def draw_some_timeserieses(plt, Timeseries, fy, ly, draw_fy, draw_ly, llabel, Month_of_RM = 13, fsizex = 20, fsizey = 6):
months, label = subroutine.get_months_and_label(fy, ly)
N = Timeseries.shape[0]
plt.rcParams['lines.linewidth'] = 2
plt.rcParams['font.size'] = 16
for i in range(0, 3):
plt.figure(figsize = (20, 6))
for j in range(0, N):
if i == 0:
plt.plot(months, Timeseries[j, :], label = llabel[j])
plt.title('Raw Timeseries')
elif i == 1:
a = ts.RawData_minus_mean_annual_cycle(Timeseries[j, :])
plt.plot(months, a, label = llabel[j])
plt.title('Interannual variation')
elif i == 2:
a = ts.running_mean(ts.RawData_minus_mean_annual_cycle(Timeseries[j, :]), Month_of_RM)
plt.plot(months, a, label = llabel[j])
plt.title(str(Month_of_RM) + ' month running mean')
else:
raise ValueError('i is out of range!')
plt = trim_Monthly_Timeseries_Figure(plt, fy, ly, fy)
plt = add_vline_at_Jan(plt, months)
plt.plot(months, np.zeros(months.size), linewidth = 0.5, color = 'k')
plt.xticks(months, label)
plt.legend(bbox_to_anchor = (1.05, 1), loc = 2, borderaxespad = 0.)
plt.show()
def load_data_of_npz(self, fy, ly, var, depth = 1, product_n = 3): import D import Var import subroutine vid = Var.var_to_id(var) formal_name = Var.VAR[vid].Get_formal_name() title_name = D.Data[product_n].title_name xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) Timeseries = subroutine.load_npz(self.AreaName + '_Area-Averaged-' + \ formal_name + '_at_' + str(zgrid[depth - 1]) + \ 'm_Year' + str(fy) + '-' + str(ly) + '_' + title_name) months, label = subroutine.get_months_and_label(fy, ly) return Timeseries, months, label
def timeseries_of_area_averaged_value(self, fy, ly, var, depth, product_n = 3): import numpy as np import D import subroutine Yn = ly - fy + 1 Timeseries = np.zeros(12 * Yn) months, label = subroutine.get_months_and_label(fy, ly) for year in range(fy, ly + 1): for month in range(1, 13): i = month - 1 + (year - fy) * 12 Data = D.get_data(year, month, var, depth, product_n) tmp, _, _ = self.Get_data_of_area(Data, var, product_n) Timeseries[i] = np.average(tmp[np.where(np.isnan(tmp) == False)]) return Timeseries, months, label
def Get_Hofmuller_of_D2Data(self, fy, ly, D2N, var, product_n = 3): import D2 import numpy as np import subroutine Yn = ly - fy + 1 Timeseries = np.zeros((12 * Yn, self.Pn)) months, label = subroutine.get_months_and_label(fy, ly) a = D2.D2Data[D2N] for year in range(fy, ly + 1): for month in range(1, 13): i = month - 1 + (year - fy) * 12 Data = a.load_data_of_npz(year, month, product_n) Data, hgrid = self.Get_VerticalSection_from_2Ddata(Data, var, product_n) Timeseries[i, :] = Data[:] return Timeseries, months, label
def Get_Hofmuller(self, fy, ly, var, depth, product_n = 3): import D import Var import numpy as np import subroutine Yn = ly - fy + 1 Timeseries = np.zeros((12 * Yn, self.Pn)) months, label = subroutine.get_months_and_label(fy, ly) for year in range(fy, ly + 1): for month in range(1, 13): i = month - 1 + (year - fy) * 12 Data, hgrid, zgrid = self.Get_VerticalSection(year, month, var, product_n) Timeseries[i, :] = Data[:, depth - 1] return Timeseries, months, label
def timeseries_of_area_averaged_D2(self, fy, ly, D2_id, product_n = 3): import numpy as np import D2 import subroutine D2D = D2.D2Data[D2_id] Yn = ly - fy + 1 Timeseries = np.zeros(12 * Yn) months, label = subroutine.get_months_and_label(fy, ly) for year in range(fy, ly + 1): for month in range(1, 13): i = month - 1 + (year - fy) * 12 Data = D2D.load_data_of_npz(year, month, product_n) tmp, _, _ = self.Get_data_of_area(Data, 's', product_n) tmp[np.where(np.abs(tmp) == np.inf)] = np.nan Timeseries[i] = np.average(tmp[np.where(np.isnan(tmp) == False)]) return Timeseries, months, label
def load_data_of_npz(self, fy, ly, var = 's', depthn = 1, product_n = 3, Rawdata_or_Anomaly = 'Rawdata', fy_of_Anomalydata = 1990, ly_of_Anomalydata = 2011):
import D
import subroutine
import Var
title_name = D.Data[product_n].title_name
xgrid, ygrid, zgrid = D.get_grid_value('s', product_n)
vid = Var.var_to_id(var)
formal_name = Var.VAR[vid].Get_formal_name()
if Rawdata_or_Anomaly == 'Rawdata':
fname_RA = formal_name
elif Rawdata_or_Anomaly == 'Anomaly':
fname_RA = '[Anomaly_of_' + formal_name + str(fy_of_Anomalydata) + '-' + str(ly_of_Anomalydata) + ']'
else:
raise ValueError('your Rawdata_or_Anomaly argument is not valid!')
Timeseries = subroutine.load_npz(self.AreaName + '_of_' + fname_RA + '_at_' + str(zgrid[depthn - 1]) + \
'm_Year' + str(fy) + '-' + str(ly) + '_' + title_name)
months, label = subroutine.get_months_and_label(fy, ly)
return Timeseries, months, label