{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Basic workflow"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We are going to load one of the very small datasets that are packaged with\n",
                "`pykanto`—this will be enough to check that everything is working as it should\n",
                "and to familiarise yourself with the package. See [project\n",
                "setup](./project-setup.md) to learn how to load your own data.\n",
                "\n",
                "```{admonition} Note:\n",
                ":class: note\n",
                "\n",
                "Creating a `KantoData` dataset requires that you have already set up your project directories (see [project setup](./project-setup.md)). Before either step, long files need to have been segmented into smaller chunks of interest (e.g., songs, song bouts). See [segmenting files](./segmenting-files.ipynb) for more information.\n",
                "Of the datasets packaged with `pykanto`, only the `GREAT_TIT` dataset has already been segmented. If you want to use another dataset, you will need to segment it first, as demonstrated in [segmenting files](./segmenting-files.ipynb) and [feature extraction](./feature-extraction.ipynb).\n",
                "```\n",
                "\n",
                "The `GREAT_TIT` dataset consists of a few songs from two male great tits (_Parus major_) in [my study population](http://wythamtits.com/), Wytham Woods, Oxfordshire, UK. Let's load the paths pointing to it and create a `KantoData` object:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "from pykanto.utils.paths import pykanto_data\n",
                "from pykanto.dataset import KantoData\n",
                "from pykanto.parameters import Parameters"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "tags": [
                    "hide-output"
                ]
            },
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "30248cdda5044587a0beccb94c5ff2e1",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Loading JSON files:   0%|          | 0/20 [00:00<?, ?it/s]"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "2022-11-28 15:01:24,902\tINFO services.py:1456 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265\u001b[39m\u001b[22m\n"
                    ]
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "c8eea2b20ab647a8bc19ef4d96d248ae",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Preparing spectrograms:   0%|          | 0/10 [00:00<?, ?it/s]"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Done\n"
                    ]
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>species</th>\n",
                            "      <th>ID</th>\n",
                            "      <th>label</th>\n",
                            "      <th>recorder</th>\n",
                            "      <th>recordist</th>\n",
                            "      <th>source_datetime</th>\n",
                            "      <th>datetime</th>\n",
                            "      <th>date</th>\n",
                            "      <th>time</th>\n",
                            "      <th>timezone</th>\n",
                            "      <th>sample_rate</th>\n",
                            "      <th>length_s</th>\n",
                            "      <th>lower_freq</th>\n",
                            "      <th>upper_freq</th>\n",
                            "      <th>max_amplitude</th>\n",
                            "      <th>min_amplitude</th>\n",
                            "      <th>bit_depth</th>\n",
                            "      <th>tech_comment</th>\n",
                            "      <th>noise</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>2021-B32-0415_05-11</th>\n",
                            "      <td>Great tit</td>\n",
                            "      <td>B32</td>\n",
                            "      <td></td>\n",
                            "      <td>24F319055FDF2205</td>\n",
                            "      <td>Nilo Merino Recalde</td>\n",
                            "      <td>2021-04-15 05:00:00</td>\n",
                            "      <td>2021-04-15 05:07:22.866667</td>\n",
                            "      <td>2021-04-15</td>\n",
                            "      <td>05:07:22.866667</td>\n",
                            "      <td>UTC</td>\n",
                            "      <td>48000</td>\n",
                            "      <td>1.139250</td>\n",
                            "      <td>2506</td>\n",
                            "      <td>5922</td>\n",
                            "      <td>0.673711</td>\n",
                            "      <td>-0.666701</td>\n",
                            "      <td>16</td>\n",
                            "      <td>Recorded at 05:00:00 15/04/2021 (UTC) by Audio...</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2021-B32-0415_05-15</th>\n",
                            "      <td>Great tit</td>\n",
                            "      <td>B32</td>\n",
                            "      <td></td>\n",
                            "      <td>24F319055FDF2205</td>\n",
                            "      <td>Nilo Merino Recalde</td>\n",
