{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QiBb5doWSSvB", "outputId": "fb742710-b8f7-490e-f620-db0f918acf92" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "CSV files found:\n", " [0] /content/S1_File.csv\n", "\n", "Using RAW CSV: /content/S1_File.csv\n", "\n", "Data shape: (162, 4)\n", "Speeds: [200, 400, 600]\n", "Angles: [-40.0, -30.0, -20.0, -10.0, 0.0, 10.0, 20.0, 30.0, 40.0]\n", "\n", "Cell counts:\n", " Speed_mm_min Angle_deg n\n", " 200 -40.0 6\n", " 200 -30.0 6\n", " 200 -20.0 6\n", " 200 -10.0 6\n", " 200 0.0 6\n", " 200 10.0 6\n", " 200 20.0 6\n", " 200 30.0 6\n", " 200 40.0 6\n", " 400 -40.0 6\n", " 400 -30.0 6\n", " 400 -20.0 6\n", " 400 -10.0 6\n", " 400 0.0 6\n", " 400 10.0 6\n", " 400 20.0 6\n", " 400 30.0 6\n", " 400 40.0 6\n", " 600 -40.0 6\n", " 600 -30.0 6\n", " 600 -20.0 6\n", " 600 -10.0 6\n", " 600 0.0 6\n", " 600 10.0 6\n", " 600 20.0 6\n", " 600 30.0 6\n", " 600 40.0 6\n", "\n", "OK: all 27 conditions have n = 6, for a total of 162 trials.\n" ] } ], "source": [ "# === Cell A: Load raw data CSV robustly ===\n", "import pandas as pd\n", "import numpy as np\n", "from pathlib import Path\n", "\n", "\n", "def load_raw_csv():\n", " \"\"\"\n", " Load the trial-level raw dataset.\n", "\n", " In Google Colab, upload S1_File.csv to /content.\n", " In a local Jupyter environment, place S1_File.csv in the same working directory as this notebook.\n", " \"\"\"\n", " search_dirs = [Path(\"/content\"), Path.cwd()]\n", " seen = set()\n", " csvs = []\n", " for d in search_dirs:\n", " if d.exists():\n", " for p in sorted(d.glob(\"*.csv\")):\n", " key = str(p.resolve())\n", " if key not in seen:\n", " csvs.append(p)\n", " seen.add(key)\n", "\n", " print(\"CSV files found:\")\n", " for i, p in enumerate(csvs):\n", " print(f\" [{i}] {p}\")\n", " assert len(csvs) >= 1, (\n", " \"No CSV file was found. Upload S1_File.csv to /content in Google Colab, \"\n", " \"or place it in the same working directory as this notebook.\"\n", " )\n", "\n", " def is_raw(path):\n", " try:\n", " tmp = pd.read_csv(path, nrows=5)\n", " cols = set(tmp.columns)\n", " return {\"Speed_mm_min\", \"Angle_deg\", \"PerforationForce_N\"}.issubset(cols)\n", " except Exception:\n", " return False\n", "\n", " raw_candidates = [p for p in csvs if is_raw(p)]\n", " assert len(raw_candidates) >= 1, (\n", " \"No trial-level raw CSV could be identified. \"\n", " \"The dataset must include Speed_mm_min, Angle_deg, and PerforationForce_N.\"\n", " )\n", "\n", " # Prefer the expected file name when present.\n", " preferred = [p for p in raw_candidates if p.name == \"S1_File.csv\"]\n", " csv_path = preferred[0] if preferred else raw_candidates[0]\n", " print(\"\\nUsing RAW CSV:\", csv_path)\n", "\n", " df = pd.read_csv(csv_path).copy()\n", "\n", " required = {\"Speed_mm_min\", \"Angle_deg\", \"PerforationForce_N\"}\n", " missing = required - set(df.columns)\n", " assert not missing, f\"Required columns are missing: {missing}. Current columns: {df.columns.tolist()}\"\n", "\n", " df[\"Speed_mm_min\"] = pd.to_numeric(df[\"Speed_mm_min\"], errors=\"raise\").astype(int)\n", " df[\"Angle_deg\"] = pd.to_numeric(df[\"Angle_deg\"], errors=\"raise\").astype(float)\n", " df[\"PerforationForce_N\"] = pd.to_numeric(df[\"PerforationForce_N\"], errors=\"raise\").astype(float)\n", "\n", " print(\"\\nData shape:\", df.shape)\n", " print(\"Speeds:\", sorted(df[\"Speed_mm_min\"].unique().tolist()))\n", " print(\"Angles:\", sorted(df[\"Angle_deg\"].unique().tolist()))\n", " return df\n", "\n", "\n", "df = load_raw_csv()\n", "\n", "# Cell-count check: 3 speeds × 9 angles × 6 trials = 162 trials.\n", "cell = (df.groupby([\"Speed_mm_min\", \"Angle_deg\"]).size().reset_index(name=\"n\")\n", " .sort_values([\"Speed_mm_min\", \"Angle_deg\"]))\n", "print(\"\\nCell counts:\")\n", "print(cell.to_string(index=False))\n", "\n", "bad = cell[cell[\"n\"] != 6]\n", "if len(bad) == 0 and df.shape[0] == 162:\n", " print(\"\\nOK: all 27 conditions have n = 6, for a total of 162 trials.\")\n", "else:\n", " print(\"\\nWARNING: cell counts or total sample size differ from the expected design.\")\n", " print(bad.to_string(index=False))\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "VO_4Sukv0XSe" }, "outputs": [], "source": [ "# === Output formatting utilities for figures ===\n", "# Requirements: TIFF, 300 dpi, LZW compression, RGB, flattened, no alpha channel;\n", "# font: Arial; axis labels: 9 pt; tick labels: 8 pt; legends: 8 pt; panel labels: 10 pt.\n", "from pathlib import Path\n", "import matplotlib as mpl\n", "from PIL import Image\n", "\n", "OUTPUT_DIR = Path(\"/content\") if Path(\"/content\").exists() else Path.cwd()\n", "\n", "mpl.rcParams.update({\n", " \"font.family\": \"Arial\",\n", " \"font.sans-serif\": [\"Arial\", \"DejaVu Sans\", \"Liberation Sans\"],\n", " \"axes.labelsize\": 9,\n", " \"xtick.labelsize\": 8,\n", " \"ytick.labelsize\": 8,\n", " \"legend.fontsize\": 8,\n", " \"legend.title_fontsize\": 8,\n", " \"axes.titlesize\": 10,\n", "})\n", "\n", "def save_tiff_rgb_lzw(fig, filename, dpi=300):\n", " \"\"\"Save a Matplotlib figure as flattened RGB TIFF with LZW compression and no alpha channel.\"\"\"\n", " out_path = OUTPUT_DIR / filename\n", " tmp_path = OUTPUT_DIR / f\"_{Path(filename).stem}_tmp.tif\"\n", " fig.savefig(\n", " tmp_path,\n", " dpi=dpi,\n", " format=\"tiff\",\n", " facecolor=\"white\",\n", " edgecolor=\"white\",\n", " transparent=False,\n", " pil_kwargs={\"compression\": \"tiff_lzw\"},\n", " )\n", " with Image.open(tmp_path) as im:\n", " im = im.convert(\"RGB\") # flatten and remove alpha channel\n", " im.save(out_path, format=\"TIFF\", compression=\"tiff_lzw\", dpi=(dpi, dpi))\n", " tmp_path.unlink(missing_ok=True)\n", " return out_path\n" ] }, { "cell_type": "markdown", "metadata": { "id": "PDeyhEiv0XSe" }, "source": [ "### Final figure-output note\n", "This notebook outputs the final figure files as 300-dpi LZW-compressed TIFF files: `Fig2.tif`, `Fig3.tif`, and `Fig4.tif`. Figure 3 is generated only as a combined three-panel figure; separate panel files are not generated.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_TihPDUBSd2b", "outputId": "5b059187-600e-4635-a665-91bab6dd0865" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " df F PR(>F) eta_p2\n", "C(Speed_mm_min) 2.0 185.147852 2.033298e-39 0.732830\n", "C(Angle_deg) 8.0 67.985466 1.302910e-43 0.801144\n", "C(Speed_mm_min):C(Angle_deg) 16.0 2.117762 1.076480e-02 0.200636\n", "\n", "Saved: /content/Table1_two_way_ANOVA_eta_p2.csv\n" ] } ], "source": [ "# === Cell B: Table 1 (Two-way ANOVA + partial eta squared) ===\n", "import statsmodels.formula.api as smf\n", "from statsmodels.stats.anova import anova_lm\n", "\n", "model = smf.ols(\"PerforationForce_N ~ C(Speed_mm_min) * C(Angle_deg)\", data=df).fit()\n", "aov = anova_lm(model, typ=2) # typ=2で主効果・交互作用\n", "\n", "ss_res = aov.loc[\"Residual\", \"sum_sq\"]\n", "aov_nores = aov.drop(index=\"Residual\").copy()\n", "aov_nores[\"eta_p2\"] = aov_nores[\"sum_sq\"] / (aov_nores[\"sum_sq\"] + ss_res)\n", "\n", "table1 = aov_nores[[\"df\", \"F\", \"PR(>F)\", \"eta_p2\"]].copy()\n", "print(table1)\n", "\n", "table1.to_csv(\"/content/Table1_two_way_ANOVA_eta_p2.csv\", index=True)\n", "print(\"\\nSaved: /content/Table1_two_way_ANOVA_eta_p2.csv\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Q01fx3t2SnBV", "outputId": "ca6ab4ef-5e1b-4312-98e2-d18695022c8c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "HC3 robust Type II ANOVA tests:\n", " Term df1 df2 F_HC3 p_HC3\n", "0 Speed 2 135 144.029147 3.277290e-34\n", "1 Angle 8 135 77.661700 9.585112e-47\n", "2 Interaction 16 135 2.277109 5.645878e-03\n", "\n", "Saved: /content/Table1_HC3_robust_Wald_tests.csv\n" ] } ], "source": [ "# === Cell B2: Sensitivity analysis (HC3 robust covariance) ===\n", "import pandas as pd\n", "from statsmodels.stats.anova import anova_lm\n", "\n", "# HC3 robust covariance Type II ANOVA.