{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A look into kiva.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kiva is a non-profit organization that hosts a platform, kiva.org, to crowdfund loans to individuals and groups who otherwise may not have had access to capital. They provide a rich dataset of historical loans from 2006 to 2019.\n",
    "\n",
    "In this notebook, we will first explore the dataset to glean insights and deeper understanding about the platform.\n",
    "\n",
    "We will then consider the expected personal loss of making a single loan, and then withdrawing any repayment as soon as possible.\n",
    "\n",
    "Finally, we will attempt to use machine learning models to predict which loans will get funded, and which will get fully repaid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.colors as mplc\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "qeds.themes.mpl_style();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# Source: https://www.kiva.org/build/data-snapshots\n",
    "loans_raw = pd.read_csv(\"datasets/loans_big.csv\", parse_dates=[\"POSTED_TIME\",\"PLANNED_EXPIRATION_TIME\",\"DISBURSE_TIME\",\"RAISED_TIME\",])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above dataset was provided by Kiva as a snapshot of their historical loans. Below is an example row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOAN_ID</th>\n",
       "      <th>LOAN_NAME</th>\n",
       "      <th>ORIGINAL_LANGUAGE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>DESCRIPTION_TRANSLATED</th>\n",
       "      <th>FUNDED_AMOUNT</th>\n",
       "      <th>LOAN_AMOUNT</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>IMAGE_ID</th>\n",
       "      <th>VIDEO_ID</th>\n",
       "      <th>ACTIVITY_NAME</th>\n",
       "      <th>SECTOR_NAME</th>\n",
       "      <th>LOAN_USE</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>COUNTRY_NAME</th>\n",
       "      <th>TOWN_NAME</th>\n",
       "      <th>CURRENCY_POLICY</th>\n",
       "      <th>CURRENCY_EXCHANGE_COVERAGE_RATE</th>\n",
       "      <th>CURRENCY</th>\n",
       "      <th>PARTNER_ID</th>\n",
       "      <th>POSTED_TIME</th>\n",
       "      <th>PLANNED_EXPIRATION_TIME</th>\n",
       "      <th>DISBURSE_TIME</th>\n",
       "      <th>RAISED_TIME</th>\n",
       "      <th>LENDER_TERM</th>\n",
       "      <th>NUM_LENDERS_TOTAL</th>\n",
       "      <th>NUM_JOURNAL_ENTRIES</th>\n",
       "      <th>NUM_BULK_ENTRIES</th>\n",
       "      <th>TAGS</th>\n",
       "      <th>BORROWER_NAMES</th>\n",
       "      <th>BORROWER_GENDERS</th>\n",
       "      <th>BORROWER_PICTURED</th>\n",
       "      <th>REPAYMENT_INTERVAL</th>\n",
       "      <th>DISTRIBUTION_MODEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77657</th>\n",
       "      <td>807094</td>\n",
       "      <td>GUSTAVO</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>Gustavo es soltero y vive con sus padres en La...</td>\n",
       "      <td>Gustavo is single and lives with his parents i...</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>funded</td>\n",
       "      <td>1745738.0</td>\n",
       "      <td>3002.0</td>\n",
       "      <td>Higher education costs</td>\n",
       "      <td>Education</td>\n",
       "      <td>to pay university tuition</td>\n",
       "      <td>BO</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>La Paz</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
       "      <td>USD</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2014-11-27 15:25:02+00:00</td>\n",
       "      <td>2015-01-25 02:20:02+00:00</td>\n",
       "      <td>2014-11-21 08:00:00+00:00</td>\n",
       "      <td>2014-12-21 15:17:44+00:00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>user_favorite, user_favorite, user_favorite</td>\n",
       "      <td>GUSTAVO</td>\n",
       "      <td>male</td>\n",
       "      <td>true</td>\n",
       "      <td>monthly</td>\n",
       "      <td>field_partner</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LOAN_ID LOAN_NAME ORIGINAL_LANGUAGE  \\\n",
       "77657   807094   GUSTAVO           Spanish   \n",
       "\n",
       "                                             DESCRIPTION  \\\n",
       "77657  Gustavo es soltero y vive con sus padres en La...   \n",
       "\n",
       "                                  DESCRIPTION_TRANSLATED  FUNDED_AMOUNT  \\\n",
       "77657  Gustavo is single and lives with his parents i...          500.0   \n",
       "\n",
       "       LOAN_AMOUNT  STATUS   IMAGE_ID  VIDEO_ID           ACTIVITY_NAME  \\\n",
       "77657        500.0  funded  1745738.0    3002.0  Higher education costs   \n",
       "\n",
       "      SECTOR_NAME                   LOAN_USE COUNTRY_CODE COUNTRY_NAME  \\\n",
       "77657   Education  to pay university tuition           BO      Bolivia   \n",
       "\n",
       "      TOWN_NAME CURRENCY_POLICY  CURRENCY_EXCHANGE_COVERAGE_RATE CURRENCY  \\\n",
       "77657    La Paz          shared                              0.1      USD   \n",
       "\n",
       "       PARTNER_ID               POSTED_TIME   PLANNED_EXPIRATION_TIME  \\\n",
       "77657        48.0 2014-11-27 15:25:02+00:00 2015-01-25 02:20:02+00:00   \n",
       "\n",
       "                  DISBURSE_TIME               RAISED_TIME  LENDER_TERM  \\\n",
       "77657 2014-11-21 08:00:00+00:00 2014-12-21 15:17:44+00:00         20.0   \n",
       "\n",
       "       NUM_LENDERS_TOTAL  NUM_JOURNAL_ENTRIES  NUM_BULK_ENTRIES  \\\n",
       "77657                 17                    2                 1   \n",
       "\n",
       "                                              TAGS BORROWER_NAMES  \\\n",
       "77657  user_favorite, user_favorite, user_favorite        GUSTAVO   \n",
       "\n",
       "      BORROWER_GENDERS BORROWER_PICTURED REPAYMENT_INTERVAL DISTRIBUTION_MODEL  \n",
       "77657             male              true            monthly      field_partner  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 40)\n",
    "loans_raw.dropna().head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 1682790 loans in the date range 2006-04-16 07:10:50+00:00 to 2019-02-25 04:12:27+00:00.\n"
     ]
    }
   ],
   "source": [
    "min_date = loans_raw[\"POSTED_TIME\"].min()\n",
    "max_date = loans_raw[\"POSTED_TIME\"].max()\n",
    "print(f\"The dataset contains {len(loans_raw)} loans in the date range {min_date} to {max_date}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first explore the quality of the data and see if there will be any issues with missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOAN_ID                                  0\n",
      "LOAN_NAME                            48555\n",
      "ORIGINAL_LANGUAGE                    44209\n",
      "DESCRIPTION                          44244\n",
      "DESCRIPTION_TRANSLATED              453635\n",
      "FUNDED_AMOUNT                            0\n",
      "LOAN_AMOUNT                              0\n",
      "STATUS                                   0\n",
      "IMAGE_ID                             44209\n",
      "VIDEO_ID                           1681943\n",
      "ACTIVITY_NAME                            0\n",
      "SECTOR_NAME                              0\n",
      "LOAN_USE                             44232\n",
      "COUNTRY_CODE                            29\n",
      "COUNTRY_NAME                             0\n",
      "TOWN_NAME                           163515\n",
      "CURRENCY_POLICY                          0\n",
      "CURRENCY_EXCHANGE_COVERAGE_RATE     337326\n",
      "CURRENCY                                 0\n",
      "PARTNER_ID                           18325\n",
      "POSTED_TIME                              0\n",
      "PLANNED_EXPIRATION_TIME             371834\n",
      "DISBURSE_TIME                         3189\n",
      "RAISED_TIME                          85150\n",
      "LENDER_TERM                             24\n",
      "NUM_LENDERS_TOTAL                        0\n",
      "NUM_JOURNAL_ENTRIES                      0\n",
      "NUM_BULK_ENTRIES                         0\n",
      "TAGS                                831171\n",
      "BORROWER_NAMES                       48555\n",
      "BORROWER_GENDERS                     44209\n",
      "BORROWER_PICTURED                    44209\n",
      "REPAYMENT_INTERVAL                       0\n",
      "DISTRIBUTION_MODEL                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(loans_raw.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some missing cells. However, the core data relating to the loans themselves are complete. Although cells relating to currencies are listed, manually investigating a sample revealed that all loan amounts are in USD.\n",
    "\n",
    "We will proceed by asking some basic questions about the loans.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "What are these loans going towards?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loans by Sector')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sectors = loans_raw[\"SECTOR_NAME\"].value_counts().sort_values()\n",
    "ax = sectors.plot.barh()\n",
    "ax.set_title(\"Loans by Sector\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "What about if we get slightly more granular?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loans by Activity')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "activities = loans_raw[\"ACTIVITY_NAME\"].value_counts().nlargest(15).sort_values()\n",
    "ax = activities.plot.barh()\n",
    "ax.set_title(\"Loans by Activity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 20 artists>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction import text\n",
    "\n",
    "additional_stopwords = ['buy', 'purchase', 'sell', 'pay', 'additional', 'items', 'materials', 'stock', 'like', 'goods',\n",
    "                        'increase', 'new', 'improve', 'build', 'make', 'products', 'supplies']\n",
    "\n",
    "stop_words = text.ENGLISH_STOP_WORDS.union(additional_stopwords)\n",
    "\n",
    "vectorizer = CountVectorizer(stop_words=stop_words)\n",
    "words = vectorizer.fit_transform(loans_raw[\"LOAN_USE\"].dropna())\n",
    "words_sum = words.sum(axis=0)\n",
    "\n",
    "words_freq = [(word, words_sum[0, idx]) for word, idx in vectorizer.vocabulary_.items()]\n",
    "words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "# The double sorting is because barh puts the first element at the bottom\n",
    "top_20_words = sorted(words_freq[0:20], key=lambda x:x[1])\n",
    "\n",
    "y = [word[0] for word in top_20_words]\n",
    "width = [freq[1] for freq in top_20_words]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"Most common specific description words\")\n",
    "ax.barh(y, width=width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the most common reasons for loans are to help small businesses, such as farming and local retail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "What does the distribution of loan amounts look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Loan Amounts in USD')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amounts = loans_raw['LOAN_AMOUNT']\n",
    "\n",
    "# Remove outliers to be able to view amounts at a reasonable scale.\n",
    "# These outliers are quite rare, but some have extremely large amounts, skewing the image.\n",
    "def filter_outliers(data, crit=4):\n",
    "    \"\"\"Removes data points with a zscore magnitude greater than the critical value crit.\"\"\"\n",
    "    z = np.abs(stats.zscore(data))\n",
    "    return data[(z < crit)]\n",
    "\n",
    "filtered_amounts = filter_outliers(amounts)\n",
    "\n",
    "ax = sns.distplot(filtered_amounts)\n",
    "ax.set_xticks(np.arange(0, np.max(filtered_amounts) + 1, 500))\n",
    "ax.set_xlabel(\"Loan Amounts in USD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can see that the most common loan amount is 250 USD, though multiples of 500 are generally more popular than surrounding amounts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "How many people tend to contribute to each loan?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8    78933\n",
      "1    72962\n",
      "9    72353\n",
      "7    72134\n",
      "5    69761\n",
      "Name: NUM_LENDERS_TOTAL, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_lenders = filter_outliers(loans_raw[\"NUM_LENDERS_TOTAL\"])\n",
    "\n",
    "ax = sns.distplot(filtered_lenders)\n",
    "ax.set_xticks(np.arange(0, 141, 10))\n",
    "ax.set_xlabel(\"Number of lenders per loan\")\n",
    "\n",
    "print(filtered_lenders.value_counts().head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We see that the distribution is centered around 8 lenders, with a long right tail. However, we can also see that a significant number of loans were funded by only a single person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The loans are not guaranteed to get fully funded. How many do not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.1% of loans do not get fully funded.\n",
      "Of those, the mean amount funded is 56.4% of the ask.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da5508b748>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_diffs = amounts - loans_raw[\"FUNDED_AMOUNT\"]\n",
    "funding_status = [\"funded\" if diff == 0 else \"unfunded\" for diff in loan_diffs]\n",
    "\n",
    "loan_diffs = loan_diffs[loan_diffs != 0]\n",
    "percent_unfunded = ((loan_diffs.count() / amounts.count()) * 100).round(1)\n",
    "print(f\"{percent_unfunded}% of loans do not get fully funded.\")\n",
    "\n",
    "unfunded_asking_amounts = amounts[loan_diffs.index]\n",
    "unfunded_ratios = loan_diffs / unfunded_asking_amounts\n",
    "how_much_funded = (unfunded_ratios.mean() * 100).round(1)\n",
    "print(f\"Of those, the mean amount funded is {how_much_funded}% of the ask.\")\n",
    "\n",
    "sns.countplot(funding_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans_raw[\"TIME_TO_FUND\"] = loans_raw[\"RAISED_TIME\"] - loans_raw[\"POSTED_TIME\"]\n",
    "loans_raw[\"DAYS_TO_FUND\"] = [time.days for time in loans_raw[\"TIME_TO_FUND\"]]\n",
    "days_to_fund = filter_outliers(loans_raw[\"DAYS_TO_FUND\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(days_to_fund)\n",
    "ax.set_xticks(np.arange(0, 71, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a relationship between the loan amount and the number of days it takes to fund the loan?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to filter outliers but don't want to filter by different amounts\n",
    "amounts_and_days = loans_raw[[\"LOAN_AMOUNT\", \"DAYS_TO_FUND\"]]\n",
    "amounts_and_days = amounts_and_days[(amounts_and_days[\"LOAN_AMOUNT\"].isin(filtered_amounts))\n",
    "                                    & (amounts_and_days[\"DAYS_TO_FUND\"].isin(days_to_fund))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1da53a875c8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = amounts_and_days.sample(250)\n",
    "sns.lmplot(x=\"LOAN_AMOUNT\", y=\"DAYS_TO_FUND\", data=sample, fit_reg=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not seem to be a clear relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Once the loans are distributed, there is no guarantee that they will be repaid. How many do not?\n",
    "\n",
    "Unfortunately, there was no information to answer this question in the original dataset. We instead turn to a previously supplied dataset of 5000 randomly sampled loans with their repayment details, taken from https://stat.duke.edu/resources/datasets/kiva-loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Source: https://stat.duke.edu/resources/datasets/kiva-loans\n",
