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{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get started, we first install `treeffuser` and import the relevant libraries." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "!pip install treeffuser\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from pathlib import Path\n", - "\n", - "from tqdm import tqdm\n", - "from treeffuser import Treeffuser\n", - "\n", - "# load autoreload extension\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, create a Kaggle account and download the data from https://www.kaggle.com/competitions/m5-forecasting-accuracy/data.\n", - "\n", - "If you're running this notebook in Colab, manually upload the necessary files (`calendar.csv`, `sales_train_validation.csv`, `sell_prices.csv`) to Colab by clicking the `Files` tab on the left sidebar and selecting `Upload`. Move the files into a new folder named `m5`. Once uploaded, the notebook will be able to read and process the data.\n", - "\n", - "If you're running this on your local machine, you can also use Kaggle's [command-line tool](https://www.kaggle.com/docs/api) and run the following from the command line:\n", - "\n", - "```bash\n", - "cd ./m5 # path to folder where you want to save the data\n", - "kaggle competitions download -c m5-forecasting-accuracy\n", - "```\n", - "\n", - "Use your favorite tool to unzip the archive. In Linux/macOS,\n", - "\n", - "```bash\n", - "unzip m5-forecasting-accuracy.zip\n", - "```\n", - "\n", - "We'll be using the following files: `calendar.csv`, `sales_train_validation.csv`, and `sell_prices.csv`.\n", - "\n", - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load the data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "data_path = Path(\"./m5\") # change with path where you extracted the data archive\n", - "\n", - "calendar_df = pd.read_csv(data_path / \"calendar.csv\")\n", - "sales_train_df = pd.read_csv(data_path / \"sales_train_validation.csv\")\n", - "sell_prices_df = pd.read_csv(data_path / \"sell_prices.csv\")\n", - "\n", - "# add explicit columns for the day, month, year for ease of processing\n", - "calendar_df[\"date\"] = pd.to_datetime(calendar_df[\"date\"])\n", - "calendar_df[\"day\"] = calendar_df[\"date\"].dt.day\n", - "calendar_df[\"month\"] = calendar_df[\"date\"].dt.month\n", - "calendar_df[\"year\"] = calendar_df[\"date\"].dt.year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The data\n", - "\n", - "### Preprocessing\n", - "`sell_prices_df` contains the prices of each item in each store at a given time. The `wm_yr_wk` is a unique identifier for the time." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id d sales\n", - "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", - "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", - "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", - "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", - "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", - " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", - " sales_df_long = pd.wide_to_long(\n", - " sales_df_wide.iloc[:100, 1:],\n", - " i=index_vars,\n", - " j=\"day\",\n", - " stubnames=[\"d\"],\n", - " sep=\"_\",\n", - " ).reset_index()\n", - "\n", - " sales_df_long = sales_df_long.rename(columns={\"d\": \"sales\", \"day\": \"d\"})\n", - "\n", - " sales_df_long[\"d\"] = \"d_\" + sales_df_long[\"d\"].astype(\n", - " \"str\"\n", - " ) # restore \"d_{i}\" format for day\n", - " return sales_df_long\n", - "\n", - "\n", - "sales_train_df_long = convert_sales_data_from_wide_to_long(sales_train_df)\n", - "sales_train_df_long.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Number of sales over the entire timespan')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(\n", - " sales_train_df_long[\"sales\"],\n", - " bins=np.arange(0, 10 + 1.5) - 0.5,\n", - " range=[0, 10],\n", - " density=True,\n", - ")\n", - "plt.xticks(range(10))\n", - "plt.ylabel(\"Frequency of number of sales\")\n", - "plt.title(\"Number of sales over the entire timespan\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train and test sets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset comprises sales data of 100 items over 1,913 days. For simplicity, we select the data from the first 365 days and discard the rest." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n_items = 100\n", - "n_days = 1913\n" - ] - } - ], - "source": [ - "print(f\"n_items = {len(sales_train_df_long['item_id'].unique())}\")\n", - "print(f\"n_days = {len(sales_train_df_long['d'].unique())}\")\n", - "\n", - "sales_train_df_long[\"day_number\"] = sales_train_df_long[\"d\"].str.extract(\"(\\d+)\").astype(int)\n", - "data = sales_train_df_long[sales_train_df_long[\"day_number\"] <= 365].copy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We compute the lags of the previous 30 days and merge the sales, calendar, and price data together." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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item_iddept_idcat_idstore_idstate_iddsalesday_numbersales_lag_1sales_lag_2...yearevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIdaysell_price
0HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14101410.00.0...2011NaNNaNNaNNaN000183.97
1HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14201420.00.0...2011Father's dayCulturalNaNNaN000193.97
2HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14301430.00.0...2011NaNNaNNaNNaN000203.97
3HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14411440.00.0...2011NaNNaNNaNNaN000213.97
4HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14501451.00.0...2011NaNNaNNaNNaN000223.97
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5 rows × 53 columns

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" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id d sales \\\n", - "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", - "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", - "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", - "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", - "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", - "\n", - " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", - "0 141 0.0 0.0 ... 2011 NaN \n", - "1 142 0.0 0.0 ... 2011 Father's day \n", - "2 143 0.0 0.0 ... 2011 NaN \n", - "3 144 0.0 0.0 ... 2011 NaN \n", - "4 145 1.0 0.0 ... 2011 NaN \n", - "\n", - " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", - "0 NaN NaN NaN 0 0 0 18 \n", - "1 Cultural NaN NaN 0 0 0 19 \n", - "2 NaN NaN NaN 0 0 0 20 \n", - "3 NaN NaN NaN 0 0 0 21 \n", - "4 NaN NaN NaN 0 0 0 22 \n", - "\n", - " sell_price \n", - "0 3.97 \n", - "1 3.97 \n", - "2 3.97 \n", - "3 3.97 \n", - "4 3.97 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_lags = 30\n", - "\n", - "# sort data before computing lags\n", - "data_index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", - "data.sort_values(data_index_vars + [\"day_number\"], inplace=True)\n", - "\n", - "for lag in range(1, n_lags + 1):\n", - " data[f\"sales_lag_{lag}\"] = data.groupby(by=data_index_vars)[\"sales\"].shift(lag)\n", - "\n", - "data = data.merge(calendar_df).merge(sell_prices_df)\n", - "\n", - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Treeffuser can handle **categorical columns**, but the dtype of those columns must be set to `category` in the DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "categorical_columns = [\n", - " \"item_id\",\n", - " \"dept_id\",\n", - " \"cat_id\",\n", - " \"store_id\",\n", - " \"state_id\",\n", - " \"d\",\n", - " \"wm_yr_wk\",\n", - " \"weekday\",\n", - " \"event_name_1\",\n", - " \"event_type_1\",\n", - " \"event_name_2\",\n", - " \"event_type_2\",\n", - " \"snap_CA\",\n", - " \"snap_TX\",\n", - " \"snap_WI\",\n", - "]\n", - "data[categorical_columns] = data[categorical_columns].astype(\"category\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, for each item, we take the first 300 days as train data and use the remaining 65 data as test data for evaluation." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(15216, 50)\n", - "(3891, 50)\n" - ] - } - ], - "source": [ - "is_train = data[\"day_number\"] <= 300\n", - "\n", - "y_name = \"sales\"\n", - "x_names = [\n", - " name for name in data.columns if name != y_name and name not in [\"day_number\", \"date\"]\n", - "]\n", - "\n", - "X_train, y_train, dates_train = (\n", - " data[is_train][x_names],\n", - " data[is_train][y_name],\n", - " data[is_train][\"date\"],\n", - ")\n", - "X_test, y_test, dates_test = (\n", - " data[~is_train][x_names],\n", - " data[~is_train][y_name],\n", - " data[~is_train][\"date\"],\n", - ")\n", - "\n", - "print(X_train.shape)\n", - "print(X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Probabilistic predictions with Treeffuser\n", - "\n", - "We regress the sales on the following covariates." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, day, sell_price\n" - ] - } - ], - "source": [ - "print(\", \".join(map(str, X_train.columns)))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", - "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" - ] - }, - { - "data": { - "text/html": [ - "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Treeffuser(extra_lightgbm_params={}, seed=0)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = Treeffuser(seed=0)\n", - "model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [05:55<00:00, 3.55s/it]\n" - ] - } - ], - "source": [ - "y_test_samples = model.sample(X_test, n_samples=100, seed=0, n_steps=50, verbose=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Newsvendor model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We illustrate the practical relevance of accurate probabilistic predictions with an application to inventory management, using the newsvendor model \\citep{arrow1951optimal}. \n", - "\n", - "Assume that every day we decide how many units $q$ of an item to buy. \n", - "We buy at a cost $c$ and sell at a price $p$. \n", - "However, the demand $y$ is random, introducing uncertainty in our decision. \n", - "The goal is to maximize the expected profit:\n", - "$$\\max_{q} p~\\mathbb{E}\\left[\\min(q, y)\\right] - c q.$$\n", - "The optimal solution to the newsvendor problem is to buy $q = F^{-1}\\left( \\frac{p-c}{p} \\right)$ units, where $F^{-1}$ is the quantile function of the distribution of $y$. \n", - "\n", - "Using Treeffuser, we can compute the quantiles from the samples and forecast the optimal quantity of units to buy.\n", - "\n", - "To compute profits, we use the observed prices, assume a margin of $50\\%$ over all products, and assume the actual number of sales of an item correspond to the demand of this item. We let Treeffuser, learn the conditional distribution of the demand of each item, estimate their quantiles, and thus determine the optimal quantity to buy. \n", - "\n", - "We use the held-out data to compute the profit made if Treeffuser was used to forecast the demand of each item and to manage the inventory of each item." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def newsvendor_utility(y_true, quantity_ordered, prices, stocking_cost):\n", - " \"\"\"\n", - " The newsvendor utility function with stock q, demand y, selling price p, stocking cost c is given by\n", - " $$ U(y, q, p, c) = p * min(y, q) - c * q $$\n", - " \"\"\"\n", - " utility = prices * np.minimum(y_true, quantity_ordered) - stocking_cost * quantity_ordered\n", - " return utility\n", - "\n", - "\n", - "def newsvendor_optimal_quantity(y_samples, prices, stocking_cost):\n", - " \"\"\"\n", - " Returns the optimal quantity to order for the newsvendor problem.