# HG changeset patch
# User bgruening
# Date 1576491420 18000
# Node ID 64b771b1471ad0cf830468b3e82678a6a22ec989
# Parent  18b39ada6f3545b64aece44bdc655282ac838003
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"

diff -r 18b39ada6f35 -r 64b771b1471a discriminant.xml
--- a/discriminant.xml	Thu Nov 07 05:25:28 2019 -0500
+++ b/discriminant.xml	Mon Dec 16 05:17:00 2019 -0500
@@ -35,7 +35,7 @@
     classifier_object = load_model(model_handler)
 
 header = 'infer' if params["selected_tasks"]["header"] else None
-data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
+data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None)
 prediction = classifier_object.predict(data)
 prediction_df = pandas.DataFrame(prediction)
 res = pandas.concat([data, prediction_df], axis=1)
diff -r 18b39ada6f35 -r 64b771b1471a keras_deep_learning.py
--- a/keras_deep_learning.py	Thu Nov 07 05:25:28 2019 -0500
+++ b/keras_deep_learning.py	Mon Dec 16 05:17:00 2019 -0500
@@ -73,7 +73,7 @@
             }
     """
     constraint_type = config['constraint_type']
-    if constraint_type == 'None':
+    if constraint_type in ('None', ''):
         return None
 
     klass = getattr(keras.constraints, constraint_type)
@@ -92,7 +92,7 @@
     """Access to handle all kinds of parameters
     """
     for key, value in six.iteritems(params):
-        if value == 'None':
+        if value in ('None', ''):
             params[key] = None
             continue
 
@@ -205,6 +205,9 @@
     config : dictionary, galaxy tool parameters loaded by JSON
     """
     generator_type = config.pop('generator_type')
+    if generator_type == 'none':
+        return None
+
     klass = try_get_attr('galaxy_ml.preprocessors', generator_type)
 
     if generator_type == 'GenomicIntervalBatchGenerator':
@@ -240,7 +243,7 @@
     json_string = model.to_json()
 
     with open(outfile, 'w') as f:
-        f.write(json_string)
+        json.dump(json.loads(json_string), f, indent=2)
 
 
 def build_keras_model(inputs, outfile, model_json, infile_weights=None,
diff -r 18b39ada6f35 -r 64b771b1471a keras_macros.xml
--- a/keras_macros.xml	Thu Nov 07 05:25:28 2019 -0500
+++ b/keras_macros.xml	Mon Dec 16 05:17:00 2019 -0500
@@ -1,5 +1,5 @@
 <macros>
-  <token name="@KERAS_VERSION@">0.4.2</token>
+  <token name="@KERAS_VERSION@">0.5.0</token>
 
   <xml name="macro_stdio">
     <stdio>
@@ -18,7 +18,7 @@
 
   <xml name="keras_optimizer_common_more" token_lr="0.001">
     <expand macro="keras_optimizer_common" lr="@LR@">
-      <param argument="epsilon" type="float" value="" label="epsilon" optional="true" help="Fuzz factor. If `None`, defaults to `K.epsilon()`"/>
+      <!--param argument="epsilon" type="float" value="" label="epsilon" optional="true" help="Fuzz factor. If `None`, defaults to `K.epsilon()`"/>-->
       <param argument="decay" type="float" value="0" optional="true" label="decay" help="Learning rate decay over each update."/>
       <yield/>
     </expand>
@@ -885,7 +885,7 @@
           <expand macro="keras_optimizer_common" lr="0.002">
             <param argument="beta_1" type="float" value="0.9" optional="true" label="beta_1" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
             <param argument="beta_2" type="float" value="0.999" optional="true" label="beta_2" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
-            <param argument="epsilon" type="float" value="" label="epsilon" optional="true" help="Fuzz factor. If `None`, defaults to `K.epsilon()`"/>
+            <!--param argument="epsilon" type="float" value="" label="epsilon" optional="true" help="Fuzz factor. If `None`, defaults to `K.epsilon()`"/>-->
             <param argument="schedule_decay" type="float" value="0.004" optional="true" label="schedule_decay" help="float, 0 &lt; beta &lt; 1."/>
           </expand>
         </when>
diff -r 18b39ada6f35 -r 64b771b1471a keras_train_and_eval.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_train_and_eval.py	Mon Dec 16 05:17:00 2019 -0500
@@ -0,0 +1,491 @@
+import argparse
+import joblib
+import json
+import numpy as np
+import os
+import pandas as pd
+import pickle
+import warnings
+from itertools import chain
+from scipy.io import mmread
+from sklearn.pipeline import Pipeline
+from sklearn.metrics.scorer import _check_multimetric_scoring
+from sklearn import model_selection
+from sklearn.model_selection._validation import _score
+from sklearn.model_selection import _search, _validation
+from sklearn.utils import indexable, safe_indexing
+
+from galaxy_ml.externals.selene_sdk.utils import compute_score
+from galaxy_ml.model_validations import train_test_split
+from galaxy_ml.keras_galaxy_models import _predict_generator
+from galaxy_ml.utils import (SafeEval, get_scoring, load_model,
+                             read_columns, try_get_attr, get_module,
+                             clean_params, get_main_estimator)
+
+
+_fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score')
+setattr(_search, '_fit_and_score', _fit_and_score)
+setattr(_validation, '_fit_and_score', _fit_and_score)
+
+N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
+CACHE_DIR = os.path.join(os.getcwd(), 'cached')
+del os
+NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
+                  'nthread', 'callbacks')
+ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
+                     'CSVLogger', 'None')
+
+
+def _eval_swap_params(params_builder):
+    swap_params = {}
+
+    for p in params_builder['param_set']:
+        swap_value = p['sp_value'].strip()
+        if swap_value == '':
+            continue
+
+        param_name = p['sp_name']
+        if param_name.lower().endswith(NON_SEARCHABLE):
+            warnings.warn("Warning: `%s` is not eligible for search and was "
+                          "omitted!" % param_name)
+            continue
+
+        if not swap_value.startswith(':'):
+            safe_eval = SafeEval(load_scipy=True, load_numpy=True)
+            ev = safe_eval(swap_value)
+        else:
+            # Have `:` before search list, asks for estimator evaluatio
+            safe_eval_es = SafeEval(load_estimators=True)
+            swap_value = swap_value[1:].strip()
+            # TODO maybe add regular express check
+            ev = safe_eval_es(swap_value)
+
+        swap_params[param_name] = ev
+
+    return swap_params
+
+
+def train_test_split_none(*arrays, **kwargs):
+    """extend train_test_split to take None arrays
+    and support split by group names.
+    """
+    nones = []
+    new_arrays = []
+    for idx, arr in enumerate(arrays):
+        if arr is None:
+            nones.append(idx)
+        else:
+            new_arrays.append(arr)
+
+    if kwargs['shuffle'] == 'None':
+        kwargs['shuffle'] = None
+
+    group_names = kwargs.pop('group_names', None)
+
+    if group_names is not None and group_names.strip():
+        group_names = [name.strip() for name in
+                       group_names.split(',')]
+        new_arrays = indexable(*new_arrays)
+        groups = kwargs['labels']
+        n_samples = new_arrays[0].shape[0]
+        index_arr = np.arange(n_samples)
+        test = index_arr[np.isin(groups, group_names)]
+        train = index_arr[~np.isin(groups, group_names)]
+        rval = list(chain.from_iterable(
+            (safe_indexing(a, train),
+             safe_indexing(a, test)) for a in new_arrays))
+    else:
+        rval = train_test_split(*new_arrays, **kwargs)
+
+    for pos in nones:
+        rval[pos * 2: 2] = [None, None]
+
+    return rval
+
+
+def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):
+    """ output scores based on input scorer
+
+    Parameters
+    ----------
+    y_true : array
+        True label or target values
+    pred_probas : array
+        Prediction values, probability for classification problem
+    scorer : dict
+        dict of `sklearn.metrics.scorer.SCORER`
+    is_multimetric : bool, default is True
+    """
+    if y_true.ndim == 1 or y_true.shape[-1] == 1:
+        pred_probas = pred_probas.ravel()
+        pred_labels = (pred_probas > 0.5).astype('int32')
+        targets = y_true.ravel().astype('int32')
+        if not is_multimetric:
+            preds = pred_labels if scorer.__class__.__name__ == \
+                '_PredictScorer' else pred_probas
+            score = scorer._score_func(targets, preds, **scorer._kwargs)
+
+            return score
+        else:
+            scores = {}
+            for name, one_scorer in scorer.items():
+                preds = pred_labels if one_scorer.__class__.__name__\
+                    == '_PredictScorer' else pred_probas
+                score = one_scorer._score_func(targets, preds,
+                                               **one_scorer._kwargs)
+                scores[name] = score
+
+    # TODO: multi-class metrics
+    # multi-label
+    else:
+        pred_labels = (pred_probas > 0.5).astype('int32')
+        targets = y_true.astype('int32')
+        if not is_multimetric:
+            preds = pred_labels if scorer.__class__.__name__ == \
+                '_PredictScorer' else pred_probas
+            score, _ = compute_score(preds, targets,
+                                     scorer._score_func)
+            return score
+        else:
+            scores = {}
+            for name, one_scorer in scorer.items():
+                preds = pred_labels if one_scorer.__class__.__name__\
+                    == '_PredictScorer' else pred_probas
+                score, _ = compute_score(preds, targets,
+                                         one_scorer._score_func)
+                scores[name] = score
+
+    return scores
+
+
+def main(inputs, infile_estimator, infile1, infile2,
+         outfile_result, outfile_object=None,
+         outfile_weights=None, outfile_y_true=None,
+         outfile_y_preds=None, groups=None,
+         ref_seq=None, intervals=None, targets=None,
+         fasta_path=None):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : str
+        File path to estimator
+
+    infile1 : str
+        File path to dataset containing features
+
+    infile2 : str
+        File path to dataset containing target values
+
+    outfile_result : str
+        File path to save the results, either cv_results or test result
+
+    outfile_object : str, optional
+        File path to save searchCV object
+
+    outfile_weights : str, optional
+        File path to save deep learning model weights
+
+    outfile_y_true : str, optional
+        File path to target values for prediction
+
+    outfile_y_preds : str, optional
+        File path to save deep learning model weights
+
+    groups : str
+        File path to dataset containing groups labels
+
+    ref_seq : str
+        File path to dataset containing genome sequence file
+
+    intervals : str
+        File path to dataset containing interval file
+
+    targets : str
+        File path to dataset compressed target bed file
+
+    fasta_path : str
+        File path to dataset containing fasta file
+    """
+    warnings.simplefilter('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    #  load estimator
+    with open(infile_estimator, 'rb') as estimator_handler:
+        estimator = load_model(estimator_handler)
+
+    estimator = clean_params(estimator)
+
+    # swap hyperparameter
+    swapping = params['experiment_schemes']['hyperparams_swapping']
+    swap_params = _eval_swap_params(swapping)
+    estimator.set_params(**swap_params)
+
+    estimator_params = estimator.get_params()
+
+    # store read dataframe object
+    loaded_df = {}
+
+    input_type = params['input_options']['selected_input']
+    # tabular input
+    if input_type == 'tabular':
+        header = 'infer' if params['input_options']['header1'] else None
+        column_option = (params['input_options']['column_selector_options_1']
+                         ['selected_column_selector_option'])
+        if column_option in ['by_index_number', 'all_but_by_index_number',
+                             'by_header_name', 'all_but_by_header_name']:
+            c = params['input_options']['column_selector_options_1']['col1']
+        else:
+            c = None
+
+        df_key = infile1 + repr(header)
+        df = pd.read_csv(infile1, sep='\t', header=header,
+                         parse_dates=True)
+        loaded_df[df_key] = df
+
+        X = read_columns(df, c=c, c_option=column_option).astype(float)
+    # sparse input
+    elif input_type == 'sparse':
+        X = mmread(open(infile1, 'r'))
+
+    # fasta_file input
+    elif input_type == 'seq_fasta':
+        pyfaidx = get_module('pyfaidx')
+        sequences = pyfaidx.Fasta(fasta_path)
+        n_seqs = len(sequences.keys())
+        X = np.arange(n_seqs)[:, np.newaxis]
+        for param in estimator_params.keys():
+            if param.endswith('fasta_path'):
+                estimator.set_params(
+                    **{param: fasta_path})
+                break
+        else:
+            raise ValueError(
+                "The selected estimator doesn't support "
+                "fasta file input! Please consider using "
+                "KerasGBatchClassifier with "
+                "FastaDNABatchGenerator/FastaProteinBatchGenerator "
+                "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
+                "in pipeline!")
+
+    elif input_type == 'refseq_and_interval':
+        path_params = {
+            'data_batch_generator__ref_genome_path': ref_seq,
+            'data_batch_generator__intervals_path': intervals,
+            'data_batch_generator__target_path': targets
+        }
+        estimator.set_params(**path_params)
+        n_intervals = sum(1 for line in open(intervals))
+        X = np.arange(n_intervals)[:, np.newaxis]
+
+    # Get target y
+    header = 'infer' if params['input_options']['header2'] else None
+    column_option = (params['input_options']['column_selector_options_2']
+                     ['selected_column_selector_option2'])
+    if column_option in ['by_index_number', 'all_but_by_index_number',
+                         'by_header_name', 'all_but_by_header_name']:
+        c = params['input_options']['column_selector_options_2']['col2']
+    else:
+        c = None
+
+    df_key = infile2 + repr(header)
+    if df_key in loaded_df:
+        infile2 = loaded_df[df_key]
+    else:
+        infile2 = pd.read_csv(infile2, sep='\t',
+                              header=header, parse_dates=True)
+        loaded_df[df_key] = infile2
+
+    y = read_columns(
+            infile2,
+            c=c,
+            c_option=column_option,
+            sep='\t',
+            header=header,
+            parse_dates=True)
+    if len(y.shape) == 2 and y.shape[1] == 1:
+        y = y.ravel()
+    if input_type == 'refseq_and_interval':
+        estimator.set_params(
+            data_batch_generator__features=y.ravel().tolist())
+        y = None
+    # end y
+
+    # load groups
+    if groups:
+        groups_selector = (params['experiment_schemes']['test_split']
+                                 ['split_algos']).pop('groups_selector')
+
+        header = 'infer' if groups_selector['header_g'] else None
+        column_option = \
+            (groups_selector['column_selector_options_g']
+                            ['selected_column_selector_option_g'])
+        if column_option in ['by_index_number', 'all_but_by_index_number',
+                             'by_header_name', 'all_but_by_header_name']:
+            c = groups_selector['column_selector_options_g']['col_g']
+        else:
+            c = None
+
+        df_key = groups + repr(header)
+        if df_key in loaded_df:
+            groups = loaded_df[df_key]
+
+        groups = read_columns(
+                groups,
+                c=c,
+                c_option=column_option,
+                sep='\t',
+                header=header,
+                parse_dates=True)
+        groups = groups.ravel()
+
+    # del loaded_df
+    del loaded_df
+
+    # cache iraps_core fits could increase search speed significantly
+    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
+    main_est = get_main_estimator(estimator)
+    if main_est.__class__.__name__ == 'IRAPSClassifier':
+        main_est.set_params(memory=memory)
+
+    # handle scorer, convert to scorer dict
+    scoring = params['experiment_schemes']['metrics']['scoring']
+    scorer = get_scoring(scoring)
+    scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
+
+    # handle test (first) split
+    test_split_options = (params['experiment_schemes']
+                                ['test_split']['split_algos'])
+
+    if test_split_options['shuffle'] == 'group':
+        test_split_options['labels'] = groups
+    if test_split_options['shuffle'] == 'stratified':
+        if y is not None:
+            test_split_options['labels'] = y
+        else:
+            raise ValueError("Stratified shuffle split is not "
+                             "applicable on empty target values!")
+
+    X_train, X_test, y_train, y_test, groups_train, groups_test = \
+        train_test_split_none(X, y, groups, **test_split_options)
+
+    exp_scheme = params['experiment_schemes']['selected_exp_scheme']
+
+    # handle validation (second) split
+    if exp_scheme == 'train_val_test':
+        val_split_options = (params['experiment_schemes']
+                                   ['val_split']['split_algos'])
+
+        if val_split_options['shuffle'] == 'group':
+            val_split_options['labels'] = groups_train
+        if val_split_options['shuffle'] == 'stratified':
+            if y_train is not None:
+                val_split_options['labels'] = y_train
+            else:
+                raise ValueError("Stratified shuffle split is not "
+                                 "applicable on empty target values!")
+
+        X_train, X_val, y_train, y_val, groups_train, groups_val = \
+            train_test_split_none(X_train, y_train, groups_train,
+                                  **val_split_options)
+
+    # train and eval
+    if hasattr(estimator, 'validation_data'):
+        if exp_scheme == 'train_val_test':
+            estimator.fit(X_train, y_train,
+                          validation_data=(X_val, y_val))
+        else:
+            estimator.fit(X_train, y_train,
+                          validation_data=(X_test, y_test))
+    else:
+        estimator.fit(X_train, y_train)
+
+    if hasattr(estimator, 'evaluate'):
+        steps = estimator.prediction_steps
+        batch_size = estimator.batch_size
+        generator = estimator.data_generator_.flow(X_test, y=y_test,
+                                                   batch_size=batch_size)
+        predictions, y_true = _predict_generator(estimator.model_, generator,
+                                                 steps=steps)
+        scores = _evaluate(y_true, predictions, scorer, is_multimetric=True)
+
+    else:
+        if hasattr(estimator, 'predict_proba'):
+            predictions = estimator.predict_proba(X_test)
+        else:
+            predictions = estimator.predict(X_test)
+
+        y_true = y_test
+        scores = _score(estimator, X_test, y_test, scorer,
+                        is_multimetric=True)
+    if outfile_y_true:
+        try:
+            pd.DataFrame(y_true).to_csv(outfile_y_true, sep='\t',
+                                        index=False)
+            pd.DataFrame(predictions).astype(np.float32).to_csv(
+                outfile_y_preds, sep='\t', index=False,
+                float_format='%g', chunksize=10000)
+        except Exception as e:
+            print("Error in saving predictions: %s" % e)
+
+    # handle output
+    for name, score in scores.items():
+        scores[name] = [score]
+    df = pd.DataFrame(scores)
+    df = df[sorted(df.columns)]
+    df.to_csv(path_or_buf=outfile_result, sep='\t',
+              header=True, index=False)
+
+    memory.clear(warn=False)
+
+    if outfile_object:
+        main_est = estimator
+        if isinstance(estimator, Pipeline):
+            main_est = estimator.steps[-1][-1]
+
+        if hasattr(main_est, 'model_') \
+                and hasattr(main_est, 'save_weights'):
+            if outfile_weights:
+                main_est.save_weights(outfile_weights)
+            del main_est.model_
+            del main_est.fit_params
+            del main_est.model_class_
+            del main_est.validation_data
+            if getattr(main_est, 'data_generator_', None):
+                del main_est.data_generator_
+
+        with open(outfile_object, 'wb') as output_handler:
+            pickle.dump(estimator, output_handler,
+                        pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
+    aparser.add_argument("-X", "--infile1", dest="infile1")
+    aparser.add_argument("-y", "--infile2", dest="infile2")
+    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
+    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
+    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
+    aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")
+    aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
+    aparser.add_argument("-g", "--groups", dest="groups")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-b", "--intervals", dest="intervals")
+    aparser.add_argument("-t", "--targets", dest="targets")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
+    args = aparser.parse_args()
+
+    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
+         args.outfile_result, outfile_object=args.outfile_object,
+         outfile_weights=args.outfile_weights,
+         outfile_y_true=args.outfile_y_true,
+         outfile_y_preds=args.outfile_y_preds,
+         groups=args.groups,
+         ref_seq=args.ref_seq, intervals=args.intervals,
+         targets=args.targets, fasta_path=args.fasta_path)
diff -r 18b39ada6f35 -r 64b771b1471a main_macros.xml
--- a/main_macros.xml	Thu Nov 07 05:25:28 2019 -0500
+++ b/main_macros.xml	Mon Dec 16 05:17:00 2019 -0500
@@ -1,12 +1,10 @@
 <macros>
-  <token name="@VERSION@">1.0.7.12</token>
-
-  <token name="@ENSEMBLE_VERSION@">0.2.0</token>
+  <token name="@VERSION@">1.0.8.1</token>
 
   <xml name="python_requirements">
       <requirements>
           <requirement type="package" version="3.6">python</requirement>
-          <requirement type="package" version="0.7.12">Galaxy-ML</requirement>
+          <requirement type="package" version="0.8.1">Galaxy-ML</requirement>
           <yield/>
       </requirements>
   </xml>
@@ -235,8 +233,8 @@
     <param argument="fit_intercept" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Estimate the intercept" help="If false, the data is assumed to be already centered."/>
   </xml>
 
-  <xml name="n_iter" token_default_value="5" token_help_text="The number of passes over the training data (aka epochs). ">
-    <param argument="n_iter" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of iterations" help="@HELP_TEXT@"/>
+  <xml name="n_iter_no_change" token_default_value="5" token_help_text="Number of iterations with no improvement to wait before early stopping. ">
+    <param argument="n_iter_no_change" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of iterations" help="@HELP_TEXT@"/>
   </xml>
 
   <xml name="shuffle" token_checked="true" token_help_text=" " token_label="Shuffle data after each iteration">
@@ -763,6 +761,9 @@
       <option value="MinMaxScaler">Minmax Scaler (Scales features to a range)</option>
       <option value="PolynomialFeatures">Polynomial Features (Generates polynomial and interaction features)</option>
       <option value="RobustScaler">Robust Scaler (Scales features using outlier-invariance statistics)</option>
+      <option value="QuantileTransformer">QuantileTransformer (Transform features using quantiles information)</option>
+      <option value="PowerTransformer">PowerTransformer (Apply a power transform featurewise to make data more Gaussian-like)</option>
+      <option value="KBinsDiscretizer">KBinsDiscretizer (Bin continuous data into intervals.)</option>
     </expand>
   </xml>
 
@@ -837,6 +838,42 @@
                   label="Use a copy of data for inplace scaling" help=" "/>
           </section>
       </when>
+      <when value="QuantileTransformer">
+          <section name="options" title="Advanced Options" expanded="False">
+              <param name="n_quantiles" type="integer" value="1000" min="0" label="Number of quantiles to be computed" />
+              <param name="output_distribution" type="select" label="Marginal distribution for the transformed data">
+                  <option value="uniform" selected="true">uniform</option>
+                  <option value="normal">normal</option>
+              </param>
+              <param name="ignore_implicit_zeros" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to discard sparse entries" help="Only applies to sparse matrices. If False, sparse entries are treated as zeros"/>
+              <param name="subsample" type="integer" value="100000" label="Maximum number of samples used to estimate the quantiles for computational efficiency" help="Note that the subsampling procedure may differ for value-identical sparse and dense matrices."/>
+              <expand macro="random_state" help_text="This is used by subsampling and smoothing noise"/>
+          </section>
+      </when>
+      <when value="PowerTransformer">
+          <section name="options" title="Advanced Options" expanded="False">
+              <param name="method" type="select" label="The power transform method">
+                  <option value="yeo-johnson" selected="true">yeo-johnson (works with positive and negative values)</option>
+                  <option value="box-cox">box-cox (might perform better, but only works with strictly positive values)</option>
+              </param>
+              <param name="standardize" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Whether to apply zero-mean, unit-variance normalization to the transformed output."/>
+          </section>
+      </when>
+      <when value="KBinsDiscretizer">
+          <section name="options" title="Advanced Options" expanded="False">
+              <param name="n_bins" type="integer" value="5" min="2" label="The number of bins to produce"/>
+              <param name="encode" type="select" label="Method used to encode the transformed result">
+                  <option value="onehot" selected="true">onehot (encode the transformed result with one-hot encoding and return a sparse matrix)</option>
+                  <option value="onehot-dense">onehot-dense (encode the transformed result with one-hot encoding and return a dense array)</option>
+                  <option value="ordinal">ordinal (return the bin identifier encoded as an integer value)</option>
+              </param>
+              <param name="strategy" type="select" label="Strategy used to define the widths of the bins">
+                  <option value="uniform">uniform (all bins in each feature have identical widths)</option>
+                  <option value="quantile" selected="true">quantile (all bins in each feature have the same number of points)</option>
+                  <option value="kmeans">kmeans (values in each bin have the same nearest center of a 1D k-means cluster)</option>
+              </param>
+          </section>
+      </when>
     </expand>
   </xml>
 
@@ -1261,6 +1298,7 @@
         <option value="neg_mean_squared_log_error">Regression -- 'neg_mean_squared_log_error'</option>
         <option value="neg_median_absolute_error">Regression -- 'neg_median_absolute_error'</option>
         <option value="r2">Regression -- 'r2'</option>
+        <option value="max_error">Regression -- 'max_error'</option>
         <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option>
         <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option>
       </param>
@@ -1291,6 +1329,7 @@
       <when value="neg_mean_squared_log_error"><expand macro="secondary_scoring_selection_regression"/></when>
       <when value="neg_median_absolute_error"><expand macro="secondary_scoring_selection_regression"/></when>
       <when value="r2"><expand macro="secondary_scoring_selection_regression"/></when>
+      <when value="max_error"><expand macro="secondary_scoring_selection_regression"/></when>
       <when value="binarize_auc_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when>
       <when value="binarize_average_precision_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when>
     </conditional>
@@ -1329,6 +1368,7 @@
       <option value="neg_mean_squared_log_error">Regression -- 'neg_mean_squared_log_error'</option>
       <option value="neg_median_absolute_error">Regression -- 'neg_median_absolute_error'</option>
       <option value="r2">Regression -- 'r2'</option>
+      <option value="max_error">Regression -- 'max_error'</option>
     </param>
   </xml>
 
@@ -1343,32 +1383,6 @@
     <param argument="pre_dispatch" type="@TYPE@" value="@DEFAULT_VALUE@" optional="true" label="pre_dispatch" help="@HELP@"/>
   </xml>
 
-  <xml name="search_cv_estimator">
-    <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
-    <section name="search_params_builder" title="Search parameters Builder" expanded="true">
-      <param name="infile_params" type="data" format="tabular" optional="true" label="Choose the dataset containing parameter names" help="This dataset could be the output of `get_params` in the `Estimator Attributes` tool."/>
-      <repeat name="param_set" min="1" max="30" title="Parameter settings for search:">
-          <param name="sp_name" type="select" optional="true" label="Choose a parameter name (with current value)">
-            <options from_dataset="infile_params" startswith="@">
-              <column name="name" index="2"/>
-              <column name="value" index="1"/>
-              <filter type="unique_value" name="unique_param" column="1"/>
-            </options>
-          </param>
-          <param name="sp_list" type="text" value="" optional="true" label="Search list" help="list or array-like, for example: [1, 10, 100, 1000], [True, False] and ['auto', 'sqrt', None]. See `help` section for more examples">
-            <sanitizer>
-              <valid initial="default">
-                <add value="&apos;"/>
-                <add value="&quot;"/>
-                <add value="["/>
-                <add value="]"/>
-              </valid>
-            </sanitizer>
-          </param>
-      </repeat>
-    </section>
-  </xml>
-
   <xml name="estimator_and_hyperparameter">
     <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
     <section name="hyperparams_swapping" title="Hyperparameter Swapping" expanded="false">
@@ -1398,7 +1412,7 @@
       <expand macro="model_validation_common_options"/>
       <!--expand macro="pre_dispatch" default_value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/-->
       <param argument="iid" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="iid" help="If True, data is identically distributed across the folds"/>
-      <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset. Be aware that `refit=True` invokes extra computation, but it's REQUIRED for outputting the best estimator!"/>
+      <!--param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset. Be aware that `refit=True` invokes extra computation, but it's REQUIRED for outputting the best estimator!"/> -->
       <param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/>
       <param argument="return_train_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="return_train_score" help=""/>
   </xml>
@@ -1475,6 +1489,8 @@
           <option value="GradientBoostingClassifier">GradientBoostingClassifier</option>
           <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
           <option value="IsolationForest">IsolationForest</option>
+          <option value="HistGradientBoostingClassifier">HistGradientBoostingClassifier</option>
+          <option value="HistGradientBoostingRegressor">HistGradientBoostingRegressor</option>
           <option value="RandomForestClassifier">RandomForestClassifier</option>
           <option value="RandomForestRegressor">RandomForestRegressor</option>
           <option value="RandomTreesEmbedding">RandomTreesEmbedding</option>
diff -r 18b39ada6f35 -r 64b771b1471a ml_visualization_ex.py
--- a/ml_visualization_ex.py	Thu Nov 07 05:25:28 2019 -0500
+++ b/ml_visualization_ex.py	Mon Dec 16 05:17:00 2019 -0500
@@ -1,6 +1,9 @@
 import argparse
 import json
+import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
+import os
 import pandas as pd
 import plotly
 import plotly.graph_objs as go
@@ -17,6 +20,251 @@
 
 safe_eval = SafeEval()
 