    "repayments = pd.read_excel(\"datasets/loan_repayment_samples.xlsx\")\n",
    "\n",
    "# basket_amount was all n/a, video.youtube_id was not used\n",
    "repayments = repayments.drop([\"basket_amount\", \"video.youtube_id\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# This dataset has a row for each individual payment; aggregate these into a single row per loan\n",
    "grouped_repayments = repayments.groupby(\"id\", as_index=False).agg(lambda x: x.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0 % of loans in this sample have defaulted.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the payment status of the loan\n",
    "grouped_repayments[\"status\"] = grouped_repayments[\"status\"].agg(lambda x: \"defaulted\" if \"defaulted\" in x else \"paid\")\n",
    "\n",
    "status_counts = grouped_repayments[\"status\"].value_counts()\n",
    "default_ratio = ((status_counts[\"defaulted\"] / len(grouped_repayments)) * 100).round(1)\n",
    "print(f\"{default_ratio} % of loans in this sample have defaulted.\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(x=status_counts, labels=[\"paid\", \"defaulted\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "What is the gender ratio of those receiving loans?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan recipients are 80.0% female and 20.0% male.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genders = loans_raw[\"BORROWER_GENDERS\"].dropna()\n",
    "\n",
    "# The gender entries include all recipients as a comma separated string, so a simple value_counts will not work.\n",
    "females = 0\n",
    "males = 0\n",
    "for entry in genders:\n",
    "    recipients = entry.split(\", \")\n",
    "    for gender in recipients:\n",
    "        if gender == \"female\":\n",
    "            females += 1\n",
    "        else:\n",
    "            males += 1\n",
    "            \n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(x=[males, females], labels=[\"males\", \"females\"])\n",
    "\n",
    "female_ratio = round((females / (females + males)) * 100, 1)\n",
    "male_ratio = round(((males / (females + males)) * 100), 1)\n",
    "\n",
    "print(f\"Loan recipients are {female_ratio}% female and {male_ratio}% male.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the lenders themselves? What can we determine about them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PERMANENT_NAME</th>\n",
       "      <th>DISPLAY_NAME</th>\n",
       "      <th>MAIN_PIC_ID</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>MEMBER_SINCE</th>\n",
       "      <th>PERSONAL_URL</th>\n",
       "      <th>OCCUPATION</th>\n",
       "      <th>LOAN_BECAUSE</th>\n",
       "      <th>OTHER_INFO</th>\n",
       "      <th>LOAN_PURCHASE_NUM</th>\n",
       "      <th>INVITED_BY</th>\n",
       "      <th>NUM_INVITED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11476</th>\n",
       "      <td>barketing</td>\n",
       "      <td>Erika Godwin</td>\n",
       "      <td>2899846.0</td>\n",
       "      <td>Ottawa</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>CA</td>\n",
       "      <td>1528393535</td>\n",
       "      <td>www.barketing.co/</td>\n",
       "      <td>Business Owner</td>\n",
       "      <td>I loan because I want to help fellow female en...</td>\n",
       "      <td>I am co-founder of ProPet Software and owner o...</td>\n",
       "      <td>5</td>\n",
       "      <td>Bella</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PERMANENT_NAME  DISPLAY_NAME  MAIN_PIC_ID    CITY    STATE COUNTRY_CODE  \\\n",
       "11476      barketing  Erika Godwin    2899846.0  Ottawa  Ontario           CA   \n",
       "\n",
       "       MEMBER_SINCE       PERSONAL_URL      OCCUPATION  \\\n",
       "11476    1528393535  www.barketing.co/  Business Owner   \n",
       "\n",
       "                                            LOAN_BECAUSE  \\\n",
       "11476  I loan because I want to help fellow female en...   \n",
       "\n",
       "                                              OTHER_INFO  LOAN_PURCHASE_NUM  \\\n",
       "11476  I am co-founder of ProPet Software and owner o...                  5   \n",
       "\n",
       "      INVITED_BY  NUM_INVITED  \n",
       "11476      Bella            0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lenders_raw = pd.read_csv(\"datasets/lenders.csv\")\n",
    "lenders_raw.dropna().head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_loans_to_lenders = pd.read_csv(\"datasets/loans_lenders.csv\")\n",
    "loans_raw = pd.merge(loans_raw, map_loans_to_lenders, on=\"LOAN_ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the nationalities of the lenders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import squarify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lenders_by_country = lenders_raw[\"COUNTRY_CODE\"].value_counts()\n",
    "ax = squarify.plot(\n",
    "    sizes=lenders_by_country.values[0:20],\n",
    "    label=lenders_by_country.index[0:20],\n",
    "    alpha=.7\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evidently the majority of the users live in the US. Which states in particular?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_mappings = pd.read_csv(\"datasets/states.csv\")\n",
    "state_pops = pd.read_csv(\"datasets/us_state_populations.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CA    57270\n",
      "NY    20708\n",
      "TX    16410\n",
      "WA    15102\n",
      "IL    12441\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        define('plotly', function(require, exports, module) {\n",
       "            /**\n",
       "* plotly.js v1.53.0\n",
       "* Copyright 2012-2020, Plotly, Inc.\n",
       "* All rights reserved.\n",
       "* Licensed under the MIT license\n",
       "*/\n",
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l=(t[o]+o*o-(t[r[s]]+r[s]*r[s]))/(2*o-2*r[s]);l<=n[s];)s--,l=(t[o]+o*o-(t[r[s]]+r[s]*r[s]))/(2*o-2*r[s]);r[++s]=o,n[s]=l,n[s+1]=+a}for(o=0,s=0;o<i;o++){for(;n[s+1]<o;)s++;e[o]=(o-r[s])*(o-r[s])+t[r[s]]}}},{clamp:117}],96:[function(t,e,r){!function(e,r){\"use strict\";function n(t,e){if(!t)throw new Error(e||\"Assertion failed\")}function a(t,e){t.super_=e;var r=function(){};r.prototype=e.prototype,t.prototype=new r,t.prototype.constructor=t}function i(t,e,r){if(i.isBN(t))return t;this.negative=0,this.words=null,this.length=0,this.red=null,null!==t&&(\"le\"!==e&&\"be\"!==e||(r=e,e=10),this._init(t||0,e||10,r||\"be\"))}var o;\"object\"==typeof e?e.exports=i:r.BN=i,i.BN=i,i.wordSize=26;try{o=t(\"buffer\").Buffer}catch(t){}function s(t,e,r){for(var n=0,a=Math.min(t.length,r),i=e;i<a;i++){var o=t.charCodeAt(i)-48;n<<=4,n|=o>=49&&o<=54?o-49+10:o>=17&&o<=22?o-17+10:15&o}return n}function l(t,e,r,n){for(var a=0,i=Math.min(t.length,r),o=e;o<i;o++){var 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h=1;for(c=l(t,u,t.length,e),u=0;u<o;u++)h*=e;this.imuln(h),this.words[0]+c<67108864?this.words[0]+=c:this._iaddn(c)}},i.prototype.copy=function(t){t.words=new Array(this.length);for(var e=0;e<this.length;e++)t.words[e]=this.words[e];t.length=this.length,t.negative=this.negative,t.red=this.red},i.prototype.clone=function(){var t=new i(null);return this.copy(t),t},i.prototype._expand=function(t){for(;this.length<t;)this.words[this.length++]=0;return this},i.prototype.strip=function(){for(;this.length>1&&0===this.words[this.length-1];)this.length--;return this._normSign()},i.prototype._normSign=function(){return 1===this.length&&0===this.words[0]&&(this.negative=0),this},i.prototype.inspect=function(){return(this.red?\"<BN-R: \":\"<BN: \")+this.toString(16)+\">\"};var c=[\"\",\"0\",\"00\",\"000\",\"0000\",\"00000\",\"000000\",\"0000000\",\"00000000\",\"000000000\",\"0000000000\",\"00000000000\",\"000000000000\",\"0000000000000\",\"00000000000000\",\"000000000000000\",\"0000000000000000\",\"00000000000000000\",\"000000000000000000\",\"0000000000000000000\",\"00000000000000000000\",\"000000000000000000000\",\"0000000000000000000000\",\"00000000000000000000000\",\"000000000000000000000000\",\"0000000000000000000000000\"],u=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],h=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];function f(t,e,r){r.negative=e.negative^t.negative;var n=t.length+e.length|0;r.length=n,n=n-1|0;var a=0|t.words[0],i=0|e.words[0],o=a*i,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var c=1;c<n;c++){for(var u=l>>>26,h=67108863&l,f=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=f;p++){var d=c-p|0;u+=(o=(a=0|t.words[d])*(i=0|e.words[p])+h)/67108864|0,h=67108863&o}r.words[c]=0|h,l=0|u}return 0!==l?r.words[c]=0|l:r.length--,r.strip()}i.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||\"hex\"===t){r=\"\";for(var a=0,i=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<a|i)).toString(16);r=0!==(i=s>>>24-a&16777215)||o!==this.length-1?c[6-l.length]+l+r:l+r,(a+=2)>=26&&(a-=26,o--)}for(0!==i&&(r=i.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0|t)&&t>=2&&t<=36){var f=u[t],p=h[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var g=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?g+r:c[f-g.length]+g+r}for(this.isZero()&&(r=\"0\"+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}n(!1,\"Base 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32-Math.clz32(t)}:i.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},i.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},i.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},i.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},i.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},i.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},i.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},i.prototype.isNeg=function(){return 0!==this.negative},i.prototype.neg=function(){return this.clone().ineg()},i.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},i.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},i.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},i.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},i.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},i.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},i.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},i.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},i.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},i.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},i.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},i.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},i.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},i.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var a=0;a<e;a++)this.words[a]=67108863&~this.words[a];return r>0&&(this.words[a]=~this.words[a]&67108863>>26-r),this.strip()},i.prototype.notn=function(t){return this.clone().inotn(t)},i.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,a=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<a:this.words[r]&~(1<<a),this.strip()},i.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var a=0,i=0;i<n.length;i++)e=(0|r.words[i])+(0|n.words[i])+a,this.words[i]=67108863&e,a=e>>>26;for(;0!==a&&i<r.length;i++)e=(0|r.words[i])+a,this.words[i]=67108863&e,a=e>>>26;if(this.length=r.length,0!==a)this.words[this.length]=a,this.length++;else if(r!==this)for(;i<r.length;i++)this.words[i]=r.words[i];return this},i.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},i.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,a=this.cmp(t);if(0===a)return this.negative=0,this.length=1,this.words[0]=0,this;a>0?(r=this,n=t):(r=t,n=this);for(var i=0,o=0;o<n.length;o++)i=(e=(0|r.words[o])-(0|n.words[o])+i)>>26,this.words[o]=67108863&e;for(;0!==i&&o<r.length;o++)i=(e=(0|r.words[o])+i)>>26,this.words[o]=67108863&e;if(0===i&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},i.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var 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mt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(a=(a=a+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((i=i+Math.imul(f,G)|0)+(a>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(m,V),a=(a=Math.imul(m,U))+Math.imul(y,V)|0,i=Math.imul(y,U),n=n+Math.imul(d,H)|0,a=(a=a+Math.imul(d,G)|0)+Math.imul(g,H)|0,i=i+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(h,W)|0)|0)+((8191&(a=(a=a+Math.imul(h,X)|0)+Math.imul(f,W)|0))<<13)|0;c=((i=i+Math.imul(f,X)|0)+(a>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,V),a=(a=Math.imul(b,U))+Math.imul(_,V)|0,i=Math.imul(_,U),n=n+Math.imul(m,H)|0,a=(a=a+Math.imul(m,G)|0)+Math.imul(y,H)|0,i=i+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,a=(a=a+Math.imul(d,X)|0)+Math.imul(g,W)|0,i=i+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(h,J)|0)|0)+((8191&(a=(a=a+Math.imul(h,K)|0)+Math.imul(f,J)|0))<<13)|0;c=((i=i+Math.imul(f,K)|0)+(a>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(k,V),a=(a=Math.imul(k,U))+Math.imul(T,V)|0,i=Math.imul(T,U),n=n+Math.imul(b,H)|0,a=(a=a+Math.imul(b,G)|0)+Math.imul(_,H)|0,i=i+Math.imul(_,G)|0,n=n+Math.imul(m,W)|0,a=(a=a+Math.imul(m,X)|0)+Math.imul(y,W)|0,i=i+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,a=(a=a+Math.imul(d,K)|0)+Math.imul(g,J)|0,i=i+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(h,$)|0)|0)+((8191&(a=(a=a+Math.imul(h,tt)|0)+Math.imul(f,$)|0))<<13)|0;c=((i=i+Math.imul(f,tt)|0)+(a>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(A,V),a=(a=Math.imul(A,U))+Math.imul(S,V)|0,i=Math.imul(S,U),n=n+Math.imul(k,H)|0,a=(a=a+Math.imul(k,G)|0)+Math.imul(T,H)|0,i=i+Math.imul(T,G)|0,n=n+Math.imul(b,W)|0,a=(a=a+Math.imul(b,X)|0)+Math.imul(_,W)|0,i=i+Math.imul(_,X)|0,n=n+Math.imul(m,J)|0,a=(a=a+Math.imul(m,K)|0)+Math.imul(y,J)|0,i=i+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,a=(a=a+Math.imul(d,tt)|0)+Math.imul(g,$)|0,i=i+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(a=(a=a+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((i=i+Math.imul(f,nt)|0)+(a>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,V),a=(a=Math.imul(L,U))+Math.imul(C,V)|0,i=Math.imul(C,U),n=n+Math.imul(A,H)|0,a=(a=a+Math.imul(A,G)|0)+Math.imul(S,H)|0,i=i+Math.imul(S,G)|0,n=n+Math.imul(k,W)|0,a=(a=a+Math.imul(k,X)|0)+Math.imul(T,W)|0,i=i+Math.imul(T,X)|0,n=n+Math.imul(b,J)|0,a=(a=a+Math.imul(b,K)|0)+Math.imul(_,J)|0,i=i+Math.imul(_,K)|0,n=n+Math.imul(m,$)|0,a=(a=a+Math.imul(m,tt)|0)+Math.imul(y,$)|0,i=i+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,a=(a=a+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,i=i+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(h,it)|0)|0)+((8191&(a=(a=a+Math.imul(h,ot)|0)+Math.imul(f,it)|0))<<13)|0;c=((i=i+Math.imul(f,ot)|0)+(a>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,V),a=(a=Math.imul(O,U))+Math.imul(z,V)|0,i=Math.imul(z,U),n=n+Math.imul(L,H)|0,a=(a=a+Math.imul(L,G)|0)+Math.imul(C,H)|0,i=i+Math.imul(C,G)|0,n=n+Math.imul(A,W)|0,a=(a=a+Math.imul(A,X)|0)+Math.imul(S,W)|0,i=i+Math.imul(S,X)|0,n=n+Math.imul(k,J)|0,a=(a=a+Math.imul(k,K)|0)+Math.imul(T,J)|0,i=i+Math.imul(T,K)|0,n=n+Math.imul(b,$)|0,a=(a=a+Math.imul(b,tt)|0)+Math.imul(_,$)|0,i=i+Math.imul(_,tt)|0,n=n+Math.imul(m,rt)|0,a=(a=a+Math.imul(m,nt)|0)+Math.imul(y,rt)|0,i=i+Math.imul(y,nt)|0,n=n+Math.imul(d,it)|0,a=(a=a+Math.imul(d,ot)|0)+Math.imul(g,it)|0,i=i+Math.imul(g,ot)|0;var kt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(a=(a=a+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((i=i+Math.imul(f,ct)|0)+(a>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(D,V),a=(a=Math.imul(D,U))+Math.imul(R,V)|0,i=Math.imul(R,U),n=n+Math.imul(O,H)|0,a=(a=a+Math.imul(O,G)|0)+Math.imul(z,H)|0,i=i+Math.imul(z,G)|0,n=n+Math.imul(L,W)|0,a=(a=a+Math.imul(L,X)|0)+Math.imul(C,W)|0,i=i+Math.imul(C,X)|0,n=n+Math.imul(A,J)|0,a=(a=a+Math.imul(A,K)|0)+Math.imul(S,J)|0,i=i+Math.imul(S,K)|0,n=n+Math.imul(k,$)|0,a=(a=a+Math.imul(k,tt)|0)+Math.imul(T,$)|0,i=i+Math.imul(T,tt)|0,n=n+Math.imul(b,rt)|0,a=(a=a+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,i=i+Math.imul(_,nt)|0,n=n+Math.imul(m,it)|0,a=(a=a+Math.imul(m,ot)|0)+Math.imul(y,it)|0,i=i+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,a=(a=a+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,i=i+Math.imul(g,ct)|0;var