text: /**
  * @license Apache-2.0
  *
  * Copyright (c) 2018 The Stdlib Authors.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *    http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
  
  'use strict';
  
  // MODULES //
  
  var tape = require( 'tape' );
  var linspace = require( '@stdlib/array/base/linspace' );
  var isnan = require( '@stdlib/math/base/assert/is-nan' );
  var gamma = require( '@stdlib/math/base/special/gamma' );
  var abs = require( '@stdlib/math/base/special/abs' );
  var exp = require( '@stdlib/math/base/special/exp' );
  var pow = require( '@stdlib/math/base/special/pow' );
  var EPS = require( '@stdlib/constants/float64/eps' );
  var G = require( '@stdlib/constants/float64/gamma-lanczos-g' );
  var gammaLanczosSumExpGScaled = require( './../lib' );
  
  
  // TESTS //
  
  tape( 'main export is a function', function test( t ) {
  	t.ok( true, __filename );
  	t.equal( typeof gammaLanczosSumExpGScaled, 'function', 'main export is a function' );
  	t.end();
  });
  
  tape( 'if provided `NaN`, the function returns `NaN`', function test( t ) {
  	var v = gammaLanczosSumExpGScaled( NaN );
  	t.equal( isnan( v ), true, 'returns NaN when provided a NaN' );
  	t.end();
  });
  
  tape( 'the function evaluates the Lanczos sum (scaled by exp(-g))', function test( t ) {
  	var expected;
  	var delta;
  	var tol;
  	var i;
  	var x;
  	var y;
  
  	x = linspace( 1.0, 100.0, 500 );
  	for ( i = 0; i < x.length; i++ ) {
  		y = gammaLanczosSumExpGScaled( x[i] );
  		expected = gamma( x[i] );
  		expected /= pow( x[i]+G-0.5, x[i]-0.5 ) / ( exp( x[i]+G-0.5 ) );
  		expected /= exp( G );
  		if ( y === expected ) {
  			t.equal( y, expected, 'x: '+x[i]+', y: '+y+', expected: '+expected );
  		} else {
  			delta = abs( y - expected );
  			tol = 10.0 * EPS * abs( expected );
  			t.ok( delta <= tol, 'within tolerance. x: '+x[i]+'. y: '+y+'. E: '+expected+'. Δ: '+delta+'. tol: '+tol );
  		}
  	}
  	t.end();
  });
  