{"spec_id":"calibration-beer-lambert","library":"plotly","language":"python","code":"\"\"\" anyplot.ai\ncalibration-beer-lambert: Beer-Lambert Calibration Curve\nLibrary: plotly 6.7.0 | Python 3.13.13\nQuality: 91/100 | Updated: 2026-06-03\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport plotly.graph_objects as go\n\n\n# Theme tokens (Imprint palette — see prompts/default-style-guide.md)\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\nPAGE_BG = \"#FAF8F1\" if THEME == \"light\" else \"#1A1A17\"\nELEVATED_BG = \"#FFFDF6\" if THEME == \"light\" else \"#242420\"\nINK = \"#1A1A17\" if THEME == \"light\" else \"#F0EFE8\"\nINK_SOFT = \"#4A4A44\" if THEME == \"light\" else \"#B8B7B0\"\nGRID = \"rgba(26,26,23,0.15)\" if THEME == \"light\" else \"rgba(240,239,232,0.15)\"\n\n# Imprint palette — positions used\nBRAND = \"#009E73\"  # calibration standards — position 1, always first series\nBLUE = \"#4467A3\"  # regression line — position 3\nRED = \"#AE3030\"  # unknown sample — semantic anchor for focal/reference point\n\n# Data - UV-Vis spectrophotometry calibration standards\nnp.random.seed(42)\nconcentration = np.array([0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0])\nmolar_absorptivity = 0.045\nabsorbance_true = molar_absorptivity * concentration\nabsorbance = absorbance_true + np.random.normal(0, 0.008, len(concentration))\nabsorbance[0] = 0.003\n\n# Linear regression\nslope, intercept = np.polyfit(concentration, absorbance, 1)\nabsorbance_pred = slope * concentration + intercept\nss_res = np.sum((absorbance - absorbance_pred) ** 2)\nss_tot = np.sum((absorbance - np.mean(absorbance)) ** 2)\nr_squared = 1 - ss_res / ss_tot\n\n# Regression line and 95% prediction interval\nconc_fit = np.linspace(-0.5, 15.5, 200)\nabs_fit = slope * conc_fit + intercept\nn = len(concentration)\nconc_mean = np.mean(concentration)\nmse = ss_res / (n - 2)\nse_pred = np.sqrt(mse * (1 + 1 / n + (conc_fit - conc_mean) ** 2 / np.sum((concentration - conc_mean) ** 2)))\nt_crit = 2.447  # t-critical for 95% two-sided, df=6 (pre-computed)\npred_upper = abs_fit + t_crit * se_pred\npred_lower = abs_fit - t_crit * se_pred\n\n# Unknown sample\nunknown_absorbance = 0.38\nunknown_concentration = (unknown_absorbance - intercept) / slope\n\n# Title — 55 chars, below 67-char baseline → default fontsize applies\ntitle = \"calibration-beer-lambert · python · plotly · anyplot.ai\"\ntitle_fontsize = round(16 * min(1.0, 67 / len(title)))\n\n# Plot\nfig = go.Figure()\n\n# 95% prediction interval band\nfig.add_trace(\n    go.Scatter(\n        x=np.concatenate([conc_fit, conc_fit[::-1]]),\n        y=np.concatenate([pred_upper, pred_lower[::-1]]),\n        fill=\"toself\",\n        fillcolor=\"rgba(68,103,163,0.12)\",\n        line={\"color\": \"rgba(0,0,0,0)\"},\n        name=\"95% Prediction Interval\",\n        showlegend=True,\n        hoverinfo=\"skip\",\n    )\n)\n\n# Regression line\nfig.add_trace(\n    go.Scatter(\n        x=conc_fit,\n        y=abs_fit,\n        mode=\"lines\",\n        name=f\"Fit: y = {slope:.4f}x + {intercept:.4f}\",\n        line={\"color\": BLUE, \"width\": 3},\n        hovertemplate=\"Conc: %{x:.2f} mg/L<br>Predicted Abs: %{y:.4f}<extra></extra>\",\n    )\n)\n\n# Calibration standards\nfig.add_trace(\n    go.Scatter(\n        x=concentration,\n        y=absorbance,\n        mode=\"markers\",\n        name=\"Calibration Standards\",\n        