def test_it_returns_the_AlmondBoard_p2_table_by_size():
fh = open('fixtures/sample_data/2012.01.PosRpt.pdf', 'rb')
pdf_page = get_pdf_page(fh, 2)
table1, _ = page_to_tables(pdf_page)
#table1, _ = getTable(fh, 2)
assert_equals(78, len(table1))
assert_equals(10, len(table1[0]))
def test_the_atomise_option_works_on_coceral_p1_by_size():
fh = open("fixtures/sample_data/1359397366Final_Coceral grain estimate_2012_December.pdf", "rb")
pdf_page = get_pdf_page(fh, 1)
table, _ = page_to_tables(pdf_page, atomise=True)
# table1, _ = getTable(fh, 2)
assert_equals(43, len(table))
assert_equals(31, len(table[0]))
def test_it_returns_the_AlmondBoard_p2_table_by_size():
fh = open("fixtures/sample_data/2012.01.PosRpt.pdf", "rb")
pdf_page = get_pdf_page(fh, 2)
table1, _ = page_to_tables(pdf_page)
# table1, _ = getTable(fh, 2)
assert_equals(78, len(table1))
assert_equals(10, len(table1[0]))
def test_it_can_use_one_hint_argentina_by_size():
fh = open("fixtures/sample_data/argentina_diputados_voting_record.pdf", "rb")
pdf_page = get_pdf_page(fh, 1)
table1, _ = page_to_tables(pdf_page, hints=["Apellido", ""])
# table1,_ = getTable(fh, 2)
assert_equals(32, len(table1))
assert_equals(4, len(table1[0]))
def test_it_can_use_hints_AlmondBoard_p1():
fh = open("fixtures/sample_data/2012.01.PosRpt.pdf", "rb")
pdf_page = get_pdf_page(fh, 1)
table, _ = page_to_tables(pdf_page, hints=[u"% Change", u"Uncommited"])
assert_equals(
[
[u"", u"Million Lbs.", u"Kernel Wt.", u"Kernel Wt.", u"% Change"],
[u"1. Carryin August 1, 2011", u"254.0", u"253,959,411", u"321,255,129", u"-20.95%"],
[u"2. Crop Receipts to Date", u"1,950.0", u"1,914,471,575", u"1,548,685,417", u"23.62%"],
[u"3. [3% Loss and Exempt]", u"58.5", u"57,434,147)(", u"46,460,563()", u""],
[u"4. New Crop Marketable (2-3)", u"1,891.5", u"1,857,037,428", u"1,502,224,854", u"23.62%"],
[u"5. [Reserve]", u"n/a", u"0", u"0", u""],
[u"6. Total Supply (1+4-5)", u"2,145.5", u"2,110,996,839", u"1,823,479,983", u"15.77%"],
[u"Shipments by Handlers7. Domestic", u"555.0", u"265,796,698", u"255,785,794", u"3.91%"],
[u"8. Export", u"1,295.0", u"755,447,255", u"664,175,807", u"13.74%"],
[u"9. Total Shipments", u"1,850.0", u"1,021,243,953", u"919,961,601", u"11.01%"],
[u"10. Forecasted Carryout", u"295.5", u"", u"", u""],
[u"11. Computed Inventory (6-9)", u"", u"1,089,752,886", u"903,518,382", u"20.61%"],
[u"Commitments (sold, not delivered)**12. Domestic", u"", u"214,522,238", u"187,492,263", u"14.42%"],
[u"13. Export", u"", u"226,349,446", u"155,042,764", u"45.99%"],
[u"14. Total Commited Shipments", u"", u"440,871,684", u"342,535,027", u"28.71%"],
[u"15. Uncommited Inventory (11-14)", u"", u"648,881,202", u"560,983,355", u"15.67%"],
],
table,
)
def test_it_exits_gracefully_when_no_tables_found():