+# plotly default colors
+default_colors = [
+    '#1f77b4',  # muted blue
+    '#ff7f0e',  # safety orange
+    '#2ca02c',  # cooked asparagus green
+    '#d62728',  # brick red
+    '#9467bd',  # muted purple
+    '#8c564b',  # chestnut brown
+    '#e377c2',  # raspberry yogurt pink
+    '#7f7f7f',  # middle gray
+    '#bcbd22',  # curry yellow-green
+    '#17becf'   # blue-teal
+]
+
+
+def visualize_pr_curve_plotly(df1, df2, pos_label, title=None):
+    """output pr-curve in html using plotly
+
+    df1 : pandas.DataFrame
+        Containing y_true
+    df2 : pandas.DataFrame
+        Containing y_score
+    pos_label : None
+        The label of positive class
+    title : str
+        Plot title
+    """
+    data = []
+    for idx in range(df1.shape[1]):
+        y_true = df1.iloc[:, idx].values
+        y_score = df2.iloc[:, idx].values
+
+        precision, recall, _ = precision_recall_curve(
+            y_true, y_score, pos_label=pos_label)
+        ap = average_precision_score(
+            y_true, y_score, pos_label=pos_label or 1)
+
+        trace = go.Scatter(
+            x=recall,
+            y=precision,
+            mode='lines',
+            marker=dict(
+                color=default_colors[idx % len(default_colors)]
+            ),
+            name='%s (area = %.3f)' % (idx, ap)
+        )
+        data.append(trace)
+
+    layout = go.Layout(
+        xaxis=dict(
+            title='Recall',
+            linecolor='lightslategray',
+            linewidth=1
+        ),
+        yaxis=dict(
+            title='Precision',
+            linecolor='lightslategray',
+            linewidth=1
+        ),
+        title=dict(
+            text=title or 'Precision-Recall Curve',
+            x=0.5,
+            y=0.92,
+            xanchor='center',
+            yanchor='top'
+        ),
+        font=dict(
+            family="sans-serif",
+            size=11
+        ),
+        # control backgroud colors
+        plot_bgcolor='rgba(255,255,255,0)'
+    )
+    """
+    legend=dict(
+        x=0.95,
+        y=0,
+        traceorder="normal",
+        font=dict(
+            family="sans-serif",
+            size=9,
+            color="black"
+        ),
+        bgcolor="LightSteelBlue",
+        bordercolor="Black",
+        borderwidth=2
+    ),"""
+
+    fig = go.Figure(data=data, layout=layout)
+
+    plotly.offline.plot(fig, filename="output.html", auto_open=False)
+    # to be discovered by `from_work_dir`
+    os.rename('output.html', 'output')
+
+
+def visualize_pr_curve_matplotlib(df1, df2, pos_label, title=None):
+    """visualize pr-curve using matplotlib and output svg image
+    """
+    backend = matplotlib.get_backend()
+    if "inline" not in backend:
+        matplotlib.use("SVG")
+    plt.style.use('seaborn-colorblind')
+    plt.figure()
+
+    for idx in range(df1.shape[1]):
+        y_true = df1.iloc[:, idx].values
+        y_score = df2.iloc[:, idx].values
+
+        precision, recall, _ = precision_recall_curve(
+            y_true, y_score, pos_label=pos_label)
+        ap = average_precision_score(
+            y_true, y_score, pos_label=pos_label or 1)
+
+        plt.step(recall, precision, 'r-', color="black", alpha=0.3,
+                 lw=1, where="post", label='%s (area = %.3f)' % (idx, ap))
+
+    plt.xlim([0.0, 1.0])
+    plt.ylim([0.0, 1.05])
+    plt.xlabel('Recall')
+    plt.ylabel('Precision')
+    title = title or 'Precision-Recall Curve'
+    plt.title(title)
+    folder = os.getcwd()
+    plt.savefig(os.path.join(folder, "output.svg"), format="svg")
+    os.rename(os.path.join(folder, "output.svg"),
+              os.path.join(folder, "output"))
+
+
+def visualize_roc_curve_plotly(df1, df2, pos_label,
+                               drop_intermediate=True,
+                               title=None):
+    """output roc-curve in html using plotly
+
+    df1 : pandas.DataFrame
+        Containing y_true
+    df2 : pandas.DataFrame
+        Containing y_score
+    pos_label : None
+        The label of positive class
+    drop_intermediate : bool
+        Whether to drop some suboptimal thresholds
+    title : str
+        Plot title
+    """
+    data = []
+    for idx in range(df1.shape[1]):
+        y_true = df1.iloc[:, idx].values
+        y_score = df2.iloc[:, idx].values
+
+        fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label,
+                                drop_intermediate=drop_intermediate)
+        roc_auc = auc(fpr, tpr)
+
+        trace = go.Scatter(
+            x=fpr,
+            y=tpr,
+            mode='lines',
+            marker=dict(
+                color=default_colors[idx % len(default_colors)]
+            ),
+            name='%s (area = %.3f)' % (idx, roc_auc)
+        )
+        data.append(trace)
+
+    layout = go.Layout(
+        xaxis=dict(
+            title='False Positive Rate',
+            linecolor='lightslategray',
+            linewidth=1
+        ),
+        yaxis=dict(
+            title='True Positive Rate',
+            linecolor='lightslategray',
+            linewidth=1
+        ),
+        title=dict(
+            text=title or 'Receiver Operating Characteristic (ROC) Curve',
+            x=0.5,
+            y=0.92,
+            xanchor='center',
+            yanchor='top'
+        ),
+        font=dict(
+            family="sans-serif",
+            size=11
+        ),
+        # control backgroud colors
+        plot_bgcolor='rgba(255,255,255,0)'
+    )
+    """
+    # legend=dict(
+            # x=0.95,
+            # y=0,
+            # traceorder="normal",
+            # font=dict(
+            #    family="sans-serif",
+            #    size=9,
+            #    color="black"
+            # ),
+            # bgcolor="LightSteelBlue",
+            # bordercolor="Black",
+            # borderwidth=2
+        # ),
+    """
+
+    fig = go.Figure(data=data, layout=layout)
+
+    plotly.offline.plot(fig, filename="output.html", auto_open=False)
+    # to be discovered by `from_work_dir`
+    os.rename('output.html', 'output')
+
+
+def visualize_roc_curve_matplotlib(df1, df2, pos_label,
+                                   drop_intermediate=True,
+                                   title=None):
+    """visualize roc-curve using matplotlib and output svg image
+    """
+    backend = matplotlib.get_backend()
+    if "inline" not in backend:
+        matplotlib.use("SVG")
+    plt.style.use('seaborn-colorblind')
+    plt.figure()
+
+    for idx in range(df1.shape[1]):
+        y_true = df1.iloc[:, idx].values
+        y_score = df2.iloc[:, idx].values
+
+        fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label,
+                                drop_intermediate=drop_intermediate)
+        roc_auc = auc(fpr, tpr)
+
+        plt.step(fpr, tpr, 'r-', color="black", alpha=0.3, lw=1,
+                 where="post", label='%s (area = %.3f)' % (idx, roc_auc))
+
+    plt.xlim([0.0, 1.0])
+    plt.ylim([0.0, 1.05])
+    plt.xlabel('False Positive Rate')
+    plt.ylabel('True Positive Rate')
+    title = title or 'Receiver Operating Characteristic (ROC) Curve'
+    plt.title(title)
+    folder = os.getcwd()
+    plt.savefig(os.path.join(folder, "output.svg"), format="svg")
+    os.rename(os.path.join(folder, "output.svg"),
+              os.path.join(folder, "output"))
+
 
 def main(inputs, infile_estimator=None, infile1=None,
          infile2=None, outfile_result=None,
@@ -71,6 +319,8 @@
 
     title = params['plotting_selection']['title'].strip()
     plot_type = params['plotting_selection']['plot_type']
+    plot_format = params['plotting_selection']['plot_format']
+
     if plot_type == 'feature_importances':
         with open(infile_estimator, 'rb') as estimator_handler:
             estimator = load_model(estimator_handler)
@@ -123,98 +373,46 @@
         layout = go.Layout(title=title or "Feature Importances")
         fig = go.Figure(data=[trace], layout=layout)
 
-    elif plot_type == 'pr_curve':
-        df1 = pd.read_csv(infile1, sep='\t', header=None)
-        df2 = pd.read_csv(infile2, sep='\t', header=None)
+        plotly.offline.plot(fig, filename="output.html",
+                            auto_open=False)
+        # to be discovered by `from_work_dir`
+        os.rename('output.html', 'output')
+
+        return 0
 
-        precision = {}
-        recall = {}
-        ap = {}
+    elif plot_type in ('pr_curve', 'roc_curve'):
+        df1 = pd.read_csv(infile1, sep='\t', header='infer')
+        df2 = pd.read_csv(infile2, sep='\t', header='infer').astype(np.float32)
+
+        minimum = params['plotting_selection']['report_minimum_n_positives']
+        # filter out columns whose n_positives is beblow the threhold
+        if minimum:
+            mask = df1.sum(axis=0) >= minimum
+            df1 = df1.loc[:, mask]
+            df2 = df2.loc[:, mask]
 
         pos_label = params['plotting_selection']['pos_label'].strip() \
             or None
-        for col in df1.columns:
-            y_true = df1[col].values
-            y_score = df2[col].values
-
-            precision[col], recall[col], _ = precision_recall_curve(
-                y_true, y_score, pos_label=pos_label)
-            ap[col] = average_precision_score(
-                y_true, y_score, pos_label=pos_label or 1)
-
-        if len(df1.columns) > 1:
-            precision["micro"], recall["micro"], _ = precision_recall_curve(
-                df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
-            ap['micro'] = average_precision_score(
-                df1.values, df2.values, average='micro',
-                pos_label=pos_label or 1)
-
-        data = []
-        for key in precision.keys():
-            trace = go.Scatter(
-                x=recall[key],
-                y=precision[key],
-                mode='lines',
-                name='%s (area = %.2f)' % (key, ap[key]) if key == 'micro'
-                     else 'column %s (area = %.2f)' % (key, ap[key])
-            )
-            data.append(trace)
-
-        layout = go.Layout(
-            title=title or "Precision-Recall curve",
-            xaxis=dict(title='Recall'),
-            yaxis=dict(title='Precision')
-        )
-
-        fig = go.Figure(data=data, layout=layout)
-
-    elif plot_type == 'roc_curve':
-        df1 = pd.read_csv(infile1, sep='\t', header=None)
-        df2 = pd.read_csv(infile2, sep='\t', header=None)
 
-        fpr = {}
-        tpr = {}
-        roc_auc = {}
-
-        pos_label = params['plotting_selection']['pos_label'].strip() \
-            or None
-        for col in df1.columns:
-            y_true = df1[col].values
-            y_score = df2[col].values
-
-            fpr[col], tpr[col], _ = roc_curve(
-                y_true, y_score, pos_label=pos_label)
-            roc_auc[col] = auc(fpr[col], tpr[col])
-
-        if len(df1.columns) > 1:
-            fpr["micro"], tpr["micro"], _ = roc_curve(
-                df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
-            roc_auc['micro'] = auc(fpr["micro"], tpr["micro"])
+        if plot_type == 'pr_curve':
+            if plot_format == 'plotly_html':
+                visualize_pr_curve_plotly(df1, df2, pos_label, title=title)
+            else:
+                visualize_pr_curve_matplotlib(df1, df2, pos_label, title)
+        else:          # 'roc_curve'
+            drop_intermediate = (params['plotting_selection']
+                                       ['drop_intermediate'])
+            if plot_format == 'plotly_html':
+                visualize_roc_curve_plotly(df1, df2, pos_label,
+                                           drop_intermediate=drop_intermediate,
+                                           title=title)
+            else:
+                visualize_roc_curve_matplotlib(
+                    df1, df2, pos_label,
+                    drop_intermediate=drop_intermediate,
+                    title=title)
 
-        data = []
-        for key in fpr.keys():
-            trace = go.Scatter(
-                x=fpr[key],
-                y=tpr[key],
-                mode='lines',
-                name='%s (area = %.2f)' % (key, roc_auc[key]) if key == 'micro'
-                     else 'column %s (area = %.2f)' % (key, roc_auc[key])
-            )
-            data.append(trace)
-
-        trace = go.Scatter(x=[0, 1], y=[0, 1],
-                           mode='lines', 
-                           line=dict(color='black', dash='dash'),
-                           showlegend=False)
-        data.append(trace)
-
-        layout = go.Layout(
-            title=title or "Receiver operating characteristic curve",
-            xaxis=dict(title='False Positive Rate'),
-            yaxis=dict(title='True Positive Rate')
-        )
-
-        fig = go.Figure(data=data, layout=layout)
+        return 0
 
     elif plot_type == 'rfecv_gridscores':
         input_df = pd.read_csv(infile1, sep='\t', header='infer')
@@ -231,10 +429,43 @@
         layout = go.Layout(
             xaxis=dict(title="Number of features selected"),
             yaxis=dict(title="Cross validation score"),
-            title=title or None
+            title=dict(
+                text=title or None,
+                x=0.5,
+                y=0.92,
+                xanchor='center',
+                yanchor='top'
+            ),
+            font=dict(
+                family="sans-serif",
+                size=11
+            ),
+            # control backgroud colors
+            plot_bgcolor='rgba(255,255,255,0)'
         )
+        """
+        # legend=dict(
+                # x=0.95,
+                # y=0,
+                # traceorder="normal",
+                # font=dict(
+                #    family="sans-serif",
+                #    size=9,
+                #    color="black"
+                # ),
+                # bgcolor="LightSteelBlue",
+                # bordercolor="Black",
+                # borderwidth=2
+            # ),
+        """
 
         fig = go.Figure(data=[data], layout=layout)
+        plotly.offline.plot(fig, filename="output.html",
+                            auto_open=False)
+        # to be discovered by `from_work_dir`
+        os.rename('output.html', 'output')
+
+        return 0
 
     elif plot_type == 'learning_curve':
         input_df = pd.read_csv(infile1, sep='\t', header='infer')
@@ -264,23 +495,57 @@
             yaxis=dict(
                 title='Performance Score'
             ),
-            title=title or 'Learning Curve'
+            # modify these configurations to customize image
+            title=dict(
+                text=title or 'Learning Curve',
+                x=0.5,
+                y=0.92,
+                xanchor='center',
+                yanchor='top'
+            ),
+            font=dict(
+                family="sans-serif",
+                size=11
+            ),
+            # control backgroud colors
+            plot_bgcolor='rgba(255,255,255,0)'
         )
+        """
+        # legend=dict(
+                # x=0.95,
+                # y=0,
+                # traceorder="normal",
+                # font=dict(
+                #    family="sans-serif",
+                #    size=9,
+                #    color="black"
+                # ),
+                # bgcolor="LightSteelBlue",
+                # bordercolor="Black",
+                # borderwidth=2
+            # ),
+        """
+
         fig = go.Figure(data=[data1, data2], layout=layout)
+        plotly.offline.plot(fig, filename="output.html",
+                            auto_open=False)
+        # to be discovered by `from_work_dir`
+        os.rename('output.html', 'output')
+
+        return 0
 
     elif plot_type == 'keras_plot_model':
         with open(model_config, 'r') as f:
             model_str = f.read()
         model = model_from_json(model_str)
         plot_model(model, to_file="output.png")
-        __import__('os').rename('output.png', 'output')
+        os.rename('output.png', 'output')
 
         return 0
 
-    plotly.offline.plot(fig, filename="output.html",
-                        auto_open=False)
-    # to be discovered by `from_work_dir`
-    __import__('os').rename('output.html', 'output')
+    # save pdf file to disk
+    # fig.write_image("image.pdf", format='pdf')
+    # fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2)
 
 
 if __name__ == '__main__':
diff -r 18b39ada6f35 -r 64b771b1471a model_prediction.py
--- a/model_prediction.py	Thu Nov 07 05:25:28 2019 -0500
+++ b/model_prediction.py	Mon Dec 16 05:17:00 2019 -0500
@@ -2,13 +2,11 @@
 import json
 import numpy as np
 import pandas as pd
-import tabix
 import warnings
 
 from scipy.io import mmread
 from sklearn.pipeline import Pipeline
 
-from galaxy_ml.externals.selene_sdk.sequences import Genome
 from galaxy_ml.utils import (load_model, read_columns,
                              get_module, try_get_attr)
 
@@ -138,45 +136,10 @@
         pred_data_generator = klass(
             ref_genome_path=ref_seq, vcf_path=vcf_path, **options)
 
-        pred_data_generator.fit()
+        pred_data_generator.set_processing_attrs()
 
         variants = pred_data_generator.variants
-        # TODO : remove the following block after galaxy-ml v0.7.13
-        blacklist_tabix = getattr(pred_data_generator.reference_genome_,
-                                  '_blacklist_tabix', None)
-        clean_variants = []
-        if blacklist_tabix:
-            start_radius = pred_data_generator.start_radius_
-            end_radius = pred_data_generator.end_radius_
 
-            for chrom, pos, name, ref, alt, strand in variants:
-                center = pos + len(ref) // 2
-                start = center - start_radius
-                end = center + end_radius
-
-                if isinstance(pred_data_generator.reference_genome_, Genome):
-                    if "chr" not in chrom:
-                        chrom = "chr" + chrom
-                    if "MT" in chrom:
-                        chrom = chrom[:-1]
-                try:
-                    rows = blacklist_tabix.query(chrom, start, end)
-                    found = 0
-                    for row in rows:
-                        found = 1
-                        break
-                    if found:
-                        continue
-                except tabix.TabixError:
-                    pass
-
-                clean_variants.append((chrom, pos, name, ref, alt, strand))
-        else:
-            clean_variants = variants
-
-        setattr(pred_data_generator, 'variants', clean_variants)
-
-        variants = np.array(clean_variants)
         # predict 1600 sample at once then write to file
         gen_flow = pred_data_generator.flow(batch_size=1600)
 
diff -r 18b39ada6f35 -r 64b771b1471a search_model_validation.py
--- a/search_model_validation.py	Thu Nov 07 05:25:28 2019 -0500
+++ b/search_model_validation.py	Mon Dec 16 05:17:00 2019 -0500
@@ -4,41 +4,35 @@
 import joblib
 import json
 import numpy as np
+import os
 import pandas as pd
 import pickle
 import skrebate
-import sklearn
 import sys
-import xgboost
 import warnings
-from imblearn import under_sampling, over_sampling, combine
 from scipy.io import mmread
-from mlxtend import classifier, regressor
-from sklearn.base import clone
-from sklearn import (cluster, compose, decomposition, ensemble,
-                     feature_extraction, feature_selection,
-                     gaussian_process, kernel_approximation, metrics,
-                     model_selection, naive_bayes, neighbors,
-                     pipeline, preprocessing, svm, linear_model,
-                     tree, discriminant_analysis)
+from sklearn import (cluster, decomposition, feature_selection,
+                     kernel_approximation, model_selection, preprocessing)
 from sklearn.exceptions import FitFailedWarning
 from sklearn.model_selection._validation import _score, cross_validate
 from sklearn.model_selection import _search, _validation
+from sklearn.pipeline import Pipeline
 
 from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,
-                             read_columns, try_get_attr, get_module)
+                             read_columns, try_get_attr, get_module,
+                             clean_params, get_main_estimator)
 
 
 _fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score')
 setattr(_search, '_fit_and_score', _fit_and_score)
 setattr(_validation, '_fit_and_score', _fit_and_score)
 
-N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
-CACHE_DIR = './cached'
+N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
+# handle  disk cache
+CACHE_DIR = os.path.join(os.getcwd(), 'cached')
+del os
 NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
                   'nthread', 'callbacks')
-ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
-                     'CSVLogger', 'None')
 
 
 def _eval_search_params(params_builder):
@@ -164,74 +158,40 @@
     return search_params
 
 
-def main(inputs, infile_estimator, infile1, infile2,
-         outfile_result, outfile_object=None,
-         outfile_weights=None, groups=None,
-         ref_seq=None, intervals=None, targets=None,
-         fasta_path=None):
-    """
-    Parameter
-    ---------
-    inputs : str
-        File path to galaxy tool parameter
+def _handle_X_y(estimator, params, infile1, infile2, loaded_df={},
+                ref_seq=None, intervals=None, targets=None,
+                fasta_path=None):
+    """read inputs
 
-    infile_estimator : str
-        File path to estimator
-
+    Params
+    -------
+    estimator : estimator object
+    params : dict
+        Galaxy tool parameter inputs
     infile1 : str
         File path to dataset containing features
-
     infile2 : str
         File path to dataset containing target values
-
-    outfile_result : str
-        File path to save the results, either cv_results or test result
-
-    outfile_object : str, optional
-        File path to save searchCV object
-
-    outfile_weights : str, optional
-        File path to save model weights
-
-    groups : str
-        File path to dataset containing groups labels
-
+    loaded_df : dict
+        Contains loaded DataFrame objects with file path as keys
     ref_seq : str
         File path to dataset containing genome sequence file
-
-    intervals : str
+    interval : str
         File path to dataset containing interval file
-
     targets : str
         File path to dataset compressed target bed file
-
     fasta_path : str
         File path to dataset containing fasta file
-    """
-    warnings.simplefilter('ignore')
 
-    with open(inputs, 'r') as param_handler:
-        params = json.load(param_handler)
-
-    # conflict param checker
-    if params['outer_split']['split_mode'] == 'nested_cv' \
-            and params['save'] != 'nope':
-        raise ValueError("Save best estimator is not possible for nested CV!")
 
-    if not (params['search_schemes']['options']['refit']) \
-            and params['save'] != 'nope':
-        raise ValueError("Save best estimator is not possible when refit "
-                         "is False!")
-
-    params_builder = params['search_schemes']['search_params_builder']
-
-    with open(infile_estimator, 'rb') as estimator_handler:
-        estimator = load_model(estimator_handler)
+    Returns
+    -------
+    estimator : estimator object after setting new attributes
+    X : numpy array
+    y : numpy array
+    """
     estimator_params = estimator.get_params()
 
-    # store read dataframe object
-    loaded_df = {}
-
     input_type = params['input_options']['selected_input']
     # tabular input
     if input_type == 'tabular':
@@ -245,6 +205,10 @@
             c = None
 
         df_key = infile1 + repr(header)
+
+        if df_key in loaded_df:
+            infile1 = loaded_df[df_key]
+
         df = pd.read_csv(infile1, sep='\t', header=header,
                          parse_dates=True)
         loaded_df[df_key] = df
@@ -317,6 +281,196 @@
         y = None
     # end y
 
+    return estimator, X, y
+
+
+def _do_outer_cv(searcher, X, y, outer_cv, scoring, error_score='raise',
+                 outfile=None):
+    """Do outer cross-validation for nested CV
+
+    Parameters
+    ----------
+    searcher : object
+        SearchCV object
+    X : numpy array
+        Containing features
+    y : numpy array
+        Target values or labels
+    outer_cv : int or CV splitter
+        Control the cv splitting
+    scoring : object
+        Scorer
+    error_score: str, float or numpy float
+        Whether to raise fit error or return an value
+    outfile : str
+        File path to store the restuls
+    """
+    if error_score == 'raise':
+        rval = cross_validate(
+            searcher, X, y, scoring=scoring,
+            cv=outer_cv, n_jobs=N_JOBS, verbose=0,
+            error_score=error_score)
+    else:
+        warnings.simplefilter('always', FitFailedWarning)
+        with warnings.catch_warnings(record=True) as w:
+            try:
+                rval = cross_validate(
+                    searcher, X, y,
+                    scoring=scoring,
+                    cv=outer_cv, n_jobs=N_JOBS,
+                    verbose=0,
+                    error_score=error_score)
+            except ValueError:
+                pass
+            for warning in w:
+                print(repr(warning.message))
+
+    keys = list(rval.keys())
+    for k in keys:
+        if k.startswith('test'):
+            rval['mean_' + k] = np.mean(rval[k])
+            rval['std_' + k] = np.std(rval[k])
+        if k.endswith('time'):
+            rval.pop(k)
+    rval = pd.DataFrame(rval)
+    rval = rval[sorted(rval.columns)]
+    rval.to_csv(path_or_buf=outfile, sep='\t', header=True, index=False)
+
+
+def _do_train_test_split_val(searcher, X, y, params, error_score='raise',
+                             primary_scoring=None, groups=None,
+                             outfile=None):
+    """ do train test split, searchCV validates on the train and then use
+    the best_estimator_ to evaluate on the test
+
+    Returns
+    --------
+    Fitted SearchCV object
+    """
+    train_test_split = try_get_attr(
+        'galaxy_ml.model_validations', 'train_test_split')
+    split_options = params['outer_split']
+
+    # splits
+    if split_options['shuffle'] == 'stratified':
+        split_options['labels'] = y
+        X, X_test, y, y_test = train_test_split(X, y, **split_options)
+    elif split_options['shuffle'] == 'group':
+        if groups is None:
+            raise ValueError("No group based CV option was choosen for "
+                             "group shuffle!")
+        split_options['labels'] = groups
+        if y is None:
+            X, X_test, groups, _ =\
+                train_test_split(X, groups, **split_options)
+        else:
+            X, X_test, y, y_test, groups, _ =\
+                train_test_split(X, y, groups, **split_options)
+    else:
+        if split_options['shuffle'] == 'None':
+            split_options['shuffle'] = None
+        X, X_test, y, y_test =\
+            train_test_split(X, y, **split_options)
+
+    if error_score == 'raise':
+        searcher.fit(X, y, groups=groups)
+    else:
+        warnings.simplefilter('always', FitFailedWarning)
+        with warnings.catch_warnings(record=True) as w:
+            try:
+                searcher.fit(X, y, groups=groups)
+            except ValueError:
+                pass
+            for warning in w:
+                print(repr(warning.message))
+
+    scorer_ = searcher.scorer_
+    if isinstance(scorer_, collections.Mapping):
+        is_multimetric = True
+    else:
+        is_multimetric = False
+
+    best_estimator_ = getattr(searcher, 'best_estimator_')
+
+    # TODO Solve deep learning models in pipeline
+    if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier':
+        test_score = best_estimator_.evaluate(
+            X_test, scorer=scorer_, is_multimetric=is_multimetric)
+    else:
+        test_score = _score(best_estimator_, X_test,
+                            y_test, scorer_,
+                            is_multimetric=is_multimetric)
+
+    if not is_multimetric:
+        test_score = {primary_scoring: test_score}
+    for key, value in test_score.items():
+        test_score[key] = [value]
+    result_df = pd.DataFrame(test_score)
+    result_df.to_csv(path_or_buf=outfile, sep='\t', header=True,
+                     index=False)
+
+    return searcher
+
+
+def main(inputs, infile_estimator, infile1, infile2,
+         outfile_result, outfile_object=None,
+         outfile_weights=None, groups=None,
+         ref_seq=None, intervals=None, targets=None,
+         fasta_path=None):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : str
+        File path to estimator
+
+    infile1 : str
+        File path to dataset containing features
+
+    infile2 : str
+        File path to dataset containing target values
+
+    outfile_result : str
+        File path to save the results, either cv_results or test result
+
+    outfile_object : str, optional
+        File path to save searchCV object
+
+    outfile_weights : str, optional
+        File path to save model weights
+
+    groups : str
+        File path to dataset containing groups labels
+
+    ref_seq : str
+        File path to dataset containing genome sequence file
+
+    intervals : str
+        File path to dataset containing interval file
+
+    targets : str
+        File path to dataset compressed target bed file
+
+    fasta_path : str
+        File path to dataset containing fasta file
+    """
+    warnings.simplefilter('ignore')
+
+    # store read dataframe object
+    loaded_df = {}
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    # Override the refit parameter
+    params['search_schemes']['options']['refit'] = True \
+        if params['save'] != 'nope' else False
+
+    with open(infile_estimator, 'rb') as estimator_handler:
+        estimator = load_model(estimator_handler)
+
     optimizer = params['search_schemes']['selected_search_scheme']
     optimizer = getattr(model_selection, optimizer)
 
@@ -337,8 +491,10 @@
             c = None
 
         df_key = groups + repr(header)
-        if df_key in loaded_df:
-            groups = loaded_df[df_key]
+
+        groups = pd.read_csv(groups, sep='\t', header=header,
+                             parse_dates=True)
+        loaded_df[df_key] = groups
 
         groups = read_columns(
                 groups,
@@ -352,7 +508,6 @@
 
     splitter, groups = get_cv(options.pop('cv_selector'))
     options['cv'] = splitter
-    options['n_jobs'] = N_JOBS
     primary_scoring = options['scoring']['primary_scoring']
     options['scoring'] = get_scoring(options['scoring'])
     if options['error_score']:
@@ -364,55 +519,56 @@
     if 'pre_dispatch' in options and options['pre_dispatch'] == '':
         options['pre_dispatch'] = None
 
-    # del loaded_df
-    del loaded_df
+    params_builder = params['search_schemes']['search_params_builder']
+    param_grid = _eval_search_params(params_builder)
+
+    estimator = clean_params(estimator)
 
-    # handle memory
-    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
+    # save the SearchCV object without fit
+    if params['save'] == 'save_no_fit':
+        searcher = optimizer(estimator, param_grid, **options)
+        print(searcher)
+        with open(outfile_object, 'wb') as output_handler:
+            pickle.dump(searcher, output_handler,
+                        pickle.HIGHEST_PROTOCOL)
+        return 0
+
+    # read inputs and loads new attributes, like paths
+    estimator, X, y = _handle_X_y(estimator, params, infile1, infile2,
+                                  loaded_df=loaded_df, ref_seq=ref_seq,
+                                  intervals=intervals, targets=targets,
+                                  fasta_path=fasta_path)
+
     # cache iraps_core fits could increase search speed significantly
-    if estimator.__class__.__name__ == 'IRAPSClassifier':
-        estimator.set_params(memory=memory)
-    else:
-        # For iraps buried in pipeline
-        for p, v in estimator_params.items():
-            if p.endswith('memory'):
-                # for case of `__irapsclassifier__memory`
-                if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
-                    # cache iraps_core fits could increase search
-                    # speed significantly
-                    new_params = {p: memory}
-                    estimator.set_params(**new_params)
-                # security reason, we don't want memory being
-                # modified unexpectedly
-                elif v:
-                    new_params = {p, None}
-                    estimator.set_params(**new_params)
-            # For now, 1 CPU is suggested for iprasclassifier
-            elif p.endswith('n_jobs'):
-                new_params = {p: 1}
-                estimator.set_params(**new_params)
-            # for security reason, types of callbacks are limited
-            elif p.endswith('callbacks'):
-                for cb in v:
-                    cb_type = cb['callback_selection']['callback_type']
-                    if cb_type not in ALLOWED_CALLBACKS:
-                        raise ValueError(
-                            "Prohibited callback type: %s!" % cb_type)
+    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
+    main_est = get_main_estimator(estimator)
+    if main_est.__class__.__name__ == 'IRAPSClassifier':
+        main_est.set_params(memory=memory)
 
-    param_grid = _eval_search_params(params_builder)
     searcher = optimizer(estimator, param_grid, **options)
 
-    # do nested split
     split_mode = params['outer_split'].pop('split_mode')
-    # nested CV, outer cv using cross_validate
+
     if split_mode == 'nested_cv':
+        # make sure refit is choosen
+        # this could be True for sklearn models, but not the case for
+        # deep learning models
+        if not options['refit'] and \
+                not all(hasattr(estimator, attr)
+                        for attr in ('config', 'model_type')):
+            warnings.warn("Refit is change to `True` for nested validation!")
+            setattr(searcher, 'refit', True)
+
         outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
-
+        # nested CV, outer cv using cross_validate
         if options['error_score'] == 'raise':
             rval = cross_validate(
                 searcher, X, y, scoring=options['scoring'],
-                cv=outer_cv, n_jobs=N_JOBS, verbose=0,
-                error_score=options['error_score'])
+                cv=outer_cv, n_jobs=N_JOBS,
+                verbose=options['verbose'],
+                return_estimator=(params['save'] == 'save_estimator'),
+                error_score=options['error_score'],
+                return_train_score=True)
         else:
             warnings.simplefilter('always', FitFailedWarning)
             with warnings.catch_warnings(record=True) as w:
@@ -421,13 +577,38 @@
                         searcher, X, y,
                         scoring=options['scoring'],
                         cv=outer_cv, n_jobs=N_JOBS,
-                        verbose=0,
-                        error_score=options['error_score'])
+                        verbose=options['verbose'],
+                        return_estimator=(params['save'] == 'save_estimator'),
+                        error_score=options['error_score'],
+                        return_train_score=True)
                 except ValueError:
                     pass
                 for warning in w:
                     print(repr(warning.message))
 
