TypeError: ufunc 'add' did not contain a loop

Question

I use Anaconda and gdsCAD and get an error when all packages are installed correctly. Like explained here: http://pythonhosted.org/gdsCAD/

TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')

My imports look like this (In the end I imported everything):

import numpy as np
from gdsCAD import *
import matplotlib.pyplot as plt

My example code looks like this:

something = core.Elements()
box=shapes.Box( (5,5),(1,5),0.5)
core.default_layer = 1
core.default_colors = 2
something.add(box)
something.show()

My error message looks like this:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2f90b960c1c1> in <module>()
 31 puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
 32 bp.add(puffer_wafer)
---> 33 bp.show()
 34 wafer = shapes.Circle((0.,0.), wafer_radius, wafer_line_thickness)
 35 bp.add(wafer)

C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _show(self)
 80     ax.margins(0.1)
 81 
---> 82     artists=self.artist()
 83     for a in artists:
 84         a.set_transform(a.get_transform() + ax.transData)

C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
952         art=[]
953         for p in self:
--> 954             art+=p.artist()
955         return art
956 

C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
475         poly = lines.buffer(self.width/2.)
476 
--> 477         return [descartes.PolygonPatch(poly, lw=0, **self._layer_properties(self.layer))]
478 
479 

C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _layer_properties(layer)
103         # Default colors from previous versions
104         colors = ['k', 'r', 'g', 'b', 'c', 'm', 'y']
--> 105         colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
106         color = colors[layer % len(colors)]
107         return {'color': color}

TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')

Did you ever find an answer to this? I am having a similar error with different code — humanbeing, Mar 02 '16 at 18:43

score 4 · Answer 1 · edited Jul 01 '16 at 15:00

4

The gdsCAD has been a pain from shapely install to this plotting issue.
This issue is because of wrong datatype being passed to colors function. It can be solved by editing the following line in core.py

colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))

to

colors += list(matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15)))

If you dont know where the core.py is located. Just type in:

from gdsCAD import *
core

This will give you the path of core.py file. Good luck !

edited Jul 01 '16 at 15:00

Delgan

18,571
11
90
141

answered May 19 '16 at 17:31

Amit Solanki

41
1
2

Exactly what I did and it worked for me. Thank you for putting the answer out here! – Raphael Pilz Jul 04 '16 at 12:10

score 1 · Answer 2 · answered Mar 08 '16 at 22:00

Well first, I'd ask that you please include actual code, as the 'example code' in the file is obviously different based on the traceback. When debugging, the details matter, and I need to be able to actually run the code.

You obviously have a data type problem. Chances are pretty good it's in the variables here:

puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)

I had the same error thrown when I was running a call to Pandas. I changed the data to str(data) and the code worked.

James · Answer 3 · 2016-05-03T09:13:14.720

I don't know if this helps I am fairly new to this myself, but I had a similar error and found that it is due to a type casting issue as suggested by previous answer. I can't see from the example in the question exactly what you are trying to do. Below is a small example of my issue and solution. My code is making a simple Random Forest model using scikit learn.

Here is an example that will give the error and it is caused by the third to last line, concatenating the results to write to file.

import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation

Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"

npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay =  npArray.shape

names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)

XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)

# Predictions results initialised 
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain)       # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)

with open(test_name,'a') as fpred :
    lenpredictions = len(RFpreds)
    lentrue = yTest.shape[0]
    if lenpredictions == lentrue :
            fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
            for i in range(0,lenpredictions) :
                    fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
    else :
            print "ERROR - names, prediction and true value array size mismatch."

This leads to an error of;

Traceback (most recent call last):
  File "min_example.py", line 40, in <module>
    fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')

The solution is to make each variable a str() type on the third to last line then write to file. No other changes to then code have been made from the above.

import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation

Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"

npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay =  npArray.shape

names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)

XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)

# Predictions results initialised 
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain)       # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)

with open(test_name,'a') as fpred :
    lenpredictions = len(RFpreds)
    lentrue = yTest.shape[0]
    if lenpredictions == lentrue :
            fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
            for i in range(0,lenpredictions) :
                    fpred.write(str(RFpreds[i])+",,"+str(yTest[i])+",\n")
    else :
            print "ERROR - names, prediction and true value array size mismatch."

These examples are from a larger code so I hope the examples are clear enough.

TypeError: ufunc 'add' did not contain a loop

3 Answers3

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