[MRG] Add tests for output formats and parser kwargs (#126)
* Remove unused image processing code * Add opencv back-compat comment * Add tests for parser special cases * Fix lattice table area test * Add tests for output format * Add openpyxl dep
This commit is contained in:
Vinayak Mehta
@@ -7,8 +7,6 @@ from operator import itemgetter
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import cv2
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import numpy as np
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from .utils import merge_tuples
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def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2):
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"""Thresholds an image using OpenCV's adaptiveThreshold.
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@@ -102,6 +100,7 @@ def find_lines(threshold, direction='horizontal', line_size_scaling=15, iteratio
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_, contours, _ = cv2.findContours(
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threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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# for opencv backward compatibility
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contours, _ = cv2.findContours(
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threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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@@ -141,6 +140,7 @@ def find_table_contours(vertical, horizontal):
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__, contours, __ = cv2.findContours(
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mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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# for opencv backward compatibility
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contours, __ = cv2.findContours(
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mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
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@@ -185,6 +185,7 @@ def find_table_joints(contours, vertical, horizontal):
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__, jc, __ = cv2.findContours(
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roi, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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# for opencv backward compatibility
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jc, __ = cv2.findContours(
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roi, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
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if len(jc) <= 4: # remove contours with less than 4 joints
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@@ -197,79 +198,3 @@ def find_table_joints(contours, vertical, horizontal):
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tables[(x, y + h, x + w, y)] = joint_coords
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return tables
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def remove_lines(threshold, line_size_scaling=15):
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"""Removes lines from a thresholded image.
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Parameters
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----------
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threshold : object
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numpy.ndarray representing the thresholded image.
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line_size_scaling : int, optional (default: 15)
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Factor by which the page dimensions will be divided to get
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smallest length of lines that should be detected.
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The larger this value, smaller the detected lines. Making it
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too large will lead to text being detected as lines.
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Returns
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-------
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threshold : object
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numpy.ndarray representing the thresholded image
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with horizontal and vertical lines removed.
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"""
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size = threshold.shape[0] // line_size_scaling
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vertical_erode_el = cv2.getStructuringElement(cv2.MORPH_RECT, (1, size))
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horizontal_erode_el = cv2.getStructuringElement(cv2.MORPH_RECT, (size, 1))
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dilate_el = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
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vertical = cv2.erode(threshold, vertical_erode_el)
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vertical = cv2.dilate(vertical, dilate_el)
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horizontal = cv2.erode(threshold, horizontal_erode_el)
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horizontal = cv2.dilate(horizontal, dilate_el)
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threshold = np.bitwise_and(threshold, np.invert(vertical))
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threshold = np.bitwise_and(threshold, np.invert(horizontal))
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return threshold
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def find_cuts(threshold, char_size_scaling=200):
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"""Finds cuts made by text projections on y-axis.
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Parameters
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----------
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threshold : object
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numpy.ndarray representing the thresholded image.
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line_size_scaling : int, optional (default: 200)
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Factor by which the page dimensions will be divided to get
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smallest length of lines that should be detected.