\n", "# This uses the same factorial model as the primary ANOVA, but with HC3 robust covariance.\n", "hc3_anova = anova_lm(model, typ=2, robust=\"hc3\").drop(index=\"Residual\")\n", "\n", "term_map = {\n", " \"C(Speed_mm_min)\": \"Speed\",\n", " \"C(Angle_deg)\": \"Angle\",\n", " \"C(Speed_mm_min):C(Angle_deg)\": \"Interaction\",\n", "}\n", "\n", "hc3_tests = pd.DataFrame({\n", " \"Term\": [term_map.get(idx, idx) for idx in hc3_anova.index],\n", " \"df1\": hc3_anova[\"df\"].astype(int).values,\n", " \"df2\": int(model.df_resid),\n", " \"F_HC3\": hc3_anova[\"F\"].astype(float).values,\n", " \"p_HC3\": hc3_anova[\"PR(>F)\"].astype(float).values,\n", "})\n", "\n", "print(\"\\nHC3 robust Type II ANOVA tests:\")\n", "print(hc3_tests)\n", "\n", "OUTPUT_DIR = Path(\"/content\") if Path(\"/content\").exists() else Path.cwd()\n", "hc3_tests.to_csv(OUTPUT_DIR / \"Table1_HC3_robust_Wald_tests.csv\", index=False)\n", "print(f\"\\nSaved: {OUTPUT_DIR / 'Table1_HC3_robust_Wald_tests.csv'}\")\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "SioypLoLStd9", "outputId": "300d4c7e-26b4-4f5e-86c9-4ed733e5e912" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Table2 all rows: 108\n", "Table2 significant rows: 48\n", "Saved: /content/Table2_all_pairs.csv\n", "Saved: /content/Table2_significant_only.csv\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " Speed_mm_min Approach_angle_1_deg Approach_angle_2_deg n1 mean1 sd1 n2 mean2 sd2 mean_diff_(2-1) CI_low CI_high p_adj test cohens_d_signed_(2-1) \\\n", "0 200 -40.0 -30.0 6 6.410000 0.354401 6 5.145000 0.377081 -1.265000 -2.083600 -0.446400 0.000300 Tukey-Kramer -3.457071 \n", "1 200 -40.0 -20.0 6 6.410000 0.354401 6 4.411667 0.539015 -1.998333 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1200-40.0-20.066.4100000.35440164.4116670.539015-1.998333-2.816900-1.1797000.000000Tukey-Kramer-4.3809164.380916TrueNaN
2200-40.0-10.066.4100000.35440164.0383330.532594-2.371667-3.190300-1.5531000.000000Tukey-Kramer-5.2428905.242890TrueNaN
3200-40.00.066.4100000.35440163.6766670.399383-2.733333-3.551900-1.9147000.000000Tukey-Kramer-7.2394227.239422TrueNaN
4200-40.010.066.4100000.35440164.0850000.430430-2.325000-3.143600-1.5064000.000000Tukey-Kramer-5.8972375.897237TrueNaN
5200-40.020.066.4100000.35440165.0366670.412100-1.373333-2.191900-0.5547000.000100Tukey-Kramer-3.5732723.573272TrueNaN
6200-40.030.066.4100000.35440165.6266670.459376-0.783333-1.6019000.0353000.070400Tukey-Kramer-1.9093581.909358FalseNaN
7200-40.040.066.4100000.35440166.1933330.371196-0.216667-1.0353000.6019000.993900Tukey-Kramer-0.5970490.597049FalseNaN
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\"num_unique_values\": 36,\n \"samples\": [\n 0.03054008957124034\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} } ], "source": [ "# =========================================================\n", "# Table2 (ALL 108 rows + Significant-only) 完全版 1セル\n", "# =========================================================\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import itertools\n", "from scipy import stats\n", "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n", "from statsmodels.stats.multitest import multipletests\n", "from IPython.display import display\n", "\n", "# -------------------------\n", "# display settings（108行を全部見せる）\n", "# -------------------------\n", "pd.set_option(\"display.max_rows\", 200)\n", "pd.set_option(\"display.max_columns\", 200)\n", "pd.set_option(\"display.width\", 200)\n", "\n", "# -------------------------\n", "# safety check: df must exist with these columns\n", "# -------------------------\n", "required = {\"Speed_mm_min\", \"Angle_deg\", \"PerforationForce_N\"}\n", "missing = required - set(df.columns)\n", "assert not missing, f\"dfに必要列がないである: {missing} / df.columns={list(df.columns)}\"\n", "\n", "# -------------------------\n", "# utility\n", "# -------------------------\n", "def cohens_d_signed(x1, x2):\n", " \"\"\"Cohen's d (mean2-mean1) / pooled SD\"\"\"\n", " x1 = np.asarray(x1, dtype=float)\n", " x2 = np.asarray(x2, dtype=float)\n", " n1, n2 = len(x1), len(x2)\n", " s1 = x1.var(ddof=1)\n", " s2 = x2.var(ddof=1)\n", " sp = np.sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))\n", " return float((x2.mean() - x1.mean()) / sp)\n", "\n", "def cohens_d_abs(x1, x2):\n", " return float(abs(cohens_d_signed(x1, x2)))\n", "\n", "# -------------------------\n", "# Tukey-Kramer (200, 400): ALL 36 pairs per speed\n", "# -------------------------\n", "def tukey_table_all(df_speed, speed_value):\n", " res = pairwise_tukeyhsd(\n", " endog=df_speed[\"PerforationForce_N\"].values,\n", " groups=df_speed[\"Angle_deg\"].values,\n", " alpha=0.05\n", " )\n", " tbl = pd.DataFrame(res._results_table.data[1:], columns=res._results_table.data[0])\n", " tbl = tbl.rename(columns={\n", " \"group1\": \"Approach_angle_1_deg\",\n", " \"group2\": \"Approach_angle_2_deg\",\n", " \"meandiff\": \"mean_diff_(2-1)\",\n", " \"p-adj\": \"p_adj\",\n", " \"lower\": \"CI_low\",\n", " \"upper\": \"CI_high\",\n", " \"reject\": \"reject_alpha0.05\"\n", " })\n", "\n", " tbl[\"Speed_mm_min\"] = speed_value\n", " tbl[\"Approach_angle_1_deg\"] = tbl[\"Approach_angle_1_deg\"].astype(float)\n", " tbl[\"Approach_angle_2_deg\"] = tbl[\"Approach_angle_2_deg\"].astype(float)\n", "\n", " # enforce mean diff direction explicitly = mean(Angle2) - mean(Angle1)\n", " means = df_speed.groupby(\"Angle_deg\")[\"PerforationForce_N\"].mean().to_dict()\n", " tbl[\"mean_diff_(2-1)\"] = tbl.apply(\n", " lambda r: means[r[\"Approach_angle_2_deg\"]] - means[r[\"Approach_angle_1_deg\"]],\n", " axis=1\n", " )\n", "\n", " # add n, mean, sd per group\n", " gstats = df_speed.groupby(\"Angle_deg\")[\"PerforationForce_N\"].agg([\"count\",\"mean\",\"std\"]).to_dict(\"index\")\n", " tbl[\"n1\"] = tbl[\"Approach_angle_1_deg\"].map(lambda a: int(gstats[a][\"count\"]))\n", " tbl[\"mean1\"] = tbl[\"Approach_angle_1_deg\"].map(lambda a: float(gstats[a][\"mean\"]))\n", " tbl[\"sd1\"] = tbl[\"Approach_angle_1_deg\"].map(lambda a: float(gstats[a][\"std\"]))\n", " tbl[\"n2\"] = tbl[\"Approach_angle_2_deg\"].map(lambda a: int(gstats[a][\"count\"]))\n", " tbl[\"mean2\"] = tbl[\"Approach_angle_2_deg\"].map(lambda a: float(gstats[a][\"mean\"]))\n", " tbl[\"sd2\"] = tbl[\"Approach_angle_2_deg\"].map(lambda a: float(gstats[a][\"std\"]))\n", "\n", " # Cohen's d\n", " d_signed, d_abs = [], []\n", " for a1, a2 in zip(tbl[\"Approach_angle_1_deg\"], tbl[\"Approach_angle_2_deg\"]):\n", " x1 = df_speed.loc[df_speed[\"Angle_deg\"] == a1, \"PerforationForce_N\"].values\n", " x2 = df_speed.loc[df_speed[\"Angle_deg\"] == a2, \"PerforationForce_N\"].values\n", " ds = cohens_d_signed(x1, x2)\n", " d_signed.append(ds)\n", " d_abs.append(abs(ds))\n", "\n", " tbl[\"cohens_d_signed_(2-1)\"] = d_signed\n", " tbl[\"cohens_d_abs\"] = d_abs\n", " tbl[\"test\"] = \"Tukey-Kramer\"\n", " tbl[\"p_raw\"] = np.nan # not applicable for Tukey\n", "\n", " return tbl[[\n", " \"Speed_mm_min\",\"Approach_angle_1_deg\",\"Approach_angle_2_deg\",\n", " \"n1\",\"mean1\",\"sd1\",\"n2\",\"mean2\",\"sd2\",\n", " \"mean_diff_(2-1)\",\"CI_low\",\"CI_high\",\"p_adj\",\"test\",\n", " \"cohens_d_signed_(2-1)\",\"cohens_d_abs\",\"reject_alpha0.05\",\"p_raw\"\n", " ]]\n", "\n", "# -------------------------\n", "# Welch + Holm (600): ALL 36 pairs\n", "# -------------------------\n", "def welch_holm_table_all(df_speed, speed_value):\n", " angles = sorted(df_speed[\"Angle_deg\"].unique().tolist())\n", " rows, p_raw_list = [], []\n", "\n", " for a1, a2 in itertools.combinations(angles, 2):\n", " x1 = df_speed.loc[df_speed[\"Angle_deg\"] == a1, \"PerforationForce_N\"].values\n", " x2 = df_speed.loc[df_speed[\"Angle_deg\"] == a2, \"PerforationForce_N\"].values\n", "\n", " n1, n2 = len(x1), len(x2)\n", " m1, m2 = x1.mean(), x2.mean()\n", " v1, v2 = x1.var(ddof=1), x2.var(ddof=1)\n", "\n", " mean_diff = m2 - m1\n", " se = np.sqrt(v1/n1 + v2/n2)\n", " df_welch = (v1/n1 + v2/n2)**2 / ((v1/n1)**2/(n1-1) + (v2/n2)**2/(n2-1))\n", " tcrit = stats.t.ppf(0.975, df_welch)\n", " ci_low = mean_diff - tcrit * se\n", " ci_high = mean_diff + tcrit * se\n", "\n", " p_raw = stats.ttest_ind(x1, x2, equal_var=False).pvalue\n", "\n", " rows.append([\n", " speed_value, a1, a2,\n", " n1, m1, x1.std(ddof=1), n2, m2, x2.std(ddof=1),\n", " mean_diff, ci_low, ci_high,\n", " p_raw, cohens_d_signed(x1, x2), cohens_d_abs(x1, x2)\n", " ])\n", " p_raw_list.append(p_raw)\n", "\n", " reject, p_adj, _, _ = multipletests(p_raw_list, alpha=0.05, method=\"holm\")\n", "\n", " out = pd.DataFrame(rows, columns=[\n", " \"Speed_mm_min\",\"Approach_angle_1_deg\",\"Approach_angle_2_deg\",\n", " \"n1\",\"mean1\",\"sd1\",\"n2\",\"mean2\",\"sd2\",\n", " \"mean_diff_(2-1)\",\"CI_low\",\"CI_high\",\n", " \"p_raw\",\"cohens_d_signed_(2-1)\",\"cohens_d_abs\"\n", " ])\n", " out[\"p_adj\"] = p_adj\n", " out[\"reject_alpha0.05\"] = reject\n", " out[\"test\"] = \"Welch+Holm\"\n", "\n", " return out[[\n", " \"Speed_mm_min\",\"Approach_angle_1_deg\",\"Approach_angle_2_deg\",\n", " \"n1\",\"mean1\",\"sd1\",\"n2\",\"mean2\",\"sd2\",\n", " \"mean_diff_(2-1)\",\"CI_low\",\"CI_high\",\"p_adj\",\"test\",\n", " \"cohens_d_signed_(2-1)\",\"cohens_d_abs\",\"reject_alpha0.05\",\"p_raw\"\n", " ]]\n", "\n", "# -------------------------\n", "# Build Table2: ALL 108 rows + significant-only\n", "# -------------------------\n", "parts_all = []\n", "for sp in [200, 400]:\n", " dfi = df[df[\"Speed_mm_min\"] == sp].copy()\n", " parts_all.append(tukey_table_all(dfi, sp))\n", "\n", "df600 = df[df[\"Speed_mm_min\"] == 600].copy()\n", "parts_all.append(welch_holm_table_all(df600, 600))\n", "\n", "table2_all = pd.concat(parts_all, ignore_index=True)\n", "table2_all = table2_all.sort_values(\n", " [\"Speed_mm_min\",\"Approach_angle_1_deg\",\"Approach_angle_2_deg\"]\n", ").reset_index(drop=True)\n", "\n", "table2_sig = table2_all.loc[table2_all[\"reject_alpha0.05\"] == True].copy()\n", "\n", "print(\"Table2 all rows:\", len(table2_all)) # 108 のはず\n", "print(\"Table2 significant rows:\", len(table2_sig))\n", "\n", "# 保存（Colab左ペインに出る）\n", "table2_all.to_csv(\"/content/Table2_all_pairs.csv\", index=False)\n", "table2_sig.to_csv(\"/content/Table2_significant_only.csv\", index=False)\n", "print(\"Saved: /content/Table2_all_pairs.csv\")\n", "print(\"Saved: /content/Table2_significant_only.csv\")\n", "\n", "# 108行を画面に全表示\n", "display(table2_all)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Ta-kcN80XSf", "outputId": "dc9faa50-66f4-4bd9-d498-0e4462f32299" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_1198/2635154741.py:97: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", " display_df.loc[i, \"Advancement speed\\n(mm/min)\"] = \"\"\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: /content/S1_Table.docx\n" ] } ], "source": [ "# === S1 Table: export full within-speed pairwise comparisons as DOCX ===\n", "# This cell generates S1_Table.docx from table2_all.\n", "# It should be run after the Table2 cell that creates table2_all.\n", "\n", "import sys\n", "import subprocess\n", "from pathlib import Path\n", "import pandas as pd\n", "from decimal import Decimal, ROUND_HALF_UP\n", "\n", "try:\n", " from docx import Document\n", " from docx.shared import Pt, Inches\n", " from docx.enum.section import WD_ORIENT\n", " from docx.enum.text import WD_ALIGN_PARAGRAPH\n", " from docx.enum.table import WD_TABLE_ALIGNMENT, WD_CELL_VERTICAL_ALIGNMENT\n", "except ModuleNotFoundError:\n", " subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"python-docx\"])\n", " from docx import Document\n", " from docx.shared import Pt, Inches\n", " from docx.enum.section import WD_ORIENT\n", " from docx.enum.text import WD_ALIGN_PARAGRAPH\n", " from docx.enum.table import WD_TABLE_ALIGNMENT, WD_CELL_VERTICAL_ALIGNMENT\n", "\n", "assert \"table2_all\" in globals(), \"Run the Table2 cell first so that table2_all exists.\"\n", "assert len(table2_all) == 108, f\"Expected 108 full pairwise comparisons, but got {len(table2_all)}.\"\n", "\n", "OUTPUT_DIR = Path(\"/content\") if Path(\"/content\").exists() else Path.cwd()\n", "\n", "def fmt_angle(x):\n", " x = int(round(float(x)))\n", " if x < 0:\n", " return f\"−{abs(x)}\" # Unicode minus for manuscript-style display\n", " if x > 0:\n", " return f\"+{x}\"\n", " return \"0\"\n", "\n", "def fmt_p(p):\n", " p = float(p)\n", " if p < 0.001:\n", " return \"< 0.001\"\n", " q = Decimal(str(p)).quantize(Decimal(\"0.001\"), rounding=ROUND_HALF_UP)\n", " return f\"{q:.3f}\"\n", "\n", "def fmt_num(x, places=2):\n", " # Use a tiny sign-aware epsilon before ROUND_HALF_UP to avoid binary floating-point\n", " # values such as 1.264999999999 from rounding down unexpectedly.\n", " d = Decimal(str(float(x)))\n", " eps = Decimal(\"1e-9\")\n", " if d > 0:\n", " d += eps\n", " elif d < 0:\n", " d -= eps\n", " q = d.quantize(Decimal(\"1.\" + \"0\" * places), rounding=ROUND_HALF_UP)\n", " # Avoid displaying -0.00 after rounding.\n", " if q == 0:\n", " q = abs(q)\n", " return f\"{q:.