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I was able to find somewhat of an answer to my question, but it was not as nested as my dictionary and so I am really unsure how to proceed as I am still very new to python. I currently have a nested dictionary like
{'140.10': {'46': {'1': '-49.50918', '2': '-50.223637', '3': '49.824406'}, '28': {'1': '-49.50918', '2': '-50.223637', '3': '49.824406'}}}:
I am wanting to plot it so that '140.10' becomes the title of the graph and '46' and '28' become the individual lines and key '1' for example is on the y axis and the x axis is the final number (in this case '-49.50918). Essentially a graph like this:
I generated this graph with a csv file that is written at another part of the code just with excel:
[![enter image description here][2]][2]
The problem I am running into is that these keys are autogenerated from a larger csv file and I will not know their exact value until the code has been run. As each of the keys are autogenerated in an earlier part of the script. As I will be running it over various files called the Graph name, and each file will have a different values for:
{key1:{key2_1: {key3_1: value1, key3_2: value2, key3_3: value3}, key_2_2 ...}}}
I have tried to do something like this:
for filename in os.listdir(Directory):
if filename.endswith('.csv'):
q = filename.split('.csv')[0]
s = q.split('_')[0]
if s in time_an_dict:
atom = list(time_an_dict[s])
ion = time_an_dict[s]
for f in time_an_dict[s]:
x_val = []
y_val = []
fz = ion[f]
for i in time_an_dict[s][f]:
pos = (fz[i])
frame = i
y_val.append(frame)
x_val.append(pos)
'''ions = atom
frame = frames
position = pos
plt.plot(frame, position, label = frames)
plt.xlabel("Frame")
plt.ylabel("Position")
plt.show()
#plt.savefig('{}_Pos.png'.format(s))'''
But it has not run as intended.
I have also tried:
for filename in os.listdir(Directory):
if filename.endswith('_Atom.csv'):
q = filename.split('.csv')[0]
s = q.split('_')[0]
if s in window_dict:
name = s + '_Atom.csv'
time_an_dict[s] = analyze_time(name,window_dict[s])
new = '{}_A_pos.csv'.format(s)
ions = list(time_an_dict.values())[0].keys()
for i in ions:
x_axis_values = []
y_axis_values = []
frame = list(time_an_dict[s][i])
x_axis_values.append(frame)
empty = []
print(x_axis_values)
for x in frame:
values = time_an_dict[s][i][x]
empty.append(values)
y_axis_values.append(empty)
plt.plot(x_axis_values, y_axis_values, label = x )
plt.show()
But keep getting the error:
Traceback (most recent call last): File "Atoms_pos.py", line 175, in
plt.plot(x_axis_values, y_axis_values, label = x ) File "/Users/hxb51/opt/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py",
line 2840, in plot
return gca().plot( File "/Users/hxb51/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py",
line 1743, in plot
lines = [*self._get_lines(*args, data=data, **kwargs)] File "/Users/hxb51/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_base.py",
line 273, in call
yield from self._plot_args(this, kwargs) File "/Users/hxb51/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_base.py",
line 394, in _plot_args
self.axes.xaxis.update_units(x) File "/Users/hxb51/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axis.py",
line 1466, in update_units
default = self.converter.default_units(data, self) File "/Users/hxb51/opt/anaconda3/lib/python3.8/site-packages/matplotlib/category.py",
line 107, in default_units
axis.set_units(UnitData(data)) File "/Users/hxb51/opt/anaconda3/lib/python3.8/site-packages/matplotlib/category.py",
line 176, in init
self.update(data) File "/Users/hxb51/opt/anaconda3/lib/python3.8/site-packages/matplotlib/category.py",
line 209, in update
for val in OrderedDict.fromkeys(data): TypeError: unhashable type: 'numpy.ndarray'
Here is the remainder of the other parts of the code that generate the files and dictionaries I am using. I was told in another question I asked that this could be helpful.
