From 62b16e0a59bdb1b313c74bafc277177bdb872ff3 Mon Sep 17 00:00:00 2001 From: Evening Date: Tue, 4 Jun 2024 17:46:08 +0800 Subject: [PATCH 1/3] Add FRRD-81 Notebook --- notebooks/frrd-80/README.md | 5 - notebooks/{frrd-80 => frrd-81}/.gitignore | 0 notebooks/frrd-81/README.md | 17 + .../epoch=0-step=150.ckpt | Bin .../epoch=1-step=300.ckpt | Bin .../epoch=2-step=450.ckpt | Bin .../epoch=3-step=600.ckpt | Bin .../epoch=4-step=750.ckpt | Bin .../epoch=5-step=900.ckpt | Bin notebooks/{frrd-80 => frrd-81}/nb.ipynb | 76588 ++++++---------- 10 files changed, 25624 insertions(+), 50986 deletions(-) delete mode 100644 notebooks/frrd-80/README.md rename notebooks/{frrd-80 => frrd-81}/.gitignore (100%) create mode 100644 notebooks/frrd-81/README.md rename notebooks/{frrd-80 => frrd-81}/epoch=0-step=150.ckpt (100%) rename notebooks/{frrd-80 => frrd-81}/epoch=1-step=300.ckpt (100%) rename notebooks/{frrd-80 => frrd-81}/epoch=2-step=450.ckpt (100%) rename notebooks/{frrd-80 => frrd-81}/epoch=3-step=600.ckpt (100%) rename notebooks/{frrd-80 => frrd-81}/epoch=4-step=750.ckpt (100%) rename notebooks/{frrd-80 => frrd-81}/epoch=5-step=900.ckpt (100%) rename notebooks/{frrd-80 => frrd-81}/nb.ipynb (73%) diff --git a/notebooks/frrd-80/README.md b/notebooks/frrd-80/README.md deleted file mode 100644 index efeb715..0000000 --- a/notebooks/frrd-80/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# Jupyter Notebooks - -## FRRD-80: Visualization of FixMatch - -This simply shows the decision boundaries constructed by the FixMatch model. diff --git a/notebooks/frrd-80/.gitignore b/notebooks/frrd-81/.gitignore similarity index 100% rename from notebooks/frrd-80/.gitignore rename to notebooks/frrd-81/.gitignore diff --git a/notebooks/frrd-81/README.md b/notebooks/frrd-81/README.md new file mode 100644 index 0000000..672e0ac --- /dev/null +++ b/notebooks/frrd-81/README.md @@ -0,0 +1,17 @@ +# Jupyter Notebooks + +## FRRD-80/81: Visualization of FixMatch + +This simply shows the embeddings of the FixMatch model. +The embeddings are generated by the model and then visualized using LDA. + +We found that the model transfer doesn't necessarily equate to a well separated +embedding space. This is likely due to the fact that the model is trained on +too small of a dataset. + +We attribute much of performance degradation to our contextual shift when +we chose our backbone model. We chose a model that was trained on ImageNet, +which in retrospect, has a large contextual shift from our dataset. + +However, it's not all bad, we reinforced the idea that this shift is a real +problem and that we need to address it in our future work. diff --git a/notebooks/frrd-80/epoch=0-step=150.ckpt b/notebooks/frrd-81/epoch=0-step=150.ckpt similarity index 100% rename from notebooks/frrd-80/epoch=0-step=150.ckpt rename to notebooks/frrd-81/epoch=0-step=150.ckpt diff --git a/notebooks/frrd-80/epoch=1-step=300.ckpt b/notebooks/frrd-81/epoch=1-step=300.ckpt similarity index 100% rename from notebooks/frrd-80/epoch=1-step=300.ckpt rename to notebooks/frrd-81/epoch=1-step=300.ckpt diff --git a/notebooks/frrd-80/epoch=2-step=450.ckpt b/notebooks/frrd-81/epoch=2-step=450.ckpt similarity index 100% rename from notebooks/frrd-80/epoch=2-step=450.ckpt rename to notebooks/frrd-81/epoch=2-step=450.ckpt diff --git a/notebooks/frrd-80/epoch=3-step=600.ckpt b/notebooks/frrd-81/epoch=3-step=600.ckpt similarity index 100% rename from notebooks/frrd-80/epoch=3-step=600.ckpt rename to notebooks/frrd-81/epoch=3-step=600.ckpt diff --git a/notebooks/frrd-80/epoch=4-step=750.ckpt b/notebooks/frrd-81/epoch=4-step=750.ckpt similarity index 100% rename from notebooks/frrd-80/epoch=4-step=750.ckpt rename to notebooks/frrd-81/epoch=4-step=750.ckpt diff --git a/notebooks/frrd-80/epoch=5-step=900.ckpt b/notebooks/frrd-81/epoch=5-step=900.ckpt similarity index 100% rename from notebooks/frrd-80/epoch=5-step=900.ckpt rename to notebooks/frrd-81/epoch=5-step=900.ckpt diff --git a/notebooks/frrd-80/nb.ipynb b/notebooks/frrd-81/nb.ipynb similarity index 73% rename from notebooks/frrd-80/nb.ipynb rename to notebooks/frrd-81/nb.ipynb index 750045b..2fcd407 100644 --- a/notebooks/frrd-80/nb.ipynb +++ b/notebooks/frrd-81/nb.ipynb @@ -2,15 +2,6 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "id": "766f20d6021303d8", - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-04T04:53:05.673267600Z", - "start_time": "2024-06-04T04:53:05.663612600Z" - } - }, "outputs": [ { "name": "stdout", @@ -27,387 +18,244 @@ ], "source": [ "from __future__ import annotations\n", + "\n", "import os\n", "from pathlib import Path\n", "\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "import torch\n", + "from scipy.spatial import ConvexHull\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "\n", + "from frdc.models.efficientnetb1 import EfficientNetB1FixMatchModule\n", + "from frdc.train.utils import preprocess\n", + "from model_tests.utils import FRDCDatasetStaticEval, val_preprocess\n", + "\n", "# disable