def test_none():
    m = [[1,None,None], [2,2,1], [2,1,2]]
    t = matrix2latex(m)
    assertEqual(t, "none")
    
    m2 = [[1,float('NaN'),float('NaN')], [2,2,1], [2,1,2]]
    t2 = matrix2latex(m)    
    assertEqual(t2, "none")

    t3 = matrix2latex(m, format='$%d$')
    assertEqual(t3, "none")

def test_pandas_columns():
    try:
        import pandas as pd
        import numpy as np
        d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
             'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
        df = pd.DataFrame(d)
        t = matrix2latex(df)
        # print 'pandas', t, df.to_records()
        assertEqual(t, "pandas_columns")

        t = matrix2latex(df, headerRow=None, headerColumn=None)
        assertEqual(t, "pandas_columns_noHeaders")
    except ImportError:
        pass

def test_pandas_series():
    try:
        import pandas as pd
        import numpy as np
        s = pd.Series([2, 4, 2, 42, 5], index=['a', 'b', 'c', 'd', 'e'])
        # print 'PANDAS\n', s
        # print 'PANDAS\n', s.to_dict(), s.tolist(), hasattr(s, 'to_dict')
        t = matrix2latex(s)
        # print 'pandas Series', t
        t2 = matrix2latex(pd.DataFrame(s))
        # print 'pandas DataFrame', t2
        assertEqual(t, "pandas_series")
        assertEqual(t2, "pandas_series_dataFrame")
    except ImportError:
        pass

def test_labels2():
    # only difference from above test is names, note how above function
    # handles having too few headerRow
    cl = ["a", "b"]
    rl = ["names", "c", "d", "e"]
    t = matrix2latex(m, None, headerColumn=cl, headerRow=rl)
    assertEqual(t, "labels2")

def test_labels3():
    # pass in environment as dictionary
    e = dict()
    e['headerColumn'] = ["a", "b"]
    e['headerRow'] = ["names", "c", "d", "e"]
    t = matrix2latex(m, None, **e)
    assertEqual(t, "labels3")

def test_infty1():
    try:
        import numpy as np
        m = [[1,np.inf,float('inf')], [2,2,float('-inf')], [-np.inf,1,2]]
        t = matrix2latex(m)
        assertEqual(t, "infty1")
    except (ImportError, AttributeError):
        pass

def test_numpy():
    try:
        import numpy as np
        for a in (np.matrix, np.array):
            t = matrix2latex(a(m), None, "align*", "pmatrix")
            assertEqual(t, "numpy")
    # Systems without numpy raises import error,
    # pypy raises attribute since matrix is not implemented, this is ok.
    except (ImportError, AttributeError):
        pass

def test_format_formatColumn_Warning():
    # Test for warning: http://stackoverflow.com/a/3892301/1942837
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always") # Cause all warnings to always be triggered.
        # specify both format and formatColumn
        t = matrix2latex([[123456e10, 123456e10]],
                             format='%g', formatColumn=['%.1g', '%g'])
        assert len(w) == 1
        assert issubclass(w[-1].category, Warning)
        assertEqual(t, 'format_formatColumn_Warning')            

def test_pandas_dataframe():
    try:
        import pandas as pd
        import numpy as np
        m = [[1, 1], [2, 4], [3, 9]] # python nested list
        m = pd.DataFrame(m)
        #m = pd.DataFrame.from_csv('http://chymera.eu/data/test/r_data.csv', parse_dates=False, index_col=False)
        # print 'PANDAS\n', m
        # print 'PANDAS\n', m.to_records()
        t = matrix2latex(m)
        assertEqual(t, "pandas_dataframe")
    except ImportError:
        pass

def tableEvtsAllRange(ch):
    if ch == "Mu": channel = "muon"
    else: channel = "electron"
    n_2btag = evalNumEvts("lljjmass", ch)
    print n_2btag 
    n_1btag = evalNumEvts("lljjmass_1btag", ch)
    n_0btag = evalNumEvts("lljjmass_0btag", ch)
    hr = [' ','0 b-tag yields', '1 b-tag yields','2 b-tag yields']
    hc = ["Background"]
    for m in masses:
        hc.append(str(m)+" GeV")
    numbers = []
    numbers.append([ n_0btag["Background"],  n_1btag["Background"],  n_2btag["Background"] ])
    for m in masses:
        signal = "GluGluToHToZZTo2L2Q_M-"+str(m)+"_8TeV"
        numbers.append([ n_0btag[signal], n_1btag[signal], n_2btag[signal]])
    