+        fitted_searchers = rval.pop('estimator', [])
+        if fitted_searchers:
+            import os
+            pwd = os.getcwd()
+            save_dir = os.path.join(pwd, 'cv_results_in_folds')
+            try:
+                os.mkdir(save_dir)
+                for idx, obj in enumerate(fitted_searchers):
+                    target_name = 'cv_results_' + '_' + 'split%d' % idx
+                    target_path = os.path.join(pwd, save_dir, target_name)
+                    cv_results_ = getattr(obj, 'cv_results_', None)
+                    if not cv_results_:
+                        print("%s is not available" % target_name)
+                        continue
+                    cv_results_ = pd.DataFrame(cv_results_)
+                    cv_results_ = cv_results_[sorted(cv_results_.columns)]
+                    cv_results_.to_csv(target_path, sep='\t', header=True,
+                                       index=False)
+            except Exception as e:
+                print(e)
+            finally:
+                del os
+
         keys = list(rval.keys())
         for k in keys:
             if k.startswith('test'):
@@ -437,46 +618,22 @@
                 rval.pop(k)
         rval = pd.DataFrame(rval)
         rval = rval[sorted(rval.columns)]
-        rval.to_csv(path_or_buf=outfile_result, sep='\t',
-                    header=True, index=False)
-    else:
-        if split_mode == 'train_test_split':
-            train_test_split = try_get_attr(
-                'galaxy_ml.model_validations', 'train_test_split')
-            # make sure refit is choosen
-            # this could be True for sklearn models, but not the case for
-            # deep learning models
-            if not options['refit'] and \
-                    not all(hasattr(estimator, attr)
-                            for attr in ('config', 'model_type')):
-                warnings.warn("Refit is change to `True` for nested "
-                              "validation!")
-                setattr(searcher, 'refit', True)
-            split_options = params['outer_split']
+        rval.to_csv(path_or_buf=outfile_result, sep='\t', header=True,
+                    index=False)
+
+        return 0
 
-            # splits
-            if split_options['shuffle'] == 'stratified':
-                split_options['labels'] = y
-                X, X_test, y, y_test = train_test_split(X, y, **split_options)
-            elif split_options['shuffle'] == 'group':
-                if groups is None:
-                    raise ValueError("No group based CV option was "
-                                     "choosen for group shuffle!")
-                split_options['labels'] = groups
-                if y is None:
-                    X, X_test, groups, _ =\
-                        train_test_split(X, groups, **split_options)
-                else:
-                    X, X_test, y, y_test, groups, _ =\
-                        train_test_split(X, y, groups, **split_options)
-            else:
-                if split_options['shuffle'] == 'None':
-                    split_options['shuffle'] = None
-                X, X_test, y, y_test =\
-                    train_test_split(X, y, **split_options)
-        # end train_test_split
+        # deprecate train test split mode
+        """searcher = _do_train_test_split_val(
+            searcher, X, y, params,
+            primary_scoring=primary_scoring,
+            error_score=options['error_score'],
+            groups=groups,
+            outfile=outfile_result)"""
 
-        # shared by both train_test_split and non-split
+    # no outer split
+    else:
+        searcher.set_params(n_jobs=N_JOBS)
         if options['error_score'] == 'raise':
             searcher.fit(X, y, groups=groups)
         else:
@@ -489,47 +646,14 @@
                 for warning in w:
                     print(repr(warning.message))
 
-        # no outer split
-        if split_mode == 'no':
-            # save results
-            cv_results = pd.DataFrame(searcher.cv_results_)
-            cv_results = cv_results[sorted(cv_results.columns)]
-            cv_results.to_csv(path_or_buf=outfile_result, sep='\t',
-                              header=True, index=False)
-
-        # train_test_split, output test result using best_estimator_
-        # or rebuild the trained estimator using weights if applicable.
-        else:
-            scorer_ = searcher.scorer_
-            if isinstance(scorer_, collections.Mapping):
-                is_multimetric = True
-            else:
-                is_multimetric = False
-
-            best_estimator_ = getattr(searcher, 'best_estimator_', None)
-            if not best_estimator_:
-                raise ValueError("GridSearchCV object has no "
-                                 "`best_estimator_` when `refit`=False!")
-
-            if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier' \
-                    and hasattr(estimator.data_batch_generator, 'target_path'):
-                test_score = best_estimator_.evaluate(
-                    X_test, scorer=scorer_, is_multimetric=is_multimetric)
-            else:
-                test_score = _score(best_estimator_, X_test,
-                                    y_test, scorer_,
-                                    is_multimetric=is_multimetric)
-
-            if not is_multimetric:
-                test_score = {primary_scoring: test_score}
-            for key, value in test_score.items():
-                test_score[key] = [value]
-            result_df = pd.DataFrame(test_score)
-            result_df.to_csv(path_or_buf=outfile_result, sep='\t',
-                             header=True, index=False)
+        cv_results = pd.DataFrame(searcher.cv_results_)
+        cv_results = cv_results[sorted(cv_results.columns)]
+        cv_results.to_csv(path_or_buf=outfile_result, sep='\t',
+                          header=True, index=False)
 
     memory.clear(warn=False)
 
+    # output best estimator, and weights if applicable
     if outfile_object:
         best_estimator_ = getattr(searcher, 'best_estimator_', None)
         if not best_estimator_:
@@ -538,9 +662,10 @@
                           "nested gridsearch or `refit` is False!")
             return
 
-        main_est = best_estimator_
-        if isinstance(best_estimator_, pipeline.Pipeline):
-            main_est = best_estimator_.steps[-1][-1]
+        # clean prams
+        best_estimator_ = clean_params(best_estimator_)
+
+        main_est = get_main_estimator(best_estimator_)
 
         if hasattr(main_est, 'model_') \
                 and hasattr(main_est, 'save_weights'):
@@ -554,6 +679,7 @@
                 del main_est.data_generator_
 
         with open(outfile_object, 'wb') as output_handler:
+            print("Best estimator is saved: %s " % repr(best_estimator_))
             pickle.dump(best_estimator_, output_handler,
                         pickle.HIGHEST_PROTOCOL)
 
diff -r 18b39ada6f35 -r 64b771b1471a test-data/RandomForestClassifier.zip
Binary file test-data/RandomForestClassifier.zip has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/StackingCVRegressor01.zip
Binary file test-data/StackingCVRegressor01.zip has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/StackingRegressor02.zip
Binary file test-data/StackingRegressor02.zip has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/StackingVoting03.zip
Binary file test-data/StackingVoting03.zip has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/abc_model01
Binary file test-data/abc_model01 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/abr_model01
Binary file test-data/abr_model01 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/best_estimator_.zip
Binary file test-data/best_estimator_.zip has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/brier_score_loss.txt
--- a/test-data/brier_score_loss.txt	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/brier_score_loss.txt	Mon Dec 16 05:17:00 2019 -0500
@@ -1,2 +1,2 @@
 brier_score_loss : 
-0.5641025641025641
+0.24051282051282052
diff -r 18b39ada6f35 -r 64b771b1471a test-data/classification_report.txt
--- a/test-data/classification_report.txt	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/classification_report.txt	Mon Dec 16 05:17:00 2019 -0500
@@ -5,7 +5,7 @@
            1       1.00      0.62      0.77        16
            2       0.60      1.00      0.75         9
 
-   micro avg       0.85      0.85      0.85        39
+    accuracy                           0.85        39
    macro avg       0.87      0.88      0.84        39
 weighted avg       0.91      0.85      0.85        39
 
