QuestionId int64 74.8M 79.8M | UserId int64 56 29.4M | QuestionTitle stringlengths 15 150 | QuestionBody stringlengths 40 40.3k | Tags stringlengths 8 101 | CreationDate stringdate 2022-12-10 09:42:47 2025-11-01 19:08:18 | AnswerCount int64 0 44 | UserExpertiseLevel int64 301 888k | UserDisplayName stringlengths 3 30 ⌀ |
|---|---|---|---|---|---|---|---|---|
76,704,696 | 5,722,359 | Why would the ttk widget only appears after there is a configuration change event and not when the button is pressed? | <p>Can you help me understand why the <code>ttk.Checkbutton</code> widget does not appear after the <code>ttk.Button</code> widget is pressed? The <code>ttk.Checkbutton</code> widget would only appear when there is a configuration change event, for example after moving the sash of the <code>ttk.PanedWindow</code> widget or by resizing the root window. Or, when the height of <code>self.f2</code> is less than the Checkbutton. This code uses a <a href="https://github.com/sunbearc22/tkinterWidgets/blob/master/scrframe.py" rel="nofollow noreferrer">vertical scroll frame</a> which is saved in the file named <code>scrframe.py</code></p>
<p>Test Code:</p>
<pre><code>import tkinter as tk
from tkinter import ttk
from PIL import Image, ImageTk
from scrframe import VerticalScrollFrame
class App(ttk.PanedWindow):
def __init__(self, parent, orient="vertical"):
super().__init__(parent, orient=orient)
self.parent = parent
self.after_id = None
self.f1 = ttk.Frame(self, style="Top.TFrame")
self.f2 = VerticalScrollFrame(self, background='green')
self.add(self.f1)
self.add(self.f2)
self.b1 = ttk.Button(self.f1, text="Button", command=self.load_image,
width=30)
self.b1.grid(row=0, column=0, sticky="nsew", padx=5, pady=5)
self.f1.bind("<Configure>", self.schedule_event)
self.f2.bind("<Configure>", self.schedule_event)
self.bind("<<SashMoved>>", self.do_something)
# Event handlers
def schedule_event(self, event):
if self.after_id:
self.after_cancel(self.after_id)
self.after_id = self.after(500, self.event_generate, "<<SashMoved>>")
def do_something(self, event):
print("do_something was called")
def load_image(self):
im = '/home/user/Pictures/image.jpeg'
im = Image.open(im)
im.thumbnail((200, 200))
self.thumbnail = ImageTk.PhotoImage(im)
self.thumbnail.image = im
self.cb = ttk.Checkbutton(self.f2.interior, text="Image",
image=self.thumbnail, compound="top")
self.cb.grid(row=0, column=0, padx=5, pady=5)
self.update_idletasks() # Does not work
if __name__ == '__main__':
root = tk.Tk()
ss = ttk.Style()
ss.configure('Top.TFrame', background="red")
app = App(root)
app.pack(fill="both", expand=True)
root.mainloop()
</code></pre>
<p>Issue: Checkbutton does not appears immediately when the height of <code>self.f2</code> is greater than the Checkbutton.</p>
<p><a href="https://i.sstatic.net/vZII7.gif" rel="nofollow noreferrer"><img src="https://i.sstatic.net/vZII7.gif" alt="Issue" /></a></p>
<p>Checkbutton appears immediately when the height of <code>self.f2</code> is less than the Checkbutton:</p>
<p><a href="https://i.sstatic.net/mJRu4.gif" rel="nofollow noreferrer"><img src="https://i.sstatic.net/mJRu4.gif" alt="Checkbutton appears immediately when the height of self.f2 is less than the Checkbutton" /></a></p>
| <python><tkinter><tcl><ttk> | 2023-07-17 12:56:05 | 1 | 8,499 | Sun Bear |
76,704,505 | 9,944,937 | Track Mouse (animal) in video using YOLO v8 trained on fiftyone.zoo dataset | <h1>The problem:</h1>
<p>I am trying to train a YOLO v8 model using a custom dataset to detect (and track) a mouse in a video but with poor results. Can you help me improve the performances of my model?</p>
<p><strong>PS: The training of the model require a quite some time, I'm asking you for tips to improve the performances so I won't waste too much time changing or optimising parameters that have little or no effect to the overall performances of the model.</strong></p>
<h2>Essential details:</h2>
<p>I'm a researcher, and I'm completely new to computer vision. I am running an experiment where I need to track a mouse's movements inside a cage from a camera (fixed angle). I am trying to train a YOLO v8 model using the fiftyone.zoo dataset "open-images-v7" however this is just my approach as a novice in the field so I'm happy to follow better suggestions:</p>
<pre class="lang-py prettyprint-override"><code>import fiftyone as fo
from ultralytics import YOLO
from pathlib import Path
from tqdm import tqdm
import shutil
# Load the FiftyOne dataset
dataset = fo.zoo.load_zoo_dataset(
"open-images-v7",
split="train",
label_types=["detections"],
classes=["Mouse"],
max_samples=100,
)
# Convert FiftyOne dataset to YOLO format
output_dir = Path("yolo_dataset")
output_dir.mkdir(exist_ok=True)
for sample in tqdm(dataset):
img_path = sample.filepath
img_filename = Path(img_path).name
yolo_labels_path = output_dir / (Path(img_filename).stem + ".txt")
with open(yolo_labels_path, "w") as f:
for detection in sample.ground_truth.detections:
if detection.label == "Mouse":
bbox = detection.bounding_box
x, y, width, height = bbox[0], bbox[1], bbox[2], bbox[3]
x_center = x + width / 2
y_center = y + height / 2
yolo_label = f"0 {x_center} {y_center} {width} {height}\n"
f.write(yolo_label)
# Copy image file to the YOLO dataset folder
shutil.copy(img_path, output_dir / img_filename)
# Load a model
model = YOLO('yolov8n.pt')
# Train the model with the YOLO dataset
model.train(data='config.yaml', epochs=100, device='mps')
# Track with the model
results = model.track(source="catmouse.mov", show=True)
</code></pre>
<p>my <code>config.yaml</code> file is:</p>
<pre class="lang-yaml prettyprint-override"><code>path: /home/path/to/code/folder
train: yolo_dataset # train images (relative to 'path')
val: yolo_dataset # val images (relative to 'path')
# Classes
names:
0: Mouse
</code></pre>
<p>as for the video <code>catmouse.mov</code> in this example is just an extract of this video from YouTube: <a href="https://youtu.be/6pbreU5ChmA" rel="nofollow noreferrer">https://youtu.be/6pbreU5ChmA</a>. Feel free to use any other video with a mouse/mice.</p>
| <python><deep-learning><computer-vision><yolo><yolov8> | 2023-07-17 12:34:44 | 1 | 1,101 | Fabio Magarelli |
76,704,445 | 7,445,658 | Difference between caching methods in python | <p>What difference is there between using the <code>@cache</code> decorator from the built-in <code>functools</code> module and the one from <code>joblib</code>?</p>
<p>Also, <a href="https://docs.python.org/3/library/functools.html#functools.cache" rel="nofollow noreferrer"><code>@functools.cache</code>'s documentation</a> mentions</p>
<blockquote>
<p>Returns the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. Because it never needs to evict old values, this is smaller and faster than lru_cache() with a size limit.</p>
</blockquote>
<p>which seems counter-intuitive to me: why would storing more values lead to a <em>smaller and faster</em> solution than <code>lru_cache()</code>?</p>
| <python><caching><joblib> | 2023-07-17 12:26:29 | 0 | 339 | alexis_thual |
76,704,151 | 1,469,954 | Service relaunch Python script when it has stalled in Linux | <p>I am trying to run a Python script as service in Linux. I found some good instructions <a href="https://websofttechs.com/tutorials/how-to-setup-python-script-autorun-in-ubuntu-18-04/" rel="nofollow noreferrer">here</a>, and how to restart a failed script in the next run <a href="https://forums.raspberrypi.com/viewtopic.php?t=324417" rel="nofollow noreferrer">here</a>.</p>
<p>However, I have another scenario, where the script does not abort with failure, but it just stalls (it is downloading some resource, and it just stays stuck at 99%). When I run it manually, I can observe it stuck for 1-2 minutes, and then I force abort the script (<code>CTRL-C</code>) and rerun and it works fine.</p>
<p>How can I make the service do that as well? I can pipe all the output of the script to a file (right now the output is being piped to <code>STDOUT</code>, where I can observe the stalling), is there a way for the service to observe that the piped output file hasn't updated in last 5 minutes, and then so that it can force restart the script, even though the script was already in running mode (but stalled)?</p>
| <python><linux><service><python-daemon> | 2023-07-17 11:49:15 | 1 | 5,353 | NedStarkOfWinterfell |
76,703,932 | 4,141,120 | Type variable ... is unbound (Python) | <p>I've realised that the following works as long as the TypeVar statement is in the same module as where it is applied:</p>
<pre><code>import pathlib
PathLike = TypeVar("PathLike", str, pathlib.Path)
</code></pre>
<p>However, when I move the TypeVar statement to another module - say customtypes - and I only use PathLike to indicate the type of the variable holding the path to my file, then this happens:</p>
<pre><code>fn: PathLike
</code></pre>
<p>Mypy: Type variable "customtypes.PathLike" is unbound
(Hint: Use "Generic[PathLike]" or "Protocol[PathLike]" base class to bind "PathLike" inside a class)
(Hint: Use "PathLike" in function signature to bind "PathLike" inside a function). I'm not using "PathLike" within a class or function. I'm not getting the hints in the first place. I don't understand the use of the keyword Generic.</p>
<p>Furthermore, typing is not able to handle the following:</p>
<pre><code>import array
from typing import NewType, TypeVar
try:
import numpy as np
ArrayLike = TypeVar("ArrayLike", array.array, np.ndarray)
except ImportError:
ArrayLike = NewType("ArrayLike", array.array)
</code></pre>
<p>The error is: Mypy: Cannot redefine "ArrayLike" as a NewType
Name "ArrayLike" already defined on line 5</p>
<p>When I try to add the definition of ArrayLike to my module customtypes, things seem to go wrong. In the module where I try to then use the new types I have this:</p>
<pre><code>import pandas as pd
from customtypes import PathLike, ArrayLike
df = pd.read_csv(fn)
result = []
for i, row in df.iterrows():
arr: ArrayLike = row.to_numpy(dtype="float32")
result.append(arr)
</code></pre>
<p>Mypy: Incompatible types in assignment (expression has type "ndarray[Any, Any]", variable has type "array[Any]"). I guess that MyPy is programmed to assume that ndarray is a multidimensional array. How can I indicate to MyPy that ndarray is a flat array?</p>
| <python><arrays><mypy><typing> | 2023-07-17 11:19:13 | 0 | 588 | Dobedani |
76,703,900 | 5,269,892 | Python shell script cannot be executed with source command | <p>I want to call a bash script from a python script using <code>subprocess.Popen()</code>. Calling the shell script as an executable works, but <code>source</code>ing it does not. Why?</p>
<p>File <em><strong>test_python.py</strong></em>:</p>
<pre><code>import sys
import os
import subprocess
os.putenv("testvar", "testvalue")
test = subprocess.Popen("./test_shell.sh", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
print(test)
test = subprocess.Popen(". test_shell.sh", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
print(test)
test = subprocess.Popen("source test_shell.sh", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
print(test)
test = subprocess.Popen("/bin/bash test_shell.sh", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
print(test)
test = subprocess.Popen("/bin/sh test_shell.sh", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
print(test)
</code></pre>
<p>File <em><strong>test_shell.sh</strong></em>:</p>
<pre><code>#!/bin/bash
echo "$testvar"
</code></pre>
<p>Output of <code>python test_python.py</code>:</p>
<pre><code>('testvalue\n', '')
('', '/bin/sh: .: test_shell.sh: cannot open [No such file or directory]\n')
('', '/bin/sh: .: test_shell.sh: cannot open [No such file or directory]\n')
('testvalue\n', '')
('testvalue\n', '')
</code></pre>
| <python><bash><shell><subprocess> | 2023-07-17 11:14:27 | 1 | 1,314 | silence_of_the_lambdas |
76,703,878 | 583,464 | swap columns from multidimensional array | <p>I have this array:</p>
<pre><code>my_array = np.arange(1216800).reshape(2, 100, 78, 78)
</code></pre>
<p>The shape now is: <code>(2, 100, 78, 78)</code> and I want to reorder to : <code>(100, 78, 78, 2)</code>.</p>
<p>I tried something like:</p>
<pre><code>my_array[:, :, 2, :], my_array[:, :, :, 3] = my_array[:, :, :, 3], my_array[:, :, 2, :].copy()
</code></pre>
<p>to swap first those columns, but I am receiving the same array.</p>
<p>I saw <a href="https://stackoverflow.com/questions/4857927/swapping-columns-in-a-numpy-array">this</a> but whatever I try, I am having the same array.</p>
| <python><numpy> | 2023-07-17 11:11:57 | 2 | 5,751 | George |
76,703,598 | 14,282,714 | Add slide numbers to Jupyter Notebook Slides | <p>I would like to add slide numbers to a Jupyter Notebook slideshow. I found an <a href="https://github.com/jupyter/nbconvert/issues/737" rel="nofollow noreferrer">issue</a> about this, but I am not sure how and if it possible to add slide numbers to your slides in Jupyter Notebook slideshow. Here is some reproducible code to make a slide show:</p>
<pre><code># Jupyter Notebook Slides
I would like to add a slide number here?
</code></pre>
<p>Using the command in the terminal:</p>
<pre><code>jupyter nbconvert test_slides.ipynb --to slides --post serve
</code></pre>
<p>So I was wondering if anyone knows how to add a page number to your slides in a Jupyter Notebook Slideshow?</p>
| <python><jupyter-notebook><jupyter><slideshow><page-numbering> | 2023-07-17 10:32:12 | 1 | 42,724 | Quinten |
76,703,551 | 2,690,578 | Statsforecast for python seems to predict values "one day ahead" | <p>I have been trying Statsforecast for Python now for a couple of weeks. It seems really good, however I noticed that my predictions always feels a bit off by one day. If you look at the first image, you can see how the graph almost matches with a difference of a day. If I displace the values one day, you can see that the graphs matches much better, however I lose the last day, 31st on this case.</p>
<p>I feel like I am missing something when I am training the model. Has anyone experienced this?</p>
<p><a href="https://i.sstatic.net/xtyjp.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/xtyjp.png" alt="initial predictions" /></a></p>
<p><a href="https://i.sstatic.net/KQfTG.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/KQfTG.png" alt="predictions displaced by -1" /></a></p>
| <python><time-series><statsforecast> | 2023-07-17 10:26:42 | 1 | 609 | Gabriel |
76,703,308 | 2,138,913 | Qt: Replacing deprecated pos() methods | <p>I have a simple app (a piano keyboard which receives midi and shows which keys are pressed) with a frameless window that I can drag around with the left mouse button.</p>
<p>This works:</p>
<pre class="lang-py prettyprint-override"><code> def mousePressEvent(self,e):
modifiers = e.modifiers()
if modifiers == Qt.ControlModifier:
app.quit()
else:
self.mpos = e.pos()
def mouseMoveEvent(self,e):
buttons = e.buttons()
if buttons == Qt.LeftButton:
dpos = e.pos() - self.mpos
newpos = self.pos() + dpos
self.move(newpos)
</code></pre>
<p>But <code>pos()</code> is deprecated and I can't figure out what to replace it with.</p>
| <python><qt> | 2023-07-17 09:56:17 | 1 | 1,292 | John Allsup |
76,703,233 | 12,131,472 | how to move dataframe's one column names(hierarchical) to index? | <p>I am sorry this seems a basic manipulation but I can't figure it out after searching. The title is probably not accurate.</p>
<p>I have this dataframe</p>
<pre><code>Bunker Port Rotterdam Singapore ... Rotterdam Singapore
bunker HSFO HSFO ... VLSFO VLSFO
period ...
Jul 23 453.318000 461.028000 ... 518.098000 553.426000
Aug 23 448.266000 454.016000 ... 513.596000 549.716000
</code></pre>
<p>There are 2 levels for the column names: 'Bunker Port' and 'bunker'</p>
<p>and I wish to convert the df to below(the 0-4 index is unecessary):</p>
<pre><code> bunker HSFO MGO VLSFO
0 period Bunker Port NaN NaN NaN
1 Jul 23 Rotterdam 453.318 723.83300 518.098
2 Aug 23 Rotterdam 448.266 735.00000 513.596
3 Jul 23 Singapore 461.028 734.04850 553.426
4 Aug 23 Singapore 454.016 738.74945 549.716
</code></pre>
<p>Thanks a lot</p>
| <python><pandas><dataframe> | 2023-07-17 09:47:01 | 1 | 447 | neutralname |
76,703,126 | 8,329,213 | Selecting all rows which contain values greater than a percentage of Average | <p>I have a <code>DataFrame</code>, which have 3 numeric columns <code>A,B,C</code>. I need to extract only those rows where values in all these 3 columns <code>A,B,C</code> is more than 40% of their <strong>row average</strong>.</p>
<pre><code>df = pd.DataFrame([['AA',10,8,12],['BB',10,2,18],['CC',10,6,14]],
columns=['ID','A', 'B', 'C'])
print(df)
ID A B C
0 AA 10 8 12
1 BB 10 2 18
2 CC 10 6 14
</code></pre>
<p>I mean the following: For Row 1, the mean of A,B,C is 30/3=10, and I want that in Row 1 all values, be it A or B or C should be more than 40% of 10, i.e; 4. Similarly, for Row 2 and Row 3. In case even one element is less than that, we remove that row.</p>
<p><strong>My attempt:</strong> I used <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.any.html" rel="nofollow noreferrer"><code>any()</code></a> function, but that does't help me when involving average of columns. I always get empty <code>DF</code>.</p>
<pre><code>df = df[(df[['A','B','C']] > (0.4*df[['A','B','C']].mean(axis=1))).all(1)]
print(df)
ID A B C
</code></pre>
<p>I was expecting this:</p>
<pre><code> ID A B C
0 AA 10 8 12
2 CC 10 6 14
</code></pre>
<p>The average of all rows is 10, so if I would have hardcoded it, it would work, like this:</p>
<pre><code>df[(df[['A','B','C']] > 0.4*10).all(1)]
</code></pre>
<p>How can I do this dynamically?
Thanks.</p>
| <python><pandas><dataframe> | 2023-07-17 09:32:10 | 2 | 7,707 | cph_sto |
76,702,936 | 348,168 | Replace $..$ within a string with \(...\) in Python | <p>I have a string here 'Let $[C | d]$ be $Star$'.</p>
<p>How to replace $..$ within the above string with \(...\).</p>
<p>The result should be 'Let \([C | d]\) be \(Star\)'.</p>
<p>How can I do it python.</p>
<p>I have tried as</p>
<pre><code>import re
x = 'Let $[C | d]$ be $Star$'
y = re.sub(r'\$.*?\$', r'\\(.*?\\)', x)
</code></pre>
<p>but not working.</p>
<p>the output was 'Let \(.<em>?\) be \(.</em>?\)'</p>
<p>Ref : <a href="https://stackoverflow.com/questions/1454913/regular-expression-to-find-a-string-included-between-two-characters-while-exclud">Regular Expression to find a string included between two characters while EXCLUDING the delimiters</a></p>
| <python><regex> | 2023-07-17 09:06:12 | 1 | 4,378 | Vinod |
76,702,921 | 2,583,670 | Forming n-grams for a dataframe (numbers) in python | <p>I found many posts explaining <strong>n-grams</strong> in words. However, I need to apply n-grams (n = 2 or 3) on a dataframe that has integer numbers of n x m. For example: Consider the below dataframe (3 x 5)</p>
<pre><code>df =
1, 2, 3, 4, 5
6, 7, 8, 9, 10
11, 12, 13, 14, 15
</code></pre>
<p>I need to apply <strong>bigram</strong> and <strong>trigram</strong> on df.</p>
<p>I tried this code, but it does not work properly</p>
<pre><code>for i in range(df.shape[0]):
row = list(str(df.iloc[i,:]))
print("row: ", row)
bigrams = [b for l in row for b in zip(l.split(" ")[:-1], l.split(" ")[1:])]
print(bigrams)
</code></pre>
<p>If the input is <code>df = [10,20,30,40,50,60,...]</code></p>
<p>Expected output</p>
<p>Bigram</p>
<p><code>(10,20)(20,30)(30,40)(40,50)...</code></p>
<p>Trigram</p>
<p><code>(10,20,30)(20,30,40)(30,40,50)...</code></p>
| <python><pandas><n-gram> | 2023-07-17 09:04:09 | 1 | 711 | Mohsen Ali |
76,702,919 | 19,155,645 | access function from a different subfolder in python project | <p>I have a python project with the following structure:</p>
<pre><code>project_folder/
__init__.py
subfolder/
__init__.py
main.py
generate_values.py
lib/
__init__.py
external.py
</code></pre>
<p>where I am running <code>subfolder/main.py</code>, and it internally calls <code>generate_values.py</code>.<br>
Inside <code>generate_values.py</code> I am calling the function calculate_external from <code>lib/external.py</code>.</p>
<p>The original code was:
<code>from lib.external import calculate_external</code><br>
this was working when the main and generate_values py files were not in a subfolder.</p>
<p>but now I get the error
<code>ModuleNotFoundError: No module named 'lib'</code></p>
<p>after some research i tried the following but still got errors:</p>
<ol>
<li><code>from project_folder.lib.external import calculate_external</code> --><br>
<code>ModuleNotFoundError: No module named 'project_folder'</code></li>
<li><code>from ..lib.external import calculate_external</code> --><br>
<code>ImportError: attempted relative import with no known parent package</code></li>
</ol>
<p>Any help in resolving this would be great. <br></p>
<p>Note that the main and generate_values must stay in a subfolder, because there will be different such subfolders that do different things but all use the lib folder.</p>
| <python> | 2023-07-17 09:03:59 | 1 | 512 | ArieAI |
76,702,772 | 6,546,694 | Why does pandas fail to join on two columns of object dtype (one of them is converted from int to object)? | <p>The following merge strategy fails:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
data1 = {'c1': ['J', 'A', 'B'],
'key': [25, 30, 35]}
df1 = pd.DataFrame(data1)
data2 = {'c2': ['A', 'B', 'C'],
'key': ["25","30","36"]}
df2 = pd.DataFrame(data2, dtype="O")
df1.key = df1.key.astype("O")
print(df1.merge(df2, on = "key"))
output:
Empty DataFrame
Columns: [c1, key, c2]
Index: []
</code></pre>
<p>Why is pandas failing in this merge? I can convert the column to string <code>dtype</code> as follows and then back to <code>object</code> and it works:</p>
<pre><code>df1.key = df1.key.astype(str).astype("O")
</code></pre>
<p>Now the merge is okay and finds the matches. How should I understand this behavior?</p>
| <python><pandas><dataframe><numpy> | 2023-07-17 08:43:30 | 1 | 5,871 | figs_and_nuts |
76,702,709 | 6,220,759 | Why is exporting a query result via SQLite command line shell so slow? | <p>To export the result of a query (~45 million records) to a CSV file I used the command line shell:</p>
<pre><code>$ sqlite3 db.db3
> .headers on
> .mode csv
> .once result.csv
> select .....
