Python Data.copy方法代码示例

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import copy [as 别名]
def xxxxtest_median_model():
    x = Data(None, None)
    x.label = 'nothing'
    d = np.random.random((100, 1, 1))
    x.data = np.ma.array(d, mask= d!=d)

    # odd number and no masked values
    a = x.copy()
    a.data[:, 0, 0] = 1.
    b = x.copy()
    b.data[:, 0, 0] = 3.
    c = x.copy()
    c.data[:, 0, 0] = 2.
    d = x.copy()
    d.data[:, 0, 0] = 5.
    e = x.copy()
    e.data[:, 0, 0] = 4.

    m = MedianModel()
    m.add_member(a)
    m.add_member(b)
    m.add_member(c)
    m.add_member(d)
    m.add_member(e)
    m.ensmedian()

    # should give the value of 3. for all timesteps

    del m

    # even number and no masked values
    a = x.copy()
    a.data[:, 0, 0] = 1.
    b = x.copy()
    b.data[:, 0, 0] = 3.
    c = x.copy()
    c.data[:, 0, 0] = 2.
    d = x.copy()
    c.data[:, 0, 0] = 4.

    m = MedianModel()
    m.add_member(a)
    m.add_member(b)
    m.add_member(c)
    m.add_member(d)

    m.ensmedian()

    # should give the value of 2.5 for all timesteps
    del m

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import copy [as 别名]
# -*- coding: utf-8 -*-
"""
This file is part of pyCMBS.
(c) 2012- Alexander Loew
For COPYING and LICENSE details, please refer to the LICENSE files
"""
from pycmbs.data import Data
from pycmbs.plots import map_difference
import matplotlib.pyplot as plt

file_name = '../../../pycmbs/examples/example_data/air.mon.mean.nc'
A = Data(file_name, 'air', lat_name='lat', lon_name='lon', read=True, label='air temperature')
B = A.copy()
B.mulc(2.3, copy=False)
a = A.get_climatology(return_object=True)
b = B.get_climatology(return_object=True)

# a quick plot as well as a projection plot
f1 = map_difference(a, b, show_stat=False, vmin=-30., vmax=30., dmin=-60., dmax=60.)  # unprojected
plt.show()

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import copy [as 别名]
class TestEOF(TestCase):

    def setUp(self):
        #init Data object for testing
        n=4 #slows down significantly! constraint is percentile  test
        x = sc.randn(n)*100. #generate dummy data
        self.D = Data(None,None)
        d=np.ones((n,1,2))
        self.D.data = d
        self.D.data[:,0,0]=x
        self.D.data = np.ma.array(self.D.data,mask=self.D.data != self.D.data)
        self.D.verbose = True
        self.D.unit = 'myunit'

        self.D.time = np.arange(n) + pl.datestr2num('2001-01-01') - 1


    def test_eof_analysis(self):
        #test of EOF
        #example taken from:
        #   http://www.atmos.washington.edu/~dennis/552_Notes_4.pdf , page 80

        #assign sampled data
        D = self.D.copy()
#        d1 = np.array([2,4,-6,8])
#        d2 = np.array([1,2,-3,4])
        #d1 = np.array([2,1])
        #d2 = np.array([4,2])
        #d3 = np.array([-6,-3])
        #d4 = np.array([8,4])

        #D.data[:,0,0] = d1; D.data[:,0,1] = d2
        #D.data[:,0,2] = d3; D.data[:,0,3] = d4

#        D.data[:,0,0] = d1
#        D.data[:,0,1] = d2
#
#        E = EOF(D,cov_norm=False) #do not normalize covariance matrix, as this is also not done in example
#        print E.C #covariance matrix


#        shape of EOF is wrong !!!!!!!
#
#        something is not really working here !!!!
#
#
#        irgendwie ist hier ein problem
#        warum einmal 4x4 und einmal 2x2 ???