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The larger this value, smaller the detected lines. Making it
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too large will lead to text being detected as lines.
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Returns
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-------
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y_cuts : list
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List of cuts on y-axis.
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"""
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size = threshold.shape[0] // char_size_scaling
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char_el = cv2.getStructuringElement(cv2.MORPH_RECT, (1, size))
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threshold = cv2.erode(threshold, char_el)
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threshold = cv2.dilate(threshold, char_el)
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try:
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__, contours, __ = cv2.findContours(threshold, cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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contours, __ = cv2.findContours(threshold, cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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contours = [cv2.boundingRect(c) for c in contours]
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y_cuts = [(c[1], c[1] + c[3]) for c in contours]
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y_cuts = list(merge_tuples(sorted(y_cuts)))
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y_cuts = [(y_cuts[i][0] + y_cuts[i - 1][1]) // 2 for i in range(1, len(y_cuts))]
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return sorted(y_cuts, reverse=True)
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@@ -641,24 +641,3 @@ def get_text_objects(layout, ltype="char", t=None):
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except AttributeError:
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pass
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return t
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def merge_tuples(tuples):
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"""Merges a list of overlapping tuples.
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Parameters
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----------
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tuples : list
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List of tuples where a tuple is a single axis coordinate pair.
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Yields
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------
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tuple
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"""
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merged = list(tuples[0])
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for s, e in tuples:
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if s <= merged[1]:
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merged[1] = max(merged[1], e)
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else:
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yield tuple(merged)
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merged[0] = s
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merged[1] = e
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yield tuple(merged)
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@@ -2,6 +2,7 @@ click==6.7
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matplotlib==2.2.3
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numpy==1.15.2
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opencv-python==3.4.2.17
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openpyxl==2.5.8
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pandas==0.23.4
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pdfminer.six==20170720
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PyPDF2==1.26.0
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+270
-82
@@ -3,17 +3,51 @@
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from __future__ import unicode_literals
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data_stream = [
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["", "Table: 5 Public Health Outlay 2012-13 (Budget Estimates) (Rs. in 000)", "", "", "", "", "", ""],
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["States-A", "Revenue", "", "Capital", "", "Total", "Others(1)", "Total"],
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["", "", "", "", "", "Revenue &", "", ""],
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["", "Medical &", "Family", "Medical &", "Family", "", "", ""],
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["", "", "", "", "", "Capital", "", ""],
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["", "Public", "Welfare", "Public", "Welfare", "", "", ""],
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["", "Health", "", "Health", "", "", "", ""],
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["Andhra Pradesh", "47,824,589", "9,967,837", "1,275,000", "15,000", "59,082,426", "14,898,243", "73,980,669"],
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["Arunachal Pradesh", "2,241,609", "107,549", "23,000", "0", "2,372,158", "86,336", "2,458,494"],
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["Assam", "14,874,821", "2,554,197", "161,600", "0", "17,590,618", "4,408,505", "21,999,123"],
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["Bihar", "21,016,708", "4,332,141", "5,329,000", "0", "30,677,849", "2,251,571", "32,929,420"],
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["Chhattisgarh", "11,427,311", "1,415,660", "2,366,592", "0", "15,209,563", "311,163", "15,520,726"],
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["Delhi", "28,084,780", "411,700", "4,550,000", "0", "33,046,480", "5,000", "33,051,480"],
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["Goa", "4,055,567", "110,000", "330,053", "0", "4,495,620", "12,560", "4,508,180"],
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["Gujarat", "26,328,400", "6,922,900", "12,664,000", "42,000", "45,957,300", "455,860", "46,413,160"],
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["Haryana", "15,156,681", "1,333,527", "40,100", "0", "16,530,308", "1,222,698", "17,753,006"],
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["Himachal Pradesh", "8,647,229", "1,331,529", "580,800", "0", "10,559,558", "725,315", "11,284,873"],
