_y, 1)
# p[0] = b, p[1] = ln(a)
y_fit = np.exp(p[1]) * np.exp(p[0] * x_sorted)
elif "${trendlineType}" == "logarithmic":
# y = a + b * ln(x)
# We do linear fit with x' = ln(x)
log_x = np.log(x_sorted)
p = np.polyfit(log_x, y_sorted, 1)
# p[0] = b, p[1] = a
y_fit = p[1] + p[0] * log_x
elif "${trendlineType}" == "power":
# y = a * x^b
# ln(y) = ln(a) + b * ln(x)
log_x = np.log(x_sorted)
log_y = np.log(y_sorted)
p = np.polyfit(log_x, log_y, 1)
# p[0] = b, p[1] = ln(a)
y_fit = np.exp(p[1]) * (x_sorted**p[0])
# Plot the fitted line
ax.plot(x_sorted, y_fit, label="Trendline", color="red")
`;
}
// Function to plot scatter plot
async function plotScatterPlot() {
const data = document.getElementById('dataInput').value;
const xColumn = document.getElementById('xSelect').value;
const yColumn = document.getElementById('ySelect').value;
const errorColumn = document.getElementById('errorSelect').value;
const xLabel = document.getElementById('xAxisLabel').value;
const yLabel = document.getElementById('yAxisLabel').value;
const dataSeriesLabel = document.getElementById('dataSeriesLabel').value;
const plotTitle = document.getElementById('plotTitle').value;
// Trendline options
const addTrendline = document.getElementById('addTrendline').checked;
const trendlineType = document.getElementById('trendlineSelect').value;
const polynomialOrder = document.getElementById('polynomialOrder').value;
// Construct Python code for the optional trendline
const trendlineCode = addTrendline
? getTrendlineCode(xColumn, yColumn, trendlineType, polynomialOrder)
: '';
try {
// Run Python code in Pyodide
await pyodide.runPythonAsync(`
import pandas as pd
from io import StringIO
import matplotlib.pyplot as plt
import base64
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
csv_data = """${data}"""
df = pd.read_csv(StringIO(csv_data), delimiter='\\t')
# Aggregate means for the chosen columns, then reset index
df = df.groupby("${xColumn}").agg({
"${yColumn}": "mean",
"${errorColumn}": "mean"
}).reset_index()
# Sort data by x for correct plotting order
df = df.sort_values("${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='${dataSeriesLabel}'
)
ax.set_title("${plotTitle}")
ax.set_xlabel("${xLabel}")
ax.set_ylabel("${yLabel}")
ax.grid(False)
ax.legend()
# Add trendline code if requested
${trendlineCode}
ax.legend()
# Save plot to PNG
canvas = FigureCanvasAgg(fig)
import io
buf = io.BytesIO()
canvas.print_png(buf)
buf.seek(0)
img_data = base64.b64encode(buf.getvalue()).decode('utf-8')
`);
// Retrieve and display the plot
const imgData = await pyodide.globals.get('img_data');
document.getElementById('plotCanvas').innerHTML = `<img src="data:image/png;base64,${imgData}" />`;
} catch (error) {
console.error('Error in plotScatterPlot:', error);
alert('An error occurred while plotting the data. Check the console for details.');
}
}
// Function to plot bar plot
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;
// Trendline options
const addTrendline = document.getElementById('addTrendline').checked;
const trendlineType = document.getElementById('trendlineSelect').value;
const polynomialOrder = document.getElementById('polynomialOrder').value;
// Construct Python code for the optional trendline (on top of bar plot)
const trendlineCode = addTrendline
? getTrendlineCode(xColumn, yColumn, trendlineType, polynomialOrder)
: '';
try {
// Run Python code in Pyodide
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
import numpy as np
csv_data = """${data}"""
df = pd.read_csv(StringIO(csv_data), delimiter='\\t')
df = df.groupby("${xColumn}").agg({
"${yColumn}": "mean",
"${errorColumn}": "mean"
}).reset_index()
# Sort data by x
df = df.sort_values("${xColumn}")
x = df["${xColumn}"].astype(str)
y = df["${yColumn}"]
yerr = df["${errorColumn}"] if "${errorColumn}" in df.columns else None
fig = Figure(figsize=(10, 6))
ax = fig.add_subplot(111)
bar_positions = range(len(x))
ax.bar(
x, y, yerr=yerr,
capsize=10, color='gray', alpha=0.5,
error_kw={'elinewidth': 2, 'capsize':5,'capthick':2},
label='${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()
# -- Add the trendline. Here we re-merge x and y as numeric for fitting. --
# We'll re-use the original numeric column from the group-by (not the string version).
# A quick approach: we can do it from df again, but let's be consistent with the group order.
${trendlineCode}
ax.legend()
# Save plot to PNG
canvas = FigureCanvasAgg(fig)
import io
buf = io.BytesIO()
canvas.print_png(buf)
buf.seek(0)
img_data = base64.b64encode(buf.getvalue()).decode('utf-8')
`);
// Retrieve and display the plot
const imgData = await pyodide.globals.get('img_data');
document.getElementById('plotCanvas').innerHTML = `<img src="data:image/png;base64,${imgData}" />`;
} catch (error) {
console.error('Error in plotBarPlot:', error);
alert('An error occurred while plotting the data. Check the console for details.');
}
}
</script>
</body>
</html>
|
