bscheibel před 5 roky
rodič
revize
b88459f444

+ 0 - 54
GV12_extracted.csv

@@ -1,54 +0,0 @@
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-Concepts, principales and rules according to:;0;2
-Dimensions according to:;0;-1
-Tolerances of form, orientation, location and run-out:;0;-3
-Edge finish according to:;0;-6
-Surface texture according to:;0;-9
-Limits according to:;0;-12
-16% rule:;0;-15
-ISO 8015;0;2
-ISO 14405 1-3;0;-1
-ISO 1101;0;-3
-ISO 13715;0;-6
-ISO 1302;0;-9
-ISO 286-2;0;-12
-not applicable;0;-15
-Rz 16;4;3
-2x;163;195
-%%c;163;199
-19,20;163;203
--;163;210
-0,00;160;211
-0,05;163;211
-%%c;152;197
-15,15;152;200
-+;149;208
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-0,00;152;209
-LAYOUT;420;297
-Concepts, principales and rules according to:;0;2
-Dimensions according to:;0;-1
-Tolerances of form, orientation, location and run-out:;0;-3
-Edge finish according to:;0;-6
-Surface texture according to:;0;-9
-Limits according to:;0;-12
-16% rule:;0;-15
-ISO 8015;0;2
-ISO 14405 1-3;0;-1
-ISO 1101;0;-3
-ISO 13715;0;-6
-ISO 1302;0;-9
-ISO 286-2;0;-12
-not applicable;0;-15
-Rz 16;4;3
-2x;163;195
-%%c;163;199
-19,20;163;203
--;163;210
-0,00;160;211
-0,05;163;211
-%%c;152;197
-15,15;152;200
-+;149;208
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-0,00;152;209

+ 2 - 2
cluster_by_distance_csv_only.py

@@ -10,12 +10,12 @@
 
 import csv
 
-csvfile1 = open('values.csv', 'r')
+csvfile1 = open('values_LH.csv', 'r')
 spamreader1 = list(csv.reader(csvfile1, dialect='excel', delimiter=','))
 csvfile1.close()
 already_merged=False
 new_rows_list = []
-with open("values.csv", "r") as csvfile:
+with open("values_LH.csv", "r") as csvfile:
     reader = csv.reader(csvfile, delimiter=',')
     for row in reader:
         #print(row)

+ 7 - 7
dbscan_clustering.py

@@ -8,15 +8,15 @@ from sklearn.preprocessing import StandardScaler
 
 def cluster(file_in, file_out):
     # #############################################################################
-    data_df = pandas.read_csv("values.csv", sep=",")
+    data_df = pandas.read_csv("values_fromhtml_GV12.csv", sep=",")
     data_df.head(3)
-    data = data_df[["X1", "Y1","X2","Y2"]]
+    data = data_df[["X1","Y1","X2","Y2"]]
     print(data)
     data = StandardScaler().fit_transform(data)
 
     # #############################################################################
     # Compute DBSCAN
-    db = DBSCAN(eps=0.1, min_samples=1).fit(data)
+    db = DBSCAN(eps=0.2, min_samples=1).fit(data)
     core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
     core_samples_mask[db.core_sample_indices_] = True
     labels = db.labels_
@@ -34,7 +34,7 @@ def cluster(file_in, file_out):
 
     # #############################################################################
     # Plot result
-    import matplotlib.pyplot as plt
+    """ort matplotlib.pyplot as plt
 
     # Black removed and is used for noise instead.
     unique_labels = set(labels)
@@ -56,11 +56,11 @@ def cluster(file_in, file_out):
                  markeredgecolor='k', markersize=6)
 
     plt.title('Estimated number of clusters: %d' % n_clusters_)
-    plt.show()
+    plt.show()"""
 
     print(data_df.head(3))
-    data_df.to_csv("values_clustered.csv")
-    data_df.groupby('cluster')['Text'].apply(' '.join).reset_index().to_csv("text_merged.csv")
+    #data_df.to_csv("values_clusteredfromPDF_GV12.csv")
+    data_df.groupby('cluster')['Text'].apply(' '.join).reset_index().to_csv("values_clusteredfromPDF_GV12.csv")
 
 
 cluster(33,33)

+ 12 - 414
drawings/5129275_Rev01-GV12.html

@@ -1,418 +1,16 @@
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+<!DOCTYPE html>
+<html>
 <head>
 <title>5129275.idw</title>
-<meta name="Subject" content=""/>
-<meta name="Keywords" content=""/>
-<meta name="Author" content="u1ac_lka"/>
-<meta name="Creator" content="PDFCreator 2.3.0.103"/>
-<meta name="Producer" content="PDFCreator 2.3.0.103"/>
-<meta name="CreationDate" content=""/>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
+<meta name="generator" content="pdftohtml 0.36"/>
+<meta name="author" content="u1ac_lka"/>
+<meta name="keywords" content=""/>
+<meta name="date" content="2018-07-17T13:01:42+00:00"/>
+<meta name="subject" content=""/>
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-  </page>
-</doc>
-</body>
+<frameset cols="100,*">
+<frame name="links" src="5129275_Rev01-GV12_ind.html"/>
+<frame name="contents" src="5129275_Rev01-GV12s.html"/>
+</frameset>
 </html>