marker={\"size\": 17, \"color\": BRAND, \"line\": {\"color\": PAGE_BG, \"width\": 2}, \"symbol\": \"circle\"},\n        hovertemplate=\"<b>Standard %{pointNumber}</b><br>Concentration: %{x:.1f} mg/L<br>Absorbance: %{y:.4f}<extra></extra>\",\n    )\n)\n\n# Unknown sample point\nfig.add_trace(\n    go.Scatter(\n        x=[unknown_concentration],\n        y=[unknown_absorbance],\n        mode=\"markers\",\n        name=f\"Unknown ({unknown_concentration:.1f} mg/L)\",\n        marker={\"size\": 21, \"color\": RED, \"line\": {\"color\": PAGE_BG, \"width\": 2}, \"symbol\": \"diamond\"},\n        hovertemplate=\"<b>Unknown Sample</b><br>Concentration: %{x:.2f} mg/L<br>Absorbance: %{y:.4f}<extra></extra>\",\n    )\n)\n\n# Dashed guide lines from unknown sample to both axes\nfig.add_shape(\n    type=\"line\",\n    x0=unknown_concentration,\n    y0=0,\n    x1=unknown_concentration,\n    y1=unknown_absorbance,\n    line={\"color\": RED, \"width\": 1.5, \"dash\": \"dash\"},\n)\nfig.add_shape(\n    type=\"line\",\n    x0=0,\n    y0=unknown_absorbance,\n    x1=unknown_concentration,\n    y1=unknown_absorbance,\n    line={\"color\": RED, \"width\": 1.5, \"dash\": \"dash\"},\n)\n\n# Regression equation and R² annotation — placed in lower-right area clear of legend\nfig.add_annotation(\n    x=0.97,\n    y=0.06,\n    xref=\"paper\",\n    yref=\"paper\",\n    text=f\"<b>y = {slope:.4f}x + {intercept:.4f}</b><br>R² = {r_squared:.5f}\",\n    showarrow=False,\n    font={\"size\": 16, \"color\": INK, \"family\": \"Arial, sans-serif\"},\n    bgcolor=ELEVATED_BG,\n    bordercolor=INK_SOFT,\n    borderwidth=1,\n    borderpad=10,\n    align=\"right\",\n    xanchor=\"right\",\n    yanchor=\"bottom\",\n)\n\n# Layout\nfig.update_layout(\n    autosize=False,\n    paper_bgcolor=PAGE_BG,\n    plot_bgcolor=PAGE_BG,\n    font={\"color\": INK},\n    title={\n        \"text\": title,\n        \"font\": {\"size\": title_fontsize, \"color\": INK, \"family\": \"Arial, sans-serif\"},\n        \"x\": 0.5,\n        \"xanchor\": \"center\",\n    },\n    xaxis={\n        \"title\": {\"text\": \"Concentration (mg/L)\", \"font\": {\"size\": 12, \"color\": INK}},\n        \"tickfont\": {\"size\": 10, \"color\": INK_SOFT},\n        \"range\": [-0.5, 15.5],\n        \"showgrid\": False,\n        \"zeroline\": False,\n        \"linecolor\": INK_SOFT,\n        \"linewidth\": 1,\n        \"ticks\": \"outside\",\n        \"tickcolor\": INK_SOFT,\n    },\n    yaxis={\n        \"title\": {\"text\": \"Absorbance\", \"font\": {\"size\": 12, \"color\": INK}},\n        \"tickfont\": {\"size\": 10, \"color\": INK_SOFT},\n        \"range\": [-0.05, 0.75],\n        \"gridcolor\": GRID,\n        \"gridwidth\": 1,\n        \"zeroline\": False,\n        \"linecolor\": INK_SOFT,\n        \"linewidth\": 1,\n        \"ticks\": \"outside\",\n        \"tickcolor\": INK_SOFT,\n    },\n    legend={\n        \"font\": {\"size\": 10, \"color\": INK_SOFT},\n        \"x\": 0.02,\n        \"y\": 0.98,\n        \"xanchor\": \"left\",\n        \"yanchor\": \"top\",\n        \"bgcolor\": ELEVATED_BG,\n        \"bordercolor\": INK_SOFT,\n        \"borderwidth\": 1,\n    },\n    margin={\"l\": 80, \"r\": 40, \"t\": 80, \"b\": 60},\n)\n\n# Save — landscape 3200×1800 (width=800, height=450, scale=4)\nfig.write_image(f\"plot-{THEME}.png\", width=800, height=450, scale=4)\nfig.write_html(f\"plot-{THEME}.html\", include_plotlyjs=\"cdn\")\n"}