fh = open("fixtures/sample_data/13_06_12_10_36_58_boletim_ingles_junho_2013.pdf", "rb")
pdf_page = get_pdf_page(fh, 5)
table, table_diagnostic_data = page_to_tables(pdf_page)
assert_equals([], table)
assert isinstance(table_diagnostic_data, TableDiagnosticData)
def test_atomise_does_not_disrupt_table_finding():
fh = open('fixtures/sample_data/13_06_12_10_36_58_boletim_ingles_junho_2013.pdf', 'rb')
pdf_page = pdftables.get_pdf_page(fh, 4)
table1, _ = pdftables.page_to_tables(pdf_page, atomise=True, extend_y=False)
table2, _ = pdftables.page_to_tables(pdf_page, atomise=False, extend_y=False)
assert_equals(table1, table2)
def test_it_can_use_one_hint_argentina_by_size():
fh = open('fixtures/sample_data/argentina_diputados_voting_record.pdf',
'rb')
pdf_page = get_pdf_page(fh, 1)
table1, _ = page_to_tables(pdf_page, hints=['Apellido', ''])
#table1,_ = getTable(fh, 2)
assert_equals(32, len(table1))
assert_equals(4, len(table1[0]))
def test_it_exits_gracefully_when_no_tables_found():
fh = open(
'fixtures/sample_data/13_06_12_10_36_58_boletim_ingles_junho_2013.pdf',
'rb')
pdf_page = get_pdf_page(fh, 5)
table, table_diagnostic_data = page_to_tables(pdf_page)
assert_equals([], table)
assert (isinstance(table_diagnostic_data, TableDiagnosticData))
def test_the_atomise_option_works_on_coceral_p1_by_size():
fh = open(
'fixtures/sample_data/1359397366Final_Coceral grain estimate_2012_December.pdf',
'rb')
pdf_page = get_pdf_page(fh, 1)
table, _ = page_to_tables(pdf_page, atomise=True)
#table1, _ = getTable(fh, 2)
assert_equals(43, len(table))
assert_equals(31, len(table[0]))
def page():
basename = os.path.basename(options.input)
inputname, inputextension = os.path.splitext(basename)
fh = open(options.input, 'rb')
pdf_page = pdftables.get_pdf_page(fh, int(options.page))
table1, _ = pdftables.page_to_tables(pdf_page)
data = pd.DataFrame(table1)
data.to_csv(options.output+inputname+"-"+options.page+".csv", encoding='utf-8',index=False)
print("Done.")
def start_reading(self, check):
total = self.doc.numPages
for num in range(0, total):
page = self.doc.getPage(num)
text = page.extractText().strip().replace('\n','')
if text.find("(SCHEME OF EXAMINATIONS)") == -1:
self.handle_result_page(text, check)
else:
table = page_to_tables(get_pdf_page(self.f, num+1))
self.subjects = self.handle_heading_page(text, table)
def test_it_can_use_hints_AlmondBoard_p1():
fh = open('fixtures/sample_data/2012.01.PosRpt.pdf', 'rb')
pdf_page = get_pdf_page(fh, 1)
table, _ = page_to_tables(pdf_page, hints=[u"% Change", u"Uncommited"])
assert_equals([
[u'', u'Million Lbs.', u'Kernel Wt.', u'Kernel Wt.', u'% Change'],
[
u'1. Carryin August 1, 2011', u'254.0', u'253,959,411',
u'321,255,129', u'-20.95%'
],
[
u'2. Crop Receipts to Date', u'1,950.0', u'1,914,471,575',
u'1,548,685,417', u'23.62%'