Rozdílová data souboru nebyla zobrazena, protože soubor je příliš velký
+ 10 - 2354
iso_documents/ISO2768-1.html


+ 0 - 92
merged.csv

@@ -1,92 +0,0 @@
-,TEXT,X,Y,Text
-0,LAYOUT,420,297,LAYOUT
-1,"Concepts, principales and rules according to:",0,2,"Concepts, principales and rules according to:ISO 8015"
-2,Dimensions according to:,0,-1,Dimensions according to:ISO 14405 1-3
-3,"Tolerances of form, orientation, location and run-out:",0,-3,"Tolerances of form, orientation, location and run-out:ISO 1101"
-4,Edge finish according to:,0,-6,Edge finish according to:ISO 13715
-5,Surface texture according to:,0,-9,Surface texture according to:ISO 1302
-6,Limits according to:,0,-12,Limits according to:ISO 286-2
-7,16% rule:,0,-15,16% rule:not applicable
-8,ISO 8015,0,2,"Concepts, principales and rules according to:ISO 8015"
-9,ISO 14405 1-3,0,-1,Dimensions according to:ISO 14405 1-3
-10,ISO 1101,0,-3,"Tolerances of form, orientation, location and run-out:ISO 1101"
-11,ISO 13715,0,-6,Edge finish according to:ISO 13715
-12,ISO 1302,0,-9,Surface texture according to:ISO 1302
-13,ISO 286-2,0,-12,Limits according to:ISO 286-2
-14,not applicable,0,-15,16% rule:not applicable
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+ 1 - 1
old/csv_to_text.py

@@ -1,7 +1,7 @@
 import pandas
 import csv
 import re
-#data_df = pandas.read_csv("values.csv", sep=",")
+#data_df = pandas.read_csv("values_LH.csv", sep=",")
 #print(data_df.head(3))
 
 #data = data_df[["X1","Y1","X2","Y2"]]

+ 0 - 28
output.csv

@@ -1,28 +0,0 @@
-Toleran zklasse,,,Grenzalbmaße für 1Nennmaßbereiche,,
-"von 0,5^)",,über 3,über 6 über 30 über 120 über 400,über 1000,über 2000
-Kurzzeichen Benennung bis 3,öCO,bis 6,bis 30 bis 120 bis 400 bis 1000,bis 2000,bis 4000
-o,,,,,
-CO,,,,,
-"f fein ±0,0+51",,"±0,05","±0,1 ±0,15 ±0,2 ±0,3","±0,5",—
-"m mittel ±0,1",,"±0,1","±0,2 ±0,3 ±0,5 ±0,8","± 1,2",±2
-"c grob ±0,2",,"±0,3","±0,5 ±0,8 ±1,2 ±2",±3,±4
-V sehr grob —,,"±0,5","±1 ±1,5 ±2,5 ±4",±6,±8
-"^) Für Nennmaße unter 0,5 mm sind die Grenzabmaße direkt an dem (den) entsprechenden Nennmaß(en) anzugeben.",,,,,
-Toleran zklasse,Greiizabmaße für Nennmaßbere iche,
-"Kurzzeichen Benennung von 0,5^) bis 3",über 3 bis 6,über 6
-f fein,,
-"±0,2","±0,5",± 1
-m mittel,,
-c grob,,
-"±0,4",± 1,±2
-V sehr grob,,
-"^) Für Nennmaße unter 0,5 mm sind die Grenzabmaße direkt an dem (den) entsprechenden Nennmaß(en) anzugeben.",,
-Toleranzklasse,,"Grenzabmaße fCir Längenbereichie, in mm, für den",
-"",,kürzeren Sch(3nkel des betreffe>nden Winkels,
-Kurzzeichen Benennung,bis 10,über 10 über 50 über 120,über 400
-"",,bis 50 bis 120 bis 400,
-f fein,,,
-"",± 1°,± 0° 30' ±0° 10',±0°5'
-m mittel,,,
-c grob,,± 1° ± 0° 30' ±0° 15',±0° 10'
-V sehr grob,,±2° ±1°,±0° 20'