Tt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(a=(a=a+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((i=i+Math.imul(f,ft)|0)+(a>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(B,V),a=(a=Math.imul(B,U))+Math.imul(N,V)|0,i=Math.imul(N,U),n=n+Math.imul(D,H)|0,a=(a=a+Math.imul(D,G)|0)+Math.imul(R,H)|0,i=i+Math.imul(R,G)|0,n=n+Math.imul(O,W)|0,a=(a=a+Math.imul(O,X)|0)+Math.imul(z,W)|0,i=i+Math.imul(z,X)|0,n=n+Math.imul(L,J)|0,a=(a=a+Math.imul(L,K)|0)+Math.imul(C,J)|0,i=i+Math.imul(C,K)|0,n=n+Math.imul(A,$)|0,a=(a=a+Math.imul(A,tt)|0)+Math.imul(S,$)|0,i=i+Math.imul(S,tt)|0,n=n+Math.imul(k,rt)|0,a=(a=a+Math.imul(k,nt)|0)+Math.imul(T,rt)|0,i=i+Math.imul(T,nt)|0,n=n+Math.imul(b,it)|0,a=(a=a+Math.imul(b,ot)|0)+Math.imul(_,it)|0,i=i+Math.imul(_,ot)|0,n=n+Math.imul(m,lt)|0,a=(a=a+Math.imul(m,ct)|0)+Math.imul(y,lt)|0,i=i+Math.imul(y,ct)|0,n=n+Math.imul(d,ht)|0,a=(a=a+Math.imul(d,ft)|0)+Math.imul(g,ht)|0,i=i+Math.imul(g,ft)|0;var Mt=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(a=(a=a+Math.imul(h,gt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((i=i+Math.imul(f,gt)|0)+(a>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,H),a=(a=Math.imul(B,G))+Math.imul(N,H)|0,i=Math.imul(N,G),n=n+Math.imul(D,W)|0,a=(a=a+Math.imul(D,X)|0)+Math.imul(R,W)|0,i=i+Math.imul(R,X)|0,n=n+Math.imul(O,J)|0,a=(a=a+Math.imul(O,K)|0)+Math.imul(z,J)|0,i=i+Math.imul(z,K)|0,n=n+Math.imul(L,$)|0,a=(a=a+Math.imul(L,tt)|0)+Math.imul(C,$)|0,i=i+Math.imul(C,tt)|0,n=n+Math.imul(A,rt)|0,a=(a=a+Math.imul(A,nt)|0)+Math.imul(S,rt)|0,i=i+Math.imul(S,nt)|0,n=n+Math.imul(k,it)|0,a=(a=a+Math.imul(k,ot)|0)+Math.imul(T,it)|0,i=i+Math.imul(T,ot)|0,n=n+Math.imul(b,lt)|0,a=(a=a+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,i=i+Math.imul(_,ct)|0,n=n+Math.imul(m,ht)|0,a=(a=a+Math.imul(m,ft)|0)+Math.imul(y,ht)|0,i=i+Math.imul(y,ft)|0;var At=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(a=(a=a+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((i=i+Math.imul(g,gt)|0)+(a>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,W),a=(a=Math.imul(B,X))+Math.imul(N,W)|0,i=Math.imul(N,X),n=n+Math.imul(D,J)|0,a=(a=a+Math.imul(D,K)|0)+Math.imul(R,J)|0,i=i+Math.imul(R,K)|0,n=n+Math.imul(O,$)|0,a=(a=a+Math.imul(O,tt)|0)+Math.imul(z,$)|0,i=i+Math.imul(z,tt)|0,n=n+Math.imul(L,rt)|0,a=(a=a+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,i=i+Math.imul(C,nt)|0,n=n+Math.imul(A,it)|0,a=(a=a+Math.imul(A,ot)|0)+Math.imul(S,it)|0,i=i+Math.imul(S,ot)|0,n=n+Math.imul(k,lt)|0,a=(a=a+Math.imul(k,ct)|0)+Math.imul(T,lt)|0,i=i+Math.imul(T,ct)|0,n=n+Math.imul(b,ht)|0,a=(a=a+Math.imul(b,ft)|0)+Math.imul(_,ht)|0,i=i+Math.imul(_,ft)|0;var St=(c+(n=n+Math.imul(m,dt)|0)|0)+((8191&(a=(a=a+Math.imul(m,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((i=i+Math.imul(y,gt)|0)+(a>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),a=(a=Math.imul(B,K))+Math.imul(N,J)|0,i=Math.imul(N,K),n=n+Math.imul(D,$)|0,a=(a=a+Math.imul(D,tt)|0)+Math.imul(R,$)|0,i=i+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,a=(a=a+Math.imul(O,nt)|0)+Math.imul(z,rt)|0,i=i+Math.imul(z,nt)|0,n=n+Math.imul(L,it)|0,a=(a=a+Math.imul(L,ot)|0)+Math.imul(C,it)|0,i=i+Math.imul(C,ot)|0,n=n+Math.imul(A,lt)|0,a=(a=a+Math.imul(A,ct)|0)+Math.imul(S,lt)|0,i=i+Math.imul(S,ct)|0,n=n+Math.imul(k,ht)|0,a=(a=a+Math.imul(k,ft)|0)+Math.imul(T,ht)|0,i=i+Math.imul(T,ft)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(a=(a=a+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((i=i+Math.imul(_,gt)|0)+(a>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),a=(a=Math.imul(B,tt))+Math.imul(N,$)|0,i=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,a=(a=a+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,i=i+Math.imul(R,nt)|0,n=n+Math.imul(O,it)|0,a=(a=a+Math.imul(O,ot)|0)+Math.imul(z,it)|0,i=i+Math.imul(z,ot)|0,n=n+Math.imul(L,lt)|0,a=(a=a+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,i=i+Math.imul(C,ct)|0,n=n+Math.imul(A,ht)|0,a=(a=a+Math.imul(A,ft)|0)+Math.imul(S,ht)|0,i=i+Math.imul(S,ft)|0;var Lt=(c+(n=n+Math.imul(k,dt)|0)|0)+((8191&(a=(a=a+Math.imul(k,gt)|0)+Math.imul(T,dt)|0))<<13)|0;c=((i=i+Math.imul(T,gt)|0)+(a>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),a=(a=Math.imul(B,nt))+Math.imul(N,rt)|0,i=Math.imul(N,nt),n=n+Math.imul(D,it)|0,a=(a=a+Math.imul(D,ot)|0)+Math.imul(R,it)|0,i=i+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,a=(a=a+Math.imul(O,ct)|0)+Math.imul(z,lt)|0,i=i+Math.imul(z,ct)|0,n=n+Math.imul(L,ht)|0,a=(a=a+Math.imul(L,ft)|0)+Math.imul(C,ht)|0,i=i+Math.imul(C,ft)|0;var Ct=(c+(n=n+Math.imul(A,dt)|0)|0)+((8191&(a=(a=a+Math.imul(A,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((i=i+Math.imul(S,gt)|0)+(a>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,it),a=(a=Math.imul(B,ot))+Math.imul(N,it)|0,i=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,a=(a=a+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,i=i+Math.imul(R,ct)|0,n=n+Math.imul(O,ht)|0,a=(a=a+Math.imul(O,ft)|0)+Math.imul(z,ht)|0,i=i+Math.imul(z,ft)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(a=(a=a+Math.imul(L,gt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((i=i+Math.imul(C,gt)|0)+(a>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),a=(a=Math.imul(B,ct))+Math.imul(N,lt)|0,i=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,a=(a=a+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,i=i+Math.imul(R,ft)|0;var Ot=(c+(n=n+Math.imul(O,dt)|0)|0)+((8191&(a=(a=a+Math.imul(O,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((i=i+Math.imul(z,gt)|0)+(a>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ht),a=(a=Math.imul(B,ft))+Math.imul(N,ht)|0,i=Math.imul(N,ft);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(a=(a=a+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((i=i+Math.imul(R,gt)|0)+(a>>>13)|0)+(zt>>>26)|0,zt&=67108863;var It=(c+(n=Math.imul(B,dt))|0)+((8191&(a=(a=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((i=Math.imul(N,gt))+(a>>>13)|0)+(It>>>26)|0,It&=67108863,l[0]=vt,l[1]=mt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=kt,l[8]=Tt,l[9]=Mt,l[10]=At,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=zt,l[18]=It,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=f),i.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?f(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,a=0,i=0;i<r.length-1;i++){var o=a;a=0;for(var s=67108863&n,l=Math.min(i,e.length-1),c=Math.max(0,i-t.length+1);c<=l;c++){var u=i-c,h=(0|t.words[u])*(0|e.words[c]),f=67108863&h;s=67108863&(f=f+s|0),a+=(o=(o=o+(h/67108864|0)|0)+(f>>>26)|0)>>>26,o&=67108863}r.words[i]=s,n=o,o=a}return 0!==n?r.words[i]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=i.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,a=0;a<e;a++)n|=(1&t)<<e-a-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,a,i){for(var o=0;o<i;o++)n[o]=e[t[o]],a[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,a,i){this.permute(i,t,e,r,n,a);for(var o=1;o<a;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<a;u+=s)for(var h=l,f=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],v=r[u+p+o],m=n[u+p+o],y=h*v-f*m;m=h*m+f*v,v=y,r[u+p]=d+v,n[u+p]=g+m,r[u+p+o]=d-v,n[u+p+o]=g-m,p!==s&&(y=l*h-c*f,f=l*f+c*h,h=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,a=0;for(r=r/2|0;r;r>>>=1)a++;return 1<<a+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var a=t[n];t[n]=t[r-n-1],t[r-n-1]=a,a=e[n],e[n]=-e[r-n-1],e[r-n-1]=-a}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var a=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&a,r=a<67108864?0:a/67108864|0}return t},g.prototype.convert13b=function(t,e,r,a){for(var i=0,o=0;o<e;o++)i+=0|t[o],r[2*o]=8191&i,i>>>=13,r[2*o+1]=8191&i,i>>>=13;for(o=2*e;o<a;++o)r[o]=0;n(0===i),n(0==(-8192&i))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),a=this.makeRBT(n),i=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,i,s,l,n,a),this.transform(c,i,u,h,n,a);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*h[p];l[p]=s[p]*h[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,f,i,n,a),this.conjugate(f,i,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},i.prototype.mul=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},i.prototype.mulf=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),d(this,t,e)},i.prototype.imul=function(t){return this.clone().mulTo(t,this)},i.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var a=(0|this.words[r])*t,i=(67108863&a)+(67108863&e);e>>=26,e+=a/67108864|0,e+=i>>>26,this.words[r]=67108863&i}return 0!==e&&(this.words[r]=e,this.length++),this},i.prototype.muln=function(t){return this.clone().imuln(t)},i.prototype.sqr=function(){return this.mul(this)},i.prototype.isqr=function(){return this.imul(this.clone())},i.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,a=r%26;e[r]=(t.words[n]&1<<a)>>>a}return e}(t);if(0===e.length)return new i(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var a=r.sqr();n<e.length;n++,a=a.sqr())0!==e[n]&&(r=r.mul(a));return r},i.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,a=(t-r)/26,i=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&i,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==a){for(e=this.length-1;e>=0;e--)this.words[e+a]=this.words[e];for(e=0;e<a;e++)this.words[e]=0;this.length+=a}return this.strip()},i.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},i.prototype.iushrn=function(t,e,r){var a;n(\"number\"==typeof t&&t>=0),a=e?(e-e%26)/26:0;var i=t%26,o=Math.min((t-i)/26,this.length),s=67108863^67108863>>>i<<i,l=r;if(a-=o,a=Math.max(0,a),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=a);c--){var h=0|this.words[c];this.words[c]=u<<26-i|h>>>i,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},i.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},i.prototype.shln=function(t){return this.clone().ishln(t)},i.prototype.ushln=function(t){return this.clone().iushln(t)},i.prototype.shrn=function(t){return this.clone().ishrn(t)},i.prototype.ushrn=function(t){return this.clone().iushrn(t)},i.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,a=1<<e;return!(this.length<=r)&&!!(this.words[r]&a)},i.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var a=67108863^67108863>>>e<<e;this.words[this.length-1]&=a}return this.strip()},i.prototype.maskn=function(t){return this.clone().imaskn(t)},i.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},i.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},i.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},i.prototype.addn=function(t){return this.clone().iaddn(t)},i.prototype.subn=function(t){return this.clone().isubn(t)},i.prototype.iabs=function(){return this.negative=0,this},i.prototype.abs=function(){return this.clone().iabs()},i.prototype._ishlnsubmul=function(t,e,r){var a,i,o=t.length+r;this._expand(o);var s=0;for(a=0;a<t.length;a++){i=(0|this.words[a+r])+s;var l=(0|t.words[a])*e;s=((i-=67108863&l)>>26)-(l/67108864|0),this.words[a+r]=67108863&i}for(;a<this.length-r;a++)s=(i=(0|this.words[a+r])+s)>>26,this.words[a+r]=67108863&i;if(0===s)return this.strip();for(n(-1===s),s=0,a=0;a<this.length;a++)s=(i=-(0|this.words[a])+s)>>26,this.words[a]=67108863&i;return this.negative=1,this.strip()},i.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),a=t,o=0|a.words[a.length-1];0!==(r=26-this._countBits(o))&&(a=a.ushln(r),n.iushln(r),o=0|a.words[a.length-1]);var s,l=n.length-a.length;if(\"mod\"!==e){(s=new i(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(a,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864*(0|n.words[a.length+h])+(0|n.words[a.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(a,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(a,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},i.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new i(0),mod:new i(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(a=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:a,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(a=s.div.neg()),{div:a,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new i(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new i(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new i(this.modn(t.words[0]))}:this._wordDiv(t,e);var a,o,s},i.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},i.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},i.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},i.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),a=t.andln(1),i=r.cmp(n);return i<0||1===a&&0===i?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},i.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,a=this.length-1;a>=0;a--)r=(e*r+(0|this.words[a]))%t;return r},i.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var a=(0|this.words[r])+67108864*e;this.words[r]=a/t|0,e=a%t}return this.strip()},i.prototype.divn=function(t){return this.clone().idivn(t)},i.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var a=new i(1),o=new i(0),s=new i(0),l=new i(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(a.isOdd()||o.isOdd())&&(a.iadd(u),o.isub(h)),a.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),a.isub(s),o.isub(l)):(r.isub(e),s.isub(a),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},i.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var a,o=new i(1),s=new i(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var h=0,f=1;0==(r.words[0]&f)&&h<26;++h,f<<=1);if(h>0)for(r.iushrn(h);h-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(a=0===e.cmpn(1)?o:s).cmpn(0)<0&&a.iadd(t),a},i.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var a=e.cmp(r);if(a<0){var i=e;e=r,r=i}else if(0===a||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},i.prototype.invm=function(t){return this.egcd(t).a.umod(t)},i.prototype.isEven=function(){return 0==(1&this.words[0])},i.prototype.isOdd=function(){return 1==(1&this.words[0])},i.prototype.andln=function(t){return this.words[0]&t},i.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,a=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=a,this;for(var i=a,o=r;0!==i&&o<this.length;o++){var s=0|this.words[o];i=(s+=i)>>>26,s&=67108863,this.words[o]=s}return 0!==i&&(this.words[o]=i,this.length++),this},i.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},i.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var a=0|this.words[0];e=a===t?0:a<t?-1:1}return 0!==this.negative?0|-e:e},i.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},i.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],a=0|t.words[r];if(n!==a){n<a?e=-1:n>a&&(e=1);break}}return e},i.prototype.gtn=function(t){return 1===this.cmpn(t)},i.prototype.gt=function(t){return 1===this.cmp(t)},i.prototype.gten=function(t){return this.cmpn(t)>=0},i.prototype.gte=function(t){return this.cmp(t)>=0},i.prototype.ltn=function(t){return-1===this.cmpn(t)},i.prototype.lt=function(t){return-1===this.cmp(t)},i.prototype.lten=function(t){return this.cmpn(t)<=0},i.prototype.lte=function(t){return this.cmp(t)<=0},i.prototype.eqn=function(t){return 0===this.cmpn(t)},i.prototype.eq=function(t){return 0===this.cmp(t)},i.red=function(t){return new w(t)},i.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},i.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},i.prototype._forceRed=function(t){return this.red=t,this},i.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},i.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},i.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},i.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},i.