Normalize according to The Cannon
978372
def cannon_normalize(spec_raw):
    
    spec = np.array([spec_raw])
    wl = np.arange(0, spec.shape[1])
    w = continuum_normalization.gaussian_weight_matrix(wl, L=50)
    ivar = np.ones(spec.shape)*0.5
    cont = continuum_normalization._find_cont_gaussian_smooth(
            wl, spec, ivar, w)
    norm_flux, norm_ivar = continuum_normalization._cont_norm(
            spec, ivar, cont)
    return norm_flux[0]
1318915
function normalize(result) {
  return {
    relevance: result.relevance || 0,
    language: result.language || null,
    value: result.value || []
  }
}
745582
def normalize()
      mag = Math.sqrt(self.x*self.x + self.y*self.y + self.z*self.z)
      return Quat.new(self.x/mag, self.y/mag, self.z/mag, self.w/mag)
    end
910414
def __normalize_args(**keywds):
        """"""
        if isinstance(keywds['name'], Callable) and \
                None is keywds['function']:
            keywds['function'] = keywds['name']
            keywds['name'] = None
        return keywds
1011042
def normalize(self) -> 'State':
        """"""
        tensor = self.tensor / bk.ccast(bk.sqrt(self.norm()))
        return State(tensor, self.qubits, self._memory)
879891
def lpnormalization(attrs, inputs, proto_obj):
    """"""
    new_attrs = translation_utils._fix_attribute_names(attrs, {'p': 'ord'})
    axis = int(attrs.get("axis", -1))
    new_attrs.update(axis=axis)
    return 'norm', new_attrs, inputs
1050282
function normalize(word) {
        var result = '';
        for (var i = 0; i < word.length; i++) {
            var kana = word[i];
            var target = transform[kana];
            if (target === false) {
                continue;
            }
            if (target) {
                kana = target;
            }
            result += kana;
        }
        return result;
    }
1260442
def normalize(self):
        ''''''
        error = self.a * self.b
        t0 = self.a - (self.b * (0.5 * error))
        t1 = self.b - (self.a * (0.5 * error))
        t2 = t0 % t1
        self.a = t0 * (1.0 / t0.length())
        self.b = t1 * (1.0 / t1.length())
        self.c = t2 * (1.0 / t2.length())
896575
@SuppressWarnings("all")
  public void normalize() {
    // Start on samples
    int samplesPerFrame = (int) ((float) getSamplesPerSecond() / (float) getFramesPerSecond());
    setFrames(getFrames() + (int) (getSamples() * (1.0f / samplesPerFrame)));
    setSamples(getSamples() % samplesPerFrame);

    setSeconds(getSeconds() + (int) ((float) getFrames() / getFramesPerSecond()));
    setFrames((int) (getFrames() % getFramesPerSecond()));

    while (getSeconds() >= 60) {
      setSeconds(getSeconds() - 60);
      setMinutes(getMinutes() + 1);
    }
    while (getMinutes() >= 60) {
      setMinutes(getMinutes() - 60);
      setHours(getHours() + 1);
    }
  }
331143
protected function normalizeItems()
    {
        foreach ($this->items as $index => $item) {
            $this->items[$index] = $this->normalizeItem( $item, $index );
        }
    }
880817
def _normalise(self):
        
        if not self.is_unit():
            n = self.norm
            if n > 0:
                self.q = self.q / n
239002
def normalize(expr):
    """"""
    lhs = normalize(expr.lhs)
    rhs = normalize(expr.rhs)
    return type(expr)(lhs, rhs, start=lhs.start, end=rhs.end)
684400
def normalize(x:TensorImage, mean:FloatTensor,std:FloatTensor)->TensorImage:
    ""
    return (x-mean[...,None,None]) / std[...,None,None]
532141
public final void normalize() {
		double norm;

		norm = (this.x*this.x + this.y*this.y + this.z*this.z + this.w*this.w);

		if (norm > 0f) {
			norm = 1f / Math.sqrt(norm);
			this.x *= norm;
			this.y *= norm;
			this.z *= norm;
			this.w *= norm;
		} else {
			this.x = 0f;
			this.y = 0f;
			this.z = 0f;
			this.w = 0f;
		}
	}
818633
def _normalize(self):
        
        # Get length
        L = self.norm()
        if not L:
            raise ValueError('Quaternion cannot have 0-length.')
        # Correct
        self.w /= L
        self.x /= L
        self.y /= L
        self.z /= L
1363236
def normalize_features(features):
    """"""
    return (features - N.min(features)) / (N.max(features) - N.min(features))
782991
def normalize_UNTL(subject):
    """"""
    subject = subject.strip()
    subject = re.sub(r'[\s]+', ' ', subject)
    return subject