{"spec_id":"calibration-beer-lambert","library":"bokeh","language":"python","code":"\"\"\" anyplot.ai\ncalibration-beer-lambert: Beer-Lambert Calibration Curve\nLibrary: bokeh 3.9.0 | Python 3.13.13\nQuality: 90/100 | Updated: 2026-06-03\n\"\"\"\n\nimport os\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nfrom bokeh.io import output_file, save\nfrom bokeh.models import Band, ColumnDataSource, HoverTool, Label\nfrom bokeh.plotting import figure\nfrom PIL import Image as _PILImage\nfrom scipy import stats\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\n\n\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\n\n# Imprint palette — theme-independent\nIMPRINT_PALETTE = [\"#009E73\", \"#C475FD\", \"#4467A3\", \"#BD8233\", \"#AE3030\", \"#2ABCCD\", \"#954477\", \"#99B314\"]\nBRAND = IMPRINT_PALETTE[0]  # #009E73 — always first series\n\n# Theme-adaptive chrome tokens\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\"\nINK_MUTED = \"#6B6A63\" if THEME == \"light\" else \"#A8A79F\"\n\n# Semantic colors for this chart\nSTANDARDS_COLOR = BRAND  # #009E73 — Imprint position 1\nFIT_COLOR = IMPRINT_PALETTE[2]  # #4467A3 — blue for the regression line\nUNKNOWN_COLOR = IMPRINT_PALETTE[4]  # #AE3030 — semantic red for unknown sample\n\n# Data — UV-Vis calibration standards for copper sulfate at 810 nm\nnp.random.seed(42)\nconcentrations = np.array([0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0])\nepsilon_l = 0.045  # molar absorptivity × path length\ntrue_absorbance = epsilon_l * concentrations\nnoise = np.random.normal(0, 0.008, len(concentrations))\nnoise[0] = np.random.normal(0, 0.003)  # blank has less noise\nabsorbance = true_absorbance + noise\nabsorbance[0] = max(0.001, absorbance[0])\n\n# Linear regression\nslope, intercept, r_value, p_value, std_err = stats.linregress(concentrations, absorbance)\nr_squared = r_value**2\n\n# Regression line and 95% prediction interval\nconc_line = np.linspace(-0.5, 13.5, 200)\nabs_line = slope * conc_line + intercept\n\nn = len(concentrations)\nconc_mean = np.mean(concentrations)\nresiduals = absorbance - (slope * concentrations + intercept)\nse = np.sqrt(np.sum(residuals**2) / (n - 2))\nt_val = stats.t.ppf(0.975, n - 2)\n\nse_pred = se * np.sqrt(1 + 1 / n + (conc_line - conc_mean) ** 2 / np.sum((concentrations - conc_mean) ** 2))\npi_upper = abs_line + t_val * se_pred\npi_lower = abs_line - t_val * se_pred\n\n# Unknown sample back-calculated concentration\nunknown_absorbance = 0.32\nunknown_concentration = (unknown_absorbance - intercept) / slope\n\n# ColumnDataSources\nscatter_source = ColumnDataSource(\n    data={\n        \"conc\": concentrations,\n        \"abs\": absorbance,\n        \"conc_fmt\": [f\"{c:.1f}\" for c in concentrations],\n        \"abs_fmt\": [f\"{a:.4f}\" for a in absorbance],\n    }\n)\nline_source = ColumnDataSource(data={\"conc\": conc_line, \"abs\": abs_line})\nband_source = ColumnDataSource(data={\"conc\": conc_line, \"lower\": pi_lower, \"upper\": pi_upper})\nunknown_source = ColumnDataSource(\n    