                            "      <td>2021-04-15 05:00:00</td>\n",
                            "      <td>2021-04-15 05:08:16.520000</td>\n",
                            "      <td>2021-04-15</td>\n",
                            "      <td>05:08:16.520000</td>\n",
                            "      <td>UTC</td>\n",
                            "      <td>48000</td>\n",
                            "      <td>1.194375</td>\n",
                            "      <td>2392</td>\n",
                            "      <td>5694</td>\n",
                            "      <td>0.356706</td>\n",
                            "      <td>-0.351275</td>\n",
                            "      <td>16</td>\n",
                            "      <td>Recorded at 05:00:00 15/04/2021 (UTC) by Audio...</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2021-B32-0415_05-21</th>\n",
                            "      <td>Great tit</td>\n",
                            "      <td>B32</td>\n",
                            "      <td></td>\n",
                            "      <td>24F319055FDF2205</td>\n",
                            "      <td>Nilo Merino Recalde</td>\n",
                            "      <td>2021-04-15 05:00:00</td>\n",
                            "      <td>2021-04-15 05:09:27.600000</td>\n",
                            "      <td>2021-04-15</td>\n",
                            "      <td>05:09:27.600000</td>\n",
                            "      <td>UTC</td>\n",
                            "      <td>48000</td>\n",
                            "      <td>1.188250</td>\n",
                            "      <td>2392</td>\n",
                            "      <td>5739</td>\n",
                            "      <td>0.189776</td>\n",
                            "      <td>-0.188388</td>\n",
                            "      <td>16</td>\n",
                            "      <td>Recorded at 05:00:00 15/04/2021 (UTC) by Audio...</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                       species   ID label          recorder  \\\n",
                            "2021-B32-0415_05-11  Great tit  B32        24F319055FDF2205   \n",
                            "2021-B32-0415_05-15  Great tit  B32        24F319055FDF2205   \n",
                            "2021-B32-0415_05-21  Great tit  B32        24F319055FDF2205   \n",
                            "\n",
                            "                               recordist      source_datetime  \\\n",
                            "2021-B32-0415_05-11  Nilo Merino Recalde  2021-04-15 05:00:00   \n",
                            "2021-B32-0415_05-15  Nilo Merino Recalde  2021-04-15 05:00:00   \n",
                            "2021-B32-0415_05-21  Nilo Merino Recalde  2021-04-15 05:00:00   \n",
                            "\n",
                            "                                       datetime        date             time  \\\n",
                            "2021-B32-0415_05-11  2021-04-15 05:07:22.866667  2021-04-15  05:07:22.866667   \n",
                            "2021-B32-0415_05-15  2021-04-15 05:08:16.520000  2021-04-15  05:08:16.520000   \n",
                            "2021-B32-0415_05-21  2021-04-15 05:09:27.600000  2021-04-15  05:09:27.600000   \n",
                            "\n",
                            "                    timezone  sample_rate  length_s  lower_freq  upper_freq  \\\n",
                            "2021-B32-0415_05-11      UTC        48000  1.139250        2506        5922   \n",
                            "2021-B32-0415_05-15      UTC        48000  1.194375        2392        5694   \n",
                            "2021-B32-0415_05-21      UTC        48000  1.188250        2392        5739   \n",
                            "\n",
                            "                     max_amplitude  min_amplitude  bit_depth  \\\n",
                            "2021-B32-0415_05-11       0.673711      -0.666701         16   \n",
                            "2021-B32-0415_05-15       0.356706      -0.351275         16   \n",
                            "2021-B32-0415_05-21       0.189776      -0.188388         16   \n",
                            "\n",
                            "                                                          tech_comment  noise  \n",
                            "2021-B32-0415_05-11  Recorded at 05:00:00 15/04/2021 (UTC) by Audio...  False  \n",
                            "2021-B32-0415_05-15  Recorded at 05:00:00 15/04/2021 (UTC) by Audio...  False  \n",
                            "2021-B32-0415_05-21  Recorded at 05:00:00 15/04/2021 (UTC) by Audio...  False  "
                        ]
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "DATASET_ID = \"GREAT_TIT\"\n",
                "DIRS = pykanto_data(dataset=DATASET_ID)\n",
                "# ---------\n",
                "params = Parameters() # Using default parameters for simplicity, which you should't!\n",
                "dataset = KantoData(DIRS, parameters=params, overwrite_dataset=True)\n",
                "dataset.data.head(3)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "```{admonition} Tip: using an IDE\n",
                ":class: tip, dropdown\n",
                "\n",