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["Jammu & Kashmir", "14,411,984", "270,840", "3,188,550", "0", "17,871,374", "166,229", "18,037,603"],
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["Jharkhand", "8,185,079", "3,008,077", "3,525,558", "0", "14,718,714", "745,139", "15,463,853"],
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["Karnataka", "34,939,843", "4,317,801", "3,669,700", "0", "42,927,344", "631,088", "43,558,432"],
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["Kerala", "27,923,965", "3,985,473", "929,503", "0", "32,838,941", "334,640", "33,173,581"],
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["Madhya Pradesh", "28,459,540", "4,072,016", "3,432,711", "0", "35,964,267", "472,139", "36,436,406"],
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["Maharashtra", "55,011,100", "6,680,721", "5,038,576", "0", "66,730,397", "313,762", "67,044,159"],
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["Manipur", "2,494,600", "187,700", "897,400", "0", "3,579,700", "0", "3,579,700"],
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["Meghalaya", "2,894,093", "342,893", "705,500", "5,000", "3,947,486", "24,128", "3,971,614"],
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["Mizoram", "1,743,501", "84,185", "10,250", "0", "1,837,936", "17,060", "1,854,996"],
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["Nagaland", "2,368,724", "204,329", "226,400", "0", "2,799,453", "783,054", "3,582,507"],
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["Odisha", "14,317,179", "2,552,292", "1,107,250", "0", "17,976,721", "451,438", "18,428,159"],
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["Puducherry", "4,191,757", "52,249", "192,400", "0", "4,436,406", "2,173", "4,438,579"],
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["Punjab", "19,775,485", "2,208,343", "2,470,882", "0", "24,454,710", "1,436,522", "25,891,232"],
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["", "Health Sector Financing by Centre and States/UTs in India [2009-10 to 2012-13](Revised) P a g e |23", "", "", "", "", "", ""]
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]
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data_stream_table_rotated = [
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["", "", "Table 21 Current use of contraception by background characteristics\u2014Continued", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""],
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["", "", "", "", "", "", "Modern method", "", "", "", "", "", "", "Traditional method", "", "", "", ""],
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["", "", "", "Any", "", "", "", "", "", "", "Other", "Any","", "", "", "Not", "", "Number"],
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["", "", "", "Any", "", "", "", "", "", "", "Other", "Any", "", "", "", "Not", "", "Number"],
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["", "", "Any", "modern", "Female", "Male", "", "", "", "Condom/", "modern", "traditional", "", "With-", "Folk", "currently", "", "of"],
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["", "Background characteristic", "method", "method", "sterilization", "sterilization", "Pill", "IUD", "Injectables", "Nirodh", "method", "method", "Rhythm", "drawal", "method", "using", "Total", "women"],
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["", "Caste/tribe", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""],
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["", "Scheduled caste", "74.8", "55.8", "42.9", "0.9", "9.7", "0.0", "0.2", "2.2", "0.0", "19.0", "11.2", "7.4", "0.4", "25.2", "100.0", "1,363"],
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["", "Scheduled tribe", "59.3", "39.0", "26.8", "0.6", "6.4", "0.6", "1.2", "3.5", "0.0", "20.3", "10.4", "5.8", "4.1", "40.7", "100.0", "256"],
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["", "Other backward class", "71.4", "51.1", "34.9", "0.0", "8.6", "1.4", "0.0", "6.2", "0.0", "20.4", "12.6", "7.8", "0.0", "28.6", "100.0", "211"],
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["", "Other", "71.1","48.8", "28.2", "0.8", "13.3", "0.9", "0.3", "5.2", "0.1", "22.3", "12.9", "9.1", "0.3", "28.9", "100.0", "3,319"],
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["", "Other", "71.1", "48.8", "28.2", "0.8", "13.3", "0.9", "0.3", "5.2", "0.1", "22.3", "12.9", "9.1", "0.3", "28.9", "100.0", "3,319"],
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["", "Wealth index", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""],
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["", "Lowest", "64.5", "48.6", "34.3", "0.5", "10.5", "0.6", "0.7", "2.0", "0.0", "15.9", "9.9", "4.6", "1.4", "35.5", "100.0", "1,258"],
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["", "Second", "68.5", "50.4", "36.2", "1.1", "11.4", "0.5", "0.1", "1.1", "0.0", "18.1", "11.2", "6.7", "0.2", "31.5", "100.0", "1,317"],
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@@ -47,18 +81,18 @@ data_stream_table_rotated = [
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["", "", "", "", "", "", "", "", "54", "", "", "", "", "", "", "", "", ""]
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]
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data_stream_table_area_single = [
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["","One Withholding"],
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["Payroll Period","Allowance"],
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["Weekly","$71.15"],
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["Biweekly","142.31"],
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["Semimonthly","154.17"],
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["Monthly","308.33"],
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["Quarterly","925.00"],
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["Semiannually","1,850.00"],
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["Annually","3,700.00"],
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["Daily or Miscellaneous","14.23"],
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["(each day of the payroll period)",""]
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data_stream_table_area = [
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["", "One Withholding"],
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["Payroll Period", "Allowance"],