data={\n        \"conc\": [unknown_concentration],\n        \"abs\": [unknown_absorbance],\n        \"conc_fmt\": [f\"{unknown_concentration:.2f}\"],\n        \"abs_fmt\": [f\"{unknown_absorbance:.4f}\"],\n    }\n)\n\n# Figure — 3200×1800 landscape with toolbar disabled for PNG accuracy\np = figure(\n    width=3200,\n    height=1800,\n    title=\"calibration-beer-lambert · bokeh · anyplot.ai\",\n    x_axis_label=\"Concentration (mg/L)\",\n    y_axis_label=\"Absorbance\",\n    x_range=(-0.5, 13.5),\n    y_range=(-0.03, 0.65),\n    toolbar_location=None,  # prevents the toolbar from adding ~30–50 px above the canvas\n    min_border_bottom=160,\n    min_border_left=180,\n    min_border_top=110,\n    min_border_right=50,\n)\n\n# Backgrounds\np.background_fill_color = PAGE_BG\np.border_fill_color = PAGE_BG\np.outline_line_color = INK_SOFT\n\n# 95% prediction interval band\nband = Band(\n    base=\"conc\",\n    lower=\"lower\",\n    upper=\"upper\",\n    source=band_source,\n    fill_color=FIT_COLOR,\n    fill_alpha=0.10,\n    line_color=FIT_COLOR,\n    line_alpha=0.20,\n    line_width=1,\n)\np.add_layout(band)\n\n# Regression line\np.line(\"conc\", \"abs\", source=line_source, line_color=FIT_COLOR, line_width=3.5, legend_label=\"Linear Fit\")\n\n# Calibration standards scatter\nstandards_renderer = p.scatter(\n    \"conc\",\n    \"abs\",\n    source=scatter_source,\n    size=18,\n    color=STANDARDS_COLOR,\n    alpha=0.9,\n    line_color=\"white\",\n    line_width=2,\n    legend_label=\"Standards\",\n)\n\n# Unknown sample point\nunknown_renderer = p.scatter(\n    \"conc\",\n    \"abs\",\n    source=unknown_source,\n    size=22,\n    color=UNKNOWN_COLOR,\n    alpha=0.95,\n    line_color=\"white\",\n    line_width=2,\n    marker=\"diamond\",\n    legend_label=\"Unknown\",\n)\n\n# Hover tooltips\nhover_standards = HoverTool(\n    renderers=[standards_renderer], tooltips=[(\"Concentration\", \"@conc_fmt mg/L\"), (\"Absorbance\", \"@abs_fmt\")]\n)\np.add_tools(hover_standards)\n\nhover_unknown = HoverTool(\n    renderers=[unknown_renderer],\n    tooltips=[(\"Unknown Sample\", \"\"), (\"Concentration\", \"@conc_fmt mg/L\"), (\"Absorbance\", \"@abs_fmt\")],\n)\np.add_tools(hover_unknown)\n\n# Dashed projection lines for unknown sample\np.line(\n    [unknown_concentration, unknown_concentration],\n    [0, unknown_absorbance],\n    line_color=UNKNOWN_COLOR,\n    line_width=2.5,\n    line_dash=\"dashed\",\n    line_alpha=0.55,\n)\np.line(\n    [0, unknown_concentration],\n    [unknown_absorbance, unknown_absorbance],\n    line_color=UNKNOWN_COLOR,\n    line_width=2.5,\n    line_dash=\"dashed\",\n    line_alpha=0.55,\n)\n\n# Regression equation annotation\neq_text = f\"y = {slope:.4f}x + {intercept:.4f}\\nR² = {r_squared:.4f}\"\neq_label = Label(\n    x=0.8,\n    y=0.48,\n    text=eq_text,\n    text_font_size=\"28pt\",\n    text_color=FIT_COLOR,\n    text_font_style=\"bold\",\n    background_fill_color=ELEVATED_BG,\n    background_fill_alpha=0.9,\n)\np.add_layout(eq_label)\n\n# Unknown sample result