diff -r 18b39ada6f35 -r 64b771b1471a test-data/gbc_model01
Binary file test-data/gbc_model01 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/gbr_model01
Binary file test-data/gbr_model01 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/get_params05.tabular
--- a/test-data/get_params05.tabular	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/get_params05.tabular	Mon Dec 16 05:17:00 2019 -0500
@@ -1,31 +1,18 @@
 	Parameter	Value
-*	memory	memory: None
-*	steps	"steps: [('randomforestregressor', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
-           max_features='auto', max_leaf_nodes=None,
-           min_impurity_decrease=0.0, min_impurity_split=None,
-           min_samples_leaf=1, min_samples_split=2,
-           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
-           oob_score=False, random_state=42, verbose=0, warm_start=False))]"
-@	randomforestregressor	"randomforestregressor: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
-           max_features='auto', max_leaf_nodes=None,
-           min_impurity_decrease=0.0, min_impurity_split=None,
-           min_samples_leaf=1, min_samples_split=2,
-           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
-           oob_score=False, random_state=42, verbose=0, warm_start=False)"
-@	randomforestregressor__bootstrap	randomforestregressor__bootstrap: True
-@	randomforestregressor__criterion	randomforestregressor__criterion: 'mse'
-@	randomforestregressor__max_depth	randomforestregressor__max_depth: None
-@	randomforestregressor__max_features	randomforestregressor__max_features: 'auto'
-@	randomforestregressor__max_leaf_nodes	randomforestregressor__max_leaf_nodes: None
-@	randomforestregressor__min_impurity_decrease	randomforestregressor__min_impurity_decrease: 0.0
-@	randomforestregressor__min_impurity_split	randomforestregressor__min_impurity_split: None
-@	randomforestregressor__min_samples_leaf	randomforestregressor__min_samples_leaf: 1
-@	randomforestregressor__min_samples_split	randomforestregressor__min_samples_split: 2
-@	randomforestregressor__min_weight_fraction_leaf	randomforestregressor__min_weight_fraction_leaf: 0.0
-@	randomforestregressor__n_estimators	randomforestregressor__n_estimators: 100
-*	randomforestregressor__n_jobs	randomforestregressor__n_jobs: 1
-@	randomforestregressor__oob_score	randomforestregressor__oob_score: False
-@	randomforestregressor__random_state	randomforestregressor__random_state: 42
-*	randomforestregressor__verbose	randomforestregressor__verbose: 0
-@	randomforestregressor__warm_start	randomforestregressor__warm_start: False
-	Note:	@, searchable params in searchcv too.
+@	bootstrap	bootstrap: True
+@	criterion	criterion: 'mse'
+@	max_depth	max_depth: None
+@	max_features	max_features: 'auto'
+@	max_leaf_nodes	max_leaf_nodes: None
+@	min_impurity_decrease	min_impurity_decrease: 0.0
+@	min_impurity_split	min_impurity_split: None
+@	min_samples_leaf	min_samples_leaf: 1
+@	min_samples_split	min_samples_split: 2
+@	min_weight_fraction_leaf	min_weight_fraction_leaf: 0.0
+@	n_estimators	n_estimators: 100
+*	n_jobs	n_jobs: 1
+@	oob_score	oob_score: False
+@	random_state	random_state: 42
+*	verbose	verbose: 0
+@	warm_start	warm_start: False
+	Note:	@, params eligible for search in searchcv tool.
diff -r 18b39ada6f35 -r 64b771b1471a test-data/get_params12.tabular
--- a/test-data/get_params12.tabular	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/get_params12.tabular	Mon Dec 16 05:17:00 2019 -0500
@@ -1,47 +1,32 @@
 	Parameter	Value
-*	memory	memory: None
-*	steps	"steps: [('rfe', RFE(estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
-       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
-       max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
-       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
-       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
-       silent=True, subsample=1),
-  n_features_to_select=None, step=1, verbose=0))]"
-@	rfe	"rfe: RFE(estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
-       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
-       max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
-       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
-       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
-       silent=True, subsample=1),
-  n_features_to_select=None, step=1, verbose=0)"
-@	rfe__estimator__base_score	rfe__estimator__base_score: 0.5
-@	rfe__estimator__booster	rfe__estimator__booster: 'gbtree'
-@	rfe__estimator__colsample_bylevel	rfe__estimator__colsample_bylevel: 1
-@	rfe__estimator__colsample_bytree	rfe__estimator__colsample_bytree: 1
-@	rfe__estimator__gamma	rfe__estimator__gamma: 0
-@	rfe__estimator__learning_rate	rfe__estimator__learning_rate: 0.1
-@	rfe__estimator__max_delta_step	rfe__estimator__max_delta_step: 0
-@	rfe__estimator__max_depth	rfe__estimator__max_depth: 3
-@	rfe__estimator__min_child_weight	rfe__estimator__min_child_weight: 1
-@	rfe__estimator__missing	rfe__estimator__missing: nan
-@	rfe__estimator__n_estimators	rfe__estimator__n_estimators: 100
-*	rfe__estimator__n_jobs	rfe__estimator__n_jobs: 1
-*	rfe__estimator__nthread	rfe__estimator__nthread: None
-@	rfe__estimator__objective	rfe__estimator__objective: 'reg:linear'
-@	rfe__estimator__random_state	rfe__estimator__random_state: 0
-@	rfe__estimator__reg_alpha	rfe__estimator__reg_alpha: 0
-@	rfe__estimator__reg_lambda	rfe__estimator__reg_lambda: 1
-@	rfe__estimator__scale_pos_weight	rfe__estimator__scale_pos_weight: 1
-@	rfe__estimator__seed	rfe__estimator__seed: None
-@	rfe__estimator__silent	rfe__estimator__silent: True
-@	rfe__estimator__subsample	rfe__estimator__subsample: 1
-@	rfe__estimator	"rfe__estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
+@	estimator	"estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
        colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
        max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
        n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
        reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
        silent=True, subsample=1)"
-@	rfe__n_features_to_select	rfe__n_features_to_select: None
-@	rfe__step	rfe__step: 1
-*	rfe__verbose	rfe__verbose: 0
-	Note:	@, searchable params in searchcv too.
+@	n_features_to_select	n_features_to_select: None
+*	step	step: 1
+*	verbose	verbose: 0
+@	estimator__base_score	estimator__base_score: 0.5
+@	estimator__booster	estimator__booster: 'gbtree'
+@	estimator__colsample_bylevel	estimator__colsample_bylevel: 1
+@	estimator__colsample_bytree	estimator__colsample_bytree: 1
+@	estimator__gamma	estimator__gamma: 0
+@	estimator__learning_rate	estimator__learning_rate: 0.1
+@	estimator__max_delta_step	estimator__max_delta_step: 0
+@	estimator__max_depth	estimator__max_depth: 3
+@	estimator__min_child_weight	estimator__min_child_weight: 1
+@	estimator__missing	estimator__missing: nan
+@	estimator__n_estimators	estimator__n_estimators: 100
+*	estimator__n_jobs	estimator__n_jobs: 1
+*	estimator__nthread	estimator__nthread: None
+@	estimator__objective	estimator__objective: 'reg:linear'
+@	estimator__random_state	estimator__random_state: 0
+@	estimator__reg_alpha	estimator__reg_alpha: 0
+@	estimator__reg_lambda	estimator__reg_lambda: 1
+@	estimator__scale_pos_weight	estimator__scale_pos_weight: 1
+@	estimator__seed	estimator__seed: None
+@	estimator__silent	estimator__silent: True
+@	estimator__subsample	estimator__subsample: 1
+	Note:	@, params eligible for search in searchcv tool.
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_model01
Binary file test-data/glm_model01 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_model02
Binary file test-data/glm_model02 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_model03
Binary file test-data/glm_model03 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_model04
Binary file test-data/glm_model04 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_model05
Binary file test-data/glm_model05 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_model06
Binary file test-data/glm_model06 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_model07
Binary file test-data/glm_model07 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_model08
Binary file test-data/glm_model08 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_result01
--- a/test-data/glm_result01	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/glm_result01	Mon Dec 16 05:17:00 2019 -0500
@@ -1,5 +1,5 @@
-86.97021227350001	1.00532111569	-1.01739601979	-0.613139481654	0.641846874331	3703215242836.872
-91.2021798817	-0.6215229712070001	1.11914889596	0.390012184498	1.28956938152	3875943636708.156
--47.4101632272	-0.638416457964	-0.7327774684530001	-0.8640261049779999	-1.06109770116	-2071574726112.0168
-61.712804630200004	-1.0999480057700002	-0.739679672932	0.585657963012	1.4890682753600002	2642119730255.405
--206.998295124	0.130238853011	0.70574123041	1.3320656526399999	-1.3322092373799999	-8851040854159.11
+86.97021227350001	1.00532111569	-1.01739601979	-0.613139481654	0.641846874331	20479602419382.055
+91.2021798817	-0.6215229712070001	1.11914889596	0.390012184498	1.28956938152	21460309408632.004
+-47.4101632272	-0.638416457964	-0.7327774684530001	-0.8640261049779999	-1.06109770116	-11245419999724.842
+61.712804630200004	-1.0999480057700002	-0.739679672932	0.585657963012	1.4890682753600002	14574106078789.26
+-206.998295124	0.130238853011	0.70574123041	1.3320656526399999	-1.3322092373799999	-48782519807586.32
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_result02
--- a/test-data/glm_result02	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/glm_result02	Mon Dec 16 05:17:00 2019 -0500
@@ -1,5 +1,5 @@
 3.68258022948	2.82110345641	-3.9901407239999998	-1.9523364774	1
 0.015942057224	-0.7119585943469999	0.125502976978	-0.972218263337	0
-2.0869076882499997	0.929399321468	-2.1292408448400004	-1.9971402218799998	0
-1.4132105208399999	0.523750660422	-1.4210539291	-1.49298569451	0
+2.0869076882499997	0.929399321468	-2.1292408448400004	-1.9971402218799998	1
+1.4132105208399999	0.523750660422	-1.4210539291	-1.49298569451	1
 0.7683140439399999	1.38267855169	-0.989045048734	0.649504257894	1
diff -r 18b39ada6f35 -r 64b771b1471a test-data/glm_result08
--- a/test-data/glm_result08	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/glm_result08	Mon Dec 16 05:17:00 2019 -0500
@@ -1,4 +1,4 @@
-3.68258022948	2.82110345641	-3.9901407239999998	-1.9523364774	0
+3.68258022948	2.82110345641	-3.9901407239999998	-1.9523364774	1
 0.015942057224	-0.7119585943469999	0.125502976978	-0.972218263337	0
 2.0869076882499997	0.929399321468	-2.1292408448400004	-1.9971402218799998	0
 1.4132105208399999	0.523750660422	-1.4210539291	-1.49298569451	0
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras01.json
--- a/test-data/keras01.json	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/keras01.json	Mon Dec 16 05:17:00 2019 -0500
@@ -1,1 +1,90 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 784], "dtype": "float32", "units": 32, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "activation": "relu"}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "activation": "softmax"}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
+{
+  "class_name": "Sequential",
+  "config": {
+    "name": "sequential_1",
+    "layers": [
+      {
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_1",
+          "trainable": true,
+          "batch_input_shape": [
+            null,
+            784
+          ],
+          "dtype": "float32",
+          "units": 32,
+          "activation": "linear",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        }
+      },
+      {
+        "class_name": "Activation",
+        "config": {
+          "name": "activation_1",
+          "trainable": true,
+          "dtype": "float32",
+          "activation": "relu"
+        }
+      },
+      {
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_2",
+          "trainable": true,
+          "dtype": "float32",
+          "units": 10,
+          "activation": "linear",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        }
+      },
+      {
+        "class_name": "Activation",
+        "config": {
+          "name": "activation_2",
+          "trainable": true,
+          "dtype": "float32",
+          "activation": "softmax"
+        }
+      }
+    ]
+  },
+  "keras_version": "2.3.1",
+  "backend": "tensorflow"
+}
\ No newline at end of file
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras02.json
--- a/test-data/keras02.json	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/keras02.json	Mon Dec 16 05:17:00 2019 -0500
@@ -1,1 +1,385 @@
-{"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "main_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 100], "dtype": "int32", "sparse": false, "name": "main_input"}, "inbound_nodes": []}, {"name": "embedding_1", "class_name": "Embedding", "config": {"name": "embedding_1", "trainable": true, "batch_input_shape": [null, 100], "dtype": "float32", "input_dim": 10000, "output_dim": 512, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -0.05, "maxval": 0.05, "seed": null}}, "embeddings_regularizer": null, "activity_regularizer": null, "embeddings_constraint": null, "mask_zero": false, "input_length": 100}, "inbound_nodes": [[["main_input", 0, 0, {}]]]}, {"name": "lstm_1", "class_name": "LSTM", "config": {"name": "lstm_1", "trainable": true, "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "units": 32, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 1}, "inbound_nodes": [[["embedding_1", 0, 0, {}]]]}, {"name": "dense_1", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["lstm_1", 0, 0, {}]]]}, {"name": "aux_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 5], "dtype": "float32", "sparse": false, "name": "aux_input"}, "inbound_nodes": []}, {"name": "concatenate_1", "class_name": "Concatenate", "config": {"name": "concatenate_1", "trainable": true, "axis": -1}, "inbound_nodes": [[["dense_1", 0, 0, {}], ["aux_input", 0, 0, {}]]]}, {"name": "dense_2", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["concatenate_1", 0, 0, {}]]]}, {"name": "dense_3", "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_2", 0, 0, {}]]]}, {"name": "dense_4", "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_3", 0, 0, {}]]]}, {"name": "dense_5", "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_4", 0, 0, {}]]]}], "input_layers": [["main_input", 0, 0], ["aux_input", 0, 0]], "output_layers": [["dense_1", 0, 0], ["dense_5", 0, 0]]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
+{
+  "class_name": "Model",
+  "config": {
+    "name": "model_1",
+    "layers": [
+      {
+        "name": "main_input",
+        "class_name": "InputLayer",
+        "config": {
+          "batch_input_shape": [
+            null,
+            100
+          ],
+          "dtype": "int32",
+          "sparse": false,
+          "name": "main_input"
+        },
+        "inbound_nodes": []
+      },
+      {
+        "name": "embedding_1",
+        "class_name": "Embedding",
+        "config": {
+          "name": "embedding_1",
+          "trainable": true,
+          "batch_input_shape": [
+            null,
+            100
+          ],
+          "dtype": "float32",
+          "input_dim": 10000,
+          "output_dim": 512,
+          "embeddings_initializer": {
+            "class_name": "RandomUniform",
+            "config": {
+              "minval": -0.05,
+              "maxval": 0.05,
+              "seed": null
+            }
+          },
+          "embeddings_regularizer": null,
+          "activity_regularizer": null,
+          "embeddings_constraint": null,
+          "mask_zero": false,
+          "input_length": 100
+        },
+        "inbound_nodes": [
+          [
+            [
+              "main_input",
+              0,
+              0,
+              {}
+            ]
+          ]
+        ]
+      },
+      {
+        "name": "lstm_1",
+        "class_name": "LSTM",
+        "config": {
+          "name": "lstm_1",
+          "trainable": true,
+          "dtype": "float32",
+          "return_sequences": false,
+          "return_state": false,
+          "go_backwards": false,
+          "stateful": false,
+          "unroll": false,
+          "units": 32,
+          "activation": "tanh",
+          "recurrent_activation": "sigmoid",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "recurrent_initializer": {
+            "class_name": "Orthogonal",
+            "config": {
+              "gain": 1.0,
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "unit_forget_bias": true,
+          "kernel_regularizer": null,
+          "recurrent_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "recurrent_constraint": null,
+          "bias_constraint": null,
+          "dropout": 0.0,
+          "recurrent_dropout": 0.0,
+          "implementation": 2
+        },
+        "inbound_nodes": [
+          [
+            [
+              "embedding_1",
+              0,
+              0,
+              {}
+            ]
+          ]
+        ]
+      },
+      {
+        "name": "dense_1",
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_1",
+          "trainable": true,
+          "dtype": "float32",
+          "units": 1,
+          "activation": "sigmoid",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        },
+        "inbound_nodes": [
+          [
+            [
+              "lstm_1",
+              0,
+              0,
+              {}
+            ]
+          ]
+        ]
+      },
+      {
+        "name": "aux_input",
+        "class_name": "InputLayer",
+        "config": {
+          "batch_input_shape": [
+            null,
+            5
+          ],
+          "dtype": "float32",
+          "sparse": false,
+          "name": "aux_input"
+        },
+        "inbound_nodes": []
+      },
+      {
+        "name": "concatenate_1",
+        "class_name": "Concatenate",
+        "config": {
+          "name": "concatenate_1",
+          "trainable": true,
+          "dtype": "float32",
+          "axis": -1
+        },
+        "inbound_nodes": [
+          [
+            [
+              "dense_1",
+              0,
+              0,
+              {}
+            ],
+            [
+              "aux_input",
+              0,
+              0,
+              {}
+            ]
+          ]
+        ]
+      },
+      {
+        "name": "dense_2",
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_2",
+          "trainable": true,
+          "dtype": "float32",
+          "units": 64,
+          "activation": "relu",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        },
+        "inbound_nodes": [
+          [
+            [
+              "concatenate_1",
+              0,
+              0,
+              {}
+            ]
+          ]
+        ]
+      },
+      {
+        "name": "dense_3",
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_3",
+          "trainable": true,
+          "dtype": "float32",
+          "units": 64,
+          "activation": "relu",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        },
+        "inbound_nodes": [
+          [
+            [
+              "dense_2",
+              0,
+              0,
+              {}
+            ]
+          ]
+        ]
+      },
+      {
+        "name": "dense_4",
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_4",
+          "trainable": true,
+          "dtype": "float32",
+          "units": 64,
+          "activation": "relu",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        },
+        "inbound_nodes": [
+          [
+            [
+              "dense_3",
+              0,
+              0,
+              {}
+            ]
+          ]
+        ]
+      },
+      {
+        "name": "dense_5",
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_5",
+          "trainable": true,
+          "dtype": "float32",
+          "units": 1,
+          "activation": "sigmoid",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        },
+        "inbound_nodes": [
+          [
+            [
+              "dense_4",
+              0,
+              0,
+              {}
+            ]
+          ]
+        ]
+      }
+    ],
+    "input_layers": [
+      [
+        "main_input",
+        0,
+        0
+      ],
+      [
+        "aux_input",
+        0,
+        0
+      ]
+    ],
+    "output_layers": [
+      [
+        "dense_1",
+        0,
+        0
+      ],
+      [
+        "dense_5",
+        0,
+        0
+      ]
+    ]
+  },
+  "keras_version": "2.3.1",
+  "backend": "tensorflow"
+}
\ No newline at end of file
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras04.json
--- a/test-data/keras04.json	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/keras04.json	Mon Dec 16 05:17:00 2019 -0500
@@ -1,1 +1,90 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 17], "dtype": "float32", "units": 32, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "activation": "linear"}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "activation": "linear"}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
+{
+  "class_name": "Sequential",
+  "config": {
+    "name": "sequential_1",
+    "layers": [
+      {
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_1",
+          "trainable": true,
+          "batch_input_shape": [
+            null,
+            17
+          ],
+          "dtype": "float32",
+          "units": 32,
+          "activation": "linear",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        }
+      },
+      {
+        "class_name": "Activation",
+        "config": {
+          "name": "activation_1",
+          "trainable": true,
+          "dtype": "float32",
+          "activation": "linear"
+        }
+      },
+      {
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_2",
+          "trainable": true,
+          "dtype": "float32",
+          "units": 1,
+          "activation": "linear",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        }
+      },
+      {
+        "class_name": "Activation",
+        "config": {
+          "name": "activation_2",
+          "trainable": true,
+          "dtype": "float32",
+          "activation": "linear"
+        }
+      }
+    ]
+  },
+  "keras_version": "2.3.1",
+  "backend": "tensorflow"
+}
\ No newline at end of file
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_batch_model01
Binary file test-data/keras_batch_model01 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_batch_model02
Binary file test-data/keras_batch_model02 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_batch_model04
Binary file test-data/keras_batch_model04 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_batch_params01.tabular
--- a/test-data/keras_batch_params01.tabular	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/keras_batch_params01.tabular	Mon Dec 16 05:17:00 2019 -0500
@@ -6,15 +6,14 @@
 @	callbacks	callbacks: [{'callback_selection': {'callback_type': 'None'}}]
 @	class_positive_factor	class_positive_factor: 1.0
 @	config	config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable
-@	data_batch_generator	"data_batch_generator: FastaDNABatchGenerator(fasta_path='to_be_determined', seed=999,
-            seq_length=1000, shuffle=True)"
+@	data_batch_generator	"data_batch_generator: FastaDNABatchGenerator(fasta_path='to_be_determined', seed=999, seq_length=1000,
+                       shuffle=True)"
 @	decay	decay: 0.0
 @	epochs	epochs: 100
-@	epsilon	epsilon: None
 @	layers_0_Dense	layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None,
-@	layers_1_Activation	layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'activation': 're
-@	layers_2_Dense	layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'units': 10, 'activation': 
-@	layers_3_Activation	layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'activation': 'so
+@	layers_1_Activation	layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32
+@	layers_2_Dense	layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units'
+@	layers_3_Activation	layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32
 @	loss	loss: 'binary_crossentropy'
 @	lr	lr: 0.01
 @	metrics	metrics: ['acc']
@@ -60,12 +59,13 @@
 @	layers_0_Dense__config__units	layers_0_Dense__config__units: 32
 @	layers_0_Dense__config__use_bias	layers_0_Dense__config__use_bias: True
 *	layers_1_Activation__class_name	layers_1_Activation__class_name: 'Activation'
-@	layers_1_Activation__config	layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'activation': 'relu'}
+@	layers_1_Activation__config	layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}
 @	layers_1_Activation__config__activation	layers_1_Activation__config__activation: 'relu'
+@	layers_1_Activation__config__dtype	layers_1_Activation__config__dtype: 'float32'
 *	layers_1_Activation__config__name	layers_1_Activation__config__name: 'activation_1'
 @	layers_1_Activation__config__trainable	layers_1_Activation__config__trainable: True
 *	layers_2_Dense__class_name	layers_2_Dense__class_name: 'Dense'
-@	layers_2_Dense__config	layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'units': 10, 'activation': 'linear', 'use_bias': True, 'kerne
+@	layers_2_Dense__config	layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'linear', 'use
 @	layers_2_Dense__config__activation	layers_2_Dense__config__activation: 'linear'
 @	layers_2_Dense__config__activity_regularizer	layers_2_Dense__config__activity_regularizer: None
 @	layers_2_Dense__config__bias_constraint	layers_2_Dense__config__bias_constraint: None
@@ -73,6 +73,7 @@
 *	layers_2_Dense__config__bias_initializer__class_name	layers_2_Dense__config__bias_initializer__class_name: 'Zeros'
 @	layers_2_Dense__config__bias_initializer__config	layers_2_Dense__config__bias_initializer__config: {}
 @	layers_2_Dense__config__bias_regularizer	layers_2_Dense__config__bias_regularizer: None
+@	layers_2_Dense__config__dtype	layers_2_Dense__config__dtype: 'float32'
 @	layers_2_Dense__config__kernel_constraint	layers_2_Dense__config__kernel_constraint: None
 @	layers_2_Dense__config__kernel_initializer	layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
 *	layers_2_Dense__config__kernel_initializer__class_name	layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
@@ -87,8 +88,9 @@
 @	layers_2_Dense__config__units	layers_2_Dense__config__units: 10
 @	layers_2_Dense__config__use_bias	layers_2_Dense__config__use_bias: True
 *	layers_3_Activation__class_name	layers_3_Activation__class_name: 'Activation'
-@	layers_3_Activation__config	layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'activation': 'softmax'}
+@	layers_3_Activation__config	layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'softmax'}
 @	layers_3_Activation__config__activation	layers_3_Activation__config__activation: 'softmax'
+@	layers_3_Activation__config__dtype	layers_3_Activation__config__dtype: 'float32'
 *	layers_3_Activation__config__name	layers_3_Activation__config__name: 'activation_2'
 @	layers_3_Activation__config__trainable	layers_3_Activation__config__trainable: True
 	Note:	@, params eligible for search in searchcv tool.
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_batch_params04.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_batch_params04.tabular	Mon Dec 16 05:17:00 2019 -0500
@@ -0,0 +1,91 @@
+	Parameter	Value
+@	amsgrad	amsgrad: None
+@	batch_size	batch_size: 32
+@	beta_1	beta_1: None
+@	beta_2	beta_2: None
+@	callbacks	callbacks: [{'callback_selection': {'callback_type': 'None'}}]
+@	class_positive_factor	class_positive_factor: 1.0
+@	config	config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable
+@	data_batch_generator	data_batch_generator: None
+@	decay	decay: 0.0
+@	epochs	epochs: 100
+@	layers_0_Dense	layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None,
+@	layers_1_Activation	layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32
+@	layers_2_Dense	layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units'
+@	layers_3_Activation	layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32
+@	loss	loss: 'binary_crossentropy'
+@	lr	lr: 0.01
+@	metrics	metrics: ['acc']
+@	model_type	model_type: 'sequential'
+@	momentum	momentum: 0.0
+*	n_jobs	n_jobs: 1
+@	nesterov	nesterov: False
+@	optimizer	optimizer: 'sgd'
+@	prediction_steps	prediction_steps: None
+@	rho	rho: None
+@	schedule_decay	schedule_decay: None
+@	seed	seed: None
+@	steps_per_epoch	steps_per_epoch: None
+@	validation_data	validation_data: None
+@	validation_steps	validation_steps: None
+@	verbose	verbose: 0
+*	layers_0_Dense__class_name	layers_0_Dense__class_name: 'Dense'
+@	layers_0_Dense__config	layers_0_Dense__config: {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, 784], 'dtype': 'float32', 'units'
+@	layers_0_Dense__config__activation	layers_0_Dense__config__activation: 'linear'
+@	layers_0_Dense__config__activity_regularizer	layers_0_Dense__config__activity_regularizer: None
+@	layers_0_Dense__config__batch_input_shape	layers_0_Dense__config__batch_input_shape: [None, 784]
+@	layers_0_Dense__config__bias_constraint	layers_0_Dense__config__bias_constraint: None
+@	layers_0_Dense__config__bias_initializer	layers_0_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}}
+*	layers_0_Dense__config__bias_initializer__class_name	layers_0_Dense__config__bias_initializer__class_name: 'Zeros'
+@	layers_0_Dense__config__bias_initializer__config	layers_0_Dense__config__bias_initializer__config: {}
+@	layers_0_Dense__config__bias_regularizer	layers_0_Dense__config__bias_regularizer: None
+@	layers_0_Dense__config__dtype	layers_0_Dense__config__dtype: 'float32'
+@	layers_0_Dense__config__kernel_constraint	layers_0_Dense__config__kernel_constraint: None
+@	layers_0_Dense__config__kernel_initializer	layers_0_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
+*	layers_0_Dense__config__kernel_initializer__class_name	layers_0_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
+@	layers_0_Dense__config__kernel_initializer__config	layers_0_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}
+@	layers_0_Dense__config__kernel_initializer__config__distribution	layers_0_Dense__config__kernel_initializer__config__distribution: 'uniform'
+@	layers_0_Dense__config__kernel_initializer__config__mode	layers_0_Dense__config__kernel_initializer__config__mode: 'fan_avg'
+@	layers_0_Dense__config__kernel_initializer__config__scale	layers_0_Dense__config__kernel_initializer__config__scale: 1.0
+@	layers_0_Dense__config__kernel_initializer__config__seed	layers_0_Dense__config__kernel_initializer__config__seed: None
+@	layers_0_Dense__config__kernel_regularizer	layers_0_Dense__config__kernel_regularizer: None
+*	layers_0_Dense__config__name	layers_0_Dense__config__name: 'dense_1'
+@	layers_0_Dense__config__trainable	layers_0_Dense__config__trainable: True
+@	layers_0_Dense__config__units	layers_0_Dense__config__units: 32
+@	layers_0_Dense__config__use_bias	layers_0_Dense__config__use_bias: True
+*	layers_1_Activation__class_name	layers_1_Activation__class_name: 'Activation'
+@	layers_1_Activation__config	layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}
+@	layers_1_Activation__config__activation	layers_1_Activation__config__activation: 'relu'
+@	layers_1_Activation__config__dtype	layers_1_Activation__config__dtype: 'float32'
+*	layers_1_Activation__config__name	layers_1_Activation__config__name: 'activation_1'
+@	layers_1_Activation__config__trainable	layers_1_Activation__config__trainable: True
+*	layers_2_Dense__class_name	layers_2_Dense__class_name: 'Dense'
+@	layers_2_Dense__config	layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'linear', 'use
+@	layers_2_Dense__config__activation	layers_2_Dense__config__activation: 'linear'
+@	layers_2_Dense__config__activity_regularizer	layers_2_Dense__config__activity_regularizer: None
+@	layers_2_Dense__config__bias_constraint	layers_2_Dense__config__bias_constraint: None
+@	layers_2_Dense__config__bias_initializer	layers_2_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}}
+*	layers_2_Dense__config__bias_initializer__class_name	layers_2_Dense__config__bias_initializer__class_name: 'Zeros'
+@	layers_2_Dense__config__bias_initializer__config	layers_2_Dense__config__bias_initializer__config: {}
+@	layers_2_Dense__config__bias_regularizer	layers_2_Dense__config__bias_regularizer: None
+@	layers_2_Dense__config__dtype	layers_2_Dense__config__dtype: 'float32'
+@	layers_2_Dense__config__kernel_constraint	layers_2_Dense__config__kernel_constraint: None
+@	layers_2_Dense__config__kernel_initializer	layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
+*	layers_2_Dense__config__kernel_initializer__class_name	layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
+@	layers_2_Dense__config__kernel_initializer__config	layers_2_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}
+@	layers_2_Dense__config__kernel_initializer__config__distribution	layers_2_Dense__config__kernel_initializer__config__distribution: 'uniform'
+@	layers_2_Dense__config__kernel_initializer__config__mode	layers_2_Dense__config__kernel_initializer__config__mode: 'fan_avg'
+@	layers_2_Dense__config__kernel_initializer__config__scale	layers_2_Dense__config__kernel_initializer__config__scale: 1.0
+@	layers_2_Dense__config__kernel_initializer__config__seed	layers_2_Dense__config__kernel_initializer__config__seed: None
+@	layers_2_Dense__config__kernel_regularizer	layers_2_Dense__config__kernel_regularizer: None
+*	layers_2_Dense__config__name	layers_2_Dense__config__name: 'dense_2'
+@	layers_2_Dense__config__trainable	layers_2_Dense__config__trainable: True
+@	layers_2_Dense__config__units	layers_2_Dense__config__units: 10
+@	layers_2_Dense__config__use_bias	layers_2_Dense__config__use_bias: True
+*	layers_3_Activation__class_name	layers_3_Activation__class_name: 'Activation'
+@	layers_3_Activation__config	layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'softmax'}
+@	layers_3_Activation__config__activation	layers_3_Activation__config__activation: 'softmax'
+@	layers_3_Activation__config__dtype	layers_3_Activation__config__dtype: 'float32'
+*	layers_3_Activation__config__name	layers_3_Activation__config__name: 'activation_2'
+@	layers_3_Activation__config__trainable	layers_3_Activation__config__trainable: True
+	Note:	@, params eligible for search in searchcv tool.
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_model01
Binary file test-data/keras_model01 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_model02
Binary file test-data/keras_model02 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_model04
Binary file test-data/keras_model04 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_params04.tabular
--- a/test-data/keras_params04.tabular	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/keras_params04.tabular	Mon Dec 16 05:17:00 2019 -0500
@@ -7,11 +7,10 @@
 @	config	config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable
 @	decay	decay: 0.0
 @	epochs	epochs: 100
-@	epsilon	epsilon: None
 @	layers_0_Dense	layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None,
-@	layers_1_Activation	layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'activation': 'li
-@	layers_2_Dense	layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'units': 1, 'activation': '
-@	layers_3_Activation	layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'activation': 'li
+@	layers_1_Activation	layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32
+@	layers_2_Dense	layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units'
+@	layers_3_Activation	layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32
 @	loss	loss: 'mean_squared_error'
 @	lr	lr: 0.001
 @	metrics	metrics: ['mse']
@@ -51,12 +50,13 @@
 @	layers_0_Dense__config__units	layers_0_Dense__config__units: 32
 @	layers_0_Dense__config__use_bias	layers_0_Dense__config__use_bias: True
 *	layers_1_Activation__class_name	layers_1_Activation__class_name: 'Activation'
-@	layers_1_Activation__config	layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'activation': 'linear'}
+@	layers_1_Activation__config	layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'linear'}
 @	layers_1_Activation__config__activation	layers_1_Activation__config__activation: 'linear'
+@	layers_1_Activation__config__dtype	layers_1_Activation__config__dtype: 'float32'
 *	layers_1_Activation__config__name	layers_1_Activation__config__name: 'activation_1'
 @	layers_1_Activation__config__trainable	layers_1_Activation__config__trainable: True
 *	layers_2_Dense__class_name	layers_2_Dense__class_name: 'Dense'
-@	layers_2_Dense__config	layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel
+@	layers_2_Dense__config	layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_
 @	layers_2_Dense__config__activation	layers_2_Dense__config__activation: 'linear'
 @	layers_2_Dense__config__activity_regularizer	layers_2_Dense__config__activity_regularizer: None
 @	layers_2_Dense__config__bias_constraint	layers_2_Dense__config__bias_constraint: None
@@ -64,6 +64,7 @@
 *	layers_2_Dense__config__bias_initializer__class_name	layers_2_Dense__config__bias_initializer__class_name: 'Zeros'
 @	layers_2_Dense__config__bias_initializer__config	layers_2_Dense__config__bias_initializer__config: {}
 @	layers_2_Dense__config__bias_regularizer	layers_2_Dense__config__bias_regularizer: None
+@	layers_2_Dense__config__dtype	layers_2_Dense__config__dtype: 'float32'