],
[
u'3. [3% Loss and Exempt]', u'58.5', u'57,434,147)(',
u'46,460,563()', u''
],
[
u'4. New Crop Marketable (2-3)', u'1,891.5', u'1,857,037,428',
u'1,502,224,854', u'23.62%'
], [u'5. [Reserve]', u'n/a', u'0', u'0', u''],
[
u'6. Total Supply (1+4-5)', u'2,145.5', u'2,110,996,839',
u'1,823,479,983', u'15.77%'
],
[
u'Shipments by Handlers7. Domestic', u'555.0', u'265,796,698',
u'255,785,794', u'3.91%'
],
[u'8. Export', u'1,295.0', u'755,447,255', u'664,175,807', u'13.74%'],
[
u'9. Total Shipments', u'1,850.0', u'1,021,243,953',
u'919,961,601', u'11.01%'
], [u'10. Forecasted Carryout', u'295.5', u'', u'', u''],
[
u'11. Computed Inventory (6-9)', u'', u'1,089,752,886',
u'903,518,382', u'20.61%'
],
[
u'Commitments (sold, not delivered)**12. Domestic', u'',
u'214,522,238', u'187,492,263', u'14.42%'
], [u'13. Export', u'', u'226,349,446', u'155,042,764', u'45.99%'],
[
u'14. Total Commited Shipments', u'', u'440,871,684',
u'342,535,027', u'28.71%'
],
[
u'15. Uncommited Inventory (11-14)', u'', u'648,881,202',
u'560,983,355', u'15.67%'
]
], table)
def test_atomise_does_not_disrupt_table_finding():
fh = open(
'fixtures/sample_data/13_06_12_10_36_58_boletim_ingles_junho_2013.pdf',
'rb')
pdf_page = pdftables.get_pdf_page(fh, 4)
table1, _ = pdftables.page_to_tables(pdf_page,
atomise=True,
extend_y=False)
table2, _ = pdftables.page_to_tables(pdf_page,
atomise=False,
extend_y=False)
assert_equals(table1, table2)
def main((i,inputPath)):
fh = open(inputPath,'rb')
pdf_page = pdftables.get_pdf_page(fh, i)
bs = "\b"
sys.stdout.write(bs*4)
sys.stdout.write("%03d " % i)
sys.stdout.flush()
try:
table1, _ = pdftables.page_to_tables(pdf_page)
except:
table1 = []
print("read error")
if len(table1)>0:
data = pd.DataFrame(table1)
data.to_csv(outputPath+"%03d" % i+".csv", encoding='utf-8')
else:
sys.stdout.write(" ")
def test_it_returns_the_AlmondBoard_p4_table():
fh = open("fixtures/sample_data/2012.01.PosRpt.pdf", "rb")
pdf_page = get_pdf_page(fh, 4)
table, _ = page_to_tables(pdf_page, extend_y=False)
assert_equals(
[
[u"Variety Name", u"Total Receipts", u"Total Receipts", u"Total Inedibles", u"Receipts", u"% Rejects"],
[u"Aldrich", u"48,455,454", u"49,181,261", u"405,555", u"2.53%", u"0.82%"],
[u"Avalon", u"7,920,179", u"8,032,382", u"91,733", u"0.41%", u"1.14%"],
[u"Butte", u"151,830,761", u"150,799,510", u"1,054,567", u"7.93%", u"0.70%"],
[u"Butte/Padre", u"215,114,812", u"218,784,885", u"1,145,000", u"11.24%", u"0.52%"],
[u"Carmel", u"179,525,234", u"178,912,935", u"1,213,790", u"9.38%", u"0.68%"],
[u"Carrion", u"507,833", u"358,580", u"2,693", u"0.03%", u"0.75%"],
[u"Fritz", u"105,479,433", u"106,650,571", u"1,209,192", u"5.51%", u"1.13%"],
[u"Harvey", u"58,755", u"58,755", u"1,416", u"0.00%", u"2.41%"],
[u"Hashem", u"430,319", u"430,014", u"1,887", u"0.02%", u"0.44%"],