</code></pre>
<p>This took about 9 hours to run. I then used Python:</p>
<pre><code>import sqlite3
import pandas as pd
conn = sqlite3.connect('db.db3')
df = pd.read_sql(query, conn)
df.to_csv('output.csv')
</code></pre>
<p>This took about 20 minutes. I understand why Python might be a little bit faster but did not expect such a huge difference. Why is the SQLite command line shell so slow?</p>
| <python><pandas><performance><sqlite><export> | 2023-07-17 08:32:54 | 1 | 11,757 | Josh Friedlander |
76,702,630 | 14,082,033 | Error during training: layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 | <p>I am training a segmentation model using TensorFlow, and I encountered an error during the training process. After approximately 6 seconds, the training stopped with the following error message:</p>
<pre><code>Epoch 1/100
2023-07-17 08:14:20.618828: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inmodel_3/dropout_15/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer
8278/8278 [==============================] - 6s 198us/step - loss: 2.1831 - accuracy: 0.8421 - val_loss: 2.2880 - val_accuracy: 0.8349
</code></pre>
<p>I am using a custom data generator (<code>DataGen</code>) to load and preprocess the input images and masks. The error seems to be related to the layout of the model, particularly the <code>dropout</code> layer. I'm not sure why the sizes of the values do not match the permutation size. I think it might be related to Data Generator.</p>
<p>I have included the relevant code snippets below:</p>
<pre class="lang-py prettyprint-override"><code># Data generator
class DataGen(tf.keras.utils.Sequence):
def __init__(self, path_input, path_mask, class_name='person', batch_size=8, image_size=128):
self.ids = os.listdir(path_mask)
self.path_input = path_input
self.path_mask = path_mask
self.class_name = class_name
self.batch_size = batch_size
self.image_size = image_size
self.on_epoch_end()
def __load__(self, id_name):
image_path = os.path.join(self.path_input, id_name)
mask_path = os.path.join(self.path_mask, id_name)
image = cv2.imread(image_path, 1) # 1 specifies RGB format
image = cv2.resize(image, (self.image_size, self.image_size)) # resizing before inserting into the network
mask = cv2.imread(mask_path, -1)
mask = cv2.resize(mask, (self.image_size, self.image_size))
mask = mask.reshape((self.image_size, self.image_size, 1))
# normalize image
image = image / 255.0
mask = mask / 255.0
return image, mask
def __getitem__(self, index):
id_name = self.ids[index]
image, mask = self.__load__(id_name)
if image is not None and mask is not None:
images = np.expand_dims(image, axis=0)
masks = np.expand_dims(mask, axis=0)
else:
images = np.empty((self.image_size, self.image_size, 3))
masks = np.empty((self.image_size, self.image_size, 1))
return images, masks
def on_epoch_end(self):
pass
def __len__(self):
return len(self.ids)
# Configure model
image_size = 128
epochs = 100
batch_size = 10
# Create data generators
train_gen = DataGen(path_input="/kaggle/input/coco-2014-dataset-for-yolov3/coco2014/images/train2014",
path_mask="/kaggle/working/mask_train_2014",
batch_size=batch_size,
image_size=image_size)
val_gen = DataGen(path_input="/kaggle/input/coco-2014-dataset-for-yolov3/coco2014/images/val2014",
path_mask="/kaggle/working/mask_val_2014",
batch_size=batch_size,
image_size=image_size)
# Define model architecture
inputs = Input(shape=(128, 128, 3))
# ...
# Compile and train the model
optimizer = tf.keras.optimizers.Adam(lr=1e-4)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_gen, validation_data=val_gen, steps_per_epoch=train_steps, epochs=epochs)
</code></pre>
<p>Any insights or suggestions on how to resolve this issue would be greatly appreciated.</p>
<p>I am using coco2014 dataset.
tf version '2.12.0'</p>
| <python><tensorflow><keras><deep-learning> | 2023-07-17 08:19:56 | 1 | 331 | Arman Asgharpoor |
76,702,574 | 726,730 | Python - PyQt5 style checkable QGroupBox (Fusion) like WindowsVista | <p>Python code:</p>
<pre><code># -*- coding: utf-8 -*-
# Form implementation generated from reading ui file '.\custom_qgroubox.ui'
#
# Created by: PyQt5 UI code generator 5.15.9
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(474, 223)
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.gridLayout_2 = QtWidgets.QGridLayout(self.centralwidget)
self.gridLayout_2.setObjectName("gridLayout_2")
self.groupBox = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox.setStyleSheet("QGroupBox{\n"
" border:1px solid #dadada;\n"
" font-size:50px;\n"
" margin-top:26px;\n"
" padding-top:35px;\n"
"}\n"
"\n"
"QGroupBox::title{\n"
" subcontrol-origin:margin;\n"
" subcontrol-position: top left;\n"
" background:green;\n"
" left:10px;\n"
" top:0px;\n"
" padding:0px;\n"
" spacing:0px;\n"
"}")
self.groupBox.setCheckable(True)
self.groupBox.setObjectName("groupBox")
self.gridLayout = QtWidgets.QGridLayout(self.groupBox)
self.gridLayout.setObjectName("gridLayout")
self.pushButton = QtWidgets.QPushButton(self.groupBox)
self.pushButton.setObjectName("pushButton")
self.gridLayout.addWidget(self.pushButton, 0, 0, 1, 1)
self.pushButton_2 = QtWidgets.QPushButton(self.groupBox)
self.pushButton_2.setObjectName("pushButton_2")
self.gridLayout.addWidget(self.pushButton_2, 1, 0, 1, 1)
self.pushButton_3 = QtWidgets.QPushButton(self.groupBox)
self.pushButton_3.setObjectName("pushButton_3")
self.gridLayout.addWidget(self.pushButton_3, 2, 0, 1, 1)
self.gridLayout_2.addWidget(self.groupBox, 0, 0, 1, 1)
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 474, 22))
self.menubar.setObjectName("menubar")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.retranslateUi(MainWindow)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow"))
self.groupBox.setTitle(_translate("MainWindow", "GroupBox"))
self.pushButton.setText(_translate("MainWindow", "PushButton"))
self.pushButton_2.setText(_translate("MainWindow", "PushButton"))
self.pushButton_3.setText(_translate("MainWindow", "PushButton"))
if __name__ == "__main__":
import sys
app = QtWidgets.QApplication(sys.argv)
app.setStyle("Fusion")
MainWindow = QtWidgets.QMainWindow()
ui = Ui_MainWindow()
ui.setupUi(MainWindow)
MainWindow.show()
sys.exit(app.exec_())
</code></pre>
<p>Output:</p>
<p><a href="https://i.sstatic.net/M9IcL.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/M9IcL.png" alt="enter image description here" /></a></p>
<p>In the green area i want to control (set/get):</p>
<ol>
<li>The space between the indicator and the groupbox title</li>
<li>The space between the groupbox title and the top of green area</li>
<li>The space between the right side of groupbox title and the end of right side of green area.</li>
</ol>
<p>For 1. i want it to be 10px, for 2. i want it to be 0px and for 3. i also want it to be 0px.</p>
<p>Is that possible?</p>
<p>Thanks.</p>
| <python><css><pyqt5><qtstylesheets><qgroupbox> | 2023-07-17 08:11:24 | 1 | 2,427 | Chris P |
76,702,453 | 1,153,290 | Django 4 backport old Oracle 12 support | <p>Django 4.0 release notes
<a href="https://docs.djangoproject.com/en/4.2/releases/4.0/#dropped-support-for-oracle-12-2-and-18c" rel="nofollow noreferrer">https://docs.djangoproject.com/en/4.2/releases/4.0/#dropped-support-for-oracle-12-2-and-18c</a></p>
<p>says:</p>
<blockquote>
<p>Dropped support for Oracle 12.2 and 18c¶</p>
<p>Upstream support for Oracle 12.2 ends in March 2022 and for Oracle 18c
it ends in June 2021. Django 3.2 will be supported until April 2024.
Django 4.0 officially supports Oracle 19c.</p>
</blockquote>
<p>So, what if I will use this simple backend for continue to use oracle 12 as secondary data sourse <strong>just for select</strong> from it, is it may cause any problems?</p>
<pre><code>from django.db.backends.oracle.base import DatabaseWrapper as DatabaseWrapperOracle
from django.db.utils import NotSupportedError
class DatabaseWrapper(DatabaseWrapperOracle):
def check_database_version_supported(self):
try:
super().check_database_version_supported()
except NotSupportedError as e:
pass # Oracle 19 or later is required (found 12.1.0.2.0).
</code></pre>
<p>Please share your experience</p>
<p>*DatabaseWrapper seems works fine</p>
| <python><django><oracle-database> | 2023-07-17 07:53:28 | 1 | 6,840 | Vladimir |
76,702,391 | 5,269,892 | Transferring variables between python and python or shell scripts | <p>From a main python script, here <code>main.py</code>, I would like to call multiple subscripts, which are either python scripts themselves (here <code>sub1.py</code> for simplicity) or bash/shell scripts (here <code>sub2.sh</code> for simplicity; these are called via <code>subprocess.Popen()</code>). The subscripts need several variables from <code>main.py</code>; and conversely <code>main.py</code> may need variables set in the subscripts. Therefore variables need to be transferred between python-python (<code>main.py</code> <--> <code>sub1.py</code>) and python-shell (<code>main.py</code> <--> <code>sub2.sh</code>).</p>
<p>Some ways I know:</p>
<ul>
<li><code>main.py</code> --> <code>sub1.py</code>: call <code>sub1.py</code> from <code>main.py</code> via <code>os.system()</code> or
<code>subprocess.Popen()</code>, pass variables as script arguments, retrieve from e.g. <code>sys.argv[1]</code> in <code>sub1.py</code></li>
<li><code>main.py</code> --> <code>sub1.py</code>: import <code>sub1.py</code> into <code>main.py</code>; variables from <code>main.py</code> must be set to <code>sub1.py</code> namespace, else not recognized by <code>sub1.py</code>; if there's non-function/-class code in <code>sub1.py</code> (i.e. "global" script code, is it executed properly as well)?</li>
<li><code>main.py</code> --> <code>sub2.sh</code>: call <code>sub2.sh</code> from <code>main.py</code> via
<code>os.system()</code> or <code>subprocess.Popen()</code>, pass variables as script
arguments, retrieve from e.g. <code>$1</code> in <code>sub2.sh</code></li>
<li>all directions / script types: store variables in some file from one script in whichever format (e.g. .txt, .dat, .pickle (for python-python transfer)), read variables from file in the other script</li>
<li>all directions / script types: store variables as environment variables from one script (in the <code>os.environ[]</code> dictionary), extract from there as needed within the other script</li>
</ul>
<p><strong>Question: What is the <em>most convenient</em> method to transfer variables (share info) between python and python or between python and shell scripts?</strong></p>
<p>Background of the question: <code>main.py</code> is a python translation of a previous shell script <code>main.sh</code>. In <code>main.sh</code>, any subscript <code>subX.sh</code> (there were fewer python scripts at that point) was called via <code>source subX.sh</code>, such that variables from <code>main.sh</code> and <code>subX.sh</code> were always known to the respective other scripts (as <code>source</code> runs the subscript in the same shell).</p>
<p>The python version used is <code>2.7.18</code> (compatibility reasons; I am not allowed to install a newer interpreter).</p>
| <python><bash><shell><global-variables> | 2023-07-17 07:43:24 | 1 | 1,314 | silence_of_the_lambdas |
76,702,390 | 10,666,991 | CPU Out of memory when training a model with pytorch lightning | <p>I am trying to train a BERT model on my data using the <code>Trainer</code> class from <code>pytorch-lightning</code>. However, I encountered an out-of-memory exception in the <strong>CPU memory</strong>.</p>
<p>Here is the code:</p>
<pre><code>from transformers.data.data_collator import DataCollatorForLanguageModeling
from datasets import load_dataset
dataset = load_dataset('text', data_path) # The dataset is only 33GB, while my GPU has 350GB
of RAM.
class BertDataModule(pl.LightningDataModule):
def __init__(self, dataset, train_split, batch_size, data_collator):
super().__init__()
self.train_dataset = None
self.dataset = dataset
self.collator = data_collator
self.batch_size = batch_size
self.train_split = train_split
def train_dataloader(self):
return torch.utils.data.DataLoader(self.train_dataset, batch_size=self.batch_size,
collate_fn=self.collator, num_workers=30)
# I defined the similar methods for val_dataloader, test_dataloader, and predict_dataloader.
bert_collator = DataCollatorForLanguageModeling(tokenizer=bert_tokenizer)
bert_data_module = BertDataModule(dataset=my_dataset['train'], train_split=0.98,
batch_size=32, data_collator=bert_collator)
bert_model = BertModel(...)
trainer = pl.Trainer(devices=1, max_epochs=50, logger=comment_logger, accelerator='gpu',
precision=16, val_check_interval=50000, callbacks=[checkpoint_callbacks])
trainer.fit(bert_model, datamodule=bert_data_module) # Crash due to CPU OOM here
</code></pre>
<p>My code crashed due to an out-of-memory (OOM) error, even though my data is only 33GB and my CPU memory in the OpenShift pod is 350GB.</p>
<p>Do you have any idea what could be causing the memory of the CPU to continue increasing during training?</p>
<p>Thank you very much.</p>
| <python><pytorch><bert-language-model><pytorch-lightning><huggingface-datasets> | 2023-07-17 07:43:11 | 1 | 781 | Ofir |
76,702,263 | 9,636,225 | making menu and sub-menu python | <p>I'm learning to make menus in python using the statemachine module,</p>
<p>here is the code i ran and it works fine</p>
<pre><code>from statemachine import State, StateMachine
class MenuStateMachine(StateMachine):
# States
main_menu = State('Main Menu', initial=True)
menu1 = State('Option1')
menu2 = State('Option2')
submenu1 = State('Sub-Menu1')
# Transitions
select_menu1 = main_menu.to(menu1)
select_menu2 = main_menu.to(menu2)
select_submenu1 = menu1.to(submenu1)
back_to_main_menu = menu1.to(main_menu) | menu2.to(main_menu)
back_to_menu1 = submenu1.to(menu1)
def on_main_menu(self):
print('=== Main Menu ===')
print('[1] Option1')
print('[2] Option2')
print('[3] Exit')
def on_menu1(self):
print('=== Option1 Menu ===')
print('[1] SubOption1')
print('[2] SubOption2')
print('[3] SubOption3')
print('[4] Previous Menu')
def on_submenu1(self):
print('=== SubOption1 Menu ===')
print('[1] Act1')
print('[2] Act2')
print('[3] Act3')
print('[4] Previous Menu')
def on_menu2(self):
print('=== Option2 Menu ===')
print('[1] Previous Menu')
# Create an instance of the MenuStateMachine class
menu = MenuStateMachine()
# Start the menu state machine
while True:
menu.on_main_menu()
choice = input('Enter Choice: ')
if choice == '1':
menu.select_menu1()
while menu.current_state.id == 'menu1':
menu.on_menu1()
menu_choice = input('Enter Choice : ')
if menu_choice == '1':
menu.select_submenu1()
while menu.current_state.id == 'submenu1':
menu.on_submenu1()
submenu_choice = input('Enter Choice : ')
if submenu_choice == '1':
print('Do Something Here')
break
elif submenu_choice == '2':
print('Do Something Here')
break
elif submenu_choice == '3':
print('Do Something Here')
break
elif submenu_choice == '4':
menu.back_to_menu1()
else:
print('Invalid choice. Please try again.')
elif menu_choice == '4':
menu.back_to_main_menu()
elif menu_choice in ['2','3']:
print('There is no menu yet, please choose another')
else:
print('Invalid choice. Please try again.')
elif choice == '2':
menu.select_menu2()
menu.on_menu2()
menu_choice = input('Enter Choice : ')
if menu_choice == '1':
menu.back_to_main_menu()
else:
print('Invalid choice. Please try again.')
elif choice == '3':
print('=== Exit ===')
exit()
else:
print("Invalid choice. Please try again.")