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["Weekly", "$71.15"],
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["Biweekly", "142.31"],
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["Semimonthly", "154.17"],
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["Monthly", "308.33"],
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["Quarterly", "925.00"],
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["Semiannually", "1,850.00"],
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["Annually", "3,700.00"],
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["Daily or Miscellaneous", "14.23"],
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["(each day of the payroll period)", ""]
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]
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data_stream_columns = [
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@@ -107,82 +141,236 @@ data_stream_columns = [
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["01", "Aguascalientes", "001", "Aguascalientes", "0226", "Hacienda Nueva"]
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]
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data_stream_split_text = [
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["FEB", "RUAR", "Y 2014 M27 (BUS)", "", "ALPHABETIC LISTING BY T", "YPE", "", "", "", "ABLPDM27"],
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["", "", "", "", "OF ACTIVE LICENSES", "", "", "", "", "3/19/2014"],
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["", "", "", "", "OKLAHOMA ABLE COMMIS", "SION", "", "", "", ""],
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["LICENSE", "", "", "", "PREMISE", "", "", "", "", ""],
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["NUMBER", "TYPE", "DBA NAME", "LICENSEE NAME", "ADDRESS", "CITY", "ST", "ZIP", "PHONE NUMBER", "EXPIRES"],
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["648765", "AAA", "ALLEGIANT AIR", "ALLEGIANT AIR LLC", "7100 TERMINAL DRIVE", "OKLAHOMA CITY", "OK", "73159", "-", "2014/12/03"],
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["", "", "", "", "7777 EAST APACHE", "", "", "", "", ""],
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["648766", "AAA", "ALLEGIANT AIR", "ALLEGIANT AIR LLC", "STREET", "TULSA", "OK", "74115", "-", "2014/12/16"],
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["82030", "AAA", "AMERICAN AIRLINES", "AMERICAN AIRLINES INC", "7100 TERMINAL DRIVE", "OKLAHOMA CITY", "OK", "73159", "(405) 680-3701", "2014/09/14"],
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["509462", "AAA", "AMERICAN AIRLINES", "AMERICAN AIRLINES INC", "7777 EAST APACHE DRIVE", "TULSA", "OK", "74115", "(918) 831-6302", "2014/08/19"],
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["", "", "", "AMERICAN EAGLE", "", "", "", "", "", ""],
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["509609", "AAA", "AMERICAN EAGLE", "AIRLINES INC", "7100 TERMINAL DRIVE", "OKLAHOMA CITY", "OK", "73159", "(405) 680-3701", "2014/08/19"],
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["", "", "", "AMERICAN EAGLE", "", "", "", "", "", ""],
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["402986", "AAA", "AMERICAN EAGLE", "AIRLINES INC", "7777 EAST APACHE DRIVE", "TULSA", "OK", "74115", "(859) 767-3747", "2014/10/22"],
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["", "", "", "", "WILL ROGERS AIRPORT", "", "", "", "", ""],
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["79145", "AAA", "DELTA AIR LINES", "DELTA AIR LINES INC", "BOX 59975", "OKLAHOMA CITY", "OK", "73159", "(404) 773-9745", "2014/05/11"],
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["600941", "AAA", "ENDEAVOR AIR", "ENDEAVOR AIR INC", "7100 TERMINAL DRIVE", "OKLAHOMA CITY", "OK", "73159", "(901) 348-4100", "2015/03/26"],
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["", "", "", "", "7100 TERMINAL DRIVE", "", "", "", "", ""],
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["478482", "AAA", "EXPRESSJET AIRLINES", "EXPRESSJET AIRLINES INC", "WILL ROGERS AIRPORT", "OKLAHOMA CITY", "OK", "73159", "(832) 353-1201", "2014/05/08"],
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["505981", "AAA", "SKYWEST AIRLINES", "SKYWEST INC", "7100 TERMINAL DRIVE", "OKLAHOMA CITY", "OK", "73159", "(405) 634-3000", "2014/05/28"],
|
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["429754", "AAA", "SOUTHWEST AIRLINES", "SOUTHWEST AIRLINES CO", "7100 TERMINAL DRIVE", "OKLAHOMA CITY", "OK", "73159", "(405) 682-4183", "2015/02/15"],
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["", "", "TULSA INTERNATIONAL", "", "", "", "", "", "", ""],
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["429755", "AAA", "AIRPORT", "SOUTHWEST AIRLINES CO", "7777 EAST APACHE DRIVE", "TULSA", "OK", "74115", "(918) 834-4495", "2015/02/16"],
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["415051", "AAA", "UNITED AIRLINES", "UNITED AIRLINES INC", "7777 EAST APACHE DRIVE", "TULSA", "OK", "74115", "(872) 825-8309", "2014/05/12"],
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["106719", "AAA", "UNITED AIRLINES", "UNITED AIRLINES INC", "WILL ROGERS AIRPORT", "OKLAHOMA CITY", "OK", "73159", "(872) 825-8309", "2014/04/11"],
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["", "", "A SENSU JAPANESE", "", "7123 SOUTH 92ND EAST", "", "", "", "", ""],
|
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["625422", "BAW", "RESTAURANT", "INFORMAL PARTNERSHIP", "AVENUE SUITE J", "TULSA", "OK", "74133", "(918) 252-0333", "2015/02/14"],
|
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["", "", "ADAMO'S ROUTE 66", "", "2132 WEST GARY", "", "", "", "", ""],
|
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["464828", "BAW", "ITALIAN VILLA", "TADJ INC", "BOULEVARD", "CLINTON", "OK", "73601", "(580) 323-5900", "2015/02/11"],
|
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["", "", "", "", "12215 NORTH", "", "", "", "", ""],
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["184066", "BAW", "AJANTA", "CABAB N' CURRY INC", "PENNSYLVANIA", "OKLAHOMA CITY", "OK", "73120", "(405) 752-5283", "2014/07/27"],
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["", "", "", "SAYRE LODGING", "", "", "", "", "", ""],
|
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["547693", "BAW", "AMERICINN OF SAYRE", "ENTERPRISES LLC", "2405 SOUTH EL CAMINO", "SAYRE", "OK", "73662", "(580) 928-2700", "2014/09/08"],
|
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["", "", "ANDOLINI'S PIZZERIA &", "", "12140 EAST 96TH STREET", "", "", "", "", ""],
