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PyQt5 custom widget .setStyleSheet doesnt change the style [duplicate]

I have several custom widget in my current project. I wish to apply stylesheets to them and when I do so inside Qt Creator, it appears to work. However, when executing the program, no stylesheet is used. The stylesheets for the Qt widgets are working normally.
Does anyone have any advice?
WidgetUnits.h
#ifndef WIDGETUNITS_H
#define WIDGETUNITS_H
#include <QList>
#include <QWidget>
#include <QPainter>
#include <Widgets/JECButton.h>
#include <Unit.h>
#include <Time.h>
namespace Ui
{
class WidgetUnits;
}
class WidgetUnits : public QWidget
{
Q_OBJECT
public:
explicit WidgetUnits(QWidget *parent = 0);
~WidgetUnits();
void setNumTimes(const int& numTimes);
public slots:
void updatePictures(const Time* time);
protected:
void paintEvent(QPaintEvent *event);
private:
void checkNewQueue(const QList<QList<Unit*>*>* units);
Ui::WidgetUnits *ui;
const int pictureWidth; // The width of the Unit pictures.
const int pictureHeight; // The height of the Unit pictures.
QList<QList<JECButton*>*> buttonPictures; // The Units' pictures. The outer QList stores the QList of pictures for a given tick.
// The inner QList stores the JECButtons for the specific tick.
};
WidgetUnits.cpp
#include "WidgetUnits.h"
#include "ui_WidgetUnits.h"
WidgetUnits::WidgetUnits(QWidget *parent):
QWidget(parent),
ui(new Ui::WidgetUnits),
pictureWidth(36),
pictureHeight(36)
{
ui->setupUi(this);
}
WidgetUnits::~WidgetUnits()
{
delete ui;
}
void WidgetUnits::updatePictures(const Time *time)
{
// Only showing units that started to get built this turn.
checkNewQueue(time->getUnits());
checkNewQueue(time->getBuildings());
checkNewQueue(time->getUpgrades());
// Updating the position of the remaining pictures (after some were removed).
// Checking the maximum number of Units made in one tick.
int maxNewQueue = 0;
for (int a = 0; a < buttonPictures.length(); ++a)
{
if (buttonPictures.at(a)->length() > maxNewQueue)
{
maxNewQueue = buttonPictures.at(a)->length();
}
}
if (buttonPictures.length() > 0)
{
this->setGeometry(0, 0, buttonPictures.length() * 130,
maxNewQueue * (pictureWidth + 10) + 20);
QList<JECButton*>* tickButtons = 0;
for (int a = 0; a < buttonPictures.length(); ++a)
{
tickButtons = buttonPictures.at(a);
for (int b = 0; b < tickButtons->length(); ++b)
{
tickButtons->at(b)->move(a * 130, b * (pictureHeight + 10));
}
}
}
update();
}
void WidgetUnits::checkNewQueue(const QList<QList<Unit *> *> *units)
{
if (units != 0)
{
const Unit* currentUnit = 0;
JECButton* currentButton = 0;
for (int a = 0; a < units->length(); ++a)
{
buttonPictures.append(new QList<JECButton*>());
for (int b = 0; b < units->at(a)->length(); ++b)
{
currentUnit = units->at(a)->at(b);
// Verifying that there is an item in the queue and the queue action was started this turn.
if (currentUnit->getQueue() != 0 && currentUnit->getAction()->getTimeStart() == currentUnit->getAction()->getTimeCurrent()
&& (currentUnit->getAction()->getType() == Action::BUILD || currentUnit->getAction()->getType() == Action::TRAIN ||
currentUnit->getAction()->getType() == Action::UPGRADE))
{
buttonPictures.last()->append(new JECButton(this));
currentButton = buttonPictures.last()->last();
QImage* image = new QImage(currentUnit->getQueue()->getUnitBase()->getImage().scaled(pictureWidth, pictureHeight));
currentButton->setImage(*image);
currentButton->setGeometry(0, 0, currentButton->getImage().width(),
currentButton->getImage().height());
currentButton->setColorHover(QColor(0, 0, 225));
currentButton->setColorPressed(QColor(120, 120, 120));
currentButton->setImageOwner(true);
currentButton->setVisible(true);
}
}
}
}
}
void WidgetUnits::setNumTimes(const int &numTimes)
{
// Appending new button lists for added ticks.
for (int a = buttonPictures.length(); a < numTimes; ++a)
{
buttonPictures.append(new QList<JECButton*>());
}
}
void WidgetUnits::paintEvent(QPaintEvent *event)
{
QWidget::paintEvent(event);
}
The widget is visible- I set a tooltip which it showed me (It's just the same color of the QScrollArea it's sitting in).