\n", - "\n", - " It is given theoeretically by:\n", - " $$ q* = argmax_{q} E[U(y, q, p, c)] $$\n", - " which has a closed form solution,\n", - " $$ q* = F^{-1}( (p - c) / p) $$\n", - " where F is the CDF of the demand distribution\n", - " \"\"\"\n", - " # compute the target quantiles (p - c) / p\n", - " target_quantiles = (prices - stocking_cost) / prices\n", - " target_quantiles = np.maximum(target_quantiles, 0.0)\n", - "\n", - " # compute the empirical quantities corresponding to the target quantiles\n", - " res = []\n", - " for i in range(y_samples.shape[1]):\n", - " optimal_quantities = np.quantile(y_samples[:, i], target_quantiles[i])\n", - " res.append(optimal_quantities)\n", - " optimal_quantities = np.array(res)\n", - " return optimal_quantities" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "185.07666666666668" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# we don't know the profit margin of each item, so we assume it is 50%.\n", - "profit_margin = 0.5\n", - "\n", - "prices = X_test[\"sell_price\"].values\n", - "stocking_cost = prices / (1 + profit_margin)\n", - "\n", - "# compute optimal quantities\n", - "optimal_quantities = newsvendor_optimal_quantity(y_test_samples, prices, stocking_cost)\n", - "\n", - "# Treeffuser models continuous responses, hence we cast the predicted quantities into int\n", - "optimal_quantities = optimal_quantities.astype(int)\n", - "\n", - "profit = newsvendor_utility(y_test, optimal_quantities, prices, stocking_cost)\n", - "profit.sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We visualize the cumulative profit, the average demand and inventory over time in the plot below." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " date profit avg_inventory_weighted avg_demand_weighted\n", - "0 2011-11-25 -2.213333 0.039476 0.784104\n", - "1 2011-11-26 1.600000 0.033068 0.933309\n", - "2 2011-11-27 0.053333 0.046308 0.796895\n", - "3 2011-11-28 5.873333 0.067636 0.789166\n", - "4 2011-11-29 1.580000 0.041286 0.913090\n", - ".. ... ... ... ...\n", - "60 2012-01-24 6.336667 0.138240 0.661504\n", - "61 2012-01-25 7.876667 0.078847 0.530883\n", - "62 2012-01-26 -3.493333 0.051774 0.894360\n", - "63 2012-01-27 -0.710000 0.102579 1.093555\n", - "64 2012-01-28 11.890000 0.116262 1.258714\n", - "\n", - "[65 rows x 4 columns]\n" - ] - } - ], - "source": [ - "performance_data = pd.DataFrame(\n", - " {\n", - " \"date\": dates_test,\n", - " \"profit\": profit,\n", - " \"demand\": y_test,\n", - " \"inventory\": optimal_quantities,\n", - " \"price\": prices,\n", - " }\n", - ")\n", - "\n", - "# for each day, compute average demand and inventory weighted by price\n", - "daily_summary = (\n", - " performance_data.groupby(\"date\")\n", - " .agg(\n", - " profit=(\"profit\", \"sum\"),\n", - " avg_inventory_weighted=(\n", - " \"inventory\",\n", - " lambda x: (x * performance_data.loc[x.index, \"price\"]).sum()\n", - " / performance_data.loc[x.index, \"price\"].sum(),\n", - " ),\n", - " avg_demand_weighted=(\n", - " \"demand\",\n", - " lambda x: (x * performance_data.loc[x.index, \"price\"]).sum()\n", - " / performance_data.loc[x.index, \"price\"].sum(),\n", - " ),\n", - " )\n", - " .reset_index()\n", - ")\n", - "\n", - "print(daily_summary)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# dictionary to store color, alpha, linewidth, and linestyle for each line\n", - "line_styles = {\n", - " \"cumulative_profit\": {\"color\": \"teal\", \"alpha\": 1, \"linewidth\": 2.5, \"linestyle\": \"-\"},\n", - " \"inventory\": {\n", - " \"color\": \"blue\",\n", - " \"alpha\": 0.5,\n", - " \"linewidth\": 1.5,\n", - " \"linestyle\": \"--\",\n", - " },\n", - " \"demand\": {\n", - " \"color\": \"orange\",\n", - " \"alpha\": 0.5,\n", - " \"linewidth\": 1.5,\n", - " \"linestyle\": \"--\",\n", - " },\n", - "}\n", - "\n", - "# create figure\n", - "fig, ax1 = plt.subplots(figsize=(10, 6))\n", - "\n", - "# define x-axis\n", - "dates = pd.to_datetime(daily_summary[\"date\"])\n", - "\n", - "# plot cumulative profit\n", - "ax1.plot(\n", - " dates,\n", - " daily_summary[\"profit\"].cumsum(),\n", - " **line_styles[\"cumulative_profit\"],\n", - " label=\"Cumulative Profit\",\n", - ")\n", - "ax1.set_xlabel(\"Date\", fontsize=12)\n", - "ax1.set_ylabel(\"Cumulative Profit ($)\", fontsize=12)\n", - "ax1.grid(True, linestyle=\"--\", alpha=0.6)\n", - "\n", - "# create second y-axis for price-weighted inventory and demand\n", - "ax2 = ax1.twinx()\n", - "ax2.plot(\n", - " dates,\n", - " daily_summary[\"avg_inventory_weighted\"],\n", - " **line_styles[\"inventory\"],\n", - " label=\"Avg Inventory (Price Weighted)\",\n", - ")\n", - "ax2.plot(\n", - " dates,\n", - " daily_summary[\"avg_demand_weighted\"],\n", - " **line_styles[\"demand\"],\n", - " label=\"Avg Demand (Price Weighted)\",\n", - ")\n", - "ax2.set_ylabel(\"Avg Inventory and Demand (Units)\", fontsize=12)\n", - "\n", - "# combine all legends into one\n", - "lines1, labels1 = ax1.get_legend_handles_labels()\n", - "lines2, labels2 = ax2.get_legend_handles_labels()\n", - "ax1.legend(\n", - " lines1 + lines2, labels1 + labels2, loc=\"upper center\", bbox_to_anchor=(0.5, 1.12), ncol=3\n", - ")\n", - "\n", - "fig.autofmt_xdate() # rotate x-tick labels\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/docs/.doctrees/nbsphinx/tutorials_m5-news-vendor-notebook_15_1.png b/docs/docs/.doctrees/nbsphinx/tutorials_m5-news-vendor-notebook_15_1.png deleted file mode 100644 index 2f4cf68b7066608e0c4e5cb68fcc629dcf99d583..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 18535 zcmc(H2UJzpx-P~TV^8c#(})U)bV0fuL6Hth6_5@aqzZ^=q7W5OkRnA8=^!9&Q?`Ig z=uIgiY>G&eE=aHMn`&}$?m6$?cgMYByfelzL)m4mx&Hb8Uz^-kI48Ys_0H7{3=HdJ 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Source code for treeffuser.samples

-from typing import Callable
-from typing import List
-from typing import Literal
-from typing import Union
-
-import numpy as np
-from jaxtyping import Float
-from sklearn.neighbors import KernelDensity
-from tqdm import tqdm
-
-
-###################################################
-# Helper functions
-###################################################
-def _check_unidimensional(array) -> None:
-    if array.ndim > 2 and array.shape[-1] > 1:
-        raise ValueError("This method only applies to unidimensional responses.")
-
-
-###################################################
-# Main class
-###################################################
-
-[docs] -class Samples: - """ - A wrapper class for the output of Treeffuser, the samples from the - conditional distribution `p(y|x)`. It provides convenient methods to - compute various statistics from the samples. - - Parameters - ---------- - input_array : np.ndarray - An array containing samples with shape (n_samples, batch, y_dim). - - Attributes - ---------- - n_samples : int - Number of samples. - batch : int - Batch size. - y_dim : int - Dimension of the response variable. - shape : tuple - Shape of the samples array. - ndim : int - Number of dimensions of the samples array. - """ - - def __init__(self, input_array: Float[np.ndarray, "n_samples batch y_dim"]): - if input_array.ndim < 2 or input_array.ndim > 3: - raise ValueError("Samples must have either 2 or 3 dimensions.") - - self._samples = input_array - self.n_samples = input_array.shape[0] - self.batch = input_array.shape[1] - self.y_dim = 1 if input_array.ndim == 2 else input_array.shape[-1] - self.shape = input_array.shape - self.ndim = input_array.ndim - -
-[docs] - def sample_apply( - self, fun: Callable[[np.ndarray], np.ndarray] - ) -> Float[np.ndarray, "batch y_dim"]: - """ - Apply a function to the samples for each `x`. - - Parameters - ---------- - func : callable - A function to apply to each sample. The function should take a numpy array of shape - (n_samples,) and return a numpy array of the same shape. - - Returns - ------- - result : np.ndarray - The result of applying the function to each row of the samples. - """ - result = np.apply_along_axis(fun, 0, self._samples) - return result
- - -
-[docs] - def sample_confidence_interval( - self, confidence: float = 0.95 - ) -> Float[np.ndarray, "2 batch y_dim"]: - """ - Estimate the confidence interval of the samples for each `x` using - the empirical quantiles of the samples. - - Parameters - ---------- - confidence : float - The confidence level for the interval. - - Returns - ------- - ci : np.ndarray - The confidence interval of the samples for each `x`. - """ - _check_unidimensional(self._samples) - alpha = 1 - confidence - return self.sample_quantile(q=[alpha / 2, 1 - alpha / 2])
- - -
-[docs] - def sample_correlation(self) -> Float[np.ndarray, "batch y_dim y_dim"]: - """ - Compute the correlation matrix of the samples for each `x`. - Estimate: `corr[Y | X = x]` for each `x`. - - Returns - ------- - correlation : np.ndarray - The correlation matrix of the samples for each `x`. - """ - correlation = np.empty((self.batch, self.y_dim, self.y_dim)) - - for i in range(self.batch): - correlation[i, :, :] = np.corrcoef(self._samples[:, i, :], rowvar=False) - - return correlation
- - -
-[docs] - def sample_kde( - self, - bandwidth: Union[float, Literal["scott", "silverman"]] = 1.0, - verbose: bool = False, - ) -> List[KernelDensity]: - """ - Compute the Kernel Density Estimate (KDE) for each `x`. - Estimate: `KDE[Y | X = x]` for each `x` using Gaussian kernels from `sklearn.neighbors`. - - Parameters - ---------- - bandwidth : float or {'scott', 'silverman'}, default=1.0 - The bandwidth of the kernel. Bandwidth can be specified as a scalar value - or as a string: - - 'scott': Scott's rule of thumb. - - 'silverman': Silverman's rule of thumb. - verbose : bool, default=False - Whether to display progress bars. - - Returns - ------- - kdes : list of KernelDensity - A list of `KernelDensity` objects, one for each `x`. - """ - kdes = [] - for i in tqdm( - range(self.batch), - disable=not verbose, - desc="Fitting kernel densities for each `x`", - ): - if self.ndim == 2: - y_i = self._samples[:, i, None] - else: - y_i = self._samples[:, i, :] - kde = KernelDensity(bandwidth=bandwidth, algorithm="auto", kernel="gaussian") - kde.fit(y_i) - kdes.append(kde) - - return kdes
- - -
-[docs] - def sample_max(self) -> Float[np.ndarray, "batch y_dim"]: - """ - Compute the maximum of the samples for each `x`. - Estimate: `max[Y | X = x]` for each `x`. - Equivalent to `np.max(samples.to_numpy(), axis=0)`. - - Returns - ------- - max : np.ndarray - The maximum of the samples for each `x`. - """ - return self._samples.max(axis=0)
- - -
-[docs] - def sample_mean(self) -> Float[np.ndarray, "batch y_dim"]: - """ - Compute the mean of the samples for each `x`. - Estimate: `E[Y | X = x]` for each `x`. - Equivalent to `np.mean(samples.to_numpy(), axis=0)`. - - Returns - ------- - mean : np.ndarray - The mean of the samples for each `x`. - """ - return self._samples.mean(axis=0)
- - -
-[docs] - def sample_median(self) -> Float[np.ndarray, "batch y_dim"]: - """ - Compute the median of the samples for each `x`. - Estimate: `median[Y | X = x]` for each `x`. - Equivalent to `np.median(samples.to_numpy(), axis=0)`. - - Returns - ------- - median : np.ndarray - The median of the samples for each `x`. - """ - return np.median(self._samples, axis=0)
- - -
-[docs] - def sample_min(self) -> Float[np.ndarray, "batch y_dim"]: - """ - Compute the minimum of the samples for each `x`. - Estimate: `min[Y | X = x]` for each `x`. - Equivalent to `np.min(samples.to_numpy(), axis=0)`. - - Returns - ------- - min : np.ndarray - The minimum of the samples for each `x`. - """ - return self._samples.min(axis=0)
- - -
