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docs(nb04): update text
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‎examples/hawks/04_MorphingShapeModes.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 80,
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"id": "51181a1c",
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"metadata": {
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"execution": {
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"shell.execute_reply": "2026-03-16T23:47:17.422247Z"
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}
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},
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"outputs": [],
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"source": "# --- Setup ---\n%load_ext autoreload\n%autoreload 2\n%matplotlib inline\n%config InlineBackend.figure_format='retina'\n\n# Reviewer default is False (~5x faster); set True to reproduce exact\n# manuscript iteration counts. Plots are unchanged; p-values are coarser.\nPAPER_MODE = False\n\nCONFIG = {\n 'rng_seed': 42,\n 'n_permutations': 500 if PAPER_MODE else 100,\n}\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nplt.rcParams['font.family'] = 'Andale Mono'\nnp.set_printoptions(suppress=True, precision=3)\n\nfrom morphing_birds import (\n Animal3D, plot_plotly, animate_plotly, animate_plotly_compare,\n animate_mode, animate_compare,\n)\n\nfrom kinematic_morphospace import (\n filter_by, run_PCA, run_PCA_birds, apply_sign_conventions,\n get_score_df, get_score_range, get_binned_raw_scores,\n reconstruct, to_bilateral, prepare_unilateral,\n bin_markers_by_distance,\n)\nfrom kinematic_morphospace.pca_core import get_PCA_input\nfrom kinematic_morphospace.null_testing import ensure_rng, validate_frame_alignment\nfrom kinematic_morphospace.plotting import (\n plot_explained, plot_components_grid,\n plot_score_multi_PCs, compare_coeffs_hawks,\n plot_3d_scatter_with_animation,\n save_figure,\n)\n\nrng = ensure_rng(CONFIG['rng_seed'])"
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"source": [
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"# --- Setup ---\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"%matplotlib inline\n",
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"%config InlineBackend.figure_format='retina'\n",
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"\n",
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"# Reviewer default is False (~5x faster); set True to reproduce exact\n",
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"# manuscript iteration counts. Plots are unchanged; p-values are coarser.\n",
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"PAPER_MODE = False\n",
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"\n",
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"CONFIG = {\n",
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" 'rng_seed': 42,\n",
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" 'n_permutations': 500 if PAPER_MODE else 100,\n",
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"}\n",
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"\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.rcParams['font.family'] = 'Andale Mono'\n",
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"np.set_printoptions(suppress=True, precision=3)\n",
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"\n",
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"from morphing_birds import (\n",
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" Animal3D, plot_plotly, animate_plotly, animate_plotly_compare,\n",
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" animate_mode, animate_compare,\n",
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")\n",
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"\n",
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"from kinematic_morphospace import (\n",
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" filter_by, run_PCA, run_PCA_birds, apply_sign_conventions,\n",
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" get_score_df, get_score_range, get_binned_raw_scores,\n",
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" reconstruct, to_bilateral, prepare_unilateral,\n",
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" bin_markers_by_distance,\n",
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")\n",
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"from kinematic_morphospace.pca_core import get_PCA_input\n",
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"from kinematic_morphospace.null_testing import ensure_rng, validate_frame_alignment\n",
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"from kinematic_morphospace.plotting import (\n",
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" plot_explained, plot_components_grid,\n",
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" plot_score_multi_PCs, compare_coeffs_hawks,\n",
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" plot_3d_scatter_with_animation,\n",
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" save_figure,\n",
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")\n",
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"\n",
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"rng = ensure_rng(CONFIG['rng_seed'])"
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]
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"execution_count": 81,
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"id": "e5c90609",
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"metadata": {
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"execution": {
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"print(f\"Frames: {len(combined_frame_info_df):,}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "052e6c3b",
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"metadata": {},
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"source": [
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"## View Feather Landmarks in 3D\n",
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"\n",
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"Drag to rotate. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 83,
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"id": "bf37ff80",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Charmander, 9 m, 2020: 9,396 frames from 121 flights\n"
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]
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}
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],
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"source": [
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"# Plot 3D scatter for a single hawk (Charmander, 9 m, 2020)\n",
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"filt = filter_by(combined_frame_info_df, hawkname=\"Charmander\", perchDist=9, year=2020)\n",
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"x = subset[:, :8, 0].flatten()\n",
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"y = subset[:, :8, 1].flatten()\n",
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"z = subset[:, :8, 2].flatten()\n",
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"_ = plot_3d_scatter_with_animation(x, y, z, browser=False)"
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"_ = plot_3d_scatter_with_animation(x, y, z, browser=True)"
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]
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},
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{
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"source": [
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"### Example Flight (Rotation-Corrected)\n",
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"\n",
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"Before decomposing the data, it is helpful to see what the rotation-corrected marker data looks like as a flight. The animation below shows the bilateral marker positions for Toothless (9 m, straight, period 2), averaged across flights by binning on horizontal distance to perch."