# importing dependencies
import math
import sys
import pandas as pd
import MDAnalysis as mda
import os
import numpy as np
import csv
import matplotlib.pyplot as plt
################################################################################
###############################################################################
Directory = '/Users/hxb51/Desktop/Q_prof/Displacement_Charge/Blah'
os.chdir(Directory)
################################################################################
''' We are only looking at the positions of the CLAs and SODs and not the DRUDE counterparts. We are assuming the DRUDE
are very close and it is not something that needs to be concerned with'''
def Positions(dcd, topo):
fields = ['Window', 'ION', 'ResID', 'Location', 'Position', 'Frame', 'Final']
with open('{}_Atoms.csv'.format(s), 'a') as d:
writer = csv.writer(d)
writer.writerow(fields)
d.close()
CLAs = u.select_atoms('segid IONS and name CLA')
SODs = u.select_atoms('segid IONS and name SOD')
CLA_res = len(CLAs)
SOD_res = len(SODs)
frame = 0
for ts in u.trajectory[-10:]:
frame +=1
CLA_pos = CLAs.positions[:,2]
SOD_pos = SODs.positions[:,2]
for i in range(CLA_res):
ids = i + 46
if CLA_pos[i] < 0:
with open('{}_Atoms.csv'.format(s), 'a') as q:
new_line = [s,'CLA', ids, 'Bottom', CLA_pos[i], frame,10]
writes = csv.writer(q)
writes.writerow(new_line)
q.close()
else:
with open('{}_Atoms.csv'.format(s), 'a') as q:
new_line = [s,'CLA', ids, 'Top', CLA_pos[i], frame, 10]
writes = csv.writer(q)
writes.writerow(new_line)
q.close()
for i in range(SOD_res):
ids = i
if SOD_pos[i] < 0:
with open('{}_Atoms.csv'.format(s), 'a') as q:
new_line = [s,'SOD', ids, 'Bottom', SOD_pos[i], frame,10]
writes = csv.writer(q)
writes.writerow(new_line)
q.close()
else:
with open('{}_Atoms.csv'.format(s), 'a') as q:
new_line = [s,'SOD', ids, 'Top', SOD_pos[i], frame, 10]
writes = csv.writer(q)
writes.writerow(new_line)
q.close()
csv_Data = pd.read_csv('{}_Atoms.csv'.format(s))
filename = s + '_Atom.csv'
sorted_df = csv_Data.sort_values(["ION", "ResID", "Frame"],
ascending=[True, True, True])
sorted_df.to_csv(filename, index = False)
os.remove('{}_Atoms.csv'.format(s))
''' this function underneath looks at the ResIds, compares them to make sure they are the same and then counts how many
times the ion flip flops around the boundaries'''
def turn_dict(f):
read = open(f)
reader = csv.reader(read, delimiter=",", quotechar = '"')
my_dict = {}
new_list = []
for row in reader:
new_list.append(row)
for i in range(len(new_list[:])):
prev = i - 1
if new_list[i][2] == new_list[prev][2]:
if new_list[i][3] != new_list[prev][3]:
if new_list[i][2] in my_dict:
my_dict[new_list[i][2]] += 1
else:
my_dict[new_list[i][2]] = 1
return my_dict
def plot_flips(f):
dict = turn_dict(f)
ions = list(dict.keys())
occ = list(dict.values())
plt.bar(range(len(dict)), occ, tick_label = ions)
plt.title("{}".format(s))
plt.xlabel("Residue ID")
plt.ylabel("Boundary Crosses")
plt.savefig('{}_Flip.png'.format(s))
def analyze_time(f, dicts):
read = open(f)
reader = csv.reader(read, delimiter=",", quotechar='"')
new_list = []
keys = list(dicts.keys())
time_dict = {}
pos_matrix = {}
for row in reader:
new_list.append(row)
fields = ['ResID', 'Position', 'Frame']
with open('{}_A_pos.csv'.format(s), 'a') as k:
writer = csv.writer(k)
writer.writerow(fields)
k.close()
for i in range(len(new_list[:])):
if new_list[i][2] in keys:
with open('{}_A_pos.csv'.format(s), 'a') as k:
new_line = [new_list[i][2], new_list[i][4], new_list[i][5]]
writes = csv.writer(k)
writes.writerow(new_line)
k.close()