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},i.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},i.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},i.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},i.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},i.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},i.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},i.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},i.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},i.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new i(e,16),this.n=this.p.bitLength(),this.k=new i(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function x(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function _(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function w(t){if(\"string\"==typeof t){var e=i._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new i(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new i(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},a(y,m),y.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=4194303&a,n=10;n<t.length;n++){var i=0|t.words[n];t.words[n-10]=(4194303&i)<<4|a>>>22,a=i}a>>>=22,t.words[n-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},y.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 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t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new i(1)).iushrn(2);return this.pow(t,r)}for(var a=this.m.subn(1),o=0;!a.isZero()&&0===a.andln(1);)o++,a.iushrn(1);n(!a.isZero());var s=new i(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new i(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var h=this.pow(u,a),f=this.pow(t,a.addn(1).iushrn(1)),p=this.pow(t,a),d=o;0!==p.cmp(s);){for(var g=p,v=0;0!==g.cmp(s);v++)g=g.redSqr();n(v<d);var m=this.pow(h,new i(1).iushln(d-v-1));f=f.redMul(m),h=m.redSqr(),p=p.redMul(h),d=v}return f},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new i(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new i(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var a=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var h=c>>u&1;a!==r[0]&&(a=this.sqr(a)),0!==h||0!==o?(o<<=1,o|=h,(4===++s||0===n&&0===u)&&(a=this.mul(a,r[o]),s=0,o=0)):s=0}l=26}return a},w.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},w.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},i.mont=function(t){return new k(t)},a(k,w),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),a=r.isub(n).iushrn(this.shift),i=a;return a.cmp(this.m)>=0?i=a.isub(this.m):a.cmpn(0)<0&&(i=a.iadd(this.m)),i._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new i(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),a=r.isub(n).iushrn(this.shift),o=a;return a.cmp(this.m)>=0?o=a.isub(this.m):a.cmpn(0)<0&&(o=a.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(\"undefined\"==typeof e||e,this)},{buffer:105}],97:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,a=t.length,i=0;for(e=0;e<a;++e)i+=t[e].length;var o=new Array(i),s=0;for(e=0;e<a;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),h=0;for(n=0;n<c;++n)n!==r&&(u[h++]=l[n]);if(1&r){var f=u[1];u[1]=u[0],u[0]=f}}}return o}},{}],98:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return n=[],c(a=t,a,u,!0),n;case 2:return\"function\"==typeof e?c(t,t,e,!0):function(t,e){return n=[],c(t,e,u,!1),n}(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}var a};var n,a=t(\"typedarray-pool\"),i=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function s(t,e){for(var r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var a=0,i=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[a++]=c[u];n[i++]=o}}return i}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var h,f=a.mallocDouble(2*u*s),p=a.mallocInt32(s);if((s=l(t,u,f,p))>0){if(1===u&&n)i.init(s),h=i.sweepComplete(u,r,0,s,f,p,0,s,f,p);else{var d=a.mallocDouble(2*u*c),g=a.mallocInt32(c);(c=l(e,u,d,g))>0&&(i.init(s+c),h=1===u?i.sweepBipartite(u,r,0,s,f,p,0,c,d,g):o(u,r,n,s,f,p,c,d,g),a.free(d),a.free(g))}a.free(f),a.free(p)}return h}}}function u(t,e){n.push([t,e])}},{\"./lib/intersect\":100,\"./lib/sweep\":104,\"typedarray-pool\":543}],99:[function(t,e,r){\"use strict\";var n=\"d\",a=\"ax\",i=\"vv\",o=\"fp\",s=\"es\",l=\"rs\",c=\"re\",u=\"rb\",h=\"ri\",f=\"rp\",p=\"bs\",d=\"be\",g=\"bb\",v=\"bi\",m=\"bp\",y=\"rv\",x=\"Q\",b=[n,a,i,l,c,u,h,p,d,g,v];function _(t){var e=\"bruteForce\"+(t?\"Full\":\"Partial\"),r=[],_=b.slice();t||_.splice(3,0,o);var w=[\"function \"+e+\"(\"+_.join()+\"){\"];function k(e,o){var _=function(t,e,r){var o=\"bruteForce\"+(t?\"Red\":\"Blue\")+(e?\"Flip\":\"\")+(r?\"Full\":\"\"),_=[\"function \",o,\"(\",b.join(),\"){\",\"var \",s,\"=2*\",n,\";\"],w=\"for(var i=\"+l+\",\"+f+\"=\"+s+\"*\"+l+\";i<\"+c+\";++i,\"+f+\"+=\"+s+\"){var x0=\"+u+\"[\"+a+\"+\"+f+\"],x1=\"+u+\"[\"+a+\"+\"+f+\"+\"+n+\"],xi=\"+h+\"[i];\",k=\"for(var j=\"+p+\",\"+m+\"=\"+s+\"*\"+p+\";j<\"+d+\";++j,\"+m+\"+=\"+s+\"){var y0=\"+g+\"[\"+a+\"+\"+m+\"],\"+(r?\"y1=\"+g+\"[\"+a+\"+\"+m+\"+\"+n+\"],\":\"\")+\"yi=\"+v+\"[j];\";return 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strict\";e.exports=function(t,e,r,i,u,S,E,L,C){!function(t,e){var r=8*a.log2(e+1)*(t+1)|0,i=a.nextPow2(b*r);w.length<i&&(n.free(w),w=n.mallocInt32(i));var o=a.nextPow2(_*r);k<o&&(n.free(k),k=n.mallocDouble(o))}(t,i+E);var P,O=0,z=2*t;T(O++,0,0,i,0,E,r?16:0,-1/0,1/0),r||T(O++,0,0,E,0,i,1,-1/0,1/0);for(;O>0;){var I=(O-=1)*b,D=w[I],R=w[I+1],F=w[I+2],B=w[I+3],N=w[I+4],j=w[I+5],V=O*_,U=k[V],q=k[V+1],H=1&j,G=!!(16&j),Y=u,W=S,X=L,Z=C;if(H&&(Y=L,W=C,X=u,Z=S),!(2&j&&(F=v(t,D,R,F,Y,W,q),R>=F)||4&j&&(R=m(t,D,R,F,Y,W,U))>=F)){var J=F-R,K=N-B;if(G){if(t*J*(J+K)<p){if(void 0!==(P=l.scanComplete(t,D,e,R,F,Y,W,B,N,X,Z)))return P;continue}}else{if(t*Math.min(J,K)<h){if(void 0!==(P=o(t,D,e,H,R,F,Y,W,B,N,X,Z)))return P;continue}if(t*J*K<f){if(void 0!==(P=l.scanBipartite(t,D,e,H,R,F,Y,W,B,N,X,Z)))return P;continue}}var Q=d(t,D,R,F,Y,W,U,q);if(R<Q)if(t*(Q-R)<h){if(void 0!==(P=s(t,D+1,e,R,Q,Y,W,B,N,X,Z)))return P}else if(D===t-2){if(void 0!==(P=H?l.sweepBipartite(t,e,B,N,X,Z,R,Q,Y,W):l.sweepBipartite(t,e,R,Q,Y,W,B,N,X,Z)))return P}else T(O++,D+1,R,Q,B,N,H,-1/0,1/0),T(O++,D+1,B,N,R,Q,1^H,-1/0,1/0);if(Q<F){var $=c(t,D,B,N,X,Z),tt=X[z*$+D],et=g(t,D,$,N,X,Z,tt);if(et<N&&T(O++,D,Q,F,et,N,(4|H)+(G?16:0),tt,q),B<$&&T(O++,D,Q,F,B,$,(2|H)+(G?16:0),U,tt),$+1===et){if(void 0!==(P=G?A(t,D,e,Q,F,Y,W,$,X,Z[$]):M(t,D,e,H,Q,F,Y,W,$,X,Z[$])))return P}else if($<et){var rt;if(G){if(rt=y(t,D,Q,F,Y,W,tt),Q<rt){var nt=g(t,D,Q,rt,Y,W,tt);if(D===t-2){if(Q<nt&&void 0!==(P=l.sweepComplete(t,e,Q,nt,Y,W,$,et,X,Z)))return P;if(nt<rt&&void 0!==(P=l.sweepBipartite(t,e,nt,rt,Y,W,$,et,X,Z)))return P}else Q<nt&&T(O++,D+1,Q,nt,$,et,16,-1/0,1/0),nt<rt&&(T(O++,D+1,nt,rt,$,et,0,-1/0,1/0),T(O++,D+1,$,et,nt,rt,1,-1/0,1/0))}}else rt=H?x(t,D,Q,F,Y,W,tt):y(t,D,Q,F,Y,W,tt),Q<rt&&(D===t-2?P=H?l.sweepBipartite(t,e,$,et,X,Z,Q,rt,Y,W):l.sweepBipartite(t,e,Q,rt,Y,W,$,et,X,Z):(T(O++,D+1,Q,rt,$,et,H,-1/0,1/0),T(O++,D+1,$,et,Q,rt,1^H,-1/0,1/0)))}}}}};var n=t(\"typedarray-pool\"),a=t(\"bit-twiddle\"),i=t(\"./brute\"),o=i.partial,s=i.full,l=t(\"./sweep\"),c=t(\"./median\"),u=t(\"./partition\"),h=128,f=1<<22,p=1<<22,d=u(\"!(lo>=p0)&&!(p1>=hi)\",[\"p0\",\"p1\"]),g=u(\"lo===p0\",[\"p0\"]),v=u(\"lo<p0\",[\"p0\"]),m=u(\"hi<=p0\",[\"p0\"]),y=u(\"lo<=p0&&p0<=hi\",[\"p0\"]),x=u(\"lo<p0&&p0<=hi\",[\"p0\"]),b=6,_=2,w=n.mallocInt32(1024),k=n.mallocDouble(1024);function T(t,e,r,n,a,i,o,s,l){var c=b*t;w[c]=e,w[c+1]=r,w[c+2]=n,w[c+3]=a,w[c+4]=i,w[c+5]=o;var u=_*t;k[u]=s,k[u+1]=l}function M(t,e,r,n,a,i,o,s,l,c,u){var h=2*t,f=l*h,p=c[f+e];t:for(var d=a,g=a*h;d<i;++d,g+=h){var v=o[g+e],m=o[g+e+t];if(!(p<v||m<p)&&(!n||p!==v)){for(var y,x=s[d],b=e+1;b<t;++b){v=o[g+b],m=o[g+b+t];var _=c[f+b],w=c[f+b+t];if(m<_||w<v)continue t}if(void 0!==(y=n?r(u,x):r(x,u)))return y}}}function A(t,e,r,n,a,i,o,s,l,c){var u=2*t,h=s*u,f=l[h+e];t:for(var p=n,d=n*u;p<a;++p,d+=u){var g=o[p];if(g!==c){var v=i[d+e],m=i[d+e+t];if(!(f<v||m<f)){for(var 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n=t(\"./partition\")(\"lo<p0\",[\"p0\"]),a=8;function i(t,e,r,n,a,i){for(var o=2*t,s=o*(r+1)+e,l=r+1;l<n;++l,s+=o)for(var c=a[s],u=l,h=o*(l-1);u>r&&a[h+e]>c;--u,h-=o){for(var f=h,p=h+o,d=0;d<o;++d,++f,++p){var g=a[f];a[f]=a[p],a[p]=g}var v=i[u];i[u]=i[u-1],i[u-1]=v}}},{\"./partition\":102}],102:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=\"abcdef\".split(\"\").concat(e),a=[];t.indexOf(\"lo\")>=0&&a.push(\"lo=e[k+n]\");t.indexOf(\"hi\")>=0&&a.push(\"hi=e[k+o]\");return r.push(n.replace(\"_\",a.join()).replace(\"$\",t)),Function.apply(void 0,r)};var n=\"for(var j=2*a,k=j*c,l=k,m=c,n=b,o=a+b,p=c;d>p;++p,k+=j){var _;if($)if(m===p)m+=1,l+=j;else{for(var s=0;j>s;++s){var t=e[k+s];e[k+s]=e[l],e[l++]=t}var u=f[p];f[p]=f[m],f[m++]=u}}return m\"},{}],103:[function(t,e,r){\"use strict\";e.exports=function(t,e){e<=4*n?a(0,e-1,t):function t(e,r,h){var 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r=8*e;p.length<r&&(n.free(p),p=n.mallocDouble(r))},sweepBipartite:function(t,e,r,n,a,h,f,v,m,y){for(var x=0,b=2*t,_=t-1,w=b-1,k=r;k<n;++k){var T=h[k],M=b*k;p[x++]=a[M+_],p[x++]=-(T+1),p[x++]=a[M+w],p[x++]=T}for(var k=f;k<v;++k){var T=y[k]+o,A=b*k;p[x++]=m[A+_],p[x++]=-T,p[x++]=m[A+w],p[x++]=T}var S=x>>>1;i(p,S);for(var E=0,L=0,k=0;k<S;++k){var C=0|p[2*k+1];if(C>=o)d(c,u,L--,C=C-o|0);else if(C>=0)d(s,l,E--,C);else if(C<=-o){C=-C-o|0;for(var P=0;P<E;++P){var O=e(s[P],C);if(void 0!==O)return O}g(c,u,L++,C)}else{C=-C-1|0;for(var P=0;P<L;++P){var O=e(C,c[P]);if(void 0!==O)return O}g(s,l,E++,C)}}},sweepComplete:function(t,e,r,n,a,o,v,m,y,x){for(var b=0,_=2*t,w=t-1,k=_-1,T=r;T<n;++T){var M=o[T]+1<<1,A=_*T;p[b++]=a[A+w],p[b++]=-M,p[b++]=a[A+k],p[b++]=M}for(var T=v;T<m;++T){var M=x[T]+1<<1,S=_*T;p[b++]=y[S+w],p[b++]=1|-M,p[b++]=y[S+k],p[b++]=1|M}var E=b>>>1;i(p,E);for(var L=0,C=0,P=0,T=0;T<E;++T){var O=0|p[2*T+1],z=1&O;if(T<E-1&&O>>1==p[2*T+3]>>1&&(z=2,T+=1),O<0){for(var 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g(t,e,r,n){t[r]=n,e[n]=r}},{\"./sort\":103,\"bit-twiddle\":94,\"typedarray-pool\":543}],105:[function(t,e,r){},{}],106:[function(t,e,r){var n=Object.create||function(t){var e=function(){};return e.prototype=t,new e},a=Object.keys||function(t){var e=[];for(var r in t)Object.prototype.hasOwnProperty.call(t,r)&&e.push(r);return r},i=Function.prototype.bind||function(t){var e=this;return function(){return e.apply(t,arguments)}};function o(){this._events&&Object.prototype.hasOwnProperty.call(this,\"_events\")||(this._events=n(null),this._eventsCount=0),this._maxListeners=this._maxListeners||void 0}e.exports=o,o.EventEmitter=o,o.prototype._events=void 0,o.prototype._maxListeners=void 0;var s,l=10;try{var c={};Object.defineProperty&&Object.defineProperty(c,\"x\",{value:0}),s=0===c.x}catch(t){s=!1}function u(t){return void 0===t._maxListeners?o.defaultMaxListeners:t._maxListeners}function h(t,e,r,a){var i,o,s;if(\"function\"!=typeof r)throw new TypeError('\"listener\" argument must be a 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c=(i=r.left=o(i)).right=o(i.right),r.left=c.right,i.right=c.left,c.right=r,c.left=i,c._color=r._color,r._color=a,i._color=a,e._color=a,l(r),l(i),l(c),u>1&&((h=t[u-2]).right===r?h.right=c:h.left=c),void(t[u-1]=c);if(i._color===a){if(r._color===n)return r._color=a,void(r.left=s(n,i));r.left=s(n,i);continue}var h;i=o(i),r.left=i.right,i.right=r,i._color=r._color,r._color=n,l(r),l(i),u>1&&((h=t[u-2]).right===r?h.right=i:h.left=i),t[u-1]=i,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}}}(e),g.left===r?g.left=null:g.right=null,new c(this.tree._compare,e[0])},Object.defineProperty(f,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(f,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(f,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return 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t||(this.autoTicks=!0),h=!0,u=!0,this._firstInit=!1),h&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort(function(t,e){return t.x-e.x});s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),i(\"tickSize\"),i(\"tickAngle\"),i(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),i(\"labelSize\"),i(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),i(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),i(\"lineTickLength\"),i(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),i(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),i(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=a(this.gl,this.bounds,this.ticks))};var p=[new f,new f,new f];function d(t,e,r,n,a){for(var i=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var h=i,f=s,p=o,d=l;c&1<<u&&(h=s,f=i,p=l,d=o),h[u]=r[0][u],f[u]=r[1][u],a[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],v={model:l,view:l,projection:l,_ortho:!1};h.isOpaque=function(){return!0},h.isTransparent=function(){return!1},h.drawTransparent=function(t){};var m=[0,0,0],y=[0,0,0],x=[0,0,0];h.draw=function(t){t=t||v;for(var e=this.gl,r=t.model||l,n=t.view||l,a=t.projection||l,i=this.bounds,s=t._ortho||!1,u=o(r,n,a,i,s),h=u.cubeEdges,f=u.axis,b=n[12],_=n[13],w=n[14],k=n[15],T=(s?2:1)*this.pixelRatio*(a[3]*b+a[7]*_+a[11]*w+a[15]*k)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=h[M],this.lastCubeProps.axis[M]=f[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,h,f);e=this.gl;var S,E=g;for(M=0;M<3;++M)this.backgroundEnable[M]?E[M]=f[M]:E[M]=0;this._background.draw(r,n,a,i,E,this.backgroundColor),this._lines.bind(r,n,a,this);for(M=0;M<3;++M){var L=[0,0,0];f[M]>0?L[M]=i[1][M]:L[M]=i[0][M];for(var C=0;C<2;++C){var P=(M+1+C)%3,O=(M+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,O,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(M+1+C)%3,O=(M+1+(1^C))%3;this.zeroEnable[O]&&Math.min(i[0][O],i[1][O])<=0&&Math.max(i[0][O],i[1][O])>=0&&this._lines.drawZero(P,O,this.bounds,L,this.zeroLineColor[O],this.zeroLineWidth[O]*this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio);var z=c(m,A[M].primalMinor),I=c(y,A[M].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=T/r[5*C];z[C]*=D[C]*R,I[C]*=D[C]*R}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,z,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,I,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,a,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,a=(t+2)%3,i=e[n],o=e[a],s=r[n],l=r[a];i>0&&l>0?N(n):i>0&&l<0?N(n):i<0&&l>0?N(n):i<0&&l<0?N(n):o>0&&s>0?N(a):o>0&&s<0?N(a):o<0&&s>0?N(a):o<0&&s<0&&N(a)}for(M=0;M<3;++M){var V=A[M].primalMinor,U=A[M].mirrorMinor,q=c(x,A[M].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[M]&&(q[C]+=T*V[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var H=[0,0,0];if(H[M]=1,this.tickEnable[M]){-3600===this.tickAngle[M]?(this.tickAngle[M]=0,this.tickAlign[M]=\"auto\"):this.tickAlign[M]=-1,F=1,\"auto\"===(S=[this.tickAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(M,V,U);for(C=0;C<3;++C)q[C]+=T*V[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],q,this.tickColor[M],H,B,S)}if(this.labelEnable[M]){F=0,B=[0,0,0],this.labels[M].length>4&&(N(M),F=1),\"auto\"===(S=[this.labelAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)q[C]+=T*V[C]*this.labelPad[C]/r[5*C];q[M]+=.5*(i[0][M]+i[1][M]),this._text.drawLabel(M,this.labelSize[M],this.labelAngle[M],q,this.labelColor[M],[0,0,0],B,S)}}this._text.unbind()},h.