Houdini Python Table Automator.md

Create Table Import node in Houdini, have it load your CSV file, then copy & paste the code below into a Houdini Python node.

Make sure you change the path in line 9 to point to your Table Import node

Works best if your CSV is encoded in UTF-8 and not UTF-8-BOM (I used Notepad++ to change the encoding for mine).

houdini_python_csv_automator.py

import csv

node = hou.pwd()
geo = node.geometry()

# csv_node points to our table node, then we evaluate the file path in the table node
# we then open the file and read it into memory so we can iterate through it and parse the data

csv_node = hou.node("../tableimport2") # change path to your table import node
file_path = csv_node.parm('file').eval()
f = open(file_path, "r")
csv_reader = csv.reader(f, delimiter=',')

# We start by establishing some variables that calculate the row and column counts of our CSV

row_count = sum(1 for row in csv_reader) 
f.seek(0)
col_count = len(next(csv_reader))

# Initialize a list and fill it with 0s, totaling the number of columns in the CSV,
# then iterate through each column and check each entry for letters. If column 0 has
# any entry that contains letters, then the first entry in the list will be 'string',
# and the for-loop will break to the next column. If column 0 has no entries with letters, 
# but has an entry with a ".", then it will store 'float' in our list and move to the next column.
# If it has neither letters or ., then it is just whole numbers and 'int' will be stored in our list

data_types = []
for column in range(col_count):
    data_types.append(0)

for column in range(col_count):
    f.seek(0)
    next(csv_reader)
    for rows in csv_reader:
        current_col = rows[column] 
        contains_letters = current_col.lower().islower()  
        if contains_letters == True:
            data_types[column] = 'string'
            break
        elif "." in current_col:
            data_types[column] = 'float'
            break
        else:
            data_types[column] = 'float'
            #data_types[column] = 'int'

# This looks at how many columns there are, creates that many attributes, 
# then loops through each attribute. It sets the column index, names the attribute (c0, c1, etc.),
# sets the attribute type by referencing the data_types array created above,
# and sets the attribute length to 1 (since we are not bothering with vectors)

csv_node.parm('numattrib').set(col_count)

for j in range(col_count):
    csv_node.parm('column%i'%(j+1)).set(j)
    csv_node.parm('attribname%i'%(j+1)).set('c%i'%j)
    csv_node.parm('attribtype%i'%(j+1)).set(data_types[j])
    csv_node.parm('attriblen%i'%(j+1)).set(1)

# This section adds a new attribute on this Python node called "column_header"
# by looking at the first row in the CSV, then iterating through each column,
# creating a point and setting the points attribute value to the value in the CSV

geo.addAttrib(hou.attribType.Point, "column_header", 'test')
f.seek(0)
first_row = next(csv_reader)

for column in range(col_count):
    point = geo.createPoint()
    point.setAttribValue("column_header", first_row[column])