read = open('{}_A_pos.csv'.format(s))
reader = csv.reader(read, delimiter=",", quotechar='"')
time_list = []
for row in reader:
time_list.append(row)
for j in range(len(keys)):
for i in range(len(time_list[1:])):
if time_list[i][0] == keys[j]:
pos_matrix[time_list[i][2]] = time_list[i][1]
time_dict[keys[j]] = pos_matrix
return time_dict
window_dict = {}
for filename in os.listdir(Directory):
s = filename.split('.dcd')[0]
fors = s + '.txt'
topos = '/Users/hxb51/Desktop/Q_prof/Displacement_Charge/topo.psf'
if filename.endswith('.dcd'):
print('We are starting with {} \n '.format(s))
u = mda.Universe(topos, filename)
Positions(filename, topos)
name = s + '_Atom.csv'
plot_flips(name)
window_dict[s] = turn_dict(name)
continue
time_an_dict = {}
for filename in os.listdir(Directory):
if filename.endswith('.csv'):
q = filename.split('.csv')[0]
s = q.split('_')[0]
if s in window_dict:
name = s + '_Atom.csv'
time_an_dict[s] = analyze_time(name,window_dict[s])
for filename in os.listdir(Directory):
if filename.endswith('.csv'):
q = filename.split('.csv')[0]
s = q.split('_')[0]
if s in time_an_dict:
atom = list(time_an_dict[s])
ion = time_an_dict[s]
for f in time_an_dict[s]:
x_val = []
y_val = []
fz = ion[f]
for i in time_an_dict[s][f]:
pos = (fz[i])
frame = i
y_val.append(frame)
x_val.append(pos)
'''ions = atom
frame = frames
position = pos
plt.plot(frame, position, label = frames)
plt.xlabel("Frame")
plt.ylabel("Position")
plt.show()
#plt.savefig('{}_Pos.png'.format(s))'''
Everything here runs well except this last bottom block of code. That deals with trying to make a graph from a nested dictionary. Any help would be appreciated!
Thanks!
I figured out the answer:
for filename in os.listdir(Directory):
if filename.endswith('_Atom.csv'):
q = filename.split('.csv')[0]
s = q.split('_')[0]
if s in window_dict:
name = s + '_Atom.csv'
time_an_dict[s] = analyze_time(name,window_dict[s])
new = '{}_A_pos.csv'.format(s)
ions = list(time_an_dict[s])
plt.yticks(np.arange(-50, 50, 5))
plt.xlabel('Frame')
plt.ylabel('Z axis position(Ang)')
plt.title([s])
for i in ions:
x_value = []
y_value = []
time_frame =len(time_an_dict[s][i]) +1
for frame in range(1,time_frame):
frame = str(frame)
x_value.append(int(frame))
y_value.append(float(time_an_dict[s][i][frame]))
plt.plot(x_value, y_value, label=[i])
plt.xticks(np.arange(1, 11, 1))
plt.legend()
plt.savefig('{}_Positions.png'.format(s))
plt.clf()
os.remove("{}_A_pos.csv".format(s))
From there, with the combo of the other parts of the code, it produces these graphs:
For more than 1 file as long as there is more '.dcd' files.
I have a list 'lst1' and wanted to append multiple values in a single line if exist. Can anyone help me out with this.
lst1 = [['cnl','fb123','ins54'],['ins45'],['abc','xyz'],['abc','xyz','fb765','ins567']]
adn = ['ab','cc']
fb = []
ins = []
otr = []
for lnk in lst1:
for lnk2 in lnk:
if 'fb' in lnk2:
try:
fb.append(lnk2)
except:
fb.append("")
elif 'ins' in lnk2:
try:
ins.append(lnk2)
except:
ins.append("")
elif ('fb' or 'ins') not in lnk2:
try:
otr.append(lnk2)
except:
otr.append("")
data = {}
data = {'fb': fb, 'ins': ins, 'otr': otr, 'adn': adn}
result = pd.DataFrame(dict([(k,pd.Series(v)) for k,v in data.items()]))
result.to_csv("raw_data.csv", index = False)
Expected Output:
fb ins otr adn
0 fb123 ins54 cnl ab
1 ins45 cc
2 abc,xyz
3 fb765 ins567 abc,xyz
Even, I have tried with 'extend' function but unable to get the desired output.
I don't understand why in the output example the third and fourth lines are empty ? And why are 'abc, xyz' in the second line?