{places}f}\".replace(\"-\", \"−\")\n", "\n", "def fmt_mean_ci(row):\n", " md = float(row[\"mean_diff_(2-1)\"])\n", " lo = float(row[\"CI_low\"])\n", " hi = float(row[\"CI_high\"])\n", " return f\"{fmt_num(md)} ({fmt_num(lo)} to {fmt_num(hi)})\"\n", "\n", "def set_cell_text(cell, text, font_size=8, bold=False, align=None):\n", " cell.text = \"\"\n", " p = cell.paragraphs[0]\n", " if align is not None:\n", " p.alignment = align\n", " run = p.add_run(str(text))\n", " run.font.name = \"Arial\"\n", " run.font.size = Pt(font_size)\n", " run.bold = bold\n", " cell.vertical_alignment = WD_CELL_VERTICAL_ALIGNMENT.CENTER\n", "\n", "# Build display table in the exact Supporting Information structure.\n", "doc_rows = []\n", "for _, r in table2_all.sort_values([\"Speed_mm_min\", \"Approach_angle_1_deg\", \"Approach_angle_2_deg\"]).iterrows():\n", " sp = int(r[\"Speed_mm_min\"])\n", " doc_rows.append({\n", " \"Advancement speed\\n(mm/min)\": sp,\n", " \"Approach angle 1 (°), Approach angle 2 (°)\": f\"{fmt_angle(r['Approach_angle_1_deg'])}, {fmt_angle(r['Approach_angle_2_deg'])}\",\n", " \"Mean difference (angle 2 – angle 1) (95% CI)\\n(N)\": fmt_mean_ci(r),\n", " \"Adjusted p value\": fmt_p(r[\"p_adj\"]),\n", " \"Cohen’s |d|\": fmt_num(r[\"cohens_d_abs\"]),\n", " })\n", "\n", "display_df = pd.DataFrame(doc_rows)\n", "assert display_df.shape == (108, 5)\n", "\n", "# Leave repeated speed entries blank after the first row for each speed, matching the submitted S1 Table style.\n", "last_speed = None\n", "for i in range(len(display_df)):\n", " sp = display_df.loc[i, \"Advancement speed\\n(mm/min)\"]\n", " if sp == last_speed:\n", " display_df.loc[i, \"Advancement speed\\n(mm/min)\"] = \"\"\n", " else:\n", " last_speed = sp\n", "\n", "# Create DOCX.\n", "doc = Document()\n", "section = doc.sections[0]\n", "section.orientation = WD_ORIENT.LANDSCAPE\n", "section.page_width, section.page_height = section.page_height, section.page_width\n", "section.top_margin = Inches(0.5)\n", "section.bottom_margin = Inches(0.5)\n", "section.left_margin = Inches(0.5)\n", "section.right_margin = Inches(0.5)\n", "\n", "styles = doc.styles\n", "styles[\"Normal\"].font.name = \"Arial\"\n", "styles[\"Normal\"].font.size = Pt(8)\n", "\n", "p = doc.add_paragraph()\n", "r = p.add_run(\"S1 Table. Full pairwise comparisons of approach angles within each advancement speed.\")\n", "r.font.name = \"Arial\"\n", "r.font.size = Pt(10)\n", "r.bold = True\n", "\n", "columns = list(display_df.columns)\n", "table = doc.add_table(rows=1, cols=len(columns))\n", "table.alignment = WD_TABLE_ALIGNMENT.CENTER\n", "table.style = \"Table Grid\"\n", "\n", "# Approximate column widths for landscape layout.\n", "widths = [Inches(1.05), Inches(2.15), Inches(3.05), Inches(1.25), Inches(1.05)]\n", "for j, col in enumerate(columns):\n", " cell = table.rows[0].cells[j]\n", " set_cell_text(cell, col, font_size=8, bold=True, align=WD_ALIGN_PARAGRAPH.CENTER)\n", " cell.width = widths[j]\n", "\n", "for _, row in display_df.iterrows():\n", " cells = table.add_row().cells\n", " values = [row[col] for col in columns]\n", " for j, value in enumerate(values):\n", " align = WD_ALIGN_PARAGRAPH.CENTER if j in [0, 1, 3, 4] else WD_ALIGN_PARAGRAPH.LEFT\n", " set_cell_text(cells[j], value, font_size=8, align=align)\n", " cells[j].width = widths[j]\n", "\n", "# Vertically merge the advancement-speed column within each speed block, matching the submitted S1 Table style.\n", "# Header row is row 0; each speed contributes 36 pairwise-comparison rows.\n", "for start_row, end_row, speed_label in [(1, 36, \"200\"), (37, 72, \"400\"), (73, 108, \"600\")]:\n", " merged = table.cell(start_row, 0).merge(table.cell(end_row, 0))\n", " set_cell_text(merged, speed_label, font_size=8, align=WD_ALIGN_PARAGRAPH.CENTER)\n", "\n", "notes = (\n", " \"Notes: Mean difference = mean(approach angle 2) − mean(approach angle 1) (N); \"\n", " \"95% CIs are in N. The effect size is reported as Cohen’s |d|. \"\n", " \"Tukey–Kramer test was used at 200 and 400 mm/min. At 600 mm/min, within-speed pairwise comparisons were performed \"\n", " \"using two-sided Welch’s t-test with Holm adjustment. Adjusted p values are reported to three decimals when p ≥ 0.001 \"\n", " \"and as thresholds when p < 0.001.\"\n", ")\n", "p = doc.add_paragraph()\n", "r = p.add_run(notes)\n", "r.font.name = \"Arial\"\n", "r.font.size = Pt(8)\n", "\n", "out_path = OUTPUT_DIR / \"S1_Table.docx\"\n", "doc.save(out_path)\n", "print(f\"Saved: {out_path}\")\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "id": "TlFUPpzdSyIN", "outputId": "4d061564-67a3-42cc-8248-5f9eee5723c7" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Advancement Speed (mm/min) Model AIC BIC R2\n", "0 200 Linear 157.45 161.42 0.006\n", "1 200 Quadratic 73.91 79.88 0.796\n", "2 200 Cubic 68.90 76.85 0.821\n", "3 400 Linear 137.54 141.52 0.026\n", "4 400 Quadratic 47.78 53.75 0.822\n", "5 400 Cubic 44.84 52.80 0.837\n", "6 600 Linear 119.57 123.55 0.005\n", "7 600 Quadratic 65.85 71.82 0.645\n", "8 600 Cubic 67.82 75.78 0.645" ], "text/html": [ "\n", "
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Advancement Speed (mm/min)ModelAICBICR2
0200Linear157.45161.420.006
1200Quadratic73.9179.880.796
2200Cubic68.9076.850.821
3400Linear137.54141.520.026
4400Quadratic47.7853.750.822
5400Cubic44.8452.800.837
6600Linear119.57123.550.005
7600Quadratic65.8571.820.645
8600Cubic67.8275.780.645
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "table3_disp", "summary": "{\n \"name\": \"table3_disp\",\n \"rows\": 9,\n \"fields\": [\n {\n \"column\": \"Advancement Speed (mm/min)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 173,\n \"min\": 200,\n \"max\": 600,\n \"num_unique_values\": 3,\n \"samples\": [\n 200,\n 400,\n 600\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Model\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Linear\",\n \"Quadratic\",\n \"Cubic\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"AIC\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"65.85\",\n \"73.91\",\n \"44.84\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"BIC\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"71.82\",\n \"79.88\",\n \"52.80\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"R2\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8,\n \"samples\": [\n \"0.796\",\n \"0.837\",\n \"0.006\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 7 } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "# ---- safety check\n", "required = {\"Speed_mm_min\", \"Angle_deg\", \"PerforationForce_N\"}\n", "missing = required - set(df.columns)\n", "assert not missing, f\"dfに必要列がない: {missing} / df.columns={list(df.columns)}\"\n", "\n", "def fit_poly_metrics(x, y, degree):\n", " \"\"\"\n", " y = b0 + b1 x + ... + bd x^d を最小二乗で当てはめ、AIC/BIC/R2を返す（yスケール）。\n", " \"\"\"\n", " x = np.asarray(x, float)\n", " y = np.asarray(y, float)\n", " n = len(y)\n", "\n", " # design matrix: [1, x, x^2, ...]