+ 2 - 2
read_html_to_csv.py

@@ -2,8 +2,8 @@ import re
 import csv
 
 
-with open("drawings/5152166_Rev04.html", "r") as f:
-    with open('values.csv', 'w') as writeFile:
+with open("drawings/5129275_Rev01-GV12.html", "r") as f:
+    with open('values_fromhtml_GV12.csv', 'w') as writeFile:
         for line in f.readlines():
             #print(line)
             row = []

+ 1 - 1
read_text_lines_from_dxf.py

@@ -11,7 +11,7 @@ def printsection(buffer, file_out):
                 #print('{},{}'.format(obj['10'], obj['20']))
                 #print("\n")
 
-                row = [values,math.floor(float(obj['10'])),math.floor(float(obj['20']))]
+                row = [values, math.floor(float(obj['10'])),math.floor(float(obj['20']))]
                 with open(file_out, 'a') as csvFile:
                     writer = csv.writer(csvFile, delimiter =';')
                     if row[0] != '':

+ 1 - 1
regex_extraction.py

@@ -88,7 +88,7 @@ reg_all = re.compile(r"(ISO\s\d\d\d\d?\W?\d?\W?\d?\W?\d?|(EN\s\d*)|^[A-Z]{1}-?[A
 extracted_dimensions = []
 #text = csv_to_text.read_csv('/home/bscheibel/PycharmProjects/dxf_reader/temporary/text_merged_GV12.csv')
 
-file = open('text_merged.csv', 'r')
+file = open('values_clusteredfromPDF_GV12.csv', 'r')
 #text = file.read()
 #file.close()
 text_df = pandas.read_csv(file)

+ 0 - 27
regex_test.py

@@ -1,27 +0,0 @@
-import re
-reg_all = re.compile(r"(^[A-Z]{1}-?[A-Z]?\s*$)|([A-Z]\W?[A-Z]?\s?\W\s?\d\d?\s?\s?:\s?\d\d?\s?\W)|((?!\d)(?!Rpk)[a-zA-Z]{3,}?\W)|(?!0)(^\d{1}\s*$|A\d{1}|\d/\d)")
-regex = r"(\S+\s{1,3}?\S*\s?\S*\S*\s?\S*\S*\s?\S*\S*\s?\S*\S*\s+)"
-file = open('/home/bscheibel/PycharmProjects/dxf_reader/drawings/5129275_Rev01-GV12.txt', 'r')
-text = file.read()
-file.close()
-extraction = []
-matches = re.findall(regex, text, re.MULTILINE)
-for match in matches:
-    #print(match)
-    extraction.append(match.strip())
-i = 0
-new_matches = []
-for match in extraction:
-    #print(match)
-    #print("blub")
-    match = match.split('\n')[0]
-    #if len(match)>1:
-    #    extraction.append(match[1])
-        #print(match[1])
-    #print([match])
-    if not re.search(reg_all, match):
-        #print("blub")
-        new_matches.append(match)
-    i += 1
-
-print(new_matches)

Rozdílová data souboru nebyla zobrazena, protože soubor je příliš velký
+ 828 - 828
values.csv


values_clustered_GV12.csv → values_clustered_GV12_dxf.csv


Rozdílová data souboru nebyla zobrazena, protože soubor je příliš velký
+ 61 - 0
values_clusteredfromPDF_GV12.csv


+ 402 - 0
values_fromhtml_GV12.csv

@@ -0,0 +1,402 @@
+X1,Y1,X2,Y2,Text,Xavg,Yavg, blub
+67.790402,11.570546,73.239534,20.626588,1,70.51496800000001,16.098567000000003,False
+268.068994,11.450546,273.518126,20.506588,3,270.79355999999996,15.978567,False
+167.989461,11.570546,173.438592,20.626588,2,170.7140265,16.098567000000003,False
+368.268523,11.450546,373.717654,20.506588,4,370.9930885,15.978567,False
+468.348387,11.450546,473.797518,20.506588,5,471.07295250000004,15.978567,False
+568.547916,11.450546,573.997047,20.506588,6,571.2724814999999,15.978567,False
+122.630951,46.695792,131.613209,60.279874,Z,127.12208000000001,53.487832999999995,False
+135.590904,46.695792,140.486308,60.279874,(,138.03860600000002,53.487832999999995,False
+144.590872,46.695792,160.924548,60.279874,20,152.75770999999997,53.487832999999995,False
+164.990792,46.695792,169.077646,60.279874,:,167.034219,53.487832999999995,False
+173.150760,46.695792,181.324468,60.279874,1,177.237614,53.487832999999995,False
+185.390712,46.695792,190.286116,60.279874,),187.838414,53.487832999999995,False
+120.761894,84.160811,129.817431,91.199965,R,125.28966249999999,87.680388,False
+121.481894,78.819233,130.537431,84.239966,2,126.00966249999999,81.52959949999999,False
+646.430741,113.055766,656.236251,126.639848,X,651.333496,119.847807,False
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