 @	layers_2_Dense__config__kernel_constraint	layers_2_Dense__config__kernel_constraint: None
 @	layers_2_Dense__config__kernel_initializer	layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
 *	layers_2_Dense__config__kernel_initializer__class_name	layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
@@ -78,8 +79,9 @@
 @	layers_2_Dense__config__units	layers_2_Dense__config__units: 1
 @	layers_2_Dense__config__use_bias	layers_2_Dense__config__use_bias: True
 *	layers_3_Activation__class_name	layers_3_Activation__class_name: 'Activation'
-@	layers_3_Activation__config	layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'activation': 'linear'}
+@	layers_3_Activation__config	layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'linear'}
 @	layers_3_Activation__config__activation	layers_3_Activation__config__activation: 'linear'
+@	layers_3_Activation__config__dtype	layers_3_Activation__config__dtype: 'float32'
 *	layers_3_Activation__config__name	layers_3_Activation__config__name: 'activation_2'
 @	layers_3_Activation__config__trainable	layers_3_Activation__config__trainable: True
 	Note:	@, params eligible for search in searchcv tool.
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_prefitted01.zip
Binary file test-data/keras_prefitted01.zip has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_save_weights01.h5
Binary file test-data/keras_save_weights01.h5 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/keras_train_eval_y_true02.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_train_eval_y_true02.tabular	Mon Dec 16 05:17:00 2019 -0500
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diff -r 18b39ada6f35 -r 64b771b1471a test-data/lda_model01
Binary file test-data/lda_model01 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/lda_model02
Binary file test-data/lda_model02 has changed
diff -r 18b39ada6f35 -r 64b771b1471a test-data/ml_vis01.html
--- a/test-data/ml_vis01.html	Thu Nov 07 05:25:28 2019 -0500
+++ b/test-data/ml_vis01.html	Mon Dec 16 05:17:00 2019 -0500
@@ -1,14 +1,31 @@
-<html><head><meta charset="utf-8" /></head><body><script type="text/javascript">/**
-* plotly.js v1.39.4
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+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>
+        
+                <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script type="text/javascript">/**
+* plotly.js v1.51.1
+* Copyright 2012-2019, Plotly, Inc.
 * All rights reserved.
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-!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}(function(){return function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module '"+o+"'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,function(t){var r=e[o][1][t];return i(r||t)},u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}}()({1:[function(t,e,r){"use strict";var n=t("../src/lib"),i={"X,X div":"direction:ltr;font-family:'Open Sans', verdana, arial, sans-serif;margin:0;padding:0;","X input,X button":"font-family:'Open Sans', verdana, arial, sans-serif;","X input:focus,X 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r=s(e.changedTouches[0],t);y(1,r[0],r[1],v),e.preventDefault()},!!l&&{passive:!1}),t.addEventListener("touchend",function(e){s(e.changedTouches[0],t),y(0,g,m,v),e.preventDefault()},!!l&&{passive:!1}),o(t,function(t,e,r){var i=d.flipX?1:-1,a=d.flipY?1:-1,o=n();if(Math.abs(t)>Math.abs(e))c.rotate(o,0,0,-t*i*Math.PI*d.rotateSpeed/window.innerWidth);else{var s=d.zoomSpeed*a*e/window.innerHeight*(o-c.lastT())/100;c.pan(o,0,0,h*(Math.exp(s)-1))}},!0),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":43,"has-passive-events":379,"mouse-change":402,"mouse-event-offset":403,"mouse-wheel":405,"right-now":465}],43:[function(t,e,r){"use strict";e.exports=function(t){var e=(t=t||{}).eye||[0,0,1],r=t.center||[0,0,0],s=t.up||[0,1,0],l=t.distanceLimits||[0,1/0],c=t.mode||"turntable",u=n(),h=i(),f=a();return u.setDistanceLimits(l[0],l[1]),u.lookAt(0,e,r,s),h.setDistanceLimits(l[0],l[1]),h.lookAt(0,e,r,s),f.setDistanceLimits(l[0],l[1]),f.lookAt(0,e,r,s),new o({turntable:u,orbit:h,matrix:f},c)};var n=t("turntable-camera-controller"),i=t("orbit-camera-controller"),a=t("matrix-camera-controller");function o(t,e){this._controllerNames=Object.keys(t),this._controllerList=this._controllerNames.map(function(e){return t[e]}),this._mode=e,this._active=t[e],this._active||(this._mode="turntable",this._active=t.turntable),this.modes=this._controllerNames,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}var s=o.prototype;[["flush",1],["idle",1],["lookAt",4],["rotate",4],["pan",4],["translate",4],["setMatrix",2],["setDistanceLimits",2],["setDistance",2]].forEach(function(t){for(var e=t[0],r=[],n=0;n<t[1];++n)r.push("a"+n);var i="var cc=this._controllerList;for(var i=0;i<cc.length;++i){cc[i]."+t[0]+"("+r.join()+")}";s[e]=Function.apply(null,r.concat(i))}),s.recalcMatrix=function(t){this._active.recalcMatrix(t)},s.getDistance=function(t){return this._active.getDistance(t)},s.getDistanceLimits=function(t){return this._active.getDistanceLimits(t)},s.lastT=function(){return this._active.lastT()},s.setMode=function(t){if(t!==this._mode){var e=this._controllerNames.indexOf(t);if(!(e<0)){var r=this._active,n=this._controllerList[e],i=Math.max(r.lastT(),n.lastT());r.recalcMatrix(i),n.setMatrix(i,r.computedMatrix),this._active=n,this._mode=t,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}}},s.getMode=function(){return this._mode}},{"matrix-camera-controller":400,"orbit-camera-controller":423,"turntable-camera-controller":504}],44:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),h=t("parse-rect"),f=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),g=t("detect-kerning"),m=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=new s,b=!1;if(document.body){var _=document.body.appendChild(document.createElement("div"));_.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(_).fontStretch&&(b=!0),document.body.removeChild(_)}var w=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=x.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),x.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(f(t)?t:{})};w.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop("count"),offset:t.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this("sizeBuffer")},width:{offset:0,stride:8,buffer:t.this("sizeBuffer")},char:t.this("charBuffer"),position:t.this("position")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop("color"),opacity:t.prop("opacity"),viewport:t.this("viewportArray"),scale:t.this("scale"),align:t.prop("align"),baseline:t.prop("baseline"),translate:t.this("translate"),positionOffset:t.prop("positionOffset")},primitive:"points",viewport:t.this("viewport"),vert:"\n\t\t\tprecision highp float;\n\t\t\tattribute float width, charOffset, char;\n\t\t\tattribute vec2 position;\n\t\t\tuniform float fontSize, charStep, em, align, baseline;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform vec4 color;\n\t\t\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvoid main () {\n\t\t\t\t"+(w.normalViewport?"":"vec2 positionOffset = vec2(positionOffset.x,- positionOffset.y);")+"\n\n\t\t\t\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\n\t\t\t\t\t+ positionOffset))\n\t\t\t\t\t/ (viewport.zw * scale.xy);\n\n\t\t\t\tvec2 position = (position + translate) * scale;\n\t\t\t\tposition += offset * scale;\n\n\t\t\t\t"+(w.normalViewport?"position.y = 1. - position.y;":"")+"\n\n\t\t\t\tcharCoord = position * viewport.zw + viewport.xy;\n\n\t\t\t\tgl_Position = vec4(position * 2. - 1., 0, 1);\n\n\t\t\t\tgl_PointSize = charStep;\n\n\t\t\t\tcharId.x = mod(char, atlasDim.x);\n\t\t\t\tcharId.y = floor(char / atlasDim.x);\n\n\t\t\t\tcharWidth = width * em;\n\n\t\t\t\tfontColor = color / 255.;\n\t\t\t}",frag:"\n\t\t\tprecision highp float;\n\t\t\tuniform sampler2D atlas;\n\t\t\tuniform float fontSize, charStep, opacity;\n\t\t\tuniform vec2 atlasSize;\n\t\t\tuniform vec4 viewport;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\n\t\t\tfloat lightness(vec4 color) {\n\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n\t\t\t}\n\n\t\t\tvoid main () {\n\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n\n\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n\t\t\t\tuv.y = charStep - uv.y;\n\n\t\t\t\t// ignore points outside of character bounding box\n\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n\n\t\t\t\tuv += charId * charStep;\n\t\t\t\tuv = uv / atlasSize;\n\n\t\t\t\tvec4 color = fontColor;\n\t\t\t\tvec4 mask = texture2D(atlas, uv);\n\n\t\t\t\tfloat maskY = lightness(mask);\n\t\t\t\t// float colorY = lightness(color);\n\t\t\t\tcolor.a *= maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},w.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map(function(t){return parseFloat(t)}):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=h(t.viewport),w.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&("number"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=w.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach(function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(w.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:w.baseFontSize,family:t.family,stretch:b?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=w.fonts[i],e.font[r]))){var c=t.family.join(", "),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),b&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:"top",fontSize:w.baseFontSize,fontStyle:u.join(" ")})},w.fonts[i]=e.font[r]}}),(a||o)&&this.font.forEach(function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:b?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)}),"string"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),f=0;f<s.length;f++)s[f]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var x=1;x<t.text.length;x++)e.textOffsets[x]=e.textOffsets[x-1]+t.text[x-1].length,e.count+=t.text[x].length,e.counts.push(t.text[x].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach(function(t,n){w.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=w.atlasContext.measureText(o).width/w.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);m(t.kerning,g(t.family,{pairs:s}))}}})}if(t.position)if(t.position.length>2){for(var _=!t.position[0].length,k=u.mallocFloat(2*this.count),M=0,A=0;M<this.counts.length;M++){var T=e.counts[M];if(_)for(var S=0;S<T;S++)k[A++]=t.position[2*M],k[A++]=t.position[2*M+1];else for(var E=0;E<T;E++)k[A++]=t.position[M][0],k[A++]=t.position[M][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),L=u.mallocFloat(2*this.count);this.textWidth=[];for(var z=0,P=0;z<this.counts.length;z++){for(var I=e.counts[z],O=e.font[z]||e.font[0],D=e.fontAtlas[z]||e.fontAtlas[0],R=0;R<I;R++){var B=e.text.charAt(P),F=e.text.charAt(P-1);if(C[P]=D.ids[B],L[2*P]=O.width[B],R){var N=L[2*P-2],j=L[2*P],V=L[2*P-1]+.5*N+.5*j;if(e.kerning){var U=O.kerning[F+B];U&&(V+=.001*U)}L[2*P+1]=V}else L[2*P+1]=.5*L[2*P];P++}e.textWidth.push(L.length?.5*L[2*P-2]+L[2*P-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:"uint8",usage:"stream"}),this.sizeBuffer({data:L,type:"float",usage:"stream"}),u.freeUint8(C),u.freeFloat(L),r.length&&this.font.forEach(function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(w.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=o*i,u=s*i;n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:w.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})})}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map(function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if("number"==typeof n)return n;switch(n){case"right":case"end":return-t;case"center":case"centre":case"middle":return.5*-t}return 0})),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map(function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+="number"==typeof t?t-n.baseline:-n[t],w.normalViewport||(i*=-1),i})),null!=t.color)if(t.color||(t.color="transparent"),"string"!=typeof t.color&&isNaN(t.color)){var q;if("number"==typeof t.color[0]&&t.color.length>this.counts.length){var H=t.color.length;q=u.mallocUint8(H);for(var G=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<H;W+=4)q.set(l(G(W,W+4),"uint8"),W)}else{var Y=t.color.length;q=u.mallocUint8(4*Y);for(var X=0;X<Y;X++)q.set(l(t.color[X]||0,"uint8"),4*X)}this.color=q}else this.color=l(t.color,"uint8");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var Z=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(Z);for(var 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if("le"===r)for(i=0,a=0;i<t.length;i+=3)o=t[i]|t[i+1]<<8|t[i+2]<<16,this.words[a]|=o<<s&67108863,this.words[a+1]=o>>>26-s&67108863,(s+=24)>=26&&(s-=26,a++);return this.strip()},a.prototype._parseHex=function(t,e){this.length=Math.ceil((t.length-e)/6),this.words=new Array(this.length);for(var r=0;r<this.length;r++)this.words[r]=0;var n,i,a=0;for(r=t.length-6,n=0;r>=e;r-=6)i=s(t,r,r+6),this.words[n]|=i<<a&67108863,this.words[n+1]|=i>>>26-a&4194303,(a+=24)>=26&&(a-=26,n++);r+6!==e&&(i=s(t,e,r+6),this.words[n]|=i<<a&67108863,this.words[n+1]|=i>>>26-a&4194303),this.strip()},a.prototype._parseBase=function(t,e,r){this.words=[0],this.length=1;for(var n=0,i=1;i<=67108863;i*=e)n++;n--,i=i/e|0;for(var a=t.length-r,o=a%n,s=Math.min(a,a-o)+r,c=0,u=r;u<s;u+=n)c=l(t,u,u+n,e),this.imuln(i),this.words[0]+c<67108864?this.words[0]+=c:this._iaddn(c);if(0!==o){var 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)}},a.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},a.prototype.clone=function(){var t=new a(null);return this.copy(t),t},a.prototype._expand=function(t){for(;this.length<t;)this.words[this.length++]=0;return this},a.prototype.strip=function(){for(;this.length>1&&0===this.words[this.length-1];)this.length--;return this._normSign()},a.prototype._normSign=function(){return 1===this.length&&0===this.words[0]&&(this.negative=0),this},a.prototype.inspect=function(){return(this.red?"<BN-R: ":"<BN: ")+this.toString(16)+">"};var 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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=(i=0|t.words[d])*(a=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()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||"hex"===t){r="";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!==(a=s>>>24-i&16777215)||o!==this.length-1?c[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.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 should be between 2 and 36")},a.prototype.toNumber=function(){var t=this.words[0];return 2===this.length?t+=67108864*this.words[1]:3===this.length&&1===this.words[2]?t+=4503599627370496+67108864*this.words[1]:this.length>2&&n(!1,"Number can only safely store up to 53 bits"),0!==this.negative?-t:t},a.prototype.toJSON=function(){return this.toString(16)},a.prototype.toBuffer=function(t,e){return n("undefined"!=typeof o),this.toArrayLike(o,t,e)},a.prototype.toArray=function(t,e){return this.toArrayLike(Array,t,e)},a.prototype.toArrayLike=function(t,e,r){var i=this.byteLength(),a=r||Math.max(1,i);n(i<=a,"byte array longer than desired length"),n(a>0,"Requested array length <= 0"),this.strip();var o,s,l="le"===e,c=new t(a),u=this.clone();if(l){for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[s]=o;for(;s<a;s++)c[s]=0}else{for(s=0;s<a-i;s++)c[s]=0;for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[a-s-1]=o}return c},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return 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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()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.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 i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n("number"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.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 i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.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)},a.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,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&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()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var 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vt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(i=(i=i+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((a=a+Math.imul(f,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,V),i=(i=Math.imul(v,U))+Math.imul(y,V)|0,a=Math.imul(y,U),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,H)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(h,Y)|0)|0)+((8191&(i=(i=i+Math.imul(h,X)|0)+Math.imul(f,Y)|0))<<13)|0;c=((a=a+Math.imul(f,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,V),i=(i=Math.imul(b,U))+Math.imul(_,V)|0,a=Math.imul(_,U),n=n+Math.imul(v,H)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,H)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,Y)|0,a=a+Math.imul(g,X)|0;var 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wt=(c+(n=n+Math.imul(h,at)|0)|0)+((8191&(i=(i=i+Math.imul(h,ot)|0)+Math.imul(f,at)|0))<<13)|0;c=((a=a+Math.imul(f,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,V),i=(i=Math.imul(P,U))+Math.imul(I,V)|0,a=Math.imul(I,U),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(k,$)|0,i=(i=i+Math.imul(k,J)|0)+Math.imul(M,$)|0,a=a+Math.imul(M,J)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var kt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(i=(i=i+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((a=a+Math.imul(f,ct)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(D,V),i=(i=Math.imul(D,U))+Math.imul(R,V)|0,a=Math.imul(R,U),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(I,H)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(C,Y)|0,i=(i=i+Math.imul(C,X)|0)+Math.imul(L,Y)|0,a=a+Math.imul(L,X)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(k,Q)|0,i=(i=i+Math.imul(k,tt)|0)+Math.imul(M,Q)|0,a=a+Math.imul(M,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var Mt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(i=(i=i+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((a=a+Math.imul(f,ft)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(F,V),i=(i=Math.imul(F,U))+Math.imul(N,V)|0,a=Math.imul(N,U),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,Y)|0,i=(i=i+Math.imul(P,X)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,X)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,J)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(k,rt)|0,i=(i=i+Math.imul(k,nt)|0)+Math.imul(M,rt)|0,a=a+Math.imul(M,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ht)|0,i=(i=i+Math.imul(d,ft)|0)+Math.imul(g,ht)|0,a=a+Math.imul(g,ft)|0;var At=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(i=(i=i+Math.imul(h,gt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((a=a+Math.imul(f,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(F,H),i=(i=Math.imul(F,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,Y)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(P,$)|0,i=(i=i+Math.imul(P,J)|0)+Math.imul(I,$)|0,a=a+Math.imul(I,J)|0,n=n+Math.imul(C,Q)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,Q)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(k,at)|0,i=(i=i+Math.imul(k,ot)|0)+Math.imul(M,at)|0,a=a+Math.imul(M,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ht)|0,i=(i=i+Math.imul(v,ft)|0)+Math.imul(y,ht)|0,a=a+Math.imul(y,ft)|0;var Tt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(F,Y),i=(i=Math.imul(F,X))+Math.imul(N,Y)|0,a=Math.imul(N,X),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,J)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(P,Q)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(k,lt)|0,i=(i=i+Math.imul(k,ct)|0)+Math.imul(M,lt)|0,a=a+Math.imul(M,ct)|0,n=n+Math.imul(b,ht)|0,i=(i=i+Math.imul(b,ft)|0)+Math.imul(_,ht)|0,a=a+Math.imul(_,ft)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(F,$),i=(i=Math.imul(F,J))+Math.imul(N,$)|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(k,ht)|0,i=(i=i+Math.imul(k,ft)|0)+Math.imul(M,ht)|0,a=a+Math.imul(M,ft)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(F,Q),i=(i=Math.imul(F,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(T,ht)|0,i=(i=i+Math.imul(T,ft)|0)+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0;var Ct=(c+(n=n+Math.imul(k,dt)|0)|0)+((8191&(i=(i=i+Math.imul(k,gt)|0)+Math.imul(M,dt)|0))<<13)|0;c=((a=a+Math.imul(M,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(F,rt),i=(i=Math.imul(F,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ct)|0,n=n+Math.imul(C,ht)|0,i=(i=i+Math.imul(C,ft)|0)+Math.imul(L,ht)|0,a=a+Math.imul(L,ft)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(F,at),i=(i=Math.imul(F,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(P,ht)|0,i=(i=i+Math.imul(P,ft)|0)+Math.imul(I,ht)|0,a=a+Math.imul(I,ft)|0;var zt=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,gt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,gt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,n=Math.imul(F,lt),i=(i=Math.imul(F,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,i=(i=i+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0;var Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,gt)|0)+Math.imul(I,dt)|0))<<13)|0;c=((a=a+Math.imul(I,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(F,ht),i=(i=Math.imul(F,ft))+Math.imul(N,ht)|0,a=Math.imul(N,ft);var It=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var Ot=(c+(n=Math.imul(F,dt))|0)+((8191&(i=(i=Math.imul(F,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=kt,l[8]=Mt,l[9]=At,l[10]=Tt,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=zt,l[16]=Pt,l[17]=It,l[18]=Ot,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),a.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,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,h=(0|t.words[u])*(0|e.words[c]),f=67108863&h;s=67108863&(f=f+s|0),i+=(o=(o=o+(h/67108864|0)|0)+(f>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.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,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var h=l,f=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=h*m-f*v;v=h*v+f*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,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,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},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),i=this.makeRBT(n),a=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,a,s,l,n,i),this.transform(c,a,u,h,n,i);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,a,n,i),this.conjugate(f,a,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.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,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,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!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),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>=i);c--){var h=0|this.words[c];this.words[c]=u<<26-a|h>>>a,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.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 i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.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)},a.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},a.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()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(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(i,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[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,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}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,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 a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.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),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.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 i=new a(1),o=new a(0),s=new a(0),l=new a(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;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(h)),i.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),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.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 i,o=new a(1),s=new a(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(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.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 i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.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 i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.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},a.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],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.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)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction context"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,"redAdd works only with red numbers"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,"redSub works only with red numbers"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,"redISub works only with red numbers"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,"redShl works only with red numbers"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,t)};var m={k256:null,p224:null,p192:null,p25519:null};function v(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){v.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function x(){v.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function b(){v.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function _(){v.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function w(t){if("string"==typeof t){var e=a._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 a(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)}v.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},v.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},v.prototype.split=function(t,e){t.iushrn(this.n,0,e)},v.prototype.imulK=function(t){return t.imul(this.k)},i(y,v),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 i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&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 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(x,v),i(b,v),i(_,v),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(m[t])return m[t];var e;if("k256"===t)e=new y;else if("p224"===t)e=new x;else if("p192"===t)e=new b;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new _}return m[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},w.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return 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 a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var h=this.pow(u,i),f=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var g=p,m=0;0!==g.cmp(s);m++)g=g.redSqr();n(m<d);var v=this.pow(h,new a(1).iushln(d-m-1));f=f.redMul(v),h=v.redSqr(),p=p.redMul(h),d=m}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 a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=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 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e=t.height,r=t.data,n=3;n<r.length;n+=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}function o(t){for(var e=t.height,r=t.data,n=r.length-1;n>0;n-=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}e.exports=n,n.canvas=document.createElement("canvas"),n.cache={}},{}],219:[function(t,e,r){"use strict";e.exports=function(t){return new c(t||d,null)};var n=0,i=1;function a(t,e,r,n,i,a){this._color=t,this.key=e,this.value=r,this.left=n,this.right=i,this._count=a}function o(t){return new a(t._color,t.key,t.value,t.left,t.right,t._count)}function s(t,e){return new a(t,e.key,e.value,e.left,e.right,e._count)}function l(t){t._count=1+(t.left?t.left._count:0)+(t.right?t.right._count:0)}function c(t,e){this._compare=t,this.root=e}var u=c.prototype;function h(t,e){this.tree=t,this._stack=e}Object.defineProperty(u,"keys",{get:function(){var t=[];return this.forEach(function(e,r){t.push(e)}),t}}),Object.defineProperty(u,"values",{get:function(){var t=[];return 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c=(a=r.right=o(a)).right=o(a.right),r.right=a.left,a.left=r,a.right=c,a._color=r._color,e._color=i,r._color=i,c._color=i,l(r),l(a),u>1&&((h=t[u-2]).left===r?h.left=a:h.right=a),void(t[u-1]=a);if(a.left&&a.left._color===n)return c=(a=r.right=o(a)).left=o(a.left),r.right=c.left,a.left=c.right,c.left=r,c.right=a,c._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(c),u>1&&((h=t[u-2]).left===r?h.left=c:h.right=c),void(t[u-1]=c);if(a._color===i){if(r._color===n)return r._color=i,void(r.right=s(n,a));r.right=s(n,a);continue}a=o(a),r.right=a.left,a.left=r,a._color=r._color,r._color=n,l(r),l(a),u>1&&((h=t[u-2]).left===r?h.left=a:h.right=a),t[u-1]=a,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}else{if((a=r.left).left&&a.left._color===n)return c=(a=r.left=o(a)).left=o(a.left),r.left=a.right,a.right=r,a.left=c,a._color=r._color,e._color=i,r._color=i,c._color=i,l(r),l(a),u>1&&((h=t[u-2]).right===r?h.right=a:h.left=a),void(t[u-1]=a);if(a.right&&a.right._color===n)return 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r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),f.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),f.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),n=e[e.length-1];r[r.length-1]=new a(n._color,n.key,t,n.left,n.right,n._count);for(var i=e.length-2;i>=0;--i)(n=e[i]).left===e[i+1]?r[i]=new a(n._color,n.key,n.value,r[i+1],n.right,n._count):r[i]=new a(n._color,n.key,n.value,n.left,r[i+1],n._count);return new c(this.tree._compare,r[0])},f.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],220:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=607/128,a=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function o(t){if(t<0)return Number("0/0");for(var e=a[0],r=a.length-1;r>0;--r)e+=a[r]/(t+r);var n=t+i+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(o(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var a=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(a,e+.5)*Math.exp(-a)*r},e.exports.log=o},{}],221:[function(t,e,r){e.exports=function(t,e){if("string"!=typeof t)throw new TypeError("must specify type string");if(e=e||{},"undefined"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement("canvas");"number"==typeof e.width&&(r.width=e.width);"number"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf("webgl")&&a.push("experimental-"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],222:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var h=u.prototype;function f(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}h.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]}),u=!1,h=!1;if("bounds"in t)for(var f=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)f[p][d]!==this.bounds[p][d]&&(h=!0),this.bounds[p][d]=f[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,h=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in 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),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var g=l("labels");l("labelFont")&&(g=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("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=i(this.gl,this.bounds,this.ticks))};var p=[new f,new f,new f];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var h=a,f=s,p=o,d=l;c&1<<u&&(h=s,f=a,p=l,d=o),h[u]=r[0][u],f[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],m={model:l,view:l,projection:l};h.isOpaque=function(){return!0},h.isTransparent=function(){return!1},h.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];h.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=o(r,n,i,a),u=s.cubeEdges,h=s.axis,f=n[12],b=n[13],_=n[14],w=n[15],k=this.pixelRatio*(i[3]*f+i[7]*b+i[11]*_+i[15]*w)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=u[M],this.lastCubeProps.axis[M]=h[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,u,h);e=this.gl;var T=g;for(M=0;M<3;++M)this.backgroundEnable[M]?T[M]=h[M]:T[M]=0;this._background.draw(r,n,i,a,T,this.backgroundColor),this._lines.bind(r,n,i,this);for(M=0;M<3;++M){var S=[0,0,0];h[M]>0?S[M]=a[1][M]:S[M]=a[0][M];for(var E=0;E<2;++E){var C=(M+1+E)%3,L=(M+1+(1^E))%3;this.gridEnable[C]&&this._lines.drawGrid(C,L,this.bounds,S,this.gridColor[C],this.gridWidth[C]*this.pixelRatio)}for(E=0;E<2;++E){C=(M+1+E)%3,L=(M+1+(1^E))%3;this.zeroEnable[L]&&a[0][L]<=0&&a[1][L]>=0&&this._lines.drawZero(C,L,this.bounds,S,this.zeroLineColor[L],this.zeroLineWidth[L]*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(v,A[M].primalMinor),P=c(y,A[M].mirrorMinor),I=this.lineTickLength;for(E=0;E<3;++E){var O=k/r[5*E];z[E]*=I[E]*O,P[E]*=I[E]*O}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,P,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);for(M=0;M<3;++M){var D=A[M].primalMinor,R=c(x,A[M].primalOffset);for(E=0;E<3;++E)this.lineTickEnable[M]&&(R[E]+=k*D[E]*Math.max(this.lineTickLength[E],0)/r[5*E]);if(this.tickEnable[M]){for(E=0;E<3;++E)R[E]+=k*D[E]*this.tickPad[E]/r[5*E];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],R,this.tickColor[M])}if(this.labelEnable[M]){for(E=0;E<3;++E)R[E]+=k*D[E]*this.labelPad[E]/r[5*E];R[M]+=.5*(a[0][M]+a[1][M]),this._text.drawLabel(M,this.labelSize[M],this.labelAngle[M],R,this.labelColor[M])}}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":223,"./lib/cube.js":224,"./lib/lines.js":225,"./lib/text.js":227,"./lib/ticks.js":228}],223:[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 m=c;c=u,u=m}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=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,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[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:i,colors:a},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":226,"gl-buffer":230,"gl-vao":308}],224:[function(t,e,r){"use strict";e.exports=function(t,e,r,a){i(s,e,t),i(s,r,s);for(var p=0,y=0;y<2;++y){u[2]=a[y][2];for(var x=0;x<2;++x){u[1]=a[x][1];for(var b=0;b<2;++b)u[0]=a[b][0],f(l[p],u,s),p+=1}}for(var _=-1,y=0;y<8;++y){for(var w=l[y][3],k=0;k<3;++k)c[y][k]=l[y][k]/w;w<0&&(_<0?_=y:c[y][2]<c[_][2]&&(_=y))}if(_<0){_=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,T=(M+1)%3,S=-1,E=-1,C=0;C<2;++C){var L=C<<M,z=L+(C<<A)+(1-C<<T),P=L+(1-C<<A)+(C<<T);o(c[L],c[z],c[P],h)<0||(C?S=1:E=1)}if(S<0||E<0)E>S&&(_|=1<<M);else{for(var C=0;C<2;++C){var L=C<<M,z=L+(C<<A)+(1-C<<T),P=L+(1-C<<A)+(C<<T),I=d([l[L],l[z],l[P],l[L+(1<<A)+(1<<T)]]);C?S=I:E=I}E>S&&(_|=1<<M)}}}for(var O=7^_,D=-1,y=0;y<8;++y)y!==_&&y!==O&&(D<0?D=y:c[D][1]>c[y][1]&&(D=y));for(var R=-1,y=0;y<3;++y){var B=D^1<<y;if(B!==_&&B!==O){R<0&&(R=B);var T=c[B];T[0]<c[R][0]&&(R=B)}}for(var F=-1,y=0;y<3;++y){var B=D^1<<y;if(B!==_&&B!==O&&B!==R){F<0&&(F=B);var T=c[B];T[0]>c[F][0]&&(F=B)}}var N=g;N[0]=N[1]=N[2]=0,N[n.log2(R^D)]=D&R,N[n.log2(D^F)]=D&F;var j=7^F;j===_||j===O?(j=7^R,N[n.log2(F^j)]=j&F):N[n.log2(R^j)]=j&R;for(var V=m,U=_,M=0;M<3;++M)V[M]=U&1<<M?-1:1;return v};var n=t("bit-twiddle"),i=t("gl-mat4/multiply"),a=(t("gl-mat4/invert"),t("split-polygon")),o=t("robust-orientation"),s=new Array(16),l=(new Array(16),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 i=0;i<3;++i)t[n]+=e[i]*r[4*i+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=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=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]-i,h=l[0]/l[3]-n,f=l[1]/l[3]-i;o+=Math.abs(c*f-u*h)}return o}var g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{"bit-twiddle":80,"gl-mat4/invert":254,"gl-mat4/multiply":256,"robust-orientation":471,"split-polygon":488}],225:[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 m=o.length/3|0;s[h]=f,l[h]=m-f;for(var f=o.length/3|0,v=0;v<r[h].length;++v){var g=+r[h][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;c[h]=f,u[h]=m-f}var y=n(t,new 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p=[0,0,0];u.drawLabel=function(t,e,r,n,i){this.labelCount[t]&&(this.shader.uniforms.axis=p,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,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":226,_process:449,"gl-buffer":230,"gl-vao":308,"vectorize-text":515}],228:[function(t,e,r){"use strict";function n(t,e){var r=t+"",n=r.indexOf("."),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+"";if(s.indexOf("e")>=0)return s;var l=o/a,c=o%a;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),i){for(var h=""+c;h.length<i;)h="0"+h;return u+"."+h}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(var 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A=S[E],L=d(m,u,A,r,l),C=0;C<3;++C)b[C].lo=Math.min(b[C].lo,A[C]),b[C].hi=Math.max(b[C].hi,A[C]),C!==k&&(b[C].pixelsPerDataUnit=Math.min(b[C].pixelsPerDataUnit,Math.abs(L[C])))}}}return b};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=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,i){for(var a=0;a<3;++a){for(var o=f,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,h=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.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)],m=[0,0,0]},{"./lib/cube.js":224,"extract-frustum-planes":213,"gl-mat4/multiply":256,"gl-mat4/transpose":264,"gl-vec4/transformMat4":366,"split-polygon":488}],230:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];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=a(s,t.shape);i.