I had a similar problem and it was solved using jecjackal's comment. As sjwarner said, it would be much more noticeable in the form of an answer. So I'll provide it. For the benefit of any future viewers. Again, it isn't my answer! Appreciate jecjackal for it!
As it is said in the Qt's stylesheets reference, applying CSS styles to custom widgets inherited from QWidget requires reimplementing paintEvent() in that way:
void CustomWidget::paintEvent(QPaintEvent *)
{
QStyleOption opt;
opt.init(this);
QPainter p(this);
style()->drawPrimitive(QStyle::PE_Widget, &opt, &p, this);
}
Without doing it your custom widgets will support only the background, background-clip and background-origin properties.
You can read about it here: Qt Stylesheets reference in the section "List of Stylable Widgets" -> QWidget.

There is an answer much easier than writing your own paintEvent: subclass QFrame instead of QWidget and it will work right away:
class WidgetUnits : public QFrame
{
Q_OBJECT
....

For completeness, the same problem is present in PyQt. You can apply a stylesheet to a subclassed QWidget by adding similar code:
def paintEvent(self, pe):
opt = QtGui.QStyleOption()
opt.init(self)
p = QtGui.QPainter(self)
s = self.style()
s.drawPrimitive(QtGui.QStyle.PE_Widget, opt, p, self)

I had same problem with pyside. I post my solution just for completeness.
It is almost like in PyQt as Pieter-Jan Busschaert proposed.
only difference is you need to call initFrom instead of init
def paintEvent(self, evt):
super(FreeDockWidget,self).paintEvent(evt)
opt = QtGui.QStyleOption()
opt.initFrom(self)
p = QtGui.QPainter(self)
s = self.style()
s.drawPrimitive(QtGui.QStyle.PE_Widget, opt, p, self)
One other thing you need to make sure is that you define your custom widget in your css file the following way:
FreeDockWidget{...}
and not like often recommended
QDockWidget#FreeDockWidget{...}

Calling setAttribute(Qt::WA_StyledBackground, true) for the custom widget worked for me.

Setting Qt::WA_StyledBackground to true only works if you remember to add Q_OBJECT to your class. With these two changes you don't need to reimplement paintEvent.

Why do I get worse results in Java than in Python when using the same Tensorflow models?