</code></pre>
<p>but I imagine that my code can be improved to be better, I think my code is not efficient especially the use of if and while. If there are a lot of sub menus or sub-sub menus will look messy, and I think this can be improved even better, any suggestions to improve my code?</p>
| <python><if-statement><state-machine> | 2023-07-17 07:23:32 | 0 | 307 | ExHunter |
76,702,207 | 15,320,579 | Two level sorting in a nested tuple of nested lists in Python | <p>I have a deeply nested tuple of nested lists as follows:</p>
<pre><code>ip = (array([[[ 50, 73]],
[[ 50, 107]],
[[ 55, 108]],
[[ 55, 121]],
[[978, 87]],
[[977, 86]],
[[977, 73]]], dtype=int32),
array([[[ 669, 3]],
[[ 668, 4]],
[[ 667, 4]],
[[1033, 71]],
[[1035, 69]],
[[1035, 4]],
[[ 848, 4]],
[[ 847, 3]],
[[ 813, 3]],
[[ 718, 4]],
[[ 717, 3]]], dtype=int32),
array([[[ 17, 3]],
[[ 16, 4]],
[[ 0, 4]],
[[ 0, 49]],
[[197, 49]],
[[197, 8]],
[[ 84, 4]],
[[ 83, 3]]], dtype=int32))
</code></pre>
<p>The length of the main tuple in above example is 3. I want to <strong>perform a 2 level sorting</strong> on the above structure. First I want to <strong>sort all the 3 elements in the main list in increasing order based on the first value of nested list</strong>. So in the above case the <strong>third element</strong> will come first as it has the <strong>lowest value</strong> of the first element i.e. <code>0</code>. Second should be the <strong>first element</strong> as it has the <strong>second lowest value</strong> of <code>50</code>and last should be the <strong>third element</strong> as it has the <strong>third lowest value</strong> of <code>1035</code>. The output of the first level sorting should be:</p>
<pre><code>op = (array([[[ 17, 3]],
[[ 16, 4]],
[[ 0, 4]],
[[ 0, 49]],
[[197, 49]],
[[197, 8]],
[[ 84, 4]],
[[ 83, 3]]], dtype=int32),
array([[[ 50, 73]],
[[ 50, 107]],
[[ 55, 108]],
[[ 55, 121]],
[[978, 87]],
[[977, 86]],
[[977, 73]]], dtype=int32),
array([[[ 669, 3]],
[[ 668, 4]],
[[ 667, 4]],
[[1033, 71]],
[[1035, 69]],
[[1035, 4]],
[[ 848, 4]],
[[ 847, 3]],
[[ 813, 3]],
[[ 718, 4]],
[[ 717, 3]]], dtype=int32),
)
</code></pre>
<p>Now I want to perform the same sorting again on the above <code>op</code> but instead of the first value of the nested list I want to <strong>sort based on the second value</strong> of the nested list. So now the final output would be as follows:</p>
<pre><code>final_op = (array([[[ 17, 3]],
[[ 16, 4]],
[[ 0, 4]],
[[ 0, 49]],
[[197, 49]],
[[197, 8]],
[[ 84, 4]],
[[ 83, 3]]], dtype=int32),
array([[[ 669, 3]],
[[ 668, 4]],
[[ 667, 4]],
[[1033, 71]],
[[1035, 69]],
[[1035, 4]],
[[ 848, 4]],
[[ 847, 3]],
[[ 813, 3]],
[[ 718, 4]],
[[ 717, 3]]], dtype=int32),
array([[[ 50, 73]],
[[ 50, 107]],
[[ 55, 108]],
[[ 55, 121]],
[[978, 87]],
[[977, 86]],
[[977, 73]]], dtype=int32)
)
</code></pre>
<p>Any help is appreciated!</p>
<p>Thanks in advance!</p>
| <python><python-3.x><list><sorting><tuples> | 2023-07-17 07:13:06 | 1 | 787 | spectre |
76,702,040 | 1,142,881 | Can I chain where clauses conditionally? | <p>I'm using Peewee as ORM extensively and within my DAO API layer I need to conditionally make a query narrower e.g.</p>
<pre><code>query = UserModel.select().where(UserModel.last_name == last_name)
if first_name is not None:
query = query.where(UserModel.first_name == first_name)
# ... more conditions then
df = pd.DataFrame(query.dicts())
</code></pre>
<p>is this the most idiomatic way to conditionally make the queries narrower in Peewee or is there another way? Are there any pros and cons of doing this?</p>
| <python><orm><peewee> | 2023-07-17 06:44:38 | 2 | 14,469 | SkyWalker |
76,701,955 | 4,260,959 | How can I draw a contour for a piecewise function with pyplot.imshow | <p>My function is <code>z = (x - 1) **10 + 5*(x - 1)**5*(y - 1)**5 + (y - 1)**10</code>. However, the contour can't show clear about negative value because of large positive value.</p>
<p>So I just need to show positive value by logarithm (<code>np.log(1 + 0)</code> if <code>z < 0</code>), but keep negative as origin.</p>
<p>How can I do this with pyplot.imshow?</p>
<p>This code doesn't work:</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
def z(x, y):
z0 = (x - 1) **10 + 5*(x - 1)**5*(y - 1)**5 + (y - 1)**10
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
return z0 if z0 < 0 else np.log(1 + z0)
def main():
x, y = np.linspace(0.0, 3.0, 300), np.linspace(0.0, 3.0, 300)
X, Y = np.meshgrid(x, y)
plt.imshow(z(X, Y), origin='lower', extent = [0, 3, 0, 3], cmap=plt.cm.hsv)
plt.colorbar()
plt.show()
if __name__ == '__main__':
main()
</code></pre>
| <python><matplotlib><imshow> | 2023-07-17 06:25:45 | 1 | 2,573 | auntyellow |
76,701,905 | 3,897,012 | How to pass selenium webdriver instance to another class in Python | <p>I am creating a few classes for some automated tests. I've created a LoginTest class that logs into a website. Once the LoginTest class is finished testing I would like to pass the logged in instance to the next class. How should I go about doing that?</p>
<p>Here is my code:</p>
<pre><code>from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from dotenv import load_dotenv
import os
import unittest
import time
class LoginTest(unittest.TestCase):
@classmethod
def setUpClass(self):
# load env
load_dotenv()
# Initialize Chrome driver
self.driver = webdriver.Chrome()
# Set implicit wait to 10 seconds
self.driver.implicitly_wait(10)
def test_login(self):
# Get email and password from environment variables
email = os.getenv("EMAIL")
password = os.getenv("PASSWORD")
driver = self.driver
# Go to login screen
driver.get("https://website.com/login")
# Locate the email and password input fields and enter the credentials
email_field = driver.find_element(By.ID, "input_email")
email_field.send_keys(email)
password_field = driver.find_element(By.ID, "input_password")
password_field.send_keys(password)
# Click the login button
driver.find_element(By.NAME, "form.submitted").click()
watchlist_title = EC.title_contains('Login | Dashboard')
# test this if it works
# probably should just check if the url is correct
self.assertIsNotNone(watchlist_title, "Login failed. Home page title is not found.")
@classmethod
def tearDownClass(self):
# Close the browser
self.driver.quit()
class DataTableTest(LoginTest):
def test_table_entries(self):
driver = self.driver
# Continue using the logged-in session for further test steps
driver.get("https://datatable.com")
class ChartTest(LoginTest):
def test_chart(self):
driver = self.driver
# Continue using the logged-in session for further test steps
driver.get("https://chart.com")
if __name__ == "__main__":
unittest.main()
</code></pre>
| <python><python-3.x><selenium-webdriver> | 2023-07-17 06:16:37 | 0 | 686 | Ryan113 |
76,701,863 | 241,515 | Iterating through a pair of DataFrames, modifying one of the two at each iteration and feeding back the results | <p>I am trying to reimplement a (rather complex) algorithm from R in Python. The algorithm operates on two dataframes (<code>short</code> and <code>long</code> for simplicity) which have this basic structure:</p>
<pre><code>index chrom chr_arm start end ratio_median size
0 chr1 p 1 100 0.789 100
</code></pre>
<p>The structures of <code>short</code> and <code>long</code> are identical, they differ from the range in the <code>size</code> column (10K-3M for <code>short</code> and > 3M for <code>long</code>). They are also not identical in length. The algorithm iteratively compares <code>short</code> with <code>long</code> in different ways and then performs some calculations which <strong>alter <code>long</code> in-place</strong>, so that at every iteration the calculations are made with an updated <code>long</code>.</p>
<p>EDIT: Someone requested a more detailed description, and I will provide one. This approach needs to merge part the data from <code>short</code> into <code>long</code> basing on whether they are above or below a certain threshold (defined as <code>threshold</code>).</p>
<p>There is also an additional data frame called <code>bins</code>, which are the raw data from where <code>short</code> and <code>long</code> were derived, with this structrure:</p>
<pre><code>chrom chr_arm start end ratio ratio_median size
1 p 1 5 0.3 0.7 5
1 p 1 5 0.9 0.7 5
</code></pre>
<p>The algorithm is as follows:</p>
<pre><code>1. Iterate through the lines (indices) of short
2. For each line in short, iterate through the lines of long
3. For each line of long, do a comparison on coordinates between short and long: if short is fully upstream (short end < long start) of long, the check is successful
4. If the check is successful:
I. measure if the absolute value of the difference between short's ratio_median and the one in long
II. if the difference is below threshold:
a. construct a new line (segment) by using the start from short and the end of long
b. Measure overlaps between the new line and bins, and recalculate a new ratio_median by doing a median of the overlapping ratios from bins
c. Replace the current line in long with a new line containing these data
III. If the difference is above threshold:
a. Append the current line from short immediately after the current one of long
6. Continue until all lines of short have been used
</code></pre>
<p>This works more or less in R, but at least in Pandas, modifying DataFrames in-place during iteration is not a good practice (and for good reason). I don't want to replicate the exact way it is done in R (in reality, it's a 600-line <code>while</code> loop with multiple nested <code>if</code>s) so I tried to make something simpler and more Pythonic.</p>
<p>My first implementation did the logic without updating the DataFrame in place:</p>
<pre><code>def adjust_segments(short: pd.DataFrame, long: pd.DataFrame) -> pd.DataFrame:
dest_long = long.copy()
for gid, group in short.groupby(["chrom", "chr_arm"], sort=False): # To reduce the number of loops
for rowid, row in group.iterrows():
for long_id, long_row in long.iterrows():
if (row.end < long_row.start): # Simplified check here, there are several
if abs(row.ratio_median - long_row.ratio_median) < threshold:
new_line = recompute_ratio(row, long_row, bins)
dest_long.loc[long_id, :] = new_line
else:
# Omitted all checks to see if it's at the end, etc.
dest_long.loc[long_id + 1, :] = new_line
</code></pre>
<p>This "works", but it doesn't feed back the changes on every iteration (it was made like this on purpose before I noticed that the R code - I didn't write it - did things differently).</p>
<p>As I have a numerical <code>index</code> column outside the DataFrame index I thought of using that to modify the DataFrame in-place without iterating, but as rows are appended (if checks are not successful) this wouldn't work well with "feeding back" the changes at every iteration.</p>
<p>What approach could be done in a case like this? I really don't want to copy the R approach, as I said, as it would lead to an unmaintainable mess.</p>
| <python><pandas><dataframe><algorithm><iteration> | 2023-07-17 06:06:45 | 0 | 4,973 | Einar |
76,701,681 | 1,990,924 | No such file or directory with import | <p>I'm a beginner with Python and I'm not able to run anything with installed modules. For example I have very simple script</p>
<pre><code>#!/usr/bin/env python3
import subprocess
import killport
subprocess.run(["killport", "1111"])
</code></pre>
<p>but the result is</p>
<pre><code>Traceback (most recent call last):
File "/Users/jklimcik/bin/infra-scripts/test.py", line 5, in <module>
subprocess.run(["killport", "1111"])
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/subprocess.py", line 505, in run
with Popen(*popenargs, **kwargs) as process:
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/subprocess.py", line 951, in __init__
self._execute_child(args, executable, preexec_fn, close_fds,
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/subprocess.py", line 1821, in _execute_child
raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'killport'
</code></pre>
<p>I have installed this module:</p>
<pre><code>$ python3 -m pip show killport
Name: killport
Version: 1.2.0
Summary:
Home-page: https://github.com/dannysepler/killport
Author: Danny Sepler
Author-email: dannysepler@gmail.com
License: MIT
Location: /Users/jklimcik/Library/Python/3.9/lib/python/site-packages
Requires: psutil
Required-by:
</code></pre>
<p>I'm using PIP. I've already tried to completely uninstall and reinstall it again.</p>
<pre><code>pip3 --version
pip 23.2 from /Users/jklimcik/Library/Python/3.9/lib/python/site-packages/pip (python 3.9)
</code></pre>
<p>What's wrong or what am I missing? I'm using Python 3.9.6 and running on MacOS, Apple M1</p>
| <python><python-3.x><pip> | 2023-07-17 05:27:08 | 2 | 4,888 | Jaroslav Klimčík |
76,701,594 | 8,726,488 | Python Dict - How to change Key name based on another dict | <p>I have Python dict with keys and values. I want to rename/mapping the dict key based on another dict keys and values.
For example</p>
<pre><code>data = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
</code></pre>
<p>my source data and mapping data is below.</p>
<pre><code>key_changes ={'a': 'a1', 'b': 'b1'}
</code></pre>
<p>expected output is</p>
<pre><code>{'a1': 1, 'b1': 2, 'c': 3, 'd': 4}
</code></pre>
<p>I tried to solve this issue using <strong>for comprehension</strong>. But I am getting Invalid syntax.</p>
<p>I have tried below solution</p>
<pre><code>res = {
key_chages[_key]:_value
else _key:_value
for _key,_value in data.items()
if _key in key_chages
}
</code></pre>
| <python><python-3.x><dictionary> | 2023-07-17 04:58:54 | 2 | 3,058 | Learn Hadoop |
76,701,561 | 1,477,364 | How to get detailed error messages for invalid OR-Tools CP-SAT models? | <p>I'm trying to recreate the Google OR-Tools <a href="https://developers.google.com/optimization/lp/stigler_diet" rel="nofollow noreferrer">Stigler Diet</a> example using the CP-SAT solver instead of the linear solver, and it results in a status of <code>MODEL_INVALID</code>. I don't know how to get detailed error messages or get any additional details as to why the model is invalid.</p>
<p>Looking into the Python source code, a code comment next to the definition of <code>MODEL_INVALID</code> says "The given CpModelProto didn't pass the validation step. You can get a detailed error by calling <code>ValidateCpModel(model_proto)</code>." But, I can't figure out how to access <code>ValidateCpModel()</code> and it's not referenced in <a href="https://developers.google.com/optimization/reference/python/sat/python/cp_model" rel="nofollow noreferrer">the docs</a> or any examples in the or-tools repo.</p>
<p>For reference, here's the program I'm trying to run:</p>
<pre><code>from ortools.sat.python import cp_model
import sys
# Minimum and maximum values for each nutrient
nutritional_requirements = [
[1, 2], # Vitamin A
[1, 2], # Vitamin B
[1, 2], # Vitamin C
]
# Nutritional value for each food
foods = [
# Vitamins
# A B C
[1, 0, 0], # Food A
[0, 1, 0], # Food B
[0, 0, 1], # Food C
]
model = cp_model.CpModel()
quantity_of_food = [model.NewIntVar(0, sys.maxsize, str(i)) for i in range(len(foods))]
for i, nutrient in enumerate(nutritional_requirements):
model.Add(
sum([food[i] * quantity_of_food[i] for food in foods]) >= nutrient[0]
)
model.Add(
sum([food[i] * quantity_of_food[i] for food in foods]) < nutrient[1]
)
model.Minimize(sum(quantity_of_food))
solver = cp_model.CpSolver()
status = solver.Solve(model)
outcomes = [
"UNKNOWN",
"MODEL_INVALID",
"FEASIBLE",
"INFEASIBLE",
"OPTIMAL",
]
print(outcomes[status]) # Prints "MODEL_INVALID"
</code></pre>
<p>This program seems pretty simple to me. How do I find a detailed error message explaining why the model is invalid?</p>
| <python><solver><or-tools><cp-sat> | 2023-07-17 04:49:43 | 1 | 2,048 | Travis |
76,701,427 | 840,352 | Reading AWS environment variables using python | <p>I've configured AWS CLI in my laptop. I'm passing the region details etc either at prompt or baked as part of the code. I'm able to connect to aws through python and read the items.
But when trying to develop a class so that it get the environment variables like AWS_REGION, AWS_SECRET_ACCESS_KEY etc all that is returned is null. Is there anything i'm missing apart from configuring the aws cli in my laptop. <strong>Take note while installing it in my (windows) laptop, at the prompt i enter the AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY etc.</strong></p>
<p>Thanks</p>
| <python><amazon-web-services> | 2023-07-17 04:06:31 | 1 | 654 | luckyluke |
76,701,351 | 23,512,643 | HTML/XML: Understanding How "Scroll Bars" Work | <p>I am working with the R programming language and trying to learn about how to use Selenium to interact with webpages.</p>
<p><strong>For example, using Google Maps - I am trying to find the name, address and longitude/latitude of all Pizza shops around a certain area.</strong> As I understand, this would involve entering the location you are interested in, clicking the "nearby" button, entering what you are looking for (e.g. "pizza"), scrolling all the way to the bottom to make sure all pizza shops are loaded - and then copying the names, address and longitude/latitudes of all pizza locations.</p>
<p>I have been self-teaching myself how to use Selenium in R and have been able to solve parts of this problem myself. Here is what I have done so far:</p>
<p><strong>Part 1:</strong> Searching for an address (e.g. Statue of Liberty, New York, USA) and returning a longitude/latitude :</p>
<pre><code>library(RSelenium)
library(wdman)
library(netstat)
selenium()
seleium_object <- selenium(retcommand = T, check = F)
remote_driver <- rsDriver(browser = "chrome", chromever = "114.0.5735.90", verbose = F, port = free_port())
remDr<- remote_driver$client
remDr$navigate("https://www.google.com/maps")
search_box <- remDr$findElement(using = 'css selector', "#searchboxinput")
search_box$sendKeysToElement(list("Statue of Liberty", key = "enter"))
Sys.sleep(5)
url <- remDr$getCurrentUrl()[[1]]
long_lat <- gsub(".*@(-?[0-9.]+),(-?[0-9.]+),.*", "\\1,\\2", url)
long_lat <- unlist(strsplit(long_lat, ","))
> long_lat
[1] "40.7269409" "-74.0906116"
</code></pre>
<p><strong>Part 2:</strong> Searching for all Pizza shops around a certain location:</p>
<pre><code>library(RSelenium)
library(wdman)
library(netstat)
selenium()
seleium_object <- selenium(retcommand = T, check = F)
remote_driver <- rsDriver(browser = "chrome", chromever = "114.0.5735.90", verbose = F, port = free_port())
remDr<- remote_driver$client
remDr$navigate("https://www.google.com/maps")
Sys.sleep(5)
search_box <- remDr$findElement(using = 'css selector', "#searchboxinput")
search_box$sendKeysToElement(list("40.7256456,-74.0909442", key = "enter"))
Sys.sleep(5)
search_box <- remDr$findElement(using = 'css selector', "#searchboxinput")
search_box$clearElement()
search_box$sendKeysToElement(list("pizza", key = "enter"))
Sys.sleep(5)
</code></pre>
<p><strong>But from here, I do not know how to proceed.</strong> I do not know how to scroll the page all the way to the bottom to view all such results that are available - and I do not know how to start extracting the names.</p>
<p>Doing some research (i.e. inspecting the HTML code), I made the following observations:</p>
<ul>
<li><p>The name of a restaurant location can be found in the following tags: <code><a class="hfpxzc" aria-label=</code></p>
</li>
<li><p>The address of a restaurant location be found in the following tags: <code><div class="W4Efsd"></code></p>
</li>
</ul>
<p><strong>In the end, I would be looking for a result like this:</strong></p>
<pre><code> name address longitude latitude
1 pizza land 123 fake st, city, state, zip code 45.212 -75.123
</code></pre>
<p><strong>Can someone please show me how to proceed?</strong></p>
<p><strong>Note:</strong> Seeing as more people likely use Selenium through Python - I am more than happy to learn how to solve this problem in Python and then try to convert the answer into R code.r</p>
<p>Thanks!</p>
<p><strong>References:</strong></p>
<ul>
<li><a href="https://medium.com/python-point/python-crawling-restaurant-data-ab395d121247" rel="nofollow noreferrer">https://medium.com/python-point/python-crawling-restaurant-data-ab395d121247</a></li>
<li><a href="https://www.youtube.com/watch?v=GnpJujF9dBw" rel="nofollow noreferrer">https://www.youtube.com/watch?v=GnpJujF9dBw</a></li>
<li><a href="https://www.youtube.com/watch?v=U1BrIPmhx10" rel="nofollow noreferrer">https://www.youtube.com/watch?v=U1BrIPmhx10</a></li>
</ul>
<p><strong>UPDATE:</strong> Some further progress with addresses</p>
<pre><code>remDr$navigate("https://www.google.com/maps")
Sys.sleep(5)
search_box <- remDr$findElement(using = 'css selector', "#searchboxinput")
search_box$sendKeysToElement(list("40.7256456,-74.0909442", key = "enter"))
Sys.sleep(5)
search_box <- remDr$findElement(using = 'css selector', "#searchboxinput")
search_box$clearElement()
search_box$sendKeysToElement(list("pizza", key = "enter"))
Sys.sleep(5)
address_elements <- remDr$findElements(using = 'css selector', '.W4Efsd')
addresses <- lapply(address_elements, function(x) x$getElementText()[[1]])
result <- data.frame(name = unlist(names), address = unlist(addresses))
</code></pre>
| <python><html><r><xml><selenium-webdriver> | 2023-07-17 03:39:03 | 2 | 6,799 | stats_noob |
76,701,326 | 8,726,488 | Python for comprehension with dict keys and values in else clause | <p>Attached is my sample in below. Aim's want to update dict based on key which is present in another list. if key is present in the list update value +1 otherwise value -1. I tried with for comprehension approach and getting syntax error. through simple for loop , will solve the problem. but i am looking for approach with for comprehension.</p>
<p><a href="https://i.sstatic.net/QsqGK.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/QsqGK.png" alt="enter image description here" /></a></p>
| <python><python-3.x><dictionary><for-loop><for-comprehension> | 2023-07-17 03:28:03 | 1 | 3,058 | Learn Hadoop |
76,701,248 | 3,099,733 | Can I annotate a member field as read only or immutable in Python? | <p>I am not looking for a "physically" immutable member field in Python as I know it is
impossible, but just a type annotation to tell type checker that this field should not be re-assign a new value.</p>
<p>For example,</p>
<pre class="lang-py prettyprint-override"><code>
class A:
def __init__(self, x: int):
self.x: Annotated[int, Immutable] = x
def do_something(self):
self.x = 1 # type checker should be able to report error
</code></pre>
<p>Can I do this in Python? Or are there any better solutions?</p>
| <python><dependency-injection><immutability><python-typing> | 2023-07-17 02:54:05 | 2 | 1,959 | link89 |
76,701,199 | 2,981,639 | Creating a Huggingface Dataset with categorical class labels from a file | <p>Most (if not all) of the huggingface <code>datasets</code> examples I've found that use <code>ClassLabel</code> hard-code the list of labels, i.e. from <a href="https://huggingface.co/datasets/ag_news/blob/main/ag_news.py" rel="nofollow noreferrer">ag_news</a></p>
<pre class="lang-py prettyprint-override"><code> def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["World", "Sports", "Business", "Sci/Tech"]),
}
),
homepage="http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html",
citation=_CITATION,
task_templates=[TextClassification(text_column="text", label_column="label")],
)
</code></pre>
<p>I'm trying to construct a dataset which has a large number of labels, and the labels have additional metadata, i.e. say I have a labels.json file containing</p>
<pre><code>{"name":"label_1", "category":"c1", "reference":123}
{"name":"label_2", "category":"c1", "reference":456}
{"name":"label_3", "category":"c2", "reference":789}
</code></pre>
<p>(Later I want to expose the additional metadata for use at inference time but it's not relevant to this question)</p>
<p>I want to read this file in order to construct the <code>DatasetInfo</code>, specifically construct the <code>names</code> array when the dataset is loaded. The issue is how do I obtain the filename - the <code>_info</code> method doesn't pass the <code>download_manager</code> which afaik is required in order to locate the local copy of the file in the cache.</p>
| <python><huggingface-datasets> | 2023-07-17 02:34:32 | 0 | 2,963 | David Waterworth |
76,701,183 | 13,916,049 | Conditional creation of row values based on another row | <p>If the <code>days_to_last_follow_up</code> row value is equal to or more than <code>1825</code>, assign the value in the <code>survival</code> row as <code>0</code>. Otherwise, if it is less than <code>1825</code> or is <code>NA</code>, assign the <code>survival</code> row as <code>1</code>.</p>
<pre><code># Long-term survival >= 5 years (1825 days)
# Short-term survival < 5 years OR NA
def survival_status(col):
if col.loc["days_to_last_follow_up"] >= 1825:
return col.loc["survival"] == 0 # lts
else:
return col.loc["survival"] == 1 # non-lts
clinical.loc["survival"] = clinical.apply(survival_status, axis=0)
</code></pre>
<p>Input:</p>
<p><code>clinical.iloc[0:4,0:4]</code></p>
<pre><code>pd.DataFrame({'TCGA-2K-A9WE-01': {'admin.batch_number': '398.45.0',
'age': '53',
'days_to_initial_pathologic_diagnosis': '0',
'days_to_last_follow_up': '207.0'},
'TCGA-2Z-A9J1-01': {'admin.batch_number': '398.45.0',
'age': '71',
'days_to_initial_pathologic_diagnosis': '0',
'days_to_last_follow_up': '2298.0'},
'TCGA-2Z-A9J3-01': {'admin.batch_number': '398.45.0',
'age': '67',
'days_to_initial_pathologic_diagnosis': '0',
'days_to_last_follow_up': nan},
'TCGA-2Z-A9J6-01': {'admin.batch_number': '398.45.0',
'age': '60',
'days_to_initial_pathologic_diagnosis': '0',
'days_to_last_follow_up': '1731.0'}})
</code></pre>
<p>Expected output:</p>
<pre><code>pd.DataFrame({'TCGA-2K-A9WE-01': {'admin.batch_number': '398.45.0',
'age': '53',
'days_to_initial_pathologic_diagnosis': '0',
'days_to_last_follow_up': '207.0',
'survival': '1'},
'TCGA-2Z-A9J1-01': {'admin.batch_number': '398.45.0',
'age': '71',
'days_to_initial_pathologic_diagnosis': '0',
'days_to_last_follow_up': '2298.0',
'survival': '0'},
'TCGA-2Z-A9J3-01': {'admin.batch_number': '398.45.0',
'age': '67',
'days_to_initial_pathologic_diagnosis': '0',
'days_to_last_follow_up': nan,
'survival': '1'},
'TCGA-2Z-A9J6-01': {'admin.batch_number': '398.45.0',
'age': '60',
'days_to_initial_pathologic_diagnosis': '0',
'days_to_last_follow_up': '1731.0',
'survival': '0'}})
</code></pre>
| <python><pandas> | 2023-07-17 02:26:19 | 1 | 1,545 | Anon |
76,701,101 | 3,560,202 | Write huge xarray dataset physically reorganized by `MultiIndex` to disk | <p>When collapsing xarray dimensions into <code>MultiIndex</code>, merely the index is changed, leaving the underlying data as is.</p>
<p>This new data organisation can then be reflected in the underlying memory the data occupies by accessing <code>.values</code>, causing the data to be computed.</p>
<p>My dataset is too big, however, to be loaded into memory with <code>.values</code>. As such, I would like to write it to memory (preferably in Zarr format) by each chunk. I do not care about the underlying dimensions and only want to persist the <code>MultiIndex</code> as a 1D array.</p>
<p>Zarr does not support <code>MultiIndex</code>, unfortunately. The solution proposed by the error message, namely to use <a href="https://cf-xarray.readthedocs.io/en/latest/generated/cf_xarray.encode_multi_index_as_compress.html" rel="nofollow noreferrer">cf_xarray.encode_multi_index_as_compress</a> makes the underlying dimensions reappear and it seems that when writing, the data will not be reorganised.</p>
<p>How do I proceed here?</p>
| <python><numpy><dask><python-xarray> | 2023-07-17 01:57:13 | 0 | 1,582 | Post Self |
76,700,916 | 5,528,270 | Reproduce the results of using Python's spectrogram() in C# | <p>I would like you to point out various insights from the perspective of a C# expert, a Python expert, and an EEG expert.</p>
<p>I would like to get the same results using MathNet in C# as I get using <code>scipy.signal.spectrogram</code> in Python.</p>
<p><a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html" rel="nofollow noreferrer">https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html</a>
<a href="https://numerics.mathdotnet.com/api/MathNet.Numerics.IntegralTransforms/Fourier.htm" rel="nofollow noreferrer">https://numerics.mathdotnet.com/api/MathNet.Numerics.IntegralTransforms/Fourier.htm</a></p>
<p>I got to the point where I could get pretty close data. I multiply the C# results by 1.5 to get results that are about equal to the Python results. But I do not want to use a magic number. I want to find out why and fix my code. Could you please point out the errors in my code?</p>
<p>The fact that they match by a factor of 1.5 leads me to believe I am making a mistake in multiplication or division. I moved the timing of using the correction factor for the Hamming window back and forth, which produced more different results.</p>
<p>What did I do wrong?</p>
<h2>Python</h2>
<pre><code># Sampling rate [Hz]