#        print 'eigval'
#        print E.eigval
#
#        print 'eigvec'
#        print E.eigvec






    pass

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import copy [as 别名]
class TestData(TestCase):

    def setUp(self):
        # init Data object for testing
        n=100  # slows down significantly! constraint is percentile  test
        x = sc.randn(n)*100.  # generate dummy data
        self.D = Data(None, None)
        d=np.ones((n, 1, 1))
        self.D.data = d
        self.D.data[:,0,0]=x
        self.D.data = np.ma.array(self.D.data, mask=self.D.data != self.D.data)
        self.D.verbose = True
        self.D.unit = 'myunit'
        self.D.label = 'testlabel'
        self.D.filename = 'testinputfilename.nc'
        self.D.varname = 'testvarname'
        self.D.long_name = 'This is the longname'
        self.D.time = np.arange(n) + pl.datestr2num('2001-01-01') - 1
        self.D.time_str = "days since 0001-01-01 00:00:00"
        self.D.calendar = 'gregorian'
        self.D.cell_area = np.ones_like(self.D.data[0,:,:])


    @unittest.skip('wait for bug free scipy')
    def test_pattern_correlation(self):
        """
        test pattern correlation function
        """
        x = self.D.copy()

        # correlation with random values
        y = self.D.copy()
        tmp = np.random.random(y.shape)
        y.data = np.ma.array(tmp, mask=tmp != tmp)
        P2 = PatternCorrelation(x, y)
        P2._correlate()
        self.assertEqual(x.nt,len(P2.r_value))
        self.assertEqual(x.nt,len(P2.t))

        for i in xrange(x.nt):
            slope, intercept, r_value, p_value, std_err = stats.mstats.linregress(x.data[i,:,:].flatten(),y.data[i,:,:].flatten())
            self.assertEqual(P2.r_value[i], r_value)
            self.assertEqual(P2.p_value[i], p_value)
            self.assertEqual(P2.slope[i], slope)
            self.assertEqual(P2.intercept[i], intercept)
            self.assertEqual(P2.std_err[i], std_err)



    def test_gleckler_index(self):
        """
        test Reichler index/Gleckler plot
        """

        # generate sample data
        # sample data
        tmp = np.zeros((5, 3, 1))
        tmp[:,0,0] = np.ones(5)*1.
        tmp[:,1,0] = np.ones(5)*2.
        tmp[:,2,0] = np.ones(5)*5.

        # The data is like ...
        #| 1 | 2 | 5 |
        #| 1 | 2 | 5 |
        #| 1 | 2 | 5 |
        #| 1 | 2 | 5 |
        #| 1 | 2 | 5 |

        x = self.D.copy()
        x._temporal_subsetting(0, 4)

        x.data = np.ma.array(tmp, mask=tmp!=tmp)
        x.std = np.ones(x.data.shape)
        x.time[0] = pl.datestr2num('2000-02-15')
        x.time[1] = pl.datestr2num('2000-03-15')
        x.time[2] = pl.datestr2num('2000-04-15')
        x.time[3] = pl.datestr2num('2000-05-15')
        x.time[4] = pl.datestr2num('2000-06-15')

        y = self.D.copy()
        y._temporal_subsetting(0, 4)
        tmp = np.ones(x.data.shape)  # sample data 2
        y.data = np.ma.array(tmp, mask=tmp!=tmp)
        y.time[0] = pl.datestr2num('2000-02-15')
        y.time[1] = pl.datestr2num('2000-03-15')
        y.time[2] = pl.datestr2num('2000-04-15')
        y.time[3] = pl.datestr2num('2000-05-15')
        y.time[4] = pl.datestr2num('2000-06-15')

        # Case 1: same area weights
        # cell area
        tmp = np.ones((3, 1))
        x.cell_area = tmp*1.

        #| 1-1 | 2-1 | 5-1 |
        #| 1-1 | 2-1 | 5-1 |
        #| 1-1 | 2-1 | 5-1 |
        #| 1-1 | 2-1 | 5-1 |
        #| 1-1 | 2-1 | 5-1 |
        #===================
#.........这里部分代码省略.........

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import copy [as 别名]
class TestPycmbsBenchmarkingModels(unittest.TestCase):

    def setUp(self):
        n=1000  # slows down significantly! constraint is percentile  test
        x = sc.randn(n)*100.  # generate dummy data
        self.D = Data(None, None)
        d=np.ones((n, 1, 1))
        self.D.data = d
        self.D.data[:,0,0]=x
        self.D.data = np.ma.array(self.D.data, mask=self.D.data != self.D.data)
        self.D.verbose = True
        self.D.unit = 'myunit'
        self.D.label = 'testlabel'
        self.D.filename = 'testinputfilename.nc'
        self.D.varname = 'testvarname'
        self.D.long_name = 'This is the longname'
        self.D.time = np.arange(n) + pl.datestr2num('2001-01-01')
        self.D.time_str = "days since 0001-01-01 00:00:00"
        self.D.calendar = 'gregorian'
        self.D.oldtime=False