-[docs] - def sample_mode( - self, - bandwidth: Union[float, Literal["scott", "silverman"]] = 1.0, - verbose: bool = False, - ) -> Float[np.ndarray, "batch"]: - """ - Compute the mode of the samples for each `x`. - Estimate: `mode[Y | X = x]` for each `x` using Kernel Density Estimation. - - Parameters - ---------- - bandwidth : float or {'scott', 'silverman'}, default=1.0 - The bandwidth of the kernel. Bandwidth can be specified as a scalar value - or as a string: - - 'scott': Scott's rule of thumb. - - 'silverman': Silverman's rule of thumb. - verbose : bool, default=False - Whether to display progress bars. - - Notes - ----- - The mode is computed via grid search on the Kernel Density Estimate (KDE). The step size - of the grid is set to be equal to the maximum between twice the number of batches and - 1,000. - - Returns - ------- - mode : np.ndarray - The mode of the samples for each `x`. - """ - _check_unidimensional(self._samples) - - kdes = self.sample_kde(bandwidth=bandwidth) - - modes = [] - n_grid = np.max([2 * self.batch, 1000]) # heuristic for the grid granularity - for i in tqdm(range(self.batch), disable=not verbose, desc="Searching for modes"): - if self._samples.ndim == 2: - y_i = self._samples[:, i, None] - else: - y_i = self._samples[:, i, :] - grid = np.linspace(np.min(y_i), np.max(y_i), n_grid) - log_density = kdes[i].score_samples(grid.reshape(-1, 1)) - modes.append(grid[np.argmax(log_density)]) - - modes = np.array(modes) - return modes
- - -
-[docs] - def sample_quantile( - self, q: Union[float, List[float]] - ) -> Float[np.ndarray, "q_dim batch y_dim"]: - """ - Compute the quantiles of the samples for each `x`. - Estimate: `q-th quantile[Y | X = x]` for each `x`. - Equivalent to `np.quantile(samples.to_numpy(), q, axis=0)`. - - Parameters - ---------- - q : float or list[float] - Quantile or sequence of quantiles to compute. - """ - quantiles = np.quantile(self._samples, q, axis=0) - return ( - quantiles - if isinstance(q, list) - else quantiles.reshape((1, self.batch, self.y_dim)) - )
- - -
-[docs] - def sample_range(self) -> Float[np.ndarray, "batch 2"]: - """ - Compute the range of the samples for each `x` using the empirical minimum and - maximum of the samples, `np.min(samples.to_numpy(), axis=0)` and - `np.max(samples.to_numpy(), axis=0)`. - - Returns - ------- - range : np.ndarray - The range of the samples for each `x`. - """ - _check_unidimensional(self._samples) - return np.stack((self._samples.min(axis=0), self._samples.max(axis=0)), axis=-1)
- - -
-[docs] - def sample_std(self, ddof: int = 0) -> Float[np.ndarray, "batch y_dim"]: - """ - Compute the standard deviation of the samples for each `x`. - Estimate: `std[Y | X = x]` for each `x`. - Equivalent to `np.std(samples.to_numpy(), axis=0, ddof=ddof)`. - - Parameters - ---------- - ddof : int - Delta Degrees of Freedom. The divisor used in the calculation is `N - ddof`, - where N represents the number of elements. - - Returns - ------- - std : np.ndarray - The standard deviation of the samples for each `x`. - """ - return self._samples.std(axis=0, ddof=ddof)
- - -
-[docs] - def to_numpy(self) -> Float[np.ndarray, "n_samples batch y_dim"]: - """ - Return the samples as a numpy array. - - Returns - ------- - samples : np.ndarray - The numpy array of the samples. - """ - return self._samples
- - - def __getitem__(self, key): - """ - Prevent the user from removing the first or second dimension of the samples. - """ - if isinstance(key, int): - key = (key,) - if isinstance(key[0], int): - raise ValueError( - f"Accessing `my_samples[{key}] would remove the first dimension of the samples," - f"which is forbidden. Instead, use `my_samples.samples[{key}]`." - ) - if len(key) >= 2 and isinstance(key[1], int): - # If key[0] is an ellipsis and self.ndim == 3 then key[1] actually refers to the - # third dimension, which is allowed - if not (key[0] is Ellipsis and self.ndim == 3): - raise ValueError( - f"Accessing `my_samples[{key}] would remove the second dimension of the " - f"samples which is forbidden. Instead, use `my_samples.samples[{key}]`." - ) - return Samples(self._samples.__getitem__(key))
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Source code for treeffuser.treeffuser

-from __future__ import annotations
-
-from typing import Literal
-
-from treeffuser._base_tabular_diffusion import BaseTabularDiffusion
-from treeffuser._score_models import LightGBMScoreModel
-from treeffuser._score_models import ScoreModel
-from treeffuser.sde import DiffusionSDE
-from treeffuser.sde import get_diffusion_sde
-
-
-
-[docs] -class Treeffuser(BaseTabularDiffusion): - def __init__( - self, - n_repeats: int = 30, - n_estimators: int = 3000, - early_stopping_rounds: int | None = 50, - eval_percent: float = 0.1, - num_leaves: int = 31, - max_depth: int = -1, - learning_rate: float = 0.1, - max_bin: int = 255, - subsample_for_bin: int = 200000, - min_child_samples: int = 20, - subsample: float = 1.0, - subsample_freq: int = 0, - n_jobs: int = -1, - sde_name: str = "vesde", - sde_initialize_from_data: bool = False, - sde_hyperparam_min: float | Literal["default"] | None = None, - sde_hyperparam_max: float | Literal["default"] | None = None, - seed: int | None = None, - verbose: int = 0, - extra_lightgbm_params: dict | None = None, - ): - """ - n_repeats : int - How many times to repeat the training dataset when fitting the score. That is, how many - noisy versions of a point to generate for training. - n_estimators : int - LightGBM: Number of boosting iterations. - early_stopping_rounds : int - LightGBM: If `None`, no early stopping is performed. Otherwise, the model will stop training - if no improvement is observed in the validation set for `early_stopping_rounds` consecutive - iterations. - eval_percent : float - LightGBM: Percentage of the training data to use for validation if `early_stopping_rounds` - is not `None`. - num_leaves : int - LightGBM: Maximum tree leaves for base learners. - max_depth : int - LightGBM: Maximum tree depth for base learners, <=0 means no limit. - learning_rate : float - LightGBM: Boosting learning rate. - max_bin : int - LightGBM: Max number of bins that feature values will be bucketed in. This is used for - lightgbm's histogram binning algorithm. - subsample_for_bin : int - LightGBM: Number of samples for constructing bins. - min_child_samples : int - LightGBM: Minimum number of data needed in a child (leaf). If less than this number, will - not create the child. - subsample : float - LightGBM: Subsample ratio of the training instance. - subsample_freq : int - LightGBM: Frequency of subsample, <=0 means no enable. How often to subsample the training - data. - n_jobs : int - LightGBM: Number of parallel threads. If set to -1, the number is set to the number of available cores. - sde_name : str - SDE: Name of the SDE to use. See `treeffuser.sde.get_diffusion_sde` for available SDEs. - sde_initialize_from_data : bool - SDE: Whether to initialize the SDE from the data. If `True`, the SDE hyperparameters are - initialized with a heuristic based on the data (see `treeffuser.sde.initialize.py`). - Otherwise, sde_hyperparam_min and sde_hyperparam_max are used. (default: False) - sde_hyperparam_min : float or "default" - SDE: The scale of the SDE at t=0 (see `VESDE`, `VPSDE`, `SubVPSDE`). - sde_hyperparam_max : float or "default" - SDE: The scale of the SDE at t=T (see `VESDE`, `VPSDE`, `SubVPSDE`). - seed : int - Random seed for generating the training data and fitting the model. - verbose : int - Verbosity of the score model. - """ - super().__init__( - sde_initialize_from_data=sde_initialize_from_data, - ) - self.sde_name = sde_name - self.n_repeats = n_repeats - self.n_estimators = n_estimators - self.eval_percent = eval_percent - self.early_stopping_rounds = early_stopping_rounds - self.num_leaves = num_leaves - self.max_depth = max_depth - self.learning_rate = learning_rate - self.max_bin = max_bin - self.subsample_for_bin = subsample_for_bin - self.min_child_samples = min_child_samples - self.subsample = subsample - self.subsample_freq = subsample_freq - self.n_jobs = n_jobs - self.seed = seed - self.verbose = verbose - self.sde_initialize_from_data = sde_initialize_from_data - self.sde_hyperparam_min = sde_hyperparam_min - self.sde_hyperparam_max = sde_hyperparam_max - self.extra_lightgbm_params = extra_lightgbm_params or {} - -
-[docs] - def get_new_sde(self) -> DiffusionSDE: - sde_cls = get_diffusion_sde(self.sde_name) - sde_kwargs = {} - if self.sde_hyperparam_min is not None: - sde_kwargs["hyperparam_min"] = self.sde_hyperparam_min - if self.sde_hyperparam_max is not None: - sde_kwargs["hyperparam_max"] = self.sde_hyperparam_max - sde = sde_cls(**sde_kwargs) - return sde
- - -
-[docs] - def get_new_score_model(self) -> ScoreModel: - score_model = LightGBMScoreModel( - n_repeats=self.n_repeats, - n_estimators=self.n_estimators, - eval_percent=self.eval_percent, - early_stopping_rounds=self.early_stopping_rounds, - num_leaves=self.num_leaves, - max_depth=self.max_depth, - learning_rate=self.learning_rate, - max_bin=self.max_bin, - subsample_for_bin=self.subsample_for_bin, - min_child_samples=self.min_child_samples, - subsample=self.subsample, - subsample_freq=self.subsample_freq, - verbose=self.verbose, - seed=self.seed, - n_jobs=self.n_jobs, - **self.extra_lightgbm_params, - ) - return score_model
- - - @property - def n_estimators_true(self) -> list[int]: - """ - The number of estimators that are actually used in the models (after early stopping), - one for each dimension of the score (i.e. the dimension of y). - """ - return self.score_model.n_estimators_true
- -
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- - Made with Sphinx and @pradyunsg's - - Furo - -
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- - - - - - diff --git a/docs/docs/_sources/getting-started.rst.txt b/docs/docs/_sources/getting-started.rst.txt deleted file mode 100644 index 97f83fdf..00000000 --- a/docs/docs/_sources/getting-started.rst.txt +++ /dev/null @@ -1,123 +0,0 @@ -==================== -Treeffuser -==================== - -.. raw:: html - - - GitHub repo stars - - - PyPI version - - - PyPI - Downloads - - - arXiv - - - Documentation -
-