[u"Le Grand", u"0", u"0", u"0", u"0.00%", u"0.00%"],
[u"Livingston", u"7,985,535", u"7,926,910", u"186,238", u"0.42%", u"2.35%"],
[u"Marchini", u"363,887", u"391,965", u"3,675", u"0.02%", u"0.94%"],
[u"Merced", u"65,422", u"66,882", u"1,167", u"0.00%", u"1.74%"],
[u"Mission", u"19,097,034", u"18,851,071", u"110,323", u"1.00%", u"0.59%"],
[u"Mixed", u"36,358,011", u"36,926,337", u"952,264", u"1.90%", u"2.58%"],
[u"Mono", u"757,637", u"689,552", u"6,785", u"0.04%", u"0.98%"],
[u"Monterey", u"220,713,436", u"212,746,409", u"2,293,892", u"11.53%", u"1.08%"],
[u"Morley", u"822,529", u"825,738", u"6,264", u"0.04%", u"0.76%"],
[u"N43", u"156,488", u"85,832", u"340", u"0.01%", u"0.40%"],
[u"Neplus", u"1,279,599", u"1,237,532", u"17,388", u"0.07%", u"1.41%"],
[u"Nonpareil", u"741,809,844", u"727,286,104", u"5,121,465", u"38.75%", u"0.70%"],
[u"Padre", u"62,905,358", u"62,417,565", u"193,168", u"3.29%", u"0.31%"],
[u"Peerless", u"5,113,472", u"5,101,245", u"20,792", u"0.27%", u"0.41%"],
[u"Price", u"25,312,529", u"25,124,463", u"143,983", u"1.32%", u"0.57%"],
[u"Ruby", u"4,163,237", u"4,057,470", u"35,718", u"0.22%", u"0.88%"],
[u"Sauret", u"55,864", u"55,864", u"517", u"0.00%", u"0.93%"],
[u"Savana", u"389,317", u"390,585", u"2,049", u"0.02%", u"0.52%"],
[u"Sonora", u"31,832,025", u"33,184,703", u"387,848", u"1.66%", u"1.17%"],
[u"Thompson", u"491,026", u"487,926", u"8,382", u"0.03%", u"1.72%"],
[u"Tokyo", u"783,494", u"794,699", u"4,511", u"0.04%", u"0.57%"],
[u"Winters", u"5,780,183", u"5,756,167", u"46,211", u"0.30%", u"0.80%"],
[u"Wood Colony", u"37,458,735", u"36,331,907", u"189,967", u"1.96%", u"0.52%"],
[u"Major Varieties Sub Total:", u"1,913,017,442", u"1,893,945,819", u"14,858,780", u"99.92%", u"0.78%"],
[u"Minor Varieties Total:", u"1,454,133", u"1,480,800", u"34,997", u"0.08%", u"2.36%"],
[u"Grand Total All Varieties", u"1,914,471,575", u"1,895,426,619", u"14,893,777", u"100.00%", u"0.79%"],
],
table,
)
def test_it_returns_the_AlmondBoard_p4_table():
fh = open('fixtures/sample_data/2012.01.PosRpt.pdf', 'rb')
pdf_page = get_pdf_page(fh, 4)
table, _ = page_to_tables(pdf_page, extend_y=False)
assert_equals(
[[u'Variety Name', u'Total Receipts', u'Total Receipts', u'Total Inedibles', u'Receipts', u'% Rejects'],
[u'Aldrich', u'48,455,454', u'49,181,261', u'405,555', u'2.53%', u'0.82%'],
[u'Avalon', u'7,920,179', u'8,032,382', u'91,733', u'0.41%', u'1.14%'],
[u'Butte', u'151,830,761', u'150,799,510', u'1,054,567', u'7.93%', u'0.70%'],
[u'Butte/Padre', u'215,114,812', u'218,784,885', u'1,145,000', u'11.24%', u'0.52%'],
[u'Carmel', u'179,525,234', u'178,912,935', u'1,213,790', u'9.38%', u'0.68%'],
[u'Carrion', u'507,833', u'358,580', u'2,693', u'0.03%', u'0.75%'],