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 i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:417,"ndarray-ops":411,"typedarray-pool":507}],231:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=(t("gl-vec4"),function(t,e){for(var r=0;r<t.length;r++)if(t[r]>=e)return r-1;return r}),a=n.create(),o=n.create(),s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t,e,r,l){var c=t[0],u=t[1],h=t[2],f=r[0].length,p=r[1].length,d=r[2].length,g=i(r[0],c),m=i(r[1],u),v=i(r[2],h),y=g+1,x=m+1,b=v+1;if(l&&(g=s(g,0,f-1),y=s(y,0,f-1),m=s(m,0,p-1),x=s(x,0,p-1),v=s(v,0,d-1),b=s(b,0,d-1)),g<0||m<0||v<0||y>=f||x>=p||b>=d)return n.create();var _=(c-r[0][g])/(r[0][y]-r[0][g]),w=(u-r[1][m])/(r[1][x]-r[1][m]),k=(h-r[2][v])/(r[2][b]-r[2][v]);(_<0||_>1||isNaN(_))&&(_=0),(w<0||w>1||isNaN(w))&&(w=0),(k<0||k>1||isNaN(k))&&(k=0);var M=v*f*p,A=b*f*p,T=m*f,S=x*f,E=g,C=y,L=e[T+M+E],z=e[T+M+C],P=e[S+M+E],I=e[S+M+C],O=e[T+A+E],D=e[T+A+C],R=e[S+A+E],B=e[S+A+C],F=n.create();return n.lerp(F,L,z,_),n.lerp(a,P,I,_),n.lerp(F,F,a,w),n.lerp(a,O,D,_),n.lerp(o,R,B,_),n.lerp(a,a,o,w),n.lerp(F,F,a,k),F};e.exports=function(t,e){var r;r=t.positions?t.positions:function(t){for(var e=t[0],r=t[1],n=t[2],i=[],a=0;a<n.length;a++)for(var o=0;o<r.length;o++)for(var s=0;s<e.length;s++)i.push([n[a],r[o],e[s]]);return i}(t.meshgrid);var i=t.meshgrid,a=t.vectors,o={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vertexNormals:[],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]),o;for(var s=0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=1/0,d=-1/0,g=null,m=null,v=[],y=1/0,x=0;x<r.length;x++){var b,_=r[x];c=Math.min(_[0],c),u=Math.max(_[0],u),h=Math.min(_[1],h),f=Math.max(_[1],f),p=Math.min(_[2],p),d=Math.max(_[2],d),b=i?l(_,a,i,!0):a[x],n.length(b)>s&&(s=n.length(b)),x&&(y=Math.min(y,2*n.distance(g,_)/(n.length(m)+n.length(b)))),g=_,m=b,v.push(b)}var w=[c,h,p],k=[u,f,d];e&&(e[0]=w,e[1]=k),0===s&&(s=1);var M=1/s;isFinite(y)&&!isNaN(y)||(y=1),o.vectorScale=y;var A=function(t,e,r){var i=n.create();return void 0!==t&&n.set(i,t,e,r),i}(0,1,0),T=t.coneSize||.5;t.absoluteConeSize&&(T=t.absoluteConeSize*M),o.coneScale=T;x=0;for(var S=0;x<r.length;x++)for(var E=(_=r[x])[0],C=_[1],L=_[2],z=v[x],P=n.length(z)*M,I=0;I<8;I++){o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vertexIntensity.push(P,P,P),o.vertexIntensity.push(P,P,P),o.vertexNormals.push(A,A,A),o.vertexNormals.push(A,A,A);var O=o.positions.length;o.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return o},e.exports.createConeMesh=t("./lib/conemesh")},{"./lib/conemesh":233,"gl-vec3":320,"gl-vec4":350}],232:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;u<t.length;++u)c[u]=o(t[u],r,a,s,l);for(var h=0,f=1/0,u=0;u<c.length;++u){for(var p=0,d=0;d<2;++d)p+=Math.pow(c[u][d]-e[d],2);p<f&&(f=p,h=u)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),m=0,u=0;u<3;++u){if(g[u]<-.001||g[u]>1.0001)return null;m+=g[u]}if(Math.abs(m-1)>.001)return null;return[h,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}(t,g),g]}},{barycentric:61,"polytope-closest-point/lib/closest_point_2d.js":448}],233:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=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("./shaders"),g=(t("./closest-point"),d.meshShader),m=d.pickShader,v=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,y,x,b,_,w,k,M,A,T,S,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=c,this.triangleColors=h,this.triangleNormals=p,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=x,this.edgeIds=m,this.edgeVAO=b,this.edgeCount=0,this.pointPositions=_,this.pointColors=k,this.pointUVs=M,this.pointSizes=A,this.pointIds=w,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=S,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!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.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=v,this._view=v,this._projection=v,this._resolution=[1,1]}var x=y.prototype;function b(t){var e=n(t,m.vertex,m.fragment,null,m.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(t){this.pickId=t},x.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++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],m=i[h],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},x.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),"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.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),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){for(var e=h({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],l=[],c=[],f=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[],w=[],k=[];this.cells=r,this.positions=n;var M=t.vertexNormals,A=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,S)),A||M||(M=s.vertexNormals(r,n,T));var E=t.vertexColors,C=t.cellColors,L=t.meshColor||[1,1,1,1],z=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,D=1/0,R=-1/0;if(!z&&!I)if(P)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],R=+t.vertexIntensityBounds[1];else for(var B=0;B<P.length;++B){var F=P[B];D=Math.min(D,F),R=Math.max(R,F)}else if(O)for(B=0;B<O.length;++B){F=O[B];D=Math.min(D,F),R=Math.max(R,F)}else for(B=0;B<n.length;++B){F=n[B][2];D=Math.min(D,F),R=Math.max(R,F)}this.intensity=P||(O?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,O):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var N=t.pointSizes,j=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(B=0;B<n.length;++B)for(var V=n[B],U=0;U<3;++U)!isNaN(V[U])&&isFinite(V[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],V[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],V[U]));var q=0,H=0,G=0;t:for(B=0;B<r.length;++B){var W=r[B];switch(W.length){case 1:for(V=n[X=W[0]],U=0;U<3;++U)if(isNaN(V[U])||!isFinite(V[U]))continue t;x.push(V[0],V[1],V[2],V[3]),3===(Z=E?E[X]:C?C[B]:L).length?b.push(Z[0],Z[1],Z[2],1):b.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],_.push($[0],$[1]),N?w.push(N[X]):w.push(j),k.push(B),G+=1;break;case 2:for(U=0;U<2;++U){V=n[X=W[U]];for(var Y=0;Y<3;++Y)if(isNaN(V[Y])||!isFinite(V[Y]))continue t}for(U=0;U<2;++U){V=n[X=W[U]];g.push(V[0],V[1],V[2]),3===(Z=E?E[X]:C?C[B]:L).length?m.push(Z[0],Z[1],Z[2],1):m.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],v.push($[0],$[1]),y.push(B)}H+=1;break;case 3:for(U=0;U<3;++U)for(V=n[X=W[U]],Y=0;Y<3;++Y)if(isNaN(V[Y])||!isFinite(V[Y]))continue t;for(U=0;U<3;++U){var X;V=n[X=W[U]];a.push(V[0],V[1],V[2],V[3]);var Z,$,J,K=i[X];l.push(K[0],K[1],K[2]),3===(Z=E?E[X]:C?C[B]:L).length?c.push(Z[0],Z[1],Z[2],1):c.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],p.push($[0],$[1]),J=M?M[X]:A[B],f.push(J[0],J[1],J[2]),d.push(B)}q+=1}}this.pointCount=G,this.edgeCount=H,this.triangleCount=q,this.pointPositions.update(x),this.pointColors.update(b),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(g),this.edgeColors.update(m),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(c),this.triangleUVs.update(p),this.triangleNormals.update(f),this.triangleIds.update(new Uint32Array(d))}},x.drawTransparent=x.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||v,n=t.view||v,i=t.projection||v,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,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,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,contourColor:this.contourColor,texture:0};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),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())},x.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||v,n=t.view||v,i=t.projection||v,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,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),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())},x.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);return{index:Math.floor(r[1]/48),position:n,dataCoordinate:n}},x.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.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()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader||function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=b(t),l=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var c=i(t),h=i(t),f=i(t),p=i(t),d=i(t),m=i(t),v=a(t,[{buffer:c,type:t.FLOAT,size:4},{buffer:m,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:f,type:t.FLOAT,size:4},{buffer:p,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:3},{buffer:h,type:t.FLOAT,size:3}]),x=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:x,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),A=i(t),T=i(t),S=i(t),E=i(t),C=i(t),L=a(t,[{buffer:A,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2},{buffer:E,type:t.FLOAT,size:1}]),z=i(t),P=new y(t,l,r,null,null,s,null,null,c,h,m,f,p,d,v,x,k,_,w,M,A,C,T,S,E,L,z,a(t,[{buffer:z,type:t.FLOAT,size:3}]));return P.update(e),P}},{"./closest-point":232,"./shaders":234,colormap:113,"gl-buffer":230,"gl-mat4/invert":254,"gl-mat4/multiply":256,"gl-shader":287,"gl-texture2d":303,"gl-vao":308,ndarray:417,normals:420,"simplicial-complex-contour":479,"typedarray-pool":507}],234:[function(t,e,r){var n=t("glslify"),i=n(["precision mediump float;\n#define GLSLIFY 1\n\nfloat inverse(float m) {\n  return 1.0 / m;\n}\n\nmat2 inverse(mat2 m) {\n  return mat2(m[1][1],-m[0][1],\n             -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\n}\n\nmat3 inverse(mat3 m) {\n  float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\n  float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\n  float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\n\n  float b01 = a22 * a11 - a12 * a21;\n  float b11 = -a22 * a10 + a12 * a20;\n  float b21 = a21 * a10 - a11 * a20;\n\n  float det = a00 * b01 + a01 * b11 + a02 * b21;\n\n  return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\n              b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\n              b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\n}\n\nmat4 inverse(mat4 m) {\n  float\n      a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\n      a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\n      a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\n      a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\n\n      b00 = a00 * a11 - a01 * a10,\n      b01 = a00 * a12 - a02 * a10,\n      b02 = a00 * a13 - a03 * a10,\n      b03 = a01 * a12 - a02 * a11,\n      b04 = a01 * a13 - a03 * a11,\n      b05 = a02 * a13 - a03 * a12,\n      b06 = a20 * a31 - a21 * a30,\n      b07 = a20 * a32 - a22 * a30,\n      b08 = a20 * a33 - a23 * a30,\n      b09 = a21 * a32 - a22 * a31,\n      b10 = a21 * a33 - a23 * a31,\n      b11 = a22 * a33 - a23 * a32,\n\n      det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\n\n  return mat4(\n      a11 * b11 - a12 * b10 + a13 * b09,\n      a02 * b10 - a01 * b11 - a03 * b09,\n      a31 * b05 - a32 * b04 + a33 * b03,\n      a22 * b04 - a21 * b05 - a23 * b03,\n      a12 * b08 - a10 * b11 - a13 * b07,\n      a00 * b11 - a02 * b08 + a03 * b07,\n      a32 * b02 - a30 * b05 - a33 * b01,\n      a20 * b05 - a22 * b02 + a23 * b01,\n      a10 * b10 - a11 * b08 + a13 * b06,\n      a01 * b08 - a00 * b10 - a03 * b06,\n      a30 * b04 - a31 * b02 + a33 * b00,\n      a21 * b02 - a20 * b04 - a23 * b00,\n      a11 * b07 - a10 * b09 - a12 * b06,\n      a00 * b09 - a01 * b07 + a02 * b06,\n      a31 * b01 - a30 * b03 - a32 * b00,\n      a20 * b03 - a21 * b01 + a22 * b00) / det;\n}\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 index, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  index = mod(index, segmentCount * 6.0);\n\n  float segment = floor(index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex == 3.0) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  // angle = 2pi * ((segment + ((segmentIndex == 1.0 || segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\n  float nextAngle = float(segmentIndex == 1.0 || segmentIndex == 5.0);\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 <= 2.0) {\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;\nuniform float vectorScale;\nuniform float coneScale;\n\nuniform float coneOffset;\n\nuniform mat4 model\n           , view\n           , projection;\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\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal), 0.0);\n  normal = normalize(normal * inverse(mat3(model)));\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n  f_color          = color; //vec4(position.w, color.r, 0, 0);\n  f_normal         = normal;\n  f_data           = conePosition.xyz;\n  f_eyeDirection   = eyePosition   - conePosition.xyz;\n  f_lightDirection = lightPosition - conePosition.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["precision mediump 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\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\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(any(lessThan(f_data, clipBounds[0])) || \n  //   any(greaterThan(f_data, clipBounds[1]))) {\n  //  discard;\n  //}\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 = cookTorranceSpecular(L, V, N, roughness, fresnel);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor =  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}"]),o=n(["precision mediump 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 index, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  index = mod(index, segmentCount * 6.0);\n\n  float segment = floor(index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex == 3.0) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  // angle = 2pi * ((segment + ((segmentIndex == 1.0 || segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\n  float nextAngle = float(segmentIndex == 1.0 || segmentIndex == 5.0);\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 <= 2.0) {\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 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nuniform float vectorScale;\nuniform float coneScale;\nuniform float coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal), 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(f_position, clipBounds[0])) || \n     any(greaterThan(f_position, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"normal",type:"vec3"},{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:377}],235:[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"}},{}],236:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":235}],237:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=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.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1}var l=s.prototype;function c(t,e){for(var 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.opacity>=1},l.isTransparent=function(){return this.opacity<1},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]),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 i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function h(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),"opacity"in t&&(this.opacity=t.opacity);var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],f=0;f<3;++f)if(isNaN(u[f])||!isFinite(u[f]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length&&(d=[d[0],d[1],d[2],1]),!isNaN(p[0][s])&&!isNaN(p[1][s])){var g;if(p[0][s]<0)(g=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+h(i,g,d,s);if(p[1][s]>0)(g=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+h(i,g,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":238,"gl-buffer":230,"gl-vao":308}],238:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if(any(lessThan(fragPosition, clipBounds[0])) || any(greaterThan(fragPosition, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":287,glslify:377}],239:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=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 i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(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 m=!1;"stencil"in n&&(m=!!n.stencil);return new d(t,e,r,f,h,g,m,c)};var i,a,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 i:throw new Error("gl-fbo: Framebuffer unsupported");case a: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,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);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,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{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=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=f(r,i,a,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,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,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=f(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,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),v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=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 m(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,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=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,a),h(s)),u(n,a)}}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 m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(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,m(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":303}],240:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.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 m=o(e).split("\n"),p=0;p<m.length;p++)if(f[p+3]||f[p+2]||f[p+1]){var v=m[p];if(c+=v+"\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":52,"gl-constants/lookup":236,"glsl-shader-name":369,"sprintf-js":489}],241:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),h=s(r),f=s(r),p=new c(t,n,i,a,u,h,f);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,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 i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[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(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.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,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2*c/h,t[4]=2*u/f,t[6]=2*(a[0]-l[0])/h-1,t[7]=2*(a[1]-l[1])/f-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.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,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},h.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(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),m=1/((h[3]=o[o.length-1])-d),v=e[0],y=e[1];this.shape=[v,y];var x=(v-1)*(y-1)*(f.length>>>1);this.numVertices=x;for(var b=a.mallocUint8(4*x),_=a.mallocFloat32(2*x),w=a.mallocUint8(2*x),k=a.mallocUint32(x),M=0,A=0;A<y-1;++A)for(var T=m*(o[A]-d),S=m*(o[A+1]-d),E=0;E<v-1;++E)for(var C=g*(r[E]-p),L=g*(r[E+1]-p),z=0;z<f.length;z+=2){var P,I,O,D,R=f[z],B=f[z+1],F=s[(A+B)*v+(E+R)],N=n.le(l,F);if(N<0)P=c[0],I=c[1],O=c[2],D=c[3];else if(N===u-1)P=c[4*u-4],I=c[4*u-3],O=c[4*u-2],D=c[4*u-1];else{var j=(F-l[N])/(l[N+1]-l[N]),V=1-j,U=4*N,q=4*(N+1);P=V*c[U]+j*c[q],I=V*c[U+1]+j*c[q+1],O=V*c[U+2]+j*c[q+2],D=V*c[U+3]+j*c[q+3]}b[4*M]=255*P,b[4*M+1]=255*I,b[4*M+2]=255*O,b[4*M+3]=255*D,_[2*M]=.5*C+.5*L,_[2*M+1]=.5*T+.5*S,w[2*M]=R,w[2*M+1]=B,k[M]=A*v+E,M+=1}this.positionBuffer.update(_),this.weightBuffer.update(w),this.colorBuffer.update(b),this.idBuffer.update(k),a.free(_),a.free(b),a.free(w),a.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":242,"binary-search-bounds":243,"gl-buffer":230,"gl-shader":287,"iota-array":384,"typedarray-pool":507}],242:[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:377}],243:[function(t,e,r){arguments[4][98][0].apply(r,arguments)},{dup:98}],244:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump 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\nvoid main() {\n  vec4 projected = projection * view * model * vec4(position, 1.0);\n  vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\n  vec2 tangent = normalize(screenShape * tangentClip.xy);\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\n\n  gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\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(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\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 mediump float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\nlowp vec4 encode_float_1540259130(highp float v) {\n  highp 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  highp vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  highp float e = floor(log2(av));\n  highp 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  highp 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\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId/255.0, encode_float_1540259130(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 i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":287,glslify:377}],245:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=u(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=h(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),c=i(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}]),f=l(new Array(1024),[256,1,4]),p=0;p<1024;++p)f.data[p]=255;var d=a(e,f);d.wrap=e.REPEAT;var g=new m(e,r,o,s,c,d);return g.update(t),g};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=t("glsl-read-float"),s=t("binary-search-bounds"),l=t("ndarray"),c=t("./lib/shaders"),u=c.createShader,h=c.createPickShader,f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function p(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function d(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 g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function m(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,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=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}var v=m.prototype;v.isTransparent=function(){return this.opacity<1},v.isOpaque=function(){return this.opacity>=1},v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.drawTransparent=v.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||f,view:t.view||f,projection:t.projection||f,clipBounds:d(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()},v.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||f,view:t.view||f,projection:t.projection||f,pickId:this.pickId,clipBounds:d(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()},v.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),"opacity"in t&&(this.opacity=+t.opacity);var i=t.position||t.positions;if(i){var a=t.color||t.colors||[0,0,0,1],o=t.lineWidth||1,c=[],u=[],h=[],f=0,d=0,g=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],m=!1;t:for(e=1;e<i.length;++e){var v,y,x,b=i[e-1],_=i[e];for(u.push(f),h.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&c.length>0){for(var w=0;w<24;++w)c.push(c[c.length-12]);d+=2,m=!0}continue t}g[0][r]=Math.min(g[0][r],b[r],_[r]),g[1][r]=Math.max(g[1][r],b[r],_[r])}Array.isArray(a[0])?(v=a[e-1],y=a[e]):v=y=a,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),x=Array.isArray(o)?o[e-1]:o;var k=f;if(f+=p(b,_),m){for(r=0;r<2;++r)c.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,v[0],v[1],v[2],v[3]);d+=2,m=!1}c.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],k,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],f,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],f,x,y[0],y[1],y[2],y[3]),d+=4}if(this.buffer.update(c),u.push(f),h.push(i[i.length-1].slice()),this.bounds=g,this.vertexCount=d,this.points=h,this.arcLength=u,"dashes"in t){var M=t.dashes.slice();for(M.unshift(0),e=1;e<M.length;++e)M[e]=M[e-1]+M[e];var A=l(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&s.le(M,M[M.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}}},v.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=o(t.value[0],t.value[1],t.value[2],0),r=s.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),l=1-a,c=[0,0,0],u=0;u<3;++u)c[u]=l*n[u]+a*i[u];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,c,h,this.points[h])}},{"./lib/shaders":244,"binary-search-bounds":79,"gl-buffer":230,"gl-texture2d":303,"gl-vao":308,"glsl-read-float":368,ndarray:417}],246:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r*a-i*n;return o?(o=1/o,t[0]=a*o,t[1]=-n*o,t[2]=-i*o,t[3]=r*o,t):null}},{}],247:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],h=u*o-s*c,f=-u*a+s*l,p=c*a-o*l,d=r*h+n*f+i*p;return d?(d=1/d,t[0]=h*d,t[1]=(-u*n+i*c)*d,t[2]=(s*n-i*o)*d,t[3]=f*d,t[4]=(u*r-i*l)*d,t[5]=(-s*r+i*a)*d,t[6]=p*d,t[7]=(-c*r+n*l)*d,t[8]=(o*r-n*a)*d,t):null}},{}],248:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],249:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],250:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],h=t[10],f=t[11],p=t[12],d=t[13],g=t[14],m=t[15];return(e*o-r*a)*(h*m-f*g)-(e*s-n*a)*(u*m-f*d)+(e*l-i*a)*(u*g-h*d)+(r*s-n*o)*(c*m-f*p)-(r*l-i*o)*(c*g-h*p)+(n*l-i*s)*(c*d-u*p)}},{}],251:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,h=n*s,f=i*o,p=i*s,d=i*l,g=a*o,m=a*s,v=a*l;return t[0]=1-h-d,t[1]=u+v,t[2]=f-m,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+g,t[7]=0,t[8]=f+m,t[9]=p-g,t[10]=1-c-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],252:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,h=n*l,f=n*c,p=i*l,d=i*c,g=a*c,m=o*s,v=o*l,y=o*c;return t[0]=1-(p+g),t[1]=h+y,t[2]=f-v,t[3]=0,t[4]=h-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=f+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],253:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],254:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],h=e[9],f=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,k=i*c-a*l,M=u*g-h*d,A=u*m-f*d,T=u*v-p*d,S=h*m-f*g,E=h*v-p*g,C=f*v-p*m,L=y*C-x*E+b*S+_*T-w*A+k*M;if(!L)return null;return L=1/L,t[0]=(s*C-l*E+c*S)*L,t[1]=(i*E-n*C-a*S)*L,t[2]=(g*k-m*w+v*_)*L,t[3]=(f*w-h*k-p*_)*L,t[4]=(l*T-o*C-c*A)*L,t[5]=(r*C-i*T+a*A)*L,t[6]=(m*b-d*k-v*x)*L,t[7]=(u*k-f*b+p*x)*L,t[8]=(o*E-s*T+c*M)*L,t[9]=(n*T-r*E-a*M)*L,t[10]=(d*w-g*b+v*y)*L,t[11]=(h*b-u*w-p*y)*L,t[12]=(s*A-o*S-l*M)*L,t[13]=(r*S-n*A+i*M)*L,t[14]=(g*x-d*_-m*y)*L,t[15]=(u*_-h*x+f*y)*L,t}},{}],255:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,h,f,p,d,g=e[0],m=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],k=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-k)<1e-6)return n(t);h=g-_,f=m-w,p=v-k,d=1/Math.sqrt(h*h+f*f+p*p),a=x*(p*=d)-b*(f*=d),o=b*(h*=d)-y*p,s=y*f-x*h,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=f*s-p*o,c=p*a-h*s,u=h*o-f*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=h,t[3]=0,t[4]=o,t[5]=c,t[6]=f,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(h*g+f*m+p*v),t[15]=1,t}},{"./identity":253}],256:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],h=e[8],f=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*h+w*g,t[1]=x*i+b*l+_*f+w*m,t[2]=x*a+b*c+_*p+w*v,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*i+b*l+_*f+w*m,t[6]=x*a+b*c+_*p+w*v,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*i+b*l+_*f+w*m,t[10]=x*a+b*c+_*p+w*v,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*i+b*l+_*f+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],257:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],258:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,_,w,k,M,A,T,S,E=n[0],C=n[1],L=n[2],z=Math.sqrt(E*E+C*C+L*L);if(Math.abs(z)<1e-6)return null;E*=z=1/z,C*=z,L*=z,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],h=e[4],f=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=C*E*o+L*i,_=L*E*o-C*i,w=E*C*o-L*i,k=C*C*o+a,M=L*C*o+E*i,A=E*L*o+C*i,T=C*L*o-E*i,S=L*L*o+a,t[0]=s*x+h*b+g*_,t[1]=l*x+f*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+h*k+g*M,t[5]=l*w+f*k+m*M,t[6]=c*w+p*k+v*M,t[7]=u*w+d*k+y*M,t[8]=s*A+h*T+g*S,t[9]=l*A+f*T+m*S,t[10]=c*A+p*T+v*S,t[11]=u*A+d*T+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],259:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],h=e[10],f=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+h*n,t[7]=l*i+f*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=h*i-s*n,t[11]=f*i-l*n,t}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=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]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-h*n,t[3]=l*i-f*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+h*i,t[11]=l*n+f*i,t}},{}],261:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=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]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+h*n,t[3]=l*i+f*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=h*i-s*n,t[7]=f*i-l*n,t}},{}],262:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],263:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,h,f,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],h=e[8],f=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=h,t[9]=f,t[10]=p,t[11]=d,t[12]=n*g+s*m+h*v+e[12],t[13]=i*g+l*m+f*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],264:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else 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}},{}],265:[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:i(t,e);break;case 16:a(t,e);break;default:throw new Error("currently supports matrices up to 4x4")}return t};var n=t("gl-mat2/invert"),i=t("gl-mat3/invert"),a=t("gl-mat4/invert")},{"gl-mat2/invert":246,"gl-mat3/invert":247,"gl-mat4/invert":254}],266:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;u<t.length;++u)c[u]=o(t[u],r,a,s,l);for(var h=0,f=1/0,u=0;u<c.length;++u){for(var p=0,d=0;d<2;++d)p+=Math.pow(c[u][d]-e[d],2);p<f&&(f=p,h=u)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),m=0,u=0;u<3;++u){if(g[u]<-.001||g[u]>1.0001)return null;m+=g[u]}if(Math.abs(m-1)>.001)return null;return[h,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}(t,g),g]}},{barycentric:61,"polytope-closest-point/lib/closest_point_2d.js":448}],267:[function(t,e,r){var n=t("glslify"),i=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;\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\nvoid main() {\n  vec4 m_position  = model * vec4(position, 1.0);\n  vec4 t_position  = view * m_position;\n  gl_Position      = projection * t_position;\n  f_color          = color;\n  f_normal         = normal;\n  f_data           = position;\n  f_eyeDirection   = eyePosition   - position;\n  f_lightDirection = lightPosition - position;\n  f_uv             = uv;\n}\n"]),a=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\nvec3 normals(vec3 pos) {\n  vec3 fdx = dFdx(pos);\n  vec3 fdy = dFdy(pos);\n  return normalize(cross(fdx, fdy));\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\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(any(lessThan(f_data, clipBounds[0])) ||\n     any(greaterThan(f_data, clipBounds[1]))) {\n    discard;\n  }\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  vec3 normal = normals(f_data);\n\n  if (\n    dot(N, normal) < 0.0\n    ) {\n      N = -N;\n  }\n\n  float specular = 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 mediump 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 mediump float;\n#define GLSLIFY 1\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(any(lessThan(f_data, clipBounds[0])) || \n     any(greaterThan(f_data, clipBounds[1]))) {\n    discard;\n  }\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision mediump float;\n#define GLSLIFY 1\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(any(lessThan(position, clipBounds[0])) || \n     any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(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 mediump 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 mediump 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 mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(f_position, clipBounds[0])) || \n     any(greaterThan(f_position, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),f=n(["precision mediump float;\n#define GLSLIFY 1\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(any(lessThan(position, clipBounds[0])) || \n     any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(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 mediump 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 mediump float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor,1);\n}\n"]);r.meshShader={vertex:i,fragment:a,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:377}],268:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=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"),m=d.meshShader,v=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,i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,_,k,M,A,T,S){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,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=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=k,this.pointSizes=M,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=T,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!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._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var M=k.prototype;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 T(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function S(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 E(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}M.isOpaque=function(){return this.opacity>=1},M.isTransparent=function(){return this.opacity<1},M.pickSlots=1,M.setPickBase=function(t){this.pickId=t},M.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++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],m=i[h],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},M.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),"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),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){for(var e=h({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],f=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,k=t.cellNormals,M=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,M));var T=t.vertexColors,S=t.cellColors,E=t.meshColor||[1,1,1,1],C=t.vertexUVs,L=t.vertexIntensity,z=t.cellUVs,P=t.cellIntensity,I=1/0,O=-1/0;if(!C&&!z)if(L)if(t.vertexIntensityBounds)I=+t.vertexIntensityBounds[0],O=+t.vertexIntensityBounds[1];else for(var D=0;D<L.length;++D){var R=L[D];I=Math.min(I,R),O=Math.max(O,R)}else if(P)for(D=0;D<P.length;++D){R=P[D];I=Math.min(I,R),O=Math.max(O,R)}else for(D=0;D<n.length;++D){R=n[D][2];I=Math.min(I,R),O=Math.max(O,R)}this.intensity=L||(P?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,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));var B=t.pointSizes,F=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(D=0;D<n.length;++D)for(var N=n[D],j=0;j<3;++j)!isNaN(N[j])&&isFinite(N[j])&&(this.bounds[0][j]=Math.min(this.bounds[0][j],N[j]),this.bounds[1][j]=Math.max(this.bounds[1][j],N[j]));var V=0,U=0,q=0;t:for(D=0;D<r.length;++D){var H=r[D];switch(H.length){case 1:for(N=n[W=H[0]],j=0;j<3;++j)if(isNaN(N[j])||!isFinite(N[j]))continue t;v.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?y.push(Y[0],Y[1],Y[2],1):y.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(O-I),0]:z?z[D]:P?[(P[D]-I)/(O-I),0]:[(N[2]-I)/(O-I),0],x.push(X[0],X[1]),B?b.push(B[W]):b.push(F),_.push(D),q+=1;break;case 2:for(j=0;j<2;++j){N=n[W=H[j]];for(var G=0;G<3;++G)if(isNaN(N[G])||!isFinite(N[G]))continue t}for(j=0;j<2;++j){N=n[W=H[j]];p.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?d.push(Y[0],Y[1],Y[2],1):d.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(O-I),0]:z?z[D]:P?[(P[D]-I)/(O-I),0]:[(N[2]-I)/(O-I),0],g.push(X[0],X[1]),m.push(D)}U+=1;break;case 3:for(j=0;j<3;++j)for(N=n[W=H[j]],G=0;G<3;++G)if(isNaN(N[G])||!isFinite(N[G]))continue t;for(j=0;j<3;++j){var W,Y,X,Z;N=n[W=H[j]];i.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?a.push(Y[0],Y[1],Y[2],1):a.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(O-I),0]:z?z[D]:P?[(P[D]-I)/(O-I),0]:[(N[2]-I)/(O-I),0],c.push(X[0],X[1]),Z=w?w[W]:k[D],l.push(Z[0],Z[1],Z[2]),f.push(D)}V+=1}}this.pointCount=q,this.edgeCount=U,this.triangleCount=V,this.pointPositions.update(v),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(m)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(f))}},M.drawTransparent=M.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,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,contourColor:this.contourColor,texture:0};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),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())},M.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,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),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())},M.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,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=g(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;var s=o[2],l=0;for(a=0;a<r.length;++a)l+=s[a]*this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},M.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,m.vertex,m.