- - -Treeffuser is an easy-to-use package for **probabilistic prediction on tabular data with tree-based diffusion models**. -It estimates distributions of the form ``p(y|x)`` where ``x`` is a feature vector and ``y`` is a target vector. -Treeffuser can model conditional distributions ``p(y|x)`` that are arbitrarily complex (e.g., multimodal, heteroscedastic, non-gaussian, heavy-tailed, etc.). - -It is designed to adhere closely to the scikit-learn API and require minimal user tuning. - -Treeffuser is detailed in the paper: `Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees `_. - -Installation -============ - -You can install Treeffuser via pip from PyPI with the following command: - -.. code-block:: bash - - pip install treeffuser - -You can also install the development version with: - -.. code-block:: bash - - pip install git+https://github.com/blei-lab/treeffuser.git@main - -The GitHub repository is located at `https://github.com/blei-lab/treeffuser `_. - -Usage Example -============= - -Here's a simple example demonstrating how to use Treeffuser. - -We generate an heteroscedastic response with two sinusoidal components and heavy tails. - -.. code-block:: python - - import matplotlib.pyplot as plt - import numpy as np - from treeffuser import Treeffuser, Samples - - # Generate data - seed = 0 - rng = np.random.default_rng(seed=seed) - n = 5000 - x = rng.uniform(0, 2 * np.pi, size=n) - z = rng.integers(0, 2, size=n) - y = z * np.sin(x - np.pi / 2) + (1 - z) * np.cos(x) + rng.laplace(scale=x / 30, size=n) - -We fit Treeffuser and generate samples. We then plot the samples against the raw data. - -.. code-block:: python - - # Fit the model - model = Treeffuser(seed=seed) - model.fit(x, y) - - # Generate and plot samples - y_samples = model.sample(x, n_samples=1, seed=seed, verbose=True) - plt.scatter(x, y, s=1, label="observed data") - plt.scatter(x, y_samples[0, :], s=1, alpha=0.7, label="Treeffuser samples") - -.. image:: README_example.png - :alt: Treeffuser on heteroscedastic data with sinuisodal response and heavy tails. - :align: center - -Treeffuser accurately learns the target conditional densities and can generate samples from them. - -These samples can be used to compute any downstream estimates of interest. - -.. code-block:: python - - y_samples = model.sample(x, n_samples=100, verbose=True) # y_samples.shape[0] is 100 - - # Estimate downstream quantities of interest - y_mean = y_samples.mean(axis=0) # conditional mean for each x - y_std = y_samples.std(axis=0) # conditional std for each x - -For convenience, we also provide a class ``Samples`` that can estimate standard quantities. - -.. code-block:: python - - y_samples = Samples(y_samples) - y_mean = y_samples.sample_mean() # same as before - y_std = y_samples.sample_std() # same as before - y_quantiles = y_samples.sample_quantile(q=[0.05, 0.95]) # conditional quantiles for each x - -Please take a look at the documentation for more information on the available methods and parameters. - -Citing Treeffuser -================= - -If you use Treeffuser or this codebase in your work, please cite the following paper: - -.. code-block:: bibtex - - @article{beltran2024treeffuser, - title={Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees}, - author={Beltran-Velez, Nicolas and Grande, Alessandro Antonio and Nazaret, Achille and Kucukelbir, Alp and Blei, David}, - journal={arXiv preprint arXiv:2406.07658}, - year={2024} - } diff --git a/docs/docs/_sources/index.rst.txt b/docs/docs/_sources/index.rst.txt deleted file mode 100644 index 76ac2137..00000000 --- a/docs/docs/_sources/index.rst.txt +++ /dev/null @@ -1,28 +0,0 @@ -.. treeffuser documentation master file, created by - sphinx-quickstart on Sun Jun 23 01:48:09 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -.. raw:: html - - - -treeffuser -========== -Welcome to `treeffuser` documentation! - -`treeffuser` forms probabilistic predictions using conditional diffusions and gradient-boosted trees. - - -.. toctree:: - :maxdepth: 2 - - Getting started - Tutorials - modules - -.. indices and tables -.. ================== -.. * :ref:`genindex` -.. * :ref:`modindex` -.. * :ref:`search` diff --git a/docs/docs/_sources/modules.rst.txt b/docs/docs/_sources/modules.rst.txt deleted file mode 100644 index ec58539f..00000000 --- a/docs/docs/_sources/modules.rst.txt +++ /dev/null @@ -1,8 +0,0 @@ -API Reference -============= - -.. toctree:: - :maxdepth: 1 - - treeffuser - samples diff --git a/docs/docs/_sources/samples.rst.txt b/docs/docs/_sources/samples.rst.txt deleted file mode 100644 index 2909426e..00000000 --- a/docs/docs/_sources/samples.rst.txt +++ /dev/null @@ -1,11 +0,0 @@ -Samples -======= - -Module contents ---------------- - -.. automodule:: treeffuser.samples - :members: - :undoc-members: - :show-inheritance: - :inherited-members: diff --git a/docs/docs/_sources/treeffuser.rst.txt b/docs/docs/_sources/treeffuser.rst.txt deleted file mode 100644 index 4116e4a3..00000000 --- a/docs/docs/_sources/treeffuser.rst.txt +++ /dev/null @@ -1,11 +0,0 @@ -Treeffuser -========== - -Module contents ---------------- - -.. automodule:: treeffuser.treeffuser - :members: - :undoc-members: - :show-inheritance: - :inherited-members: diff --git a/docs/docs/_sources/tutorials.rst.txt b/docs/docs/_sources/tutorials.rst.txt deleted file mode 100644 index 5a4925a9..00000000 --- a/docs/docs/_sources/tutorials.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Tutorials -========= - -.. toctree:: - :maxdepth: 1 - - tutorials/m5-news-vendor-notebook diff --git a/docs/docs/_sources/tutorials/m5-news-vendor-notebook.ipynb.txt b/docs/docs/_sources/tutorials/m5-news-vendor-notebook.ipynb.txt deleted file mode 100644 index 39eb21ec..00000000 --- a/docs/docs/_sources/tutorials/m5-news-vendor-notebook.ipynb.txt +++ /dev/null @@ -1,1859 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Forecasting Walmart sales with Treeffuser\n", - "\n", - "In this tutorial we show how to use Treeffuser to model and forecast Walmart sales using the M5 forecasting dataset from Kaggle." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting started\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get started, we first install `treeffuser` and import the relevant libraries." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "!pip install treeffuser\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from pathlib import Path\n", - "\n", - "from tqdm import tqdm\n", - "from treeffuser import Treeffuser\n", - "\n", - "# load autoreload extension\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, create a Kaggle account and download the data from https://www.kaggle.com/competitions/m5-forecasting-accuracy/data.\n", - "\n", - "If you're running this notebook in Colab, manually upload the necessary files (`calendar.csv`, `sales_train_validation.csv`, `sell_prices.csv`) to Colab by clicking the `Files` tab on the left sidebar and selecting `Upload`. Move the files into a new folder named `m5`. Once uploaded, the notebook will be able to read and process the data.\n", - "\n", - "If you're running this on your local machine, you can also use Kaggle's [command-line tool](https://www.kaggle.com/docs/api) and run the following from the command line:\n", - "\n", - "```bash\n", - "cd ./m5 # path to folder where you want to save the data\n", - "kaggle competitions download -c m5-forecasting-accuracy\n", - "```\n", - "\n", - "Use your favorite tool to unzip the archive. In Linux/macOS,\n", - "\n", - "```bash\n", - "unzip m5-forecasting-accuracy.zip\n", - "```\n", - "\n", - "We'll be using the following files: `calendar.csv`, `sales_train_validation.csv`, and `sell_prices.csv`.\n", - "\n", - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load the data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "data_path = Path(\"./m5\") # change with path where you extracted the data archive\n", - "\n", - "calendar_df = pd.read_csv(data_path / \"calendar.csv\")\n", - "sales_train_df = pd.read_csv(data_path / \"sales_train_validation.csv\")\n", - "sell_prices_df = pd.read_csv(data_path / \"sell_prices.csv\")\n", - "\n", - "# add explicit columns for the day, month, year for ease of processing\n", - "calendar_df[\"date\"] = pd.to_datetime(calendar_df[\"date\"])\n", - "calendar_df[\"day\"] = calendar_df[\"date\"].dt.day\n", - "calendar_df[\"month\"] = calendar_df[\"date\"].dt.month\n", - "calendar_df[\"year\"] = calendar_df[\"date\"].dt.year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The data\n", - "\n", - "### Preprocessing\n", - "`sell_prices_df` contains the prices of each item in each store at a given time. The `wm_yr_wk` is a unique identifier for the time." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datewm_yr_wkweekdaywdaymonthyeardevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIday
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" - ], - "text/plain": [ - " date wm_yr_wk weekday wday month year d event_name_1 \\\n", - "0 2011-01-29 11101 Saturday 1 1 2011 d_1 NaN \n", - "1 2011-01-30 11101 Sunday 2 1 2011 d_2 NaN \n", - "2 2011-01-31 11101 Monday 3 1 2011 d_3 NaN \n", - "3 2011-02-01 11101 Tuesday 4 2 2011 d_4 NaN \n", - "4 2011-02-02 11101 Wednesday 5 2 2011 d_5 NaN \n", - "\n", - " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \n", - "0 NaN NaN NaN 0 0 0 29 \n", - "1 NaN NaN NaN 0 0 0 30 \n", - "2 NaN NaN NaN 0 0 0 31 \n", - "3 NaN NaN NaN 1 1 0 1 \n", - "4 NaN NaN NaN 1 0 1 2 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "calendar_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`sales_train_df` contains the number of units sold for an item in each department and store. The sales are grouped by day: for example, the `d_1907` column has the number of units sold on the 1907-th day." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1HOBBIES_1_002_CA_1_validationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CA0000...0000010000
2HOBBIES_1_003_CA_1_validationHOBBIES_1_003HOBBIES_1HOBBIESCA_1CA0000...2121110111
3HOBBIES_1_004_CA_1_validationHOBBIES_1_004HOBBIES_1HOBBIESCA_1CA0000...1054101372
4HOBBIES_1_005_CA_1_validationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CA0000...2110112224
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5 rows × 1919 columns

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" - ], - "text/plain": [ - " id item_id dept_id cat_id store_id \\\n", - "0 HOBBIES_1_001_CA_1_validation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 \n", - "1 HOBBIES_1_002_CA_1_validation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 \n", - "2 HOBBIES_1_003_CA_1_validation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 \n", - "3 HOBBIES_1_004_CA_1_validation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 \n", - "4 HOBBIES_1_005_CA_1_validation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 \n", - "\n", - " state_id d_1 d_2 d_3 d_4 ... d_1904 d_1905 d_1906 d_1907 d_1908 \\\n", - "0 CA 0 0 0 0 ... 1 3 0 1 1 \n", - "1 CA 0 0 0 0 ... 0 0 0 0 0 \n", - "2 CA 0 0 0 0 ... 2 1 2 1 1 \n", - "3 CA 0 0 0 0 ... 1 0 5 4 1 \n", - "4 CA 0 0 0 0 ... 2 1 1 0 1 \n", - "\n", - " d_1909 d_1910 d_1911 d_1912 d_1913 \n", - "0 1 3 0 1 1 \n", - "1 1 0 0 0 0 \n", - "2 1 0 1 1 1 \n", - "3 0 1 3 7 2 \n", - "4 1 2 2 2 4 \n", - "\n", - "[5 rows x 1919 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sales_train_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To align the sales data with the other DataFrames, we convert `sales_train_df` to a long format. We collapse the daily sales columns `d_{i}` into a single `sales` column, with an additional `day` column indicating the day corresponding to each sales entry." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id d sales\n", - "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", - "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", - "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", - "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", - "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", - " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", - " sales_df_long = pd.wide_to_long(\n", - " sales_df_wide.iloc[:100, 1:],\n", - " i=index_vars,\n", - " j=\"day\",\n", - " stubnames=[\"d\"],\n", - " sep=\"_\",\n", - " ).reset_index()\n", - "\n", - " sales_df_long = sales_df_long.rename(columns={\"d\": \"sales\", \"day\": \"d\"})\n", - "\n", - " sales_df_long[\"d\"] = \"d_\" + sales_df_long[\"d\"].astype(\n", - " \"str\"\n", - " ) # restore \"d_{i}\" format for day\n", - " return sales_df_long\n", - "\n", - "\n", - "sales_train_df_long = convert_sales_data_from_wide_to_long(sales_train_df)\n", - "sales_train_df_long.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Number of sales over the entire timespan')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(\n", - " sales_train_df_long[\"sales\"],\n", - " bins=np.arange(0, 10 + 1.5) - 0.5,\n", - " range=[0, 10],\n", - " density=True,\n", - ")\n", - "plt.xticks(range(10))\n", - "plt.ylabel(\"Frequency of number of sales\")\n", - "plt.title(\"Number of sales over the entire timespan\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train and test sets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset comprises sales data of 100 items over 1,913 days. For simplicity, we select the data from the first 365 days and discard the rest." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n_items = 100\n", - "n_days = 1913\n" - ] - } - ], - "source": [ - "print(f\"n_items = {len(sales_train_df_long['item_id'].unique())}\")\n", - "print(f\"n_days = {len(sales_train_df_long['d'].unique())}\")\n", - "\n", - "sales_train_df_long[\"day_number\"] = sales_train_df_long[\"d\"].str.extract(\"(\\d+)\").astype(int)\n", - "data = sales_train_df_long[sales_train_df_long[\"day_number\"] <= 365].copy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We compute the lags of the previous 30 days and merge the sales, calendar, and price data together." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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item_iddept_idcat_idstore_idstate_iddsalesday_numbersales_lag_1sales_lag_2...yearevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIdaysell_price
0HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14101410.00.0...2011NaNNaNNaNNaN000183.97
1HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14201420.00.0...2011Father's dayCulturalNaNNaN000193.97
2HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14301430.00.0...2011NaNNaNNaNNaN000203.97
3HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14411440.00.0...2011NaNNaNNaNNaN000213.97
4HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14501451.00.0...2011NaNNaNNaNNaN000223.97
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5 rows × 53 columns

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" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id d sales \\\n", - "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", - "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", - "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", - "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", - "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", - "\n", - " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", - "0 141 0.0 0.0 ... 2011 NaN \n", - "1 142 0.0 0.0 ... 2011 Father's day \n", - "2 143 0.0 0.0 ... 2011 NaN \n", - "3 144 0.0 0.0 ... 2011 NaN \n", - "4 145 1.0 0.0 ... 2011 NaN \n", - "\n", - " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", - "0 NaN NaN NaN 0 0 0 18 \n", - "1 Cultural NaN NaN 0 0 0 19 \n", - "2 NaN NaN NaN 0 0 0 20 \n", - "3 NaN NaN NaN 0 0 0 21 \n", - "4 NaN NaN NaN 0 0 0 22 \n", - "\n", - " sell_price \n", - "0 3.97 \n", - "1 3.97 \n", - "2 3.97 \n", - "3 3.97 \n", - "4 3.97 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_lags = 30\n", - "\n", - "# sort data before computing lags\n", - "data_index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", - "data.sort_values(data_index_vars + [\"day_number\"], inplace=True)\n", - "\n", - "for lag in range(1, n_lags + 1):\n", - " data[f\"sales_lag_{lag}\"] = data.groupby(by=data_index_vars)[\"sales\"].shift(lag)\n", - "\n", - "data = data.merge(calendar_df).merge(sell_prices_df)\n", - "\n", - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Treeffuser can handle **categorical columns**, but the dtype of those columns must be set to `category` in the DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "categorical_columns = [\n", - " \"item_id\",\n", - " \"dept_id\",\n", - " \"cat_id\",\n", - " \"store_id\",\n", - " \"state_id\",\n", - " \"d\",\n", - " \"wm_yr_wk\",\n", - " \"weekday\",\n", - " \"event_name_1\",\n", - " \"event_type_1\",\n", - " \"event_name_2\",\n", - " \"event_type_2\",\n", - " \"snap_CA\",\n", - " \"snap_TX\",\n", - " \"snap_WI\",\n", - "]\n", - "data[categorical_columns] = data[categorical_columns].astype(\"category\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, for each item, we take the first 300 days as train data and use the remaining 65 data as test data for evaluation." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(15216, 50)\n", - "(3891, 50)\n" - ] - } - ], - "source": [ - "is_train = data[\"day_number\"] <= 300\n", - "\n", - "y_name = \"sales\"\n", - "x_names = [\n", - " name for name in data.columns if name != y_name and name not in [\"day_number\", \"date\"]\n", - "]\n", - "\n", - "X_train, y_train, dates_train = (\n", - " data[is_train][x_names],\n", - " data[is_train][y_name],\n", - " data[is_train][\"date\"],\n", - ")\n", - "X_test, y_test, dates_test = (\n", - " data[~is_train][x_names],\n", - " data[~is_train][y_name],\n", - " data[~is_train][\"date\"],\n", - ")\n", - "\n", - "print(X_train.shape)\n", - "print(X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Probabilistic predictions with Treeffuser\n", - "\n", - "We regress the sales on the following covariates." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, day, sell_price\n" - ] - } - ], - "source": [ - "print(\", \".join(map(str, X_train.columns)))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", - "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" - ] - }, - { - "data": { - "text/html": [ - "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Treeffuser(extra_lightgbm_params={}, seed=0)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = Treeffuser(seed=0)\n", - "model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [05:55<00:00, 3.55s/it]\n" - ] - } - ], - "source": [ - "y_test_samples = model.sample(X_test, n_samples=100, seed=0, n_steps=50, verbose=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Newsvendor model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We illustrate the practical relevance of accurate probabilistic predictions with an application to inventory management, using the newsvendor model \\citep{arrow1951optimal}. \n", - "\n", - "Assume that every day we decide how many units $q$ of an item to buy. \n", - "We buy at a cost $c$ and sell at a price $p$. \n", - "However, the demand $y$ is random, introducing uncertainty in our decision. \n", - "The goal is to maximize the expected profit:\n", - "$$\\max_{q} p~\\mathbb{E}\\left[\\min(q, y)\\right] - c q.$$\n", - "The optimal solution to the newsvendor problem is to buy $q = F^{-1}\\left( \\frac{p-c}{p} \\right)$ units, where $F^{-1}$ is the quantile function of the distribution of $y$. \n", - "\n", - "Using Treeffuser, we can compute the quantiles from the samples and forecast the optimal quantity of units to buy.\n", - "\n", - "To compute profits, we use the observed prices, assume a margin of $50\\%$ over all products, and assume the actual number of sales of an item correspond to the demand of this item. We let Treeffuser, learn the conditional distribution of the demand of each item, estimate their quantiles, and thus determine the optimal quantity to buy. \n", - "\n", - "We use the held-out data to compute the profit made if Treeffuser was used to forecast the demand of each item and to manage the inventory of each item." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def newsvendor_utility(y_true, quantity_ordered, prices, stocking_cost):\n", - " \"\"\"\n", - " The newsvendor utility function with stock q, demand y, selling price p, stocking cost c is given by\n", - " $$ U(y, q, p, c) = p * min(y, q) - c * q $$\n", - " \"\"\"\n", - " utility = prices * np.minimum(y_true, quantity_ordered) - stocking_cost * quantity_ordered\n", - " return utility\n", - "\n", - "\n", - "def newsvendor_optimal_quantity(y_samples, prices, stocking_cost):\n", - " \"\"\"\n", - " Returns the optimal quantity to order for the newsvendor problem.\n", - "\n", - " It is given theoeretically by:\n", - " $$ q* = argmax_{q} E[U(y, q, p, c)] $$\n", - " which has a closed form solution,\n", - " $$ q* = F^{-1}( (p - c) / p) $$\n", - " where F is the CDF of the demand distribution\n", - " \"\"\"\n", - " # compute the target quantiles (p - c) / p\n", - " target_quantiles = (prices - stocking_cost) / prices\n", - " target_quantiles = np.maximum(target_quantiles, 0.0)\n", - "\n", - " # compute the empirical quantities corresponding to the target quantiles\n", - " res = []\n", - " for i in range(y_samples.shape[1]):\n", - " optimal_quantities = np.quantile(y_samples[:, i], target_quantiles[i])\n", - " res.append(optimal_quantities)\n", - " optimal_quantities = np.array(res)\n", - " return optimal_quantities" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "185.07666666666668" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# we don't know the profit margin of each item, so we assume it is 50%.\n", - "profit_margin = 0.5\n", - "\n", - "prices = X_test[\"sell_price\"].values\n", - "stocking_cost = prices / (1 + profit_margin)\n", - "\n", - "# compute optimal quantities\n", - "optimal_quantities = newsvendor_optimal_quantity(y_test_samples, prices, stocking_cost)\n", - "\n", - "# Treeffuser models continuous responses, hence we cast the predicted quantities into int\n", - "optimal_quantities = optimal_quantities.astype(int)\n", - "\n", - "profit = newsvendor_utility(y_test, optimal_quantities, prices, stocking_cost)\n", - "profit.sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We visualize the cumulative profit, the average demand and inventory over time in the plot below." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " date profit avg_inventory_weighted avg_demand_weighted\n", - "0 2011-11-25 -2.213333 0.039476 0.784104\n", - "1 2011-11-26 1.600000 0.033068 0.933309\n", - "2 2011-11-27 0.053333 0.046308 0.796895\n", - "3 2011-11-28 5.873333 0.067636 0.789166\n", - "4 2011-11-29 1.580000 0.041286 0.913090\n", - ".. ... ... ... ...\n", - "60 2012-01-24 6.336667 0.138240 0.661504\n", - "61 2012-01-25 7.876667 0.078847 0.530883\n", - "62 2012-01-26 -3.493333 0.051774 0.894360\n", - "63 2012-01-27 -0.710000 0.102579 1.093555\n", - "64 2012-01-28 11.890000 0.116262 1.258714\n", - "\n", - "[65 rows x 4 columns]\n" - ] - } - ], - "source": [ - "performance_data = pd.DataFrame(\n", - " {\n", - " \"date\": dates_test,\n", - " \"profit\": profit,\n", - " \"demand\": y_test,\n", - " \"inventory\": optimal_quantities,\n", - " \"price\": prices,\n", - " }\n", - ")\n", - "\n", - "# for each day, compute average demand and inventory weighted by price\n", - "daily_summary = (\n", - " performance_data.groupby(\"date\")\n", - " .agg(\n", - " profit=(\"profit\", \"sum\"),\n", - " avg_inventory_weighted=(\n", - " \"inventory\",\n", - " lambda x: (x * performance_data.loc[x.index, \"price\"]).sum()\n", - " / performance_data.loc[x.index, \"price\"].sum(),\n", - " ),\n", - " avg_demand_weighted=(\n", - " \"demand\",\n", - " lambda x: (x * performance_data.loc[x.index, \"price\"]).sum()\n", - " / performance_data.loc[x.index, \"price\"].sum(),\n", - " ),\n", - " )\n", - " .reset_index()\n", - ")\n", - "\n", - "print(daily_summary)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# dictionary to store color, alpha, linewidth, and linestyle for each line\n", - "line_styles = {\n", - " \"cumulative_profit\": {\"color\": \"teal\", \"alpha\": 1, \"linewidth\": 2.5, \"linestyle\": \"-\"},\n", - " \"inventory\": {\n", - " \"color\": \"blue\",\n", - " \"alpha\": 0.5,\n", - " \"linewidth\": 1.5,\n", - " \"linestyle\": \"--\",\n", - " },\n", - " \"demand\": {\n", - " \"color\": \"orange\",\n", - " \"alpha\": 0.5,\n", - " \"linewidth\": 1.5,\n", - " \"linestyle\": \"--\",\n", - " },\n", - "}\n", - "\n", - "# create figure\n", - "fig, ax1 = plt.subplots(figsize=(10, 6))\n", - "\n", - "# define x-axis\n", - "dates = pd.to_datetime(daily_summary[\"date\"])\n", - "\n", - "# plot cumulative profit\n", - "ax1.plot(\n", - " dates,\n", - " daily_summary[\"profit\"].cumsum(),\n", - " **line_styles[\"cumulative_profit\"],\n", - " label=\"Cumulative