[u'Fritz', u'105,479,433', u'106,650,571', u'1,209,192', u'5.51%', u'1.13%'],
[u'Harvey', u'58,755', u'58,755', u'1,416', u'0.00%', u'2.41%'],
[u'Hashem', u'430,319', u'430,014', u'1,887', u'0.02%', u'0.44%'],
[u'Le Grand', u'0', u'0', u'0', u'0.00%', u'0.00%'],
[u'Livingston', u'7,985,535', u'7,926,910', u'186,238', u'0.42%', u'2.35%'],
[u'Marchini', u'363,887', u'391,965', u'3,675', u'0.02%', u'0.94%'],
[u'Merced', u'65,422', u'66,882', u'1,167', u'0.00%', u'1.74%'],
[u'Mission', u'19,097,034', u'18,851,071', u'110,323', u'1.00%', u'0.59%'],
[u'Mixed', u'36,358,011', u'36,926,337', u'952,264', u'1.90%', u'2.58%'],
[u'Mono', u'757,637', u'689,552', u'6,785', u'0.04%', u'0.98%'],
[u'Monterey', u'220,713,436', u'212,746,409', u'2,293,892', u'11.53%', u'1.08%'],
[u'Morley', u'822,529', u'825,738', u'6,264', u'0.04%', u'0.76%'],
[u'N43', u'156,488', u'85,832', u'340', u'0.01%', u'0.40%'],
[u'Neplus', u'1,279,599', u'1,237,532', u'17,388', u'0.07%', u'1.41%'],
[u'Nonpareil', u'741,809,844', u'727,286,104', u'5,121,465', u'38.75%', u'0.70%'],
[u'Padre', u'62,905,358', u'62,417,565', u'193,168', u'3.29%', u'0.31%'],
[u'Peerless', u'5,113,472', u'5,101,245', u'20,792', u'0.27%', u'0.41%'],
[u'Price', u'25,312,529', u'25,124,463', u'143,983', u'1.32%', u'0.57%'],
[u'Ruby', u'4,163,237', u'4,057,470', u'35,718', u'0.22%', u'0.88%'],
[u'Sauret', u'55,864', u'55,864', u'517', u'0.00%', u'0.93%'],
[u'Savana', u'389,317', u'390,585', u'2,049', u'0.02%', u'0.52%'],
[u'Sonora', u'31,832,025', u'33,184,703', u'387,848', u'1.66%', u'1.17%'],
[u'Thompson', u'491,026', u'487,926', u'8,382', u'0.03%', u'1.72%'],
[u'Tokyo', u'783,494', u'794,699', u'4,511', u'0.04%', u'0.57%'],
[u'Winters', u'5,780,183', u'5,756,167', u'46,211', u'0.30%', u'0.80%'],
[u'Wood Colony', u'37,458,735', u'36,331,907', u'189,967', u'1.96%', u'0.52%'],
[u'Major Varieties Sub Total:', u'1,913,017,442', u'1,893,945,819', u'14,858,780', u'99.92%', u'0.78%'],
[u'Minor Varieties Total:', u'1,454,133', u'1,480,800', u'34,997', u'0.08%', u'2.36%'],
[u'Grand Total All Varieties', u'1,914,471,575', u'1,895,426,619', u'14,893,777', u'100.00%', u'0.79%']]
, table
)
def page():
basename = os.path.basename(options.input)
inputname, inputextension = os.path.splitext(basename)
fh = open(options.input, 'rb')
pdf_page = pdftables.get_pdf_page(fh, int(options.page))
table1, _ = pdftables.page_to_tables(pdf_page)
table = []
for row in table1:
if uni(row[1])!='' and uni(row[1])!='Wage':
newrow = []
#Append the column description
newrow.append(uni(row[1]))
for cell in row[1:]:
naIndex = [m.start() for m in re.finditer('N/A', uni(cell))]
dotIndex = [m.start() for m in re.finditer('\.', uni(cell))]
if len(naIndex)+len(dotIndex)==1:
#It's fine, it's just one value
newrow.append(uni(cell))
else:
#We need to separate things...