Implemented based only on the description. If you want to exclude duplication, you can additionally transform the *_check list to set .
import pandas as pd
lst1 = [['cnl', 'fb123', 'ins54'], ['ins45'], ['abc', 'xyz'], ['abc', 'xyz', 'fb765', 'ins567']]
adn = ['ab', 'cc']
fb = []
ins = []
otr = []
for lnk in lst1:
fb_check = [word for word in lnk if word.startswith('fb')]
ins_check = [word for word in lnk if word.startswith('ins')]
otr_check = [word for word in lnk if not word.startswith('fb') and not word.startswith('ins')]
fb.append(','.join(fb_check) if fb_check else '')
ins.append(','.join(ins_check) if ins_check else '')
otr.append(','.join(otr_check) if otr_check else '')
while len(adn) != len(fb):
adn.append('')
data = {'fb': fb, 'ins': ins, 'otr': otr, 'adn': adn}
result = pd.DataFrame(dict([(k, pd.Series(v)) for k, v in data.items()]))
print(result)
result.to_csv("raw_data.csv", index=False)
Output:
fb ins otr adn
0 fb123 ins54 cnl ab
1 ins45 cc
2 abc,xyz
3 fb765 ins567 abc,xyz
I'm getting number from a HTML, some of them are %, 4 digits and 7 digits (37.89%, 3.464, 2,193.813). I would like to save just the numbers, not the percentages, without the thousand separators (".").
list_of_rows = []
for row in table.findAll('div', attrs={'class': 'quadrado'}):
list_of_cells = []
for cell in row.findAll('span', attrs={'class': 'circulo'}):
text = cell.text
# print(text)
for cell_index in row.findAll('span', attrs={'class': 'triangulo'}):
text_index = cell_index.text
list_of_cells_index = [text, text_index]
list_of_cells_index_clean = ','.join(list_of_cells_index) # remove brackets and ''
# print(list_of_cells_index_clean)
list_of_cells.append(list_of_cells_index_clean)
list_of_rows.append(list_of_cells)
outfile = open("./list.csv", "a")
writer = csv.writer(outfile, lineterminator = '\n')
writer.writerows(list_of_rows)
I would like to get:
37.89%, 3464, 2193,813.
How can I do it?
I don't know all your input parameters, but this works for the ones that you provided.
s = ('37.89%', '3.464', '2,193.813')
for item in s:
remove_comma = item.replace(',', '')
keep_percentage = re.findall(r'\d{1,4}\.\d{1,4}%', remove_comma)
if keep_percentage:
keep_percentage = ''.join(keep_percentage)
print (keep_percentage)
else:
if (len(remove_comma)) == 5:
print (remove_comma.replace('.', ''))
else:
print (remove_comma.replace('.', ','))
**OUTPUTS**
37.89%
3464
2193,813
thank you for your help. I want to use the following python code, to read and process data from an affymetrix microarray data set. I want to elucidate differential gene expression in disease conditions of Crohn's disease and Ulcerative colitis, in mononuclear cells. The code runs perfectly, but when I try to see the content of X, I get an empty array at the output (like this : array([], dtype=float64)), which of course is not useful. Here is a link to the raw data set : https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS1615
I have tried long to figure out why I have an empty and unprocessable output, but to no avail. Here is the code:
import gzip
import numpy as np
"""
Read in a SOFT format data file. The following values can be exported:
GID : A list of gene identifiers of length d
SID : A list of sample identifiers of length n
STP : A list of sample descriptions of length d
X : A dxn array of gene expression values
"""
#path to the data file
fname = "../data/GDS1615_full.soft.gz"
## Open the data file directly as a gzip file
with gzip.open(fname) as fid:
SIF = {}
for line in fid:
if line.startswith(line, len("!dataset_table_begin")):
break
elif line.startswith(line, len("!subject_description")):
subset_description = line.split("=")[1].strip()
elif line.startswith(line, len("!subset_sample_id")):
subset_ids = [x.strip() for x in subset_ids]
for k in subset_ids:
SIF[k] = subset_description
## Next line is the column headers (sample id's)
SID = next(fid).split("\t")
## The column indices that contain gene expression data
I = [i for i,x in enumerate(SID) if x.startswith("GSM")]
## Restrict the column headers to those that we keep
SID = [SID[i] for i in I]
## Get a list of sample labels
STP = [SIF[k] for k in SID]
## Read the gene expression data as a list of lists, also get the gene
## identifiers
GID,X = [],[]
for line in fid:
## This is what signals the end of the gene expression data
## section in the file
if line.startswith("!dataset_table_end"):
break
V = line.split("\t")
## Extract the values that correspond to gene expression measures
## and convert the strings to numbers
x = [float(V[i]) for i in I]
X.append(x)
GID.append(V[0] + ";" + V[1])
X = np.array(X)
## The indices of samples for the ulcerative colitis group
UC = [i for i,x in enumerate(STP) if x == "ulcerative colitis"]
## The indices of samples for the Crohn's disease group
CD = [i for i,x in enumerate(STP) if x == "Crohn's disease"]
At the console, I get such output:
X
Out[94]: array([], dtype=float64)
X.shape
Out[95]: (0,)
Thank you once more for your suggestions.