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":237,\"./lib/cube.js\":238,\"./lib/lines.js\":239,\"./lib/text.js\":241,\"./lib/ticks.js\":242}],237:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,h=[0,0,0],f=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),h[l]=p,f[l]=p;for(var d=-1;d<=1;d+=2){h[c]=d;for(var g=-1;g<=1;g+=2)h[u]=g,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),s+=1}var v=c;c=u,u=v}var m=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=a(t,[{buffer:m,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:m,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=i(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,m,x,b)};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,a,i){for(var o=!1,s=0;s<3;++s)o=o||a[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:a,colors:i},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":240,\"gl-buffer\":244,\"gl-vao\":329}],238:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,i,p){a(s,e,t),a(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=i[x][2];for(var b=0;b<2;++b){u[1]=i[b][1];for(var _=0;_<2;++_)u[0]=i[_][0],f(l[y],u,s),y+=1}}for(var w=-1,x=0;x<8;++x){for(var k=l[x][3],T=0;T<3;++T)c[x][T]=l[x][T]/k;p&&(c[x][2]*=-1),k<0&&(w<0?w=x:c[x][2]<c[w][2]&&(w=x))}if(w<0){w=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,S=(M+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=C<<M,O=P+(C<<A)+(1-C<<S),z=P+(1-C<<A)+(C<<S);o(c[P],c[O],c[z],h)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<M);else{for(var C=0;C<2;++C){var P=C<<M,O=P+(C<<A)+(1-C<<S),z=P+(1-C<<A)+(C<<S),I=d([l[P],l[O],l[z],l[P+(1<<A)+(1<<S)]]);C?E=I:L=I}L>E&&(w|=1<<M)}}}for(var D=7^w,R=-1,x=0;x<8;++x)x!==w&&x!==D&&(R<0?R=x:c[R][1]>c[x][1]&&(R=x));for(var F=-1,x=0;x<3;++x){var B=R^1<<x;if(B!==w&&B!==D){F<0&&(F=B);var S=c[B];S[0]<c[F][0]&&(F=B)}}for(var N=-1,x=0;x<3;++x){var B=R^1<<x;if(B!==w&&B!==D&&B!==F){N<0&&(N=B);var S=c[B];S[0]>c[N][0]&&(N=B)}}var j=g;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^N)]=R&N;var V=7^N;V===w||V===D?(V=7^F,j[n.log2(N^V)]=V&N):j[n.log2(F^V)]=V&F;for(var U=v,q=w,M=0;M<3;++M)U[M]=q&1<<M?-1:1;return m};var n=t(\"bit-twiddle\"),a=t(\"gl-mat4/multiply\"),i=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),h=[0,0,0];function f(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var a=0;a<3;++a)t[n]+=e[a]*r[4*a+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=i.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],a=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-a,h=l[0]/l[3]-n,f=l[1]/l[3]-a;o+=Math.abs(c*f-u*h)}return o}var g=[1,1,1],v=[0,0,0],m={cubeEdges:g,axis:v}},{\"bit-twiddle\":94,\"gl-mat4/multiply\":270,\"robust-orientation\":508,\"split-polygon\":525}],239:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var h=0;h<3;++h){for(var f=o.length/3|0,d=0;d<r[h].length;++d){var g=+r[h][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3|0;s[h]=f,l[h]=v-f;for(var f=o.length/3|0,m=0;m<r[h].length;++m){var g=+r[h][m].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3|0;c[h]=f,u[h]=v-f}var y=n(t,new Float32Array(o)),x=a(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=i(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function h(t){return t[0]=t[1]=t[2]=0,t}function f(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,a,i,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=a,this.tickOffset=i,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,a){var i=h(s);this.shader.uniforms.majorAxis=s,i[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=i;var o,u=f(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=n,(o=h(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=h(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,a){if(this.tickCount[t]){var i=h(o);i[t]=1,this.shader.uniforms.majorAxis=i,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=a;var s=h(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,a,i){if(this.gridCount[t]){var u=h(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=f(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=h(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=h(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=a,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,a,i){var o=h(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=f(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=h(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=a,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":240,\"gl-buffer\":244,\"gl-vao\":329}],240:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),a=t(\"gl-shader\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return a(t,i,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return a(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return a(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":304,glslify:410}],241:[function(t,e,r){(function(r){\"use strict\";e.exports=function(t,e,r,i,s,l){var u=n(t),h=a(t,[{buffer:u,size:3}]),f=o(t);f.attributes.position.location=0;var p=new c(t,f,u,h);return p.update(e,r,i,s,l),p};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,h=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var a=this.shader.uniforms;a.model=t,a.view=e,a.projection=r,a.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,a){var o=[];function s(t,e,r,n,a,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return i(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:a,styletags:s}));for(var h=(n||12)/12,f=u.positions,p=u.cells,d=0,g=p.length;d<g;++d)for(var v=p[d],m=2;m>=0;--m){var y=f[v[m]];o.push(h*y[0],-h*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],h=[0,0,0],f=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){h[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),f[d]=(o.length/3|0)-h[d],c[d]=o.length/3|0;for(var g=0;g<n[d].length;++g)n[d][g].text&&s(n[d][g].x,n[d][g].text,n[d][g].font||a,n[d][g].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=h,this.labelCount=f},u.drawTicks=function(t,e,r,n,a,i,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=i,this.shader.uniforms.color=a,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,a,i,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=i,this.shader.uniforms.color=a,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this,t(\"_process\"))},{\"./shaders\":240,_process:483,\"gl-buffer\":244,\"gl-vao\":329,\"vectorize-text\":548}],242:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),a=0;n>=0&&(a=r.length-n-1);var i=Math.pow(10,a),o=Math.round(t*e*i),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/i,c=o%i;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),a){for(var h=\"\"+c;h.length<a;)h=\"0\"+h;return u+\".\"+h}return u}r.create=function(t,e){for(var r=[],a=0;a<3;++a){for(var i=[],o=(t[0][a],t[1][a],0);o*e[a]<=t[1][a];++o)i.push({x:o*e[a],text:n(e[a],o)});for(var o=-1;o*e[a]>=t[0][a];--o)i.push({x:o*e[a],text:n(e[a],o)});r.push(i)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var a=t[r][n],i=e[r][n];if(a.x!==i.x||a.text!==i.text||a.font!==i.font||a.fontColor!==i.fontColor||a.fontSize!==i.fontSize||a.dx!==i.dx||a.dy!==i.dy)return!1}}return!0}},{}],243:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,h){var f=e.model||c,p=e.view||c,m=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(h=h||i(f,p,m,x,y)).axis;o(u,p,f),o(u,m,u);for(var _=g,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var k=n(s(u,u));s(u,u);for(var T=0;T<3;++T){var M=(T+1)%3,A=(T+2)%3,S=v;t:for(var w=0;w<2;++w){var E=[];if(b[T]<0!=!!w){S[T]=x[w][T];for(var L=0;L<2;++L){S[M]=x[L^w][M];for(var C=0;C<2;++C)S[A]=x[C^L^w][A],E.push(S.slice())}for(var P=y?5:4,L=P;L===P;++L){if(0===E.length)continue t;E=a.positive(E,k[L])}for(var L=0;L<E.length;++L)for(var A=E[L],O=d(v,u,A,r,l),C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,A[C]),_[C].hi=Math.max(_[C].hi,A[C]),C!==T&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(O[C])))}}}return _};var n=t(\"extract-frustum-planes\"),a=t(\"split-polygon\"),i=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function h(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var f=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,a){for(var i=0;i<3;++i){for(var o=f,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[i]+=1,l(s,s,e),s[3]<0&&(t[i]=1/0),o[i]-=1,l(o,o,e),o[3]<0&&(t[i]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,h=(o[1]/o[3]-s[1]/s[3])*a;t[i]=.25*Math.sqrt(u*u+h*h)}return t}var g=[new h(1/0,-1/0,1/0),new h(1/0,-1/0,1/0),new h(1/0,-1/0,1/0)],v=[0,0,0]},{\"./lib/cube.js\":238,\"extract-frustum-planes\":227,\"gl-mat4/multiply\":270,\"gl-mat4/transpose\":279,\"gl-vec4/transformMat4\":400,\"split-polygon\":525}],244:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),a=t(\"ndarray-ops\"),i=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,a){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=a}var l=s.prototype;function c(t,e,r,n,a,i){var o=a.length*a.BYTES_PER_ELEMENT;if(i<0)return t.bufferData(e,a,n),o;if(o+i>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,i,a),r}function u(t,e){for(var r=n.malloc(t.length,e),a=t.length,i=0;i<a;++i)r[i]=t[i];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&\"undefined\"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=i(s,t.shape);a.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var h;h=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?h:h.subarray(0,t.length),e),n.free(h)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var a=t.createBuffer(),i=new s(t,r,a,0,n);return i.update(e),i}},{ndarray:451,\"ndarray-ops\":445,\"typedarray-pool\":543}],245:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,a=t.vectors,i={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),i;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=null,d=null,g=[],v=1/0,m=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),h=Math.min(x[2],h),f=Math.max(x[2],f);var b=a[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(v=Math.min(v,_),m=!1):m=!0}m||(p=x,d=b),g.push(b)}var w=[s,c,h],k=[l,u,f];e&&(e[0]=w,e[1]=k),0===o&&(o=1);var T=1/o;isFinite(v)||(v=1),i.vectorScale=v;var M=t.coneSize||.5;t.absoluteConeSize&&(M=t.absoluteConeSize*T),i.coneScale=M;y=0;for(var A=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=g[y],P=n.length(C)*T,O=0;O<8;O++){i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vertexIntensity.push(P,P,P),i.vertexIntensity.push(P,P,P);var z=i.positions.length;i.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return i};var a=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:a,traceType:\"cone\"})}},{\"./create_mesh\":246,\"./lib/shaders\":247,\"gl-vec3\":348}],246:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(t,e,r,n,a,i,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=a,this.triangleVectors=i,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=h,this._view=h,this._projection=h,this._resolution=[1,1]}var p=f.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var a=e[n],i=0;i<3;++i)r[4*n+i]=a[i];r[4*n+3]=255*a[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,a=t.vectors;if(n&&r&&a){var i=[],o=[],s=[],l=[],h=[];this.cells=r,this.positions=n,this.vectors=a;var f=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var v=0;v<p.length;++v){var m=p[v];d=Math.min(d,m),g=Math.max(g,m)}else for(v=0;v<n.length;++v){m=n[v][2];d=Math.min(d,m),g=Math.max(g,m)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(v=0;v<n.length;++v)for(var y=n[v],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(v=0;v<r.length;++v){var _=r[v];switch(_.length){case 3:for(x=0;x<3;++x){y=n[k=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var k;y=n[k=_[2-x]];i.push(y[0],y[1],y[2],y[3]);var T=a[k];o.push(T[0],T[1],T[2],T[3]||0);var M,A=f;3===A.length?s.push(A[0],A[1],A[2],1):s.push(A[0],A[1],A[2],A[3]),M=p?[(p[k]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(M[0],M[1]),h.push(v)}b+=1}}this.triangleCount=b,this.trianglePositions.update(i),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(h))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,a=t.projection||h,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:a,inverseModel:h.slice(),clipBounds:i,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var f=u[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/f}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,a=t.projection||h,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(a),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:a,clipBounds:i,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),a={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?a.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(a.intensity=this.intensity[r[1]],a.velocity=this.vectors[r[1]].slice(0,3),a.divergence=this.vectors[r[1]][3],a.index=e),a},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),u=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),h=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));h.generateMipmap(),h.minFilter=t.LINEAR_MIPMAP_LINEAR,h.magFilter=t.LINEAR;var p=a(t),d=a(t),g=a(t),v=a(t),m=a(t),y=new f(t,h,l,u,p,d,m,g,v,i(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:m,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return y.update(e),y}},{colormap:128,\"gl-buffer\":244,\"gl-mat4/invert\":268,\"gl-mat4/multiply\":270,\"gl-shader\":304,\"gl-texture2d\":324,\"gl-vao\":329,ndarray:451}],247:[function(t,e,r){var n=t(\"glslify\"),a=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),i=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:a,fragment:i,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:410}],248:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34000:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],249:[function(t,e,r){var 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r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,a=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var i=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(a[3]*i+a[7]*s+a[11]*l+a[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]*this.pixelRatio),r.capSize=this.capSize[h]*u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var a=-1;a<=1;a+=2){var i=[0,0,0];i[(n+e)%3]=a,r.push(i)}t[e]=r}return t}();function h(t,e,r,n){for(var a=u[n],i=0;i<a.length;++i){var 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fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return a(t,i,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":304,glslify:410}],252:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){a||(a=t.FRAMEBUFFER_UNSUPPORTED,i=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var a=new Array(r),i=0;i<n;++i)a[i]=t.COLOR_ATTACHMENT0+i;for(var i=n;i<r;++i)a[i]=t.NONE;l[n]=a}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if(\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var h=1;if(\"color\"in(n=n||{})){if((h=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(h>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(h>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+h+\" draw buffers\")}}var f=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&h>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");f=t.FLOAT}else n.preferFloat&&h>0&&p&&(f=t.FLOAT);var g=!0;\"depth\"in n&&(g=!!n.depth);var v=!1;\"stencil\"in n&&(v=!!n.stencil);return new d(t,e,r,f,h,g,v,c)};var a,i,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function h(t){switch(t){case a:throw new Error(\"gl-fbo: Framebuffer unsupported\");case i:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function f(t,e,r,a,i,o){if(!a)return null;var s=n(t,e,r,i,a);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,a){var i=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,i),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,a,t.RENDERBUFFER,i),i}function d(t,e,r,n,a,i,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(a);for(var d=0;d<a;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=i,this._useStencil=o;var g=this,v=[0|e,0|r];Object.defineProperties(v,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=v,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),a=t._shape[0],i=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,v=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var m=0;m<o;++m)t.color[m]=f(r,a,i,v,r.RGBA,r.COLOR_ATTACHMENT0+m);0===o?