sliceIndicies = []
#Slice 3 characters after N/A
for indx in naIndex:
sliceIndicies.append(indx+3)
#Slice 2 characters after .
for indx in dotIndex:
sliceIndicies.append(indx+3)
#Sort
sliceIndicies.sort()
#Add zero for the beginning
sliceIndicies.insert(0,0)
#Slice
indxLen = len(sliceIndicies)
for i in range(1,indxLen):
start = sliceIndicies[i-1]
end = sliceIndicies[i]
strSlice = uni(cell)[start:end]
newrow.append(strSlice)
table.append(newrow)
data = pd.DataFrame(table)
data.to_csv(options.output+inputname+"-"+options.page+".csv", encoding='utf-8',index=False)
print("Done.")
def test_it_can_use_hints_AlmondBoard_p1():
fh = open('fixtures/sample_data/2012.01.PosRpt.pdf', 'rb')
pdf_page = get_pdf_page(fh, 1)
table, _ = page_to_tables(pdf_page, hints=[u"% Change", u"Uncommited"])
assert_equals(
[[u'Salable', u'Million Lbs.', u'Kernel Wt.', u'Kernel Wt.', u'% Change'],
[u'1. Carryin August 1, 2011', u'254.0', u'253,959,411', u'321,255,129', u'-20.95%'],
[u'2. Crop Receipts to Date', u'1,950.0', u'1,914,471,575', u'1,548,685,417', u'23.62%'],
[u'3. [3% Loss and Exempt]', u'58.5', u'57,434,147)(', u'46,460,563(', u')'],
[u'4. New Crop Marketable (2-3)', u'1,891.5', u'1,857,037,428', u'1,502,224,854', u'23.62%'],
[u'5. [Reserve]', u'n/a', u'0', u'0', u''],
[u'6. Total Supply (1+4-5)Shipments by Handlers', u'2,145.5', u'2,110,996,839', u'1,823,479,983', u'15.77%'],
[u'7. Domestic', u'555.0', u'265,796,698', u'255,785,794', u'3.91%'],
[u'8. Export', u'1,295.0', u'755,447,255', u'664,175,807', u'13.74%'],
[u'9. Total Shipments', u'1,850.0', u'1,021,243,953', u'919,961,601', u'11.01%'],
[u'10. Forecasted Carryout', u'295.5', u'', u'', u''],
[u'11. Computed Inventory (6-9)Commitments (sold, not delivered)**', u'', u'1,089,752,886', u'903,518,382', u'20.61%'],
[u'12. Domestic', u'', u'214,522,238', u'187,492,263', u'14.42%'], [u'13. Export', u'', u'226,349,446', u'155,042,764', u'45.99%'],
[u'14. Total Commited Shipments', u'', u'440,871,684', u'342,535,027', u'28.71%'],
[u'15. Uncommited Inventory (11-14)', u'', u'648,881,202', u'560,983,355', u'15.67%']]
, table)
import pdftables
my_pdf = open('data/WEF_GlobalCompetitivenessReport_2014-15.pdf', 'rb')
chart_page = pdftables.get_pdf_page(my_pdf, 29)
table = pdftables.page_to_tables(chart_page)
titles = zip(table[0][0], table[0][1])[:5]
titles = [''.join([title[0], title[1]]) for title in titles]
print(titles)
all_rows = []
for row_data in table[0][2:]:
all_rows.extend([row_data[:5], row_data[5:]])
print(all_rows)
# pagenumber = 1
# SelectedPDF = "pdf_prc_prod_1_7_1288_acucar-vhp-vendido-mercado-externo_sao-paulo_mensal.pdf"
# pagenumber = 1
# SelectedPDF = "commodity-prices_en.pdf"
# pagenumber = 1
SelectedPDF = "AnimalExampleTables.pdf" # 7 pages works fine in pdfminer, 4 for first test 2012.01.PosRpt.pdf