This worked perfectly:
import gzip
import numpy as np
"""
Read in a SOFT format data file. The following values can be exported:
GID : A list of gene identifiers of length d
SID : A list of sample identifiers of length n
STP : A list of sample desriptions of length d
X : A dxn array of gene expression values
"""
#path to the data file
fname = "../data/GDS1615_full.soft.gz"
## Open the data file directly as a gzip file
with gzip.open(fname) as fid:
SIF = {}
for line in fid:
if line.startswith(b"!dataset_table_begin"):
break
elif line.startswith(b"!subset_description"):
subset_description = line.decode('utf8').split("=")[1].strip()
elif line.startswith(b"!subset_sample_id"):
subset_ids = line.decode('utf8').split("=")[1].split(",")
subset_ids = [x.strip() for x in subset_ids]
for k in subset_ids:
SIF[k] = subset_description
## Next line is the column headers (sample id's)
SID = next(fid).split(b"\t")
## The column indices that contain gene expression data
I = [i for i,x in enumerate(SID) if x.startswith(b"GSM")]
## Restrict the column headers to those that we keep
SID = [SID[i] for i in I]
## Get a list of sample labels
STP = [SIF[k.decode('utf8')] for k in SID]
## Read the gene expression data as a list of lists, also get the gene
## identifiers
GID,X = [],[]
for line in fid:
## This is what signals the end of the gene expression data
## section in the file
if line.startswith(b"!dataset_table_end"):
break
V = line.split(b"\t")
## Extract the values that correspond to gene expression measures
## and convert the strings to numbers
x = [float(V[i]) for i in I]
X.append(x)
GID.append(V[0].decode() + ";" + V[1].decode())
X = np.array(X)
## The indices of samples for the ulcerative colitis group
UC = [i for i,x in enumerate(STP) if x == "ulcerative colitis"]
## The indices of samples for the Crohn's disease group
CD = [i for i,x in enumerate(STP) if x == "Crohn's disease"]
results:
X.shape
Out[4]: (22283, 127)
I’m working on a Python script that takes a set of input lines and assigns a mullion to the corresponding gridline that they intersect. However, I’m getting a strange error:
that I don’t know how to correct towards the end of the script. Python is telling me that it expected a MullionType and got a Family Type (see image). I’m using a modified version of Spring Nodes’ Collector.WallTypes that collects Mullion Types instead but the output of the node is a Family Type, which the script won’t accept. Any idea how to get the Mullion Type to feed into the final Python node?
