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_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_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_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_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_alignment_withoutTable():
t = matrix2latex(m,
None,
"align*",
"pmatrix",
format="$%.2f$",
alignment='c')
assertEqual(t, "alignment_withoutTable")
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_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_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_multicolumn():
hr = [[
'Item', 'Item', 'Item', 'Item', 'Price', 'Price', 'test', '', 'Money',
'Money', 'Money'
], ['Animal', 'Description', '(\$)']]
t = matrix2latex(m, headerRow=hr)
t = t.split('\n')[4].strip() # pick out only third line
assert t == r"\multicolumn{4}{c}{Item} & \multicolumn{2}{c}{Price} & {test} & {} & \multicolumn{3}{c}{Money}\\\cmidrule(r){1-4}\cmidrule(r){5-6}\cmidrule(r){9-11}", t
def test_alignment2b():
rl = ["a", "b"]
cl = ["names", "c", "d", "e"]
t = matrix2latex(m,
alignment='r',
headerColumn=cl,
headerRow=rl,
transpose=True)
t = t.split('\n')[2].strip()
assert t == r"\begin{tabular}{rrr}", t
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_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_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 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_empty():
t = matrix2latex([])
assertEqual(t, 'empty')
def test_environment2():
t = matrix2latex(m, None, "foo", "bar")
assertEqual(t, "environment2")
def test_file():
matrix2latex(m, 'tmp.tex')
f = open('tmp.tex')
content = f.read()
f.close()
assertEqual(content, "file")
def test_transpose1():
t = matrix2latex(m, transpose=True)
assertEqual(t, "transpose1")
def test_nicefloat():
t = matrix2latex([123456e-10, 1e-15, 12345e5])
assertEqual(t, 'nicefloat')
def test_non_rectangular():
"""Test a nested list with 'missing' elements"""
t = matrix2latex([[1, 2], [1, 2, 3], [5]])
assertEqual(t, 'non_rectangular')
def test_nicefloat_4g():
t = matrix2latex([123456e-10, 1e-15, 12345e5], format='$%.4g$')
assertEqual(t, 'nicefloat_4g')
def test_file():
matrix2latex(m, 'tmp.tex')
f = open('tmp.tex')
content = f.read()
f.close()
assertEqual(content, "file")
def test_environment1():
t = matrix2latex(m, None, "table", "center", "tabular")
assertEqual(t, "environment1")
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_labels4():
t = matrix2latex(m, None, caption="Hello", label="la")
assertEqual(t, "labels4")
def test_environment2():
t = matrix2latex(m, None, "foo", "bar")
assertEqual(t, "environment2")
def test_alignment1():
t = matrix2latex(m, alignment='r')
t = t.split('\n')[2].strip()
assert t == r"\begin{tabular}{rrr}", t
def test_labels4():
t = matrix2latex(m, None, caption="Hello", label="la")
assertEqual(t, "labels4")
def test_nicefloat():
t = matrix2latex([123456e-10, 1e-15, 12345e5])
assertEqual(t, 'nicefloat')
for func in (factorialMath, factorialRecursive, factorialSequential):
row = list()
for n in N:
res = func(n) # call func
row.append(res) # append result to row
table.append(row) # append row to table
# convert to string for labeling
cl = ["${n}$".format(n=n) for n in N]
# row labels
rl = ['$n$', 'Built-in', 'Recursive', 'Sequential']
caption = '''Vertifying that the different factorial
implementations gives the same results'''
matrix2latex(table,
'facV',
caption=caption,
columnLabels=cl,
rowLabels=rl,
alignment='r')
import timeit
table = list()
for func in ('factorialMath', 'factorialRecursive', 'factorialSequential'):
row = list()
for n in N:
statement = 'factorial.{func}({n})'.format(func=func, n=n)
setup = 'import factorial'
# measure time
res = timeit.repeat(statement, setup)
row.append(min(res)) # append result
table.append(row) # append row to table
def test_non_rectangular():
"""Test a nested list with 'missing' elements"""
t = matrix2latex([[1,2],
[1, 2, 3],
[5]])
assertEqual(t, 'non_rectangular')
def test_transpose2():
cl = ["a", "b"]
t = matrix2latex(m, transpose=True, headerRow=cl)
assertEqual(t, "transpose2")
def test_nicefloat_4g():
t = matrix2latex([123456e-10, 1e-15, 12345e5], format='$%.4g$')
assertEqual(t, 'nicefloat_4g')
def test_string():
t = matrix2latex([['a', 'b', '1'], ['1', '2', '3']], format='%s')
assertEqual(t, "string")
def test_simple():
t = matrix2latex(m)
assertEqual(t, "simple")
def test_transpose1():
t = matrix2latex(m, transpose=True)
assertEqual(t, "transpose1")
def test_transpose2():
cl = ["a", "b"]
t = matrix2latex(m, transpose=True, headerRow=cl)
assertEqual(t, "transpose2")
def test_simple():
t = matrix2latex(m)
assertEqual(t, "simple")
def test_environment1():
t = matrix2latex(m, None, "table", "center", "tabular")
assertEqual(t, "environment1")
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_multicolumn():
hr = [['Item', 'Item', 'Item', 'Item', 'Price', 'Price', 'test', '', 'Money', 'Money', 'Money'],
['Animal', 'Description', '(\$)']]
t = matrix2latex(m, headerRow=hr)
t = t.split('\n')[4].strip() # pick out only third line
assert t == r"\multicolumn{4}{c}{Item} & \multicolumn{2}{c}{Price} & {test} & {} & \multicolumn{3}{c}{Money}\\\cmidrule(r){1-4}\cmidrule(r){5-6}\cmidrule(r){9-11}", t
def test_empty():
t = matrix2latex([])
assertEqual(t, 'empty')
def test_labels1():
cl = ["a", "b"]
rl = ["c", "d", "e"]
t = matrix2latex(m, None, headerColumn=cl, headerRow=rl)
assertEqual(t, "labels1")
def test_alignment1():
t = matrix2latex(m, alignment='r')
t = t.split('\n')[2].strip()
assert t == r"\begin{tabular}{rrr}", t
from uncertainties import correlated_values
from matrix2latex import matrix2latex
from scipy.special import legendre
#Polarplot
alpha, A1, A2, A3 = np.genfromtxt('data/peaks.txt', unpack=True)
hr = ['$\alpha$/°', '$A_1$', '$A_2$', '$A_3$']
m = np.zeros((19, 4))
m[:, 0] = alpha
m[:, 1] = A1
m[:, 2] = A2
m[:, 3] = A3
t = matrix2latex(m, headerRow=hr, format='%.2f')
print(t)
# Test mit Scipy
# Erklärung: Pn = legendre(n) erschafft ein Objekt namens Pn, dass ein Legendrepolynom
# des Grades n ist. Damit kann man plotten, wie unten auch zu sehen ist.
# Damit kriegst du auch diese Schleifen hin!
plt.figure(3)
P1 = legendre(1)
philin = np.linspace(0, 2 * np.pi, 1000)
theta = np.arccos(1 / 2 * np.cos(alpha / 360 * 2 * np.pi) - 1 / 2)
plt.polar(theta, A1 / np.max(A1), "rx", mew=0.5, label="Messwerte")
plt.polar(philin,
np.abs(P1(np.cos(philin))),
"b-",
label="Theorie",
def simple(matrix, headerRow=None, headerColumn=None, Filename=None, font_size=None, clean_latex=True):
"""A simple pagination function, that creates a minimal LaTeX document code for an input matrix,
compiles it, and removes the LaTeX traces.
Arguments:
matrix
A numpy matrix or a nested list
Filename
File to place output, extension .tex is added automatically. File can be included in a LaTeX
document by \input{filename}. Output will always be returned in a string. If filename is None
or not a string it is ignored.
headerRow
A row at the top used to label the columns.
Must be a list of strings. Can be a nested list for multiple headings.
If two or more items are repeated, a multicolumn is inserted, so:
headerRow=['a', 'a']
will produces "\multicolumn{2}{c}{Item}" with an appropriate cmidrule beneath.