        # generate dummy Model object
        data_dir = './test/'
        varmethods = {'albedo':'get_albedo()', 'sis': 'get_sis()'}
        self.model = models.Model(data_dir, varmethods, name='testmodel', intervals='monthly')

        sis = self.D.copy()
        sis.mulc(5., copy=False)
        sis.label='sisdummy'

        alb = self.D.copy()
        alb.label='albedodummy'

        # add some dummy data variable
        self.model.variables = {'albedo':alb, 'sis':sis}

    def test_save_prefix_missing(self):
        m = self.model
        odir = './odir/'
        with self.assertRaises(ValueError):
            m.save(odir)

    def test_save_create_odir(self):
        m = self.model
        odir = './odir/'
        if os.path.exists(odir):
            os.system('rm -rf ' + odir)
        m.save(odir, prefix='test')
        self.assertTrue(os.path.exists(odir))
        os.system('rm -rf ' + odir)

    def test_save(self):
        m = self.model
        odir = './odir/'

        sisfile = odir + 'testoutput_SIS.nc'
        albfile = odir + 'testoutput_ALBEDO.nc'
        if os.path.exists(sisfile):
            os.remove(sisfile)
        if os.path.exists(albfile):
            os.remove(albfile)

        m.save(odir, prefix='testoutput')
        self.assertTrue(os.path.exists(sisfile))
        self.assertTrue(os.path.exists(albfile))

        if os.path.exists(sisfile):
            os.remove(sisfile)
        if os.path.exists(albfile):
            os.remove(albfile)
        os.system('rm -rf ' + odir)

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import copy [as 别名]
class TestPycmbsBenchmarkingModels(unittest.TestCase):

    def setUp(self):
        self.D = Data(None, None)
        self.D._init_sample_object(nt=1000, ny=1, nx=1)

        # generate dummy Model object
        data_dir = './test/'
        varmethods = {'albedo':'get_albedo()', 'sis': 'get_sis()'}
        self.model = models.Model(data_dir, varmethods, name='testmodel', intervals='monthly')

        sis = self.D.copy()
        sis.mulc(5., copy=False)
        sis.label='sisdummy'

        alb = self.D.copy()
        alb.label='albedodummy'

        # add some dummy data variable
        self.model.variables = {'albedo':alb, 'sis':sis}

    def test_save_prefix_missing(self):
        m = self.model
        odir = tempfile.mkdtemp() + os.sep
        with self.assertRaises(ValueError):
            m.save(odir)

    def test_save_create_odir(self):
        m = self.model
        odir = tempfile.mkdtemp() + os.sep
        if os.path.exists(odir):
            os.system('rm -rf ' + odir)
        m.save(odir, prefix='test')
        self.assertTrue(os.path.exists(odir))
        os.system('rm -rf ' + odir)

    def test_save(self):
        m = self.model
        odir = tempfile.mkdtemp() + os.sep

        sisfile = odir + 'testoutput_SIS.nc'
        albfile = odir + 'testoutput_ALBEDO.nc'
        if os.path.exists(sisfile):
            os.remove(sisfile)
        if os.path.exists(albfile):
            os.remove(albfile)

        m.save(odir, prefix='testoutput')
        self.assertTrue(os.path.exists(sisfile))
        self.assertTrue(os.path.exists(albfile))

        if os.path.exists(sisfile):
            os.remove(sisfile)
        if os.path.exists(albfile):
            os.remove(albfile)
        os.system('rm -rf ' + odir)

    def test_cmip5_init_singlemember(self):
        data_dir = tempfile.mkdtemp()

        # invalid model identifier
        with self.assertRaises(ValueError):
            M = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR1', 'amip', {}, intervals='monthly')
        with self.assertRaises(ValueError):
            M = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR#1#2', 'amip', {}, intervals='monthly')
        M1 = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR#1', 'amip', {}, intervals='monthly')
        M2 = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR#728', 'amip', {}, intervals='monthly')
        self.assertEqual(M1.ens_member, 1)
        self.assertEqual(M2.ens_member, 728)

    def test_cmip5_singlemember_filename(self):
        data_dir = tempfile.mkdtemp()

        # generate testfile
        testfile = data_dir + os.sep + 'MPI-M' + os.sep + 'MPI-ESM-LR' + os.sep + 'amip' + os.sep + 'mon' + os.sep + 'atmos' + os.sep + 'Amon' + os.sep + 'r1i1p1' + os.sep + 'ta' + os.sep + 'ta_Amon_MPI-ESM-LR_amip_r1i1p1_197901-200812.nc'
        os.makedirs(os.path.dirname(testfile))
        os.system('touch ' + testfile)
        self.assertTrue(os.path.exists(testfile))