pagenumber = 2
filepath = os.path.join(PDF_TEST_FILES, SelectedPDF)
fh = open(filepath, "rb")
# pta.plotAllPages(fh)
pdf_page = get_pdf_page(fh, pagenumber)
table, diagnosticData = page_to_tables(pdf_page, extend_y=False, hints=hints, atomise=False)
fig, ax1 = pta.plotpage(diagnosticData)
result = StringIO()
(columns, rows) = get_dimensions(table)
result.write(" {} columns, {} rows\n".format(columns, rows))
print to_string(table)
# BoxList = plotAllPages(open(filepath, 'rb'))
def test_it_does_not_crash_on_m30_p5():
fh = open('fixtures/sample_data/m30-JDent36s15-20.pdf', 'rb')
pdf_page = get_pdf_page(fh, 5)
table, _ = page_to_tables(pdf_page)
"""Put this in for more aggressive test"""
#pagenumber = 1
#SelectedPDF = "pdf_prc_prod_1_7_1288_acucar-vhp-vendido-mercado-externo_sao-paulo_mensal.pdf"
#pagenumber = 1
#SelectedPDF = "commodity-prices_en.pdf"
#pagenumber = 1
SelectedPDF = "AnimalExampleTables.pdf" # 7 pages works fine in pdfminer, 4 for first test 2012.01.PosRpt.pdf
pagenumber = 2
filepath = os.path.join(PDF_TEST_FILES, SelectedPDF)
fh = open(filepath, 'rb')
#pta.plotAllPages(fh)
pdf_page = get_pdf_page(fh, pagenumber)
table, diagnosticData = page_to_tables(pdf_page,
extend_y=False,
hints=hints,
atomise=False)
fig, ax1 = pta.plotpage(diagnosticData)
result = StringIO()
(columns, rows) = get_dimensions(table)
result.write(" {} columns, {} rows\n".format(columns, rows))
print to_string(table)
# BoxList = plotAllPages(open(filepath, 'rb'))
def test_it_returns_the_AlmondBoard_p4_table():
fh = open('fixtures/sample_data/2012.01.PosRpt.pdf', 'rb')
pdf_page = get_pdf_page(fh, 4)
table, _ = page_to_tables(pdf_page, extend_y=False)
assert_equals([
[
u'Variety Name', u'Total Receipts', u'Total Receipts',
u'Total Inedibles', u'Receipts', u'% Rejects'
],
[
u'Aldrich', u'48,455,454', u'49,181,261', u'405,555', u'2.53%',
u'0.82%'
],
[u'Avalon', u'7,920,179', u'8,032,382', u'91,733', u'0.41%', u'1.14%'],
[
u'Butte', u'151,830,761', u'150,799,510', u'1,054,567', u'7.93%',
u'0.70%'
],
[
u'Butte/Padre', u'215,114,812', u'218,784,885', u'1,145,000',
u'11.24%', u'0.52%'
],
[
u'Carmel', u'179,525,234', u'178,912,935', u'1,213,790', u'9.38%',
u'0.68%'
], [u'Carrion', u'507,833', u'358,580', u'2,693', u'0.03%', u'0.75%'],
[
u'Fritz', u'105,479,433', u'106,650,571', u'1,209,192', u'5.51%',
u'1.13%'
], [u'Harvey', u'58,755', u'58,755', u'1,416', u'0.00%', u'2.41%'],
[u'Hashem', u'430,319', u'430,014', u'1,887', u'0.02%', u'0.44%'],
[u'Le Grand', u'0', u'0', u'0', u'0.00%', u'0.00%'],
[
u'Livingston', u'7,985,535', u'7,926,910', u'186,238', u'0.42%',
u'2.35%'
], [u'Marchini', u'363,887', u'391,965', u'3,675', u'0.02%', u'0.94%'],
[u'Merced', u'65,422', u'66,882', u'1,167', u'0.00%', u'1.74%'],