Introduction:
For education purpose I developed a Java class that enables students to load Tensorflow models in the Tensorflow SavedModel format and use them for classification purpose in Java. For example, they can create a model online with Google's Teachable Machine, download that and use that model right in Java. This also works with many image classification models on tfhub.dev. Thereby I tried to use the new but not well documented Java API and not the deprecated old libtensorflow-API (when I understood everything correctly). As I use BlueJ for that, everything is based on pure Java code linking the required libraries directly in BlueJ's preferences after downloading them. The documentation in the Java code shows where to download the libraries.
Note: I know that "the normal way today" is using Gradle or Maven or sth. but students do not work with these tools. Another note: In the following I only use a few Code excerpts in order to simplify everything to fit into this minimum example.
Problem:
The results of all my loaded models in Java are OK but not that performant as in Python resp. the online demonstrations linked on the Tensorflow website, mainly in Jupyter notebooks. So there seems to be one step wrong in my code.
As a representative test I will now compare the performance of the MoveNet model when using Python and Java. The MoveNet model "Thunder" detects 17 keypoints of a body in an image with 256x256 pixels. I will use exactly the same image (the same file without touching and resizing it) in both setups (I uploaded it to my webspace; this step was done when updating this text, however there are no differences in the results).
Python:
The MoveNet Model comes with a nice online Python demo in a Jupyter notebook:
https://www.tensorflow.org/hub/tutorials/movenet
The code can be found here (Note: I linked to the same image as in my Java project by uploading it to my webspace and linking to it) and the classification result of the image looks like this:
Java:
My Java-based approach ends up in an image like this:
I think that it is not bad, but it isn't perfect. With other models like e.g. Google's imagenet_mobilenet model I get similar results that are ok, but I suppose they are always a bit better when running online demos in Jupyter notebooks. I do not have more evidence - only a feeling. Im some cases the same image from the online demo is recognized as a different class - but not always. I might provide more data on that later.
Assumption and work done yet:
There might be an error in the data structures or algorithms on them in my Java code. I really searched the web for some weeks now, but I am unsure if my code really is precise, mainly as there are too few examples out there. E.g., I tried to change the order of RGB or the way it is calculated in the method that converts an image into a ND array. However, I saw no significant changes. Maybe the error is anywhere else. However, probably it is just as it is. If my code works well and is correct, that it is also ok for me - but I am still wondering why there are differences. Thanks for answers!
Code:
Here is a fully working example with two classes (I know, the Frame with the Panel drawing is bad - I coded this just fast for this example)
/**
* 1. TensorFlow Core API Library: org.tensorflow -> tensorflow-core-api
* https://mvnrepository.com/artifact/org.tensorflow/tensorflow-core-api
* -> tensorflow-core-api-0.4.0.jar
*
* 2. additionally click "View All" and open:
* https://repo1.maven.org/maven2/org/tensorflow/tensorflow-core-api/0.4.0/
* Download the correct native library for your OS
* -> tensorflow-core-api-0.4.0-macosx-x86_64.jar
* -> tensorflow-core-api-0.4.0-windows-x86_64.jar
* -> tensorflow-core-api-0.4.0-linux-x86_64.jar
*
* 3. TensorFlow Framework Library: org.tensorflow -> tensorflow-framework
* https://mvnrepository.com/artifact/org.tensorflow/tensorflow-framework/0.4.0
* -> tensorflow-framework-0.4.0.jar
*
* 4. Protocol Buffers [Core]: com.google.protobuf -> protobuf-java
* https://mvnrepository.com/artifact/com.google.protobuf/protobuf-java
* -> protobuf-java-4.0.0-rc-2.jar
*
* 5. JavaCPP: org.bytedeco -> javacpp
* https://mvnrepository.com/artifact/org.bytedeco/javacpp
* -> javacpp-1.5.7.jar
*
* 6. TensorFlow NdArray Library: org.tensorflow -> ndarray
* https://mvnrepository.com/artifact/org.tensorflow/ndarray
* -> ndarray-0.3.3.jar
*/
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Tensor;
import org.tensorflow.ndarray.IntNdArray;
import org.tensorflow.ndarray.NdArrays;
import org.tensorflow.ndarray.Shape;
import org.tensorflow.types.TInt32;
import java.util.HashMap;
import java.util.Map;
import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;
import java.awt.Color;
import java.io.File;
import javax.swing.JFrame;
import javax.swing.JButton;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.BorderLayout;
public class MoveNetDemo {
private SavedModelBundle model;
private String inputLayerName;
private String outputLayerName;
private String keyName;
private BufferedImage image;
private float[][] output;
private int width;
private int height;
public MoveNetDemo(String pFoldername, int pImageWidth, int pImageHeight) {
width = pImageWidth;
height = pImageHeight;
model = SavedModelBundle.load(pFoldername, "serve");
// Read input and output layer names from file
inputLayerName = model.signatures().get(0).getInputs().keySet().toString();
outputLayerName = model.signatures().get(0).getOutputs().keySet().toString();
inputLayerName = inputLayerName.substring(1, inputLayerName.length()-1);
outputLayerName = outputLayerName.substring(1, outputLayerName.length()-1);
keyName = model.signatures().get(0).key();
}
// not necessary here
public String getModelInformation() {
String infos = "";
for (int i=0; i<model.signatures().size(); i++) {
infos += model.signatures().get(i).toString();
}
return infos;
}
public void setData(String pFilename) {
image = null;
try {
image = ImageIO.read(new File(pFilename));
}
catch (Exception e) {
}
}
public BufferedImage getData() {
return image;
}
private IntNdArray fillIntNdArray(IntNdArray pMatrix, BufferedImage pImage) {
try {
int w = pImage.getWidth();
int h = pImage.getHeight();
for (int i = 0; i < h; i++) {
for (int j = 0; j < w; j++) {
Color mycolor = new Color(pImage.getRGB(j, i));
int red = mycolor.getRed();
int green = mycolor.getGreen();
int blue = mycolor.getBlue();
pMatrix.setInt(red, 0, j, i, 0);
pMatrix.setInt(green, 0, j, i, 1);
pMatrix.setInt(blue, 0, j, i, 2);
}
}
}
catch (Exception e) {
}
return pMatrix;
}
public void run() {
Map<String, Tensor> feed_dict = null;
IntNdArray input_matrix = NdArrays.ofInts(Shape.of(1, width, height, 3));
input_matrix = fillIntNdArray(input_matrix, image);
Tensor input_tensor = TInt32.tensorOf(input_matrix);
feed_dict = new HashMap<>();
feed_dict.put(inputLayerName, input_tensor);
Map<String, Tensor> res = model.function(keyName).call(feed_dict);
Tensor output_tensor = res.get(outputLayerName);
output = new float[17][3];
for (int i= 0; i<17; i++) {
output[i][0] = output_tensor.asRawTensor().data().asFloats().getFloat(i*3)*256;
output[i][1] = output_tensor.asRawTensor().data().asFloats().getFloat(i*3+1)*256;
output[i][2] = output_tensor.asRawTensor().data().asFloats().getFloat(i*3+2);
}
}
public float[][] getOutputArray() {
return output;
}
public static void main(String[] args) {
MoveNetDemo im = new MoveNetDemo("/Users/myname/Downloads/Code/TF_Test_04_NEW/movenet_singlepose_thunder_4", 256, 256);
im.setData("/Users/myname/Downloads/Code/TF_Test_04_NEW/test.jpeg");
JFrame jf = new JFrame("TEST");
jf.setSize(300, 300);
jf.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
ImagePanel ip = new ImagePanel(im.getData());
jf.add(ip, BorderLayout.CENTER);
JButton st = new JButton("RUN");
st.addActionListener(new ActionListener() {
public void actionPerformed(ActionEvent e) {
im.run();
ip.update(im.getOutputArray());
}
});
jf.add(st, BorderLayout.NORTH);
jf.setVisible(true);
}
}
and the ImagePanel class:
import javax.swing.JPanel;
import java.awt.image.BufferedImage;
import java.awt.Graphics;
import java.awt.Color;
public class ImagePanel extends JPanel {
private BufferedImage image;
private float[][] points;
public ImagePanel(BufferedImage pImage) {
image = pImage;
}
public void update(float[][] pPoints) {
points = pPoints;
repaint();
}
#Override
protected void paintComponent(Graphics g) {
super.paintComponent(g);
g.drawImage(image, 0,0,null);
g.setColor(Color.GREEN);
if (points != null) {
for (int j=0; j<17; j++) {
g.fillOval((int)points[j][0], (int)points[j][1], 5, 5);
}
}
}
}