        M = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR#1', 'amip', {}, intervals='monthly')
        f = M.get_single_ensemble_file('ta', mip='Amon', realm='atmos')
        self.assertTrue(os.path.exists(f))
        self.assertEqual(f, testfile)

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import copy [as 别名]
class TestPycmbsPlots(unittest.TestCase):

    def setUp(self):
        self.D = Data(None, None)
        self.D._init_sample_object(nt=1000, ny=1, nx=1)
        self._tmpdir = tempfile.mkdtemp()

    def test_ReichlerPlotGeneral(self):
        RP = ReichlerPlot()
        for i in xrange(10):
            RP.add([i*12.], 'test'+str(i))
        RP.simple_plot()
        RP.bar(title='some title', vmin=-10., vmax=10.)
        #~ RP.circle_plot()

    def test_ScatterPlot_General(self):
        x = self.D
        S = ScatterPlot(x)
        S.plot(x)
        S.legend()

    def test_rotate_ticks(self):
        f = plt.figure()
        ax=f.add_subplot(111)
        ax.plot(np.random.random(1000))
        rotate_ticks(ax, 20.)

    def test_correlation_analysis(self):
        x = self.D
        y = self.D
        C = CorrelationAnalysis(x, y)
        C.do_analysis()

    def test_ScatterPlot_GeneralWithNormalization(self):
        x = self.D
        S = ScatterPlot(x, normalize_data=True)
        S.plot(x)
        S.legend()

    def test_ScatterPlot_FldemeanFalse(self):
        x = self.D
        S = ScatterPlot(x)
        S.plot(x, fldmean=False)
        S.legend()

    def test_ScatterPlot_InvalidShape(self):
        x = self.D
        S = ScatterPlot(x)
        y = self.D.copy()
        y.data = np.random.random((10,20,30,40))
        with self.assertRaises(ValueError):
            S.plot(y, fldmean=False)

    def test_LinePlot_General(self):
        x = self.D
        L = LinePlot()
        L1 = LinePlot(regress=True)
        L.plot(x)
        L1.plot(x)

    def test_LinePlot_WithAxis(self):
        x = self.D
        f = plt.figure()
        ax = f.add_subplot(111)
        L = LinePlot(ax=ax)
        L.plot(x)

    def test_HistogrammPlot_General(self):
        H = HistogrammPlot(normalize=True)
        H.plot(self.D, bins=10, shown=True)

    def test_ZonalPlot(self):
        Z = ZonalPlot()
        Z.plot(self.D)

    def test_map_difference_General(self):
        map_difference(self.D, self.D)


    def test_GlecklerPlot_InvalidNumberOfObservations(self):
        G = GlecklerPlot()
        G.add_model('echam5')
        G.add_model('mpi-esm')
        G.add_variable('ta')
        G.add_data('ta', 'echam5', 0.5,pos=1)
        G.add_data('ta', 'echam5',0.25,pos=2)
        G.add_data('ta', 'echam5',-0.25,pos=3)
        G.add_data('ta', 'mpi-esm',-0.25,pos=4)
        G.add_data('ta', 'mpi-esm',-0.25,pos=5)
        with self.assertRaises(ValueError):
            G.plot()


    def test_GlecklerPlot_4obs(self):
        G = GlecklerPlot()
        G.add_model('echam5')
        G.add_model('mpi-esm')
        G.add_variable('ta')
        G.add_variable('P')
        G.add_data('P', 'echam5', 0.5,pos=1)
#.........这里部分代码省略.........

# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import copy [as 别名]
For COPYING and LICENSE details, please refer to the LICENSE file
"""

from pycmbs.data import Data
import numpy as np

fname = '../pycmbs/examples/example_data/air.mon.mean.nc'

d=Data(fname, 'air', read=True)
c=d.get_climatology(return_object=True)

print 'c raw: ', c.fldmean()
print c.date
print ''

# create some invalid data
d1=d.copy()
t = d1.time*1.
d1.time[20:] = t[0:-20]
d1.time[0:20] = t[-20:]

tmp = d1.data*1.
d1.data[20:,:,:] = tmp[0:-20,:,:]
d1.data[0:20,:,:] = tmp[-20:,:,:]

c1=d1.get_climatology(return_object=True, ensure_start_first=True)

print ''
print c1.date
print c1.fldmean()