Mean Value T Test

STMV Class

Calculate the mean T-test for a single sample and the mean T-test for two samples

Parameter:

  • data1 (np.ndarray or xr.DataArray): test data1

  • data2 (np.ndarray or xr.DataArray): test data2, if data2 is None, function will use one sample test.

  • wrap (bool): if data are wrapped to original xarray coords. Defaults to True.

  • *param: parameter transports to sts.ttest_1samp or sts.ttest_2samp

  • **kwargs: parameter transports to sts.ttest_1samp or sts.ttest_2samp

[1]:

import sacpy as scp
import numpy as np
import matplotlib.pyplot as plt
sst = scp.load_sst()["sst"]
ssta = scp.get_anom(sst, method=0)
ssta_djf = scp.XrTools.spec_moth_yrmean(ssta,[12,1,2])
Nino34 = ssta_djf.loc[:, -5:5, 190:240].mean(axis=(1, 2))

[13]:

select1 = (Nino34 >= 0.8) & (Nino34 < 1.1)
ssta_sl1 = ssta_djf[select1]
select2 = Nino34 >= 1.3
ssta_sl2 = ssta_djf[select2]

# 2 examples ttest
ttest = scp.STMV(ssta_sl2,ssta_sl1)

[14]:

import sacpy.Map
plt.figure(figsize=[7, 3])
m,ax = ttest.mean.splot()
ax.sig_plot(sst.lon,sst.lat,ttest.p_value)

[14]:

<cartopy.mpl.contour.GeoContourSet at 0x15da956d0>
../_images/notebooks_MValueTTest_4_1.png

one_mean_test

Parameter:

  • data (np.ndarray): test data

  • expected_mean (np.ndarray, optional): expected mean. Defaults to None.

  • *param: parameter transports to sts.ttest_1samp

Raises:

  • ValueError: expected_mean shape can match data

Returns:

  • origin_mean,pvalue : np.ndarray: origin_mean and res.value

Example

[8]:

import sacpy as scp
import numpy as np
import matplotlib.pyplot as plt
sst = scp.load_sst()["sst"]
ssta = scp.get_anom(sst, method=0)
ssta_djf = scp.XrTools.spec_moth_yrmean(ssta,[12,1,2])

[9]:

Nino34 = ssta_djf.loc[:, -5:5, 190:240].mean(axis=(1, 2))
print(Nino34.std())
Nino34.plot()

<xarray.DataArray 'sst' ()>
array(0.93506366)

[9]:

[<matplotlib.lines.Line2D at 0x15c593550>]
../_images/notebooks_MValueTTest_7_2.png 
[10]:

select = Nino34 >= 1
ssta_sl = ssta_djf[select]
mean, pv = scp.one_mean_test(np.array(ssta_sl))

[11]:

import sacpy.Map
import cartopy.crs as ccrs
fig = plt.figure(figsize=[7, 3])
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180))
lon ,lat = np.array(ssta.lon) , np.array(ssta.lat)
m = ax.scontourf(lon,lat,mean)
n = ax.sig_plot(lon,lat,pv,color="k",marker="..")
ax.init_map(stepx=50, smally=2.5)
plt.colorbar(m)

[11]:

<matplotlib.colorbar.Colorbar at 0x15c648b20>
../_images/notebooks_MValueTTest_9_1.png

two_mean_test

two samples t test

Parameter

  • data1 (np.ndarray): test data1

  • data2 (np.ndarray): test data2

Returns:

  • mean_diff (np.ndarray): mean difference

  • pvalue (np.ndarray): p-value of t-test

Example

Load Modules and SST data

[16]:

import sacpy as scp
import numpy as np
import matplotlib.pyplot as plt

sst = scp.load_sst()["sst"]
ssta = scp.get_anom(sst, method=0)
ssta_djf = scp.XrTools.spec_moth_yrmean(ssta,[12,1,2])
Nino34 = ssta_djf.loc[:, -5:5, 190:240].mean(axis=(1, 2))

Select Normal and Super El nino Cases

[14]:

select1 = (Nino34 >= 0.8) & (Nino34 < 1.1)
ssta_sl1 = ssta_djf[select1]
select2 = Nino34 >= 1.3
ssta_sl2 = ssta_djf[select2]

# cal difference
mean_diff, pv2 = scp.two_mean_test(ssta_sl2,ssta_sl1)

Plot the results

[15]:

fig = plt.figure(figsize=[7, 3])
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180))
lon ,lat = np.array(ssta.lon) , np.array(ssta.lat)
m = ax.scontourf(lon,lat,mean_diff)
n = ax.sig_plot(lon,lat,pv2,color="k",marker="..")
ax.init_map(stepx=50, smally=2.5)
plt.colorbar(m)

[15]:

<matplotlib.colorbar.Colorbar at 0x15cae6730>
../_images/notebooks_MValueTTest_16_1.png