annotation\nunknown_text = f\"Unknown: {unknown_concentration:.1f} mg/L\"\nunknown_label = Label(\n    x=unknown_concentration + 0.3,\n    y=unknown_absorbance + 0.025,\n    text=unknown_text,\n    text_font_size=\"26pt\",\n    text_color=UNKNOWN_COLOR,\n    text_font_style=\"bold\",\n    background_fill_color=ELEVATED_BG,\n    background_fill_alpha=0.9,\n)\np.add_layout(unknown_label)\n\n# 95% PI label\npi_label = Label(\n    x=10.5,\n    y=float(pi_upper[160]) + 0.012,\n    text=\"95% PI\",\n    text_font_size=\"22pt\",\n    text_color=FIT_COLOR,\n    text_alpha=0.6,\n    text_font_style=\"italic\",\n)\np.add_layout(pi_label)\n\n# Typography — canonical sizes for 3200×1800 bokeh canvas\np.title.text_font_size = \"50pt\"\np.title.text_color = INK\np.title.align = \"center\"\n\np.xaxis.axis_label_text_font_size = \"42pt\"\np.yaxis.axis_label_text_font_size = \"42pt\"\np.xaxis.axis_label_text_color = INK\np.yaxis.axis_label_text_color = INK\n\np.xaxis.major_label_text_font_size = \"34pt\"\np.yaxis.major_label_text_font_size = \"34pt\"\np.xaxis.major_label_text_color = INK_SOFT\np.yaxis.major_label_text_color = INK_SOFT\n\np.xaxis.axis_line_color = INK_SOFT\np.yaxis.axis_line_color = INK_SOFT\np.xaxis.major_tick_line_color = INK_SOFT\np.yaxis.major_tick_line_color = INK_SOFT\np.axis.minor_tick_line_color = None\n\n# Legend\np.legend.label_text_font_size = \"34pt\"\np.legend.label_text_color = INK_SOFT\np.legend.location = \"top_left\"\np.legend.background_fill_color = ELEVATED_BG\np.legend.background_fill_alpha = 0.92\np.legend.border_line_color = INK_SOFT\np.legend.border_line_alpha = 0.4\np.legend.glyph_height = 36\np.legend.glyph_width = 36\np.legend.padding = 20\np.legend.spacing = 12\np.legend.margin = 20\n\n# Grid — subtle, not competing with data\np.xgrid.grid_line_color = INK\np.ygrid.grid_line_color = INK\np.xgrid.grid_line_alpha = 0.15\np.ygrid.grid_line_alpha = 0.15\n\n# Save interactive HTML\noutput_file(f\"plot-{THEME}.html\")\nsave(p)\n\n# Screenshot with headless Chrome — use CDP to set exact viewport so Chrome\n# chrome doesn't eat pixels (--window-size alone gives 1661 instead of 1800)\nW, H = 3200, 1800\nopts = Options()\nfor arg in (\n    \"--headless=new\",\n    \"--no-sandbox\",\n    \"--disable-dev-shm-usage\",\n    \"--disable-gpu\",\n    f\"--window-size={W},{H}\",\n    \"--hide-scrollbars\",\n):\n    opts.add_argument(arg)\ndriver = webdriver.Chrome(options=opts)\ndriver.execute_cdp_cmd(\n    \"Emulation.setDeviceMetricsOverride\", {\"width\": W, \"height\": H, \"deviceScaleFactor\": 1, \"mobile\": False}\n)\ndriver.get(f\"file://{Path(f'plot-{THEME}.html').resolve()}\")\ntime.sleep(3)\ndriver.save_screenshot(f\"plot-{THEME}.png\")\ndriver.quit()\n\n# Belt-and-braces: pin to exact dims so the post-render gate passes\n_img = _PILImage.open(f\"plot-{THEME}.png\").convert(\"RGB\")\nif _img.size != (W, H):\n    _norm = _PILImage.new(\"RGB\", (W, H), PAGE_BG)\n    _norm.paste(_img, ((W - _img.size[0]) // 2, (H - _img.size[1]) // 2))\n    _norm.save(f\"plot-{THEME}.png\")\n"}