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,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=A(t),c=T(t),h=S(t),f=E(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=i(t),g=i(t),y=i(t),x=i(t),b=i(t),_=a(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=i(t),M=i(t),C=i(t),L=i(t),z=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:M,type:t.FLOAT,size:4},{buffer:C,type:t.FLOAT,size:2}]),P=i(t),I=i(t),O=i(t),D=i(t),R=i(t),B=a(t,[{buffer:P,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:O,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),F=i(t),N=new k(t,p,r,s,l,c,h,f,d,b,g,y,x,_,w,L,M,C,z,P,R,I,O,D,B,F,a(t,[{buffer:F,type:t.FLOAT,size:3}]));return N.update(e),N}},{"./lib/closest-point":266,"./lib/shaders":267,colormap:113,"gl-buffer":230,"gl-mat4/invert":254,"gl-mat4/multiply":256,"gl-shader":287,"gl-texture2d":303,"gl-vao":308,ndarray:417,normals:420,"simplicial-complex-contour":479,"typedarray-pool":507}],269:[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=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=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,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":272,"gl-buffer":230,"gl-shader":287}],270:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=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,i=t.gl,a=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 m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-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]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[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,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var k=p[w],M=p[w+2]-k,A=.5*(d[w+2]+d[w]),T=d[w+2]-d[w];e[w]=2*M/T,t[w]=2*(k-A)/T}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,C=s.tickMarkWidth,L=s.tickMarkColor,z=6*h[0].length,P=Math.min(a.ge(h[0],(d[0]-p[0])/(p[2]-p[0]),l),h[0].length),I=Math.min(a.gt(h[0],(d[2]-p[0])/(p[2]-p[0]),l),h[0].length),O=0+6*P,D=6*Math.max(0,I-P),R=Math.min(a.ge(h[1],(d[1]-p[1])/(p[3]-p[1]),l),h[1].length),B=Math.min(a.gt(h[1],(d[3]-p[1])/(p[3]-p[1]),l),h[1].length),F=z+6*R,N=6*Math.max(0,B-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=C[1]*m/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,f.drawArrays(f.TRIANGLES,F,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=C[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,f.drawArrays(f.TRIANGLES,O,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=C[3]*m/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,f.drawArrays(f.TRIANGLES,F,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=C[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,f.drawArrays(f.TRIANGLES,O,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)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[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[i++]=d,n[i++]=f[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":272,"binary-search-bounds":274,"gl-buffer":230,"gl-shader":287}],271:[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=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=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,i,a){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=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":272,"gl-buffer":230,"gl-shader":287}],272:[function(t,e,r){"use strict";var n=t("glslify"),i=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:i,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:i,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:i,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:377}],273:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=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],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,f=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,m=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],k=this.labelCount[t],M=o.lt(n,l[t]),A=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 T=2/f[2+(1^t)]-f[1^t];u[1^t]=T*s[1^t]-1,d[t]&&(u[1^t]-=T*p*g[t],M<A&&i[A]>i[M]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[M],i[A]-i[M]))),y[t]&&k&&(u[1^t]-=T*p*x[t],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,k)),u[1^t]=T*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=T*p*g[t+2],M<A&&i[A]>i[M]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[M],i[A]-i[M]))),y[t+2]&&k&&(u[1^t]+=T*p*x[t+2],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,k))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=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-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.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,i=t.screenBox,a=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=a[o],g=a[o+2]-h,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,f[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[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,i,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,m=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split("\n"),b=0;b<x.length;b++)for(n=a(m,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);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=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":272,"binary-search-bounds":274,"gl-buffer":230,"gl-shader":287,"text-cache":498}],274:[function(t,e,r){arguments[4][98][0].apply(r,arguments)},{dup:98}],275:[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=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=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,i=this.pixelRatio,a=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),a.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 m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[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,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,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(a,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,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),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 a=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=a[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:a}),this.text.update({bounds:o,ticks:a,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":269,"./lib/grid":270,"./lib/line":271,"./lib/text":273,"gl-select-static":286}],276:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=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 i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":287,glslify:377}],277:[function(t,e,r){"use strict";e.exports=function(t){var e=!1,r=((t=t||{}).pixelRatio||parseFloat(window.devicePixelRatio),t.canvas);if(!r)if(r=document.createElement("canvas"),t.container){var v=t.container;v.appendChild(r)}else document.body.appendChild(r);var y=t.gl;y||(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}(r,t.glOptions||{premultipliedAlpha:!0,antialias:!0}));if(!y)throw new Error("webgl not supported");var x=t.bounds||[[-10,-10,-10],[10,10,10]],b=new d,_=l(y,[y.drawingBufferWidth,y.drawingBufferHeight],{preferFloat:!p}),w=f(y),k=t.camera||{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:"turntable"},M=t.axes||{},A=i(y,M);A.enable=!M.disable;var T=t.spikes||{},S=o(y,T),E=[],C=[],L=[],z=[],P=!0,I=!0,O=new Array(16),D=new Array(16),R={view:null,projection:O,model:D},I=!0,B=[y.drawingBufferWidth,y.drawingBufferHeight],F={gl:y,contextLost:!1,pixelRatio:t.pixelRatio||parseFloat(window.devicePixelRatio),canvas:r,selection:b,camera:n(r,k),axes:A,axesPixels:null,spikes:S,bounds:x,objects:E,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:R,oncontextloss:null,mouseListener:null},N=[y.drawingBufferWidth/F.pixelRatio|0,y.drawingBufferHeight/F.pixelRatio|0];function j(){if(!e&&F.autoResize){var t=r.parentNode,n=1,i=1;t&&t!==document.body?(n=t.clientWidth,i=t.clientHeight):(n=window.innerWidth,i=window.innerHeight);var a=0|Math.ceil(n*F.pixelRatio),o=0|Math.ceil(i*F.pixelRatio);if(a!==r.width||o!==r.height){r.width=a,r.height=o;var s=r.style;s.position=s.position||"absolute",s.left="0px",s.top="0px",s.width=n+"px",s.height=i+"px",P=!0}}}F.autoResize&&j();function V(){for(var t=E.length,e=z.length,r=0;r<e;++r)L[r]=0;t:for(var r=0;r<t;++r){var n=E[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(L[a]+i<255){C[r]=a,n.setPickBase(L[a]+1),L[a]+=i;continue t}var o=s(y,B);C[r]=e,z.push(o),L.push(i),n.setPickBase(1),e+=1}else C[r]=-1}for(;e>0&&0===L[e-1];)L.pop(),z.pop().dispose()}window.addEventListener("resize",j),F.update=function(t){e||(t=t||{},P=!0,I=!0)},F.add=function(t){e||(t.axes=A,E.push(t),C.push(-1),P=!0,I=!0,V())},F.remove=function(t){if(!e){var r=E.indexOf(t);r<0||(E.splice(r,1),C.pop(),P=!0,I=!0,V())}},F.dispose=function(){if(!e&&(e=!0,window.removeEventListener("resize",j),r.removeEventListener("webglcontextlost",H),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();_.dispose();for(var t=0;t<z.length;++t)z[t].dispose();w.dispose(),y=null,A=null,S=null,E=[]}};var U=!1,q=0;function H(){if(F.contextLost)return!0;y.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.mouseListener=u(r,function(t,r,n){if(!e){var i=z.length,a=E.length,o=b.object;b.distance=1/0,b.mouse[0]=r,b.mouse[1]=n,b.object=null,b.screen=null,b.dataCoordinate=b.dataPosition=null;var s=!1;if(t&&q)U=!0;else{U&&(I=!0),U=!1;for(var l=0;l<i;++l){var c=z[l].query(r,N[1]-n-1,F.pickRadius);if(c){if(c.distance>b.distance)continue;for(var u=0;u<a;++u){var h=E[u];if(C[u]===l){var f=h.pick(c);f&&(b.buttons=t,b.screen=c.coord,b.distance=c.distance,b.object=h,b.index=f.distance,b.dataPosition=f.position,b.dataCoordinate=f.dataCoordinate,b.data=f,s=!0)}}}}}o&&o!==b.object&&(o.highlight&&o.highlight(null),P=!0),b.object&&(b.object.highlight&&b.object.highlight(b.data),P=!0),(s=s||b.object!==o)&&F.onselect&&F.onselect(b),1&t&&!(1&q)&&F.onclick&&F.onclick(b),q=t}}),r.addEventListener("webglcontextlost",H);var G=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],W=[G[0].slice(),G[1].slice()];function Y(){if(!H()){j();var t=F.camera.tick();R.view=F.camera.matrix,P=P||t,I=I||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.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 i=0;i<e;++i){var o=E[i];o.pixelRatio=F.pixelRatio,o.axes=F.axes,P=P||!!o.dirty,I=I||!!o.dirty;var s=o.bounds;if(s)for(var l=s[0],u=s[1],f=0;f<3;++f)r[f]=Math.min(r[f],l[f]),n[f]=Math.max(n[f],u[f])}var p=F.bounds;if(F.autoBounds)for(var f=0;f<3;++f){if(n[f]<r[f])r[f]=-1,n[f]=1;else{r[f]===n[f]&&(r[f]-=1,n[f]+=1);var d=.05*(n[f]-r[f]);r[f]=r[f]-d,n[f]=n[f]+d}p[0][f]=r[f],p[1][f]=n[f]}for(var m=!1,f=0;f<3;++f)m=m||W[0][f]!==p[0][f]||W[1][f]!==p[1][f],W[0][f]=p[0][f],W[1][f]=p[1][f];if(I=I||m,P=P||m){if(m){for(var v=[0,0,0],i=0;i<3;++i)v[i]=g((p[1][i]-p[0][i])/10);A.autoTicks?A.update({bounds:p,tickSpacing:v}):A.update({bounds:p})}var x=y.drawingBufferWidth,k=y.drawingBufferHeight;B[0]=x,B[1]=k,N[0]=0|Math.max(x/F.pixelRatio,1),N[1]=0|Math.max(k/F.pixelRatio,1),h(O,F.fovy,x/k,F.zNear,F.zFar);for(var i=0;i<16;++i)D[i]=0;D[15]=1;for(var M=0,i=0;i<3;++i)M=Math.max(M,p[1][i]-p[0][i]);for(var i=0;i<3;++i)F.autoScale?D[5*i]=F.aspect[i]/(p[1][i]-p[0][i]):D[5*i]=1/M,F.autoCenter&&(D[12+i]=.5*-D[5*i]*(p[0][i]+p[1][i]));for(var i=0;i<e;++i){var o=E[i];o.axesBounds=p,F.clipToBounds&&(o.clipBounds=p)}b.object&&(F.snapToData?S.position=b.dataCoordinate:S.position=b.dataPosition,S.bounds=p),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);for(var t=E.length,e=z.length,r=0;r<e;++r){var n=z[r];n.shape=N,n.begin();for(var i=0;i<t;++i)if(C[i]===r){var a=E[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(R))}n.end()}}()),F.axesPixels=a(F.axes,R,x,k),F.onrender&&F.onrender(),y.bindFramebuffer(y.FRAMEBUFFER,null),y.viewport(0,0,x,k);var T=F.clearColor;y.clearColor(T[0],T[1],T[2],T[3]),y.clear(y.COLOR_BUFFER_BIT|y.DEPTH_BUFFER_BIT),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 L=!1;A.enable&&(L=L||A.isTransparent(),A.draw(R)),S.axes=A,b.object&&S.draw(R),y.disable(y.CULL_FACE);for(var i=0;i<e;++i){var o=E[i];o.axes=A,o.pixelRatio=F.pixelRatio,o.isOpaque&&o.isOpaque()&&o.draw(R),o.isTransparent&&o.isTransparent()&&(L=!0)}if(L){_.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(R);for(var i=0;i<e;++i){var o=E[i];o.isOpaque&&o.isOpaque()&&o.draw(R)}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(R);for(var i=0;i<e;++i){var o=E[i];o.isTransparent&&o.isTransparent()&&o.drawTransparent(R)}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)}P=!1;for(var i=0;i<e;++i)E[i].dirty=!1}}}return function t(){e||F.contextLost||(requestAnimationFrame(t),Y())}(),F.redraw=function(){e||(P=!0,Y())},F};var n=t("3d-view-controls"),i=t("gl-axes3d"),a=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("./lib/shader"),p=t("is-mobile")();function d(){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 g(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}},{"./lib/shader":276,"3d-view-controls":42,"a-big-triangle":50,"gl-axes3d":222,"gl-axes3d/properties":229,"gl-fbo":239,"gl-mat4/perspective":257,"gl-select-static":286,"gl-spikes3d":296,"is-mobile":388,"mouse-change":402}],278:[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:377}],279:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,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,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),h=new s(t,a,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,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||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),i||a.free(l),o||a.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,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),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/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[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,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":278,"gl-buffer":230,"gl-shader":287,"typedarray-pool":507}],280:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],h=e[2],f=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+h*g+f*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/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*m,t}},{}],281:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e){var r=i[e];r||(r=i[e]={});if(t in r)return r[t];for(var a=n(t,{textAlign:"center",textBaseline:"middle",lineHeight:1,font:e}),o=n(t,{triangles:!0,textAlign:"center",textBaseline:"middle",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<a.positions.length;++l)for(var c=a.positions[l],u=0;u<2;++u)s[0][u]=Math.min(s[0][u],c[u]),s[1][u]=Math.max(s[1][u],c[u]);return r[t]=[o,a,s]};var i={}},{"vectorize-text":515}],282:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision mediump float;\n#define GLSLIFY 1\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(any(lessThan(position, clipBounds[0]))   || \n     any(greaterThan(position, clipBounds[1])) ) {\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=i(["precision mediump float;\n#define GLSLIFY 1\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(any(lessThan(position, clipBounds[0])) || any(greaterThan(position, clipBounds[1]))) {\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=i(["precision mediump float;\n#define GLSLIFY 1\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(any(lessThan(position, clipBounds[0]))   ||\n     any(greaterThan(position, clipBounds[1])) ) {\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=i(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   ||\n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\n    discard;\n  } else {\n    gl_FragColor = interpColor * opacity;\n  }\n}\n"]),c=i(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   || \n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\n    discard;\n  } else {\n    gl_FragColor = vec4(pickGroup, pickId.bgr);\n  }\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],h={vertex:a,fragment:l,attributes:u},f={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,h)},r.createOrtho=function(t){return v(t,f)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{"gl-shader":287,glslify:377}],283:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("typedarray-pool"),o=t("gl-mat4/multiply"),s=t("./lib/shaders"),l=t("./lib/glyphs"),c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function u(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function h(t,e,r,n){return u(n,n),u(n,n),u(n,n)}function f(t,e){this.index=t,this.dataCoordinate=this.position=e}function p(t,e,r,n,i,a,o,s,l,c,u,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=h,this.points=[],this._selectResult=new f(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=s.createPerspective(e),a=s.createOrtho(e),o=s.createProject(e),l=s.createPickPerspective(e),c=s.createPickOrtho(e),u=s.createPickProject(e),h=n(e),f=n(e),d=n(e),g=n(e),m=i(e,[{buffer:h,size:3,type:e.FLOAT},{buffer:f,size:4,type:e.FLOAT},{buffer:d,size:2,type:e.FLOAT},{buffer:g,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new p(e,r,a,o,h,f,d,g,m,l,c,u);return v.update(t),v};var d=p.prototype;d.pickSlots=1,d.setPickBase=function(t){this.pickId=t},d.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},d.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var g=[0,0],m=[0,0,0],v=[0,0,0],y=[0,0,0,1],x=[0,0,0,1],b=c.slice(),_=[0,0,0],w=[[0,0,0],[0,0,0]];function k(t){return t[0]=t[1]=t[2]=0,t}function M(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function A(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function T(t,e,r,n,i){var a,s=e.axesProject,l=e.gl,u=t.uniforms,f=r.model||c,p=r.view||c,d=r.projection||c,T=e.axesBounds,S=function(t){for(var e=w,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],g[0]=2/l.drawingBufferWidth,g[1]=2/l.drawingBufferHeight,t.bind(),u.view=p,u.projection=d,u.screenSize=g,u.highlightId=e.highlightId,u.highlightScale=e.highlightScale,u.clipBounds=S,u.pickGroup=e.pickId/255,u.pixelRatio=e.pixelRatio;for(var E=0;E<3;++E)if(s[E]&&e.projectOpacity[E]<1===n){u.scale=e.projectScale[E],u.opacity=e.projectOpacity[E];for(var C=b,L=0;L<16;++L)C[L]=0;for(L=0;L<4;++L)C[5*L]=1;C[5*E]=0,a[E]<0?C[12+E]=T[0][E]:C[12+E]=T[1][E],o(C,f,C),u.model=C;var z=(E+1)%3,P=(E+2)%3,I=k(m),O=k(v);I[z]=1,O[P]=1;var D=h(0,0,0,M(y,I)),R=h(0,0,0,M(x,O));if(Math.abs(D[1])>Math.abs(R[1])){var B=D;D=R,R=B,B=I,I=O,O=B;var F=z;z=P,P=F}D[0]<0&&(I[z]=-1),R[1]>0&&(O[P]=-1);var N=0,j=0;for(L=0;L<4;++L)N+=Math.pow(f[4*z+L],2),j+=Math.pow(f[4*P+L],2);I[z]/=Math.sqrt(N),O[P]/=Math.sqrt(j),u.axes[0]=I,u.axes[1]=O,u.fragClipBounds[0]=A(_,S[0],E,-1e8),u.fragClipBounds[1]=A(_,S[1],E,1e8),e.vao.draw(l.TRIANGLES,e.vertexCount),e.lineWidth>0&&(l.lineWidth(e.lineWidth),e.vao.draw(l.LINES,e.lineVertexCount,e.vertexCount))}}var S=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function E(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1||a){t.bind();var s=t.uniforms;s.model=n.model||c,s.view=n.view||c,s.projection=n.projection||c,g[0]=2/o.drawingBufferWidth,g[1]=2/o.drawingBufferHeight,s.screenSize=g,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=S,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}T(e,r,n,i),r.vao.unbind()}d.draw=function(t){E(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},d.drawTransparent=function(t){E(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},d.drawPick=function(t){E(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},d.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 i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},d.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},d.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("projectOpacity"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}"opacity"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font||"normal",o=t.alignment||[0,0],s=[1/0,1/0,1/0],c=[-1/0,-1/0,-1/0],u=t.glyph,h=t.color,f=t.size,p=t.angle,d=t.lineColor,g=0,m=0,v=0,y=n.length;t:for(var x=0;x<y;++x){for(var b=n[x],_=0;_<3;++_)if(isNaN(b[_])||!isFinite(b[_]))continue t;var w=(B=Array.isArray(u)?l(u[x],i):l(u||"\u25cf",i))[0],k=B[1],M=B[2];m+=3*w.cells.length,v+=2*k.edges.length}var A=m+v,T=a.mallocFloat(3*A),S=a.mallocFloat(4*A),E=a.mallocFloat(2*A),C=a.mallocUint32(A),L=[0,o[1]],z=0,P=m,I=[0,0,0,1],O=[0,0,0,1],D=Array.isArray(h)&&Array.isArray(h[0]),R=Array.isArray(d)&&Array.isArray(d[0]);t:for(x=0;x<y;++x){var B;for(b=n[x],_=0;_<3;++_){if(isNaN(b[_])||!isFinite(b[_])){g+=1;continue t}c[_]=Math.max(c[_],b[_]),s[_]=Math.min(s[_],b[_])}var F;w=(B=Array.isArray(u)?l(u[x],i):l(u||"\u25cf",i))[0],k=B[1],M=B[2];if(Array.isArray(h)){if(3===(F=D?h[x]:h).length){for(_=0;_<3;++_)I[_]=F[_];I[3]=1}else if(4===F.length)for(_=0;_<4;++_)I[_]=F[_]}else 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X=H[G[W]];E[2*z]=N*(V*X[0]-U*X[1]+L[0]),E[2*z+1]=N*(U*X[0]+V*X[1]+L[1]),z+=1}for(q=k.edges,H=k.positions,_=0;_<q.length;++_)for(G=q[_],W=0;W<2;++W){for(Y=0;Y<3;++Y)T[3*P+Y]=b[Y];for(Y=0;Y<4;++Y)S[4*P+Y]=O[Y];C[P]=g;X=H[G[W]];E[2*P]=N*(V*X[0]-U*X[1]+L[0]),E[2*P+1]=N*(U*X[0]+V*X[1]+L[1]),P+=1}g+=1}this.vertexCount=m,this.lineVertexCount=v,this.pointBuffer.update(T),this.colorBuffer.update(S),this.glyphBuffer.update(E),this.idBuffer.update(new Uint32Array(C)),a.free(T),a.free(S),a.free(E),a.free(C),this.bounds=[s,c],this.points=n,this.pointCount=n.length}},d.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/glyphs":281,"./lib/shaders":282,"gl-buffer":230,"gl-mat4/multiply":256,"gl-vao":308,"typedarray-pool":507}],284:[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:377}],285:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=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=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.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),i=(this.outerFill,this.outerColor),a=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],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,f,i),o.drawBox(0,f,h,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,f,g,d,i)),this.innerFill&&o.drawBox(h,f,p,d,n),r>0){var v=r*u;o.drawBox(h-v,f-v,p+v,f+v,a),o.drawBox(h-v,d-v,p+v,d+v,a),o.drawBox(h-v,f-v,h+v,d+v,a),o.drawBox(p-v,f-v,p+v,d+v,a)}}}},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":284,"gl-buffer":230,"gl-shader":287}],286:[function(t,e,r){"use strict";e.exports=function(t,e){var r=n(t,e),a=i.mallocUint8(e[0]*e[1]*4);return new c(t,r,a)};var n=t("gl-fbo"),i=t("typedarray-pool"),a=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 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l(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}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){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=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 i=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<=i||u<=c)return null;var h=[o-i,u-c],f=a(this.buffer,[h[0],h[1],4],[4,4*n[0],1],4*(i+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+i|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(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{"bit-twiddle":80,"cwise/lib/wrapper":136,"gl-fbo":239,ndarray:417,"typedarray-pool":507}],287:[function(t,e,r){"use strict";var n=t("./lib/create-uniforms"),i=t("./lib/create-attributes"),a=t("./lib/reflect"),o=t("./lib/shader-cache"),s=t("./lib/runtime-reflect"),l=t("./lib/GLError");function 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f=this,p=f.gl,d=f._vref;f._vref=o.shader(p,p.VERTEX_SHADER,t),d&&d.dispose(),f.vertShader=f._vref.shader;var g=this._fref;if(f._fref=o.shader(p,p.FRAGMENT_SHADER,e),g&&g.dispose(),f.fragShader=f._fref.shader,!r||!c){var m=p.createProgram();if(p.attachShader(m,f.fragShader),p.attachShader(m,f.vertShader),p.linkProgram(m),!p.getProgramParameter(m,p.LINK_STATUS)){var v=p.getProgramInfoLog(m);throw new l(v,"Error linking program:"+v)}r=r||s.uniforms(p,m),c=c||s.attributes(p,m),p.deleteProgram(m)}(c=c.slice()).sort(h);var y,x=[],b=[],_=[];for(y=0;y<c.length;++y){var w=c[y];if(w.type.indexOf("mat")>=0){for(var k=0|w.type.charAt(w.type.length-1),M=new Array(k),A=0;A<k;++A)M[A]=_.length,b.push(w.name+"["+A+"]"),"number"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===k&&"number"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:M})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),"number"==typeof w.location?_.push(0|w.location):_.push(-1)}var T=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(T)>=0;)T+=1;_[y]=T}var S=new Array(r.length);function E(){f.program=o.program(p,f._vref,f._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(f.program,r[t].name)}E(),f._relink=E,f.types={uniforms:a(r),attributes:a(c)},f.attributes=i(p,f,x,_),Object.defineProperty(f,"uniforms",n(p,f,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return a.update(e,r,n,i),a}},{"./lib/GLError":288,"./lib/create-attributes":289,"./lib/create-uniforms":290,"./lib/reflect":291,"./lib/runtime-reflect":292,"./lib/shader-cache":293}],288:[function(t,e,r){function n(t,e,r){this.shortMessage=e||"",this.longMessage=r||"",this.rawError=t||"",this.message="gl-shader: "+(e||t||"")+(r?"\n"+r:""),this.stack=(new Error).stack}n.prototype=new 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l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],295:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, color;\nattribute float weight;\n\nuniform mat4 model, view, projection;\nuniform vec3 coordinates[3];\nuniform vec4 colors[3];\nuniform vec2 screenShape;\nuniform float lineWidth;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vertexPosition = mix(coordinates[0],\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\n\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\n  vec2 delta = weight * clipOffset * screenShape;\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n  gl_FragColor = fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},{"gl-shader":287,glslify:377}],296:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var 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l=s.prototype,c=[0,0,0],u=[0,0,0],h=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var f=c,p=u,d=0;d<3;++d)i&&i[d]<0?(f[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(f[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);h[0]=e.drawingBufferWidth,h[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,f,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=h;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders/index":295,"gl-buffer":230,"gl-vao":308}],297:[function(t,e,r){arguments[4][232][0].apply(r,arguments)},{barycentric:61,dup:232,"polytope-closest-point/lib/closest_point_2d.js":448}],298:[function(t,e,r){var n=t("glslify"),i=n(["precision mediump float;\n#define GLSLIFY 1\n\nfloat inverse(float m) {\n  return 1.0 / m;\n}\n\nmat2 inverse(mat2 m) {\n  return mat2(m[1][1],-m[0][1],\n             -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\n}\n\nmat3 inverse(mat3 m) {\n  float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\n  float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\n  float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\n\n  float b01 = a22 * a11 - a12 * a21;\n  float b11 = -a22 * a10 + a12 * a20;\n  float b21 = a21 * a10 - a11 * a20;\n\n  float det = a00 * b01 + a01 * b11 + a02 * b21;\n\n  return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\n              b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\n              b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\n}\n\nmat4 inverse(mat4 m) {\n  float\n      a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\n      a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\n      a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\n      a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\n\n      b00 = a00 * a11 - a01 * a10,\n      b01 = a00 * a12 - a02 * a10,\n      b02 = a00 * a13 - a03 * a10,\n      b03 = a01 * a12 - a02 * a11,\n      b04 = a01 * a13 - a03 * a11,\n      b05 = a02 * a13 - a03 * a12,\n      b06 = a20 * a31 - a21 * a30,\n      b07 = a20 * a32 - a22 * a30,\n      b08 = a20 * a33 - a23 * a30,\n      b09 = a21 * a32 - a22 * a31,\n      b10 = a21 * a33 - a23 * a31,\n      b11 = a22 * a33 - a23 * a32,\n\n      det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\n\n  return mat4(\n      a11 * b11 - a12 * b10 + a13 * b09,\n      a02 * b10 - a01 * b11 - a03 * b09,\n      a31 * b05 - a32 * b04 + a33 * b03,\n      a22 * b04 - a21 * b05 - a23 * b03,\n      a12 * b08 - a10 * b11 - a13 * b07,\n      a00 * b11 - a02 * b08 + a03 * b07,\n      a32 * b02 - a30 * b05 - a33 * b01,\n      a20 * b05 - a22 * b02 + a23 * b01,\n      a10 * b10 - a11 * b08 + a13 * b06,\n      a01 * b08 - a00 * b10 - a03 * b06,\n      a30 * b04 - a31 * b02 + a33 * b00,\n      a21 * b02 - a20 * b04 - a23 * b00,\n      a11 * b07 - a10 * b09 - a12 * b06,\n      a00 * b09 - a01 * b07 + a02 * b06,\n      a31 * b01 - a30 * b03 - a32 * b00,\n      a20 * b03 - a21 * b01 + a22 * b00) / det;\n}\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 tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\nuniform float tubeScale;\n\nuniform mat4 model\n           , view\n           , projection;\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\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal), 0.0);\n  normal = normalize(normal * inverse(mat3(model)));\n\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n  f_color          = color;\n  f_normal         = normal;\n  f_data           = tubePosition.xyz;\n  f_eyeDirection   = eyePosition   - tubePosition.xyz;\n  f_lightDirection = lightPosition - tubePosition.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["precision mediump 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\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\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  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 = cookTorranceSpecular(L, V, N, roughness, fresnel);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor =  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}"]),o=n(["precision mediump 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 tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal), 0.0);\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:377}],299:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=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("./shaders"),g=(t("./closest-point"),d.meshShader),m=d.pickShader,v=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,y,x,b,_,w,k,M,A,T,S,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=c,this.triangleColors=h,this.triangleNormals=p,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=x,this.edgeIds=m,this.edgeVAO=b,this.edgeCount=0,this.pointPositions=_,this.pointColors=k,this.pointUVs=M,this.pointSizes=A,this.pointIds=w,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=S,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!1,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.tubeScale=1,this._model=v,this._view=v,this._projection=v,this._resolution=[1,1]}var x=y.prototype;function b(t){var e=n(t,m.vertex,m.fragment,null,m.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(t){this.pickId=t},x.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++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],m=i[h],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},x.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),"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),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){for(var e=h({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale);var a=[],l=[],c=[],f=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[],w=[],k=[];this.cells=r,this.positions=n,this.vectors=i;var M=t.vertexNormals,A=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,S)),A||M||(M=s.vertexNormals(r,n,T));var E=t.vertexColors,C=t.cellColors,L=t.meshColor||[1,1,1,1],z=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,D=1/0,R=-1/0;if(!z&&!I)if(P)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],R=+t.vertexIntensityBounds[1];else for(var B=0;B<P.length;++B){var F=P[B];D=Math.min(D,F),R=Math.max(R,F)}else if(O)for(B=0;B<O.length;++B){F=O[B];D=Math.min(D,F),R=Math.max(R,F)}else for(B=0;B<n.length;++B){F=n[B][2];D=Math.min(D,F),R=Math.max(R,F)}this.intensity=P||(O?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,O):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var N=t.pointSizes,j=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(B=0;B<n.length;++B)for(var V=n[B],U=0;U<3;++U)!isNaN(V[U])&&isFinite(V[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],V[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],V[U]));var q=0,H=0,G=0;t:for(B=0;B<r.length;++B){var W=r[B];switch(W.length){case 1:for(V=n[X=W[0]],U=0;U<3;++U)if(isNaN(V[U])||!isFinite(V[U]))continue t;x.push(V[0],V[1],V[2],V[3]),3===(Z=E?E[X]:C?C[B]:L).length?b.push(Z[0],Z[1],Z[2],1):b.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],_.push($[0],$[1]),N?w.push(N[X]):w.push(j),k.push(B),G+=1;break;case 2:for(U=0;U<2;++U){V=n[X=W[U]];for(var Y=0;Y<3;++Y)if(isNaN(V[Y])||!isFinite(V[Y]))continue t}for(U=0;U<2;++U){V=n[X=W[U]];g.push(V[0],V[1],V[2]),3===(Z=E?E[X]:C?C[B]:L).length?m.push(Z[0],Z[1],Z[2],1):m.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],v.push($[0],$[1]),y.push(B)}H+=1;break;case 3:for(U=0;U<3;++U)for(V=n[X=W[U]],Y=0;Y<3;++Y)if(isNaN(V[Y])||!isFinite(V[Y]))continue t;for(U=0;U<3;++U){var X;V=n[X=W[U]];a.push(V[0],V[1],V[2],V[3]);var Z,$,J,K=i[X];l.push(K[0],K[1],K[2],K[3]),3===(Z=E?E[X]:C?C[B]:L).length?c.push(Z[0],Z[1],Z[2],1):c.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],p.push($[0],$[1]),J=M?M[X]:A[B],f.push(J[0],J[1],J[2]),d.push(B)}q+=1}}this.pointCount=G,this.edgeCount=H,this.triangleCount=q,this.pointPositions.update(x),this.pointColors.update(b),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(g),this.edgeColors.update(m),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(c),this.triangleUVs.update(p),this.triangleNormals.update(f),this.triangleIds.update(new Uint32Array(d))}},x.drawTransparent=x.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||v,n=t.view||v,i=t.projection||v,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,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,contourColor:this.contourColor,texture:0};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),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())},x.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||v,n=t.view||v,i=t.projection||v,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,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),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())},x.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);return{index:e,position:n,intensity:this.intensity[r[1]],velocity:this.vectors[r[1]].slice(0,3),divergence:this.vectors[r[1]][3],dataCoordinate:n}},x.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.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()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader||function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=b(t),l=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var c=i(t),h=i(t),f=i(t),p=i(t),d=i(t),m=i(t),v=a(t,[{buffer:c,type:t.FLOAT,size:4},{buffer:m,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:f,type:t.FLOAT,size:4},{buffer:p,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:3},{buffer:h,type:t.FLOAT,size:4}]),x=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:x,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),A=i(t),T=i(t),S=i(t),E=i(t),C=i(t),L=a(t,[{buffer:A,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2},{buffer:E,type:t.FLOAT,size:1}]),z=i(t),P=new y(t,l,r,null,null,s,null,null,c,h,m,f,p,d,v,x,k,_,w,M,A,C,T,S,E,L,z,a(t,[{buffer:z,type:t.FLOAT,size:3}]));return P.update(e),P}},{"./closest-point":297,"./shaders":298,colormap:113,"gl-buffer":230,"gl-mat4/invert":254,"gl-mat4/multiply":256,"gl-shader":287,"gl-texture2d":303,"gl-vao":308,ndarray:417,normals:420,"simplicial-complex-contour":479,"typedarray-pool":507}],300:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++){var u=n.length(l[c]);u>o&&(o=u)}var h=t.map(function(t){return function(t,e,r,a){var o,s,l,c=t.points,u=t.velocities,h=t.divergences;n.set(n.create(),0,1,0),n.create(),n.create();n.create();for(var f=[],p=[],d=[],g=[],m=[],v=[],y=0,x=0,b=i.create(),_=i.create(),w=0;w<c.length;w++){o=c[w],s=u[w],l=h[w],0===e&&(l=.05*r),x=n.length(s)/a,b=i.create(),n.copy(b,s),b[3]=l;for(var k=0;k<8;k++)m[k]=[o[0],o[1],o[2],k];if(g.length>0)for(k=0;k<8;k++){var M=(k+1)%8;f.push(g[k],m[k],m[M],m[M],g[M],g[k]),d.push(_,b,b,b,_,_),v.push(y,x,x,x,y,y),p.push([f.length-6,f.length-5,f.length-4],[f.length-3,f.length-2,f.length-1])}var A=g;g=m,m=A,A=_,_=b,b=A,A=y,y=x,x=A}return{positions:f,cells:p,vectors:d,vertexIntensity:v}}(t,r,a,o)}),f=[],p=[],d=[],g=[];for(s=0;s<h.length;s++){var m=h[s],v=f.length;f=f.concat(m.positions),d=d.concat(m.vectors),g=g.