\n", " X = np.column_stack([x**k for k in range(degree + 1)])\n", " beta, _, _, _ = np.linalg.lstsq(X, y, rcond=None)\n", " yhat = X @ beta\n", "\n", " rss = np.sum((y - yhat)**2)\n", " tss = np.sum((y - y.mean())**2)\n", " r2 = 1.0 - rss/tss if tss > 0 else np.nan\n", "\n", " # Gaussian log-likelihood with sigma^2 = RSS/n (MLE)\n", " rss = max(rss, 1e-12)\n", " ll = -n/2 * (np.log(2*np.pi) + 1 + np.log(rss/n))\n", "\n", " k = degree + 1 # number of parameters\n", " aic = 2*k - 2*ll\n", " bic = k*np.log(n) - 2*ll\n", " return aic, bic, r2\n", "\n", "rows = []\n", "for sp in [200, 400, 600]:\n", " dfi = df[df[\"Speed_mm_min\"] == sp].copy()\n", " x = dfi[\"Angle_deg\"].values\n", " y = dfi[\"PerforationForce_N\"].values\n", "\n", " aic, bic, r2 = fit_poly_metrics(x, y, degree=1)\n", " rows.append([sp, \"Linear\", aic, bic, r2])\n", "\n", " aic, bic, r2 = fit_poly_metrics(x, y, degree=2)\n", " rows.append([sp, \"Quadratic\", aic, bic, r2])\n", "\n", " aic, bic, r2 = fit_poly_metrics(x, y, degree=3)\n", " rows.append([sp, \"Cubic\", aic, bic, r2])\n", "\n", "table3 = pd.DataFrame(rows, columns=[\"Advancement Speed (mm/min)\", \"Model\", \"AIC\", \"BIC\", \"R2\"])\n", "table3[[\"AIC\",\"BIC\",\"R2\"]] = table3[[\"AIC\",\"BIC\",\"R2\"]].astype(float)\n", "\n", "# 表示用整形\n", "table3_disp = table3.copy()\n", "table3_disp[\"AIC\"] = table3_disp[\"AIC\"].map(lambda v: f\"{v:.2f}\")\n", "table3_disp[\"BIC\"] = table3_disp[\"BIC\"].map(lambda v: f\"{v:.2f}\")\n", "table3_disp[\"R2\"] = table3_disp[\"R2\"].map(lambda v: f\"{v:.3f}\")\n", "\n", "table3_disp\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 163 }, "id": "g2O1FrZdS2ga", "outputId": "fc7ca4d2-f6ed-4010-903d-ea9056677d23" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Speed_mm_min R2_quadratic p_add_quadratic_term p_model_quadratic b0 b1 b2\n", "0 200 0.796 < 0.001 < 0.001 3.940267 0.003125 0.001527\n", "1 400 0.822 < 0.001 < 0.001 3.366595 0.005242 0.001288\n", "2 600 0.645 < 0.001 < 0.001 2.793658 0.001844 0.000968" ], "text/html": [ "\n", "
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Speed_mm_minR2_quadraticp_add_quadratic_termp_model_quadraticb0b1b2
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "disp", "summary": "{\n \"name\": \"disp\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"Speed_mm_min\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 200,\n \"min\": 200,\n \"max\": 600,\n \"num_unique_values\": 3,\n \"samples\": [\n 200,\n 400,\n 600\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"R2_quadratic\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"0.796\",\n \"0.822\",\n \"0.645\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"p_add_quadratic_term\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"< 0.001\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"p_model_quadratic\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"< 0.001\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"b0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.573304512666486,\n \"min\": 2.7936580086580087,\n \"max\": 3.9402669552669556,\n \"num_unique_values\": 3,\n \"samples\": [\n 3.9402669552669556\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"b1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.001715673780948376,\n \"min\": 0.001844444444444428,\n \"max\": 0.00524166666666662,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.0031249999999999577\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"b2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0002805983849169203,\n \"min\": 0.0009675685425685426,\n \"max\": 0.001526821789321789,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.001526821789321789\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 8 } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import statsmodels.formula.api as smf\n", "import statsmodels.api as sm\n", "\n", "speeds = [200, 400, 600]\n", "out_rows = []\n", "\n", "def fmt_p(p):\n", " if p < 0.001:\n", " return \"< 0.001\"\n", " return f\"{p:.3f}\"\n", "\n", "for spd in speeds:\n", " dfi = df[df[\"Speed_mm_min\"] == spd].copy()\n", "\n", " # linear and quadratic (trial-level)\n", " m_lin = smf.ols(\"PerforationForce_N ~ Angle_deg\", data=dfi).fit()\n", " m_quad = smf.ols(\"PerforationForce_N ~ Angle_deg + I(Angle_deg**2)\", data=dfi).fit()\n", "\n", " # nested F-test: does adding Angle^2 improve fit?\n", " an = sm.stats.anova_lm(m_lin, m_quad)\n", " p_add_quad = float(an[\"Pr(>F)\"].iloc[1])\n", "\n", " # overall model p-value (quadratic vs intercept-only)\n", " p_model = float(m_quad.f_pvalue)\n", "\n", " out_rows.append({\n", " \"Speed_mm_min\": spd,\n", " \"R2_quadratic\": float(m_quad.rsquared),\n", " \"p_add_quadratic_term\": p_add_quad,\n", " \"p_model_quadratic\": p_model,\n", " \"b0\": float(m_quad.params[\"Intercept\"]),\n", " \"b1\": float(m_quad.params[\"Angle_deg\"]),\n", " \"b2\": float(m_quad.params[\"I(Angle_deg ** 2)\"]),\n", " })\n", "\n", "res_fig3 = pd.DataFrame(out_rows)\n", "\n", "# 表示（論文に書きやすい形）\n", "disp = res_fig3.copy()\n", "disp[\"R2_quadratic\"] = disp[\"R2_quadratic\"].map(lambda v: f\"{v:.3f}\")\n", "disp[\"p_add_quadratic_term\"] = disp[\"p_add_quadratic_term\"].map(fmt_p)\n", "disp[\"p_model_quadratic\"] = disp[\"p_model_quadratic\"].map(fmt_p)\n", "disp\n" ] }, { "cell_type": "markdown", "metadata": { "id": "81b05121" }, "source": [ "# Complete regression-model outputs\n", "\n", "This cell reports complete coefficient-level results for the linear, quadratic, and cubic regression models at each advancement speed. The output includes coefficient estimates, standard errors, 95% confidence intervals, term-specific p values, model-level p values, R², adjusted R², AIC, and BIC. The same results are exported as `Regression_model_full_results.csv`.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "cc1a9553", "outputId": "e162c0ce-cb14-4d1e-a256-ba3e12398e66" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " Speed_mm_min Model Term Coefficient Standard_error CI95_lower CI95_upper Term_p_value N R2 Adjusted_R2 Model_p_value AIC BIC\n", "0 200 Linear Intercept 4.95815 0.138932 4.67936 5.23694 3.04489e-38 54 0.00644454 -0.0126623 0.563907 157.445 161.423\n", "1 200 Linear Angle_deg 0.003125 0.00538082 -0.00767241 0.0139224 0.563907 54 0.00644454 -0.0126623 0.563907 157.445 161.423\n", "2 200 Quadratic Intercept 3.94027 0.0963418 3.74685 4.13368 1.16272e-40 54 0.796155 0.788161 2.43867e-18 73.9132 79.8802\n", "3 200 Quadratic Angle_deg 0.003125 0.00246104 -0.00181575 0.00806575 0.209924 54 0.796155 0.788161 2.43867e-18 73.9132 79.8802\n", "4 200 Quadratic I(Angle_deg ** 2) 0.00152682 0.000108622 0.00130875 0.00174489 3.56905e-19 54 0.796155 0.788161 2.43867e-18 73.9132 79.8802\n", "5 200 Cubic Intercept 3.94027 0.0911819 3.75712 4.12341 2.89947e-41 54 0.820986 0.810245 1.09404e-18 68.899 76.8549\n", "6 200 Cubic Angle_deg 0.0179743 0.00610076 0.0057206 0.0302281 0.00487419 54 0.820986 0.810245 1.09404e-18 68.899 76.8549\n", "7 200 Cubic I(Angle_deg ** 2) 0.00152682 0.000102805 0.00132033 0.00173331 5.75394e-20 54 0.820986 0.810245 1.09404e-18 68.899 76.8549\n", "8 200 Cubic I(Angle_deg ** 3) -1.25842e-05 4.77848e-06 -2.2182e-05 -2.98631e-06 0.011216 54 0.820986 0.810245 1.09404e-18 68.899 76.8549\n", "9 400 Linear Intercept 4.225 0.115552 3.99313 4.45687 9.04574e-39 54 0.0257029 0.00696641 0.24684 137.545 141.523\n", "10 400 Linear Angle_deg 0.00524167 0.0044753 -0.00373869 0.014222 0.24684 54 0.0257029 0.00696641 0.24684 137.545 