|
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["428377", "BAW", "ITALIAN RESTAURANT", "ANDOLINI'S LLC", "NORTH #106", "OWASSO", "OK", "74055", "(918) 272-9325", "2015/02/10"],
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["", "", "ASAHI JAPANESE", "", "", "", "", "", "", ""],
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["446957", "BAW", "RESTAURANT", "JIN CORPORATION", "7831 EAST 71ST STREET", "TULSA", "OK", "74133", "(918) 307-9151", "2014/12/22"],
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["", "", "", "SMOKEHOUSE", "", "", "", "", "", ""],
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["632501", "BAW", "BACK DOOR BARBECUE", "ASSOCIATES INC", "315 NORTHWEST 23RD", "OKLAHOMA CITY", "OK", "73103", "-", "2014/08/01"],
|
||||
["598515", "BAW", "BAMBOO THAI BISTRO", "BAMBOO THAI BISTRO INC", "5079 SOUTH YALE AVENUE", "TULSA", "OK", "74135", "(918) 828-0740", "2015/03/11"],
|
||||
["", "", "BANDANA RED'S", "", "", "", "", "", "", ""],
|
||||
["618693", "BAW", "STEAKHOUSE", "BRADSHAW, STEVE_LEN", "37808 OLD HIGHWAY 270", "SHAWNEE", "OK", "74804", "-", "2014/08/20"],
|
||||
["", "", "", "", "1522 WEST LINDSEY", "", "", "", "", ""],
|
||||
["632575", "BAW", "BASHU LEGENDS", "HYH HE CHUANG LLC", "STREET", "NORMAN", "OK", "73069", "-", "2014/07/21"],
|
||||
["", "", "", "DEEP FORK HOLDINGS", "", "", "", "", "", ""],
|
||||
["543149", "BAW", "BEDLAM BAR-B-Q", "LLC", "610 NORTHEAST 50TH", "OKLAHOMA CITY", "OK", "73105", "(405) 528-7427", "2015/02/23"],
|
||||
["", "", "", "", "Page 1 of 151", "", "", "", "", ""]
|
||||
]
|
||||
|
||||
data_stream_flag_size = [
|
||||
["", "TABLE 125: STATE-WISE COMPOSITION OF OUTSTANDING LIABILITIES - 1997 <s>(Contd.)</s>", "", "", "", "", "", "", "", "", ""],
|
||||
["", "", "", "", "(As at end-March)", "", "", "", "", "", ""],
|
||||
["", "", "", "", "", "", "", "", "", "", "(<s>`</s> Billion)"],
|
||||
["States", "Total", "Market", "NSSF", "WMA", "Loans", "Loans", "Loans", "Loans", "Loans", "Loans"],
|
||||
["", "Internal", "Loans", "", "from", "from", "from", "from", "from", "from SBI", "from"],
|
||||
["", "Debt", "", "", "RBI", "Banks", "LIC", "GIC", "NABARD", "& Other", "NCDC"],
|
||||
["", "", "", "", "", "& FIs", "", "", "", "Banks", ""],
|
||||
["1", "2=", "3", "4", "5", "6=", "7", "8", "9", "10", "11"],
|
||||
["", "(3 to 6)+14", "", "", "", "(7 to13)", "", "", "", "", ""],
|
||||
["Andhra Pradesh", "48.11", "40.45", "-", "3.26", "4.4", "2.62", "-", "0.91", "-", "0.25"],
|
||||
["Arunachal Pradesh", "1.23", "1.1", "-", "-", "0.13", "-", "-", "-", "-", "-"],
|
||||
["Assam", "12.69", "10.02", "-", "2.41", "0.26", "0.08", "-", "-0.06", "0.01", "0.24"],
|
||||
["Bihar", "40.75", "41.54", "-", "-", "-1.42", "0.19", "-", "-1.01", "-0.36", "0.2"],
|
||||
["Chhattisgarh", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-"],
|
||||
["Goa", "1.4", "1.02", "-", "-", "0.38", "0.31", "-", "0.07", "-", "-"],
|
||||
["Gujarat", "19.75", "17.1", "-", "-", "2.64", "1.17", "-", "1.11", "-", "0.44"],
|
||||
["Haryana", "11.53", "9.67", "-", "0.06", "1.8", "0.55", "-", "0.64", "-", "0.49"],
|
||||
["Himachal Pradesh", "8.02", "2.94", "-", "4.55", "0.53", "0.13", "-", "0.05", "-", "0.25"],
|
||||
["Jammu and Kashmir", "11.72", "4.49", "-", "-", "7.23", "0.66", "-", "0.02", "6.08", "-"],
|
||||
["Jharkhand", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-"],
|
||||
["Karnataka", "22.44", "19.59", "-", "-", "2.86", "1.22", "-", "0.89", "-", "0.69"],
|
||||
["Kerala", "29.03", "24.91<s>2</s>", "-", "-", "4.11", "1.77", "-", "0.48", "-", "1.45"],
|
||||
["Madhya Pradesh", "27.13", "23.57", "-", "-", "3.56", "0.38", "-", "1.86", "-", "1.28"],
|
||||
["Maharashtra", "30.47", "26.07", "-", "-", "4.39", "0.21", "-", "-0.12", "0.02", "2.89"],
|
||||
["Manipur", "2.17", "1.61", "-", "0.26", "0.29", "0.08", "-", "-", "-", "0.09"],
|
||||
["Meghalaya", "1.36", "1.38", "-", "-", "-0.02", "0.04", "-", "-0.05", "-", "0.03"],
|
||||
["Mizoram", "1.17", "0.46", "-", "0.27", "0.43", "0.11", "-", "-", "-", "0.03"],
|
||||
["Nagaland", "2.99", "2.6", "-", "-", "0.39", "0.24", "-", "-", "-", "0.04"],
|
||||
["Odisha", "34.04", "27.58", "-", "4.4", "2.06", "0.56", "-", "0.66", "-", "0.2"],
|
||||
["Punjab", "19.18", "10.93", "-", "1.03", "7.23", "0.17", "-", "0.71", "5.9", "0.46"],
|
||||
["Rajasthan", "36.77", "28.63", "-", "4.99", "3.16", "0.57", "-", "1.64", "-", "0.81"],
|
||||
["Sikkim", "0.16", "-", "-", "-", "0.16", "0.03", "-", "-", "-", "0.01"],
|
||||
["Tamil Nadu", "34.11", "31.41", "-", "-", "2.7", "1.3", "-", "0.6", "-", "0.68"],
|
||||
["Tripura", "2.3", "1.89", "-", "-", "0.41", "0.41", "-", "-0.05", "-", "0.02"],
|
||||
["Uttaranchal", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-"],
|
||||
["Uttar Pradesh", "80.62", "74.89", "-", "4.34", "1.34", "0.6", "-", "-0.21", "0.18", "0.03"],
|
||||
["West Bengal", "34.23", "32.19", "-", "-", "2.04", "0.77", "-", "0.06", "-", "0.51"],
|
||||
["NCT Delhi", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-"],
|
||||
["ALL STATES", "513.38", "436.02", "-", "25.57", "51.06", "14.18", "-", "8.21", "11.83", "11.08"],
|
||||
["<s>2</s> Includes `2.45 crore outstanding under “Market Loan Suspense”.", "", "", "", "", "", "", "", "", "", ""],
|
||||
["", "", "", "", "445", "", "", "", "", "", ""]
|
||||
]
|
||||
|
||||
data_lattice = [
|
||||
["Cycle Name","KI (1/km)","Distance (mi)","Percent Fuel Savings","","",""],
|
||||
["","","","Improved Speed","Decreased Accel","Eliminate Stops","Decreased Idle"],
|
||||
["2012_2","3.30","1.3","5.9%","9.5%","29.2%","17.4%"],
|
||||
["2145_1","0.68","11.2","2.4%","0.1%","9.5%","2.7%"],
|
||||
["4234_1","0.59","58.7","8.5%","1.3%","8.5%","3.3%"],
|
||||
["2032_2","0.17","57.8","21.7%","0.3%","2.7%","1.2%"],
|
||||
["4171_1","0.07","173.9","58.1%","1.6%","2.1%","0.5%"]
|
||||
["Cycle Name", "KI (1/km)", "Distance (mi)", "Percent Fuel Savings", "", "", ""],
|
||||
["", "", "", "Improved Speed", "Decreased Accel", "Eliminate Stops", "Decreased Idle"],
|
||||
["2012_2", "3.30", "1.3", "5.9%", "9.5%", "29.2%", "17.4%"],
|
||||
["2145_1", "0.68", "11.2", "2.4%", "0.1%", "9.5%", "2.7%"],
|
||||
["4234_1", "0.59", "58.7", "8.5%", "1.3%", "8.5%", "3.3%"],
|
||||
["2032_2", "0.17", "57.8", "21.7%", "0.3%", "2.7%", "1.2%"],
|
||||
["4171_1", "0.07", "173.9", "58.1%", "1.6%", "2.1%", "0.5%"]
|
||||
]
|
||||
|
||||
data_lattice_table_rotated = [
|
||||
["State","Nutritional Assessment (No. of individuals)","","","","IYCF Practices (No. of mothers: 2011-12)","Blood Pressure (No. of adults: 2011-12)","","Fasting Blood Sugar (No. of adults:2011-12)",""],