    t = matrix2latex(numbers, "evtsAllRange_"+ch, headerColumn=hc, headerRow=hr, format='%.2f', alignment = '|c|c|c|',caption='List of expected background and signal yields in the '+channel+' channel with 1~fb$^{-1}$of data after all selection and within the $ZZ$ invariant mass range [0,1000]')    

	def get_notes(self):
		# get notes as a latex matrix

		notes = self._get_notes()
		# used by matrix2latex
		# header row
		hr = ['dataset', 'recipe', 'note']
		# format column
		fc = ['%s', '%s', '%s']
		alignment = 'l l p{7.5cm}'

		m = []
		for note in notes:
			txt = note.get('note')
			txt = unicode(txt).encode('utf-8').replace('æ', '\\ae ').replace('å','\\aa ').replace('ø','\\o ')
			m.append([note.get('dataset'), note.get('recipe_type'), txt])
		caption = 'Comments to videos in questionnaire'
		label = 'tab:notes'
		t = matrix2latex(m, 'notes', headerRow=hr, caption=caption, label=label, formatColumn=fc, alignment=alignment)			
		return t

def tableEvtsMassWind(var, ch):

    if ch == "Mu": channel = "$2\mu2j$"
    else: channel = "$2e2j$"
    if var == "lljjmass": cat = "2 b-tag"
    elif var == "lljjmass_1btag": cat = "1 b-tag"
    else: cat = "0 b-tag"
  
    hr = ['Mass [GeV]','Signal', '$Z$+Jets', '$tt$', 'ZZ/WZ/WW', 'TotalBkg']
    hc = [str(m) for m in masses]
    numbers = []
    for m in masses:
        nEvts = evalNumEvts(var, ch, m)
        signal = "GluGluToHToZZTo2L2Q_M-"+str(m)+"_8TeV"
        numbers.append([
                        nEvts[signal],
                        nEvts["DYJetsToLL_M-50"],
                        nEvts["TT"],
                        nEvts["ZZ/WZ/WW"],
                        nEvts["Background"]
                        ])


        t = matrix2latex(numbers, var+"_"+ch+"_evts", headerColumn=hc, headerRow=hr, format='%.2f', alignment = '|c|c|c|c|c|',caption='Expected yields of signal and background with  1~fb$^{-1}$ based on simulation in the '+cat+' category. The numbers show  '+channel+' expectations. Tighter $mZZ$ mass requirements are applied as  (-6\%, +\0\%) of the mass hypothesis.')

def test_string():
    t = matrix2latex([['a', 'b', '1'], ['1', '2', '3']], format='%s')
    assertEqual(t, "string")

def test_infty2():
    # same as above but without numpy
    inf = float('inf')
    m = [[1,inf,float('inf')], [2,2,float('-inf')], [-inf,1,2]]
    t = matrix2latex(m)
    assertEqual(t, "infty1")

def test_labels1():
    cl = ["a", "b"]
    rl = ["c", "d", "e"]
    t = matrix2latex(m, None, headerColumn=cl, headerRow=rl)
    assertEqual(t, "labels1")

def test_environment2():
    t = matrix2latex(m, None, "foo", "bar")
    assertEqual(t, "environment2")

def test_environment1():
    t = matrix2latex(m, None, "table", "center", "tabular")
    assertEqual(t, "environment1")

def test_nicefloat_4g():
    t = matrix2latex([123456e-10, 1e-15, 12345e5], format='$%.4g$')
    assertEqual(t, 'nicefloat_4g')

def test_alignment2():
    cl = ["a", "b"]
    rl = ["names", "c", "d", "e"]
    t = matrix2latex(m, alignment='r', headerColumn=cl, headerRow = rl)
    t = t.split('\n')[2].strip()
    assert t == r"\begin{tabular}{rrrr}", t

def test_transpose1():
    t = matrix2latex(m, transpose=True)
    assertEqual(t, "transpose1")