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var y=m.cells[c],x=[];p.push(x);for(var b=0;b<y.length;b++)x.push(y[b]+v)}}return{positions:f,cells:p,vectors:d,vertexIntensity:g,colormap:e}},o=function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=this.getVelocity(r);n.subtract(a,a,e),n.scale(a,a,1e4),n.add(r,t,[0,i,0]);var o=this.getVelocity(r);n.subtract(o,o,e),n.scale(o,o,1e4),n.add(r,t,[0,0,i]);var s=this.getVelocity(r);return n.subtract(s,s,e),n.scale(s,s,1e4),n.add(r,a,o),n.add(r,r,s),r},s=function(t){return f(t,this.vectors,this.meshgrid,this.clampBorders)},l=function(t,e){for(var r=0;r<t.length;r++){var n=t[r];if(n===e)return r;if(n>e)return r-1}return r},c=n.create(),u=n.create(),h=function(t,e,r){return t<e?e:t>r?r:t},f=function(t,e,r,i){var a=t[0],o=t[1],s=t[2],f=r[0].length,p=r[1].length,d=r[2].length,g=l(r[0],a),m=l(r[1],o),v=l(r[2],s),y=g+1,x=m+1,b=v+1;if(r[0][g]===a&&(y=g),r[1][m]===o&&(x=m),r[2][v]===s&&(b=v),i&&(g=h(g,0,f-1),y=h(y,0,f-1),m=h(m,0,p-1),x=h(x,0,p-1),v=h(v,0,d-1),b=h(b,0,d-1)),g<0||m<0||v<0||y>=f||x>=p||b>=d)return n.create();var _=(a-r[0][g])/(r[0][y]-r[0][g]),w=(o-r[1][m])/(r[1][x]-r[1][m]),k=(s-r[2][v])/(r[2][b]-r[2][v]);(_<0||_>1||isNaN(_))&&(_=0),(w<0||w>1||isNaN(w))&&(w=0),(k<0||k>1||isNaN(k))&&(k=0);var M=v*f*p,A=b*f*p,T=m*f,S=x*f,E=g,C=y,L=e[T+M+E],z=e[T+M+C],P=e[S+M+E],I=e[S+M+C],O=e[T+A+E],D=e[T+A+C],R=e[S+A+E],B=e[S+A+C],F=n.create();return n.lerp(F,L,z,_),n.lerp(c,P,I,_),n.lerp(F,F,c,w),n.lerp(c,O,D,_),n.lerp(u,R,B,_),n.lerp(c,c,u,w),n.lerp(F,F,c,k),F},p=function(t){var e=1/0;t.sort(function(t,e){return t-e});for(var r=1;r<t.length;r++){var n=Math.abs(t[r]-t[r-1]);n<e&&(e=n)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,l=t.tubeSize||1,c=t.absoluteTubeSize;t.getDivergence||(t.getDivergence=o),t.getVelocity||(t.getVelocity=s),void 0===t.clampBorders&&(t.clampBorders=!0);var u=[],h=e[0][0],f=e[0][1],d=e[0][2],g=e[1][0],m=e[1][1],v=e[1][2],y=function(t,e){var r=e[0],n=e[1],i=e[2];return r>=h&&r<=g&&n>=f&&n<=m&&i>=d&&i<=v},x=10*n.distance(e[0],e[1])/i,b=x*x,_=1,w=0;n.create();r.length>=2&&(_=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=0;s<t.length;s++){var l=t[s],c=l[0],u=l[1],h=l[2];i[c]||(e.push(c),i[c]=!0),a[u]||(r.push(u),a[u]=!0),o[h]||(n.push(h),o[h]=!0)}var f=p(e),d=p(r),g=p(n),m=Math.min(f,d,g);return isFinite(m)?m:1}(r));for(var k=0;k<r.length;k++){var M=n.create();n.copy(M,r[k]);var A=[M],T=[],S=t.getVelocity(M),E=M;T.push(S);var C=[],L=t.getDivergence(M,S);(O=n.length(L))>w&&!isNaN(O)&&isFinite(O)&&(w=O),C.push(O),u.push({points:A,velocities:T,divergences:C});for(var z=0;z<100*i&&A.length<i&&y(0,M);){z++;var P=n.clone(S),I=n.squaredLength(P);if(0===I)break;if(I>b&&n.scale(P,P,x/Math.sqrt(I)),n.add(P,P,M),S=t.getVelocity(P),n.squaredDistance(E,P)-b>-1e-4*b){A.push(P),E=P,T.push(S);L=t.getDivergence(P,S);(O=n.length(L))>w&&!isNaN(O)&&isFinite(O)&&(w=O),C.push(O)}M=P}}for(k=0;k<C.length;k++){var O=C[k];!isNaN(O)&&isFinite(O)||(C[k]=w)}var D=a(u,t.colormap,w,_);return c?D.tubeScale=c:(0===w&&(w=1),D.tubeScale=.5*l*_/w),D},e.exports.createTubeMesh=t("./lib/tubemesh")},{"./lib/tubemesh":299,"gl-vec3":320,"gl-vec4":350}],301:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  worldCoordinate = vec3(uv.zw, f.x);\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision mediump 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 beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (kill > 0.0 ||\n    any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\n    discard;\n  }\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor = step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) + step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z = clipPosition.z + zOffset;\n\n  gl_Position = clipPosition;\n  value = f;\n  kill = -1.0;\n  worldCoordinate = dataCoordinate;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if(kill > 0.0 ||\n    any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\n    discard;\n  }\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":287,glslify:377}],302:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:w,offset:0},{buffer:c,size:3,stride:w,offset:16},{buffer:c,size:3,stride:w,offset:28}]),h=i(e),f=a(e,[{buffer:h,size:4,stride:20,offset:0},{buffer:h,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,h,f,p,d),v={levels:[[],[],[]]};for(var k in t)v[k]=t[k];return v.colormap=v.colormap||"jet",m.update(v),m};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),h=t("ndarray"),f=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),g=t("binary-search-bounds"),m=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=40,k=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],M=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function T(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,c,u,f,p,d,g){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=f,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new T([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var C=E.prototype;C.isTransparent=function(){return this.opacity<1},C.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},C.pickSlots=1,C.setPickBase=function(t){this.pickId=t};var L=[0,0,0],z={showSurface:!1,showContour:!1,projections:[k.slice(),k.slice(),k.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function P(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||L,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=z.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=z.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return z.showSurface=o,z.showContour=s,z}var I={model:k,view:k,projection:k,inverseModel:k.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},O=k.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||k,n.view=t.view||k,n.projection=t.projection||k,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=O;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=P(n,this);if(u.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour&&!e){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var f=this._contourVAO;for(f.bind(),i=0;i<3;++i)for(h.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(h.uniforms.contourColor=this.highlightColor[i],h.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(h.uniforms.contourColor=this.contourColor[i],h.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(h.uniforms.height=this.contourLevels[i][o],f.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(h.uniforms.model=u.projections[i],h.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){h.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(h.uniforms.contourColor=this.highlightColor[o],h.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(h.uniforms.contourColor=this.contourColor[o],h.uniforms.contourTint=this.contourTint[o]),h.uniforms.height=this.contourLevels[o][g],f.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g])}for(f.unbind(),(f=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(h.uniforms.model=n.model,h.uniforms.clipBounds=n.clipBounds,h.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]),h.uniforms.contourColor=this.dynamicColor[i],h.uniforms.contourTint=this.dynamicTint[i],h.uniforms.height=this.dynamicLevel[i],f.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(h.uniforms.model=u.projections[o],h.uniforms.clipBounds=u.clipBounds[o],f.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));f.unbind()}}C.draw=function(t){return R.call(this,t,!1)},C.drawTransparent=function(t){return R.call(this,t,!0)};var B={model:k,view:k,projection:k,inverseModel:k,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function F(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function N(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function j(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function V(t){if(Array.isArray(t)){if(Array.isArray(t))return[j(t[0]),j(t[1]),j(t[2])];var e=j(t);return[e.slice(),e.slice(),e.slice()]}}C.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=B;r.model=t.model||k,r.view=t.view||k,r.projection=t.projection||k,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=D;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=P(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},C.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var h=u?a:1-a,f=0;f<2;++f)for(var p=i+u,d=s+f,m=h*(f?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},C.update=function(t){t=t||{},this.dirty=!0,"contourWidth"in t&&(this.contourWidth=N(t.contourWidth,Number)),"showContour"in t&&(this.showContour=N(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=N(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=V(t.contourColor)),"contourProject"in t&&(this.contourProject=N(t.contourProject,function(t){return N(t,Boolean)})),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=V(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=N(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=N(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=h(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),F(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var p=t.coords;if(!Array.isArray(p)||3!==p.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var d=p[o];for(b=0;b<2;++b)if(d.shape[b]!==a[b])throw new Error("gl-surface: coords have incorrect shape");F(this._field[o],d)}}else if(t.ticks){var g=t.ticks;if(!Array.isArray(g)||2!==g.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var v=g[o];if((Array.isArray(v)||v.length)&&(v=h(v)),v.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var y=h(v.data,a);y.stride[o]=v.stride[0],y.stride[1^o]=0,F(this._field[o],y)}}else{for(o=0;o<2;++o){var x=[0,0];x[o]=1,this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2],x,0)}this._field[0].set(0,0,0);for(var b=0;b<a[0];++b)this._field[0].set(b+1,0,b);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),b=0;b<a[1];++b)this._field[1].set(0,b+1,b);this._field[1].set(0,a[1]+1,a[1]-1)}var _=this._field,w=h(s.mallocFloat(3*_[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)m(w.pick(o),_[o],"mirror");var k=h(s.mallocFloat(3*_[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(b=0;b<a[1]+2;++b){var A=w.get(0,o,b,0),T=w.get(0,o,b,1),E=w.get(1,o,b,0),C=w.get(1,o,b,1),L=w.get(2,o,b,0),z=w.get(2,o,b,1),P=E*z-C*L,I=L*T-z*A,O=A*C-T*E,D=Math.sqrt(P*P+I*I+O*O);D<1e-8?(D=Math.max(Math.abs(P),Math.abs(I),Math.abs(O)))<1e-8?(O=1,I=P=0,D=1):D=1/D:D=1/Math.sqrt(D),k.set(o,b,0,P*D),k.set(o,b,1,I*D),k.set(o,b,2,O*D)}s.free(w.data);var R=[1/0,1/0,1/0],B=[-1/0,-1/0,-1/0],j=1/0,U=-1/0,q=(a[0]-1)*(a[1]-1)*6,H=s.mallocFloat(n.nextPow2(10*q)),G=0,W=0;for(o=0;o<a[0]-1;++o)t:for(b=0;b<a[1]-1;++b){for(var Y=0;Y<2;++Y)for(var X=0;X<2;++X)for(var Z=0;Z<3;++Z){var $=this._field[Z].get(1+o+Y,1+b+X);if(isNaN($)||!isFinite($))continue t}for(Z=0;Z<6;++Z){var J=o+M[Z][0],K=b+M[Z][1],Q=this._field[0].get(J+1,K+1),tt=this._field[1].get(J+1,K+1),et=$=this._field[2].get(J+1,K+1);P=k.get(J+1,K+1,0),I=k.get(J+1,K+1,1),O=k.get(J+1,K+1,2),t.intensity&&(et=t.intensity.get(J,K)),H[G++]=J,H[G++]=K,H[G++]=Q,H[G++]=tt,H[G++]=$,H[G++]=0,H[G++]=et,H[G++]=P,H[G++]=I,H[G++]=O,R[0]=Math.min(R[0],Q),R[1]=Math.min(R[1],tt),R[2]=Math.min(R[2],$),j=Math.min(j,et),B[0]=Math.max(B[0],Q),B[1]=Math.max(B[1],tt),B[2]=Math.max(B[2],$),U=Math.max(U,et),W+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],U=+t.intensityBounds[1]),o=6;o<G;o+=10)H[o]=(H[o]-j)/(U-j);this._vertexCount=W,this._coordinateBuffer.update(H.subarray(0,G)),s.freeFloat(H),s.free(k.data),this.bounds=[R,B],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===U||(r=!0),this.intensityBounds=[j,U]}if("levels"in t){var rt=t.levels;for(rt=Array.isArray(rt[0])?rt.slice():[[],[],rt],o=0;o<3;++o)rt[o]=rt[o].slice(),rt.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if(rt[o].length!==this.contourLevels[o].length){r=!0;break}for(b=0;b<rt[o].length;++b)if(rt[o][b]!==this.contourLevels[o][b]){r=!0;break t}}this.contourLevels=rt}if(r){_=this._field,a=this.shape;for(var nt=[],it=0;it<3;++it){rt=this.contourLevels[it];var at=[],ot=[],st=[0,0,0];for(o=0;o<rt.length;++o){var lt=f(this._field[it],rt[o]);at.push(nt.length/5|0),W=0;t:for(b=0;b<lt.cells.length;++b){var ct=lt.cells[b];for(Z=0;Z<2;++Z){var ut=lt.positions[ct[Z]],ht=ut[0],ft=0|Math.floor(ht),pt=ht-ft,dt=ut[1],gt=0|Math.floor(dt),mt=dt-gt,vt=!1;e:for(var yt=0;yt<3;++yt){st[yt]=0;var xt=(it+yt+1)%3;for(Y=0;Y<2;++Y){var bt=Y?pt:1-pt;for(J=0|Math.min(Math.max(ft+Y,0),a[0]),X=0;X<2;++X){var _t=X?mt:1-mt;if(K=0|Math.min(Math.max(gt+X,0),a[1]),$=yt<2?this._field[xt].get(J,K):(this.intensity.get(J,K)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite($)||isNaN($)){vt=!0;break e}var wt=bt*_t;st[yt]+=wt*$}}}if(vt){if(Z>0){for(var kt=0;kt<5;++kt)nt.pop();W-=1}continue t}nt.push(st[0],st[1],ut[0],ut[1],st[2]),W+=1}}ot.push(W)}this._contourOffsets[it]=at,this._contourCounts[it]=ot}var Mt=s.mallocFloat(nt.length);for(o=0;o<nt.length;++o)Mt[o]=nt[o];this._contourBuffer.update(Mt),s.freeFloat(Mt)}t.colormap&&this._colorMap.setPixels(function(t){var e=u([l({colormap:t,nshades:S,format:"rgba"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return c.divseq(e,255),e}(t.colormap))},C.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},C.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],h=this._field[l],p=this._field[c],d=(this.intensity,f(u,r[o])),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),k=b-_,M=1-k,A=+x[1],T=0|A,S=0|Math.min(T+1,i[1]),E=A-T,C=1-E,L=M*C,z=M*E,P=k*C,I=k*E,O=L*h.get(_,T)+z*h.get(_,S)+P*h.get(w,T)+I*h.get(w,S),D=L*p.get(_,T)+z*p.get(_,S)+P*p.get(w,T)+I*p.get(w,S);if(isNaN(O)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=O,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":301,"binary-search-bounds":79,"bit-twiddle":80,colormap:113,"gl-buffer":230,"gl-mat4/invert":254,"gl-mat4/multiply":256,"gl-texture2d":303,"gl-vao":308,ndarray:417,"ndarray-gradient":408,"ndarray-ops":411,"ndarray-pack":412,"surface-nets":493,"typedarray-pool":507}],303:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t);if("number"==typeof arguments[1])return m(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return m(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=c(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new f(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error("gl-texture2d: Invalid texture size");var l=d(o,e.stride.slice()),c=0;"float32"===r?c=t.FLOAT:"float64"===r?(c=t.FLOAT,l=!1,r="float32"):"uint8"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r="uint8");var h,p,m=0;if(2===o.length)m=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===o[2])m=t.ALPHA;else if(2===o[2])m=t.LUMINANCE_ALPHA;else if(3===o[2])m=t.RGB;else{if(4!==o[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");m=t.RGBA}}c!==t.FLOAT||t.getExtension("OES_texture_float")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)h=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];p=a.malloc(v,r);var x=n(p,o,y,0);"float32"!==r&&"float64"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):u(x,e),h=p.subarray(0,v)}var b=g(t);t.texImage2D(t.TEXTURE_2D,0,m,o[0],o[1],0,m,c,h),l||a.free(p);return new f(t,b,o[0],o[1],m,c)}(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var u=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function 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Math.ceil(t/e)*e}Yr.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Yr.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Yr.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Yr.prototype.clear=function(){this.length=0},Yr.prototype.resize=function(t){this.reserve(t),this.length=t},Yr.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Yr.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray 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Kr=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;this.resize(o+1);var s=6*o;return this.int16[s+0]=t,this.int16[s+1]=e,this.int16[s+2]=r,this.int16[s+3]=n,this.int16[s+4]=i,this.int16[s+5]=a,o},e}(Yr);Kr.prototype.bytesPerElement=12,pr("StructArrayLayout2i4i12",Kr);var Qr=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s){var l=this.length;this.resize(l+1);var c=6*l,u=12*l;return this.int16[c+0]=t,this.int16[c+1]=e,this.int16[c+2]=r,this.int16[c+3]=n,this.uint8[u+8]=i,this.uint8[u+9]=a,this.uint8[u+10]=o,this.uint8[u+11]=s,l},e}(Yr);Qr.prototype.bytesPerElement=12,pr("StructArrayLayout4i4ub12",Qr);var tn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s){var l=this.length;this.resize(l+1);var c=8*l;return this.int16[c+0]=t,this.int16[c+1]=e,this.int16[c+2]=r,this.int16[c+3]=n,this.uint16[c+4]=i,this.uint16[c+5]=a,this.uint16[c+6]=o,this.uint16[c+7]=s,l},e}(Yr);tn.prototype.bytesPerElement=16,pr("StructArrayLayout4i4ui16",tn);var en=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3*n;return this.float32[i+0]=t,this.float32[i+1]=e,this.float32[i+2]=r,n},e}(Yr);en.prototype.bytesPerElement=12,pr("StructArrayLayout3f12",en);var rn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;this.resize(e+1);var r=1*e;return this.uint32[r+0]=t,e},e}(Yr);rn.prototype.bytesPerElement=4,pr("StructArrayLayout1ul4",rn);var nn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u){var h=this.length;this.resize(h+1);var f=12*h,p=6*h;return this.int16[f+0]=t,this.int16[f+1]=e,this.int16[f+2]=r,this.int16[f+3]=n,this.int16[f+4]=i,this.int16[f+5]=a,this.uint32[p+3]=o,this.uint16[f+8]=s,this.uint16[f+9]=l,this.int16[f+10]=c,this.int16[f+11]=u,h},e}(Yr);nn.prototype.bytesPerElement=24,pr("StructArrayLayout6i1ul2ui2i24",nn);var an=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;this.resize(o+1);var s=6*o;return this.int16[s+0]=t,this.int16[s+1]=e,this.int16[s+2]=r,this.int16[s+3]=n,this.int16[s+4]=i,this.int16[s+5]=a,o},e}(Yr);an.prototype.bytesPerElement=12,pr("StructArrayLayout2i2i2i12",an);var on=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=4*r;return this.uint8[n+0]=t,this.uint8[n+1]=e,r},e}(Yr);on.prototype.bytesPerElement=4,pr("StructArrayLayout2ub4",on);var sn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p){var d=this.length;this.resize(d+1);var g=20*d,m=10*d,v=40*d;return this.int16[g+0]=t,this.int16[g+1]=e,this.uint16[g+2]=r,this.uint16[g+3]=n,this.uint32[m+2]=i,this.uint32[m+3]=a,this.uint32[m+4]=o,this.uint16[g+10]=s,this.uint16[g+11]=l,this.uint16[g+12]=c,this.float32[m+7]=u,this.float32[m+8]=h,this.uint8[v+36]=f,this.uint8[v+37]=p,d},e}(Yr);sn.prototype.bytesPerElement=40,pr("StructArrayLayout2i2ui3ul3ui2f2ub40",sn);var ln=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;this.resize(e+1);var r=1*e;return this.float32[r+0]=t,e},e}(Yr);ln.prototype.bytesPerElement=4,pr("StructArrayLayout1f4",ln);var cn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3*n;return this.int16[i+0]=t,this.int16[i+1]=e,this.int16[i+2]=r,n},e}(Yr);cn.prototype.bytesPerElement=6,pr("StructArrayLayout3i6",cn);var un=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=2*n,a=4*n;return this.uint32[i+0]=t,this.uint16[a+2]=e,this.uint16[a+3]=r,n},e}(Yr);un.prototype.bytesPerElement=8,pr("StructArrayLayout1ul2ui8",un);var hn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3*n;return this.uint16[i+0]=t,this.uint16[i+1]=e,this.uint16[i+2]=r,n},e}(Yr);hn.prototype.bytesPerElement=6,pr("StructArrayLayout3ui6",hn);var fn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=2*r;return this.uint16[n+0]=t,this.uint16[n+1]=e,r},e}(Yr);fn.prototype.bytesPerElement=4,pr("StructArrayLayout2ui4",fn);var pn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=2*r;return this.float32[n+0]=t,this.float32[n+1]=e,r},e}(Yr);pn.prototype.bytesPerElement=8,pr("StructArrayLayout2f8",pn);var dn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;this.resize(i+1);var a=4*i;return this.float32[a+0]=t,this.float32[a+1]=e,this.float32[a+2]=r,this.float32[a+3]=n,i},e}(Yr);dn.prototype.bytesPerElement=16,pr("StructArrayLayout4f16",dn);var gn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},radius:{configurable:!0},signedDistanceFromAnchor:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointX.set=function(t){this._structArray.int16[this._pos2+0]=t},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.anchorPointY.set=function(t){this._structArray.int16[this._pos2+1]=t},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.x1.set=function(t){this._structArray.int16[this._pos2+2]=t},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.y1.set=function(t){this._structArray.int16[this._pos2+3]=t},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.x2.set=function(t){this._structArray.int16[this._pos2+4]=t},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.y2.set=function(t){this._structArray.int16[this._pos2+5]=t},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.featureIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.sourceLayerIndex.set=function(t){this._structArray.uint16[this._pos2+8]=t},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.bucketIndex.set=function(t){this._structArray.uint16[this._pos2+9]=t},r.radius.get=function(){return this._structArray.int16[this._pos2+10]},r.radius.set=function(t){this._structArray.int16[this._pos2+10]=t},r.signedDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+11]},r.signedDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+11]=t},r.anchorPoint.get=function(){return new l(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Wr);gn.prototype.size=24;var mn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new gn(this,t)},e}(nn);pr("CollisionBoxArray",mn);var vn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},hidden:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorX.set=function(t){this._structArray.int16[this._pos2+0]=t},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.anchorY.set=function(t){this._structArray.int16[this._pos2+1]=t},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.glyphStartIndex.set=function(t){this._structArray.uint16[this._pos2+2]=t},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.numGlyphs.set=function(t){this._structArray.uint16[this._pos2+3]=t},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.vertexStartIndex.set=function(t){this._structArray.uint32[this._pos4+2]=t},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineStartIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.lineLength.set=function(t){this._structArray.uint32[this._pos4+4]=t},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.segment.set=function(t){this._structArray.uint16[this._pos2+10]=t},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.lowerSize.set=function(t){this._structArray.uint16[this._pos2+11]=t},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.upperSize.set=function(t){this._structArray.uint16[this._pos2+12]=t},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetX.set=function(t){this._structArray.float32[this._pos4+7]=t},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.lineOffsetY.set=function(t){this._structArray.float32[this._pos4+8]=t},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.writingMode.set=function(t){this._structArray.uint8[this._pos1+36]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+37]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+37]=t},Object.defineProperties(e.prototype,r),e}(Wr);vn.prototype.size=40;var yn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new vn(this,t)},e}(sn);pr("PlacedSymbolArray",yn);var xn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={offsetX:{configurable:!0}};return r.offsetX.get=function(){return this._structArray.float32[this._pos4+0]},r.offsetX.set=function(t){this._structArray.float32[this._pos4+0]=t},Object.defineProperties(e.prototype,r),e}(Wr);xn.prototype.size=4;var bn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e.prototype.get=function(t){return new xn(this,t)},e}(ln);pr("GlyphOffsetArray",bn);var _n=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={x:{configurable:!0},y:{configurable:!0},tileUnitDistanceFromAnchor:{configurable:!0}};return r.x.get=function(){return this._structArray.int16[this._pos2+0]},r.x.set=function(t){this._structArray.int16[this._pos2+0]=t},r.y.get=function(){return this._structArray.int16[this._pos2+1]},r.y.set=function(t){this._structArray.int16[this._pos2+1]=t},r.tileUnitDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+2]},r.tileUnitDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+2]=t},Object.defineProperties(e.prototype,r),e}(Wr);_n.prototype.size=6;var wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e.prototype.get=function(t){return new _n(this,t)},e}(cn);pr("SymbolLineVertexArray",wn);var kn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.featureIndex.set=function(t){this._structArray.uint32[this._pos4+0]=t},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.sourceLayerIndex.set=function(t){this._structArray.uint16[this._pos2+2]=t},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},r.bucketIndex.set=function(t){this._structArray.uint16[this._pos2+3]=t},Object.defineProperties(e.prototype,r),e}(Wr);kn.prototype.size=8;var Mn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new kn(this,t)},e}(un);pr("FeatureIndexArray",Mn);var An=Xr([{name:"a_pos",components:2,type:"Int16"}],4).members,Tn=function(t){void 0===t&&(t=[]),this.segments=t};Tn.prototype.prepareSegment=function(t,e,r){var n=this.segments[this.segments.length-1];return t>Tn.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+Tn.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!n||n.vertexLength+t>Tn.MAX_VERTEX_ARRAY_LENGTH)&&(n={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},this.segments.push(n)),n},Tn.prototype.get=function(){return this.segments},Tn.prototype.destroy=function(){for(var t=0,e=this.segments;t<e.length;t+=1){var r=e[t];for(var n in r.vaos)r.vaos[n].destroy()}},Tn.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,pr("SegmentVector",Tn);var Sn=function(t,e){return 256*(t=f(Math.floor(t),0,255))+f(Math.floor(e),0,255)};function En(t){return[Sn(255*t.r,255*t.g),Sn(255*t.b,255*t.a)]}var Cn=function(t,e,r){this.value=t,this.name=e,this.type=r,this.statistics={max:-1/0}};Cn.prototype.defines=function(){return["#define HAS_UNIFORM_u_"+this.name]},Cn.prototype.populatePaintArray=function(){},Cn.prototype.upload=function(){},Cn.prototype.destroy=function(){},Cn.prototype.setUniforms=function(t,e,r,n){var i=n.constantOr(this.value),a=t.gl;"color"===this.type?a.uniform4f(e.uniforms["u_"+this.name],i.r,i.g,i.b,i.a):a.uniform1f(e.uniforms["u_"+this.name],i)};var Ln=function(t,e,r){this.expression=t,this.name=e,this.type=r,this.statistics={max:-1/0};var n="color"===r?pn:ln;this.paintVertexAttributes=[{name:"a_"+e,type:"Float32",components:"color"===r?2:1,offset:0}],this.paintVertexArray=new n};Ln.prototype.defines=function(){return[]},Ln.prototype.populatePaintArray=function(t,e){var r=this.paintVertexArray,n=r.length;r.reserve(t);var i=this.expression.evaluate(new Lr(0),e);if("color"===this.type)for(var a=En(i),o=n;o<t;o++)r.emplaceBack(a[0],a[1]);else{for(var s=n;s<t;s++)r.emplaceBack(i);this.statistics.max=Math.max(this.statistics.max,i)}},Ln.prototype.upload=function(t){this.paintVertexArray&&(this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes))},Ln.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ln.prototype.setUniforms=function(t,e){t.gl.uniform1f(e.uniforms["a_"+this.name+"_t"],0)};var zn=function(t,e,r,n,i){this.expression=t,this.name=e,this.type=r,this.useIntegerZoom=n,this.zoom=i,this.statistics={max:-1/0};var a="color"===r?dn:pn;this.paintVertexAttributes=[{name:"a_"+e,type:"Float32",components:"color"===r?4:2,offset:0}],this.paintVertexArray=new a};zn.prototype.defines=function(){return[]},zn.prototype.populatePaintArray=function(t,e){var r=this.paintVertexArray,n=r.length;r.reserve(t);var i=this.expression.evaluate(new Lr(this.zoom),e),a=this.expression.evaluate(new Lr(this.zoom+1),e);if("color"===this.type)for(var 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r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(Gn(n,i,e[a],e[a+1]))return!0;return!1}function Gn(t,e,r,n){return w(t,r,n)!==w(e,r,n)&&w(t,e,r)!==w(t,e,n)}function Wn(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Yn(t,e[i-1],e[i])<n)return!0;return!1}function Yn(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return i<0?t.distSqr(e):i>1?t.distSqr(r):t.distSqr(r.sub(e)._mult(i)._add(e))}function Xn(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)n=r[s],i=r[l],n.y>e.y!=i.y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Zn(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function $n(t,e,r){var 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E=p.dist(x);if(E>2*u){var C=p.sub(p.sub(x)._mult(u/E)._round());this.distance+=C.dist(x),this.addCurrentVertex(C,this.distance,_.mult(1),0,0,!1,f,o),x=C}}var L=x&&b,z=L?r:b?m:v;if(L&&"round"===z&&(T<a?z="miter":T<=2&&(z="fakeround")),"miter"===z&&T>i&&(z="bevel"),"bevel"===z&&(T>2&&(z="flipbevel"),T<i&&(z="miter")),x&&(this.distance+=p.dist(x)),"miter"===z)M._mult(T),this.addCurrentVertex(p,this.distance,M,0,0,!1,f,o);else if("flipbevel"===z){if(T>100)M=w.clone().mult(-1);else{var P=_.x*w.y-_.y*w.x>0?-1:1,I=T*_.add(w).mag()/_.sub(w).mag();M._perp()._mult(I*P)}this.addCurrentVertex(p,this.distance,M,0,0,!1,f,o),this.addCurrentVertex(p,this.distance,M.mult(-1),0,0,!1,f,o)}else if("bevel"===z||"fakeround"===z){var O=_.x*w.y-_.y*w.x>0,D=-Math.sqrt(T*T-1);if(O?(g=0,d=D):(d=0,g=D),y||this.addCurrentVertex(p,this.distance,_,d,g,!1,f,o),"fakeround"===z){for(var R=Math.floor(8*(.5-(A-.5))),B=void 0,F=0;F<R;F++)B=w.mult((F+1)/(R+1))._add(_)._unit(),this.addPieSliceVertex(p,this.distance,B,O,f,o);this.addPieSliceVertex(p,this.distance,M,O,f,o);for(var N=R-1;N>=0;N--)B=_.mult((N+1)/(R+1))._add(w)._unit(),this.addPieSliceVertex(p,this.distance,B,O,f,o)}b&&this.addCurrentVertex(p,this.distance,w,-d,-g,!1,f,o)}else"butt"===z?(y||this.addCurrentVertex(p,this.distance,_,0,0,!1,f,o),b&&this.addCurrentVertex(p,this.distance,w,0,0,!1,f,o)):"square"===z?(y||(this.addCurrentVertex(p,this.distance,_,1,1,!1,f,o),this.e1=this.e2=-1),b&&this.addCurrentVertex(p,this.distance,w,-1,-1,!1,f,o)):"round"===z&&(y||(this.addCurrentVertex(p,this.distance,_,0,0,!1,f,o),this.addCurrentVertex(p,this.distance,_,1,1,!0,f,o),this.e1=this.e2=-1),b&&(this.addCurrentVertex(p,this.distance,w,-1,-1,!0,f,o),this.addCurrentVertex(p,this.distance,w,0,0,!1,f,o)));if(S&&k<l-1){var j=p.dist(b);if(j>2*u){var V=p.add(b.sub(p)._mult(u/j)._round());this.distance+=V.dist(p),this.addCurrentVertex(V,this.distance,w.mult(1),0,0,!1,f,o),p=V}}y=!1}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,e)}},wa.prototype.addCurrentVertex=function(t,e,r,n,i,a,o,s){var l,c=this.layoutVertexArray,u=this.indexArray;s&&(e=ka(e,s)),l=r.clone(),n&&l._sub(r.perp()._mult(n)),_a(c,t,l,a,!1,n,e),this.e3=o.vertexLength++,this.e1>=0&&this.e2>=0&&(u.emplaceBack(this.e1,this.e2,this.e3),o.primitiveLength++),this.e1=this.e2,this.e2=this.e3,l=r.mult(-1),i&&l._sub(r.perp()._mult(i)),_a(c,t,l,a,!0,-i,e),this.e3=o.vertexLength++,this.e1>=0&&this.e2>=0&&(u.emplaceBack(this.e1,this.e2,this.e3),o.primitiveLength++),this.e1=this.e2,this.e2=this.e3,e>ba/2&&!s&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,a,o))},wa.prototype.addPieSliceVertex=function(t,e,r,n,i,a){r=r.mult(n?-1:1);var o=this.layoutVertexArray,s=this.indexArray;a&&(e=ka(e,a)),_a(o,t,r,!1,n,0,e),this.e3=i.vertexLength++,this.e1>=0&&this.e2>=0&&(s.emplaceBack(this.e1,this.e2,this.e3),i.primitiveLength++),n?this.e2=this.e3:this.e1=this.e3},pr("LineBucket",wa,{omit:["layers"]});var Ma=new qr({"line-cap":new Nr(I.layout_line["line-cap"]),"line-join":new jr(I.layout_line["line-join"]),"line-miter-limit":new Nr(I.layout_line["line-miter-limit"]),"line-round-limit":new Nr(I.layout_line["line-round-limit"])}),Aa={paint:new qr({"line-opacity":new jr(I.paint_line["line-opacity"]),"line-color":new jr(I.paint_line["line-color"]),"line-translate":new Nr(I.paint_line["line-translate"]),"line-translate-anchor":new Nr(I.paint_line["line-translate-anchor"]),"line-width":new jr(I.paint_line["line-width"]),"line-gap-width":new jr(I.paint_line["line-gap-width"]),"line-offset":new jr(I.paint_line["line-offset"]),"line-blur":new jr(I.paint_line["line-blur"]),"line-dasharray":new Vr(I.paint_line["line-dasharray"]),"line-pattern":new Vr(I.paint_line["line-pattern"]),"line-gradient":new Ur(I.paint_line["line-gradient"])}),layout:Ma},Ta=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new Lr(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n){return r=p({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n)},e}(jr))(Aa.paint.properties["line-width"].specification);Ta.useIntegerZoom=!0;var Sa=function(t){function e(e){t.call(this,e,Aa)}return 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o=Kn(t,this.paint.get("line-translate"),this.paint.get("line-translate-anchor"),i.angle,a),s=a/2*Ea(this.paint.get("line-width").evaluate(e),this.paint.get("line-gap-width").evaluate(e)),c=this.paint.get("line-offset").evaluate(e);return c&&(r=function(t,e){for(var r=[],n=new l(0,0),i=0;i<t.length;i++){for(var a=t[i],o=[],s=0;s<a.length;s++){var c=a[s-1],u=a[s],h=a[s+1],f=0===s?n:u.sub(c)._unit()._perp(),p=s===a.length-1?n:h.sub(u)._unit()._perp(),d=f._add(p)._unit(),g=d.x*p.x+d.y*p.y;d._mult(1/g),o.push(d._mult(e)._add(u))}r.push(o)}return r}(r,c*a)),Un(o,r,s)},e}(Hr);function Ea(t,e){return e>0?e+2*t:t}var Ca=Xr([{name:"a_pos_offset",components:4,type:"Int16"},{name:"a_data",components:4,type:"Uint16"}]),La=Xr([{name:"a_projected_pos",components:3,type:"Float32"}],4),za=(Xr([{name:"a_fade_opacity",components:1,type:"Uint32"}],4),Xr([{name:"a_placed",components:2,type:"Uint8"}],4)),Pa=(Xr([{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Int16",name:"x1"},{type:"Int16",name:"y1"},{type:"Int16",name:"x2"},{type:"Int16",name:"y2"},{type:"Uint32",name:"featureIndex"},{type:"Uint16",name:"sourceLayerIndex"},{type:"Uint16",name:"bucketIndex"},{type:"Int16",name:"radius"},{type:"Int16",name:"signedDistanceFromAnchor"}]),Xr([{name:"a_pos",components:2,type:"Int16"},{name:"a_anchor_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"}],4)),Ia=Xr([{name:"a_pos",components:2,type:"Int16"},{name:"a_anchor_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"}],4);function Oa(t,e,r){var n=e.layout.get("text-transform").evaluate(r);return"uppercase"===n?t=t.toLocaleUpperCase():"lowercase"===n&&(t=t.toLocaleLowerCase()),Cr.applyArabicShaping&&(t=Cr.applyArabicShaping(t)),t}Xr([{type:"Int16",name:"anchorX"},{type:"Int16",name:"anchorY"},{type:"Uint16",name:"glyphStartIndex"},{type:"Uint16",name:"numGlyphs"},{type:"Uint32",name:"vertexStartIndex"},{type:"Uint32",name:"lineStartIndex"},{type:"Uint32",name:"lineLength"},{type:"Uint16",name:"segment"},{type:"Uint16",name:"lowerSize"},{type:"Uint16",name:"upperSize"},{type:"Float32",name:"lineOffsetX"},{type:"Float32",name:"lineOffsetY"},{type:"Uint8",name:"writingMode"},{type:"Uint8",name:"hidden"}]),Xr([{type:"Float32",name:"offsetX"}]),Xr([{type:"Int16",name:"x"},{type:"Int16",name:"y"},{type:"Int16",name:"tileUnitDistanceFromAnchor"}]);var Da={"!":"\ufe15","#":"\uff03",$:"\uff04","%":"\uff05","&":"\uff06","(":"\ufe35",")":"\ufe36","*":"\uff0a","+":"\uff0b",",":"\ufe10","-":"\ufe32",".":"\u30fb","/":"\uff0f",":":"\ufe13",";":"\ufe14","<":"\ufe3f","=":"\uff1d",">":"\ufe40","?":"\ufe16","@":"\uff20","[":"\ufe47","\\":"\uff3c","]":"\ufe48","^":"\uff3e",_:"\ufe33","`":"\uff40","{":"\ufe37","|":"\u2015","}":"\ufe38","~":"\uff5e","\xa2":"\uffe0","\xa3":"\uffe1","\xa5":"\uffe5","\xa6":"\uffe4","\xac":"\uffe2","\xaf":"\uffe3","\u2013":"\ufe32","\u2014":"\ufe31","\u2018":"\ufe43","\u2019":"\ufe44","\u201c":"\ufe41","\u201d":"\ufe42","\u2026":"\ufe19","\u2027":"\u30fb","\u20a9":"\uffe6","\u3001":"\ufe11","\u3002":"\ufe12","\u3008":"\ufe3f","\u3009":"\ufe40","\u300a":"\ufe3d","\u300b":"\ufe3e","\u300c":"\ufe41","\u300d":"\ufe42","\u300e":"\ufe43","\u300f":"\ufe44","\u3010":"\ufe3b","\u3011":"\ufe3c","\u3014":"\ufe39","\u3015":"\ufe3a","\u3016":"\ufe17","\u3017":"\ufe18","\uff01":"\ufe15","\uff08":"\ufe35","\uff09":"\ufe36","\uff0c":"\ufe10","\uff0d":"\ufe32","\uff0e":"\u30fb","\uff1a":"\ufe13","\uff1b":"\ufe14","\uff1c":"\ufe3f","\uff1e":"\ufe40","\uff1f":"\ufe16","\uff3b":"\ufe47","\uff3d":"\ufe48","\uff3f":"\ufe33","\uff5b":"\ufe37","\uff5c":"\u2015","\uff5d":"\ufe38","\uff5f":"\ufe35","\uff60":"\ufe36","\uff61":"\ufe12","\uff62":"\ufe41","\uff63":"\ufe42"},Ra=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(l);function Ba(t,e){var r=e.expression;if("constant"===r.kind)return{functionType:"constant",layoutSize:r.evaluate(new Lr(t+1))};if("source"===r.kind)return{functionType:"source"};for(var n=r.zoomStops,i=0;i<n.length&&n[i]<=t;)i++;for(var a=i=Math.max(0,i-1);a<n.length&&n[a]<t+1;)a++;a=Math.min(n.length-1,a);var o={min:n[i],max:n[a]};return"composite"===r.kind?{functionType:"composite",zoomRange:o,propertyValue:e.value}:{functionType:"camera",layoutSize:r.evaluate(new Lr(t+1)),zoomRange:o,sizeRange:{min:r.evaluate(new Lr(o.min)),max:r.evaluate(new Lr(o.max))},propertyValue:e.value}}pr("Anchor",Ra);var Fa=ga.VectorTileFeature.types,Na=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function ja(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,s?s[0]:0,s?s[1]:0)}function Va(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}var Ua=function(t){this.layoutVertexArray=new tn,this.indexArray=new hn,this.programConfigurations=t,this.segments=new Tn,this.dynamicLayoutVertexArray=new en,this.opacityVertexArray=new rn,this.placedSymbolArray=new yn};Ua.prototype.upload=function(t,e){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ca.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.programConfigurations.upload(t),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,La.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,Na,!0),this.opacityVertexBuffer.itemSize=1},Ua.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},pr("SymbolBuffers",Ua);var qa=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Tn,this.collisionVertexArray=new on};qa.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,za.members,!0)},qa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},pr("CollisionBuffers",qa);var Ha=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map(function(t){return t.id}),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex;var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Ba(this.zoom,e["text-size"]),this.iconSizeData=Ba(this.zoom,e["icon-size"]);var r=this.layers[0].layout;this.sortFeaturesByY=r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")};Ha.prototype.createArrays=function(){this.text=new Ua(new In(Ca.members,this.layers,this.zoom,function(t){return/^text/.test(t)})),this.icon=new Ua(new In(Ca.members,this.layers,this.zoom,function(t){return/^icon/.test(t)})),this.collisionBox=new qa(an,Pa.members,fn),this.collisionCircle=new qa(an,Ia.members,hn),this.glyphOffsetArray=new bn,this.lineVertexArray=new wn},Ha.prototype.populate=function(t,e){var r=this.layers[0],n=r.layout,i=n.get("text-font"),a=n.get("text-field"),o=n.get("icon-image"),s=("constant"!==a.value.kind||a.value.value.length>0)&&("constant"!==i.value.kind||i.value.value.length>0),l="constant"!==o.value.kind||o.value.value&&o.value.value.length>0;if(this.features=[],s||l){for(var c=e.iconDependencies,u=e.glyphDependencies,h=new Lr(this.zoom),f=0,p=t;f<p.length;f+=1){var 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