|
||||
["","1975-79","1988-90","1996-97","2011-12","","Men","Women","Men","Women"],
|
||||
["Kerala","5738","6633","8864","8297","245","2161","3195","1645","2391"],
|
||||
["Tamil Nadu","7387","10217","5813","7851","413","2134","2858","1119","1739"],
|
||||
["Karnataka","6453","8138","12606","8958","428","2467","2894","1628","2028"],
|
||||
["Andhra Pradesh","5844","9920","9545","8300","557","1899","2493","1111","1529"],
|
||||
["Maharashtra","5161","7796","6883","9525","467","2368","2648","1417","1599"],
|
||||
["Gujarat","4403","5374","4866","9645","477","2687","3021","2122","2503"],
|
||||
["Madhya Pradesh","*","*","*","7942","470","1965","2150","1579","1709"],
|
||||
["Orissa","3756","5540","12024","8473","398","2040","2624","1093","1628"],
|
||||
["West Bengal","*","*","*","8047","423","2058","2743","1413","2027"],
|
||||
["Uttar Pradesh","*","*","*","9860","581","2139","2415","1185","1366"],
|
||||
["Pooled","38742","53618","60601","86898","4459","21918","27041","14312","18519"]
|
||||
["State", "Nutritional Assessment (No. of individuals)", "", "", "", "IYCF Practices (No. of mothers: 2011-12)", "Blood Pressure (No. of adults: 2011-12)", "", "Fasting Blood Sugar (No. of adults:2011-12)", ""],
|
||||
["", "1975-79", "1988-90", "1996-97", "2011-12", "", "Men", "Women", "Men", "Women"],
|
||||
["Kerala", "5738", "6633", "8864", "8297", "245", "2161", "3195", "1645", "2391"],
|
||||
["Tamil Nadu", "7387", "10217", "5813", "7851", "413", "2134", "2858", "1119", "1739"],
|
||||
["Karnataka", "6453", "8138", "12606", "8958", "428", "2467", "2894", "1628", "2028"],
|
||||
["Andhra Pradesh", "5844", "9920", "9545", "8300", "557", "1899", "2493", "1111", "1529"],
|
||||
["Maharashtra", "5161", "7796", "6883", "9525", "467", "2368", "2648", "1417", "1599"],
|
||||
["Gujarat", "4403", "5374", "4866", "9645", "477", "2687", "3021", "2122", "2503"],
|
||||
["Madhya Pradesh", "*", "*", "*", "7942", "470", "1965", "2150", "1579", "1709"],
|
||||
["Orissa", "3756", "5540", "12024", "8473", "398", "2040", "2624", "1093", "1628"],
|
||||
["West Bengal", "*", "*", "*", "8047", "423", "2058", "2743", "1413", "2027"],
|
||||
["Uttar Pradesh", "*", "*", "*", "9860", "581", "2139", "2415", "1185", "1366"],
|
||||
["Pooled", "38742", "53618", "60601", "86898", "4459", "21918", "27041", "14312", "18519"]
|
||||
]
|
||||
|
||||
data_lattice_table_area = [
|
||||
["", "", "", "", "", "", "", "", ""],
|
||||
["State", "n", "Literacy Status", "", "", "", "", "", ""],
|
||||
["", "", "Illiterate", "Read & Write", "1-4 std.", "5-8 std.", "9-12 std.", "College", ""],
|
||||
["Kerala", "2400", "7.2", "0.5", "25.3", "20.1", "41.5", "5.5", ""],
|
||||
["Tamil Nadu", "2400", "21.4", "2.3", "8.8", "35.5", "25.8", "6.2", ""],
|
||||
["Karnataka", "2399", "37.4", "2.8", "12.5", "18.3", "23.1", "5.8", ""],
|
||||
["Andhra Pradesh", "2400", "54.0", "1.7", "8.4", "13.2", "18.8", "3.9", ""],
|
||||
["Maharashtra", "2400", "22.0", "0.9", "17.3", "20.3", "32.6", "7.0", ""],
|
||||
["Gujarat", "2390", "28.6", "0.1", "14.4", "23.1", "26.9", "6.8", ""],
|
||||
["Madhya Pradesh", "2402", "29.1", "3.4", "8.5", "35.1", "13.3", "10.6", ""],
|
||||
["Orissa", "2405", "33.2", "1.0", "10.4", "25.7", "21.2", "8.5", ""],
|
||||
["West Bengal", "2293", "41.7", "4.4", "13.2", "17.1", "21.2", "2.4", ""],
|
||||
["Uttar Pradesh", "2400", "35.3", "2.1", "4.5", "23.3", "27.1", "7.6", ""],
|
||||
["Pooled", "23889", "30.9", "1.9", "12.3", "23.2", "25.2", "6.4", ""],
|
||||
["", "", "", "", "", "", "", "", ""]
|
||||
]
|
||||
|
||||
data_lattice_process_background = [
|
||||
["State","Date","Halt stations","Halt days","Persons directly reached(in lakh)","Persons trained","Persons counseled","Persons testedfor HIV"],
|
||||
["Delhi","1.12.2009","8","17","1.29","3,665","2,409","1,000"],
|
||||
["Rajasthan","2.12.2009 to 19.12.2009","","","","","",""],
|
||||
["Gujarat","20.12.2009 to 3.1.2010","6","13","6.03","3,810","2,317","1,453"],
|
||||
["Maharashtra","4.01.2010 to 1.2.2010","13","26","1.27","5,680","9,027","4,153"],
|
||||
["Karnataka","2.2.2010 to 22.2.2010","11","19","1.80","5,741","3,658","3,183"],
|
||||
["Kerala","23.2.2010 to 11.3.2010","9","17","1.42","3,559","2,173","855"],
|
||||
["Total","","47","92","11.81","22,455","19,584","10,644"]
|
||||
["State", "Date", "Halt stations", "Halt days", "Persons directly reached(in lakh)", "Persons trained", "Persons counseled" ,"Persons testedfor HIV"],
|
||||
["Delhi", "1.12.2009", "8", "17", "1.29", "3,665", "2,409", "1,000"],
|
||||
["Rajasthan", "2.12.2009 to 19.12.2009", "", "", "", "", "", ""],
|
||||
["Gujarat", "20.12.2009 to 3.1.2010", "6", "13", "6.03", "3,810", "2,317", "1,453"],
|
||||
["Maharashtra", "4.01.2010 to 1.2.2010", "13", "26", "1.27", "5,680", "9,027", "4,153"],
|
||||
["Karnataka", "2.2.2010 to 22.2.2010", "11", "19", "1.80", "5,741", "3,658", "3,183"],
|
||||
["Kerala", "23.2.2010 to 11.3.2010", "9", "17", "1.42", "3,559", "2,173", "855"],
|
||||
["Total", "", "47", "92", "11.81", "22,455", "19,584", "10,644"]
|
||||
]
|
||||
|
||||
data_lattice_copy_text = [
|
||||
["Plan Type","County","Plan Name","Totals"],
|
||||
["GMC","Sacramento","Anthem Blue Cross","164,380"],
|
||||
["GMC","Sacramento","Health Net","126,547"],
|
||||
["GMC","Sacramento","Kaiser Foundation","74,620"],
|
||||
["GMC","Sacramento","Molina Healthcare","59,989"],
|
||||
["GMC","San Diego","Care 1st Health Plan","71,831"],
|
||||
["GMC","San Diego","Community Health Group","264,639"],
|
||||
["GMC","San Diego","Health Net","72,404"],
|
||||
["GMC","San Diego","Kaiser","50,415"],
|
||||
["GMC","San Diego","Molina Healthcare","206,430"],
|
||||
["GMC","Total GMC Enrollment","","1,091,255"],
|
||||
["COHS","Marin","Partnership Health Plan of CA","36,006"],
|
||||
["COHS","Mendocino","Partnership Health Plan of CA","37,243"],
|
||||
["COHS","Napa","Partnership Health Plan of CA","28,398"],
|
||||
["COHS","Solano","Partnership Health Plan of CA","113,220"],
|
||||
["COHS","Sonoma","Partnership Health Plan of CA","112,271"],
|
||||
["COHS","Yolo","Partnership Health Plan of CA","52,674"],
|
||||
["COHS","Del Norte","Partnership Health Plan of CA","11,242"],
|
||||
["COHS","Humboldt","Partnership Health Plan of CA","49,911"],
|
||||
["COHS","Lake","Partnership Health Plan of CA","29,149"],
|
||||
["COHS","Lassen","Partnership Health Plan of CA","7,360"],
|
||||
["COHS","Modoc","Partnership Health Plan of CA","2,940"],
|
||||
["COHS","Shasta","Partnership Health Plan of CA","61,763"],