(t._color_rb=p(r,a,i,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=f(r,a,i,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=f(r,a,i,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,a,i,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,a,i,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,a,i,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),m=0;m<t.color.length;++m)t.color[m].dispose(),t.color[m]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),h(x)}u(r,e)}(this)}var g=d.prototype;function v(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,a=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var i=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,i),h(s)),u(n,i)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return v(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return v(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,v(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":324}],253:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,a=t(\"gl-constants/lookup\"),i=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=i(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===a.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),h=t.split(\"\\n\"),f={},p=0;p<h.length;p++){var d=h[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));f[g]=d}}for(var v=o(e).split(\"\\n\"),p=0;p<v.length;p++)if(f[p+3]||f[p+2]||f[p+1]){var m=v[p];if(c+=m+\"\\n\",f[p+1]){var y=f[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":64,\"gl-constants/lookup\":249,\"glsl-shader-name\":402,\"sprintf-js\":526}],254:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),a=o(r,l.pickVertex,l.pickFragment),i=s(r),u=s(r),h=s(r),f=s(r),p=new c(t,n,a,i,u,h,f);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),a=t(\"iota-array\"),i=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,a,i,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=a,this.colorBuffer=i,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,h=c.prototype,f=[0,0,1,0,0,1,1,0,1,1,0,1];h.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var a=t.gl,i=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=i[2]-i[0],c=i[3]-i[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-i[0])/l-1,u[7]=2*(r[1]-i[1])/c-1,e.bind();var h=e.uniforms;h.viewTransform=u,h.shape=this.shape;var f=e.attributes;this.positionBuffer.bind(),f.position.pointer(),this.weightBuffer.bind(),f.weight.pointer(a.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(a.UNSIGNED_BYTE,!0),a.drawArrays(a.TRIANGLES,0,n)}}),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,a=this.pickShader,i=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=i[2]-i[0],u=i[3]-i[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2*c/h,t[4]=2*u/f,t[6]=2*(i[0]-l[0])/h-1,t[7]=2*(i[1]-l[1])/f-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,a.bind();var d=a.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=a.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,a=this.shape[0]*this.shape[1];if(r<n||r>=n+a)return null;var i=r-n,o=this.xData,s=this.yData;return{object:this,pointId:i,dataCoord:[o[i%this.shape[0]],s[i/this.shape[0]|0]]}},h.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||a(e[0]),o=t.y||a(e[1]),s=t.z||new Float32Array(e[0]*e[1]);this.xData=r,this.yData=o;var l=t.colorLevels||[0],c=t.colorValues||[0,0,0,1],u=l.length,h=this.bounds,p=h[0]=r[0],d=h[1]=o[0],g=1/((h[2]=r[r.length-1])-p),v=1/((h[3]=o[o.length-1])-d),m=e[0],y=e[1];this.shape=[m,y];var x=(m-1)*(y-1)*(f.length>>>1);this.numVertices=x;for(var b=i.mallocUint8(4*x),_=i.mallocFloat32(2*x),w=i.mallocUint8(2*x),k=i.mallocUint32(x),T=0,M=0;M<y-1;++M)for(var A=v*(o[M]-d),S=v*(o[M+1]-d),E=0;E<m-1;++E)for(var L=g*(r[E]-p),C=g*(r[E+1]-p),P=0;P<f.length;P+=2){var O,z,I,D,R=f[P],F=f[P+1],B=s[(M+F)*m+(E+R)],N=n.le(l,B);if(N<0)O=c[0],z=c[1],I=c[2],D=c[3];else if(N===u-1)O=c[4*u-4],z=c[4*u-3],I=c[4*u-2],D=c[4*u-1];else{var j=(B-l[N])/(l[N+1]-l[N]),V=1-j,U=4*N,q=4*(N+1);O=V*c[U]+j*c[q],z=V*c[U+1]+j*c[q+1],I=V*c[U+2]+j*c[q+2],D=V*c[U+3]+j*c[q+3]}b[4*T]=255*O,b[4*T+1]=255*z,b[4*T+2]=255*I,b[4*T+3]=255*D,_[2*T]=.5*L+.5*C,_[2*T+1]=.5*A+.5*S,w[2*T]=R,w[2*T+1]=F,k[T]=M*m+E,T+=1}this.positionBuffer.update(_),this.weightBuffer.update(w),this.colorBuffer.update(b),this.idBuffer.update(k),i.free(_),i.free(b),i.free(w),i.free(k)},h.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":255,\"binary-search-bounds\":256,\"gl-buffer\":244,\"gl-shader\":304,\"iota-array\":417,\"typedarray-pool\":543}],255:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:410}],256:[function(t,e,r){arguments[4][113][0].apply(r,arguments)},{dup:113}],257:[function(t,e,r){var n=t(\"glslify\"),a=t(\"gl-shader\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return a(t,i,o,null,l)},r.createPickShader=function(t){return a(t,i,s,null,l)}},{\"gl-shader\":304,glslify:410}],258:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=h(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=a(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=i(e,u);d.wrap=e.REPEAT;var g=new m(e,r,o,s,l,d);return g.update(t),g};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),h=u.createShader,f=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var a=t[n]-e[n];r+=a*a}return Math.sqrt(r)}function g(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function v(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function m(t,e,r,n,a,i){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=a,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=i,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=m.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:g(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:g(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var 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t[0]=x*n+b*s+_*h+w*g,t[1]=x*a+b*l+_*f+w*v,t[2]=x*i+b*c+_*p+w*m,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*h+w*g,t[5]=x*a+b*l+_*f+w*v,t[6]=x*i+b*c+_*p+w*m,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*h+w*g,t[9]=x*a+b*l+_*f+w*v,t[10]=x*i+b*c+_*p+w*m,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*h+w*g,t[13]=x*a+b*l+_*f+w*v,t[14]=x*i+b*c+_*p+w*m,t[15]=x*o+b*u+_*d+w*y,t}},{}],271:[function(t,e,r){e.exports=function(t,e,r,n,a,i,o){var s=1/(e-r),l=1/(n-a),c=1/(i-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(a+n)*l,t[14]=(o+i)*c,t[15]=1,t}},{}],272:[function(t,e,r){e.exports=function(t,e,r,n,a){var i=1/Math.tan(e/2),o=1/(n-a);return t[0]=i/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=i,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(a+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*a*n*o,t[15]=0,t}},{}],273:[function(t,e,r){e.exports=function(t,e,r,n){var 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t[4]=i*a+c*n,t[5]=o*a+u*n,t[6]=s*a+h*n,t[7]=l*a+f*n,t[8]=c*a-i*n,t[9]=u*a-o*n,t[10]=h*a-s*n,t[11]=f*a-l*n,t}},{}],275:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),a=Math.cos(r),i=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],h=e[10],f=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=i*a-c*n,t[1]=o*a-u*n,t[2]=s*a-h*n,t[3]=l*a-f*n,t[8]=i*n+c*a,t[9]=o*n+u*a,t[10]=s*n+h*a,t[11]=l*n+f*a,t}},{}],276:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),a=Math.cos(r),i=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],h=e[6],f=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=i*a+c*n,t[1]=o*a+u*n,t[2]=s*a+h*n,t[3]=l*a+f*n,t[4]=c*a-i*n,t[5]=u*a-o*n,t[6]=h*a-s*n,t[7]=f*a-l*n,t}},{}],277:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],a=r[1],i=r[2];return 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t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],280:[function(t,e,r){\"use strict\";e.exports=function(t,e){switch(e.length){case 0:break;case 1:t[0]=1/e[0];break;case 4:n(t,e);break;case 9:a(t,e);break;case 16:i(t,e);break;default:throw new Error(\"currently supports matrices up to 4x4\")}return t};var n=t(\"gl-mat2/invert\"),a=t(\"gl-mat3/invert\"),i=t(\"gl-mat4/invert\")},{\"gl-mat2/invert\":260,\"gl-mat3/invert\":261,\"gl-mat4/invert\":268}],281:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),a=t(\"polytope-closest-point/lib/closest_point_2d.js\");function i(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var a=0;a<4;++a)r[a]+=t[4*n+a]*e[n];return r}function o(t,e,r,n,a){for(var o=i(n,i(r,i(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*a[0]*(1+o[0]),.5*a[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var a=t[n],i=e[n],o=0;o<3;++o)r[o]+=i*a[o];return r}e.exports=function(t,e,r,i,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),h=0;h<t.length;++h)u[h]=o(t[h],r,i,l,c);for(var f=0,p=1/0,h=0;h<u.length;++h){for(var d=0,g=0;g<2;++g)d+=Math.pow(u[h][g]-e[g],2);d<p&&(p=d,f=h)}for(var v=function(t,e){if(2===t.length){for(var r=0,i=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),i+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),i=Math.sqrt(i),r+i<1e-6?[1,0]:[i/(r+i),r/(i+r)]}if(3===t.length){var s=[0,0];return a(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),m=0,h=0;h<3;++h){if(v[h]<-.001||v[h]>1.0001)return null;m+=v[h]}if(Math.abs(m-1)>.001)return null;return[f,s(t,v),v]}},{barycentric:75,\"polytope-closest-point/lib/closest_point_2d.js\":482}],282:[function(t,e,r){var n=t(\"glslify\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),i=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:a,fragment:i,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:f,fragment:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:410}],283:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),h=t(\"colormap\"),f=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),v=d.meshShader,m=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function k(t,e,r,n,a,i,o,s,l,c,u,h,f,p,d,g,v,m,y,x,b,_,k,T,M,A,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=a,this.pickShader=i,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=v,this.edgeUVs=m,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=k,this.pointSizes=T,this.pointIds=b,this.pointVAO=M,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var T=k.prototype;function M(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function A(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}T.isOpaque=function(){return!this.hasAlpha},T.isTransparent=function(){return this.hasAlpha},T.pickSlots=1,T.setPickBase=function(t){this.pickId=t},T.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,a=e.vertexWeights,i=r.length,o=p.mallocFloat32(6*i),s=0,l=0;l<i;++l)for(var c=r[l],u=0;u<2;++u){var h=c[0];2===c.length&&(h=c[u]);for(var d=n[h][0],g=n[h][1],v=a[h],m=1-v,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=v*y[b]+m*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},T.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=h({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),a=0;a<256;++a){for(var i=r[a],o=0;o<3;++o)n[4*a+o]=i[o];n[4*a+3]=e?255*M(a/255,e):255*i[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var a=[],i=[],l=[],c=[],f=[],p=[],d=[],g=[],v=[],m=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,k=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,A=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=s.faceNormals(r,n,A)),k||w||(w=s.vertexNormals(r,n,T));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,O=t.cellUVs,z=t.cellIntensity,I=1/0,D=-1/0;if(!C&&!O)if(P)if(t.vertexIntensityBounds)I=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];I=Math.min(I,F),D=Math.max(D,F)}else if(z)if(t.cellIntensityBounds)I=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<z.length;++R){F=z[R];I=Math.min(I,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];I=Math.min(I,F),D=Math.max(D,F)}this.intensity=P||(z||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(z||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],V=0;V<3;++V)!isNaN(j[V])&&isFinite(j[V])&&(this.bounds[0][V]=Math.min(this.bounds[0][V],j[V]),this.bounds[1][V]=Math.max(this.bounds[1][V],j[V]));var U=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],V=0;V<3;++V)if(isNaN(j[V])||!isFinite(j[V]))continue t;m.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?i.push(X[0],X[1],X[2],this.opacity*M((P[W]-I)/(D-I),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-I)/(D-I),0]:O?O[R]:z?[(z[R]-I)/(D-I),0]:[(j[2]-I)/(D-I),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),H+=1;break;case 2:for(V=0;V<2;++V){j=n[W=G[V]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(V=0;V<2;++V){j=n[W=G[V]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?i.push(X[0],X[1],X[2],this.opacity*M((P[W]-I)/(D-I),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-I)/(D-I),0]:O?O[R]:z?[(z[R]-I)/(D-I),0]:[(j[2]-I)/(D-I),0],g.push(Z[0],Z[1]),v.push(R)}q+=1;break;case 3:for(V=0;V<3;++V)for(j=n[W=G[V]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(V=0;V<3;++V){var W,X,Z,J;j=n[W=G[2-V]];a.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?i.push(X[0],X[1],X[2],this.opacity*M((P[W]-I)/(D-I),this.opacityscale)):3===X.length?i.push(X[0],X[1],X[2],this.opacity):(i.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):i.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-I)/(D-I),0]:O?O[R]:z?[(z[R]-I)/(D-I),0]:[(j[2]-I)/(D-I),0],c.push(Z[0],Z[1]),J=w?w[W]:k[R],l.push(J[0],J[1],J[2]),f.push(R)}U+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=U,this.pointPositions.update(m),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(v)),this.trianglePositions.update(a),this.triangleColors.update(i),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(f))}},T.drawTransparent=T.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,a=t.projection||w,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:a,inverseModel:w.slice(),clipBounds:i,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var h,f=u[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/f}this.triangleCount>0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},T.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,a=t.projection||w,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(a),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:a,clipBounds:i,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},T.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,a=new Array(r.length),i=0;i<r.length;++i)a[i]=n[r[i]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],h=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:h}}var f=g(a,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!f)return null;var p=f[2],d=0;for(i=0;i<r.length;++i)d+=p[i]*this.intensity[r[i]];return{position:f[1],index:r[f[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[f[0]]]}},T.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\")))throw new Error(\"derivatives not supported\");var r=function(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=A(t),c=S(t),h=E(t),f=L(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=a(t),g=a(t),y=a(t),x=a(t),b=a(t),_=i(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:y,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=a(t),T=a(t),M=a(t),C=a(t),P=i(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:M,type:t.FLOAT,size:2}]),O=a(t),z=a(t),I=a(t),D=a(t),R=a(t),F=i(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:I,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=a(t),N=new k(t,p,r,s,l,c,h,f,d,b,g,y,x,_,w,C,T,M,P,O,R,z,I,D,F,B,i(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},{\"./lib/closest-point\":281,\"./lib/shaders\":282,colormap:128,\"gl-buffer\":244,\"gl-mat4/invert\":268,\"gl-mat4/multiply\":270,\"gl-shader\":304,\"gl-texture2d\":324,\"gl-vao\":329,ndarray:451,normals:454,\"simplicial-complex-contour\":516,\"typedarray-pool\":543}],284:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=a(e,i.boxVert,i.