fs = 600.0
# FFT window length [sec].
win_sec = 3.0
# Upper frequency [Hz]
fq_lim = 50
# Sampling interval [sec]
dt = 1. / fs
# Data length [points]
n = int(fs * win_sec)
# Power of 2 greater than argument calculated
nfft = 2 ** math.ceil(math.log2(n))
# Frequency signal interval [/sec].
df_f = 1.0 / (nfft * dt)
# Spectrogram calculation Frequency (freq) Time (t) Power spectral density PSD (μV²/Hz) calculation
freq, t, spctgram = signal.spectrogram(sig, fs=fs, nperseg=n, nfft=nfft, noverlap=(win_sec-1)*fs, window='hamming', mode='psd', return_onesided=True)
</code></pre>
<h2>C#</h2>
<pre><code> public Dictionary<string, float> Analyze(List<float> brainwaveData)
{
// power of 2 greater than the sampling number
var fftWindowSize = (int)Math.Pow(2, Math.Ceiling(Math.Log(Const.NumOfSampling, 2)));
// Fill with zeros until sampling number is next power of 2
while (brainwaveData.Count < fftWindowSize)
{
brainwaveData.Add(0f);
}
// Apply Hamming window for FFT
var coefficientHammingWindow = 0.54f;
double[] window = Window.HammingPeriodic(fftWindowSize);
var signalWindow = brainwaveData.Select((d, i) => new Complex32((float)(d * window[i] / coefficientHammingWindow), 0)).ToArray();
// Execute FFT
Fourier.Forward(signalWindow, FourierOptions.Matlab);
// Power spectrum [μV²] calculation Amplitude squared
var powerSpectrum = signalWindow.Select(d => (d.Real * d.Real) + (d.Imaginary * d.Imaginary)).ToArray();
// power spectrum normalization [μV²]
var normalizedPowerSpectrum = powerSpectrum.Select(d => d / fftWindowSize).ToArray();
// Power spectral density [μV²/Hz] calculation
var powerSpectrumDensity = normalizedPowerSpectrum.Select(d => d / Const.SamplingFrequencyHz).ToArray();
// Calculated for each EEG component
Dictionary<string, float> result = new Dictionary<string, float>();
foreach (var listBand in listBands)
{
result.Add(listBand.BandName, calcFrequencyBandAverage(powerSpectrumDensity, Const.SamplingFrequencyHz, listBand.MinFreqHz, listBand.MaxFreqHz));
}
return result;
}
private float calcFrequencyBandAverage(float[] spectrum, int sampleRate, double minHz, double maxHz)
{
// Find the index for a given frequency band (Hz)
var minIndex = (int)Math.Ceiling(spectrum.Length * minHz / sampleRate);
var maxIndex = (int)Math.Floor(spectrum.Length * maxHz / sampleRate);
// Calculate average
return spectrum.Skip(minIndex).Take(maxIndex - minIndex).Average();
}
List<BrainwaveFrequencyBands> listBands = new List<BrainwaveFrequencyBands>
{
new BrainwaveFrequencyBands{ BandName = "Delta", MinFreqHz = 1f, MaxFreqHz = 4f },
new BrainwaveFrequencyBands{ BandName = "Theta", MinFreqHz = 4f, MaxFreqHz = 8f },
new BrainwaveFrequencyBands{ BandName = "Alpha1", MinFreqHz = 8f, MaxFreqHz = 10f },
new BrainwaveFrequencyBands{ BandName = "Alpha2", MinFreqHz = 10f, MaxFreqHz = 12f },
new BrainwaveFrequencyBands{ BandName = "Beta1", MinFreqHz = 12f, MaxFreqHz = 20f },
new BrainwaveFrequencyBands{ BandName = "Beta2", MinFreqHz = 20f, MaxFreqHz = 30f },
new BrainwaveFrequencyBands{ BandName = "Gamma1", MinFreqHz = 30f, MaxFreqHz = 40f },
new BrainwaveFrequencyBands{ BandName = "Gamma2", MinFreqHz = 40f, MaxFreqHz = 50f },
};
</code></pre>
| <python><c#><scipy><fft><mathnet-numerics> | 2023-07-17 00:28:23 | 0 | 1,024 | Ganessa |
76,700,778 | 3,334,355 | Numpy einsum getting crazy slow after certain problem size | <p>I have the following benchmarking script</p>
<pre><code>import numpy as np
import timeit
import matplotlib.pyplot as plt
n1, n2, n3, n4, n5, m = (101, 33, 1, 32, 2, 32)
def make_arrays(aOrder, bOrder, cOrder):
a = np.random.randn(n1, m) + 1j * np.random.randn(n1, m)
b = np.random.randn(n2, m) + 1j * np.random.randn(n2, m)
c = np.random.randn(n1, n2, n3, n4, n5) + 1j * np.random.randn(n1, n2, n3, n4, n5)
return (
np.array(a, order=aOrder),
np.array(b, order=bOrder),
np.array(c, order=cOrder),
)
# used in B()
blockSize = 84
resA = []
resB = []
resC = []
sizes = np.unique(np.exp(np.linspace(2, 6, 8)).astype(np.int64))
numTrials = 10
# overrides m form above
for m in sizes:
a, b, c = make_arrays("F", "F", "F")
path = np.einsum_path(
a,
[0, 5],
b,
[1, 5],
c,
[0, 1, Ellipsis],
[Ellipsis, 5],
optimize="greedy",
)
def A():
np.einsum(
a,
[0, 5],
b,
[1, 5],
c,
[0, 1, 2, 3, 4],
[5, 2, 3, 4],
optimize="greedy",
order="F",
)
# print("einsum\n", res.flags)
def B():
numBlocks = int(a.shape[1] // blockSize)
np.concatenate(
tuple(
np.einsum(
c,
[1, 2, Ellipsis],
a[:, kk * blockSize : (kk + 1) * blockSize],
[1, 0],
b[:, kk * blockSize : (kk + 1) * blockSize],
[2, 0],
[0, Ellipsis],
optimize="greedy",
order="F",
)
for kk in range(numBlocks)
)
+ (
np.einsum(
c,
[1, 2, Ellipsis],
a[:, numBlocks * blockSize :],
[1, 0],
b[:, numBlocks * blockSize :],
[2, 0],
[0, Ellipsis],
optimize="greedy",
order="F",
),
),
axis=0,
)
def C():
tmp = np.einsum(a, [0, 5], c, [0, 1, 2, 3, 4], [1, 2, 3, 4, 5], order="F")
np.einsum(b, [1, 5], tmp, [1, 2, 3, 4, 5], [2, 3, 4, 5], order="F")
A()
B()
C()
measured = np.mean(timeit.repeat(A, number=numTrials, repeat=numTrials)) / (
numTrials * m
)
resA.append(measured)
measured = np.mean(timeit.repeat(B, number=numTrials, repeat=numTrials)) / (
numTrials * m
)
resB.append(measured)
measured = np.median(timeit.repeat(C, number=numTrials, repeat=numTrials)) / (
numTrials * m
)
resC.append(measured)
plt.figure()
plt.semilogy(sizes, resA, label="A")
plt.semilogy(sizes, resB, label="B")
plt.semilogy(sizes, resC, label="C")
plt.legend()
plt.show()
</code></pre>
<p>I think one only needs <code>numpy</code> and <code>matplotlib</code> to run it.</p>
<p>Approach <code>A</code> is my naive way of using einsum, since I expect this to work well. After some time this code has been residing in my codebase, I noticed that after a certain size of <code>m</code>, the computation just gets very slow. Hence, I went ahead and implemented <code>B</code>, which is producing satisfactory results across the board, but especially in the beginning it looses against <code>A</code>. Also, no matter how I toss and turn this in terms of memory-layout of the input and output-arrays, I see no noticeable difference in qualitative behavior.</p>
<p>Just to retain my sanity, I went ahead and tried out an even more naive way by using <code>C</code>, which is superslow as expected.</p>
<p>On a very powerful <code>AMD EPYC 7343 16-Core Processor</code> with numpy-intelpython, i.e. using MKL, where I have forced the MKL to only use one core for the computation, I get the following result:</p>
<p><a href="https://i.sstatic.net/T18Fk.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/T18Fk.png" alt="enter image description here" /></a></p>
<p>Essentially I divide the runtime by the problem size <code>m</code>, to get an estimate for the cost of a single "slice" of the problems.</p>
<p>To iron out any relation to the CPU or MKL, I used my Macbook Air with the M2 chip first with a vanilla numpy:</p>
<p><a href="https://i.sstatic.net/AP9C1.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/AP9C1.png" alt="enter image description here" /></a></p>
<p>and then also with a numpy linking again the accelerateBLAS library to make use of the GPU, or whatever, don't really care. Then I get</p>
<p><a href="https://i.sstatic.net/YlpCF.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/YlpCF.png" alt="enter image description here" /></a></p>
<p>So my question is: what the effing heck is wrong with <code>A</code>?</p>
<p>As a kinda off-topic sidenote: I am also running the same code on cupy from time to time and there this behavior is not visible. There the corresponding version of <code>A</code> is scaling nicely, at least for the exact same problem sizes as used above.</p>
<p>Another off-topic sidenote: If I use <code>opt_einsum</code> for the contraction, basically with the code of <code>A</code>, I get something similar to <code>B</code> from above, but slightly slower.</p>
| <python><numpy><optimization><benchmarking><einsum> | 2023-07-16 23:17:30 | 1 | 333 | Labello |
76,700,701 | 2,867,356 | additional TaintStep for taint tracking in python programs | <p>I am using codeql TaintTracking and I noticed by default it does not follow data for functions it doesn't know.</p>
<p>for exapmple for this code:</p>
<pre class="lang-py prettyprint-override"><code>import pd
a = src + anything
df = pd.DataFrame(a)
</code></pre>
<p>if src is the source, then a is defined as a sink (as expected)
but df isn't.</p>
<p>I want to arrive to any "contaminated" variable, including df. Any ideas how to do that?
I saw the documentation for overriding <code>isAdditionalTaintStep</code> in <code>TaintTracking::Configuration</code> which seems like a good direction but I only found examples of it crossing a specific function, and not any value assignment by any function (which I believe can be useful to many cases)</p>
<p>An</p>
| <python><static-analysis><codeql> | 2023-07-16 22:46:56 | 1 | 592 | Atlantis |
76,700,620 | 9,588,300 | Difference between PySpark functions write.parquet vs write.format('parquet') | <p>In PySpark DataFrames can be saved in two ways, irrespective of the data it contains</p>
<pre><code>df.write.format('parquet').save(<path>)
</code></pre>
<p>and</p>
<pre><code>df.write.parquet(<path>)
</code></pre>
<p>What is the difference between these two functions?</p>
| <python><apache-spark><pyspark><databricks><parquet> | 2023-07-16 22:17:24 | 1 | 462 | Eugenio.Gastelum96 |
76,700,489 | 8,560,600 | How to play Audio file at specific time? | <p>I have a video which I am processing, frame by frame, and I would like based on the result of frame processing to add multiple (short) audio file to it.</p>
<p>For example, let's say I have a program for <strong>SQUAT COUNTING</strong>, and every time I detect a <strong>successful</strong> or <strong>unsuccessful</strong> squat, I want to add a specific sound.</p>
<p>How would I do that, which libraries to use? I am using OpenCV for iterating through video frames, so the code looks something like this:</p>
<pre><code>while(True):
# Capture the video frame
# by frame
try:
ret, frame = vid.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
out_frame, curr_proper_squat_num, curr_improper_squat_num = process_frame.process(frame, pose)
if curr_proper_squat_num != prev_proper_squat_num:
prev_proper_squat_num = curr_proper_squat_num
# APPEND AUDIO STARTING FROM THIS FRAME
elif curr_improper_squat_num != prev_improper_squat_num:
prev_improper_squat_num = curr_improper_squat_num
# APPEND AUDIO STARTING FROM THIS FRAME
out_frame = cv2.cvtColor(out_frame, cv2.COLOR_RGB2BGR)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
except Exception as err:
break
</code></pre>
<p>Which libraries would I use to append audio when I detect the frame from which I need audio to play?</p>
<p>Thanks!</p>
| <python><audio> | 2023-07-16 21:27:28 | 0 | 1,608 | Stefan Radonjic |
76,700,162 | 442,351 | sklearn - ModuleNotFound despite is installing successfully | <p>I'm importing <code>get_embedding</code> from <code>openai.embeddings_utils</code>.</p>
<p>I received a <code>ModuleNotFound</code> error and, after installing</p>
<ol>
<li><code>matplotlib</code></li>
<li><code>plotly</code></li>
<li><code>scipy</code></li>
</ol>
<p>I got another saying that <code>sklearn</code> wasn't found. I've installed that, apparently successfully:</p>
<pre><code>Successfully built sklearn
Installing collected packages: sklearn
Successfully installed sklearn-0.0.post5
</code></pre>
<p>When I run the code though:</p>
<pre class="lang-py prettyprint-override"><code>from openai.embeddings_utils import get_embedding
df['embedding'] = df['text'].apply(lambda x:
get_embedding(x, engine='text-embedding-ada-002'))
</code></pre>
<p>I still get the exception:</p>
<p><code>ModuleNotFoundError: No module named 'sklearn'</code></p>
<p>I'm <em>very</em> new to Python and Pip, is there some magic I need to do for this particular package?</p>
| <python><scikit-learn><pip><openai-api> | 2023-07-16 19:51:54 | 1 | 28,726 | BanksySan |
76,700,157 | 12,787,469 | Unable to receive email during AWS Cognito signup phase | <p>After have client_id and client_secret properly imported into my code and have the hash secret generated using the credential and username, this is what i have for sign up:</p>
<pre><code>user_attributes = [{"Name": "email", "Value": email}]
cognito_client.sign_up(
ClientId=_CLIENT_ID,
SecretHash=secret_hash,
Username=username,
UserAttributes=user_attributes,
Password=password,
)
</code></pre>
<p>I tried to trigger this <code>sign_up()</code> method using a Lambda function, and this is what I get in the terminal every time when I wanted to create a new user</p>
<pre><code>on 'AuthFunction' timed out after 10 seconds
"errorMessage": "An error occurred (CodeDeliveryFailureException) when calling the SignUp operation: Unable to deliver message".
</code></pre>
<p>I checked my userpool in AWS Cognito console, and I can see a user created but with the email field left unverified. The email I used here has been verified on SES (I am currently in the sandbox mode)</p>
<p>Anyone ran into this issue before?</p>
<p>UPDATE:
I might have to contact AWS support for the issue described in this thread because it looks like this very Userpool I created also has other weird problems: 1. I can't update REPLY-TO email config (originally left blank) under the Messaging tab 2. I can't deactivate Deletion Protection, which in turn prevents me from deleting the Userpool as a whole. The error message I received after implementing any of the actions listed above is <code>code: InvalidParameterException message: Unable to deliver message</code>. So what I did was I created a new Userpool with a valid REPLY-TO email (updated the corresponding credentials), and I received an automated email with verification code.</p>
| <python><aws-lambda><amazon-cognito> | 2023-07-16 19:50:18 | 0 | 397 | TechSelfLearner |
76,700,089 | 15,098,472 | Adding a column in a DataFrame based on thresholds and size of group | <p>I have a DataFrame with x and y coordinates, where the index represents a timestamp. We may assume it is an object that moves every timestep. The distance between consecutive timestamps is expected to increase. However, if the distance doesn't increase by a certain threshold, I consider it a potential "waiting" position.
