Python interpolate.spline方法代码示例

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import spline [as 别名]
def line_plot(self):
        """
            The plot uses matplotlib library and for smoothing purposes spline method is used
        :return: matplotlib plot object
        """
        _x_axis = self._x()
        _x_new = np.linspace(_x_axis[0], _x_axis[-1], self.no_of_points*10)
        _y_smooth = spline(_x_axis, self._y(), _x_new)
        plt.plot(_x_new, _y_smooth)
        plt.grid(True)
        plt.xlabel(str(self.x_axis).capitalize())
        plt.ylabel(str(self.func).capitalize())
        plt.title(str(self.func).capitalize() + " vs "+str(self.x_axis).capitalize())
        return plt 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import spline [as 别名]
def periodsSplineRiskFreeInterestRate(options, date):
    """
    params: options: 计算VIX的当天的options数据用来获取expDate
            date: 计算哪天的VIX
    return：shibor：该date到每个到期日exoDate的risk free rate

    """
    date = datetime.strptime(date,'%Y/%m/%d')
    #date = datetime(date.year,date.month,date.day)
    exp_dates = np.sort(options.EXE_ENDDATE.unique())
    periods = {}
    for epd in exp_dates:
        epd = pd.to_datetime(epd)
        periods[epd] = (epd - date).days*1.0/365.0
    shibor_date = datetime.strptime(shibor_rate.index[0], "%Y-%m-%d") 
    if date >= shibor_date:
        date_str = shibor_rate.index[0]
        shibor_values = shibor_rate.ix[0].values
        #shibor_values = np.asarray(list(map(float,shibor_values)))
    else:
        date_str = date.strftime("%Y-%m-%d") 
        shibor_values = shibor_rate.loc[date_str].values
        #shibor_values = np.asarray(list(map(float,shibor_values)))
        
    shibor = {}
    period = np.asarray([1.0, 7.0, 14.0, 30.0, 90.0, 180.0, 270.0, 360.0]) / 360.0
    min_period = min(period)
    max_period = max(period)
    for p in periods.keys():
        tmp = periods[p]
        if periods[p] > max_period:
            tmp = max_period * 0.99999
        elif periods[p] < min_period:
            tmp = min_period * 1.00001
        # 此处使用SHIBOR来插值
        sh = interpolate.spline(period, shibor_values, tmp, order=3)
        shibor[p] = sh/100.0
    return shibor 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import spline [as 别名]
def skyline_plot(csv_data, output_file):
    """
    Creates a skyline style plot from data on a csv_file. This csv file should
    be compliant with the one generated by Tracer and is parsed by parse_csv
    function.
    """

    fig, ax = plt.subplots()

    #x_data = list(csv_data.keys())
    x_data = np.arange(len(csv_data))

    median_data = np.array([x[0] for x in csv_data.values()])
    lower_hpd = np.array([x[1] for x in csv_data.values()])
    higher_hpd = np.array([x[2] for x in csv_data.values()])

    plt.xticks(x_data, ["%.2E" % x for x in csv_data.keys()], rotation=45,
               ha="right")

    xnew = np.linspace(x_data.min(),x_data.max(), 200)

    smooth_median = spline(x_data, median_data, xnew)
    smooth_lower = spline(x_data, lower_hpd, xnew)
    smooth_higher = spline(x_data, higher_hpd, xnew)

    ax.plot(xnew, smooth_median, "--", color="black")
    #ax.fill_between(x_data, higher_hpd, lower_hpd, facecolor="blue", alpha=0.5)
    ax.plot(xnew, smooth_lower, color="blue")
    ax.plot(xnew, smooth_higher, color="blue")
    ax.fill_between(xnew, smooth_higher, smooth_lower, facecolor="blue", alpha=0.3)


    plt.xlabel("Time")
    plt.ylabel("Ne")

    plt.tight_layout()
    plt.savefig("%s.svg" % (output_file)) 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import spline [as 别名]
def plot_density(self, ax, num=300, **kwargs):
        """Returns a density plot on an Pyplot Axes object.

        Args:
            :ax: (`Axes`)
                An matplotlib Axes object on which the histogram will be plot
            :num: (`int`)
                The number of x values the line is plotted on. Default: 300
            :**kwargs:
                Keyword arguments that are passed on to the pyplot.plot function.
        """
        colors = []

        self.build()
        bin_centers = np.asarray(self._get_bin_centers())
        x_new = np.linspace(bin_centers.min(), bin_centers.max(), num)

        if 'color' in kwargs:
            colors = kwargs['color']
            del kwargs['color']

        power_smooth = []

        for (colname, bin_values) in self.hist_dict.items():
            normed_values, ble = np.histogram(self._get_bin_centers(),
                                              bins=self.bin_list,
                                              weights=bin_values,
                                              density=True
                                              )

            power_smooth.append(x_new)
            power_smooth.append(spline(bin_centers, normed_values, x_new))

        lines = ax.plot(*power_smooth, **kwargs)

        for i, line in enumerate(lines):
            if len(colors) > 0:
                plt.setp(line, color=colors[i], label=list(self.hist_dict.keys())[i])
            else:
                plt.setp(line, label=list(self.hist_dict.keys())[i])

        return lines