gpu\n", "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n", + "torch.set_grad_enabled(False)\n", "\n", "curr_dir = Path(os.path.abspath(\"\"))\n", "model_paths = list(curr_dir.glob(\"*.ckpt\"))\n", "for model_path in model_paths:\n", " print(model_path.name)" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-06-04T09:40:03.558651700Z", + "start_time": "2024-06-04T09:39:49.665390Z" + } + }, + "id": "23149afcfcfc144e", + "execution_count": 1 }, { "cell_type": "code", "outputs": [], "source": [ - "from pathlib import Path\n", - "\n", - "import numpy as np\n", - "import plotly.express as px\n", - "import plotly.graph_objects as go\n", - "from scipy.spatial import ConvexHull\n", - "from sklearn.preprocessing import OrdinalEncoder\n", - "from sklearn.svm import SVC\n", - "\n", - "\n", - "def get_df_lda(model_paths: list[str | Path]):\n", - " from model_tests.utils import FRDCDatasetStaticEval, val_preprocess\n", - " import torch\n", - "\n", - " ds = FRDCDatasetStaticEval(\n", - " \"chestnut_nature_park\",\n", - " \"20201218\",\n", - " None,\n", - " transform=val_preprocess(255),\n", - " )\n", - " xs_raw, ys_raw = list(zip(*ds))\n", - " xs_raw = torch.stack(xs_raw)\n", - "\n", - " def extract_model(model_path):\n", - " m = EfficientNetB1FixMatchModule.load_from_checkpoint(\n", - " model_path\n", - " ).eval()\n", - " m: EfficientNetB1FixMatchModule\n", - " m_eff = m.eff\n", - " m_fc = m.fc[0]\n", - " return m_eff, m_fc, m.x_scaler, m.y_encoder\n", - "\n", - " def extract_lda(xs_eff, xs_fc, ys, lda_eff, lda_fc, n_components=2):\n", - " if lda_eff is None:\n", - " lda_eff = LinearDiscriminantAnalysis(n_components=n_components)\n", - " lda_eff.fit(xs_eff, ys)\n", - " if lda_fc is None:\n", - " lda_fc = LinearDiscriminantAnalysis(n_components=n_components)\n", - " lda_fc.fit(xs_fc, ys)\n", - " xs_eff_lda = lda_eff.transform(xs_eff)\n", - " xs_fc_lda = lda_fc.transform(xs_fc)\n", - " return xs_eff_lda, xs_fc_lda, lda_eff, lda_fc\n", - "\n", - " dfs = []\n", - "\n", - " lda_eff = None\n", - " lda_fc = None\n", - "\n", - " for step, model_path in tqdm(\n", - " enumerate(model_paths),\n", - " total=len(model_paths),\n", - " desc=\"Extracting LDA\",\n", - " ):\n", - " # Extracts the Eff and FC layer\n", - " # and the X scaler and Y encoder\n", - " m_eff, m_fc, x_scaler, y_encoder = extract_model(model_path)\n", - "\n", - " # Preprocess the data\n", - " (xs, ys), _xs_unl = preprocess(xs_raw, ys_raw, x_scaler, y_encoder)\n", - "\n", - " # Extract the LDA\n", - " xs_eff_lda, xs_fc_lda, lda_eff, lda_fc = extract_lda(\n", - " (xs_eff := m_eff(xs)).detach().numpy(),\n", - " m_fc(xs_eff).detach().numpy(),\n", - " ys,\n", - " lda_eff=lda_eff,\n", - " lda_fc=lda_fc,\n", - " )\n", - "\n", - " # Construct the dataframe\n", - " df = pd.DataFrame(xs_fc_lda, columns=[\"x\", \"y\"])\n", - " df[\"label\"] = ys_raw\n", - " df[\"label\"] = df[\"label\"].astype(\"category\")\n", - " df[\"step\"] = step\n", - " df[[\"orient\", \"sample\"]] = [(i % 8, i // 8) for i in range(len(df))]\n", - " dfs.append(df)\n", - " df = pd.concat(dfs, ignore_index=True)\n", - " return df\n", - "\n", - "\n", - "def plot_svm_boundaries(\n", - " x,\n", - " y,\n", - " labels,\n", - " xy_range=25,\n", - " resolution=200,\n", - " contour_opacity=0.5,\n", - " width=800,\n", - " height=800,\n", - ") -> go.Figure:\n", - " \"\"\"Plot the data points and the decision boundaries.\n", - "\n", - " Args:\n", - " x: The x values of the data points.\n", - " y: The y values of the data points.\n", - " labels: The labels of the data points.\n", - " xy_range: The range of the x and y axes.\n", - " resolution: The resolution of the meshgrid.\n", - " contour_opacity: The opacity of the decision boundaries.\n", - " width: The width of the plot.\n", - " height: The height of the plot.\n", - "\n", - " \"\"\"\n", - " labels_int = (\n", - " OrdinalEncoder()\n", - " .fit_transform(np.array(labels).reshape(-1, 1))\n", - " .flatten()\n", - " )\n", - " xy = np.c_[x, y]\n", - " # Create a scatter plot\n", - " fig = go.Figure(\n", - " data=go.Scatter(\n", - " x=x,\n", - " y=y,\n", - " mode=\"markers\",\n", - " marker=dict(\n", - " color=labels_int, # Use 'Jet' color scale\n", - " size=10,\n", - " colorscale=\"jet\",\n", - " # Border\n", - " line=dict(color=\"white\", width=1),\n", - " ),\n", - " # Add names as text\n", - " text=labels,\n", - " hovertemplate=\"%{text}\",\n", - " ),\n", - " )\n", - "\n", - " # For each class in ys_p, we retrieve the xs_lda values\n", - " # then find the convex hull and draw it onto the plot\n", - " for lab in np.unique(labels):\n", - " xy_lab = xy[labels == lab]\n", - " hull = ConvexHull(xy_lab)\n", - " for simplex in hull.simplices:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=xy_lab[simplex, 0],\n", - " y=xy_lab[simplex, 1],\n", - " mode=\"lines\",\n", - " line=dict(color=\"white\"),\n", - " hoverinfo=\"skip\", # No hover info\n", - " ),\n", - " )\n", - "\n", - " # Fit the SVC model\n", - " clf = SVC()\n", - " clf.fit(xy, labels_int)\n", - "\n", - " # Create a meshgrid for contour plot\n", - " domain = np.linspace(-xy_range, xy_range, resolution)\n", - " xx, yy = np.meshgrid(domain, domain)\n", - " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", - " Z = Z.reshape(xx.shape)\n", - "\n", - " # Add contour plot to the figure with transparency\n", - " fig.add_trace(\n", - " go.Contour(\n", - " x=domain,\n", - " y=domain,\n", - " z=Z,\n", - " showscale=False,\n", - " opacity=contour_opacity,\n", - " colorscale=\"jet\",\n", - " hoverinfo=\"skip\", # No hover info\n", - " )\n", - " )\n", - "\n", - " # Make the aspect ratio square\n", - " fig.update_yaxes(scaleanchor=\"x\", scaleratio=1)\n", - " fig.update_layout(showlegend=False, width=width, height=height)\n", - " return fig\n", - "\n", - "\n", - "def plot_anim(\n", - " x, y, label, step, xy_range=25, width=800, height=800\n", - ") -> go.Figure:\n", - " \"\"\"Plot an animation of the data points.\n", - "\n", - " Args:\n", - " x: The x values of the data points.\n", - " y: The y values of the data points.\n", - " label: The labels of the data points.\n", - " step: The step of the animation.\n", - " xy_range: The range of the x and y axes.\n", - " width: The width of the plot.\n", - " height: The height of the plot.\n", - " \"\"\"\n", - " fig = px.scatter(\n", - " x=x,\n", - " y=y,\n", - " animation_frame=step,\n", - " color=label,\n", - " hover_name=label,\n", - " range_x=[-xy_range, xy_range],\n", - " range_y=[-xy_range, xy_range],\n", - " )\n", - " fig.update_layout(width=width, height=height)\n", - " return fig\n" + "m = EfficientNetB1FixMatchModule.load_from_checkpoint(model_paths[-1]).eval()\n", + "m: EfficientNetB1FixMatchModule" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-06-04T04:53:08.555154Z", - "start_time": "2024-06-04T04:53:05.678486700Z" + "end_time": "2024-06-04T09:40:04.084655200Z", + "start_time": "2024-06-04T09:40:03.556463900Z" } }, - "id": "2c65e7d2029dab4b", + "id": "8f43a473429a5e58", "execution_count": 2 }, { "cell_type": "code", - "execution_count": 3, - "id": "d36dce5890dba29c", + "outputs": [], + "source": [ + "ds_chs = FRDCDatasetStaticEval(\n", + " \"chestnut_nature_park\",\n", + " \"20201218\",\n", + " None,\n", + " transform=val_preprocess(255),\n", + ")\n", + "x_chs_raw, y_chs_raw = list(zip(*ds_chs))\n", + "x_chs_raw = torch.stack(x_chs_raw).detach()\n", + "(x_chs, y_chs), _ = preprocess(\n", + " x_chs_raw, y_chs_raw, m.x_scaler, m.y_encoder\n", + ")\n", + "x_chs_emb = m.eff(x_chs)" + ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-06-04T04:53:22.493880800Z", - "start_time": "2024-06-04T04:53:08.558559200Z" + "end_time": "2024-06-04T09:40:13.377009700Z", + "start_time": "2024-06-04T09:40:04.089937100Z" } }, + "id": "ac546548a044fa8b", + "execution_count": 3 + }, + { + "cell_type": "code", "outputs": [], "source": [ - "from model_tests.utils import FRDCDatasetStaticEval, val_preprocess\n", - "import torch\n", - "\n", - "ds = FRDCDatasetStaticEval(\n", - " \"chestnut_nature_park\", \"20201218\", None,\n", + "ds_cas = FRDCDatasetStaticEval(\n", + " \"casuarina\",\n", + " \"20220418\",\n", + " \"93deg\",\n", " transform=val_preprocess(255),\n", ")\n", - "xs_raw, ys_raw = list(zip(*ds))\n", - "xs_raw = torch.stack(xs_raw)" - ] + "x_cas_raw, y_cas_raw = list(zip(*ds_cas))\n", + "x_cas_raw = torch.stack(x_cas_raw).detach()\n", + "m.y_encoder.encoded_missing_value = -1\n", + "(x_cas, _), _ = preprocess(\n", + " x_cas_raw, y_cas_raw, m.x_scaler, m.y_encoder, nan_mask=False\n", + ")\n", + "x_cas_emb = m.eff(x_cas)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-06-04T09:40:37.637881100Z", + "start_time": "2024-06-04T09:40:13.379650300Z" + } + }, + "id": "8e24199179540e21", + "execution_count": 4 }, { "cell_type": "code", - "execution_count": 4, - "id": "ad591487ed18579b", + "outputs": [], + "source": [ + "lda = LinearDiscriminantAnalysis(n_components=2)\n", + "lda.fit(x_chs_emb.numpy(), y_chs)\n", + "x_chs_lda = lda.transform(x_chs_emb.numpy())\n", + "x_cas_lda = lda.transform(x_cas_emb.numpy())" + ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-06-04T04:53:50.135046600Z", - "start_time": "2024-06-04T04:53:22.497162200Z" + "end_time": "2024-06-04T09:40:37.846233400Z", + "start_time": "2024-06-04T09:40:37.637881100Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Extracting LDA: 100%|██████████| 6/6 [00:23<00:00, 3.95s/it]\n" - ] - } + "id": "941de67f0e9e68ca", + "execution_count": 5 + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "df = pd.concat([\n", + " pd.DataFrame(x_chs_lda, columns=[\"x\", \"y\"])\n", + " .assign(label=y_chs_raw, site=\"chestnut_nature_park\"),\n", + " pd.DataFrame(x_cas_lda, columns=[\"x\", \"y\"])\n", + " .assign(label=y_cas_raw, site=\"casuarina\"),\n", + "]).astype({\"label\": \"category\"})\n", + "df[['orient', 'sample']] = [(i % 8, i // 8) for i in range(len(df))]" ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-06-04T09:40:37.861506200Z", + "start_time": "2024-06-04T09:40:37.848361400Z" + } + }, + "id": "adedbd77c4248168", + "execution_count": 6 + }, + { + "cell_type": "code", + "outputs": [], "source": [ - "from tqdm import tqdm\n", - "from frdc.train.utils import preprocess\n", - "from frdc.models.efficientnetb1 import EfficientNetB1FixMatchModule\n", - "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", - "\n", - "import pandas as pd\n", - "\n", - "\n", - "def extract_model(model_path):\n", - " m = EfficientNetB1FixMatchModule.load_from_checkpoint(model_path).eval()\n", - " m: EfficientNetB1FixMatchModule\n", - " m_eff = m.eff\n", - " m_fc = m.fc[0]\n", - " return m_eff, m_fc, m.x_scaler, m.y_encoder\n", - "\n", - "\n", - "def extract_lda(xs_eff, xs_fc, ys, lda_eff, lda_fc, n_components=2):\n", - " if lda_eff is None:\n", - " lda_eff = LinearDiscriminantAnalysis(n_components=n_components)\n", - " lda_eff.fit(xs_eff, ys)\n", - " if lda_fc is None:\n", - " lda_fc = LinearDiscriminantAnalysis(n_components=n_components)\n", - " lda_fc.fit(xs_fc, ys)\n", - " xs_eff_lda = lda_eff.transform(xs_eff)\n", - " xs_fc_lda = lda_fc.transform(xs_fc)\n", - " return xs_eff_lda, xs_fc_lda, lda_eff, lda_fc\n", - "\n", - "\n", - "dfs = []\n", - "\n", - "lda_eff = None\n", - "lda_fc = None\n", - "\n", - "for step, model_path in tqdm(\n", - " enumerate(model_paths),\n", - " total=len(model_paths),\n", - " desc=\"Extracting LDA\",\n", - "):\n", - " # Extracts the Eff and FC layer\n", - " # and the X scaler and Y encoder\n", - " m_eff, m_fc, x_scaler, y_encoder = extract_model(model_path)\n", + "import numpy as np\n", "\n", - " # Preprocess the data\n", - " (xs, ys), _xs_unl = preprocess(xs_raw, ys_raw, x_scaler, y_encoder)\n", "\n", - " # Extract the LDA\n", - " xs_eff_lda, xs_fc_lda, lda_eff, lda_fc = extract_lda(\n", - " (xs_eff := m_eff(xs)).detach().numpy(),\n", - " m_fc(xs_eff).detach().numpy(),\n", - " ys,\n", - " lda_eff=lda_eff,\n", - " lda_fc=lda_fc,\n", + "def plot(df_: pd.DataFrame):\n", + " fig = px.scatter(\n", + " df_,\n", + " x=\"x\",\n", + " y=\"y\",\n", + " color=\"label\",\n", + " hover_name=\"label\",\n", + " range_x=[-12, 12],\n", + " range_y=[-12, 12],\n", + " # Different marker for different site\n", + " symbol=\"site\",\n", " )\n", + " \n", + " has_legend = False\n", + " for lab, samp in df_[[\"label\", \"sample\"]].drop_duplicates().values:\n", + " xy_lab = df_[\n", + " (df_[\"label\"] == lab) & (df_[\"sample\"] == samp)\n", + " ][[\"x\", \"y\"]].values\n", + " hull = ConvexHull(xy_lab)\n", + " for simplex in hull.simplices:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=xy_lab[simplex, 0],\n", + " y=xy_lab[simplex, 1],\n", + " mode=\"lines\",\n", + " line=dict(color=\"white\", width=1),\n", + " hoverinfo=\"skip\",\n", + " showlegend=False if has_legend else True,\n", + " legendgroup=\"Sample Convex Hull\",\n", + " name=\"Sample Convex Hull\",\n", + " ),\n", + " )\n", + " has_legend = True\n", + " \n", + " has_legend = False\n", + " for lab in np.unique(df_[\"label\"]):\n", + " xy_lab = df_[df_[\"label\"] == lab][[\"x\", \"y\"]].values \n", + " hull = ConvexHull(xy_lab)\n", + " for simplex in hull.simplices:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=xy_lab[simplex, 0],\n", + " y=xy_lab[simplex, 1],\n", + " mode=\"lines\",\n", + " line=dict(color=\"white\", width=3),\n", + " hoverinfo=\"skip\",\n", + " showlegend=False if has_legend else True,\n", + " legendgroup=\"Species Convex Hull\",\n", + " name=\"Species Convex Hull\",\n", + " ),\n", + " )\n", + " has_legend = True\n", "\n", - " # Construct the dataframe\n", - " df = pd.DataFrame(xs_fc_lda, columns=['x', 'y'])\n", - " df['label'] = ys_raw\n", - " df['label'] = df['label'].astype('category')\n", - " df['step'] = step\n", - " df[['orient', 'sample']] = [(i % 8, i // 8) for i in range(len(df))]\n", - " dfs.append(df)\n", - "df = pd.concat(dfs, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "478f6eb2a9eaf3c4", + " fig.update_layout(width=1200, height=800)\n", + " fig.show()" + ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-06-04T04:53:50.154188800Z", - "start_time": "2024-06-04T04:53:50.133624400Z" + "end_time": "2024-06-04T09:40:37.867148Z", + "start_time": "2024-06-04T09:40:37.860048800Z" } }, - "outputs": [ - { - "data": { - "text/plain": " x y label step orient sample\n0 -6.896686 -3.340312 Falcataria Moluccana 0 0 0\n1 -6.009223 -3.940270 Falcataria Moluccana 0 1 0\n2 -4.735628 -1.184250 Falcataria Moluccana 0 2 0\n3 -6.142573 -3.102567 Falcataria Moluccana 0 3 0\n4 -6.750273 -2.793355 Falcataria Moluccana 0 4 0\n... ... ... ... ... ... ...\n1531 -0.068610 3.011702 Alstonia Angulstiloba 5 3 31\n1532 1.226064 1.432768 Alstonia Angulstiloba 5 4 31\n1533 -0.517754 3.480461 Alstonia Angulstiloba 5 5 31\n1534 0.238426 4.833471 Alstonia Angulstiloba 5 6 31\n1535 -1.051879 5.066136 Alstonia Angulstiloba 5 7 31\n\n[1536 rows x 6 columns]", - 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xylabelsteporientsample
0-6.896686-3.340312Falcataria Moluccana000
1-6.009223-3.940270Falcataria Moluccana010
2-4.735628-1.184250Falcataria Moluccana020
3-6.142573-3.102567Falcataria Moluccana030
4-6.750273-2.793355Falcataria Moluccana040
.....................