I use the word potential, because the data is quite noisy, and a single 'waiting' condition is not enough to be really sure that the object was not moving. Thus, I require at least 3 or more consecutive 'waiting' conditions, before I can be sure the object was indeed not moving.</p>
<p>I would like to detect these waiting positions and label them accordingly in a new column.</p>
<pre><code>Example :
x y
timestamp
2023-07-01 00:00:00 1 5
2023-07-01 00:01:00 2 6
2023-07-01 00:02:00 3 7
2023-07-01 00:03:00 4 8
2023-07-01 00:04:00 4 8
2023-07-01 00:05:00 5 9
2023-07-01 00:06:00 6 9
2023-07-01 00:07:00 7 10
2023-07-01 00:08:00 7 10
2023-07-01 00:09:00 7 10
2023-07-01 00:10:00 7 10
2023-07-01 00:11:00 8 11
2023-07-01 00:12:00 9 11
</code></pre>
<p>To compute the distance, I already shifted the dataframe by 1, and caluclate the distance:</p>
<pre><code> x y distance
timestamp
2023-07-01 00:00:00 1 5 NaN
2023-07-01 00:01:00 2 6 1.414214
2023-07-01 00:02:00 3 7 1.414214
2023-07-01 00:03:00 4 8 1.414214
2023-07-01 00:04:00 4 8 0.000000
2023-07-01 00:05:00 5 9 1.414214
2023-07-01 00:06:00 6 9 1.000000
2023-07-01 00:07:00 7 10 1.414214
2023-07-01 00:08:00 7 10 0.000000
2023-07-01 00:09:00 7 10 0.000000
2023-07-01 00:10:00 7 10 0.000000
2023-07-01 00:11:00 8 11 1.414214
2023-07-01 00:12:00 9 11 1.000000
</code></pre>
<p>Now, assume if the distance is lower than 1, it could potentially be a waiting position:</p>
<pre><code> x y distance condition_fulfilled
timestamp
2023-07-01 00:00:00 1 5 NaN NaN
2023-07-01 00:01:00 2 6 1.414214 False
2023-07-01 00:02:00 3 7 1.414214 False
2023-07-01 00:03:00 4 8 1.414214 False
2023-07-01 00:04:00 4 8 0.000000 True
2023-07-01 00:05:00 5 9 1.414214 False
2023-07-01 00:06:00 6 9 1.000000 False
2023-07-01 00:07:00 7 10 1.414214 False
2023-07-01 00:08:00 7 10 0.000000 True
2023-07-01 00:09:00 7 10 0.000000 True
2023-07-01 00:10:00 7 10 0.000000 True
2023-07-01 00:11:00 8 11 1.414214 False
2023-07-01 00:12:00 9 11 1.000000 False
</code></pre>
<p>Since I require at least 3 consecutive fulfilled condtions, the expected output would be:</p>
<pre><code> x y distance status
timestamp
2023-07-01 00:00:00 1 5 NaN moving
2023-07-01 00:01:00 2 6 1.414214 moving
2023-07-01 00:02:00 3 7 1.414214 moving
2023-07-01 00:03:00 4 8 1.414214 moving
2023-07-01 00:04:00 4 8 0.000000 moving
2023-07-01 00:05:00 5 9 1.414214 moving
2023-07-01 00:06:00 6 9 1.000000 moving
2023-07-01 00:07:00 7 10 1.414214 moving
2023-07-01 00:08:00 7 10 0.000000 waiting
2023-07-01 00:09:00 7 10 0.000000 waiting
2023-07-01 00:10:00 7 10 0.000000 waiting
2023-07-01 00:11:00 8 11 1.414214 moving
2023-07-01 00:12:00 9 11 1.000000 moving
</code></pre>
| <python><pandas> | 2023-07-16 19:34:01 | 3 | 574 | kklaw |
76,700,067 | 9,588,300 | Pyspark get max value of column of a csv the quickest way possible | <p>I am trying to get the max value of a column using this:
<code>df.agg(max(col('some_integer_column')),min(col('some_integer_column')))</code></p>
<p>The df is a csv file. Which I know if it was a parquet/delta it would be much easier and faster. As the csv file needs to shuffle data because it doesn't have the metadata stats that a parquet/delta has. But I am not interested in rewriting the csv as parquet/delta</p>
<p>So in my df from the csv, I checked the execution plan from that command, and I see it does some exchange of partitions. While I know it theoretically needs to do so because data is scattered across partitions. Can't there just exist a quicker way to minimize the shuffle?</p>
<p>Like letting each executor check for each of his partitions what is the maximum value within each partition. And then share that value in the exchange. For example, if I have 200 partitions, then I can get 200 values. So now I just have to shuffle 200 values and get the max of 200 values.</p>
<p>Instead of shuffling all the data inside the 200 partitions which is what I understand this execution plan is doing:</p>
<p><a href="https://i.sstatic.net/t4CPY.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/t4CPY.png" alt="Execution Plan" /></a></p>
| <python><pyspark><databricks><distributed-computing><spark-ui> | 2023-07-16 19:29:02 | 1 | 462 | Eugenio.Gastelum96 |
76,699,993 | 19,366,064 | Python How to move/centralize pycache files | <p>Below is what my project looks like</p>
<pre><code>Project
src
folder1
__pycache__
...
folder2
__pycache__
...
main
__pycache__
tests
__pycache__
...
</code></pre>
<p>Is there a way to centralize all the pycache files in one folder so that it looks something like this?</p>
<pre><code>Project
__pycache__
src
folder1
folder2
main
tests
</code></pre>
| <python> | 2023-07-16 19:10:48 | 2 | 544 | Michael Xia |
76,699,846 | 19,574,336 | form element is not valid | <p>I'm trying to make a form (without using django's template forms) but for some reason my forms are never valid when I try them. Here is my code:</p>
<p>forms.py:</p>
<pre><code>class AddressForm(forms.Form):
address_long = forms.CharField(max_length=500)
class Meta:
model = Address
fields = ['address_long']
def save(self, commit=True):
address = Address.objects.create()
address.address_long = self.cleaned_data['address_long']
if commit:
address.save()
return address
</code></pre>
<p>views.py:</p>
<pre><code>@login_required
def add_address_view(request):
if request.method == 'POST':
form = AddressForm(request.POST)
print(request.POST)
if form.is_valid():
print("Valid")
address = form.save()
user_profile = request.user.userprofile
user_profile.addresses.add(address) # Add to many to many field
user_profile.save()
return redirect('core:profile') # Redirect to the user's profile page
else:
form = AddressForm()
return render(request, 'Test_site/add-address.html', {'form': form, 'countries': COUNTRIES})
</code></pre>
<p>And my html looks like this:</p>
<pre><code>...
<form method="POST" class="col-md-5 border-right">
{% csrf_token %}
...
<div class="col-md-12">
<label class="labels">Address Long</label>
<input name="{{ form.address_long.name }}" id="{{ form.address_long.auto_id }}" type="text" class="input-field form-control" placeholder="enter address line" value="">
</div>
...
</form>
</code></pre>
<p>When I try this on my local host I get this output from the print statements in the view function:</p>
<pre><code><QueryDict: {'csrfmiddlewaretoken': ['my_generated_token'], 'address_long': ['Long address try']}>
</code></pre>
<p>But no matter what I do it just doesn't pass the '.is_valid()' check. Also I can't look up on what 'form' holds, like trying to do <code>form.cleaned_data['address_long']</code> causes a 'form doesnt have member cleaned_data' error. It acts like it can't create the object. For example form.address_long is also invalid, eventhough I've created the form, it says it's not there.</p>
<p>What's the issue here?</p>
<p>Note: I have more members in AddressForm then address_long, but they are all char fields and I made sure all the fields are filled on every step.</p>
| <python><django><forms> | 2023-07-16 18:33:37 | 1 | 859 | Turgut |
76,699,785 | 8,176,763 | Airflow SqlSensor | <p>I have a dag that checks if a database is in recovery mode. The task is a sqlsensor, and I'm struggling to get this correct. I want to check every 5 minutes to see if a database is in recovery mode or not. The sql query will return one row, either false or true. I want to mark the task as Failed if it returns False and Success if it return True. The problem is that this code for some reason is continuously checking the database every second, it seems the cacthup argument is not working and is backfilling everything. I set the poke_interval differently for 5 minutes but this does not work as I expect. I also don't know how to handle the parameters <code>success</code> and <code>failure</code> in this case when the sql query returns <code>false</code> and <code>true</code>.</p>
<pre><code>from airflow.sensors.sql import SqlSensor
from airflow import DAG
from datetime import datetime, timedelta
# Define the DAG
default_args = {
'start_date': datetime(2023, 7, 15),
'catchup': False,
}
dag = DAG(
'database_monitor',
default_args=default_args,
schedule_interval='*/10 * * * *' # Runs every 10 minutes
)
check_db_alive = SqlSensor(
task_id="check_db_alive",
conn_id="evergreen",
sql="SELECT pg_is_in_recovery()",
#failure= lambda x: x == 'false',
poke_interval= 60 * 5,
timeout = 60 * 2,
mode = "reschedule",
dag=dag
)
check_db_alive
</code></pre>
| <python><airflow> | 2023-07-16 18:16:34 | 0 | 2,459 | moth |
76,699,483 | 13,849,446 | Handle Add Extension Alert from webstore using only Selenium to make it work in headless | <p>All the current solutions on Stack Overflow use pyautogui or some other method to click on the add extension alert. Or some will say to download and load the extension using ChromeOptions. Well, I do not want any of these. The reason is I need to make it work in headless mode and I can not download the extension for some reason.</p>
<p>Here is a link to similar question: <a href="https://stackoverflow.com/questions/69269098/python-selenium-how-to-handle-chrome-store-alert">Python Selenium How to handle chrome store alert?</a></p>
<p>I have tried the steps in this post but they do not work. The code snippet to work with is:</p>
<pre><code>import time
import pyautogui
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.support.ui import WebDriverWait
from webdriver_manager.chrome import ChromeDriverManager
from undetected_chromedriver import Chrome, ChromeOptions
from selenium.webdriver.support import expected_conditions as EC
service = Service(ChromeDriverManager().install())
options = ChromeOptions()
options.add_argument("--disable-web-security")
options.add_argument("--disable-xss-auditor")
options.add_argument("--no-sandbox")
driver = Chrome(service=service, chrome_options=options)
driver.execute_script("Object.defineProperty(navigator, 'webdriver', {get: function() {return false}})")
wait = WebDriverWait(driver, 50)
driver.get("https://chrome.google.com/webstore/detail/chatgpt-chrome-extension/gabfffndnhbbjlgmbogpfaciajcbpkdn")
wait.until(EC.element_to_be_clickable((By.XPATH, '//div[contains(@class, "webstore-test-button-label") and contains(text(), "Add")]')))
driver.find_element(By.XPATH, '//div[contains(@class, "webstore-test-button-label") and contains(text(), "Add")]').click()
time.sleep(5)
pyautogui.hotkey('tab','enter', interval=0.1)
time.sleep(5)
driver.quit()
</code></pre>
| <python><python-3.x><selenium-webdriver> | 2023-07-16 17:04:05 | 0 | 1,146 | farhan jatt |
76,699,398 | 3,071,728 | pyftpdlib ftp server unavailable from outside local network | <p>my server is simple:</p>
<pre><code>from pyftpdlib import servers, handlers
address = ("0.0.0.0", 21)
server = servers.FTPServer(address, handlers.FTPHandler)
server.banner = "Welcome to My FTP Server"
handler = server.handler
handler.authorizer.add_user("username", "password", ".", perm="elr")
handler.authorizer.add_anonymous(".", perm="elr")
handler.masquerade_address = '97.117.28.178' # from `curl ifconfig.me`
handler.passive_ports = range(60000, 60100)
server.serve_forever()
</code></pre>
<p>running in a docker container:</p>
<pre><code>docker run --rm -it -p 20:20 -p 21:21 -p 60000-60100:60000-60100 python:slim bash
</code></pre>
<p>then I run a client (in the same image):</p>
<pre><code>import os
from ftplib import FTP
class FTPClient:
def __init__(self, host):
self.host = host
def _connect(self):
self.ftp = FTP(self.host)
self.ftp.set_pasv(True)
self.ftp.login('username', 'password')
def _disconnect(self):
''' Disconnect from the FTP server '''
self.ftp.quit()
def connect(self):
self.ftp = FTP(self.host)
self.ftp.login('username', 'password')
@staticmethod
def manageConnection(func):
def wrapper(self, *args, **kwargs):
self._connect()
result = func(self, *args, **kwargs)
self._disconnect()
return result
return wrapper
@manageConnection
def pull(self, filename: str, path: str = None, local: str = '.') -> bool:
if path is not None:
self.ftp.cwd(path)
if filename in self.ftp.nlst():
# Specify the desired folder or path here
local_path = os.path.join(local, filename)
with open(local_path, 'wb') as file:
self.ftp.retrbinary('RETR ' + filename, file.write)
return True
return False
@manageConnection
def view(self, path: str = None) -> bool:
if path is not None:
self.ftp.cwd(path)
for file in self.ftp.nlst():
print(file)
return False
</code></pre>
<p>and it works fine locally:</p>
<pre><code>f = FTPClient('127.0.0.1')
f.connect() # works - I see logs on server
f.pull('utils.py') # works - downloads files
</code></pre>
<p>but when I point it to the public address it fails:</p>
<pre><code>f = FTPClient('97.117.28.178')
f.connect() # doesn't work - no log entries on server, it doesn't even reach it
> Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 13, in wrapper
File "<stdin>", line 5, in _connect
File "/usr/local/lib/python3.11/ftplib.py", line 121, in __init__
self.connect(host)
File "/usr/local/lib/python3.11/ftplib.py", line 158, in connect
self.sock = socket.create_connection((self.host, self.port), self.timeout,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/socket.py", line 851, in create_connection
raise exceptions[0]
File "/usr/local/lib/python3.11/socket.py", line 836, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
</code></pre>
<p>What could be the cause of this? I want the FTP server to be accessible online.</p>
| <python><docker><ftp><connection-refused> | 2023-07-16 16:44:30 | 0 | 3,848 | MetaStack |
76,699,105 | 8,207,754 | ~900 line error message when pip installing numpy | <p>I'm using MacOS Ventura 13.4.1 (Apple M1 chip). Here's some info about my local setup:</p>
<pre><code>> uname -m
arm64
> pyenv -v
pyenv 2.3.22
> pyenv versions
system
* 3.6.15 (set by /Users/user1/.pyenv/version)
3.7.17
> python -V
Python 3.6.15
> pip -V
pip 21.3.1 from /Users/user1/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pip (python 3.6)
</code></pre>
<p>I can run Python files just fine. But when I do <code>pip install numpy</code>, I get the ~900 line error message below (truncated in the middle). I've also tried with <code>--no-cache-dir</code>. Other pip installs are successful, e.g. pytest. I do need to use Python 3.6 here, which I know is an older version.</p>
<pre><code>(conda_env) user1@xxx directory % pip install numpy --no-cache-dir
Collecting numpy
Downloading numpy-1.19.5.zip (7.3 MB)
|████████████████████████████████| 7.3 MB 4.4 MB/s
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Building wheels for collected packages: numpy
Building wheel for numpy (pyproject.toml) ... error
ERROR: Command errored out with exit status 1:
command: /Users/user1/.pyenv/versions/3.6.15/bin/python3.6 /Users/user1/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pip/_vendor/pep517/in_process/_in_process.py build_wheel /var/folders/sm/906grd657hd0rc9std54b39w0000gn/T/tmp10f0xbak
cwd: /private/var/folders/sm/906grd657hd0rc9std54b39w0000gn/T/pip-install-2mc8sdc8/numpy_e35df2512ab24c5382ec786ebe6e6e01
Complete output (901 lines):
numpy/random/_bounded_integers.pxd.in has not changed
numpy/random/_philox.pyx has not changed
numpy/random/_bounded_integers.pyx.in has not changed
numpy/random/_sfc64.pyx has not changed
numpy/random/_mt19937.pyx has not changed
numpy/random/bit_generator.pyx has not changed
Processing numpy/random/_bounded_integers.pyx
numpy/random/mtrand.pyx has not changed
numpy/random/_generator.pyx has not changed
numpy/random/_pcg64.pyx has not changed
numpy/random/_common.pyx has not changed
Cythonizing sources
blas_opt_info:
blas_mkl_info:
customize UnixCCompiler
libraries mkl_rt not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
blis_info:
libraries blis not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
openblas_info:
libraries openblas not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
atlas_3_10_blas_threads_info:
Setting PTATLAS=ATLAS
libraries tatlas not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
atlas_3_10_blas_info:
libraries satlas not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
atlas_blas_threads_info:
Setting PTATLAS=ATLAS
libraries ptf77blas,ptcblas,atlas not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
atlas_blas_info:
libraries f77blas,cblas,atlas not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
accelerate_info:
libraries accelerate not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
Library accelerate was not found. Ignoring
libraries veclib not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
Library veclib was not found. Ignoring
FOUND:
extra_compile_args = ['-faltivec', '-I/System/Library/Frameworks/vecLib.framework/Headers']
extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
FOUND:
extra_compile_args = ['-faltivec', '-I/System/Library/Frameworks/vecLib.framework/Headers']
extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
non-existing path in 'numpy/distutils': 'site.cfg'
lapack_opt_info:
lapack_mkl_info:
libraries mkl_rt not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
openblas_lapack_info:
libraries openblas not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
openblas_clapack_info:
libraries openblas,lapack not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
flame_info:
libraries flame not found in ['/Users/user1/.pyenv/versions/3.6.15/lib', '/usr/local/lib', '/usr/lib', '/opt/local/lib']
NOT AVAILABLE
atlas_3_10_threads_info:
Setting PTATLAS=ATLAS
libraries lapack_atlas not found in /Users/user1/.pyenv/versions/3.6.15/lib
libraries tatlas,tatlas not found in /Users/user1/.pyenv/versions/3.6.15/lib
libraries lapack_atlas not found in /usr/local/lib
libraries tatlas,tatlas not found in /usr/local/lib
libraries lapack_atlas not found in /usr/lib
libraries tatlas,tatlas not found in /usr/lib
libraries lapack_atlas not found in /opt/local/lib
libraries tatlas,tatlas not found in /opt/local/lib
<class 'numpy.distutils.system_info.atlas_3_10_threads_info'>
NOT AVAILABLE
atlas_3_10_info:
libraries lapack_atlas not found in /Users/user1/.pyenv/versions/3.6.15/lib
libraries satlas,satlas not found in /Users/user1/.pyenv/versions/3.6.15/lib
libraries lapack_atlas not found in /usr/local/lib
libraries satlas,satlas not found in /usr/local/lib
libraries lapack_atlas not found in /usr/lib
libraries satlas,satlas not found in /usr/lib
libraries lapack_atlas not found in /opt/local/lib
libraries satlas,satlas not found in /opt/local/lib
<class 'numpy.distutils.system_info.atlas_3_10_info'>
NOT AVAILABLE
atlas_threads_info:
Setting PTATLAS=ATLAS
libraries lapack_atlas not found in /Users/user1/.pyenv/versions/3.6.15/lib
libraries ptf77blas,ptcblas,atlas not found in /Users/user1/.pyenv/versions/3.6.15/lib
libraries lapack_atlas not found in /usr/local/lib
libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib
libraries lapack_atlas not found in /usr/lib
libraries ptf77blas,ptcblas,atlas not found in /usr/lib
libraries lapack_atlas not found in /opt/local/lib
libraries ptf77blas,ptcblas,atlas not found in /opt/local/lib
<class 'numpy.distutils.system_info.atlas_threads_info'>
NOT AVAILABLE
atlas_info:
libraries lapack_atlas not found in /Users/user1/.pyenv/versions/3.6.15/lib
libraries f77blas,cblas,atlas not found in /Users/user1/.pyenv/versions/3.6.15/lib
libraries lapack_atlas not found in /usr/local/lib
libraries f77blas,cblas,atlas not found in /usr/local/lib
libraries lapack_atlas not found in /usr/lib
libraries f77blas,cblas,atlas not found in /usr/lib
libraries lapack_atlas not found in /opt/local/lib
libraries f77blas,cblas,atlas not found in /opt/local/lib
<class 'numpy.distutils.system_info.atlas_info'>
NOT AVAILABLE
FOUND:
extra_compile_args = ['-faltivec', '-I/System/Library/Frameworks/vecLib.framework/Headers']
extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
running bdist_wheel
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
build_src
building py_modules sources
building library "npymath" sources
Could not locate executable gfortran
Could not locate executable f95
Could not locate executable f90
Could not locate executable f77
Could not locate executable xlf90
Could not locate executable xlf
Could not locate executable ifort
Could not locate executable ifc
Could not locate executable g77
Could not locate executable g95
Could not locate executable pgfortran
don't know how to compile Fortran code on platform 'posix'
adding 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath' to include_dirs.
None - nothing done with h_files = ['build/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath/npy_math_internal.h']
building library "npysort" sources
adding 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/common' to include_dirs.