To avoid this behavior ensure each consecutive item is unique, for instance:
headerRow=['a', 'a ']
will produces the expected "a & a".
headerColumn
A column used to label the rows.
Must be a list of strings
font_size
Specify the global (document and table) font size.
Accepted values are integers from 1 to 10 - these are mapped on the available LaTeX font sizes
https://en.wikibooks.org/wiki/LaTeX/Fonts
clean_latex
Used to optionally turn off the delete phase for LaTeX traces
Must be bool
"""
latex_font_sizes = {
1: "\\tiny",
2: "\\scriptsize",
3: "\\footnotesize",
4: "\\small",
5: "\\normalsize",
6: "\\large",
7: "\\Large",
8: "\\LARGE",
9: "\\huge",
10: "\\Huge"
}
if not Filename:
Filename = "_temp"
table = matrix2latex(matrix, headerRow=headerRow, headerColumn=headerColumn, environments=['tabular'])
#determine document font size
if font_size:
document_fontsize = latex_font_sizes[font_size]+"\n"
else:
document_fontsize = ""
#add header elements (with the prepend operator "+"y in reverse order)
tex = "\\sbox\mt{%\n" + table
tex = document_fontsize + tex
tex = "\\begin{document}\n" + tex
tex = "\\pagenumbering{gobble}\n" + tex
tex = "\\newsavebox\mt\n" + tex
tex = "\\usepackage{booktabs}\n" + tex
tex = "\\usepackage{geometry}\n\\geometry{a4paper,total={210mm,297mm},left=15mm,right=15mm,top=15mm,bottom=15mm}\n" + tex
tex = "\\documentclass{article}\n" + tex
#add footer elements
tex = tex + "%\n}\n"
tex = tex + \
"\\makeatletter\n" + \
"\\ifdim\\wd\\mt>\\textwidth\n" + \
"\\setlength\\@tempdima {\\paperheight}%\n" + \
"\\setlength\\paperheight {\\paperwidth}%\n" + \
"\\setlength\\paperwidth {\\@tempdima}%\n" + \
"\\setlength\\pdfpageheight{\\paperheight}%\n" + \
"\\setlength\\pdfpagewidth{\\paperwidth}%\n" + \
"\\setlength{\\textwidth}{\\paperwidth}%\n" + \
"\\addtolength{\\textwidth}{-3cm}%\n" + \
"\\setlength{\\hsize}{\\textwidth}%\n" + \
"\\fi\n" + \
"\\makeatother\n" + \
"\\begin{table}[htp]\\setlength{\\hsize}{\\textwidth}%\n" + \
"\\centering\n" + \
"\\usebox\\mt\n" + \
"\\end{table}\n" + \
"\\end{document}\n"
file_ = open(Filename+".tex", 'w')
file_.write(tex)
file_.close()
call(["pdflatex", Filename+".tex"])
if clean_latex:
all_files = os.listdir(".")
latex_files = [one_file for one_file in all_files if Filename in one_file]
non_pdf_latex_files = [latex_file for latex_file in latex_files if ".pdf" not in latex_file]
for non_pdf_latex_file in non_pdf_latex_files:
os.remove(non_pdf_latex_file)
ylabel("Average performance")
grid()
axis([0, 410, -2.2, 8])
savefig("Exp14.pdf")
# <codecell>
from scipy.stats import ks_2samp
from matrix2latex import matrix2latex
table = zeros((len(Abcissas), 2))
for i in range(0, len(Abcissas)):
y_csi = Y_CSI[i]
y_scirl = Y_SCIRL[i]
table[i][0] = Abcissas[i]
table[i][1] = ks_2samp(y_csi, y_scirl)[1]
t = matrix2latex(table, headerRow=["Number of expert samples", "$p$-value"])
print t
# <codecell>
def student_t(X1, X2):
return abs((mean(X1) - mean(X2))) / sqrt(
abs(pow(X1.var(), 2) / len(X1) - pow(X2.var(), 2) / len(X2)))
[student_t(Y_SCIRL[i], Y_CSI[i]) for i in range(0, 5)]
# <codecell>
plot(Abcissas[-4:],
