{"spec_id":"parallel-basic","library":"plotnine","language":"python","code":"\"\"\" anyplot.ai\nparallel-basic: Basic Parallel Coordinates Plot\nLibrary: plotnine 0.15.7 | Python 3.13.14\nQuality: 86/100 | Updated: 2026-07-24\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom plotnine import (\n    aes,\n    annotate,\n    element_blank,\n    element_line,\n    element_rect,\n    element_text,\n    geom_line,\n    geom_point,\n    geom_vline,\n    ggplot,\n    labs,\n    scale_color_manual,\n    scale_x_continuous,\n    theme,\n    theme_minimal,\n)\n\n\n# Theme tokens\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\"\n\nIMPRINT = [\"#009E73\", \"#C475FD\", \"#4467A3\", \"#BD8233\", \"#AE3030\", \"#2ABCCD\", \"#954477\"]\n\n# Data — synthetic iris-like measurements, 50 samples per species (balanced)\nnp.random.seed(42)\nn = 50\ncov_setosa = np.diag([0.25, 0.09, 0.25, 0.04])\ncov_versicol = np.diag([0.25, 0.09, 0.25, 0.04])\ncov_virginica = np.diag([0.25, 0.09, 0.25, 0.04])\nsetosa = np.random.multivariate_normal([5.01, 3.42, 1.46, 0.24], cov_setosa, size=n)\nversicol = np.random.multivariate_normal([5.94, 2.77, 4.26, 1.33], cov_versicol, size=n)\nvirginica = np.random.multivariate_normal([6.59, 2.97, 5.55, 2.03], cov_virginica, size=n)\n\ncols = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\ndf = pd.DataFrame(np.vstack([setosa, versicol, virginica]), columns=cols)\ndf[\"species\"] = [\"Setosa\"] * n + [\"Versicolor\"] * n + [\"Virginica\"] * n\ndf[\"id\"] = range(len(df))\n\n# Clip to realistic ranges\ndf[\"sepal_length\"] = df[\"sepal_length\"].clip(4.0, 8.5)\ndf[\"sepal_width\"] = df[\"sepal_width\"].clip(2.0, 4.5)\ndf[\"petal_length\"] = df[\"petal_length\"].clip(1.0, 7.0)\ndf[\"petal_width\"] = df[\"petal_width\"].clip(0.1, 2.8)\n\n# Normalize each dimension to 0–1 scale for fair comparison\ndimensions = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\ndf_norm = df.copy()\nfor col in dimensions:\n    df_norm[col] = (df[col] - df[col].min()) / (df[col].max() - df[col].min())\n\n# Transform to long format for parallel coordinates\ndf_long = pd.melt(df_norm, id_vars=[\"id\", \"species\"], value_vars=dimensions, var_name=\"dimension\", value_name=\"value\")\ndim_map = {dim: i for i, dim in enumerate(dimensions)}\ndf_long[\"dim_num\"] = df_long[\"dimension\"].map(dim_map)\n\n# Imprint palette colors — first series always #009E73\nspecies_order = [\"Setosa\", \"Versicolor\", \"Virginica\"]\ncolors = {sp: IMPRINT[i] for i, sp in enumerate(species_order)}\n\n# Plot\nplot = (\n    ggplot(df_long, aes(x=\"dim_num\", y=\"value\", group=\"id\", color=\"species\"))\n    + geom_vline(xintercept=list(range(len(dimensions))), color=INK_SOFT, size=0.5, alpha=0.4)\n    + geom_line(alpha=0.35, size=1.0)\n    + geom_point(size=2.5, alpha=0.55)\n    + annotate(\n        \"text\",\n        x=2.5,\n        y=0.32,\n        label=\"Setosa separates cleanly on petal dimensions\",\n        color=IMPRINT[0],\n        size=7,\n        ha=\"center\",\n        fontstyle=\"italic\",\n    )\n    + scale_color_manual(values=colors, breaks=species_order)\n    + scale_x_continuous(\n        breaks=list(range(len(dimensions))),\n        labels=[\"Sepal Length\\n(cm)\", \"Sepal Width\\n(cm)\", \"Petal Length\\n(cm)\", \"Petal Width\\n(cm)\"],\n    )\n    + labs(\n        x=\"Dimension\", y=\"Normalized Value (0–1)\", title=\"parallel-basic · plotnine · anyplot.ai\", color=\"Iris Species\"\n    )\n    + theme_minimal()\n    + theme(\n        figure_size=(8, 4.5),\n        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),\n        panel_background=element_rect(fill=PAGE_BG),\n        panel_border=element_blank(),\n        panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),\n        panel_grid_minor=element_blank(),\n        axis_title=element_text(color=INK, size=10),\n        axis_text=element_text(color=INK_SOFT, size=8),\n        axis_line=element_line(color=INK_SOFT, size=0.4),\n        plot_title=element_text(color=INK, size=12),\n        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),\n        legend_text=element_text(color=INK_SOFT, size=8),\n        legend_title=element_text(color=INK, size=9),\n        text=element_text(size=8),\n    )\n)\n\n# Save\nplot.save(f\"plot-{THEME}.png\", dpi=400, width=8, height=4.5, units=\"in\", verbose=False)\n"}