|
||||
["COHS","Siskiyou","Partnership Health Plan of CA","16,715"],
|
||||
["COHS","Trinity","Partnership Health Plan of CA","4,542"],
|
||||
["COHS","Merced","Central California Alliance for Health","123,907"],
|
||||
["COHS","Monterey","Central California Alliance for Health","147,397"],
|
||||
["COHS","Santa Cruz","Central California Alliance for Health","69,458"],
|
||||
["COHS","Santa Barbara","CenCal","117,609"],
|
||||
["COHS","San Luis Obispo","CenCal","55,761"],
|
||||
["COHS","Orange","CalOptima","783,079"],
|
||||
["COHS","San Mateo","Health Plan of San Mateo","113,202"],
|
||||
["COHS","Ventura","Gold Coast Health Plan","202,217"],
|
||||
["COHS","Total COHS Enrollment","","2,176,064"],
|
||||
["Subtotal for Two-Plan, Regional Model, GMC and COHS","","","10,132,022"],
|
||||
["PCCM","Los Angeles","AIDS Healthcare Foundation","828"],
|
||||
["PCCM","San Francisco","Family Mosaic","25"],
|
||||
["PCCM","Total PHP Enrollment","","853"],
|
||||
["All Models Total Enrollments","","","10,132,875"],
|
||||
["Source: Data Warehouse 12/14/15","","",""]
|
||||
["Plan Type", "County", "Plan Name", "Totals"],
|
||||
["GMC", "Sacramento", "Anthem Blue Cross", "164,380"],
|
||||
["GMC", "Sacramento", "Health Net", "126,547"],
|
||||
["GMC", "Sacramento", "Kaiser Foundation", "74,620"],
|
||||
["GMC", "Sacramento", "Molina Healthcare", "59,989"],
|
||||
["GMC", "San Diego", "Care 1st Health Plan", "71,831"],
|
||||
["GMC", "San Diego", "Community Health Group", "264,639"],
|
||||
["GMC", "San Diego", "Health Net", "72,404"],
|
||||
["GMC", "San Diego", "Kaiser", "50,415"],
|
||||
["GMC", "San Diego", "Molina Healthcare", "206,430"],
|
||||
["GMC", "Total GMC Enrollment", "", "1,091,255"],
|
||||
["COHS", "Marin", "Partnership Health Plan of CA", "36,006"],
|
||||
["COHS", "Mendocino", "Partnership Health Plan of CA", "37,243"],
|
||||
["COHS", "Napa", "Partnership Health Plan of CA", "28,398"],
|
||||
["COHS", "Solano", "Partnership Health Plan of CA", "113,220"],
|
||||
["COHS", "Sonoma", "Partnership Health Plan of CA", "112,271"],
|
||||
["COHS", "Yolo", "Partnership Health Plan of CA", "52,674"],
|
||||
["COHS", "Del Norte", "Partnership Health Plan of CA", "11,242"],
|
||||
["COHS", "Humboldt", "Partnership Health Plan of CA", "49,911"],
|
||||
["COHS", "Lake", "Partnership Health Plan of CA", "29,149"],
|
||||
["COHS", "Lassen", "Partnership Health Plan of CA", "7,360"],
|
||||
["COHS", "Modoc", "Partnership Health Plan of CA", "2,940"],
|
||||
["COHS", "Shasta", "Partnership Health Plan of CA", "61,763"],
|
||||
["COHS", "Siskiyou", "Partnership Health Plan of CA", "16,715"],
|
||||
["COHS", "Trinity", "Partnership Health Plan of CA", "4,542"],
|
||||
["COHS", "Merced", "Central California Alliance for Health", "123,907"],
|
||||
["COHS", "Monterey", "Central California Alliance for Health", "147,397"],
|
||||
["COHS", "Santa Cruz", "Central California Alliance for Health", "69,458"],
|
||||
["COHS", "Santa Barbara", "CenCal", "117,609"],
|
||||
["COHS", "San Luis Obispo", "CenCal", "55,761"],
|
||||
["COHS", "Orange", "CalOptima", "783,079"],
|
||||
["COHS", "San Mateo", "Health Plan of San Mateo", "113,202"],
|
||||
["COHS", "Ventura", "Gold Coast Health Plan", "202,217"],
|
||||
["COHS", "Total COHS Enrollment", "", "2,176,064"],
|
||||
["Subtotal for Two-Plan, Regional Model, GMC and COHS", "", "", "10,132,022"],
|
||||
["PCCM", "Los Angeles", "AIDS Healthcare Foundation", "828"],
|
||||
["PCCM", "San Francisco", "Family Mosaic", "25"],
|
||||
["PCCM", "Total PHP Enrollment", "", "853"],
|
||||
["All Models Total Enrollments", "", "", "10,132,875"],
|
||||
["Source: Data Warehouse 12/14/15", "", "", ""]
|
||||
]
|
||||
|
||||
data_lattice_shift_text_left_top = [
|
||||
["Investigations", "No. ofHHs", "Age/Sex/Physiological Group", "Preva-lence", "C.I*", "RelativePrecision", "Sample sizeper State"],
|
||||
["Anthropometry", "2400", "All the available individuals", "", "", "", ""],
|
||||
["Clinical Examination", "", "", "", "", "", ""],
|
||||
["History of morbidity", "", "", "", "", "", ""],
|
||||
["Diet survey", "1200", "All the individuals partaking meals in the HH", "", "", "", ""],
|
||||
["Blood Pressure #", "2400", "Men (≥ 18yrs)", "10%", "95%", "20%", "1728"],
|
||||
["", "", "Women (≥ 18 yrs)", "", "", "", "1728"],
|
||||
["Fasting blood glucose", "2400", "Men (≥ 18 yrs)", "5%", "95%", "20%", "1825"],
|
||||
["", "", "Women (≥ 18 yrs)", "", "", "", "1825"],
|
||||
["Knowledge &Practices on HTN &DM", "2400", "Men (≥ 18 yrs)", "-", "-", "-", "1728"],
|
||||
["", "2400", "Women (≥ 18 yrs)", "-", "-", "-", "1728"]
|
||||
]
|
||||
|
||||
data_lattice_shift_text_disable = [
|
||||
["Investigations", "No. ofHHs", "Age/Sex/Physiological Group", "Preva-lence", "C.I*", "RelativePrecision", "Sample sizeper State"],
|
||||
["Anthropometry", "", "", "", "", "", ""],
|
||||
["Clinical Examination", "2400", "", "All the available individuals", "", "", ""],
|
||||
["History of morbidity", "", "", "", "", "", ""],
|
||||
["Diet survey", "1200", "", "All the individuals partaking meals in the HH", "", "", ""],
|
||||
["", "", "Men (≥ 18yrs)", "", "", "", "1728"],
|
||||
["Blood Pressure #", "2400", "Women (≥ 18 yrs)", "10%", "95%", "20%", "1728"],
|
||||
["", "", "Men (≥ 18 yrs)", "", "", "", "1825"],
|
||||
["Fasting blood glucose", "2400", "Women (≥ 18 yrs)", "5%", "95%", "20%", "1825"],
|
||||
["Knowledge &Practices on HTN &", "2400", "Men (≥ 18 yrs)", "-", "-", "-", "1728"],
|
||||
["DM", "2400", "Women (≥ 18 yrs)", "-", "-", "-", "1728"]
|
||||
]
|
||||
|
||||
data_lattice_shift_text_right_bottom = [
|
||||
["Investigations", "No. ofHHs", "Age/Sex/Physiological Group", "Preva-lence", "C.I*", "RelativePrecision", "Sample sizeper State"],
|
||||
["Anthropometry", "", "", "", "", "", ""],
|
||||
["Clinical Examination", "", "", "", "", "", ""],
|
||||
["History of morbidity", "2400", "", "", "", "", "All the available individuals"],
|
||||
["Diet survey", "1200", "", "", "", "", "All the individuals partaking meals in the HH"],
|
||||
["", "", "Men (≥ 18yrs)", "", "", "", "1728"],
|
||||
["Blood Pressure #", "2400", "Women (≥ 18 yrs)", "10%", "95%", "20%", "1728"],
|
||||
["", "", "Men (≥ 18 yrs)", "", "", "", "1825"],
|
||||
["Fasting blood glucose", "2400", "Women (≥ 18 yrs)", "5%", "95%", "20%", "1825"],
|
||||
["", "2400", "Men (≥ 18 yrs)", "-", "-", "-", "1728"],
|
||||
["Knowledge &Practices on HTN &DM", "2400", "Women (≥ 18 yrs)", "-", "-", "-", "1728"]
|
||||
]
|
||||
Executable
BIN
Binary file not shown.