SpringNodes script:
#Copyright(c) 2016, Dimitar Venkov
# #5devene, dimitar.ven#gmail.com
import clr
clr.AddReference("RevitServices")
import RevitServices
from RevitServices.Persistence import DocumentManager
doc = DocumentManager.Instance.CurrentDBDocument
clr.AddReference("RevitAPI")
from Autodesk.Revit.DB import *
clr.AddReference("RevitNodes")
import Revit
clr.ImportExtensions(Revit.Elements)
def tolist(obj1):
if hasattr(obj1,"__iter__"): return obj1
else: return [obj1]
fn = tolist(IN[0])
fn = [str(n) for n in fn]
result, similar, names = [], [], []
fec = FilteredElementCollector(doc).OfClass(MullionType)
for i in fec:
n1 = Element.Name.__get__(i)
names.append(n1)
if any(fn1 == n1 for fn1 in fn):
result.append(i.ToDSType(True))
elif any(fn1.lower() in n1.lower() for fn1 in fn):
similar.append(i.ToDSType(True))
if len(result) > 0:
OUT = result,similar
if len(result) == 0 and len(similar) > 0:
OUT = "No exact match found. Check partial below:",similar
if len(result) == 0 and len(similar) == 0:
OUT = "No match found! Check names below:", names
The SpringNodes script outputs a Family Type, even though the collector is for Mullion Types (see above image)
Here's my script:
import clr
# Import RevitAPI
clr.AddReference("RevitAPI")
import Autodesk
from Autodesk.Revit.DB import *
# Import DocumentManager and TransactionManager
clr.AddReference("RevitServices")
import RevitServices
from RevitServices.Persistence import DocumentManager
from RevitServices.Transactions import TransactionManager
# Import ToDSType(bool) extension method
clr.AddReference("RevitNodes")
import Revit
clr.ImportExtensions(Revit.GeometryConversion)
from System import Array
clr.AddReference('ProtoGeometry')
from Autodesk.DesignScript.Geometry import *
import math
doc = DocumentManager.Instance.CurrentDBDocument
app = DocumentManager.Instance.CurrentUIApplication.Application
walls = UnwrapElement(IN[0])
toggle = IN[1]
inputLine = IN[2]
mullionType = IN[3]
wallSrf = []
heights = []
finalPoints = []
directions = []
isPrimary = []
projectedCrvs = []
keySegments = []
keySegmentsGeom = []
gridSegments = []
gridSegmentsGeom = []
gridLines = []
gridLinesGeom = []
keyGridLines = []
keyGridLinesGeom = []
projectedGridlines = []
lineDirections = []
gridLineDirection = []
allTrueFalse = []
if toggle == True:
TransactionManager.Instance.EnsureInTransaction(doc)
for w, g in zip(walls,inputLine):
pointCoords = []
primary = []
## Get curtain wall element sketch line
originLine = Revit.GeometryConversion.RevitToProtoCurve.ToProtoType( w.Location.Curve, True )
originLineLength = w.Location.Curve.ApproximateLength
## Get curtain wall element height, loft to create surface
for p in w.Parameters:
if p.Definition.Name == 'Unconnected Height':
height = p.AsDouble()
topLine = originLine.Translate(0,0,height)
srfCurves = [originLine,topLine]
wallSrf = NurbsSurface.ByLoft(srfCurves)
## Get centerpoint of curve, determine whether it extends across entire gridline
projectedCrvCenterpoint = []
for d in g:
lineDirection = d.Direction.Normalized()
lineDirections.append(lineDirection)
curveProject= d.PullOntoSurface(wallSrf)
if abs(lineDirection.Z) == 1:
if curveProject.Length >= height-.5:
primary.append(False)
else:
primary.append(True)
else:
if curveProject.Length >= originLineLength-.5:
primary.append(False)
else:
primary.append(True)
centerPoint = curveProject.PointAtParameter(0.5)
pointList = []
projectedCrvCenterpoint.append(centerPoint)
## Project centerpoint of curve onto wall surface
for h in [centerPoint]:
pointUnwrap = UnwrapElement(centerPoint)
pointList.append(pointUnwrap.X)
pointList.append(pointUnwrap.Y)
pointList.append(pointUnwrap.Z)
pointCoords.append(pointList)
finalPoints.append(pointCoords)
isPrimary.append(primary)
projectedCrvs.append(projectedCrvCenterpoint)
TransactionManager.Instance.TransactionTaskDone()
TransactionManager.Instance.EnsureInTransaction(doc)
##Gather all segments of gridline geometry
for wall in UnwrapElement(walls):
gridSegments2 = []
gridSegmentsGeom2 = []
gridLines1 = []
gridLinesGeom1 = []
for id1 in wall.CurtainGrid.GetVGridLineIds():