[
u'Mission', u'19,097,034', u'18,851,071', u'110,323', u'1.00%',
u'0.59%'
],
[
u'Mixed', u'36,358,011', u'36,926,337', u'952,264', u'1.90%',
u'2.58%'
], [u'Mono', u'757,637', u'689,552', u'6,785', u'0.04%', u'0.98%'],
[
u'Monterey', u'220,713,436', u'212,746,409', u'2,293,892',
u'11.53%', u'1.08%'
], [u'Morley', u'822,529', u'825,738', u'6,264', u'0.04%', u'0.76%'],
[u'N43', u'156,488', u'85,832', u'340', u'0.01%', u'0.40%'],
[u'Neplus', u'1,279,599', u'1,237,532', u'17,388', u'0.07%', u'1.41%'],
[
u'Nonpareil', u'741,809,844', u'727,286,104', u'5,121,465',
u'38.75%', u'0.70%'
],
[
u'Padre', u'62,905,358', u'62,417,565', u'193,168', u'3.29%',
u'0.31%'
],
[
u'Peerless', u'5,113,472', u'5,101,245', u'20,792', u'0.27%',
u'0.41%'
],
[
u'Price', u'25,312,529', u'25,124,463', u'143,983', u'1.32%',
u'0.57%'
], [
u'Ruby', u'4,163,237', u'4,057,470', u'35,718', u'0.22%', u'0.88%'
], [u'Sauret', u'55,864', u'55,864', u'517', u'0.00%', u'0.93%'],
[u'Savana', u'389,317', u'390,585', u'2,049', u'0.02%', u'0.52%'],
[
u'Sonora', u'31,832,025', u'33,184,703', u'387,848', u'1.66%',
u'1.17%'
], [u'Thompson', u'491,026', u'487,926', u'8,382', u'0.03%', u'1.72%'],
[u'Tokyo', u'783,494', u'794,699', u'4,511', u'0.04%', u'0.57%'],
[
u'Winters', u'5,780,183', u'5,756,167', u'46,211', u'0.30%',
u'0.80%'
],
[
u'Wood Colony', u'37,458,735', u'36,331,907', u'189,967', u'1.96%',
u'0.52%'
],
[
u'Major Varieties Sub Total:', u'1,913,017,442', u'1,893,945,819',
u'14,858,780', u'99.92%', u'0.78%'
],
[
u'Minor Varieties Total:', u'1,454,133', u'1,480,800', u'34,997',
u'0.08%', u'2.36%'
],
[
u'Grand Total All Varieties', u'1,914,471,575', u'1,895,426,619',
u'14,893,777', u'100.00%', u'0.79%'
]
], table)
def test_it_does_not_crash_on_m30_p5():
fh = open("fixtures/sample_data/m30-JDent36s15-20.pdf", "rb")
pdf_page = get_pdf_page(fh, 5)
table, _ = page_to_tables(pdf_page)
"""Put this in for more aggressive test"""
def test_it_copes_with_CONAB_p8():
fh = open(
'fixtures/sample_data/13_06_12_10_36_58_boletim_ingles_junho_2013.pdf',
'rb')
pdf_page = get_pdf_page(fh, 8)
table, _ = page_to_tables(pdf_page, atomise=True)
import pdftables
my_pdf = open('data/WEF_GlobalCompetitivenessReport_2014-15.pdf', 'rb')
chart_page = pdftables.get_pdf_page(my_pdf, 29)
table = pdftables.page_to_tables(chart_page)
titles = zip(table[0][0], table[0][1])[:5]
titles = [''.join([title[0], title[1]]) for title in titles]
print(titles)
all_rows = []
for row_data in table[0][2:]:
all_rows.extend([row_data[:5], row_data[5:]])
print(all_rows)
def test_it_copes_with_CONAB_p8():
fh = open("fixtures/sample_data/13_06_12_10_36_58_boletim_ingles_junho_2013.pdf", "rb")
pdf_page = get_pdf_page(fh, 8)
table, _ = page_to_tables(pdf_page, atomise=True)