141.523\n", "11 400 Quadratic Intercept 3.36659 0.0756387 3.21474 3.51845 1.74626e-42 54 0.821881 0.814896 7.81851e-20 47.7844 53.7514\n", "12 400 Quadratic Angle_deg 0.00524167 0.00193218 0.00136264 0.00912069 0.00907395 54 0.821881 0.814896 7.81851e-20 47.7844 53.7514\n", "13 400 Quadratic I(Angle_deg ** 2) 0.00128761 8.52803e-05 0.0011164 0.00145882 1.86014e-20 54 0.821881 0.814896 7.81851e-20 47.7844 53.7514\n", "14 400 Cubic Intercept 3.36659 0.0729736 3.22002 3.51317 1.19132e-42 54 0.837462 0.82771 9.88087e-20 44.841 52.797\n", "15 400 Cubic Angle_deg 0.0151214 0.00488249 0.00531459 0.0249281 0.00320186 54 0.837462 0.82771 9.88087e-20 44.841 52.797\n", "16 400 Cubic I(Angle_deg ** 2) 0.00128761 8.22755e-05 0.00112235 0.00145286 6.61511e-21 54 0.837462 0.82771 9.88087e-20 44.841 52.797\n", "17 400 Cubic I(Angle_deg ** 3) -8.37262e-06 3.82426e-06 -1.60539e-05 -6.91368e-07 0.033267 54 0.837462 0.82771 9.88087e-20 44.841 52.797\n", "18 600 Linear Intercept 3.4387 0.097838 3.24238 3.63503 6.52871e-38 54 0.00453573 -0.0146078 0.628476 119.573 123.551\n", "19 600 Linear Angle_deg 0.00184444 0.00378925 -0.00575924 0.00944813 0.628476 54 0.00453573 -0.0146078 0.628476 119.573 123.551\n", "20 600 Quadratic Intercept 2.79366 0.089413 2.61415 2.97316 6.34356e-35 54 0.645272 0.631361 3.32926e-12 65.8525 71.8194\n", "21 600 Quadratic Angle_deg 0.00184444 0.00228405 -0.00274097 0.00642986 0.423109 54 0.645272 0.631361 3.32926e-12 65.8525 71.8194\n", "22 600 Quadratic I(Angle_deg ** 2) 0.000967569 0.00010081 0.000765183 0.00116995 5.12819e-13 54 0.645272 0.631361 3.32926e-12 65.8525 71.8194\n", "23 600 Cubic Intercept 2.79366 0.0902772 2.61233 2.97499 2.83668e-34 54 0.645471 0.6242 2.5621e-11 67.8221 75.778\n", "24 600 Cubic Angle_deg 0.00278143 0.00604023 -0.00935073 0.0149136 0.647166 54 0.645471 0.6242 2.5621e-11 67.8221 75.778\n", "25 600 Cubic I(Angle_deg ** 2) 0.000967569 0.000101785 0.000763128 0.00117201 8.59461e-13 54 0.645471 0.6242 2.5621e-11 67.8221 75.778\n", "26 600 Cubic I(Angle_deg ** 3) -7.94052e-07 4.73107e-06 -1.02967e-05 8.70858e-06 0.867388 54 0.645471 0.6242 2.5621e-11 67.8221 75.778" ], "text/html": [ "\n", "
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Speed_mm_minModelTermCoefficientStandard_errorCI95_lowerCI95_upperTerm_p_valueNR2Adjusted_R2Model_p_valueAICBIC
0200LinearIntercept4.958150.1389324.679365.236943.04489e-38540.00644454-0.01266230.563907157.445161.423
1200LinearAngle_deg0.0031250.00538082-0.007672410.01392240.563907540.00644454-0.01266230.563907157.445161.423
2200QuadraticIntercept3.940270.09634183.746854.133681.16272e-40540.7961550.7881612.43867e-1873.913279.8802
3200QuadraticAngle_deg0.0031250.00246104-0.001815750.008065750.209924540.7961550.7881612.43867e-1873.913279.8802
4200QuadraticI(Angle_deg ** 2)0.001526820.0001086220.001308750.001744893.56905e-19540.7961550.7881612.43867e-1873.913279.8802
5200CubicIntercept3.940270.09118193.757124.123412.89947e-41540.8209860.8102451.09404e-1868.89976.8549
6200CubicAngle_deg0.01797430.006100760.00572060.03022810.00487419540.8209860.8102451.09404e-1868.89976.8549
7200CubicI(Angle_deg ** 2)0.001526820.0001028050.001320330.001733315.75394e-20540.8209860.8102451.09404e-1868.89976.8549
8200CubicI(Angle_deg ** 3)-1.25842e-054.77848e-06-2.2182e-05-2.98631e-060.011216540.8209860.8102451.09404e-1868.89976.8549
9400LinearIntercept4.2250.1155523.993134.456879.04574e-39540.02570290.006966410.24684137.545141.523
10400LinearAngle_deg0.005241670.0044753-0.003738690.0142220.24684540.02570290.006966410.24684137.545141.523
11400QuadraticIntercept3.366590.07563873.214743.518451.74626e-42540.8218810.8148967.81851e-2047.784453.7514
12400QuadraticAngle_deg0.005241670.001932180.001362640.009120690.00907395540.8218810.8148967.81851e-2047.784453.7514
13400QuadraticI(Angle_deg ** 2)0.001287618.52803e-050.00111640.001458821.86014e-20540.8218810.8148967.81851e-2047.784453.7514
14400CubicIntercept3.366590.07297363.220023.513171.19132e-42540.8374620.827719.88087e-2044.84152.797
15400CubicAngle_deg0.01512140.004882490.005314590.02492810.00320186540.8374620.827719.88087e-2044.84152.797
16400CubicI(Angle_deg ** 2)0.001287618.22755e-050.001122350.001452866.61511e-21540.8374620.827719.88087e-2044.84152.797
17400CubicI(Angle_deg ** 3)-8.37262e-063.82426e-06-1.60539e-05-6.91368e-070.033267540.8374620.827719.88087e-2044.84152.797
18600LinearIntercept3.43870.0978383.242383.635036.52871e-38540.00453573-0.01460780.628476119.573123.551
19600LinearAngle_deg0.001844440.00378925-0.005759240.009448130.628476540.00453573-0.01460780.628476119.573123.551
20600QuadraticIntercept2.793660.0894132.614152.973166.34356e-35540.6452720.6313613.32926e-1265.852571.8194
21600QuadraticAngle_deg0.001844440.00228405-0.002740970.006429860.423109540.6452720.6313613.32926e-1265.852571.8194
22600QuadraticI(Angle_deg ** 2)0.0009675690.000100810.0007651830.001169955.12819e-13540.6452720.6313613.32926e-1265.852571.8194
23600CubicIntercept2.793660.09027722.612332.974992.83668e-34540.6454710.62422.5621e-1167.822175.778
24600CubicAngle_deg0.002781430.00604023-0.009350730.01491360.647166540.6454710.62422.5621e-1167.822175.778
25600CubicI(Angle_deg ** 2)0.0009675690.0001017850.0007631280.001172018.59461e-13540.6454710.62422.5621e-1167.822175.778
26600CubicI(Angle_deg ** 3)-7.94052e-074.73107e-06-1.02967e-058.70858e-060.867388540.6454710.62422.5621e-1167.822175.778
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "regression_full_results_display", "summary": "{\n \"name\": \"regression_full_results_display\",\n \"rows\": 27,\n \"fields\": [\n {\n \"column\": \"Speed_mm_min\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 166,\n \"min\": 200,\n \"max\": 600,\n \"num_unique_values\": 3,\n \"samples\": [\n 200,\n 400,\n 600\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Model\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Linear\",\n \"Quadratic\",\n \"Cubic\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Term\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Angle_deg\",\n \"I(Angle_deg ** 3)\",\n \"Intercept\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Coefficient\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 18,\n \"samples\": [\n \"4.95815\",\n \"0.003125\",\n \"3.36659\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Standard_error\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 27,\n \"samples\": [\n \"4.77848e-06\",\n \"8.52803e-05\",\n \"0.115552\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CI95_lower\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 27,\n \"samples\": [\n \"-2.2182e-05\",\n \"0.0011164\",\n \"3.99313\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CI95_upper\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 27,\n \"samples\": [\n \"-2.98631e-06\",\n \"0.00145882\",\n \"4.45687\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Term_p_value\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 27,\n \"samples\": [\n \"0.011216\",\n \"1.86014e-20\",\n \"9.04574e-39\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"N\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 