None - nothing done with h_files = ['build/src.macosx-13.4-arm64-3.6/numpy/core/src/common/npy_sort.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/common/npy_partition.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/common/npy_binsearch.h']
building library "npyrandom" sources
building extension "numpy.core._multiarray_tests" sources
building extension "numpy.core._multiarray_umath" sources
adding 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/umath' to include_dirs.
adding 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath' to include_dirs.
adding 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/common' to include_dirs.
numpy.core - nothing done with h_files = ['build/src.macosx-13.4-arm64-3.6/numpy/core/src/umath/funcs.inc', 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/umath/simd.inc', 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/umath/loops.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/umath/matmul.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/umath/clip.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath/npy_math_internal.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/src/common/templ_common.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/include/numpy/config.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/include/numpy/_numpyconfig.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/include/numpy/__multiarray_api.h', 'build/src.macosx-13.4-arm64-3.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core._umath_tests" sources
building extension "numpy.core._rational_tests" sources
building extension "numpy.core._struct_ufunc_tests" sources
building extension "numpy.core._operand_flag_tests" sources
building extension "numpy.fft._pocketfft_internal" sources
building extension "numpy.linalg.lapack_lite" sources
building extension "numpy.linalg._umath_linalg" sources
building extension "numpy.random._mt19937" sources
building extension "numpy.random._philox" sources
building extension "numpy.random._pcg64" sources
building extension "numpy.random._sfc64" sources
building extension "numpy.random._common" sources
building extension "numpy.random.bit_generator" sources
building extension "numpy.random._generator" sources
building extension "numpy.random._bounded_integers" sources
building extension "numpy.random.mtrand" sources
building data_files sources
build_src: building npy-pkg config files
running build_py
(Removed chunk of error message)
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
22 warnings generated.
ar: adding 7 object files to build/temp.macosx-13.4-arm64-3.6/libnpysort.a
ranlib:@ build/temp.macosx-13.4-arm64-3.6/libnpysort.a
building 'npyrandom' library
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include
creating build/temp.macosx-13.4-arm64-3.6/numpy/random
creating build/temp.macosx-13.4-arm64-3.6/numpy/random/src
creating build/temp.macosx-13.4-arm64-3.6/numpy/random/src/distributions
compile options: '-Inumpy/core/include -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/Users/user1/.pyenv/versions/3.6.15/include/python3.6m -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/common -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath -c'
clang: numpy/random/src/distributions/logfactorial.c
clang: numpy/random/src/distributions/distributions.c
clang: numpy/random/src/distributions/random_mvhg_marginals.c
clang: numpy/random/src/distributions/random_mvhg_count.c
clang: numpy/random/src/distributions/random_hypergeometric.c
ar: adding 5 object files to build/temp.macosx-13.4-arm64-3.6/libnpyrandom.a
ranlib:@ build/temp.macosx-13.4-arm64-3.6/libnpyrandom.a
running build_ext
customize UnixCCompiler
customize UnixCCompiler using new_build_ext
building 'numpy.core._multiarray_tests' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include
creating build/temp.macosx-13.4-arm64-3.6/build/src.macosx-13.4-arm64-3.6/numpy/core/src/multiarray
creating build/temp.macosx-13.4-arm64-3.6/numpy/core/src/common
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/Users/user1/.pyenv/versions/3.6.15/include/python3.6m -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/common -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath -c'
clang: build/src.macosx-13.4-arm64-3.6/numpy/core/src/multiarray/_multiarray_tests.c
clang: numpy/core/src/common/mem_overlap.c
In file included from numpy/core/src/multiarray/_multiarray_tests.c.src:7:
In file included from numpy/core/include/numpy/npy_math.h:596:
numpy/core/src/npymath/npy_math_internal.h.src:490:21: warning: incompatible pointer types passing 'npy_longdouble *' (aka 'double *') to parameter of type 'long double *' [-Wincompatible-pointer-types]
return modfl(x, iptr);
^~~~
/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/math.h:394:52: note: passing argument to parameter here
extern long double modfl(long double, long double *);
^
numpy/core/src/multiarray/_multiarray_tests.c.src:1895:61: warning: format specifies type 'long double' but the argument has type 'npy_longdouble' (aka 'double') [-Wformat]
PyOS_snprintf(str, sizeof(str), "%.*Lg", precision, x);
~~~~~ ^
%.*g
2 warnings generated.
clang -bundle -undefined dynamic_lookup -L/opt/homebrew/opt/readline/lib -L/opt/homebrew/opt/readline/lib -L/opt/homebrew/opt/openssl@3/lib -L/Users/user1/.pyenv/versions/3.6.15/lib -Wl,-rpath,/Users/user1/.pyenv/versions/3.6.15/lib -L/opt/homebrew/lib -Wl,-rpath,/opt/homebrew/lib -L/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/lib -L/opt/homebrew/opt/readline/lib -L/opt/homebrew/opt/readline/lib -L/opt/homebrew/opt/openssl@3/lib -L/Users/user1/.pyenv/versions/3.6.15/lib -Wl,-rpath,/Users/user1/.pyenv/versions/3.6.15/lib -L/opt/homebrew/lib -Wl,-rpath,/opt/homebrew/lib -L/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/lib build/temp.macosx-13.4-arm64-3.6/build/src.macosx-13.4-arm64-3.6/numpy/core/src/multiarray/_multiarray_tests.o build/temp.macosx-13.4-arm64-3.6/numpy/core/src/common/mem_overlap.o -L/Users/user1/.pyenv/versions/3.6.15/lib -Lbuild/temp.macosx-13.4-arm64-3.6 -lnpymath -o build/lib.macosx-13.4-arm64-3.6/numpy/core/_multiarray_tests.cpython-36m-darwin.so
building 'numpy.core._multiarray_umath' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include
creating build/temp.macosx-13.4-arm64-3.6/numpy/core/src/multiarray
creating build/temp.macosx-13.4-arm64-3.6/numpy/core/src/umath
creating build/temp.macosx-13.4-arm64-3.6/build/src.macosx-13.4-arm64-3.6/numpy/core/src/umath
creating build/temp.macosx-13.4-arm64-3.6/build/src.macosx-13.4-arm64-3.6/numpy/core/src/common
creating build/temp.macosx-13.4-arm64-3.6/private
creating build/temp.macosx-13.4-arm64-3.6/private/var
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders/sm
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders/sm/906grd657hd0rc9std54b39w0000gn
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders/sm/906grd657hd0rc9std54b39w0000gn/T
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders/sm/906grd657hd0rc9std54b39w0000gn/T/pip-install-2mc8sdc8
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders/sm/906grd657hd0rc9std54b39w0000gn/T/pip-install-2mc8sdc8/numpy_e35df2512ab24c5382ec786ebe6e6e01
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders/sm/906grd657hd0rc9std54b39w0000gn/T/pip-install-2mc8sdc8/numpy_e35df2512ab24c5382ec786ebe6e6e01/numpy
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders/sm/906grd657hd0rc9std54b39w0000gn/T/pip-install-2mc8sdc8/numpy_e35df2512ab24c5382ec786ebe6e6e01/numpy/_build_utils
creating build/temp.macosx-13.4-arm64-3.6/private/var/folders/sm/906grd657hd0rc9std54b39w0000gn/T/pip-install-2mc8sdc8/numpy_e35df2512ab24c5382ec786ebe6e6e01/numpy/_build_utils/src
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/umath -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/Users/user1/.pyenv/versions/3.6.15/include/python3.6m -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/common -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath -c'
extra options: '-faltivec -I/System/Library/Frameworks/vecLib.framework/Headers'
clang: numpy/core/src/multiarray/alloc.c
clang: numpy/core/src/multiarray/array_assign_scalar.c
clang: numpy/core/src/multiarray/common.c
clang: numpy/core/src/multiarray/datetime_strings.c
clang: numpy/core/src/multiarray/descriptor.c
clang: numpy/core/src/multiarray/buffer.c
clang: numpy/core/src/multiarray/conversion_utils.c
clang: build/src.macosx-13.4-arm64-3.6/numpy/core/src/multiarray/einsum.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/multiarray/hashdescr.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: build/src.macosx-13.4-arm64-3.6/numpy/core/src/multiarray/lowlevel_strided_loops.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/multiarray/multiarraymodule.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/multiarray/nditer_constr.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/multiarray/refcount.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/multiarray/scalarapi.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/multiarray/temp_elide.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/multiarray/vdot.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: build/src.macosx-13.4-arm64-3.6/numpy/core/src/umath/loops.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/umath/ufunc_object.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/umath/ufunc_type_resolution.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: build/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath/ieee754.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: numpy/core/src/common/array_assign.c
clang: numpy/core/src/common/ucsnarrow.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: build/src.macosx-13.4-arm64-3.6/numpy/core/src/common/npy_cpu_features.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: /private/var/folders/sm/906grd657hd0rc9std54b39w0000gn/T/pip-install-2mc8sdc8/numpy_e35df2512ab24c5382ec786ebe6e6e01/numpy/_build_utils/src/apple_sgemv_fix.c
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
Running from numpy source directory.
/Users/user1/.pyenv/versions/3.6.15/lib/python3.6/distutils/dist.py:261: UserWarning: Unknown distribution option: 'define_macros'
warnings.warn(msg)
error: Command "clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include -DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/umath -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/Users/user1/.pyenv/versions/3.6.15/include/python3.6m -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/common -Ibuild/src.macosx-13.4-arm64-3.6/numpy/core/src/npymath -c numpy/core/src/multiarray/array_assign_scalar.c -o build/temp.macosx-13.4-arm64-3.6/numpy/core/src/multiarray/array_assign_scalar.o -MMD -MF build/temp.macosx-13.4-arm64-3.6/numpy/core/src/multiarray/array_assign_scalar.o.d -faltivec -I/System/Library/Frameworks/vecLib.framework/Headers" failed with exit status 1
----------------------------------------
ERROR: Failed building wheel for numpy
Failed to build numpy
ERROR: Could not build wheels for numpy, which is required to install pyproject.toml-based projects
</code></pre>
| <python><numpy><pip><apple-m1> | 2023-07-16 15:40:01 | 1 | 794 | K-- |
76,698,958 | 2,393,472 | There is an error adding a custom LibreOffice extension | <p>Good afternoon.</p>
<p>I wrote an extension for LibreOffice. Here is its structure:</p>
<p><a href="https://i.sstatic.net/8jDgc.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/8jDgc.png" alt="enter image description here" /></a></p>
<p>The contents of the file <strong>datalink.py</strong>:</p>
<pre><code>import uno
import unohelper
from com.sun.star.task import XJobExecutor
from pythonpath.services.messageboxservice import MessageBoxService
class DataLink(unohelper.Base, XJobExecutor):
implementationName = "vnd.datalink"
serviceNames = ("com.sun.star.task.Job",)
def __init__(self, context):
self.context = context
def trigger(self, args):
MessageBoxService("Hello World!!! Args: " + args, "Info")
g_ImplementationHelper = unohelper.ImplementationHelper()
g_ImplementationHelper.addImplementation(DataLink, DataLink.implementationName, DataLink.serviceNames,)
</code></pre>
<p>The contents of the file <strong>messageboxservice.py</strong>:</p>
<pre><code>import uno
from com.sun.star.awt.MessageBoxType import INFOBOX
from com.sun.star.awt.MessageBoxButtons import BUTTONS_OK
class MessageBoxService:
@staticmethod
def Show(text, caption, type = INFOBOX):
context = uno.getComponentContext()
sManager = context.ServiceManager
toolkit = sManager.createInstance("com.sun.star.awt.Toolkit")
msgbox = toolkit.createMessageBox(None, type, BUTTONS_OK, caption, text)
return msgbox.execute()
</code></pre>
<p>The contents of the file <strong>manifest.xml</strong>:</p>
<pre><code><?xml version="1.0" encoding="UTF-8"?>
<manifest:manifest xmlns:manifest="http://openoffice.org/2001/manifest">
<manifest:file-entry manifest:full-path="Addons.xcu" manifest:media-type="application/vnd.sun.star.configuration-data"/>
<manifest:file-entry manifest:full-path="datalink.py" manifest:media-type="application/vnd.sun.star.uno-component;type=Python"/>
</manifest:manifest>
</code></pre>
<p>When you add a <strong>DataLink.oxt</strong> extension file to LibreOffice, the following error occurs:
<a href="https://i.sstatic.net/S9SSW.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/S9SSW.png" alt="enter image description here" /></a></p>
<p><strong>Question</strong>: How did I correct this error?</p>
<p><strong>P.S.</strong> may need to make any changes to the above files</p>
| <python><libreoffice-calc> | 2023-07-16 15:09:58 | 1 | 333 | Anton |
76,698,934 | 14,068,964 | getting upgrade pip warning but requirement already satisfied | <p>I have started a fresh venv in a new project location and after activating it used <code>pip list</code> to see what a fresh venv included. The listing included a warning that I was not using the latest version of pip:</p>
<pre><code>WARNING: You are using pip version 21.2.4; however, version 23.2 is available.
You should consider upgrading via the '<path to project>/venv/bin/python3 -m pip install --upgrade pip' command.
</code></pre>
<p>However, when I execute the recommended command I get another warning that the requirement is already satisfied:</p>
<pre><code>Requirement already satisfied: pip in ./venv/lib/python3.9/site-packages (23.2)
</code></pre>
<p>which just hangs there forever until I kill it.</p>
<p>What am I missing here? Is this a PATH conflict and it might be looking elsewhere in my environment?</p>
<p>Thanks, in advance.</p>
| <python><pip><warnings><upgrade> | 2023-07-16 15:04:50 | 2 | 329 | Bruce Altner |
76,698,792 | 8,485,638 | Setting the type of a hard coded string in a column_property in SQLAlchemy? | <p>Given the following model using flask_sqlalchemy:</p>
<pre><code>class Student(DB.Model):
"""A student."""
id_: DB.Mapped[uuid.UUID] = DB.mapped_column(
primary_key=True, default=uuid.uuid4
)
first_name: DB.Mapped[str] = DB.mapped_column(StrippedString(16))
last_name: DB.Mapped[str] = DB.mapped_column(StrippedString(32))
full_name: DB.Mapped[str] = DB.column_property(
first_name + " " + last_name,
)
</code></pre>
<p>Whenever printing out <code>full_name</code>, the space in between is not there. I figured that this is because <code>first_name</code> is of type <code>StrippedString</code>:</p>
<pre><code>class StrippedString(TypeDecorator):
"""An augmented string type.
It strips whitespace upon the binding of the value as a parameter to a
query.
"""
impl = DB.String
cache_ok = True
def process_bind_param(self, value, dialect):
"""Strip the value (if it exists) from leading and trailing whitespace."""
return value.strip() if value else None
</code></pre>
<p>The <code>process_bind_param</code> function above is applied to the <code>" "</code> as well, resulting in no space between <code>first_name</code> and <code>last_name</code>.</p>
<p>If I change the column type of <code>first_name</code> to <code>DB.String(16)</code>, all is well. Except, of course, I want to retain the <code>StrippedString</code> type for <code>first_name</code>.</p>
<p>So, now my question is: how can I set (or influence) the type of the plain string <code>" "</code>? Basically, I want the hard coded <code>" "</code> to be left alone, and not seen as another <code>StrippedString</code>.</p>
| <python><sqlalchemy> | 2023-07-16 14:30:37 | 1 | 1,530 | Bart Van Loon |
76,698,709 | 1,451,346 | In what way does `runpy` leave functions or classes in an incorrect state? | <p>The documentation for <code>runpy</code> <a href="https://docs.python.org/3.11/library/runpy.html" rel="nofollow noreferrer">warns</a></p>
<blockquote>
<p>…any functions and classes defined by the executed code are not guaranteed to work correctly after a <code>runpy</code> function has returned.</p>
</blockquote>
<p>What does that mean in practice? If they functioned normally during <code>runpy</code> execution, what can happen after <code>runpy</code> returns to make them go awry?</p>
| <python><python-import><runpy> | 2023-07-16 14:11:03 | 0 | 3,525 | Kodiologist |
76,698,680 | 3,489,155 | Curl works but python requests doesnt (curl gives 304 and 200, requests a 403). Datadome, Cloudflare? | <p>When I go to vinted.nl a cookie is set. This allows me to do an api call in the browser and i can also do it in curl, but not using python requests. I am also wondering if this is because they use <a href="https://datadome.co/products/" rel="nofollow noreferrer">Datadome</a> to prevent certain http requests? Or is it just a mistake in my python requests code? Or is it CloudFlare causing this?</p>
<p>I've tried VPN, home network, work network etc. Always this response when i use python requests, but when i use curl or postman i do get a correct response from the api.</p>
<p>This works:</p>
<pre class="lang-bash prettyprint-override"><code>curl 'https://www.vinted.nl/api/v2/languages' \
-H 'cookie: anon_id=2ec2c37b-7d7a-4e65-b010-f9779852300c; v_udt=dFpFa0JZbDhLcnpXTzBMRGNHQ1Qwa3RGZm1vNFg0aVhOM29WTmc9PS0tVXIrVEtZRDNhMFdVMnhnbS0tVE1qVVVUeHRFMEk3LzVESlVwUEZ0UT09; v_sid=2e6ae0379a464a29cd97ead68aae344f; ab.optOut=This-cookie-will-expire-in-2024; _pbjs_userid_consent_data=7944749324711140; __cf_bm=dYupWIvIxLP6czwXium_JL.Sa1ALZDQydFWupYQS2lc-1689514518-0-Aev5HXwCkVwTwm5ecWnXi203ANDsvXiys1gB77le1YfP7/taQfDKbSTZadENhutigSewOGb98KaDPQrzXAIwJQYsYnIhckhdrhgaUdvJ3pkogtE6Jwxqiy2dZrbksApOrA==; viewport_size=374; _vinted_fr_session=b1psTUhzYXE0cjEwMGw4ck9CNWlVeEVtcG5kRTRES1dneHE0c3pEOEtxbUxNM0ZpTFdSS0NpZDdBdSs0a0FnR0RsZmtRL25wbDJBNjV0NFBKZzkwTWhBQzB4bndtNEhsWUFGVEpHM2xYRkY2K2ozcEhsUVNDdGgraHJBdlZVSVlRNUc0WHRHb0tidEFLQmI3NFovVEVTQUtDWnJvNkFKdlloVTRrL01UdjU3SW9vdWxmT3Y5RG12dTYvNGNMQmtxdkdyTHRMdUZuYWc0VW1vdGJYZGZydnFrbURUZFBHa2Z1b040VHM2WXpGT0dvbzJNZEFmY3FTYWVXcFR5OS9BYXZuMFU1NFpLTjY3WW83QlBlTjVNL0pDWm5WTXVoRmQ3Zm5Obmh3SjdsOTdrTU5rWjd5MGNydFlBVVJ1RmJjKzhFZkNEMy81MEoyeDB2Zm5kdGRMcmMzaElYSlkyNW9nTjZWRks2VXpjUVp2WW9TY3dDOWFtVnNNVDBabHhzZXhvZS9HQ2p4NTh6VHdKTWhCajVpSlRZQUlZdXBlZXVEUFlCaSt1ejU4U1pLUTNMMVNiTmR4ZEdBa1RKMDBLTUVJV0JrYkdmaStoN3BEcWNSWWgwdCttNE1Gd0FOSG9aeS9mRTFRZmhSaStZN3A5TXhiOXJjdE5LWWxPNzBBK29ydHl5MDhDbEd6RlU5OVJLYS95cVBNLzhnRWRjRlV0U00vL3RzYVZjdGVlY1dWTVl1cURLa1BjWnFabm5Ga01vUUNOYzMxUTRua3lyKyt3d2c1SlR5SU1KS3UvT3N3YUxsYzUzWURXQy95Q2V6TUpXeE1hdXk5eUR5dGFUSWZpYStLeEZLcU13NHBFcW9CT2d0VEZMWGZQbjdDMnFMV21FMk5WRmRxclhGYWlYaFNxa0lQeW9OYXpQSmpjVGQ5bGx5WmhaUGN0NjMxVmRNUDViTWE2ZnJ1bUlhNkh0R09FeCtJSCtrMERrTnVoRGFJWkhPc1JLa2x4QUlyb1dwYk1wSWJmNEM1ZGFSQlNVaGs2QndYRnBZZC90Z29ZNTZ2UXZVWWVHWFg1TVA3ZDY2RDFhbzJjKzVKMU04d0JyWEZQWUtsdlpFanAwRSt1R0FiR0EwTklCeDJyM0Mwc25LUktTMzRXNU1VaU45RGJweWdPNHJyUGx3UkxUenJ5Sy9PcndZb1JBemhqWUJUZTl4bGJadGtWUjEzSHRYNjRodkNETU9MS1ZTWXBkUTdQQ21OdDdTdWMvNzZLazRQY0ZiZlJpRXFjVHFTQWZJZ3B0RXdCS2dTTWRvb0J4RUFhTzZ2cFBKM0JpNFpNZnpValFYZ29va0FtRGgvZVc0VWhCNkd5aGFSVXJNVUZmRDEwVmNxbkMwU05QeW8vSXVpV3AvOER3cEd4Kzk5K0tISndNWkZJTzFpRGVGQXJVZmFxd1lpb1c0ZFllakUzYVNVQmswUDFzVFNRNmNDVkFYRUVRWjlPTnY2QXN0Zmw0MC94S3Ewd0JOR2gyNCtONjhQNnVtOXNodi9TbThLSUhhbEduM1h5N0IxZjg0UUh0NDFrMHhvejJkZUtKVHBUQ3hjdk1DST0tLVJnaVJLemdFamhpZ1pZQWphd3dpaUE9PQ%3D%3D--3c0df38decfa6be41b27f65b0f8073cf25f84a48; datadome=2~IXr6AtVrQ0uioqk1ZON1BWpEibj33CUCIOS8wuwAYvBWYjeE60K14p5Njb3e4VqYk2k3NOFnLFTDgRwSX4fC9dCfPRXpwgksoLXck2Y56oUZ2yK~nbLHpZdGyOnZh4; OptanonConsent=isGpcEnabled=0&datestamp=Sun+Jul+16+2023+15%3A49%3A51+GMT%2B0200+(Central+European+Summer+Time)&version=202305.1.0&browserGpcFlag=0&isIABGlobal=false&consentId=2ec2c37b-7d7a-4e65-b010-f9779852300c&hosts=&interactionCount=0&landingPath=https%3A%2F%2Fwww.vinted.nl%2F&groups=C0001%3A1%2CC0002%3A0%2CC0003%3A0%2CC0004%3A0%2CSTACK42%3A0%2CC0015%3A0; _dd_s=rum=0&expire=1689516288792'
</code></pre>
<br>
I saw that curl sets some additional default headers, so i added those also to my python request:
<pre class="lang-bash prettyprint-override"><code>> GET /api/v2/languages HTTP/2
> Host: www.vinted.nl
> user-agent: curl/7.88.1
> accept: */*
> cookie: anon_id=2ec2c37b-7d7a-4e65-b010-f9779852300c etc etc
</code></pre>
<p>However when i try this with python requests, I get a 403:</p>
<pre class="lang-py prettyprint-override"><code>import requests
cookie = "anon_id=2ec2c37b-7d7a-4e65-b010-f9779852300c; v_udt=dFpFa0JZbDhLcnpXTzBMRGNHQ1Qwa3RGZm1vNFg0aVhOM29WTmc9PS0tVXIrVEtZRDNhMFdVMnhnbS0tVE1qVVVUeHRFMEk3LzVESlVwUEZ0UT09; v_sid=2e6ae0379a464a29cd97ead68aae344f; ab.optOut=This-cookie-will-expire-in-2024; _pbjs_userid_consent_data=7944749324711140; __cf_bm=dYupWIvIxLP6czwXium_JL.Sa1ALZDQydFWupYQS2lc-1689514518-0-Aev5HXwCkVwTwm5ecWnXi203ANDsvXiys1gB77le1YfP7/taQfDKbSTZadENhutigSewOGb98KaDPQrzXAIwJQYsYnIhckhdrhgaUdvJ3pkogtE6Jwxqiy2dZrbksApOrA==; viewport_size=374; _vinted_fr_session=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%3D%3D--3c0df38decfa6be41b27f65b0f8073cf25f84a48; datadome=2~IXr6AtVrQ0uioqk1ZON1BWpEibj33CUCIOS8wuwAYvBWYjeE60K14p5Njb3e4VqYk2k3NOFnLFTDgRwSX4fC9dCfPRXpwgksoLXck2Y56oUZ2yK~nbLHpZdGyOnZh4; OptanonConsent=isGpcEnabled=0&datestamp=Sun+Jul+16+2023+15%3A49%3A51+GMT%2B0200+(Central+European+Summer+Time)&version=202305.1.0&browserGpcFlag=0&isIABGlobal=false&consentId=2ec2c37b-7d7a-4e65-b010-f9779852300c&hosts=&interactionCount=0&landingPath=https%3A%2F%2Fwww.vinted.nl%2F&groups=C0001%3A1%2CC0002%3A0%2CC0003%3A0%2CC0004%3A0%2CSTACK42%3A0%2CC0015%3A0; _dd_s=rum=0&expire=1689516288792"