                "If you don't already I highly recommend that you use an IDE to write and run your code, such as [vscode](https://code.visualstudio.com/) or [PyCharm](https://www.jetbrains.com/pycharm/). Among many other benefits, you will be able to see the documentation for each function in `pykanto` on hover:\n",
                "\n",
                "![IDE](../custom/IDE_example.png)\n",
                "\n",
                "```"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "tags": [
                    "hide-output"
                ]
            },
            "source": [
                "We now have an object `dataset`, which is an instance of the `KantoData` class and has all of its methods.\n",
                "\n",
                "```{admonition} Tip:\n",
                ":class: tip\n",
                "\n",
                "See [how to create and use a `KantoData` object](./kantodata-dataset.ipynb) for more details.\n",
                "```"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "For example, you might want to segment your songs into discrete notes using `pykanto`'s algorithm, which is a simple amplitude-based method that works reasonably well (based on Tim Sainburg's [vocalseg](https://github.com/timsainb/vocalization-segmentation) and Robert Lachlan's de-echoing method in [Luscinia](https://rflachlan.github.io/Luscinia/))."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Using existing unit onset/offset information.\n",
                        "Found and segmented 169 units.\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 616.32x322.56 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Segment:\n",
                "dataset.segment_into_units()\n",
                "\n",
                "# Plot an example:\n",
                "dataset.plot(dataset.data.index[0], segmented=True)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Next, you can create spectrogram representations of the units or the average of the units present in the vocalisations of each individual ID in the dataset, project and cluster them, and prepare compressed representations that can be used with the interactive app:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {
                "tags": [
                    "hide-output"
                ]
            },
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "d7319d8211e44c4284f7c76e50c3da42",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Calculating and saving unit spectrograms:   0%|          | 0/2 [00:00<?, ?it/s]"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Found 2 individual IDs. They will be processed in 2 chunks of length 1.\n"
                    ]
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "84eea252f5a14aefa0705f4fb848da6a",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Projecting and clustering vocalisations:   0%|          | 0/2 [00:00<?, ?it/s]"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "\u001b[2m\u001b[36m(_reduce_and_cluster_r pid=40309)\u001b[0m UserWarning:\n",
                        "\u001b[2m\u001b[36m(_reduce_and_cluster_r pid=40309)\u001b[0m     `min_cluster_size` too small, setting it to 2\n",
                        "\u001b[2m\u001b[36m(_reduce_and_cluster_r pid=40306)\u001b[0m UserWarning:\n",
                        "\u001b[2m\u001b[36m(_reduce_and_cluster_r pid=40306)\u001b[0m     `min_cluster_size` too small, setting it to 2\n"
                    ]
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Found 2 individual IDs. They will be processed in 2 chunks of length 1.\n"
                    ]
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "fdac99d5405544ad93856cb9ce40d036",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Prepare interactive visualisation:   0%|          | 0/2 [00:00<?, ?it/s]"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "dataset.get_units()\n",
                "dataset.cluster_ids(min_sample=5)\n",
                "dataset.prepare_interactive_data()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now you can start the interactive app on your browser by simply running `dataset.open_label_app()`."
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "There are not enough data in this minimal dataset to show how the app works:\n",
                "clustering cannot work well with such small sample sizes. But here is a gif\n",
                "showing what it would look like with a real dataset from these birds:\n",
                "\n",
                "![label app demo](../custom/labelapp.gif)"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "To learn how to use the interactive app, see [using the interactive app](./interactive-app.md).\n",
                "Once you are done checking the automatically assigned labels you need to reload the dataset:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [],
            "source": [
                "dataset = dataset.reload()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now you have a dataset containing the raw audio files, their spectrogram representations, onset and offset times, and element or song type labels. Happy sciencing!"
            ]
        }
    ],
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