54,\n \"max\": 54,\n \"num_unique_values\": 1,\n \"samples\": [\n 54\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"R2\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"0.645272\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Adjusted_R2\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"0.631361\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Model_p_value\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"3.32926e-12\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"AIC\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"65.8525\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"BIC\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"71.8194\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: Regression_model_full_results.csv (27 coefficient rows)\n" ] } ], "source": [ "# === Complete coefficient-level outputs for all regression models ===\n", "import pandas as pd\n", "import statsmodels.formula.api as smf\n", "from IPython.display import display\n", "\n", "model_specs = {\n", " \"Linear\": \"PerforationForce_N ~ Angle_deg\",\n", " \"Quadratic\": \"PerforationForce_N ~ Angle_deg + I(Angle_deg**2)\",\n", " \"Cubic\": (\n", " \"PerforationForce_N ~ Angle_deg + I(Angle_deg**2) \"\n", " \"+ I(Angle_deg**3)\"\n", " ),\n", "}\n", "\n", "regression_rows = []\n", "\n", "for speed in [200, 400, 600]:\n", " df_speed = df[df[\"Speed_mm_min\"] == speed].copy()\n", "\n", " for model_name, formula in model_specs.items():\n", " fitted_model = smf.ols(formula, data=df_speed).fit()\n", " confidence_intervals = fitted_model.conf_int(alpha=0.05)\n", "\n", " for term in fitted_model.params.index:\n", " regression_rows.append(\n", " {\n", " \"Speed_mm_min\": speed,\n", " \"Model\": model_name,\n", " \"Term\": term,\n", " \"Coefficient\": float(fitted_model.params[term]),\n", " \"Standard_error\": float(fitted_model.bse[term]),\n", " \"CI95_lower\": float(confidence_intervals.loc[term, 0]),\n", " \"CI95_upper\": float(confidence_intervals.loc[term, 1]),\n", " \"Term_p_value\": float(fitted_model.pvalues[term]),\n", " \"N\": int(fitted_model.nobs),\n", " \"R2\": float(fitted_model.rsquared),\n", " \"Adjusted_R2\": float(fitted_model.rsquared_adj),\n", " \"Model_p_value\": float(fitted_model.f_pvalue),\n", " \"AIC\": float(fitted_model.aic),\n", " \"BIC\": float(fitted_model.bic),\n", " }\n", " )\n", "\n", "regression_full_results = pd.DataFrame(regression_rows)\n", "\n", "# Save the full-precision numerical results.\n", "regression_full_results.to_csv(\n", " \"Regression_model_full_results.csv\",\n", " index=False,\n", ")\n", "\n", "# Display a readable version in the notebook.\n", "regression_full_results_display = regression_full_results.copy()\n", "numeric_columns = [\n", " \"Coefficient\",\n", " \"Standard_error\",\n", " \"CI95_lower\",\n", " \"CI95_upper\",\n", " \"Term_p_value\",\n", " \"R2\",\n", " \"Adjusted_R2\",\n", " \"Model_p_value\",\n", " \"AIC\",\n", " \"BIC\",\n", "]\n", "\n", "for column in numeric_columns:\n", " regression_full_results_display[column] = (\n", " regression_full_results_display[column]\n", " .map(lambda value: f\"{value:.6g}\")\n", " )\n", "\n", "display(regression_full_results_display)\n", "\n", "print(\n", " \"Saved: Regression_model_full_results.csv \"\n", " f\"({len(regression_full_results)} coefficient rows)\"\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "V6pdC2cat2XT" }, "source": [ "# Figure 2: Dependence of perforation force on approach angle\n", "This cell reproduces the mean ± SD plot across approach angles for each advancement speed and saves it as a 300-dpi LZW-compressed TIFF file.\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "wuVmb2p6t2XU", "outputId": "d69d76d2-25f8-4480-acce-d81b282df5ef" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: /content/Fig2.tif\n" ] } ], "source": [ "# === Figure 2: Dependence of perforation force on approach angle ===\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "speeds = [200, 400, 600]\n", "angles = [-40, -30, -20, -10, 0, 10, 20, 30, 40]\n", "\n", "summary_fig2 = (df.groupby([\"Speed_mm_min\", \"Angle_deg\"])[\"PerforationForce_N\"]\n", " .agg(mean=\"mean\", sd=\"std\", n=\"count\")\n", " .reset_index()\n", " .sort_values([\"Speed_mm_min\", \"Angle_deg\"]))\n", "\n", "# Horizontal offsets are used only for visual clarity; x-axis tick labels show the nominal angles.\n", "offset = {200: -1.5, 400: 0.0, 600: 1.5}\n", "\n", "fig, ax = plt.subplots(figsize=(7.5, 5.2))\n", "\n", "for sp in speeds:\n", " dfi = summary_fig2[summary_fig2[\"Speed_mm_min\"] == sp]\n", " x = dfi[\"Angle_deg\"].values + offset[sp]\n", " ax.errorbar(\n", " x,\n", " dfi[\"mean\"].values,\n", " yerr=dfi[\"sd\"].values,\n", " marker=\"o\",\n", " linestyle=\"-\",\n", " capsize=3,\n", " label=f\"{sp} mm/min\"\n", " )\n", "\n", "ax.set_xlabel(\"Approach angle (°)\", fontsize=9)\n", "ax.set_ylabel(\"Perforation force (N)\", fontsize=9)\n", "ax.set_xticks(angles)\n", "ax.set_xticklabels([str(a) for a in angles])\n", "ax.legend(title=\"Advancement speed\", fontsize=8, title_fontsize=8)\n", "ax.grid(True, axis=\"y\", alpha=0.3)\n", "ax.tick_params(axis=\"both\", labelsize=8)\n", "fig.tight_layout()\n", "\n", "out_path = save_tiff_rgb_lzw(fig, \"Fig2.tif\", dpi=300)\n", "plt.show()\n", "\n", "print(f\"Saved: {out_path}\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "CYBiHEL1t2XU" }, "source": [ "# Figure 3: Quadratic regression models\n", "This cell reproduces the combined three-panel quadratic regression figure for 200, 400, and 600 mm/min and saves it as a 300-dpi LZW-compressed TIFF file. Separate panel files are not generated.\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "iJU6icgPt2XV", "outputId": "d629f606-635a-48e5-d049-3e4be14a4288" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font 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not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: /content/Fig3.tif\n" ] } ], "source": [ "# === Figure 3: Quadratic regression models ===\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import statsmodels.formula.api as smf\n", "\n", "speeds = [200, 400, 600]\n", "angles = [-40, -30, -20, -10, 0, 10, 20, 30, 40]\n", "panel_labels = {200: \"a\", 400: \"b\", 600: \"c\"}\n", "\n", "# Combined 3-panel figure only\n", "fig, axes = plt.subplots(1, 3, figsize=(15, 4.6), sharey=True)\n", "\n", "for ax, sp in zip(axes, speeds):\n", " dfi = df[df[\"Speed_mm_min\"] == sp].copy()\n", " summary = (dfi.groupby(\"Angle_deg\")[\"PerforationForce_N\"]\n", " .agg(mean=\"mean\", sd=\"std\", n=\"count\")\n", " .reset_index()\n", " .sort_values(\"Angle_deg\"))\n", "\n", " model_quad = smf.ols(\"PerforationForce_N ~ Angle_deg + I(Angle_deg**2)\", data=dfi).fit()\n", " x_grid = np.linspace(min(angles), max(angles), 300)\n", " pred = model_quad.predict(pd.DataFrame({\"Angle_deg\": x_grid}))\n", "\n", " ax.errorbar(\n", " summary[\"Angle_deg\"],\n", " summary[\"mean\"],\n", " yerr=summary[\"sd\"],\n", " marker=\"o\",\n", " linestyle=\"None\",\n", " capsize=3,\n", " label=\"Mean ± SD\"\n", " )\n", " ax.plot(x_grid, pred, linestyle=\"--\", label=f\"Quadratic fit, R² = {model_quad.rsquared:.2f}\")\n", "\n", " ax.set_title(f\"({panel_labels[sp]}) {sp} mm/min\", fontsize=10)\n", " ax.set_xlabel(\"Approach angle (°)\", fontsize=9)\n", " ax.set_xticks(angles)\n", " ax.grid(True, axis=\"y\", alpha=0.3)\n", " ax.tick_params(axis=\"both\", labelsize=8)\n", " ax.legend(fontsize=8)\n", "\n", "axes[0].set_ylabel(\"Perforation force (N)\", fontsize=9)\n", "fig.tight_layout()\n", "out_path = save_tiff_rgb_lzw(fig, \"Fig3.tif\", dpi=300)\n", "plt.show()\n", "\n", "print(f\"Saved: {out_path}\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "SLqEKovdt2XV" }, "source": [ "# Figure 4: Signed Cohen's d heatmaps\n", "This cell reproduces the combined three-panel heatmap figure in a horizontal layout consistent with Figure 3 and saves it as a 300-dpi LZW-compressed TIFF file.