Profit\",\n", - ")\n", - "ax1.set_xlabel(\"Date\", fontsize=12)\n", - "ax1.set_ylabel(\"Cumulative Profit ($)\", fontsize=12)\n", - "ax1.grid(True, linestyle=\"--\", alpha=0.6)\n", - "\n", - "# create second y-axis for price-weighted inventory and demand\n", - "ax2 = ax1.twinx()\n", - "ax2.plot(\n", - " dates,\n", - " daily_summary[\"avg_inventory_weighted\"],\n", - " **line_styles[\"inventory\"],\n", - " label=\"Avg Inventory (Price Weighted)\",\n", - ")\n", - "ax2.plot(\n", - " dates,\n", - " daily_summary[\"avg_demand_weighted\"],\n", - " **line_styles[\"demand\"],\n", - " label=\"Avg Demand (Price Weighted)\",\n", - ")\n", - "ax2.set_ylabel(\"Avg Inventory and Demand (Units)\", fontsize=12)\n", - "\n", - "# combine all legends into one\n", - "lines1, labels1 = ax1.get_legend_handles_labels()\n", - "lines2, labels2 = ax2.get_legend_handles_labels()\n", - "ax1.legend(\n", - " lines1 + lines2, labels1 + labels2, loc=\"upper center\", bbox_to_anchor=(0.5, 1.12), ncol=3\n", - ")\n", - "\n", - "fig.autofmt_xdate() # rotate x-tick labels\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/docs/_static/basic.css b/docs/docs/_static/basic.css deleted file mode 100644 index 91cf78fe..00000000 --- a/docs/docs/_static/basic.css +++ /dev/null @@ -1,925 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; 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- padding-right: 5px; -} - -dl.field-list > dd { - padding-left: 0.5em; - margin-top: 0em; - margin-left: 0em; - margin-bottom: 0em; -} - -dl { - margin-bottom: 15px; -} - -dd > :first-child { - margin-top: 0px; -} - -dd ul, dd table { - margin-bottom: 10px; -} - -dd { - margin-top: 3px; - margin-bottom: 10px; - margin-left: 30px; -} - -.sig dd { - margin-top: 0px; - margin-bottom: 0px; -} - -.sig dl { - margin-top: 0px; - margin-bottom: 0px; -} - -dl > dd:last-child, -dl > dd:last-child > :last-child { - margin-bottom: 0; -} - -dt:target, span.highlighted { - background-color: #fbe54e; -} - -rect.highlighted { - fill: #fbe54e; -} - -dl.glossary dt { - font-weight: bold; - font-size: 1.1em; -} - -.versionmodified { - font-style: italic; -} - -.system-message { - background-color: #fda; - padding: 5px; - border: 3px solid red; -} - -.footnote:target { - background-color: #ffa; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; 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-} - -table.highlighttable tbody { - display: block; -} - -table.highlighttable tr { - display: flex; -} - -table.highlighttable td { - margin: 0; - padding: 0; -} - -table.highlighttable td.linenos { - padding-right: 0.5em; -} - -table.highlighttable td.code { - flex: 1; - overflow: hidden; -} - -.highlight .hll { - display: block; -} - -div.highlight pre, -table.highlighttable pre { - margin: 0; -} - -div.code-block-caption + div { - margin-top: 0; -} - -div.code-block-caption { - margin-top: 1em; - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -table.highlighttable td.linenos, -span.linenos, -div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; - -webkit-user-select: text; /* Safari fallback only */ - -webkit-user-select: none; /* Chrome/Safari */ - -moz-user-select: none; /* Firefox */ - -ms-user-select: none; /* IE10+ */ -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - margin: 1em 0; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: absolute; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; 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-} -.sb-footer-content { - background: plum; -} -.sb-footer-content__inner { - background: palevioletred; -} -.sb-footer { - background: pink; -} -.sb-footer__inner { - background: salmon; -} -.sb-article { - background: white; -} diff --git a/docs/docs/_static/doctools.js b/docs/docs/_static/doctools.js deleted file mode 100644 index 4d67807d..00000000 --- a/docs/docs/_static/doctools.js +++ /dev/null @@ -1,156 +0,0 @@ -/* - * doctools.js - * ~~~~~~~~~~~ - * - * Base JavaScript utilities for all Sphinx HTML documentation. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ -"use strict"; - -const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", -]); - -const _ready = (callback) => { - if (document.readyState !== "loading") { - callback(); - } else { - document.addEventListener("DOMContentLoaded", callback); - } -}; - -/** - * Small JavaScript module for the documentation. - */ -const Documentation = { - init: () => { - Documentation.initDomainIndexTable(); - Documentation.initOnKeyListeners(); - }, - - /** - * i18n support - */ - TRANSLATIONS: {}, - PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), - LOCALE: "unknown", - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext: (string) => { - const translated = Documentation.TRANSLATIONS[string]; - switch (typeof translated) { - case "undefined": - return string; // no translation - case "string": - return translated; // translation exists - default: - return translated[0]; // (singular, plural) translation tuple exists - } - }, - - ngettext: (singular, plural, n) => { - const translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated !== "undefined") - return translated[Documentation.PLURAL_EXPR(n)]; - return n === 1 ? singular : plural; - }, - - addTranslations: (catalog) => { - Object.assign(Documentation.TRANSLATIONS, catalog.messages); - Documentation.PLURAL_EXPR = new Function( - "n", - `return (${catalog.plural_expr})` - ); - Documentation.LOCALE = catalog.locale; - }, - - /** - * helper function to focus on search bar - */ - focusSearchBar: () => { - document.querySelectorAll("input[name=q]")[0]?.focus(); - }, - - /** - * Initialise the domain index toggle buttons - */ - initDomainIndexTable: () => { - const toggler = (el) => { - const idNumber = el.id.substr(7); - const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); - if (el.src.substr(-9) === "minus.png") { - el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; - toggledRows.forEach((el) => (el.style.display = "none")); - } else { - el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; - toggledRows.forEach((el) => (el.style.display = "")); - } - }; - - const togglerElements = document.querySelectorAll("img.toggler"); - togglerElements.forEach((el) => - el.addEventListener("click", (event) => toggler(event.currentTarget)) - ); - togglerElements.forEach((el) => (el.style.display = "")); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); - }, - - initOnKeyListeners: () => { - // only install a listener if it is really needed - if ( - !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS - ) - return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.altKey || event.ctrlKey || event.metaKey) return; - - if (!event.shiftKey) { - switch (event.key) { - case "ArrowLeft": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const prevLink = document.querySelector('link[rel="prev"]'); - if (prevLink && prevLink.href) { - window.location.href = prevLink.href; - event.preventDefault(); - } - break; - case "ArrowRight": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const nextLink = document.querySelector('link[rel="next"]'); - if (nextLink && nextLink.href) { - window.location.href = nextLink.href; - event.preventDefault(); - } - break; - } - } - - // some keyboard layouts may need Shift to get / - switch (event.key) { - case "/": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.focusSearchBar(); - event.preventDefault(); - } - }); - }, -}; - -// quick alias for translations -const _ = Documentation.gettext; - -_ready(Documentation.init); diff --git a/docs/docs/_static/documentation_options.js b/docs/docs/_static/documentation_options.js deleted file mode 100644 index a1e9248a..00000000 --- a/docs/docs/_static/documentation_options.js +++ /dev/null @@ -1,13 +0,0 @@ -const DOCUMENTATION_OPTIONS = { - VERSION: '0.1.3', - LANGUAGE: 'en', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false, - SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: true, -}; diff --git a/docs/docs/_static/file.png b/docs/docs/_static/file.png deleted file mode 100644 index a858a410e4faa62ce324d814e4b816fff83a6fb3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( diff --git a/docs/docs/_static/language_data.js b/docs/docs/_static/language_data.js deleted file mode 100644 index 434cd3d5..00000000 --- a/docs/docs/_static/language_data.js +++ /dev/null @@ -1,198 +0,0 @@ -/* - * language_data.js - * ~~~~~~~~~~~~~~~~ - * - * This script contains the language-specific data used by searchtools.js, - * namely the list of stopwords, stemmer, scorer and splitter. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; - - -/* Non-minified version is copied as a separate JS file, if available */ - -/** - * Porter Stemmer - */ -var Stemmer = function() { - - var step2list = { - ational: 'ate', - tional: 'tion', - enci: 'ence', - anci: 'ance', - izer: 'ize', - bli: 'ble', - alli: 'al', - entli: 'ent', - eli: 'e', - ousli: 'ous', - ization: 'ize', - ation: 'ate', - ator: 'ate', - alism: 'al', - iveness: 'ive', - fulness: 'ful', - ousness: 'ous', - aliti: 'al', - iviti: 'ive', - biliti: 'ble', - logi: 'log' - }; - - var step3list = { - icate: 'ic', - ative: '', - alize: 'al', - iciti: 'ic', - ical: 'ic', - ful: '', - ness: '' - }; - - var c = "[^aeiou]"; // consonant - var v = "[aeiouy]"; // vowel - var C = c + "[^aeiouy]*"; // consonant sequence - var V = v + "[aeiou]*"; // vowel sequence - - var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 - var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 - var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 - var s_v = "^(" + C + ")?" + v; // vowel in stem - - this.stemWord = function (w) { - var stem; - var suffix; - var firstch; - var origword = w; - - if (w.length < 3) - return w; - - var re; - var re2; - var re3; - var re4; - - firstch = w.substr(0,1); - if (firstch == "y") - w = firstch.toUpperCase() + w.substr(1); - - // Step 1a - re = /^(.+?)(ss|i)es$/; - re2 = /^(.+?)([^s])s$/; - - if (re.test(w)) - w = w.replace(re,"$1$2"); - else if (re2.test(w)) - w = w.replace(re2,"$1$2"); - - // Step 1b - re = /^(.+?)eed$/; - re2 = /^(.+?)(ed|ing)$/; - if (re.test(w)) { - var fp = re.exec(w); - re = new RegExp(mgr0); - if (re.test(fp[1])) { - re = /.$/; - w = w.replace(re,""); - } - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = new RegExp(s_v); - if (re2.test(stem)) { - w = stem; - re2 = /(at|bl|iz)$/; - re3 = new RegExp("([^aeiouylsz])\\1$"); - re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re2.test(w)) - w = w + "e"; - else if (re3.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - else if (re4.test(w)) - w = w + "e"; - } - } - - // Step 1c - re = /^(.+?)y$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(s_v); - if (re.test(stem)) - w = stem + "i"; - } - - // Step 2 - re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step2list[suffix]; - } - - // Step 3 - re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step3list[suffix]; - } - - // Step 4 - re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - re2 = /^(.+?)(s|t)(ion)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - if (re.test(stem)) - w = stem; - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = new RegExp(mgr1); - if (re2.test(stem)) - w = stem; - } - - // Step 5 - re = /^(.+?)e$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - re2 = new RegExp(meq1); - re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) - w = stem; - } - re = /ll$/; - re2 = new RegExp(mgr1); - if (re.test(w) && re2.