1531-0.0686103.011702Alstonia Angulstiloba5331
15321.2260641.432768Alstonia Angulstiloba5431
1533-0.5177543.480461Alstonia Angulstiloba5531
15340.2384264.833471Alstonia Angulstiloba5631
1535-1.0518795.066136Alstonia Angulstiloba5731
\n

1536 rows × 6 columns

\n
" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] + "id": "6716a6b49801f91c", + "execution_count": 7 }, { "cell_type": "markdown", - "id": "476315d917e28669", + "source": [ + "# Plots \n", + "\n", + "## Interpretation\n", + "The \"dots\" are simply the embedding of each sample/instance.\n", + "Not\n", + "\n", + "## Chestnut" + ], "metadata": { "collapsed": false }, - "source": [ - "# Plots" - ] + "id": "2cda55b02e4cf6f6" }, { "cell_type": "code", - "execution_count": 7, - "id": "1376f2c23cf7041f", - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-04T04:54:05.728691Z", - "start_time": "2024-06-04T04:54:04.446003100Z" - } - }, "outputs": [ { "data": { @@ -421,300 +269,8 @@ "application/vnd.plotly.v1+json": { "data": [ { - "hovertemplate": "%{text}", - "marker": { - "color": [ - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 5.0, - 5.0, - 5.0, - 5.0, - 5.0, - 5.0, - 5.0, - 5.0, - 11.0, - 11.0, - 11.0, - 11.0, - 11.0, - 11.0, - 11.0, - 11.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 12.0, - 12.0, - 12.0, - 12.0, - 12.0, - 12.0, - 12.0, - 12.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 3.0, - 3.0, - 3.0, - 3.0, - 3.0, - 3.0, - 3.0, - 3.0, - 10.0, - 10.0, - 10.0, - 10.0, - 10.0, - 10.0, - 10.0, - 10.0, - 7.0, - 7.0, - 7.0, - 7.0, - 7.0, - 7.0, - 7.0, - 7.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 13.0, - 13.0, - 13.0, - 13.0, - 13.0, - 13.0, - 13.0, - 13.0, - 17.0, - 17.0, - 17.0, - 17.0, - 17.0, - 17.0, - 17.0, - 17.0, - 6.0, - 6.0, - 6.0, - 6.0, - 6.0, - 6.0, - 6.0, - 6.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 9.0, - 9.0, - 9.0, - 9.0, - 9.0, - 9.0, - 9.0, - 9.0, - 4.0, - 4.0, - 4.0, - 4.0, - 4.0, - 4.0, - 4.0, - 4.0, - 6.0, - 6.0, - 6.0, - 6.0, - 6.0, - 6.0, - 6.0, - 6.0, - 14.0, - 14.0, - 14.0, - 14.0, - 14.0, - 14.0, - 14.0, - 14.0, - 17.0, - 17.0, - 17.0, - 17.0, - 17.0, - 17.0, - 17.0, - 17.0, - 4.0, - 4.0, - 4.0, - 4.0, - 4.0, - 4.0, - 4.0, - 4.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 15.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 9.0, - 9.0, - 9.0, - 9.0, - 9.0, - 9.0, - 9.0, - 9.0, - 18.0, - 18.0, - 18.0, - 18.0, - 18.0, - 18.0, - 18.0, - 18.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "colorscale": [ - [ - 0.0, - "rgb(0,0,131)" - ], - [ - 0.2, - "rgb(0,60,170)" - ], - [ - 0.4, - "rgb(5,255,255)" - ], - [ - 0.6, - "rgb(255,255,0)" - ], - [ - 0.8, - "rgb(250,0,0)" - ], - [ - 1.0, - "rgb(128,0,0)" - ] - ], - "line": { - "color": "white", - "width": 1 - }, - "size": 10 - }, - "mode": "markers", - "text": [ + "hovertemplate": "%{hovertext}

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y=%{y}", + "hovertext": [ "Pennisetum Purpureum", "Pennisetum Purpureum", "Pennisetum Purpureum", @@ -739,6 +416,44 @@ "Pennisetum Purpureum", "Pennisetum Purpureum", "Pennisetum Purpureum", + "Pennisetum Purpureum" + ], + "legendgroup": "Pennisetum Purpureum, chestnut_nature_park", + "marker": { + "color": "#00cc96", + "symbol": "circle" + }, + "mode": "markers", + "name": "Pennisetum Purpureum, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + -1.5768322944641113, + -2.2321584224700928, + -0.8032571077346802, + -2.4566855430603027, + -2.175772190093994, + -1.754042387008667, + -0.5213509798049927, + -0.08018822222948074 + ], + "xaxis": "x", + "y": [ + -3.5984158515930176, + -3.0331926345825195, + -2.4752800464630127, + -3.9949440956115723, + -3.80399489402771, + -3.644489288330078, + -1.9284720420837402, + -4.427600860595703 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Bridelia Sp.