I found the answer. I mixed up height and width twice! No idea, why this behaves so strange (nearly correct but not perfect) but it works now.
In the Jupyter notebook it says:
input_image: A [1, height, width, 3]
so I changed the method fillIntArray to:
private IntNdArray fillIntNdArray(IntNdArray pMatrix, BufferedImage pImage) {
try {
int w = pImage.getWidth();
int h = pImage.getHeight();
for (int i = 0; i < h; i++) {
for (int j = 0; j < w; j++) {
Color mycolor = new Color(pImage.getRGB(j, i));
int red = mycolor.getRed();
int green = mycolor.getGreen();
int blue = mycolor.getBlue();
pMatrix.setInt(red, 0, i, j, 0); // switched j and i
pMatrix.setInt(green, 0, i, j, 1); // switched j and i
pMatrix.setInt(blue, 0, i, j, 2); // switched j and i
}
}
}
catch (Exception e) {
}
return pMatrix;
}
and accordingly in the run()-method:
IntNdArray input_matrix = NdArrays.ofInts(Shape.of(1, height, width, 3));
In the Jupyter notebook you can toggle the helper functions for visualization and see that at first y and then x coordinates are taken. Height first, then width. Changing this in the ImagePanel class too, solves the problem and the classification is as expected and the same quality as in the online demonstration!
if (points != null) {
for (int j=0; j<17; j++) {
// switched 0 and 1
g.fillOval((int)points[j][1], (int)points[j][0], 5, 5);
}
}
Here it is:

How to get two RichText features to be mutually exclusive

So basically I've added two custom features for coloring text to a RichTextBlock, and I'd like to make them so selecting one for a portion of text would automatically unselect the other color button, much like it's already the case for h tags.
I've searched for a bit but didn't find much, so I guess I could use some help, be it advice, instruction or even code.
My features go like this :
#hooks.register('register_rich_text_features')
def register_redtext_feature(features):
feature_name = 'redtext'
type_ = 'RED_TEXT'
tag = 'span'
control = {
'type': type_,
'label': 'Red',
'style': {'color': '#bd003f'},
}
features.register_editor_plugin(
'draftail', feature_name, draftail_features.InlineStyleFeature(control)
)
db_conversion = {
'from_database_format': {tag: InlineStyleElementHandler(type_)},
'to_database_format': {
'style_map': {
type_: {'element': tag, 'props': {'class': 'text-primary'}}
}
},
}
features.register_converter_rule(
'contentstate', feature_name, db_conversion
)
The other one is similar but color is different.

This is possible, but it requires jumping through many hoops in Wagtail. The h1…h6 tags work like this out of the box because they are block-level formatting – each block within the editor can only be of one type. Here you’re creating this RED_TEXT formatting as inline formatting ("inline style"), which, intentionally supports multiple formats being applied to the same text.
If you want to achieve this mutually exclusive implementation anyway – you’ll need to write custom JS code to auto-magically remove the desired styles from the text when attempting to add a new style.
Here is a function that does just that. It goes through all of the characters in the user’s selection, and removes the relevant styles from them:
/**
* Remove all of the COLOR_ styles from the current selection.
* This is to ensure only one COLOR_ style is applied per range of text.
* Replicated from https://github.com/thibaudcolas/draftjs-filters/blob/f997416a0c076eb6e850f13addcdebb5e52898e5/src/lib/filters/styles.js#L7,
* with additional "is the character in the selection" logic.
*/
export const filterColorStylesFromSelection = (
content: ContentState,
selection: SelectionState,
) => {
const blockMap = content.getBlockMap();
const startKey = selection.getStartKey();
const endKey = selection.getEndKey();
const startOffset = selection.getStartOffset();
const endOffset = selection.getEndOffset();
let isAfterStartKey = false;
let isAfterEndKey = false;
const blocks = blockMap.map((block) => {
const isStartBlock = block.getKey() === startKey;
const isEndBlock = block.getKey() === endKey;
isAfterStartKey = isAfterStartKey || isStartBlock;
isAfterEndKey = isAfterEndKey || isEndBlock;
const isBeforeEndKey = isEndBlock || !isAfterEndKey;
const isBlockInSelection = isAfterStartKey && isBeforeEndKey;
// Skip filtering through the block chars if out of selection.
if (!isBlockInSelection) {
return block;
}
let altered = false;
const chars = block.getCharacterList().map((char, i) => {
const isAfterStartOffset = i >= startOffset;
const isBeforeEndOffset = i < endOffset;
const isCharInSelection =
// If the selection is on a single block, the char needs to be in-between start and end offsets.
(isStartBlock &&
isEndBlock &&
isAfterStartOffset &&
isBeforeEndOffset) ||
// Start block only: after start offset
(isStartBlock && !isEndBlock && isAfterStartOffset) ||
// End block only: before end offset.
(isEndBlock && !isStartBlock && isBeforeEndOffset) ||
// Neither start nor end: just "in selection".
(isBlockInSelection && !isStartBlock && !isEndBlock);
let newChar = char;
if (isCharInSelection) {
char
.getStyle()
.filter((type) => type.startsWith("COLOR_"))
.forEach((type) => {
altered = true;
newChar = CharacterMetadata.removeStyle(newChar, type);
});
}
return newChar;
});
return altered ? block.set("characterList", chars) : block;
});
return content.merge({
blockMap: blockMap.merge(blocks),
});
};
This is taken from the Draftail ColorPicker demo, which you can see running in the Draftail Storybook’s "Custom formats" example.
To implement this kind of customisation in Draftail, you’d need to use the controls API. Unfortunately that API isn’t currently supported out of the box in Wagtail’s integration of the editor (see wagtail/wagtail#5580), so at the moment in order for this to work you’d need to customize Draftail’s initialisation within Wagtail as well.

How do I call global functions on Python objects?

I've seen this page: https://docs.python.org/3/c-api/object.html but there doesn't seem to be any way to call functions like long_lshift or long_or.
It's not essential to me to call these functions, I could also live with the more generic versions, although I'd prefer to call these. Anyways, is there any way to use these? What do I need to include? Below is some example code, where I'd like to use them (simplified):
size_t parse_varint(parse_state* state) {
int64_t value[2] = { 0, 0 };
size_t parsed = parse_varint_impl(state, value);
PyObject* low = PyLong_FromLong(value[0]);
PyObject* high;
if (value[1] > 0) {
high = PyLong_FromLong(value[1]);
PyObject* shift = PyLong_FromLong(64L);
PyObject* high_shifted = long_lshift(high, shift);
state->out = long_or(low, high_shifted);
} else {
state->out = low;
}
PyObject_Print(state->out, stdout, 0);
return 0;
}

I couldn't find these functions in documentation, but they appear to be exported in Python.h header:
PyNumber_Lshift is the replacement for long_shift in my code.
Similarly, PyNumber_Or is the replacement for long_or in my code.

fgetc causes a segfault after running the second time

I have an application that tries to read a specific key file and this can happen multiple times during the program's lifespan. Here is the function for reading the file:
__status
_read_key_file(const char * file, char ** buffer)
{
FILE * pFile = NULL;
long fsize = 0;
pFile = fopen(file, "rb");
if (pFile == NULL) {
_set_error("Could not open file: ", 1);
return _ERROR;
}
// Get the filesize
while(fgetc(pFile) != EOF) {
++fsize;
}
*buffer = (char *) malloc(sizeof(char) * (fsize + 1));
// Read the file and write it to the buffer
rewind(pFile);
size_t result = fread(*buffer, sizeof(char), fsize, pFile);
if (result != fsize) {
_set_error("Reading error", 0);
fclose(pFile);
return _ERROR;
}
fclose(pFile);
pFile = NULL;
return _OK;
}
Now the problem is that for a single open/read/close it works just fine, except when I run the function the second time - it will always segfault at this line: while(fgetc(pFile) != EOF)
Tracing with gdb, it shows that the segfault occurs deeper within the fgetc function itself.
I am a bit lost, but obviously am doing something wrong, since if I try to tell the size with fseek/ftell, I always get a 0.
Some context:
Language: C
System: Linux (Ubuntu 16 64bit)
Please ignore functions
and names with underscores as they are defined somewhere else in the
code.
Program is designed to run as a dynamic library to load in Python via ctypes
EDIT
Right, it seems there's more than meets the eye. Jean-François Fabre spawned an idea that I tested and it worked, however I am still confused to why.
Some additional context:
Suppose there's a function in C that looks something like this:
_status
init(_conn_params cp) {
_status status = _NONE;
if (!cp.pkey_data) {
_set_error("No data, open the file", 0);
if(!cp.pkey_file) {
_set_error("No public key set", 0);
return _ERROR;
}
status = _read_key_file(cp.pkey_file, &cp.pkey_data);
if (status != _OK) return status;
}
/* SOME ADDITIONAL WORK AND CHECKING DONE HERE */
return status;
}
Now in Python (using 3.5 for testing), we generate those conn_params and then call the init function:
from ctypes import *
libCtest = CDLL('./lib/lib.so')
class _conn_params(Structure):
_fields_ = [
# Some params
('pkey_file', c_char_p),
('pkey_data', c_char_p),
# Some additonal params
]
#################### PART START #################
cp = _conn_params()
cp.pkey_file = "public_key.pem".encode('utf-8')
status = libCtest.init(cp)
status = libCtest.init(cp) # Will cause a segfault
##################### PART END ###################
# However if we do
#################### PART START #################
cp = _conn_params()
cp.pkey_file = "public_key.pem".encode('utf-8')
status = libCtest.init(cp)
# And then
cp = _conn_params()
cp.pkey_file = "public_key.pem".encode('utf-8')
status = libCtest.init(cp)
##################### PART END ###################
The second PART START / PART END will not cause the segfault in this context.
Would anyone know a reason to why?