diff --git a/gml_extrusion/gml_parsing.py b/gml_extrusion/gml_parsing.py
new file mode 100644
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+++ b/gml_extrusion/gml_parsing.py
@@ -0,0 +1,161 @@
+import lxml.etree as ET
+import pandas as pd
+import numpy as np
+
+#------------------------------------------------------------------------------
+### Class GML Parsing ###
+#------------------------------------------------------------------------------
+
+class little_parser:
+ def __init__(self, file):
+ self.file = file
+ #------------------------------------------------------------------------------
+ ### start parsing gml file ###
+ #------------------------------------------------------------------------------
+
+ def little_parser(self):
+ #------------------------------------------------------------------------------
+ ### functions ###
+ #------------------------------------------------------------------------------
+
+ # function to generate needed prefix and tagsc(pt) to access text with simply using
+ # tags for all kinds of CityGML files
+ def tags(tag):
+ pt = prefix_tag['pt'].iloc[prefix_tag[prefix_tag['tag']==tag].index]
+ pt = pt.values
+ pt = str(".//"+pt).strip('['']')[1:-1]
+ return pt
+
+ # calculate areas from points
+ def plane_area(points):
+ # Convert the points to a numpy array
+ points = np.array(points)
+ # Extract x, y, and z coordinates
+ x = points[:, 0]
+ y = points[:, 1]
+ z = points[:, 2]
+ # Calculate the coefficients of the equation of the plane
+ a = np.sum((y - y[0]) * (z - z[0]))
+ b = np.sum((z - z[0]) * (x - x[0]))
+ c = np.sum((x - x[0]) * (y - y[0]))
+ d = -(a * x[0] + b * y[0] + c * z[0])
+ # Calculate the normal vector of the plane
+ normal_vector = np.array([a, b, c])
+ # Calculate the area of the parallelogram
+ area = np.linalg.norm(normal_vector)
+ return area
+
+ # changing structure of points
+ def string_to_points_list(string):
+ # Split the string into a list of strings, each representing a point
+ point_strings = string.split()
+ # Convert each string to a float and group the three floats into a list
+ points = [[float(point_strings[i]), float(point_strings[i+1]),
+ float(point_strings[i+2])] for i in range(0, len(point_strings), 3)]
+ return points
+ # file = r'C:\Users\ge29duf\Documents\02_Forschung\P62\Tool_ne\Grombühl_LoD2_mini.gml'
+ # file = r'C:\Users\ge29duf\Documents\02_Forschung\P62\Tool_ne\Grombuehl_LoD2.gml'
+ # parse the CityGML file
+ tree = ET.parse(self.file)
+ root = tree.getroot()
+
+ #------------------------------------------------------------------------------
+ ### generate prefix and tags of gml file ###
+ #------------------------------------------------------------------------------
+ prefix_tag = []
+ for element in root.iter():
+ prefix = {}
+ prefix['prefix'], prefix['tag'] = element.tag.rsplit("}", 1)
+ prefix_tag.append(prefix)
+
+ # list of all prefixes and tags in gml
+ prefix_tag = pd.DataFrame(prefix_tag).drop_duplicates(subset=['tag'])\
+ .reset_index().drop(columns=['index'])
+ prefix_tag['pt'] = prefix_tag[prefix_tag.columns[0:]].apply(
+ lambda x: '}'.join(x.astype(str)), axis=1)
+
+ #------------------------------------------------------------------------------
+ ### extracting information from gml file and creating a DataFrame ###
+ # building id, name, year, ....
+ # areas of surfaces
+ #------------------------------------------------------------------------------
+
+ buildings = []
+ # find all the building elements in the CityGML file
+ for b in root.findall(tags('Building')):
+ #print(b.find(tags('name')))
+ tags_in_b = {elem.tag.split('}')[-1] for elem in b.iter()} # extract the general information from GML
+ building = {}
+ building['id'] = b.get("{http://www.opengis.net/gml}id")
+ building['name'] = b.find(tags('name')).text\
+ if b.find(tags('name')) is not None\
+ else b.find(tags('name'))
+ # check if the building has year of construction, function, roof type, and measured height
+ if 'yearOfConstruction' in tags_in_b:
+ building['yearOfConstruction'] = b.find(tags('yearOfConstruction')).text\
+ if b.find(tags('yearOfConstruction')) is not None\
+ else b.find(tags('yearOfConstruction'))
+ # add random yearOfConstruction between 1900 and 2020 if None
+ else:
+ building['yearOfConstruction'] = np.random.randint(1900, 2020)
+ print(str(building['id']) + ': yearOfConstruction is missing. Random year is added.')
+
+ if 'function' in tags_in_b:
+ building['function'] = b.find(tags('function')).text\
+ if b.find(tags('function')) is not None\
+ else b.find(tags('function'))
+ else:
+ building['function'] = 'unknown'
+ print(str(building['id']) + ': function is missing. Unknown is added.')
+
+ if 'roofType' in tags_in_b:
+ building['roofType'] = b.find(tags('roofType')).text\
+ if b.find(tags('roofType')) is not None\
+ else b.find(tags('roofType'))
+ else:
+ building['roofType'] = 'unknown'
+ print(str(building['id']) + ': roofType is missing. Unknown is added.')
+
+ if 'measuredHeight' in tags_in_b:
+ building['measuredHeight'] = b.find(tags('measuredHeight')).text\
+ if b.find(tags('measuredHeight')) is not None\
+ else b.find(tags('roofType'))
+ else:
+ building['measuredHeight'] = 'unknown'
+
+ # access building areas
+ ### go one level down to surfaces. findall is needed to make a seperation between different surfaces
+ # Roof
+ i=0
+ area = 0
+ building['Roof'] = 0
+ for i, s in enumerate(b.findall(tags('RoofSurface'))):
+ string = s.find(tags('posList')).text
+ points = string_to_points_list(string)
+ area = plane_area(points)
+ # building['RoofSurface'+str(i)] = area
+ building['Roof'] += area
+ # Wall
+ j=0
+ building['Wall'] = 0
+ for j, s in enumerate(b.findall(tags('WallSurface'))):
+ string = s.find(tags('posList')).text
+ points = string_to_points_list(string)
+ area = plane_area(points)
+ # building['WallSurface'+str(j)] = area
+ building['Wall'] += area
+ # Ground
+ k=0
+ building['Ground'] = 0
+ for k, s in enumerate(b.findall(tags('GroundSurface'))):
+ string = s.find(tags('posList')).text
+ points = string_to_points_list(string)
+ area = plane_area(points)
+ # building['GroundSurface'+str(k)] = area
+ building['Ground'] += area
+ # add the building data to the list
+ buildings.append(building)
+
+ # create a Pandas dataframe from the list of building data
+ building_info = pd.DataFrame(buildings)
+ return building_info