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Bridelia Sp.", "Bridelia Sp.", "Bridelia Sp.", @@ -747,15 +462,160 @@ "Bridelia Sp.", "Bridelia Sp.", "Bridelia Sp.", + "Bridelia Sp.", + "Bridelia Sp.", + "Bridelia Sp.", + "Bridelia Sp.", + "Bridelia Sp.", + "Bridelia Sp.", + "Bridelia Sp.", + "Bridelia Sp." + ], + "legendgroup": "Bridelia Sp., chestnut_nature_park", + "marker": { + "color": "#ab63fa", + "symbol": "circle" + }, + "mode": "markers", + "name": "Bridelia Sp., chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + -0.25504976511001587, + 0.5301423072814941, + 2.0618255138397217, + 1.2671260833740234, + 1.8951125144958496, + 3.404362916946411, + 0.6684303879737854, + 0.47354766726493835, + 0.8980615735054016, + 0.18977709114551544, + -0.09134437888860703, + 0.5992942452430725, + 2.0742404460906982, + -0.6919328570365906, + 2.047966480255127, + -1.717325210571289 + ], + "xaxis": "x", + "y": [ + -2.3101890087127686, + -2.273353338241577, + -3.3556292057037354, + -2.0669636726379395, + -1.0206074714660645, + -2.442203998565674, + -1.188420295715332, + -2.324533462524414, + -2.484633684158325, + -3.208245038986206, + -2.3651182651519775, + -3.490729808807373, + -3.214282989501953, + -3.9511358737945557, + -2.6724441051483154, + -1.9166983366012573 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Spathodea Campanulatum
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label=Pometia Pinnata
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Pometia Pinnata", "Pometia Pinnata", "Pometia Pinnata", @@ -763,6 +623,44 @@ "Pometia Pinnata", "Pometia Pinnata", "Pometia Pinnata", + "Pometia Pinnata" + ], + "legendgroup": "Pometia Pinnata, chestnut_nature_park", + "marker": { + "color": "#19d3f3", + "symbol": "circle" + }, + "mode": "markers", + "name": "Pometia Pinnata, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + 7.1016716957092285, + 5.498776912689209, + 6.77592658996582, + 6.727469444274902, + 6.540652275085449, + 7.991696357727051, + 6.888737678527832, + 7.131957530975342 + ], + "xaxis": "x", + "y": [ + -0.5273712277412415, + -0.9412727355957031, + -2.90822434425354, + -1.6870486736297607, + -3.5113561153411865, + -1.9532681703567505, + 1.3905977010726929, + -0.9272468090057373 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Sterculia Parviflora
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Sterculia Parviflora", "Sterculia Parviflora", "Sterculia Parviflora", @@ -771,14 +669,6 @@ "Sterculia Parviflora", "Sterculia Parviflora", "Sterculia Parviflora", - "Calophyllum", - "Calophyllum", - "Calophyllum", - "Calophyllum", - "Calophyllum", - "Calophyllum", - "Calophyllum", - "Calophyllum", "Sterculia Parviflora", "Sterculia Parviflora", "Sterculia Parviflora", @@ -787,6 +677,152 @@ "Sterculia Parviflora", "Sterculia Parviflora", "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora", + "Sterculia Parviflora" + ], + "legendgroup": "Sterculia Parviflora, chestnut_nature_park", + "marker": { + "color": "#FF6692", + "symbol": "circle" + }, + "mode": "markers", + "name": "Sterculia Parviflora, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + 6.184960842132568, + 4.292339324951172, + 3.155700445175171, + 6.36042594909668, + 3.1191327571868896, + 4.562215805053711, + 4.141836166381836, + 4.647774696350098, + 4.238309383392334, + 4.483465671539307, + 5.549316883087158, + 4.218283653259277, + 4.848672866821289, + 4.653240203857422, + 4.561854362487793, + 4.147789001464844, + 3.3567655086517334, + 5.789372444152832, + 5.114755630493164, + 4.789064407348633, + 5.707193374633789, + 3.266106128692627, + 4.564957141876221, + 3.766212224960327, + 3.5590004920959473, + 5.3018293380737305, + 4.821725845336914, + 3.728187084197998, + 2.7988600730895996, + 4.291358470916748, + 3.5338172912597656, + 4.347221374511719 + ], + "xaxis": "x", + "y": [ + -2.9733011722564697, + -2.625730514526367, + -0.24375037848949432, + -0.8035494089126587, + 0.008396178483963013, + -0.9310495853424072, + -1.1438024044036865, + 0.7928268313407898, + -1.1431225538253784, + -1.1148929595947266, + -0.734988272190094, + -1.6101949214935303, + -1.6828646659851074, + -2.477553367614746, + 0.1214686930179596, + -0.012092545628547668, + -0.7217229008674622, + 1.1846158504486084, + -0.3347240090370178, + -0.5419343113899231, + -3.268531560897827, + -0.548242449760437, + -0.07972145080566406, + -1.7464616298675537, + -2.0784499645233154, + 0.4445035457611084, + 0.47801756858825684, + -0.5981073975563049, + 1.7576109170913696, + -0.9619389772415161, + -0.3696235418319702, + 0.2212485373020172 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Calophyllum
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ + "Calophyllum", + "Calophyllum", + "Calophyllum", + "Calophyllum", + "Calophyllum", + "Calophyllum", + "Calophyllum", + "Calophyllum" + ], + "legendgroup": "Calophyllum, chestnut_nature_park", + "marker": { + "color": "#B6E880", + "symbol": "circle" + }, + "mode": "markers", + "name": "Calophyllum, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + 4.404155254364014, + 1.81601881980896, + 3.285297393798828, + 3.6634976863861084, + 3.7802658081054688, + 5.755464553833008, + 4.653932571411133, + 4.64644193649292 + ], + "xaxis": "x", + "y": [ + -3.3053436279296875, + -4.417731761932373, + -4.728030204772949, + -5.0114593505859375, + -5.144756317138672, + -1.8732606172561646, + -1.8457210063934326, + -4.177268981933594 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Campnosperma Auriculatum