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),a=t(\"gl-shader\"),i=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,a){var i=this.plot,o=this.shader,c=i.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=a,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":287,\"gl-buffer\":244,\"gl-shader\":304}],285:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),i=a(e,o.gridVert,o.gridFrag),l=a(e,o.tickVert,o.gridFrag);return new s(t,r,i,l)};var n=t(\"gl-buffer\"),a=t(\"gl-shader\"),i=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,h,f,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],h=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,a=t.gl,i=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,f=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var v=i[g],m=i[g+2]-v,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*m/x,c[g]=2*(v-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){h[0]=h[1]=0,h[g]=1,r.uniforms.dataAxis=h,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=f[g];var _=6*n[g].length;p[g]&&_&&a.drawArrays(a.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],a=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,h=this.ticks,f=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,v=s.pixelRatio,m=s.screenBox,y=m[2]-m[0],x=m[3]-m[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var k=p[w],T=p[w+2]-k,M=.5*(d[w+2]+d[w]),A=d[w+2]-d[w];e[w]=2*T/A,t[w]=2*(k-M)/A}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*h[0].length,O=Math.min(i.ge(h[0],(d[0]-p[0])/(p[2]-p[0]),l),h[0].length),z=Math.min(i.gt(h[0],(d[2]-p[0])/(p[2]-p[0]),l),h[0].length),I=0+6*O,D=6*Math.max(0,z-O),R=Math.min(i.ge(h[1],(d[1]-p[1])/(p[3]-p[1]),l),h[1].length),F=Math.min(i.gt(h[1],(d[3]-p[1])/(p[3]-p[1]),l),h[1].length),B=P+6*R,N=6*Math.max(0,F-R);a[0]=2*(g[0]-E[1])/y-1,a[1]=(g[3]+g[1])/x-1,o[0]=E[1]*v/y,o[1]=L[1]*v/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=a,f.drawArrays(f.TRIANGLES,B,N)),a[0]=(g[2]+g[0])/y-1,a[1]=2*(g[1]-E[0])/x-1,o[0]=L[0]*v/y,o[1]=E[0]*v/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=a,f.drawArrays(f.TRIANGLES,I,D)),a[0]=2*(g[2]+E[3])/y-1,a[1]=(g[3]+g[1])/x-1,o[0]=E[3]*v/y,o[1]=L[3]*v/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=a,f.drawArrays(f.TRIANGLES,B,N)),a[0]=(g[2]+g[0])/y-1,a[1]=2*(g[3]+E[2])/x-1,o[0]=L[2]*v/y,o[1]=E[2]*v/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=a,f.drawArrays(f.TRIANGLES,I,D))}}(),d.update=(f=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),a=(this.plot.zeroLineEnable,0),i=[[],[]],o=0;o<2;++o)for(var s=i[o],l=e[o],c=r[o],u=r[o+2],h=0;h<l.length;++h){var d=(l[h].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[a++]=d,n[a++]=f[g],n[a++]=p[g]}this.ticks=i,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":287,\"binary-search-bounds\":289,\"gl-buffer\":244,\"gl-shader\":304}],286:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=a(e,i.lineVert,i.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),a=t(\"gl-shader\"),i=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,a,i){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=a*o.pixelRatio,c.uniforms.color=i,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":287,\"gl-buffer\":244,\"gl-shader\":304}],287:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),a=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:a,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:a,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:a,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:410}],288:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),i=a(e,s.textVert,s.textFrag);return new l(t,r,i)};var n=t(\"gl-buffer\"),a=t(\"gl-shader\"),i=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,h,f,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],h=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],a=this.tickOffset[t],i=e.gl,s=e.viewBox,l=e.dataBox,f=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,v=e.tickColor,m=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],k=this.labelCount[t],T=o.lt(n,l[t]),M=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(f[2+t]-f[t])-1;var A=2/f[2+(1^t)]-f[1^t];u[1^t]=A*s[1^t]-1,d[t]&&(u[1^t]-=A*p*g[t],T<M&&a[M]>a[T]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=v[t],r.uniforms.angle=m[t],i.drawArrays(i.TRIANGLES,a[T],a[M]-a[T]))),y[t]&&k&&(u[1^t]-=A*p*x[t],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],i.drawArrays(i.TRIANGLES,w,k)),u[1^t]=A*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=A*p*g[t+2],T<M&&a[M]>a[T]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=v[t+2],r.uniforms.angle=m[t+2],i.drawArrays(i.TRIANGLES,a[T],a[M]-a[T]))),y[t+2]&&k&&(u[1^t]+=A*p*x[t+2],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],i.drawArrays(i.TRIANGLES,w,k))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,a=r.gl,i=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-i[u])/(i[2+u]-i[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,a.drawArrays(a.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(f=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,a=t.screenBox,i=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],h=i[o],g=i[o+2]-h,v=a[o],m=a[o+2]-v;p[o]=2*l/u*g/m,f[o]=2*(s-c)/u*g/m}d[1]=2*t.pixelRatio/(a[3]-a[1]),d[0]=d[1]*(a[3]-a[1])/(a[2]-a[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=f,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,a,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],h=[-1/0],f=l[o];for(e=0;e<f.length;++e){var p=f[e],d=p.x,g=p.text,v=p.font||\"sans-serif\";a=p.fontSize||12;for(var m=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=i(v,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*a,-n[r+1]*a-b*a*1.2,(d-y)*m);u.push(Math.floor(s.length/3)),h.push(d)}this.tickOffset[o]=u,this.tickX[o]=h}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=i(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,a=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*a,-n[e+1]*a,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=i(t.titleFont,t.title).data,a=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*a,-n[e+1]*a,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":287,\"binary-search-bounds\":289,\"gl-buffer\":244,\"gl-shader\":304,\"text-cache\":534}],289:[function(t,e,r){arguments[4][113][0].apply(r,arguments)},{dup:113}],290:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=a(c),c.text=i(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),a=t(\"./lib/grid\"),i=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function h(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,a=this.pixelRatio,i=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),i.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,p=this.zeroLineWidth;if(h[0]||h[1]){o.bind();for(var d=0;d<2;++d)if(h[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],f[d]):o.drawLine(e[0],g,e[2],g,p[d],f[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var v=this.borderLineEnable,m=this.borderLineWidth,y=this.borderLineColor;for(v[1]&&o.drawLine(r[0],r[1]-.5*m[1]*a,r[0],r[3]+.5*m[3]*a,m[1],y[1]),v[0]&&o.drawLine(r[0]-.5*m[0]*a,r[1],r[2]+.5*m[2]*a,r[1],m[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5*m[1]*a,r[2],r[3]+.5*m[3]*a,m[3],y[3]),v[2]&&o.drawLine(r[0]-.5*m[0]*a,r[3],r[2]+.5*m[2]*a,r[3],m[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,a=this.viewBox,i=0|Math.round((t-a[0]/r)*n),o=0|Math.round((e-a[1]/r)*n),s=this.pickBuffer.query(i,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var h=c[u].pick(i,o,l);if(h)return h}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,a=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/a,10,10/a]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t&&!t.titleEnable),this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var i=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=i[s].slice(0);0!==l.length&&(l.sort(h),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:i}),this.text.update({bounds:o,ticks:i,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":284,\"./lib/grid\":285,\"./lib/line\":286,\"./lib/text\":288,\"gl-select-static\":303}],291:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=a({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],h=0,f=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,a=e-2*r;c.idle(e-r),c.recalcMatrix(a),c.flush(e-(100+2*r));for(var i=!0,o=c.computedMatrix,s=0;s<16;++s)i=i&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===f&&t.clientHeight===p;return f=t.clientWidth,p=t.clientHeight,i?!l:(h=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),a=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var i=n();c._active.lookAt(i,r,a,e),c._active.lookAt(i+500,r,a,[0,0,1]),c._active.flush(i)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return h},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",function(t){return t.preventDefault(),!1}),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,a,i){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,f=!!i.control,p=!!i.alt,g=!!i.shift,v=!!(1&e),m=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(a-d._lastY),w=d.flipX?1:-1,k=d.flipY?1:-1,T=Math.PI*d.rotateSpeed,M=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&v&&!f&&!p&&!g||v&&!f&&!p&&g)&&c.rotate(M,w*T*b,-k*T*_,0),(l&&v&&!f&&!p&&!g||m||v&&f&&!p&&!g)&&c.pan(M,-d.translateSpeed*b*h,d.translateSpeed*_*h,0),u&&v&&!f&&!p&&!g||y||v&&!f&&p&&!g)){var A=-d.zoomSpeed*_/window.innerHeight*(M-c.lastT())*100;c.pan(M,0,0,h*(Math.exp(A)-1))}return d._lastX=r,d._lastY=a,d._lastMods=i,!0}}d.mouseListener=i(t,e),t.addEventListener(\"touchstart\",function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods),r.preventDefault()},!!l&&{passive:!1}),t.addEventListener(\"touchmove\",function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()},!!l&&{passive:!1}),t.addEventListener(\"touchend\",function(t){e(0,d._lastX,d._lastY,d._lastMods),t.preventDefault()},!!l&&{passive:!1}),d.wheelListener=o(t,function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,a=d.flipY?1:-1,i=n();if(Math.abs(t)>Math.abs(e))c.rotate(i,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*a*e/window.innerHeight*(i-c.lastT())/20;c.pan(i,0,0,h*(Math.exp(o)-1))}}},!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),a=t(\"3d-view\"),i=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":54,\"has-passive-events\":412,\"mouse-change\":436,\"mouse-event-offset\":437,\"mouse-wheel\":439,\"right-now\":502}],292:[function(t,e,r){var n=t(\"glslify\"),a=t(\"gl-shader\"),i=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return a(t,i,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":304,glslify:410}],293:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),a=t(\"gl-axes3d\"),i=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),h=t(\"gl-mat4/perspective\"),f=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function v(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function m(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e)if(e=document.createElement(\"canvas\"),t.container){var r=t.container;r.appendChild(e)}else document.body.appendChild(e);var y=t.gl;y||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),y=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!y)throw new Error(\"webgl not supported\");var x=t.bounds||[[-10,-10,-10],[10,10,10]],b=new g,_=l(y,y.drawingBufferWidth,y.drawingBufferHeight,{preferFloat:!d}),w=p(y),k=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:k},M=t.axes||{},A=a(y,M);A.enable=!M.disable;var S=t.spikes||{},E=o(y,S),L=[],C=[],P=[],O=[],z=!0,I=!0,D=new Array(16),R=new Array(16),F={view:null,projection:D,model:R,_ortho:!1},I=!0,B=[y.drawingBufferWidth,y.drawingBufferHeight],N=t.cameraObject||n(e,T),j={gl:y,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:b,camera:N,axes:A,axesPixels:null,spikes:E,bounds:x,objects:L,shape:B,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:m(t.autoResize),autoBounds:m(t.autoBounds),autoScale:!!t.autoScale,autoCenter:m(t.autoCenter),clipToBounds:m(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:F,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},V=[y.drawingBufferWidth/j.pixelRatio|0,y.drawingBufferHeight/j.pixelRatio|0];function U(){if(!j._stopped&&j.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var a=0|Math.ceil(r*j.pixelRatio),i=0|Math.ceil(n*j.pixelRatio);if(a!==e.width||i!==e.height){e.width=a,e.height=i;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",z=!0}}}j.autoResize&&U();function q(){for(var t=L.length,e=O.length,r=0;r<e;++r)P[r]=0;t:for(var r=0;r<t;++r){var n=L[r],a=n.pickSlots;if(a){for(var i=0;i<e;++i)if(P[i]+a<255){C[r]=i,n.setPickBase(P[i]+1),P[i]+=a;continue t}var o=s(y,B);C[r]=e,O.push(o),P.push(a),n.setPickBase(1),e+=1}else C[r]=-1}for(;e>0&&0===P[e-1];)P.pop(),O.pop().dispose()}function H(){if(j.contextLost)return!0;y.isContextLost()&&(j.contextLost=!0,j.mouseListener.enabled=!1,j.selection.object=null,j.oncontextloss&&j.oncontextloss())}window.addEventListener(\"resize\",U),j.update=function(t){j._stopped||(t=t||{},z=!0,I=!0)},j.add=function(t){j._stopped||(t.axes=A,L.push(t),C.push(-1),z=!0,I=!0,q())},j.remove=function(t){if(!j._stopped){var e=L.indexOf(t);e<0||(L.splice(e,1),C.pop(),z=!0,I=!0,q())}},j.dispose=function(){if(!j._stopped&&(j._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),j.mouseListener.enabled=!1,!j.contextLost)){A.dispose(),E.dispose();for(var t=0;t<L.length;++t)L[t].dispose();_.dispose();for(var t=0;t<O.length;++t)O[t].dispose();w.dispose(),y=null,A=null,E=null,L=[]}},j._mouseRotating=!1,j._prevButtons=0,j.enableMouseListeners=function(){j.mouseListener=u(e,function(t,e,r){if(!j._stopped){var n=O.length,a=L.length,i=b.object;b.distance=1/0,b.mouse[0]=e,b.mouse[1]=r,b.object=null,b.screen=null,b.dataCoordinate=b.dataPosition=null;var o=!1;if(t&&j._prevButtons)j._mouseRotating=!0;else{j._mouseRotating&&(I=!0),j._mouseRotating=!1;for(var s=0;s<n;++s){var l=O[s].query(e,V[1]-r-1,j.pickRadius);if(l){if(l.distance>b.distance)continue;for(var c=0;c<a;++c){var u=L[c];if(C[c]===s){var h=u.pick(l);h&&(b.buttons=t,b.screen=l.coord,b.distance=l.distance,b.object=u,b.index=h.distance,b.dataPosition=h.position,b.dataCoordinate=h.dataCoordinate,b.data=h,o=!0)}}}}}i&&i!==b.object&&(i.highlight&&i.highlight(null),z=!0),b.object&&(b.object.highlight&&b.object.highlight(b.data),z=!0),(o=o||b.object!==i)&&j.onselect&&j.onselect(b),1&t&&!(1&j._prevButtons)&&j.onclick&&j.onclick(b),j._prevButtons=t}})},e.addEventListener(\"webglcontextlost\",H);var G=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],Y=[G[0].slice(),G[1].slice()];function W(){if(!H()){U();var t=j.camera.tick();F.view=j.camera.matrix,z=z||t,I=I||t,A.pixelRatio=j.pixelRatio,E.pixelRatio=j.pixelRatio;var e=L.length,r=G[0],n=G[1];r[0]=r[1]=r[2]=1/0,n[0]=n[1]=n[2]=-1/0;for(var a=0;a<e;++a){var o=L[a];o.pixelRatio=j.pixelRatio,o.axes=j.axes,z=z||!!o.dirty,I=I||!!o.dirty;var s=o.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)r[p]=Math.min(r[p],l[p]),n[p]=Math.max(n[p],u[p])}var d=j.bounds;if(j.autoBounds)for(var p=0;p<3;++p){if(n[p]<r[p])r[p]=-1,n[p]=1;else{r[p]===n[p]&&(r[p]-=1,n[p]+=1);var g=.05*(n[p]-r[p]);r[p]=r[p]-g,n[p]=n[p]+g}d[0][p]=r[p],d[1][p]=n[p]}for(var m=!1,p=0;p<3;++p)m=m||Y[0][p]!==d[0][p]||Y[1][p]!==d[1][p],Y[0][p]=d[0][p],Y[1][p]=d[1][p];if(I=I||m,z=z||m){if(m){for(var x=[0,0,0],a=0;a<3;++a)x[a]=v((d[1][a]-d[0][a])/10);A.autoTicks?A.update({bounds:d,tickSpacing:x}):A.update({bounds:d})}var T=y.drawingBufferWidth,M=y.drawingBufferHeight;B[0]=T,B[1]=M,V[0]=0|Math.max(T/j.pixelRatio,1),V[1]=0|Math.max(M/j.