@@ -50,3 +50,30 @@ def test_cli_stream():
|
||||
result = runner.invoke(cli, ['--output', outfile, 'stream', infile])
|
||||
format_error = 'Please specify output file format using --format'
|
||||
assert format_error in result.output
|
||||
|
||||
|
||||
def test_cli_output_format():
|
||||
with TemporaryDirectory() as tempdir:
|
||||
infile = os.path.join(testdir, 'health.pdf')
|
||||
outfile = os.path.join(tempdir, 'health.{}')
|
||||
runner = CliRunner()
|
||||
|
||||
# json
|
||||
result = runner.invoke(cli, ['--format', 'json', '--output', outfile.format('json'),
|
||||
'stream', infile])
|
||||
assert result.exit_code == 0
|
||||
|
||||
# excel
|
||||
result = runner.invoke(cli, ['--format', 'excel', '--output', outfile.format('xlsx'),
|
||||
'stream', infile])
|
||||
assert result.exit_code == 0
|
||||
|
||||
# html
|
||||
result = runner.invoke(cli, ['--format', 'html', '--output', outfile.format('html'),
|
||||
'stream', infile])
|
||||
assert result.exit_code == 0
|
||||
|
||||
# zip
|
||||
result = runner.invoke(cli, ['--zip', '--format', 'csv', '--output', outfile.format('csv'),
|
||||
'stream', infile])
|
||||
assert result.exit_code == 0
|
||||
+60
-2
@@ -12,8 +12,25 @@ testdir = os.path.dirname(os.path.abspath(__file__))
|
||||
testdir = os.path.join(testdir, "files")
|
||||
|
||||
|
||||
def test_parsing_report():
|
||||
parsing_report = {
|
||||
'accuracy': 99.02,
|
||||
'whitespace': 12.24,
|
||||
'order': 1,
|
||||
'page': 1
|
||||
}
|
||||
|
||||
filename = os.path.join(testdir, "foo.pdf")
|
||||
tables = camelot.read_pdf(filename)
|
||||
assert tables[0].parsing_report == parsing_report
|
||||
|
||||
|
||||
def test_stream():
|
||||
pass
|
||||
df = pd.DataFrame(data_stream)
|
||||
|
||||
filename = os.path.join(testdir, "health.pdf")
|
||||
tables = camelot.read_pdf(filename, flavor="stream")
|
||||
assert df.equals(tables[0].df)
|
||||
|
||||
|
||||
def test_stream_table_rotated():
|
||||
@@ -29,7 +46,7 @@ def test_stream_table_rotated():
|
||||
|
||||
|
||||
def test_stream_table_area():
|
||||
df = pd.DataFrame(data_stream_table_area_single)
|
||||
df = pd.DataFrame(data_stream_table_area)
|
||||
|
||||
filename = os.path.join(testdir, "tabula/us-007.pdf")
|
||||
tables = camelot.read_pdf(filename, flavor="stream", table_area=["320,500,573,335"])
|
||||
@@ -45,6 +62,23 @@ def test_stream_columns():
|
||||
assert df.equals(tables[0].df)
|
||||
|
||||
|
||||
def test_stream_split_text():
|
||||
df = pd.DataFrame(data_stream_split_text)
|
||||
|
||||
filename = os.path.join(testdir, "tabula/m27.pdf")
|
||||
tables = camelot.read_pdf(
|
||||
filename, flavor="stream", columns=["72,95,209,327,442,529,566,606,683"], split_text=True)
|
||||
assert df.equals(tables[0].df)
|
||||
|
||||
|
||||
def test_stream_flag_size():
|
||||
df = pd.DataFrame(data_stream_flag_size)
|
||||
|
||||
filename = os.path.join(testdir, "superscript.pdf")
|
||||
tables = camelot.read_pdf(filename, flavor="stream", flag_size=True)
|
||||
assert df.equals(tables[0].df)
|
||||
|
||||
|
||||
def test_lattice():
|
||||
df = pd.DataFrame(data_lattice)
|
||||
|
||||
@@ -66,6 +100,14 @@ def test_lattice_table_rotated():
|
||||
assert df.equals(tables[0].df)
|
||||
|
||||
|
||||
def test_lattice_table_area():
|
||||
df = pd.DataFrame(data_lattice_table_area)
|
||||
|
||||
filename = os.path.join(testdir, "twotables_2.pdf")
|
||||
tables = camelot.read_pdf(filename, table_area=["80,693,535,448"])
|
||||
assert df.equals(tables[0].df)
|
||||
|
||||
|
||||
def test_lattice_process_background():
|
||||
df = pd.DataFrame(data_lattice_process_background)
|
||||
|
||||
@@ -80,3 +122,19 @@ def test_lattice_copy_text():
|
||||
filename = os.path.join(testdir, "row_span_1.pdf")
|
||||
tables = camelot.read_pdf(filename, line_size_scaling=60, copy_text="v")
|
||||
assert df.equals(tables[0].df)
|
||||
|
||||
|
||||
def test_lattice_shift_text():
|
||||
df_lt = pd.DataFrame(data_lattice_shift_text_left_top)
|
||||
df_disable = pd.DataFrame(data_lattice_shift_text_disable)
|
||||
df_rb = pd.DataFrame(data_lattice_shift_text_right_bottom)
|
||||
|
||||
filename = os.path.join(testdir, "column_span_2.pdf")
|
||||
tables = camelot.read_pdf(filename, line_size_scaling=40)
|
||||
assert df_lt.equals(tables[0].df)
|
||||
|
||||
tables = camelot.read_pdf(filename, line_size_scaling=40, shift_text=[''])
|
||||
assert df_disable.equals(tables[0].df)
|
||||
|
||||
tables = camelot.read_pdf(filename, line_size_scaling=40, shift_text=['r', 'b'])
|
||||
assert df_rb.equals(tables[0].df)
|
||||
@@ -1 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
Reference in New Issue
Block a user