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "X7Y5RFEdS6zA", "outputId": "fd17199f-9088-4bc6-e63a-a221a56174c5" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", 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family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", 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not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", "WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n", 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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: /content/Fig4.tif\n" ] } ], "source": [ "# === Figure 4: Heatmaps of signed Cohen's d values ===\n", "# Horizontal three-panel layout consistent with Figure 3\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "\n", "angles = [-40, -30, -20, -10, 0, 10, 20, 30, 40]\n", "\n", "def cohens_d_signed(x1, x2):\n", " x1 = np.asarray(x1, float)\n", " x2 = np.asarray(x2, float)\n", " n1, n2 = len(x1), len(x2)\n", " s1 = x1.var(ddof=1)\n", " s2 = x2.var(ddof=1)\n", " sp = np.sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))\n", " return float((x2.mean() - x1.mean()) / sp) # column - row\n", "\n", "def d_matrix_for_speed(dfi):\n", " M = np.zeros((len(angles), len(angles)), float)\n", " for i, a1 in enumerate(angles):\n", " x1 = dfi.loc[dfi[\"Angle_deg\"] == a1, \"PerforationForce_N\"].values\n", " for j, a2 in enumerate(angles):\n", " x2 = dfi.loc[dfi[\"Angle_deg\"] == a2, \"PerforationForce_N\"].values\n", " M[i, j] = 0.0 if i == j else cohens_d_signed(x1, x2)\n", " return M\n", "\n", "def format_signed_d_for_fig4(value):\n", " \"\"\"Format signed Cohen's d for cell labels and suppress negative zero.\"\"\"\n", " value_rounded = np.round(float(value), 2)\n", " if value_rounded == 0:\n", " return \"0.00\"\n", " return f\"{value_rounded:.2f}\"\n", "\n", "# Color adjustment\n", "cmap_name = \"seismic\"\n", "blend = 0.52\n", "v_scale = 1.25\n", "alpha = 0.92\n", "\n", "base = plt.get_cmap(cmap_name)\n", "colors = base(np.linspace(0, 1, 256))\n", "colors[:, :3] = 1 - (1 - colors[:, :3]) * (1 - blend)\n", "cmap = ListedColormap(colors)\n", "\n", "speeds = [200, 400, 600]\n", "panel_labels = {200: \"a\", 400: \"b\", 600: \"c\"}\n", "mats = [d_matrix_for_speed(df[df[\"Speed_mm_min\"] == sp].copy()) for sp in speeds]\n", "\n", "# Common scale across the three panels\n", "max_abs = max(np.max(np.abs(M)) for M in mats)\n", "v = max_abs * v_scale\n", "\n", "# Horizontal 3-panel figure to match Fig 3.\n", "# At 300 dpi, this produces a 5400 x 1920 px image.\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 6.4), constrained_layout=True)\n", "\n", "for ax, sp, M in zip(axes, speeds, mats):\n", " im = ax.imshow(M, vmin=-v, vmax=v, cmap=cmap, aspect=\"equal\", alpha=alpha)\n", "\n", " ax.set_title(f\"({panel_labels[sp]}) {sp} mm/min\", fontsize=10)\n", " ax.set_xticks(range(len(angles)))\n", " ax.set_yticks(range(len(angles)))\n", " ax.set_xticklabels(angles)\n", " ax.set_yticklabels(angles)\n", " ax.set_xlabel(\"Approach angle [column, °]\", fontsize=9)\n", " ax.set_ylabel(\"Approach angle [row, °]\", fontsize=9)\n", " ax.tick_params(axis=\"both\", labelsize=8)\n", "\n", " for i in range(len(angles)):\n", " for j in range(len(angles)):\n", " label = format_signed_d_for_fig4(M[i, j])\n", " ax.text(j, i, label,\n", " ha=\"center\", va=\"center\", fontsize=7, color=\"black\")\n", "\n", "# Use one common colorbar for the three panels to save space and keep the scale identical.\n", "cbar = fig.colorbar(im, ax=axes.ravel().tolist(), fraction=0.025, pad=0.02)\n", "cbar.set_label(\"Signed Cohen's d\", fontsize=9)\n", "cbar.ax.tick_params(labelsize=8)\n", "\n", "out_path = save_tiff_rgb_lzw(fig, \"Fig4.tif\", dpi=300)\n", "plt.show()\n", "\n", "print(f\"Saved: {out_path}\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "pSzD2kTht2XW" }, "source": [ "# Sensitivity analysis for potential within-animal correlation\n", "\n", "Because source-animal identifiers were unavailable, animal-level clustering could not be modeled directly. Therefore, an ICC-based design-effect approach was used to estimate the potential reduction in effective sample size under assumed within-animal correlation scenarios.\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KWu1BSagt2XW", "outputId": "5616ddff-dd82-4f0d-8a0e-4fe686aafb77" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "ICC-based sensitivity analysis for potential within-animal correlation\n", " Assumed_ICC Average_cluster_size Design_effect Effective_sample_size\n", "0 0.05 5.06 1.20 135\n", "1 0.10 5.06 1.41 115\n", "2 0.20 5.06 1.81 89\n", "3 0.30 5.06 2.22 73\n" ] } ], "source": [ "# =========================================================\n", "# Sensitivity analysis for potential within-animal correlation\n", "# =========================================================\n", "\n", "import pandas as pd\n", "\n", "# Total number of trials/specimens and source animals\n", "n_total = 162\n", "n_source_animals = 32\n", "\n", "# Average number of specimens per source animal\n", "m = n_total / n_source_animals\n", "\n", "# Assumed intraclass correlation coefficients\n", "icc_values = [0.05, 0.10, 0.20, 0.30]\n", "\n", "sensitivity_results = []\n", "\n", "for icc in icc_values:\n", " design_effect = 1 + (m - 1) * icc\n", " effective_sample_size = n_total / design_effect\n", "\n", " sensitivity_results.append({\n", " \"Assumed_ICC\": icc,\n", " \"Average_cluster_size\": m,\n", " \"Design_effect\": design_effect,\n", " \"Effective_sample_size\": effective_sample_size\n", " })\n", "\n", "sensitivity_df = pd.DataFrame(sensitivity_results)\n", "\n", "# Round values for reporting in the manuscript\n", "sensitivity_df[\"Average_cluster_size\"] = sensitivity_df[\"Average_cluster_size\"].round(2)\n", "sensitivity_df[\"Design_effect\"] = sensitivity_df[\"Design_effect\"].round(2)\n", "sensitivity_df[\"Effective_sample_size\"] = sensitivity_df[\"Effective_sample_size\"].round(0).astype(int)\n", "\n", "print(\"\\nICC-based sensitivity analysis for potential within-animal correlation\")\n", "print(sensitivity_df)\n" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }