diff options
| -rw-r--r-- | templates/patent.html | 526 |
1 files changed, 351 insertions, 175 deletions
diff --git a/templates/patent.html b/templates/patent.html index 3f97761..8bf862e 100644 --- a/templates/patent.html +++ b/templates/patent.html @@ -1,233 +1,409 @@ <!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> + <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 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> - <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> + <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="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 class="mb-3"> + <label for="errorSelect" class="form-label">Select Error column:</label> + <select id="errorSelect" class="form-select"></select> </div> - <div id="plotCanvas" class="mt-4"></div> + </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; - } + <!-- 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> - // Initialize Pyodide on page load - window.onload = async () => { - await initPyodide(); - } + <!-- 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> - // 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)); - }); - } + <!-- 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> - // 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; + <!-- 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; - 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(` + // 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') -df = df.groupby('${xColumn}').agg({ '${yColumn}': 'mean', '${errorColumn}': 'mean' }).reset_index() -x = df['${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 +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}') -# 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('${yLabel}') +ax.errorbar( + x, y, yerr=yerr, + fmt='o', capsize=5, capthick=2, + color='grey', ecolor='black', elinewidth=2, + markerfacecolor='black', markersize=10, + label='${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,${imgData}" />`; - } catch (error) { - console.error('Error in plotScatterPlot:', error); - alert('An error occurred while plotting the data. Please check the console for details.'); - } - } +img_data = base64.b64encode(buf.getvalue()).decode('utf-8') + `); + + // Retrieve and display the plot + const imgData = await pyodide.globals.get('img_data'); + document.getElementById('plotCanvas').innerHTML = `<img src="data:image/png;base64,${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; - 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; + // Construct Python code for the optional trendline (on top of bar plot) + const trendlineCode = addTrendline + ? getTrendlineCode(xColumn, yColumn, trendlineType, polynomialOrder) + : ''; - try { - // Send the data to Pyodide for processing - await pyodide.runPythonAsync(` + 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 = '''${data}''' -df = pd.read_csv(StringIO(csv_data), delimiter='\t') -df = df.groupby('${xColumn}').agg({ '${yColumn}': 'mean', '${errorColumn}': 'mean' }).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 '${errorColumn}' in df.columns else None +import numpy as np + +csv_data = """${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) -# 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='${dataSeriesLabel}') +ax.bar( + x, y, yerr=yerr, + capsize=10, color='gray', alpha=0.5, + error_kw={'elinewidth': 2, 'capsize':5,'capthick':2}, + label='${dataSeriesLabel}' +) + ax.set_xticks(bar_positions) ax.set_xticklabels(x) -ax.set_title('${plotTitle}') -ax.set_xlabel('${xLabel}') -ax.set_ylabel('${yLabel}') +ax.set_title("${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 plotScatterPlot:', error); - alert('An error occurred while plotting the data. Please check the console for details.'); - } - } - </script> - </script> +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> |