headers = {
"Host": "www.vinted.nl",
"User-Agent": "curl/7.88.1",
"Accept-Encoding": None,
"Connection": None,
"accept": "*/*",
"cookie": cookie,
}
url = "https://www.vinted.nl/api/v2/languages"
response = requests.get(url=url, headers=headers)
print(response)
</code></pre>
<p>The cookie probably expires, so you probably have to get the cookie value from your browser developer tools if you want to try my code.</p>
<p>This is a screenshot of how i get the cookie using Chrome dev tools:
<a href="https://i.sstatic.net/Rdm6ym.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Rdm6ym.png" alt="chrome dev tools Copy as Curl vinted.nl" /></a></p>
<p>response.status_code: 403</p>
<p>response.text:</p>
<pre><code> <h1>Access denied</h1>
<p>You do not have access to www.vinted.nl.</p><p>The site owner may have set restrictions that prevent you from accessing the site.</p>
<ul class="cferror_details">
<li>Ray ID: 7e90b3ed3aa9b8c6</li>
<li>Timestamp: 2023-07-19 05:53:13 UTC</li>
<li>Your IP address: 86.etc</li>
<li class="XXX_no_wrap_overflow_hidden">Requested URL: www.vinted.nl/api/v2/languages </li>
<li>Error reference number: 1020</li>
<li>Server ID: FL_521F90</li>
<li>User-Agent: Mozilla/5.0 (Linux; Android 8.0.0; SM-G955U Build/R16NW) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.141 Mobile Safari/537.36</li>
</ul>
</code></pre>
<p>response.headers</p>
<pre><code>{'Date': 'Wed, 19 Jul 2023 05:53:13 GMT',
'Content-Type': 'text/html; charset=UTF-8',
'Transfer-Encoding': 'chunked',
'Connection': 'keep-alive',
'X-Frame-Options': 'SAMEORIGIN',
'Referrer-Policy': 'same-origin',
'Cache-Control': 'private, max-age=0, no-store, no-cache, must-revalidate, post-check=0, pre-check=0', 'Expires': 'Thu, 01 Jan 1970 00:00:01 GMT',
'Set-Cookie': '__cf_bm=LPhOJ80GUwCYRjFvS6m.8i.7NVl_YXcuEJufH5WHhrQ-1689745993-0-AWu4bkuqNL4hf1bx/IwLfM/RZY9dzgtyBMXq6m/gLv+s+nUHb1Lbc5Cw3/XPcVX9OnUAQURGbclo/OI7TyglbdEdVQr7S5r0Yq334L15kkKx; path=/; expires=Wed, 19-Jul-23 06:23:13 GMT; domain=.vinted.nl; HttpOnly; Secure; SameSite=None',
'Vary': 'Accept-Encoding',
'Server': 'cloudflare',
'CF-RAY': '7e90b3ed3aa9b8c6-AMS',
'Content-Encoding': 'gzip'}
</code></pre>
<p>response.request.headers:</p>
<pre><code>{
'User-Agent': 'Mozilla/5.0 (Linux; Android 8.0.0; SM-G955U Build/R16NW) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.141 Mobile Safari/537.36',
'Accept-Encoding': 'gzip, deflate',
'Accept': '*/*',
'Connection': 'keep-alive',
'host': 'www.vinted.nl',
'Cache-Control': 'no-cache',
'cookie': '_vinted_fr_session=MElmUmdUcmNOb etc etc'}
</code></pre>
| <python><python-3.x><curl><python-requests><cloudflare> | 2023-07-16 14:06:45 | 3 | 13,022 | Sander van den Oord |
76,698,670 | 1,303,562 | Fixture not found although it exist in conftest.py | <p>This is my test class:</p>
<pre><code>import pytest
class TestInstallation:
def test_some(self, fix_test):
print(fix_test)
</code></pre>
<p><strong>conftest.py</strong></p>
<pre><code>import pytest
@pytest.fixture()
def fix_test():
return 123
</code></pre>
<p>And when I try to run my test I received this <code>error</code>:</p>
<blockquote>
<pre><code>> def test_install(self, fix_test): E fixture 'fix_test' not found
> > available fixtures: cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig,
> record_property, record_testsuite_property, record_xml_attribute,
> recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory
> > use 'pytest --fixtures [testpath]' for help on them.
</code></pre>
</blockquote>
<p>When I put my <code>fixture</code> inside my test class this works fine.</p>
<p>What I am doing wrong ?</p>
| <python><pytest><fixtures> | 2023-07-16 14:03:33 | 0 | 721 | Dana Yeger |
76,698,668 | 4,386,557 | tmpfs disk usage : python vs bash | <p>I have a ramdrive created through fstab as</p>
<pre><code>tmpfs /mnt/ram tmpfs defaults,size=2048M 0 0
</code></pre>
<p>This space filled up to 86% so I deleted some large files.</p>
<p>Now the python view and the linux view differ on this disk usage. It looks like the python view does not account for the deleted files.</p>
<pre><code>>>> from os import system
>>> import psutil
>>> import shutil
>>> cmd = 'du -s /mnt/ram/'
>>> system(cmd)
632 /mnt/ram/
0
>>> psutil.disk_usage('/mnt/ram/')
sdiskusage(total=2147483648, used=1862307840, free=285175808, percent=86.7)
>>> shutil.disk_usage('/mnt/ram/')
usage(total=2147483648, used=1862500352, free=284983296)
</code></pre>
| <python><du> | 2023-07-16 14:02:57 | 0 | 1,239 | Stephen Boston |
76,698,632 | 1,938,552 | How to access PEP-526 local variable annotations in Python? | <p>I made a variable annotation of a local variable:</p>
<pre><code>def a():
x: int = 1
</code></pre>
<p>But the annotations dict is empty:</p>
<pre><code>> a.__annotations__
{}
</code></pre>
<p>Same when I use <code>inspect.get_annotations()</code> or <code>typing.get_type_hints()</code>. How can I access the annotation? Is this feature implemented at all?</p>
<p>I'm using Python 3.10.7.</p>
| <python><python-typing> | 2023-07-16 13:56:51 | 3 | 1,059 | haael |
76,698,515 | 15,547,292 | Difference between `multiprocessing.shared_memory` and anonymous mmap? | <p>What is the technical difference between multiprocessing SharedMemory and an anonymous mmap? Is one better than the other?</p>
| <python><shared-memory><mmap> | 2023-07-16 13:29:18 | 0 | 2,520 | mara004 |
76,698,405 | 8,523,868 | Unable to post files to Telegram from pycharm | <p>I searched google Bard and ChatGPT and found the above code.
I am trying to send files from my directory To telegram to my channel
It's is showing the below error.</p>
<p>Below configuration:
Windows 11 OS and pycharm as the IDE.
Installed selenium and telegram in the pycharm addon</p>
<p>My code:-</p>
<pre><code>Import os
From telegram import Bot
bot=Bot(token="xxxxxx")
#Replace'YOUR_TELEGRAM_TOKEN'withyouractualTelegrambottoken
TOKEN='xxxx'
#Replace'YOUR_CHAT_ID'withyouractualTelegramchatID
CHAT_ID='xxxx'
#Directorypathcontainingthefilestoupload
DIRECTORY_PATH='E:\\upload\\2017'
defupload_files_to_telegram():
bot=Bot(token=TOKEN)
forfilenameinos.listdir(DIRECTORY_PATH):
file_path=os.path.join(DIRECTORY_PATH,filename)
ifos.path.isfile(file_path):
withopen(file_path,'rb')asfile:
bot.send_document(chat_id=CHAT_ID,document=file)
print(f"Uploaded{filename}successfully.")
</code></pre>
<p>Error Message:-</p>
<pre><code>Traceback (most recent call last):
File "E:\vss coding\Dayscoding100\venv\telegramupload.py", line 2, in <module>
from telegram import Bot
ImportError: cannot import name 'Bot' from 'telegram' (E:\vss coding\Dayscoding100\venv\lib\site-packages\telegram\__init__.py)
</code></pre>
<p>Process finished with exit code 1</p>
| <python><selenium-webdriver><pycharm><telegram><python-telegram-bot> | 2023-07-16 13:04:01 | 1 | 911 | vivek rajagopalan |
76,698,367 | 4,534 | How to visualize a Panda with a lot of columns interactively? | <p><a href="https://i.sstatic.net/rxqlx.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/rxqlx.jpg" alt="plotting a panda" /></a></p>
<p>I have a panda with a 200+ columns, that I plot currently with</p>
<pre><code>ranked.plot.line(figsize=(20,10),title='Rank Over Time')
</code></pre>
<p>I would like to interactively look at one or several at a time. Ideally tracing the line for exact values. How can I do this in my Jupyter Notebook with my dataframe please?</p>
| <python><pandas> | 2023-07-16 12:54:18 | 1 | 11,105 | hendry |
76,698,314 | 1,172,907 | How to display a picture completely in tkinter? | <p>I tried many approaches to no avail (commented lines). Any other ideas?</p>
<p><img src="https://i.imgur.com/bu4HDX0.png" alt="screenshot" title="Screenshot" /></p>
<p>Get the picture first via curl <code>curl https://i.imgur.com/VYdf3vp.png --output pic.png</code></p>
<pre class="lang-py prettyprint-override"><code>from tkinter import Tk, Label, PhotoImage,Canvas
# Create the main window
window = Tk()
window.title("Image Viewer")
window.geometry("600x600")
# Load and display the image
# curl https://i.imgur.com/VYdf3vp.png --output pic.png
image = PhotoImage(file="./pic.png")
label = Label(window, image=image)
# Doesn't help
# label = Label(window, image=image, anchor="nw", padx=10, pady=10)
label.pack()
# Doesn't help
# label.config(width=image.width(), height=image.height())
# Doesn't help
# desired_width = image.width()
# desired_height = image.height()
# label.config(width=desired_width, height=desired_height)
# Doesn't help
# label.place(x=0, y=0, width=desired_width, height=desired_height)
# Doesn't help
# canvas = Canvas(window, width=image.width(), height=image.height())
# canvas.pack()
# canvas.create_image(0, 0, anchor="nw", image=image)
# Start the tkinter event loop
window.mainloop()
</code></pre>
| <python><tkinter> | 2023-07-16 12:42:07 | 0 | 605 | jjk |
76,698,128 | 4,409,163 | How serialize a Gtk.Textview buffer in Gtk4 | <p>I’m working on a flatpak python+gtk4 application which is, roughly speaking, a text editor, and I need a way to save the contents of a TextView including all the tags.</p>
<p>What I know: In Gtk 4, TextView buffer no longer has built-in serialization methods. To seralilize the contents of the buffer it is necessary to create my own serializer, for that I must use <code>gdk_content_register_serializer</code> (and the corresponding one to create the deserializer). Something like that:</p>
<pre><code>def _r_serializer(self):
Gdk.content_register_serializer(Gtk.TextBuffer,
"text/plain;charset=utf-8",
self._serialize_text,
None,
None)
</code></pre>
<p>What I don’t know: While the use of <code>gdk_content_register_serializer</code> is fairly straightforward and simple, the same is not true about <code>GdkContentSerializeFunc</code> (<code>self._serialize_text</code>). From the documentation, I couldn’t understand what this function should be exactly. I tried looking at how Textbuffer’s serializer worked in GTk3, but I’m fairly new to working with Gtk/Gdk and don’t fully understand the workflow, and besides, my knowledge of C is limited.</p>
<p>What I need: I would like a minimal code example (python would be nice, but it can be any language) of a <code>GdkContentSerializeFunc</code> that serializes at least one tag (italics, for example) to plain text, so that I can use it as a basis for build my own serializer.</p>
| <python><serialization><gtk4><gtktextview> | 2023-07-16 11:57:06 | 0 | 544 | Kripto |
76,698,069 | 719,001 | Speed up call to secret manager in bash | <p>I have a bash script in each environment that with it I set up all the env variables before each script. Recently changed the script and added a python script that gets a list of aws secrets and store them in env variables. So in <code>env.sh</code> instead of:</p>
<pre><code>pass=1234
</code></pre>
<p>now I have:</p>
<pre><code>pass=$(get-key.py dev/pass)
</code></pre>
<p><code>get-key.py</code> (which was based of <a href="https://docs.aws.amazon.com/code-library/latest/ug/python_3_secrets-manager_code_examples.html" rel="nofollow noreferrer">this</a>) runs in about 0.4s which make all my scripts 0.4s slower (and much more if it is a script calling a script etc...). I'm open to hear how can I make <code>env.sh</code> faster while keeping security, caching is one thing that comes into mind. Not sure how though.</p>
| <python><linux><bash><caching> | 2023-07-16 11:41:30 | 2 | 2,677 | Nir |
76,697,938 | 3,423,825 | How to get the column with the lowest number of na cells in Pandas? | <p>I have a dataframe with multiple columns and some of them has a lot of <code>nan</code>. How can I select the column with the lowest number of <code>nan</code> cells ?</p>
| <python><pandas> | 2023-07-16 11:10:44 | 1 | 1,948 | Florent |
76,697,883 | 16,454,377 | delta packages installation on local with pyspark: ivy-cache file not found | <p>I am triying to use delta format so trying this from this site <a href="https://docs.delta.io/0.8.0/quick-start.html" rel="nofollow noreferrer">delta lake</a>. On cmd <code>pyspark --packages io.delta:delta-core_2.12:0.8.0 --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"</code> but giving
error like.</p>
<p><a href="https://i.sstatic.net/FwGIn.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/FwGIn.png" alt="this is the error" /></a></p>
<p>I tried manually put jar file but not working.</p>
| <python><pyspark><delta-lake> | 2023-07-16 10:56:07 | 0 | 469 | Avinash |
76,697,815 | 10,848,158 | How can I listen to the onFieldChange event in Odoo 16 using JavaScript? | <p>I would like to listen for the onChange event of a field in Odoo 16 using JavaScript. I have attempted the following code snippets, but they did not yield the desired results.</p>
<p>my_module/static/src/js/on_field_changes.js</p>
<pre><code>odoo.define('my_module.on_field_changes', function (require) {
"use strict";
const fieldRegistry = require('web.field_registry');
const { FieldChar } = require('web.basic_fields');
const onchange_widget = FieldChar.extend({
_onFieldChanged(ev) {
console.log('========= > > > > Hello')
this._super.apply(this, arguments);
},
});
fieldRegistry.add('onchange_widget', onchange_widget); });
</code></pre>
<p>In form view:</p>
<pre><code><field name="father_name" widget="onchange_widget"/>
</code></pre>
<p>In manifest.py:</p>
<pre><code>'assets': {
'web.assets_backend': [
'my_module/static/src/js/on_field_changes.js',
]
}
</code></pre>
<p>In addition, I have attempted several other code snippets, but unfortunately, none of them proved successful either.</p>
| <javascript><python><python-3.x><odoo><odoo-16> | 2023-07-16 10:38:53 | 0 | 789 | Habib Mhamadi |
76,697,781 | 2,173,773 | pytest-qt: using qtbot gives segmentation fault | <p>I am on Ubuntu 22.04, using <a href="https://python-poetry.org/" rel="nofollow noreferrer">Poetry</a>, Python 3.10, <a href="https://pypi.org/project/PyQt6/" rel="nofollow noreferrer">PyQt6</a>, and <a href="https://docs.pytest.org/en/stable/" rel="nofollow noreferrer">pytest</a> with the <a href="https://pytest-qt.readthedocs.io/en/latest/index.html" rel="nofollow noreferrer">pytest-qt</a> plugin. When I run a test with pytest using the <a href="https://pytest-qt.readthedocs.io/en/latest/reference.html#module-pytestqt.qtbot" rel="nofollow noreferrer"><code>qtbot</code> fixture</a> I get a segmentation fault. If I remove the <code>qtbot</code> fixture from the test file, it works fine. Here is a minimal example (my real test case is more complicated):</p>
<pre><code>$ poetry new --src myproject
$ cd myproject
$ poetry add pyqt6
$ poetry add --group=dev pytest
$ poetry add --group=dev pytest-qt
$ cat <<'END' > pytest.ini
[pytest]
qt_api=pyqt6
END
$ cat <<'END' > src/myproject/main.py
from PyQt6.QtWidgets import QApplication
def qapp(app: QApplication) -> None: # This is the function that we will test with pytest
app.quit()
END
$ cat <<'END' > tests/test_main.py # This is the test file
import pytest
from PyQt6.QtWidgets import QApplication
import myproject.main
from typing import Any
QtBot = Any # Missing type hints here
def test_app(qtbot: QtBot) -> None:
app = QApplication([])
myproject.main.qapp(app)
assert 1 == 1
END
$ poetry install
$ poetry shell
$ pytest
============================================================================ test session starts ============================================================================
platform linux -- Python 3.10.4, pytest-7.4.0, pluggy-1.2.0
PyQt6 6.5.1 -- Qt runtime 6.5.1 -- Qt compiled 6.5.1
rootdir: /home/hakon/test/python/pytest-qt/qtbot/myproject
plugins: qt-4.2.0
collected 1 item
tests/test_main.py Fatal Python error: Segmentation fault
Current thread 0x00007f24e2fc1740 (most recent call first):
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/python.py", line 194 in pytest_pyfunc_call
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_callers.py", line 80 in _multicall
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_manager.py", line 112 in _hookexec
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_hooks.py", line 433 in __call__
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/python.py", line 1788 in runtest
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/runner.py", line 169 in pytest_runtest_call
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_callers.py", line 80 in _multicall
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_manager.py", line 112 in _hookexec
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_hooks.py", line 433 in __call__
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/runner.py", line 262 in <lambda>
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/runner.py", line 341 in from_call
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/runner.py", line 261 in call_runtest_hook
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/runner.py", line 222 in call_and_report
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/runner.py", line 133 in runtestprotocol
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/runner.py", line 114 in pytest_runtest_protocol
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_callers.py", line 80 in _multicall
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_manager.py", line 112 in _hookexec
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_hooks.py", line 433 in __call__
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/main.py", line 349 in pytest_runtestloop
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_callers.py", line 80 in _multicall
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_manager.py", line 112 in _hookexec
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_hooks.py", line 433 in __call__
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/main.py", line 324 in _main
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/main.py", line 270 in wrap_session
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/main.py", line 317 in pytest_cmdline_main
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_callers.py", line 80 in _multicall
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_manager.py", line 112 in _hookexec
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/pluggy/_hooks.py", line 433 in __call__
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/config/__init__.py", line 166 in main
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/lib/python3.10/site-packages/_pytest/config/__init__.py", line 189 in console_main
File "/home/hakon/.cache/pypoetry/virtualenvs/myproject-rfHFWcwh-py3.10/bin/pytest", line 8 in <module>
Extension modules: PyQt6.QtCore, PyQt6.QtGui, PyQt6.QtWidgets, PyQt6.QtTest (total: 4)
Segmentation fault (core dumped)
</code></pre>
| <python><pytest><python-poetry><pyqt6><pytest-qt> | 2023-07-16 10:28:37 | 0 | 40,918 | Håkon Hægland |
76,697,684 | 1,255,819 | How to force pip to install latest requirement.txt versions without use cache? | <p>I have this requirements.txt</p>
<pre><code>esphome
tornado
esptool
pyparsing >=3.0
</code></pre>
<p>When I call it with <code>pip install -r requirements.txt</code>, it says:</p>
<blockquote>
<p>Requirement already satisfied: esphome in /usr/local/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (2023.6.4)</p>
</blockquote>
<p>But if I see on searching on <a href="https://pypi.org/search/?q=esphome" rel="nofollow noreferrer">https://pypi.org/search/?q=esphome</a> I see:
<a href="https://i.sstatic.net/z5Wbp.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/z5Wbp.png" alt="newer esphome version" /></a></p>
<p>How can I let <code>pip</code> known that a new version exists? As workaround I am changing the dependency manually but I always want to use the latest versions of <code>esphome</code>.</p>
<p>I already tried the <code>--no-cache-dir</code> flag but it does not help.</p>
| <python><pip><requirements.txt> | 2023-07-16 10:04:41 | 1 | 7,101 | distante |
76,697,681 | 5,733,709 | Retrieval QA with custom prompt with multiple inputs and memory | <p>I am trying to provide a custom prompt for doing Q&A in langchain.</p>
<p>I wasn't able to do that with RetrievalQA as it was not allowing for multiple custom inputs in custom prompt.I have loaded a sample pdf file, chunked it and stored the embeddings in vector store which I am using as a retriever and passing to Retreival QA chain.</p>
<p>How do I add <strong>memory + custom prompt with multiple inputs to Retrieval QA</strong> in langchain?</p>
<pre><code>import openai
import numpy as np
import pandas as pd
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA, ConversationalRetrievalChain,RetrievalQAWithSourcesChain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import UnstructuredFileLoader
from langchain.prompts import PromptTemplate
from langchain.document_loaders import UnstructuredExcelLoader
loader = UnstructuredFileLoader("../Test.pdf", mode="elements")
documents = loader.load()
from langchain.docstore.document import Document
import json
# Opening JSON file
with open('Customer_profile.json', 'r') as openfile:
# Reading from json file
json_object = json.load(openfile)
cName=json_object['Customer_Name']
cState=json_object['Customer_State']
cGen=json_object['Customer_Gender']
cProfile = "Customer's Name is "+cName+"\nCustomer's Resident State is "+cState+"\nCustomer's Gender is "+cGen
print(cProfile)
# cProfileDoc = Document(page_content=cProfile, metadata={"source": "customerProfile.json"})
# documents.insert(0, cProfileDoc)
prompt_template = """You are a Chat customer support agent.