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - - // and turn initial Y back to y - if (firstch == "y") - w = firstch.toLowerCase() + w.substr(1); - return w; - } -} diff --git a/docs/docs/_static/logo.svg b/docs/docs/_static/logo.svg deleted file mode 100644 index 4590ed73..00000000 --- a/docs/docs/_static/logo.svg +++ /dev/null @@ -1,25 +0,0 @@ - - - - - - - - - - - - - - - - diff --git a/docs/docs/_static/minus.png b/docs/docs/_static/minus.png deleted file mode 100644 index d96755fdaf8bb2214971e0db9c1fd3077d7c419d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK diff --git a/docs/docs/_static/nbsphinx-broken-thumbnail.svg b/docs/docs/_static/nbsphinx-broken-thumbnail.svg deleted file mode 100644 index 4919ca88..00000000 --- a/docs/docs/_static/nbsphinx-broken-thumbnail.svg +++ /dev/null @@ -1,9 +0,0 @@ - - - - diff --git a/docs/docs/_static/nbsphinx-code-cells.css b/docs/docs/_static/nbsphinx-code-cells.css deleted file mode 100644 index f3547347..00000000 --- a/docs/docs/_static/nbsphinx-code-cells.css +++ /dev/null @@ -1,258 +0,0 @@ -/* remove conflicting styling from Sphinx themes */ -div.nbinput.container div.prompt *, -div.nboutput.container div.prompt *, -div.nbinput.container div.input_area pre, -div.nboutput.container div.output_area pre, -div.nbinput.container div.input_area .highlight, -div.nboutput.container div.output_area .highlight { - border: none; 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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n var bounds = elem.getBoundingClientRect();\n var offset = getOffset(settings);\n if (bottom) {\n return (\n parseInt(bounds.bottom, 10) <\n (window.innerHeight || document.documentElement.clientHeight)\n );\n }\n return parseInt(bounds.top, 10) <= offset;\n };\n\n /**\n * Check if at the bottom of the viewport\n * @return {Boolean} If true, page is at the bottom of the viewport\n */\n var isAtBottom = function () {\n if (\n Math.ceil(window.innerHeight + window.pageYOffset) >=\n getDocumentHeight()\n )\n return true;\n return false;\n };\n\n /**\n * Check if the last item should be used (even if not at the top of the page)\n * @param {Object} item The last item\n * @param {Object} settings The settings for this instantiation\n * @return {Boolean} If true, use the last item\n */\n var useLastItem = function (item, settings) {\n if (isAtBottom() && isInView(item.content, settings, true)) return true;\n return false;\n };\n\n /**\n * Get the active content\n * @param {Array} contents The content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(publicAPIs.detect);\n };\n\n /**\n * Update content sorting on resize\n * Debounced for performance\n */\n var resizeHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(function () {\n sortContents(contents);\n publicAPIs.detect();\n });\n };\n\n /**\n * Destroy the current instantiation\n */\n publicAPIs.destroy = function () {\n // Undo DOM changes\n if (current) {\n deactivate(current, settings);\n }\n\n // Remove event listeners\n window.removeEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.removeEventListener(\"resize\", resizeHandler, false);\n }\n\n // Reset variables\n contents = null;\n navItems = null;\n current = null;\n timeout = null;\n settings = null;\n };\n\n /**\n * Initialize the current instantiation\n */\n var init = function () {\n // Merge user options into defaults\n settings = extend(defaults, options || {});\n\n // Setup variables based on the current DOM\n publicAPIs.setup();\n\n // Find the currently active content\n publicAPIs.detect();\n\n // Setup event listeners\n window.addEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.addEventListener(\"resize\", resizeHandler, false);\n }\n };\n\n //\n // Initialize and return the public APIs\n //\n\n init();\n return publicAPIs;\n };\n\n //\n // Return the Constructor\n //\n\n return Constructor;\n },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n if (Math.floor(header.getBoundingClientRect().top) == 0) {\n header.classList.add(\"scrolled\");\n } else {\n header.classList.remove(\"scrolled\");\n }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n if (positionY < GO_TO_TOP_OFFSET) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n } else {\n if (positionY < lastScrollTop) {\n document.documentElement.classList.add(\"show-back-to-top\");\n } else if (positionY > lastScrollTop) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n }\n }\n lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n if (tocScroll === null) {\n return;\n }\n\n // top of page.\n if (positionY == 0) {\n tocScroll.scrollTo(0, 0);\n } else if (\n // bottom of page.\n Math.ceil(positionY) >=\n Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n ) {\n tocScroll.scrollTo(0, tocScroll.scrollHeight);\n } else {\n // somewhere in the middle.\n const current = document.querySelector(\".scroll-current\");\n if (current == null) {\n return;\n }\n\n // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n // // scroll the currently \"active\" heading in toc, into view.\n // const rect = 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Resetting to auto.`);\n mode = \"auto\";\n }\n\n document.body.dataset.theme = mode;\n localStorage.setItem(\"theme\", mode);\n console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n if (prefersDark) {\n // Auto (dark) -> Light -> Dark\n if (currentTheme === \"auto\") {\n setTheme(\"light\");\n } else if (currentTheme == \"light\") {\n setTheme(\"dark\");\n } else {\n setTheme(\"auto\");\n }\n } else {\n // Auto (light) -> Dark -> Light\n if (currentTheme === \"auto\") {\n setTheme(\"dark\");\n } else if (currentTheme == \"dark\") {\n setTheme(\"light\");\n } else {\n setTheme(\"auto\");\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n let last_known_scroll_position = 0;\n let ticking = false;\n\n window.addEventListener(\"scroll\", function (e) {\n last_known_scroll_position = window.scrollY;\n\n if (!ticking) {\n window.requestAnimationFrame(function () {\n scrollHandler(last_known_scroll_position);\n ticking = false;\n });\n\n ticking = true;\n }\n });\n window.scroll();\n}\n\nfunction setupScrollSpy() {\n if (tocScroll === null) {\n return;\n }\n\n // Scrollspy -- highlight table on contents, based on scroll\n new Gumshoe(\".toc-tree a\", {\n reflow: true,\n recursive: true,\n navClass: \"scroll-current\",\n offset: () => {\n let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n return header.getBoundingClientRect().height + 2.5 * rem + 1;\n },\n });\n}\n\nfunction setupTheme() {\n // Attach event handlers for toggling themes\n const buttons = document.getElementsByClassName(\"theme-toggle\");\n Array.from(buttons).forEach((btn) => {\n btn.addEventListener(\"click\", cycleThemeOnce);\n });\n}\n\nfunction setup() {\n setupTheme();\n setupScrollHandler();\n setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n document.body.parentNode.classList.remove(\"no-js\");\n\n header = document.querySelector(\"header\");\n tocScroll = document.querySelector(\".toc-scroll\");\n\n setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","floor","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""} diff --git a/docs/docs/_static/searchtools.js b/docs/docs/_static/searchtools.js deleted file mode 100644 index 92da3f8b..00000000 --- a/docs/docs/_static/searchtools.js +++ /dev/null @@ -1,619 +0,0 @@ -/* - * searchtools.js - * ~~~~~~~~~~~~~~~~ - * - * Sphinx JavaScript utilities for the full-text search. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ -"use strict"; - -/** - * Simple result scoring code. - */ -if (typeof Scorer === "undefined") { - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [docname, title, anchor, descr, score, filename] - // and returns the new score. - /* - score: result => { - const [docname, title, anchor, descr, score, filename] = result - return score - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2, - }; -} - -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); -}; - -/** - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping - */ -const _escapeRegExp = (string) => - string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string - -const _displayItem = (item, searchTerms, highlightTerms) => { - const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; - const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; - const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - const contentRoot = document.documentElement.dataset.content_root; - - const [docName, title, anchor, descr, score, _filename] = item; - - let listItem = document.createElement("li"); - let requestUrl; - let linkUrl; - if (docBuilder === "dirhtml") { - // dirhtml builder - let dirname = docName + "/"; - if (dirname.match(/\/index\/$/)) - dirname = dirname.substring(0, dirname.length - 6); - else if (dirname === "index/") dirname = ""; - requestUrl = contentRoot + dirname; - linkUrl = requestUrl; - } else { - // normal html builders - requestUrl = contentRoot + docName + docFileSuffix; - linkUrl = docName + docLinkSuffix; - } - let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + anchor; - linkEl.dataset.score = score; - linkEl.innerHTML = title; - if (descr) { - listItem.appendChild(document.createElement("span")).innerHTML = - " (" + descr + ")"; - // highlight search terms in the description - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - } - else if (showSearchSummary) - fetch(requestUrl) - .then((responseData) => responseData.text()) - .then((data) => { - if (data) - listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, anchor) - ); - // highlight search terms in the summary - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - "Search finished, found ${resultCount} page(s) matching the search query." - ).replace('${resultCount}', resultCount); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms, - highlightTerms, -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms, highlightTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; -// Helper function used by query() to order search results. -// Each input is an array of [docname, title, anchor, descr, score, filename]. -// Order the results by score (in opposite order of appearance, since the -// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. -const _orderResultsByScoreThenName = (a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString, anchor) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - for (const removalQuery of [".headerlinks", "script", "style"]) { - htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); - } - if (anchor) { - const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); - if (anchorContent) return anchorContent.textContent; - - console.warn( - `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` - ); - } - - // if anchor not specified or not found, fall back to main content - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent) return docContent.textContent; - - console.warn( - "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - _parseQuery: (query) => { - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; - }, - - /** - * execute search (requires search index to be loaded) - */ - _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // Collect multiple result groups to be sorted separately and then ordered. - // Each is an array of [docname, title, anchor, descr, score, filename]. - const normalResults = []; - const nonMainIndexResults = []; - - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase().trim(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - normalResults.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id, isMain] of foundEntries) { - const score = Math.round(100 * queryLower.length / entry.length); - const result = [ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - ]; - if (isMain) { - normalResults.push(result); - } else { - nonMainIndexResults.push(result); - } - } - } - } - - // lookup as object - objectTerms.forEach((term) => - normalResults.