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Campnosperma Auriculatum", "Campnosperma Auriculatum", "Campnosperma Auriculatum", @@ -794,8 +830,44 @@ "Campnosperma Auriculatum", "Campnosperma Auriculatum", "Campnosperma Auriculatum", - "Campnosperma Auriculatum", - "Leea Indica", + "Campnosperma Auriculatum" + ], + "legendgroup": "Campnosperma Auriculatum, chestnut_nature_park", + "marker": { + "color": "#FF97FF", + "symbol": "circle" + }, + "mode": "markers", + "name": "Campnosperma Auriculatum, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + -5.826648235321045, + -6.778146266937256, + -3.867398738861084, + -5.119288921356201, + -5.6866302490234375, + -4.654855251312256, + -6.076643943786621, + -4.92719841003418 + ], + "xaxis": "x", + "y": [ + -4.657229900360107, + -2.4602272510528564, + -4.403306007385254, + -3.6613335609436035, + -1.201369285583496, + -3.7680487632751465, + -4.744860649108887, + -4.5701446533203125 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Leea Indica
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Leea Indica", "Leea Indica", "Leea Indica", @@ -803,6 +875,44 @@ "Leea Indica", "Leea Indica", "Leea Indica", + "Leea Indica" + ], + "legendgroup": "Leea Indica, chestnut_nature_park", + "marker": { + "color": "#FECB52", + "symbol": "circle" + }, + "mode": "markers", + "name": "Leea Indica, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + -1.2409319877624512, + -0.14542461931705475, + -2.659909725189209, + -0.48774254322052, + -0.5050564408302307, + 0.8067618608474731, + -0.6180833578109741, + -0.6633522510528564 + ], + "xaxis": "x", + "y": [ + 7.776524543762207, + 7.6825408935546875, + 7.96218204498291, + 8.281291961669922, + 11.010896682739258, + 7.613468647003174, + 7.6433305740356445, + 9.480361938476562 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Dillenia Suffruticosa
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Dillenia Suffruticosa", "Dillenia Suffruticosa", "Dillenia Suffruticosa", @@ -810,24 +920,44 @@ "Dillenia Suffruticosa", "Dillenia Suffruticosa", "Dillenia Suffruticosa", - "Dillenia Suffruticosa", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sterculia Parviflora", - "Sandoricum Koetjape", + "Dillenia Suffruticosa" + ], + "legendgroup": "Dillenia Suffruticosa, chestnut_nature_park", + "marker": { + "color": "#636efa", + "symbol": "circle" + }, + "mode": "markers", + "name": "Dillenia Suffruticosa, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + 3.005591630935669, + 2.6278443336486816, + 0.47947192192077637, + 1.5605134963989258, + 4.004590034484863, + 3.925246238708496, + 5.4332122802734375, + 3.881726026535034 + ], + "xaxis": "x", + "y": [ + -2.6127331256866455, + -1.706722378730774, + 0.16096751391887665, + -2.158160448074341, + -0.7572104930877686, + -0.8929039239883423, + -1.7211620807647705, + -1.0383542776107788 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Sandoricum Koetjape
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Sandoricum Koetjape", "Sandoricum Koetjape", "Sandoricum Koetjape", @@ -835,6 +965,44 @@ "Sandoricum Koetjape", "Sandoricum Koetjape", "Sandoricum Koetjape", + "Sandoricum Koetjape" + ], + "legendgroup": "Sandoricum Koetjape, chestnut_nature_park", + "marker": { + "color": "#EF553B", + "symbol": "circle" + }, + "mode": "markers", + "name": "Sandoricum Koetjape, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + 3.8632612228393555, + 3.8187155723571777, + 3.6136369705200195, + 3.566622734069824, + 4.123162269592285, + 4.722501754760742, + 4.487424850463867, + 3.3763558864593506 + ], + "xaxis": "x", + "y": [ + 5.8475213050842285, + 4.3448896408081055, + 5.668377876281738, + 2.3804078102111816, + 5.16605281829834, + 5.9415411949157715, + 4.334445476531982, + 4.363962650299072 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Syzygium Polyanthum
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Syzygium Polyanthum", "Syzygium Polyanthum", "Syzygium Polyanthum", @@ -843,38 +1011,136 @@ "Syzygium Polyanthum", "Syzygium Polyanthum", "Syzygium Polyanthum", + "Syzygium Polyanthum", + "Syzygium Polyanthum", + "Syzygium Polyanthum", + "Syzygium Polyanthum", + "Syzygium Polyanthum", + "Syzygium Polyanthum", + "Syzygium Polyanthum", + "Syzygium Polyanthum" + ], + "legendgroup": "Syzygium Polyanthum, chestnut_nature_park", + "marker": { + "color": "#00cc96", + "symbol": "circle" + }, + "mode": "markers", + "name": "Syzygium Polyanthum, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + 5.111335754394531, + 6.60610294342041, + 5.381496429443359, + 6.1848297119140625, + 3.9853579998016357, + 3.6576356887817383, + 5.787599086761475, + 4.870671272277832, + 5.683246612548828, + 4.422710418701172, + 3.7947394847869873, + 5.425734519958496, + 3.9325761795043945, + 4.243100643157959, + 4.830308437347412, + 5.529813766479492 + ], + "xaxis": "x", + "y": [ + 1.515824317932129, + 1.4241135120391846, + 0.21098753809928894, + 2.029207229614258, + 1.475165605545044, + 1.24684476852417, + 2.962848663330078, + 2.0759143829345703, + 1.6501895189285278, + 3.151578903198242, + 0.7415168881416321, + 3.4123971462249756, + -0.7886474132537842, + 1.722311019897461, + 2.3306162357330322, + 1.4810709953308105 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Clausena Excavata