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"Before decomposing the data, it is helpful to see what the rotation-corrected marker data looks like as a flight. The animation below shows the bilateral marker positions for Toothless (9 m, straight, period 2), averaged across flights by binning on horizontal distance to perch.\n",
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"\n",
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"Press play/pause, drag the scrollbar, or click and drag the plot to rotate. "
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]
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{
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}
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"outputs": [],
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"source": "# Select a flight: Toothless, 9 m, straight, 2020\nflight = dict(hawkname=\"Toothless\", perchDist=\"9m\", turn=\"Straight\", year=2020)\n\n# --- Original bilateral markers, binned by distance (ground truth) ---\nn = len(combined_frame_info_df)\nbilateral_mask = filter_by(scores_df.iloc[:n], **flight)\nbins = scores_df.iloc[:n].loc[bilateral_mask, \"bins\"]\norig = transformed_markers[bilateral_mask, :8, :]\n\norig_df = pd.DataFrame(orig.reshape(-1, 24), index=bins.index)\norig_df[\"bins\"] = bins.values\norig_binned = orig_df.groupby(\"bins\", observed=True).mean()\n\n# --- Bin raw scores using the same distance bins ---\nleft_binned = get_binned_raw_scores(scores, scores_df, **flight, left=True)\nright_binned = get_binned_raw_scores(scores, scores_df, **flight, left=False)\n\n# Align all to the same bin index\ncommon_bins = orig_binned.index.intersection(left_binned.index).intersection(right_binned.index)\nprint(f\"Bins: {len(orig_binned)} original, {len(left_binned)} left, {len(right_binned)} right, {len(common_bins)} common\")\n\norig_bilateral = orig_binned.loc[common_bins].to_numpy().reshape(-1, 8, 3)\nleft_np = left_binned.loc[common_bins].to_numpy()\nright_np = right_binned.loc[common_bins].to_numpy()\n\nmu = mean_shape.reshape(1, -1, 3)\n\n# --- Sanity check: 12-mode reconstruction must match original ---\nall_modes = list(range(12))\nfull_recon = to_bilateral(\n reconstruct(right_np, principal_components, mu, all_modes),\n left=reconstruct(left_np, principal_components, mu, all_modes),\n)\nmax_err = np.abs(full_recon - orig_bilateral).max()\nprint(f\"Max error (12-mode vs original): {max_err:.2e} m\")\nassert max_err < 1e-10, f\"12-mode reconstruction does not match original! Max error: {max_err}\"\n\n# --- 4-mode reconstruction ---\nmodes = [0, 1, 2, 3]\nrecon_bilateral = to_bilateral(\n reconstruct(right_np, principal_components, mu, modes),\n left=reconstruct(left_np, principal_components, mu, modes),\n)\n\nprint(f\"Reconstructed {len(common_bins)} distance bins using {len(modes)} modes\")"
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"source": [
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"# Select a flight: Toothless, 9 m, straight, 2020\n",
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"flight = dict(hawkname=\"Toothless\", perchDist=\"9m\", turn=\"Straight\", year=2020)\n",
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"\n",
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"# --- Original bilateral markers, binned by distance (ground truth) ---\n",
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"n = len(combined_frame_info_df)\n",
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"bilateral_mask = filter_by(scores_df.iloc[:n], **flight)\n",
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"bins = scores_df.iloc[:n].loc[bilateral_mask, \"bins\"]\n",
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"orig = transformed_markers[bilateral_mask, :8, :]\n",
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"\n",
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"orig_df = pd.DataFrame(orig.reshape(-1, 24), index=bins.index)\n",
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"orig_df[\"bins\"] = bins.values\n",
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"orig_binned = orig_df.groupby(\"bins\", observed=True).mean()\n",
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"\n",
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"# --- Bin raw scores using the same distance bins ---\n",
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"left_binned = get_binned_raw_scores(scores, scores_df, **flight, left=True)\n",
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"right_binned = get_binned_raw_scores(scores, scores_df, **flight, left=False)\n",
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"\n",
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"# Align all to the same bin index\n",
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"common_bins = orig_binned.index.intersection(left_binned.index).intersection(right_binned.index)\n",
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"print(f\"Bins: {len(orig_binned)} original, {len(left_binned)} left, {len(right_binned)} right, {len(common_bins)} common\")\n",
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"\n",
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"orig_bilateral = orig_binned.loc[common_bins].to_numpy().reshape(-1, 8, 3)\n",
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"left_np = left_binned.loc[common_bins].to_numpy()\n",
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"right_np = right_binned.loc[common_bins].to_numpy()\n",
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"\n",
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"mu = mean_shape.reshape(1, -1, 3)\n",
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"\n",
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"# --- Sanity check: 12-mode reconstruction must match original ---\n",
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"all_modes = list(range(12))\n",
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"full_recon = to_bilateral(\n",
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" reconstruct(right_np, principal_components, mu, all_modes),\n",
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" left=reconstruct(left_np, principal_components, mu, all_modes),\n",
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")\n",
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"max_err = np.abs(full_recon - orig_bilateral).max()\n",
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"print(f\"Max error (12-mode vs original): {max_err:.2e} m\")\n",
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"assert max_err < 1e-10, f\"12-mode reconstruction does not match original! Max error: {max_err}\"\n",
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"\n",
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"# --- 4-mode reconstruction ---\n",
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"modes = [0, 1, 2, 3]\n",
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"recon_bilateral = to_bilateral(\n",
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" reconstruct(right_np, principal_components, mu, modes),\n",
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" left=reconstruct(left_np, principal_components, mu, modes),\n",
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")\n",
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"\n",
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"print(f\"Reconstructed {len(common_bins)} distance bins using {len(modes)} modes\")"
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]
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"cell_type": "code",
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

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