gridLinesGeom1.append(Revit.GeometryConversion.RevitToProtoCurve.ToProtoType(doc.GetElement(id1).FullCurve))
gridLines1.append(doc.GetElement(id1))
VgridSegments1 = []
VgridSegmentsGeom1 = []
for i in doc.GetElement(id1).AllSegmentCurves:
VgridSegments1.append(i)
VgridSegmentsGeom1.append(Revit.GeometryConversion.RevitToProtoCurve.ToProtoType(i,True))
gridSegments2.append(VgridSegments1)
gridSegmentsGeom2.append(VgridSegmentsGeom1)
for id2 in wall.CurtainGrid.GetUGridLineIds():
gridLinesGeom1.append(Revit.GeometryConversion.RevitToProtoCurve.ToProtoType(doc.GetElement(id2).FullCurve))
gridLines1.append(doc.GetElement(id2))
UgridSegments1 = []
UgridSegmentsGeom1 = []
for i in doc.GetElement(id2).AllSegmentCurves:
UgridSegments1.append(i)
UgridSegmentsGeom1.append(Revit.GeometryConversion.RevitToProtoCurve.ToProtoType(i,True))
gridSegments2.append(UgridSegments1)
gridSegmentsGeom2.append(UgridSegmentsGeom1)
gridSegments.append(gridSegments2)
gridSegmentsGeom.append(gridSegmentsGeom2)
gridLines.append(gridLines1)
gridLinesGeom.append(gridLinesGeom1)
boolFilter = [[[[b.DoesIntersect(x) for x in d] for d in z] for b in a] for a,z in zip(projectedCrvs, gridSegmentsGeom)]
boolFilter2 = [[[b.DoesIntersect(x) for x in z] for b in a] for a,z in zip(projectedCrvs, gridLinesGeom)]
##Select gridline segments that intersect with centerpoint of projected lines
for x,y in zip(boolFilter,gridSegments):
keySegments2 = []
keySegmentsGeom2 = []
for z in x:
keySegments1 = []
keySegmentsGeom1 = []
for g,l in zip(z,y):
for d,m in zip(g,l):
if d == True:
keySegments1.append(m)
keySegmentsGeom1.append(Revit.GeometryConversion.RevitToProtoCurve.ToProtoType(m,True))
keySegments2.append(keySegments1)
keySegmentsGeom2.append(keySegmentsGeom1)
keySegments.append(keySegments2)
keySegmentsGeom.append(keySegmentsGeom2)
##Order gridlines according to intersection with projected points
for x,y in zip(boolFilter2, gridLines):
keyGridLines1 = []
keyGridLinesGeom1 = []
for z in x:
for g,l in zip(z,y):
if g == True:
keyGridLines1.append(l)
keyGridLinesGeom1.append(Revit.GeometryConversion.RevitToProtoCurve.ToProtoType(l.FullCurve,True))
keyGridLines.append(keyGridLines1)
keyGridLinesGeom.append(keyGridLinesGeom1)
##Add mullions at intersected gridline segments
TransactionManager.Instance.TransactionTaskDone()
TransactionManager.Instance.EnsureInTransaction(doc)
for x,y,z in zip(keyGridLines,keySegments,isPrimary):
projectedGridlines1 = []
for h,j,k in zip(x,y,z):
for i in j:
if i != None:
h.AddMullions(i,mullionType,k)
projectedGridlines1.append(h)
projectedGridlines.append(projectedGridlines1)
else:
None
if toggle == True:
OUT = projectedGridlines
else:
None
TransactionManager.Instance.TransactionTaskDone()
Apologies for the messiness of the code, it's a modification of another node that I've been working on. Thanks for your help.
Bo,
Your problem is rooted in how Dynamo is wrapping elements to use with its own model. That last call .ToDSType(True) is the gist of the issue. MullionType class is a subclass (it inherits properties) from a ElementType class in Revit. When Dynamo team wraps that object into a custom wrapper they only wrote a top level wrapper that treats all ElementTypes the same, hence this outputs an ElementType/FamilyType rather than a specific MullionType.
First I would suggest that you replace the line of code in your code:
mullionType = IN[3]
with:
mullionType = UnwrapElement(IN[3])
This is their built in method for unwrapping elements to be used with calls to Revit API.
If that still somehow remains an issue, you could try and retrieve the MullionType object again, this time directly in your script, before you use it. You can do so like this:
for x,y,z in zip(keyGridLines,keySegments,isPrimary):
projectedGridlines1 = []
for h,j,k in zip(x,y,z):
for i in j:
if i != None:
h.AddMullions(i,doc.GetElement(mullionType.Id),k)
projectedGridlines1.append(h)
projectedGridlines.append(projectedGridlines1)
This should make sure that you get the MullionType element before it was wrapped.
Again, try unwrapping it first, then GetElement() call if first doesn't work.