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Spathodea Campanulatum", - "Spathodea Campanulatum", - "Spathodea Campanulatum", - "Spathodea Campanulatum", - "Spathodea Campanulatum", - "Spathodea Campanulatum", - "Spathodea Campanulatum", - "Spathodea Campanulatum", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", - "Falcataria Moluccana", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata", + "Clausena Excavata" + ], + "legendgroup": "Clausena Excavata, chestnut_nature_park", + "marker": { + "color": "#ab63fa", + "symbol": "circle" + }, + "mode": "markers", + "name": "Clausena Excavata, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + 1.0575857162475586, + 2.0317912101745605, + 2.902047634124756, + 0.3971206843852997, + 0.5372660756111145, + 0.44847530126571655, + 2.027556896209717, + 3.2083539962768555, + 3.6181297302246094, + 2.505894899368286, + 2.1448471546173096, + 2.0604453086853027, + 3.8398687839508057, + 2.6101484298706055, + 3.9308955669403076, + 2.3512799739837646 + ], + "xaxis": "x", + "y": [ + -0.08270604908466339, + 0.14807790517807007, + -0.4187530279159546, + -0.7946305274963379, + 1.2825591564178467, + 0.18204069137573242, + 0.39373064041137695, + 0.41177648305892944, + 2.077887535095215, + -1.21475350856781, + -0.7700870037078857, + -0.47557032108306885, + -1.351039171218872, + -0.713493824005127, + -0.6456183791160583, + 0.7350282073020935 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Ficus Variegata
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Ficus Variegata", "Ficus Variegata", "Ficus Variegata", @@ -883,6 +1149,67 @@ "Ficus Variegata", "Ficus Variegata", "Ficus Variegata", + "Ficus Variegata", + "Ficus Variegata", + "Ficus Variegata", + "Ficus Variegata", + "Ficus Variegata", + "Ficus Variegata", + "Ficus Variegata", + "Ficus Variegata" + ], + "legendgroup": "Ficus Variegata, chestnut_nature_park", + "marker": { + "color": "#FFA15A", + "symbol": "circle" + }, + "mode": "markers", + "name": "Ficus Variegata, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + -4.930440902709961, + -2.9946022033691406, + -4.119160175323486, + -3.682558059692383, + -3.8170182704925537, + -3.48366641998291, + -4.929508209228516, + -4.47011661529541, + -5.041943073272705, + -2.5963101387023926, + -4.0586466789245605, + -5.044648170471191, + -6.1278462409973145, + -4.922389507293701, + -3.534058094024658, + -6.340842247009277 + ], + "xaxis": "x", + "y": [ + -0.8142274618148804, + 0.0075242891907691956, + -0.17636741697788239, + -0.7036334276199341, + -0.4019842743873596, + -0.5731493234634399, + -3.3817546367645264, + 0.01867617666721344, + -0.059571344405412674, + -1.1243318319320679, + -1.4044281244277954, + -1.549052119255066, + -1.4849154949188232, + -2.979710578918457, + -1.3537830114364624, + -1.0730973482131958 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Cinnamomum Iners
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ "Cinnamomum Iners", "Cinnamomum Iners", "Cinnamomum Iners", @@ -891,31 +1218,6 @@ "Cinnamomum Iners", "Cinnamomum Iners", "Cinnamomum Iners", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Clausena Excavata", - "Shorea Leprosula", - "Shorea Leprosula", - "Shorea Leprosula", - "Shorea Leprosula", - "Shorea Leprosula", - "Shorea Leprosula", - "Shorea Leprosula", - "Shorea Leprosula", - "Syzygium Polyanthum", - "Syzygium Polyanthum", - "Syzygium Polyanthum", - "Syzygium Polyanthum", - "Syzygium Polyanthum", - "Syzygium Polyanthum", - "Syzygium Polyanthum", - "Syzygium Polyanthum", - "Cinnamomum Iners", "Cinnamomum Iners", "Cinnamomum Iners", "Cinnamomum Iners", @@ -923,6 +1225,105 @@ "Cinnamomum Iners", "Cinnamomum Iners", "Cinnamomum Iners", + "Cinnamomum Iners" + ], + "legendgroup": "Cinnamomum Iners, chestnut_nature_park", + "marker": { + "color": "#19d3f3", + "symbol": "circle" + }, + "mode": "markers", + "name": "Cinnamomum Iners, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + -1.7479363679885864, + -0.5090059041976929, + -1.6657984256744385, + -3.662828207015991, + -0.11791189014911652, + -0.8801651000976562, + -0.15230894088745117, + -1.198506236076355, + -2.4163730144500732, + -1.5620609521865845, + -1.7599608898162842, + -1.4562256336212158, + -1.999084234237671, + -1.8601893186569214, + -2.102099657058716, + 0.21557213366031647 + ], + "xaxis": "x", + "y": [ + 8.410784721374512, + 7.088747024536133, + 8.290816307067871, + 6.066162109375, + 5.944489002227783, + 5.041205406188965, + 6.664888381958008, + 6.84027099609375, + 6.688454627990723, + 8.401176452636719, + 6.284826278686523, + 7.047152042388916, + 7.923769950866699, + 6.9387335777282715, + 7.51273775100708, + 8.089821815490723 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Shorea Leprosula
site=chestnut_nature_park
x=%{x}
y=%{y}", + "hovertext": [ + "Shorea Leprosula", + "Shorea Leprosula", + "Shorea Leprosula", + "Shorea Leprosula", + "Shorea Leprosula", + "Shorea Leprosula", + "Shorea Leprosula", + "Shorea Leprosula" + ], + "legendgroup": "Shorea Leprosula, chestnut_nature_park", + "marker": { + "color": "#FF6692", + "symbol": "circle" + }, + "mode": "markers", + "name": "Shorea Leprosula, chestnut_nature_park", + "orientation": "v", + "showlegend": true, + "x": [ + 9.252948760986328, + 8.714714050292969, + 9.327483177185059, + 6.875175476074219, + 7.106823444366455, + 4.804249286651611, + 6.063521385192871, + 8.500020027160645 + ], + "xaxis": "x", + "y": [ + -1.5626335144042969, + -1.81861412525177, + -3.381708860397339, + -1.447744369506836, + -2.5851266384124756, + -1.5330942869186401, + -1.876153588294983, + -1.5395747423171997 + ], + "yaxis": "y", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}

label=Alstonia Angulstiloba
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