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,a=n.projection,i=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(f(a,-p,p,-1,1,l,c),n._ortho=!0):(h(a,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)i[d]=0;i[15]=1;for(var g=0,d=0;d<3;++d)g=Math.max(g,r[1][d]-r[0][d]);for(var d=0;d<3;++d)t.autoScale?i[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):i[5*d]=1/g,t.autoCenter&&(i[12+d]=.5*-i[5*d]*(r[0][d]+r[1][d]))}(j,k);for(var a=0;a<e;++a){var o=L[a];o.axesBounds=d,j.clipToBounds&&(o.clipBounds=d)}b.object&&(j.snapToData?E.position=b.dataCoordinate:E.position=b.dataPosition,E.bounds=d),I&&(I=!1,function(){if(H())return;y.colorMask(!0,!0,!0,!0),y.depthMask(!0),y.disable(y.BLEND),y.enable(y.DEPTH_TEST),y.depthFunc(y.LEQUAL);for(var t=L.length,e=O.length,r=0;r<e;++r){var n=O[r];n.shape=V,n.begin();for(var a=0;a<t;++a)if(C[a]===r){var i=L[a];i.drawPick&&(i.pixelRatio=1,i.drawPick(F))}n.end()}}()),j.axesPixels=i(j.axes,F,T,M),j.onrender&&j.onrender(),y.bindFramebuffer(y.FRAMEBUFFER,null),y.viewport(0,0,T,M),j.clearRGBA(),y.depthMask(!0),y.colorMask(!0,!0,!0,!0),y.enable(y.DEPTH_TEST),y.depthFunc(y.LEQUAL),y.disable(y.BLEND),y.disable(y.CULL_FACE);var S=!1;A.enable&&(S=S||A.isTransparent(),A.draw(F)),E.axes=A,b.object&&E.draw(F),y.disable(y.CULL_FACE);for(var a=0;a<e;++a){var o=L[a];o.axes=A,o.pixelRatio=j.pixelRatio,o.isOpaque&&o.isOpaque()&&o.draw(F),o.isTransparent&&o.isTransparent()&&(S=!0)}if(S){_.shape=B,_.bind(),y.clear(y.DEPTH_BUFFER_BIT),y.colorMask(!1,!1,!1,!1),y.depthMask(!0),y.depthFunc(y.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(F);for(var a=0;a<e;++a){var o=L[a];o.isOpaque&&o.isOpaque()&&o.draw(F)}y.enable(y.BLEND),y.blendEquation(y.FUNC_ADD),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.colorMask(!0,!0,!0,!0),y.depthMask(!1),y.clearColor(0,0,0,0),y.clear(y.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(F);for(var a=0;a<e;++a){var o=L[a];o.isTransparent&&o.isTransparent()&&o.drawTransparent(F)}y.bindFramebuffer(y.FRAMEBUFFER,null),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.disable(y.DEPTH_TEST),w.bind(),_.color[0].bind(0),w.uniforms.accumBuffer=0,c(y),y.disable(y.BLEND)}z=!1;for(var a=0;a<e;++a)L[a].dirty=!1}}}return j.enableMouseListeners(),function t(){j._stopped||j.contextLost||(W(),requestAnimationFrame(t))}(),j.redraw=function(){j._stopped||(z=!0,W())},j},createCamera:n}},{\"./camera.js\":291,\"./lib/shader\":292,\"a-big-triangle\":62,\"gl-axes3d\":236,\"gl-axes3d/properties\":243,\"gl-fbo\":252,\"gl-mat4/ortho\":271,\"gl-mat4/perspective\":272,\"gl-select-static\":303,\"gl-spikes3d\":313,\"is-mobile\":421,\"mouse-change\":436}],294:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:410}],295:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,a){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=a,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,i=a(r),l=a(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),h=new s(t,i,l,c,u);return h.update(e),t.addObject(h),h};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,a=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=a?s:i.mallocFloat32(s.length),c=o?t.idToIndex:i.mallocInt32(n);if(a||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),a||i.free(l),o||i.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,a=this.plot.dataBox;if(0===this.pointCount)return t;var i=a[2]-a[0],o=a[3]-a[1],s=function(t,e){var r,n=0,a=t.length>>>1;for(r=0;r<a;r++){var i=t[2*r],o=t[2*r+1];i>=e[0]&&i<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,a),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/i,l[4]=2/o,l[6]=-2*a[0]/i-1,l[7]=-2*a[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var h=n.getParameter(n.BLEND),f=n.getParameter(n.DITHER);return h&&!this.blend&&n.disable(n.BLEND),f&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),h&&!this.blend&&n.enable(n.BLEND),f&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,a=this.pointCount;if(r<n||r>=n+a)return null;var i=r-n,o=this.points;return{object:this,pointId:i,dataCoord:[o[2*i],o[2*i+1]]}}},{\"./lib/shader\":294,\"gl-buffer\":244,\"gl-shader\":304,\"typedarray-pool\":543}],296:[function(t,e,r){e.exports=function(t,e,r,n){var a,i,o,s,l,c=e[0],u=e[1],h=e[2],f=e[3],p=r[0],d=r[1],g=r[2],v=r[3];(i=c*p+u*d+h*g+f*v)<0&&(i=-i,p=-p,d=-d,g=-g,v=-v);1-i>1e-6?(a=Math.acos(i),o=Math.sin(a),s=Math.sin((1-n)*a)/o,l=Math.sin(n*a)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*h+l*g,t[3]=s*f+l*v,t}},{}],297:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],298:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var i=a[e];i||(i=a[e]={});if(t in i)return i[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var h=[[1/0,1/0],[-1/0,-1/0]],f=u.positions.length;for(l=0;l<f;++l){var p=u.positions[l];for(c=0;c<2;++c)h[0][c]=Math.min(h[0][c],p[c]),h[1][c]=Math.max(h[1][c],p[c])}return i[t]=[s,u,h]};var a={}},{\"vectorize-text\":548}],299:[function(t,e,r){var n=t(\"gl-shader\"),a=t(\"glslify\"),i=a([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=a([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=a([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=a([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=a([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],h={vertex:i,fragment:l,attributes:u},f={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:i,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},v={vertex:s,fragment:c,attributes:u};function m(t,e){var r=n(t,e),a=r.attributes;return a.position.location=0,a.color.location=1,a.glyph.location=2,a.id.location=3,r}r.createPerspective=function(t){return m(t,h)},r.createOrtho=function(t){return m(t,f)},r.createProject=function(t){return m(t,p)},r.createPickPerspective=function(t){return m(t,d)},r.createPickOrtho=function(t){return m(t,g)},r.createPickProject=function(t){return m(t,v)}},{\"gl-shader\":304,glslify:410}],300:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),a=t(\"gl-buffer\"),i=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(t,e){var r=t[0],n=t[1],a=t[2],i=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*a+e[12]*i,t[1]=e[1]*r+e[5]*n+e[9]*a+e[13]*i,t[2]=e[2]*r+e[6]*n+e[10]*a+e[14]*i,t[3]=e[3]*r+e[7]*n+e[11]*a+e[15]*i,t}function p(t,e,r,n){return f(n,n),f(n,n),f(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t?1:t>1?1:t}function v(t,e,r,n,a,i,o,s,l,c,u,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=a,this.colorBuffer=i,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=h,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),h=a(e),f=a(e),p=a(e),d=a(e),g=i(e,[{buffer:h,size:3,type:e.FLOAT},{buffer:f,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),m=new v(e,r,n,o,h,f,p,d,g,s,c,u);return m.update(t),m};var m=v.prototype;m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},m.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],k=h.slice(),T=[0,0,0],M=[[0,0,0],[0,0,0]];function A(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var a,i=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||h,u=r.view||h,f=r.projection||h,d=e.axesBounds,g=function(t){for(var e=M,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);a=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=f,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var v=0;v<3;++v)if(i[v]){l.scale=e.projectScale[v],l.opacity=e.projectOpacity[v];for(var m=k,L=0;L<16;++L)m[L]=0;for(L=0;L<4;++L)m[5*L]=1;m[5*v]=0,a[v]<0?m[12+v]=d[0][v]:m[12+v]=d[1][v],s(m,c,m),l.model=m;var C=(v+1)%3,P=(v+2)%3,O=A(x),z=A(b);O[C]=1,z[P]=1;var I=p(0,0,0,S(_,O)),D=p(0,0,0,S(w,z));if(Math.abs(I[1])>Math.abs(D[1])){var R=I;I=D,D=R,R=O,O=z,z=R;var F=C;C=P,P=F}I[0]<0&&(O[C]=-1),D[1]>0&&(z[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);O[C]/=Math.sqrt(B),z[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=z,l.fragClipBounds[0]=E(T,g[0],v,-1e8),l.fragClipBounds[1]=E(T,g[1],v,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,a,i,o){var s=r.gl;if((i===r.projectHasAlpha||o)&&L(e,r,n,a),i===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||h,l.view=n.view||h,l.projection=n.projection||h,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=a,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*a),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function O(t,e,r,a){var i;i=Array.isArray(t)?e<t.length?t[e]:void 0:t,i=u(i);var o=!0;n(i)&&(i=\"\\u25bc\",o=!1);var s=c(i,r,a);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}m.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},m.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},m.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var a=0;a<3;++a)n.position[a]=n.dataCoordinate[a]=r[a];return n},m.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,a=e>>16&255;this.highlightId=[r/255,n/255,a/255,0]}else this.highlightId=[1,1,1,1]},m.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var a,i,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)a=c[0],i=c[1];else{a=[],i=[];for(n=0;n<c.length;++n)a[n]=c[n][0],i[n]=c[n][1]}var u=[1/0,1/0,1/0],h=[-1/0,-1/0,-1/0],f=t.glyph,p=t.color,d=t.size,v=t.angle,m=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],k=0;k<3;++k)if(isNaN(w[k])||!isFinite(w[k]))continue t;var T=(N=O(f,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;x+=3*T.cells.length,b+=2*M.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var z=0,I=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(m)&&Array.isArray(m[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],k=0;k<3;++k){if(isNaN(w[k])||!isFinite(w[k]))continue t;h[k]=Math.max(h[k],w[k]),u[k]=Math.min(u[k],w[k])}T=(N=O(f,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(V=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(k=0;k<3;++k)D[k]=V[k];D[3]=1}else if(4===V.length){for(k=0;k<4;++k)D[k]=V[k];!this.hasAlpha&&V[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(m)){var V;if(3===(V=B?n<m.length?m[n]:[0,0,0,0]:m).length){for(k=0;k<3;++k)R[k]=V[k];R[k]=1}else if(4===V.length){for(k=0;k<4;++k)R[k]=V[k];!this.hasAlpha&&V[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var U=.5;j?Array.isArray(d)?U=n<d.length?+d[n]:12:d?U=+d:this.useOrtho&&(U=12):U=0;var q=0;Array.isArray(v)?q=n<v.length?+v[n]:0:v&&(q=+v);var H=Math.cos(q),G=Math.sin(q);for(w=s[n],k=0;k<3;++k)h[k]=Math.max(h[k],w[k]),u[k]=Math.min(u[k],w[k]);var Y=a,W=i;Y=0;Array.isArray(a)?Y=n<a.length?a[n]:0:a&&(Y=a);W=0;Array.isArray(i)?W=n<i.length?i[n]:0:i&&(W=i);var X=[Y*=Y>0?1-A[0][0]:Y<0?1+A[1][0]:1,W*=W>0?1-A[0][1]:W<0?1+A[1][1]:1],Z=T.cells||[],J=T.positions||[];for(k=0;k<Z.length;++k)for(var K=Z[k],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=D[$];P[z]=y;var tt=J[K[Q]];C[2*z]=U*(H*tt[0]-G*tt[1]+X[0]),C[2*z+1]=U*(G*tt[0]+H*tt[1]+X[1]),z+=1}for(Z=M.edges,J=M.positions,k=0;k<Z.length;++k)for(K=Z[k],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*I+$]=w[$];for($=0;$<4;++$)L[4*I+$]=R[$];P[I]=y;tt=J[K[Q]];C[2*I]=U*(H*tt[0]-G*tt[1]+X[0]),C[2*I+1]=U*(G*tt[0]+H*tt[1]+X[1]),I+=1}}}this.bounds=[u,h],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},m.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":297,\"./lib/glyphs\":298,\"./lib/shaders\":299,\"gl-buffer\":244,\"gl-mat4/multiply\":270,\"gl-vao\":329,\"is-string-blank\":424,\"typedarray-pool\":543}],301:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:410}],302:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=a(r,[0,0,0,1,1,0,1,1]),l=n(r,i.boxVertex,i.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),a=(this.outerFill,this.outerColor),i=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,h=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],f=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(h=Math.max(h,c[0]),f=Math.max(f,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<h||d<f)){o.bind();var g=s[2]-s[0],v=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,f,a),o.drawBox(0,f,h,d,a),o.drawBox(0,d,g,v,a),o.drawBox(p,f,g,d,a)),this.innerFill&&o.drawBox(h,f,p,d,n),r>0){var m=r*u;o.drawBox(h-m,f-m,p+m,f+m,i),o.drawBox(h-m,d-m,p+m,d+m,i),o.drawBox(h-m,f-m,h+m,d+m,i),o.drawBox(p-m,f-m,p+m,d+m,i)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":301,\"gl-buffer\":244,\"gl-shader\":304}],303:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],i=e[1],o=n(t,r,i,{}),s=a.mallocUint8(r*i*4);return new c(t,o,s)};var n=t(\"gl-fbo\"),a=t(\"typedarray-pool\"),i=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2,s=t(\"cwise/lib/wrapper\")({args:[\"array\",{offset:[0,0,1],array:0},{offset:[0,0,2],array:0},{offset:[0,0,3],array:0},\"scalar\",\"scalar\",\"index\"],pre:{body:\"{this_closestD2=1e8,this_closestX=-1,this_closestY=-1}\",args:[],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[]},body:{body:\"{if(_inline_16_arg0_<255||_inline_16_arg1_<255||_inline_16_arg2_<255||_inline_16_arg3_<255){var _inline_16_l=_inline_16_arg4_-_inline_16_arg6_[0],_inline_16_a=_inline_16_arg5_-_inline_16_arg6_[1],_inline_16_f=_inline_16_l*_inline_16_l+_inline_16_a*_inline_16_a;_inline_16_f<this_closestD2&&(this_closestD2=_inline_16_f,this_closestX=_inline_16_arg6_[0],this_closestY=_inline_16_arg6_[1])}}\",args:[{name:\"_inline_16_arg0_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_16_arg1_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_16_arg2_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_16_arg3_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_16_arg4_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_16_arg5_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_16_arg6_\",lvalue:!1,rvalue:!0,count:4}],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[\"_inline_16_a\",\"_inline_16_f\",\"_inline_16_l\"]},post:{body:\"{return[this_closestX,this_closestY,this_closestD2]}\",args:[],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[]},debug:!1,funcName:\"cwise\",blockSize:64});function l(t,e,r,n,a){this.coord=[t,e],this.id=r,this.value=n,this.distance=a}function c(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var u=c.prototype;Object.defineProperty(u,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){a.free(this.buffer);for(var n=this.buffer=a.mallocUint8(o(r*e*4)),i=0;i<r*e*4;++i)n[i]=255}return t}}}),u.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},u.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},u.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var a=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),c=0|Math.min(Math.max(e-r,0),n[1]),u=0|Math.min(Math.max(e+r,0),n[1]);if(o<=a||u<=c)return null;var h=[o-a,u-c],f=i(this.buffer,[h[0],h[1],4],[4,4*n[0],1],4*(a+n[0]*c)),p=s(f.hi(h[0],h[1],1),r,r),d=p[0],g=p[1];return d<0||Math.pow(this.radius,2)<p[2]?null:new l(d+a|0,g+c|0,f.get(d,g,0),[f.get(d,g,1),f.get(d,g,2),f.get(d,g,3)],Math.sqrt(p[2]))},u.dispose=function(){this.gl&&(this.fbo.dispose(),a.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":94,\"cwise/lib/wrapper\":151,\"gl-fbo\":252,ndarray:451,\"typedarray-pool\":543}],304:[function(t,e,r){\"use strict\";var n=t(\"./lib/create-uniforms\"),a=t(\"./lib/create-attributes\"),i=t(\"./lib/reflect\"),o=t(\"./lib/shader-cache\"),s=t(\"./lib/runtime-reflect\"),l=t(\"./lib/GLError\");function c(t){this.gl=t,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var u=c.prototype;function h(t,e){return t.name<e.name?-1:1}u.bind=function(){var t;this.program||this._relink();var e=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),r=this.gl.lastAttribCount;if(e>r)for(t=r;t<e;t++)this.gl.enableVertexAttribArray(t);else if(r>e)for(t=e;t<r;t++)this.gl.disableVertexAttribArray(t);this.gl.lastAttribCount=e,this.gl.useProgram(this.program)},u.dispose=function(){for(var t=this.gl.lastAttribCount,e=0;e<t;e++)this.gl.disableVertexAttribArray(e);this.gl.lastAttribCount=0,this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},u.update=function(t,e,r,c){if(!e||1===arguments.length){var u=t;t=u.vertex,e=u.fragment,r=u.uniforms,c=u.attributes}var f=this,p=f.gl,d=f._vref;f._vref=o.shader(p,p.VERTEX_SHADER,t),d&&d.dispose(),f.vertShade