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) { - normalResults.forEach((item) => (item[4] = Scorer.score(item))); - nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); - } - - // Sort each group of results by score and then alphabetically by name. - normalResults.sort(_orderResultsByScoreThenName); - nonMainIndexResults.sort(_orderResultsByScoreThenName); - - // Combine the result groups in (reverse) order. - // Non-main index entries are typically arbitrary cross-references, - // so display them after other results. - let results = [...nonMainIndexResults, ...normalResults]; - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - return results.reverse(); - }, - - query: (query) => { - const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); - const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms, highlightTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - const arr = [ - { files: terms[word], score: Scorer.term }, - { files: titleTerms[word], score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - if (!terms.hasOwnProperty(word)) { - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - } - if (!titleTerms.hasOwnProperty(word)) { - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); - }); - } - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, {}); - scoreMap.get(file)[word] = record.score; - }); - }); - - // create the mapping - files.forEach((file) => { - if (!fileMap.has(file)) fileMap.set(file, [word]); - else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords, anchor) => { - const text = Search.htmlToText(htmlText, anchor); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/docs/docs/_static/skeleton.css b/docs/docs/_static/skeleton.css deleted file mode 100644 index 467c878c..00000000 --- a/docs/docs/_static/skeleton.css +++ /dev/null @@ -1,296 +0,0 @@ -/* Some sane resets. */ -html { - height: 100%; -} - -body { - margin: 0; - min-height: 100%; -} - -/* All the flexbox magic! */ -body, -.sb-announcement, -.sb-content, -.sb-main, -.sb-container, -.sb-container__inner, -.sb-article-container, -.sb-footer-content, -.sb-header, -.sb-header-secondary, -.sb-footer { - display: flex; -} - -/* These order things vertically */ -body, -.sb-main, -.sb-article-container { - flex-direction: column; -} - -/* Put elements in the center */ -.sb-header, -.sb-header-secondary, -.sb-container, -.sb-content, -.sb-footer, -.sb-footer-content { - justify-content: center; -} -/* Put elements at the ends */ -.sb-article-container { - justify-content: space-between; -} - -/* These elements grow. */ -.sb-main, -.sb-content, -.sb-container, -article { - flex-grow: 1; -} - -/* Because padding making this wider is not fun */ -article { - box-sizing: border-box; -} - -/* The announcements element should never be wider than the page. */ -.sb-announcement { - max-width: 100%; -} - -.sb-sidebar-primary, -.sb-sidebar-secondary { - flex-shrink: 0; - width: 17rem; -} - -.sb-announcement__inner { - justify-content: center; - - box-sizing: border-box; - height: 3rem; - - overflow-x: auto; - white-space: nowrap; -} - -/* Sidebars, with checkbox-based toggle */ -.sb-sidebar-primary, -.sb-sidebar-secondary { - position: fixed; - height: 100%; - top: 0; -} - -.sb-sidebar-primary { - left: -17rem; - transition: left 250ms ease-in-out; -} -.sb-sidebar-secondary { - right: -17rem; - transition: right 250ms ease-in-out; -} - -.sb-sidebar-toggle { - display: none; -} -.sb-sidebar-overlay { - position: fixed; - top: 0; - width: 0; - height: 0; - - transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease; - - opacity: 0; - background-color: rgba(0, 0, 0, 0.54); -} - -#sb-sidebar-toggle--primary:checked - ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"], -#sb-sidebar-toggle--secondary:checked - ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] { - width: 100%; - height: 100%; - opacity: 1; - transition: width 0ms ease, height 0ms ease, opacity 250ms ease; -} - -#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary { - left: 0; -} -#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary { - right: 0; -} - -/* Full-width mode */ -.drop-secondary-sidebar-for-full-width-content - .hide-when-secondary-sidebar-shown { - display: none !important; -} -.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary { - display: none !important; -} - -/* Mobile views */ -.sb-page-width { - width: 100%; -} - -.sb-article-container, -.sb-footer-content__inner, -.drop-secondary-sidebar-for-full-width-content .sb-article, -.drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 100vw; -} - -.sb-article, -.match-content-width { - padding: 0 1rem; - box-sizing: border-box; -} - -@media (min-width: 32rem) { - .sb-article, - .match-content-width { - padding: 0 2rem; - } -} - -/* Tablet views */ -@media (min-width: 42rem) { - .sb-article-container { - width: auto; - } - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 42rem; - } - .sb-article, - .match-content-width { - width: 42rem; - } -} -@media (min-width: 46rem) { - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 46rem; - } - .sb-article, - .match-content-width { - width: 46rem; - } -} -@media (min-width: 50rem) { - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 50rem; - } - .sb-article, - .match-content-width { - width: 50rem; - } -} - -/* Tablet views */ -@media (min-width: 59rem) { - .sb-sidebar-secondary { - position: static; - } - .hide-when-secondary-sidebar-shown { - display: none !important; - } - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 59rem; - } - .sb-article, - .match-content-width { - width: 42rem; - } -} -@media (min-width: 63rem) { - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 63rem; - } - .sb-article, - .match-content-width { - width: 46rem; - } -} -@media (min-width: 67rem) { - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 67rem; - } - .sb-article, - .match-content-width { - width: 50rem; - } -} - -/* Desktop views */ -@media (min-width: 76rem) { - .sb-sidebar-primary { - position: static; - } - .hide-when-primary-sidebar-shown { - display: none !important; - } - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 59rem; - } - .sb-article, - .match-content-width { - width: 42rem; - } -} - -/* Full desktop views */ -@media (min-width: 80rem) { - .sb-article, - .match-content-width { - width: 46rem; - } - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 63rem; - } -} - -@media (min-width: 84rem) { - .sb-article, - .match-content-width { - width: 50rem; - } - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 67rem; - } -} - -@media (min-width: 88rem) { - .sb-footer-content__inner, - .drop-secondary-sidebar-for-full-width-content .sb-article, - .drop-secondary-sidebar-for-full-width-content .match-content-width { - width: 67rem; - } - .sb-page-width { - width: 88rem; - } -} diff --git a/docs/docs/_static/sphinx_highlight.js b/docs/docs/_static/sphinx_highlight.js deleted file mode 100644 index 8a96c69a..00000000 --- a/docs/docs/_static/sphinx_highlight.js +++ /dev/null @@ -1,154 +0,0 @@ -/* Highlighting utilities for Sphinx HTML documentation. */ -"use strict"; - -const SPHINX_HIGHLIGHT_ENABLED = true - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - const rest = document.createTextNode(val.substr(pos + text.length)); - parent.insertBefore( - span, - parent.insertBefore( - rest, - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - /* There may be more occurrences of search term in this node. So call this - * function recursively on the remaining fragment. - */ - _highlight(rest, addItems, text, className); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - -/** - * Small JavaScript module for the documentation. - */ -const SphinxHighlight = { - - /** - * highlight the search words provided in localstorage in the text - */ - highlightSearchWords: () => { - if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight - - // get and clear terms from localstorage - const url = new URL(window.location); - const highlight = - localStorage.getItem("sphinx_highlight_terms") - || url.searchParams.get("highlight") - || ""; - localStorage.removeItem("sphinx_highlight_terms") - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - - // get individual terms from highlight string - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(() => { - /* Do not call highlightSearchWords() when we are on the search page. - * It will highlight words from the *previous* search query. - */ - if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); - SphinxHighlight.initEscapeListener(); -}); diff --git a/docs/docs/_static/styles/furo-extensions.css b/docs/docs/_static/styles/furo-extensions.css deleted file mode 100644 index 0c2199de..00000000 --- a/docs/docs/_static/styles/furo-extensions.css +++ /dev/null @@ -1,2 +0,0 @@ -#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement 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Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n border-style: none;\n padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto-light\n display: block\n\n @media (prefers-color-scheme: dark)\n .theme-toggle svg.theme-icon-when-auto-dark\n display: block\n .theme-toggle svg.theme-icon-when-auto-light\n display: none\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n\n --color-api-added: #21632c;\n --color-api-added-border: #38a84d;\n --color-api-changed: #046172;\n --color-api-changed-border: #06a1bc;\n --color-api-deprecated: #605706;\n --color-api-deprecated-border: #f0d90f;\n --color-api-removed: #b30000;\n --color-api-removed-border: #ff5c5c;\n\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n --color-api-added: #3db854;\n --color-api-added-border: #267334;\n --color-api-changed: #09b0ce;\n --color-api-changed-border: #056d80;\n --color-api-deprecated: #b1a10b;\n --color-api-deprecated-border: #6e6407;\n --color-api-removed: #ff7575;\n --color-api-removed-border: #b03b3b;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\n$icon-size: 1.25rem\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: $icon-size\n width: $icon-size\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: $icon-size\n width: $icon-size\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page, .view-this-page\n svg\n color: inherit\n height: $icon-size\n width: $icon-size\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: $icon-size\n width: $icon-size\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$full-width: $content-width + 2 * ($content-padding + $sidebar-width);\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 4.25rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated,\ndiv.versionremoved\n border-left: 0.1875rem solid\n border-radius: 0.125rem\n\n padding-left: 0.75rem\n\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\ndiv.versionadded\n border-color: var(--color-api-added-border)\n .versionmodified\n color: var(--color-api-added)\n\ndiv.versionchanged\n border-color: var(--color-api-changed-border)\n .versionmodified\n color: var(--color-api-changed)\n\ndiv.deprecated\n border-color: var(--color-api-deprecated-border)\n .versionmodified\n color: var(--color-api-deprecated)\n\ndiv.versionremoved\n border-color: var(--color-api-removed-border)\n .versionmodified\n color: var(--color-api-removed)\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(2.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n color: var(--color-sidebar-link-text)\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the

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