[Bug] - How to remove black background in Jupyter

[lrstolk]

Question:

With the dark and modern dark theme I get these black backgrounds. Currently only happens in jupyter files.
How do I remove them?

Platform & Language Details:

Python
PyCharm

PyCharm 2023.2.1 (Professional Edition)
Build #PY-232.9559.58, built on August 22, 2023
Licensed to Lorenzo Stolk
Subscription is active until April 8, 2024.
For educational use only.
Runtime version: 17.0.8+7-b1000.8 amd64
VM: OpenJDK 64-Bit Server VM by JetBrains s.r.o.
Windows 11.0
GC: G1 Young Generation, G1 Old Generation
Memory: 2048M
Cores: 12
Registry:
  editor.focus.mode.color.light=737374
  ide.editor.tab.selection.animation=true
  debugger.new.tool.window.layout=true
  ide.animate.toolwindows=true
  ide.new.editor.tabs.vertical.borders=true
  ide.experimental.ui=true
  ide.balloon.shadow.size=0
  editor.focus.mode.color.dark=737374

Non-Bundled Plugins:
  dracula-vscode (1.0.2)
  com.samdark.intellij-visual-studio-code-dark-plus (2.8)
  com.mallowigi (93.1.0)
  monokai-pro (1.10)
  com.github.dinbtechit.vscodetheme (1.10.3)
  com.chrisrm.idea.MaterialThemeUI (8.12.2)

[dinbtechit]

interesting. I am not a python developer. Are you able to provide a sample github repo that I can use for testing?

[lrstolk]

interesting. I am not a python developer. Are you able to provide a sample github repo that I can use for testing?

https://github.com/lrstolk/temp
U can download something like PyCharm, create an evironment and install the packages.

[lrstolk]

Just found out it is also on the light modern.

[dinbtechit]

A temp fix has been added in v1.10.4

[lrstolk]

I updated to 1.10.7, still got the problem. Most of it is gone.

[lrstolk]

predict = h(x_input, theta)
def grad(X, y):
return 1/m * X.transpose().dot(hyp - y)
def h(X, theta):
return X.dot(theta)
df.head(10)
df['year'].apply(convert_int)
J_history.append(J(X, y, theta))
ax.set_xlabel('size of house')
plt.ylabel('0')
pd.read_csv()
x_input = np.array([1650, 3])
x_input = featureNormalize(x_input)
ones = np.ones((1))
x_input = np.hstack((ones, x_input))
def J(X, y, theta):
m = len(y)
hyp = h(X, theta)
square_err = (hyp - y)**2
return 1/(2 * m) * np.sum(square_err)
s = df.apply(lambda x: pd.Series(x['genres']),axis=1).stack().reset_index(level=1, drop=True)

[dinbtechit]

Reopening this. I need to think of a permanent solution for this.

[dinbtechit]

sorry between personal life and my full time job. I haven't been had a chance to get back to this. I try to get this in the next couple of days or during the Christmas holidays.

[github-actions]

Stale issue message

[github-actions]

Stale issue message

[lrstolk]

@dinbtechit how are you?
Have you been able to take look at this?