Address the customer as Dear Mr. or Miss. depending on customer's gender followed by Customer's First Name.
Use the following customer related information (delimited by <cp></cp>) context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question at the end:
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Below are the details of the customer:\n
<cp>
Customer's Name: {Customer_Name}
Customer's Resident State: {Customer_State}
Customer's Gender: {Customer_Gender}
</cp>
<ctx>
{context}
</ctx>
<hs>
{history}
</hs>
Question: {query}
Answer: """
#print(prompt_template.format(cProfile))
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["history","context", "query","Customer_Name","Customer_State","Customer_Gender"]
)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
#embeddings = OpenAIEmbeddings()
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vectorDB = Chroma.from_documents(texts,embeddings)
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="history",input_key="query" ,output_key='answer',return_messages=True)
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type='stuff',
retriever=vectorDB.as_retriever(),
verbose=True,
chain_type_kwargs={
"verbose": True,
"prompt": PROMPT,
"memory": memory
}
)
qa({"query": "who's the client's friend?","Customer_Gender":"Male","Customer_State":"New York","Customer_Name":"Aaron"})
</code></pre>
| <python><openai-api><langchain><py-langchain> | 2023-07-16 10:04:12 | 2 | 786 | Jason |
76,697,678 | 15,002,748 | Extract google reviews from google map | <pre><code>from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.keys import Keys
import time
# Specify the URL of the business page on Google Maps
url = 'https://www.google.com/maps/place/FRUYO+MALAYSIA/@2.2916032,111.8210233,17z/data=!4m8!3m7!1s0x31f77f4fb024a7e1:0x468c52dc9e9179c3!8m2!3d2.2916032!4d111.8210233!9m1!1b1!16s%2Fg%2F11p65htbhd?entry=ttu'
# Create an instance of the Chrome driver
driver = webdriver.Chrome()
# Navigate to the specified URL
driver.get(url)
# Wait for the reviews to load
wait = WebDriverWait(driver, 20) # Increased the waiting time
# Scroll down to load more reviews
body = driver.find_element(By.XPATH, '//body')
num_reviews = len(driver.find_elements(By.CLASS_NAME, 'wiI7pd'))
while True:
body.send_keys(Keys.END)
time.sleep(2) # Adjust the delay based on your internet speed and page loading time
new_num_reviews = len(driver.find_elements(By.CLASS_NAME, 'wiI7pd'))
if new_num_reviews == num_reviews:
# Scroll to the top to ensure all reviews are loaded
body.send_keys(Keys.HOME)
time.sleep(2)
break
num_reviews = new_num_reviews
# Wait for the reviews to load completely
wait.until(EC.presence_of_all_elements_located((By.CLASS_NAME, 'wiI7pd')))
# Extract the text of each review
review_elements = driver.find_elements(By.CLASS_NAME, 'wiI7pd')
reviews = [element.text for element in review_elements]
# Print the reviews
print(reviews)
# Close the browser
driver.quit()
</code></pre>
<p>Hi Everyone,</p>
<p>I need help in scraping the google reviews. The code above works fine, but it only scrap the first 8 reviews without scrolling to the bottom even though I already tried scroll down to load more reviews in my code but it doesn't work. Any one have any idea why is it so? Any help or advise is greatly appreciated!</p>
| <python><selenium-webdriver><web-scraping> | 2023-07-16 10:03:15 | 1 | 1,127 | weizer |
76,697,535 | 1,690,805 | setter methods and Restricted Python | <p>I have a class "MyClass" with a class attribute and its methods.</p>
<pre><code>class MyClass:
def __init__(self):
self._my_attr = None
@property
def my_attr(self):
return self._my_attr
@my_attr.setter
def my_attr(self, value):
self._my_attr = value
def set_my_attr(self, value):
self._my_attr = value
</code></pre>
<p>When I try to assign a value in Zope (in a Python Script, which uses Restricted Python), I get a TypeError <code>attribute-less object (assign or del)</code> when I try to assign the value with <code>=</code>.</p>
<p>It also says in the traceback: <code>AttributeError: 'MyClass' object has no attribute '__guarded_setattr__'</code></p>
<pre><code>obj = MyClass()
obj.my_attr = 42 # error in Restricted Python
obj.set_my_attr(value=42) # does work in Restricted Python
</code></pre>
<p>What does the error mean? Is there a way to allow setter methods for direct assignment in Restricted Python or do I have to implement a normal method to set the value?</p>
| <python><setter><zope><restrictedpython> | 2023-07-16 09:28:03 | 1 | 1,629 | Georg Pfolz |
76,697,534 | 13,557,319 | Cleaning Latitudes and Longitudes Data | <p>I have a csv file containing Latitudes, longitudes and Date but <code>lat</code> and <code>long</code> contains some special characters like comma(,) and (\t) etc.</p>
<p>CSV file look like this:</p>
<pre><code>| lat | long | date |
| 30.970695, | 72.482265 | 05/23/2015 |
| 30.284493\t | 73.106275 | 07/01/2015 |
| 30.311023\t | 73.153053 | 07/01/2015 |
| 30.289273` | 73.067512 | 07/02/2015 |
| 30.309319\t | 73.068774 | 07/03/2015 |
</code></pre>
<p>The data types are:</p>
<pre><code> lat object
long object
date object
dtype: object
</code></pre>
<p>I tried to remove special characters from latitude and longitude</p>
<pre><code>df['lat'] = df['lat'].str.replace(r'[^\d.-]', '', regex=True)
df['long'] = df['long'].str.replace(r'[^\d.-]', '', regex=True)
</code></pre>
<p>I want to convert lat longs into float but I noticed this result after removing special charachters.</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>index</th>
<th>lat</th>
<th>long</th>
<th>date</th>
</tr>
</thead>
<tbody>
<tr>
<td>5514</td>
<td>30.30116873.070213</td>
<td>30.30116873.070213</td>
<td>12/28/2015</td>
</tr>
</tbody>
</table>
</div>
<p>lat and long values of row <code>5514</code> get merged.</p>
<p>Why am I getting this behavior? How do I solve this?
I am new with spatiotemporal data any recommendations and help will be appreciated.</p>
| <python><pandas> | 2023-07-16 09:27:47 | 2 | 342 | Linear Data Structure |
76,697,516 | 15,528,750 | Debugging in VSCode Not Working because of ImportError | <p>When I launch VSCode in debug mode, I get an ImportError for <code>numpy</code>. However, I have <code>numpy</code> installed in my conda environment and was hence wondering why this error message occurs? When I only import Python built-in modules and no third-party libraries, the debug mode runs fine.</p>
| <python><visual-studio-code><debugging> | 2023-07-16 09:22:40 | 1 | 566 | Imahn |
76,697,301 | 8,491,363 | Is the file read in import module saved in memory in Jupyter Notebook? | <p>There are a bunch of datasets that I have to import/preprocess many times.</p>
<p>What I'm doing is putting all of <code>pd.read_csv()</code> inside a single <code>my_datasets.py</code> file like this:</p>
<pre class="lang-py prettyprint-override"><code># my_datasets.py
import pandas as pd
dataset1 = pd.read_csv('file1.csv')
dataset2 = pd.read_csv('file2.csv')
dataset3 = pd.read_csv('file3.csv')
</code></pre>
<p>and then I simply import this module from Jupyter Notebook whenever I need some data.</p>
<p>When I do this, on <code>EDA.ipynb</code>, am I storing dataset1, 2, 3 in RAM memory so that I don't make file IO every time I call <code>my_datasets.dataset1</code>?</p>
<p>Is there any other inefficiencies that you'd like to address?</p>
| <python><pandas><jupyter-notebook><io> | 2023-07-16 08:19:02 | 1 | 4,040 | user8491363 |
76,697,253 | 5,239,250 | The second call to a tkinter dialog does not fill the list | <p>In a Python script, I display twice the same tkinter dialog.</p>
<p>The first time, it's correctly filled with the value returned from <code>get_items()</code>, the second time the list is shown empty.</p>
<p>Here is a simplified version of my code, that reproduces the behavior:</p>
<pre class="lang-py prettyprint-override"><code>import tkinter as tk
class ItemSelector:
def __init__(self, title='title'):
self.selection = None
self.root = tk.Tk()
self.root.attributes('-type', 'dialog')
self.root.title(title)
self.root.bind('<Escape>', self.cancel)
self.root.bind('<Return>', self.send)
frame = tk.Frame(self.root) # we need a frame, if we want scrollbar
frame.pack()
items = self.get_items()
self.items_tk = tk.Variable(value=items) # FIX: tk.Variable(frame, value=items)
self.items_ui = tk.Listbox(frame, listvariable=self.items_tk, height=12)
scrollbar = tk.Scrollbar(frame, orient="vertical")
scrollbar.config(command=self.items_ui.yview)
scrollbar.pack(side="right", fill="y")
self.items_ui.config(yscrollcommand=scrollbar.set)
self.items_ui.pack()
submitButton = tk.Button(self.root, text='Submit', command=self.send)
submitButton.pack()
self.root.mainloop()
def get_items(self):
return ['a', 'b', 'c', 'd']
def cancel(self, *args):
self.root.quit()
self.root.withdraw()
def send(self, *args):
self.root.withdraw()
self.root.quit()
def main():
itemSelector = ItemSelector('A')
itemSelector = ItemSelector('B')
if __name__ == '__main__':
main()
</code></pre>
<p>How can I get the list of items to be shown also in the second call?</p>
<h2>First solution</h2>
<p>Swifty's answer helped me understand and circumscribe the issue: It's because I have multiple Tk instances.</p>
<p>His solution with <code>root</code> as a class variable does work, but, if I have one single root, I'd prefer to have it <em>outside</em> of the list dialog and share it with both classes. (I don't have working code for that solution but I will post if and when I get to that.)</p>
<p>As a first solution, I've added a <code>#FIX</code> in the original code applying <em>TheLizzard</em>'s hints from <a href="https://stackoverflow.com/a/69062053/5239250">https://stackoverflow.com/a/69062053/5239250</a> to my code: if you have multiple Tk instances you need to always explicitly pass the Tk context when creating Tk variables!</p>
| <python><tkinter> | 2023-07-16 08:05:28 | 1 | 878 | a.l.e |
76,696,870 | 22,234,318 | How to import variable from a root directory | <p>i have Telegram Bot project:</p>
<pre><code>bot.py
----- handlers/
----- --------- create_ticket.py
</code></pre>
<p>in file create_ticket.py
i need import a variable "bot" from file bot.py</p>
<p>i try</p>
<pre><code>from ..bot import bot
</code></pre>
<p>and get an error:</p>
<pre><code>ImportError: attempted relative import beyond top-level package
</code></pre>
| <python><import> | 2023-07-16 05:52:48 | 1 | 569 | jetgreen |
76,696,832 | 1,279,318 | How to send attachment on s/mime email | <p>I'm trying to send an encrypted mail, using s/mime protocol, but the mail client doesn't acknowledge the attachments, and doesn't decrypt them. I've checked some answers on stack overflow, but none of them worked for me. Please note that the encryption itself does work (see below)</p>
<p>Here is my code:</p>
<pre><code>import os
import ssl
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
from smtplib import SMTP
from typing import List, Optional, Union
from M2Crypto import BIO, SMIME, X509
port = 587
def send_email_by_smtp(sender: str, receiver: List[str], message: Union[bytes, str]):
# getting the credentials fron evironemnt
host = os.environ.get("SMTP_FQDN")
user = os.environ.get("SMTP_USER_ID")
password = os.environ.get("SMTP_PASSWORD")
# setting up ssl context
context = ssl.create_default_context()
# creating an unsecure smtp connection
with SMTP(host, port) as server:
# securing using tls
server.starttls(context=context)
# authenticating with the server to prove our identity
server.login(user=user, password=password)
# sending a plain text email
return server.sendmail(sender, receiver, message)
def send_mail(
*,
recipient: List[str],
subject: str,
body: str,
attachments: Optional[List[str]] = None,
):
if attachments is None:
attachments = []
message = MIMEMultipart()
from_address = os.environ.get("FROM_ADDRESS")
body_part = MIMEText(body, "html")
message.attach(body_part)
for attachment in attachments:
with open(attachment, "rb") as fh:
attachment_part = MIMEApplication(fh.read())
filename = os.path.basename(fh.name)
attachment_part.add_header(
"Content-Disposition",
"attachment",
filename=filename,
Content_Transfer_Encoding="base64",
Content_ID=f"<{filename}>",
Content_Type="text/csv",
Content_Disposition=f"attachment; filename={filename}",
)
message.attach(attachment_part)
# data = message.as_string()
data = encrypt_message(
from_address,
recipient,
subject,
message.as_bytes(),
from_key="certs/new/signer_key.pem",
from_cert="certs/new/signer.pem",
to_certs=["certs/new/recipient.pem"],
)
return send_email_by_smtp(from_address, recipient, data)
def encrypt_message(from_addr, to_addrs, subject, msg, from_key, from_cert=None, to_certs=None):
msg_bio = BIO.MemoryBuffer(msg)
sign = from_key
encrypt = to_certs
p7 = None
s = SMIME.SMIME()
if sign:
s.load_key(from_key, from_cert)
if encrypt:
p7 = s.sign(msg_bio, flags=SMIME.PKCS7_TEXT)
else:
p7 = s.sign(msg_bio, flags=SMIME.PKCS7_TEXT | SMIME.PKCS7_DETACHED)
msg_bio = BIO.MemoryBuffer(msg) # Recreate coz sign() has consumed it.
if encrypt:
sk = X509.X509_Stack()
for x in to_certs:
sk.push(X509.load_cert(x))
s.set_x509_stack(sk)
s.set_cipher(SMIME.Cipher("aes_256_cbc"))
tmp_bio = BIO.MemoryBuffer()
if sign:
s.write(tmp_bio, p7)
else:
tmp_bio.write(msg)
p7 = s.encrypt(tmp_bio)
out = BIO.MemoryBuffer()
out.write(f"From: {from_addr}\r\n")
out.write(f"To: {', '.join(to_addrs)}\r\n")
out.write(f"Subject: {subject}\r\n")
if encrypt:
s.write(out, p7)
else:
if sign:
s.write(out, p7, msg_bio, SMIME.PKCS7_TEXT)
else:
out.write("\r\n")
out.write(msg)
out.close()
return out.read()
if __name__ == "__main__":
res = send_mail(recipient=os.environ.get("TO_ADDRESS", "").split(","), subject="some subject", body="some body", attachments=[__file__])
print(res)
</code></pre>
<p>and the results on the client :</p>
<pre><code>Content-Type: multipart/mixed; boundary="===============2300897735206349356=="
MIME-Version: 1.0
--===============2300897735206349356==
Content-Type: text/html; charset="us-ascii"
MIME-Version: 1.0
Content-Transfer-Encoding: 7bit
some body
--===============2300897735206349356==
Content-Type: application/octet-stream
MIME-Version: 1.0
Content-Transfer-Encoding: base64
Content-Disposition: attachment; filename="smtp_test.py";
Content-Transfer-Encoding="base64"; Content-ID="<smtp_test.py>";
Content-Type="text/csv"; Content-Disposition="attachment;
filename=smtp_test.py"
aW1wb3J0IG9zCmltcG9ydCBzc2wKZnJvbSBlbWFpbC5taW1lLnRleHQgaW1wb3J0IE1JTUVUZXh0
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--===============2300897735206349356==--
</code></pre>
| <python><smime> | 2023-07-16 05:33:46 | 1 | 706 | eplaut |
76,696,752 | 13,060,649 | AssertionError: Expected a `Response`, `HttpResponse` or `HttpStreamingResponse` to be returned from the view, but received a `<class 'coroutine'>` | <p>I want to async view with my django rest framework <code>api_view</code> but I am getting the above error even if I had awaited my response. Here is my view</p>
<pre><code>@api_view(['POST'])
@permission_classes([AllowAny])
@authentication_classes([])
async def login(request):
email = request.data.get('email')
if email is None or email == '':
return Response(data={'success': False, 'message': 'Invalid credentials'})
user = await sync_to_async(User.get_by_email)(email=email)
if user is not None and user.is_active and user.check_password(raw_password=request.data.get('password')):
serializer = UserSerializer(user)
# TODO async
tokens_map = await sync_to_async(AuthUtils.generate_token)(request=request, user=user)
return Response({'success': True, 'user': serializer.data, 'tokens': tokens_map})
return Response(data={'success': False, 'message': 'Invalid login credentials'}, status=status.HTTP_404_NOT_FOUND)
</code></pre>
<p>Is there any way to use async views with django in an efficient way?</p>
<p>Also for async views will the traditional django middlewares needs to changed?</p>
| <python><django><django-rest-framework><python-asyncio><asgi> | 2023-07-16 05:00:14 | 1 | 928 | suvodipMondal |
76,696,731 | 446,215 | 'langchain' is not a package | <p>Running into this error while trying to run a basic tutorial script for langchain:</p>
<pre><code>ModuleNotFoundError: No module named 'langchain.llms'; 'langchain' is not a package
</code></pre>
<p>Here is the code snippet:</p>
<pre><code>from langchain.llms import OpenAI
llm = OpenAI(temperature=0.9)
text = "What is a popular recipe using coconut milk?"
print(llm(text))
</code></pre>
| <python><langchain><chatgpt-api> | 2023-07-16 04:50:32 | 2 | 6,056 | Deepak Joy Cheenath |
76,696,685 | 6,294,231 | Unable to import holidays from fb prophet package | <p>Getting error while importing holidays from fb prophet as below. Seems like an intermittent issue.</p>
<pre><code>import fbprophet.hdays as hdays_part2
TypeError: This is a python-holidays entity loader class. For entity inheritance purposes please import a class you want to derive from directly: e.g., `from holidays.countries import Entity` or `from holidays.financial import Entity`.
fbprophet version == 0.7.1
</code></pre>
| <python><facebook-prophet><python-holidays> | 2023-07-16 04:33:06 | 0 | 2,021 | Sarang Manjrekar |
76,696,590 | 6,929,343 | Tkinter Treeview how to change tree.item(Id)['text'] | <p>Getting a Tkinter Treeview item's text is straight forward:</p>
<pre class="lang-py prettyprint-override"><code>my_text = tree.item(Id)['text']
</code></pre>
<p>I can't seem to find a way of setting the <code>text</code> to a new value.</p>
<p>I've tried:</p>
<pre class="lang-py prettyprint-override"><code>tree.set(Id, text=legal_string)
# TypeError: set() got an unexpected keyword argument 'text'
tree.set(Id, ['text']=legal_string)
# SyntaxError: keyword can't be an expression
tree.set(Id)['text'] = legal_string
# Has no effect
tree.set(Id, "#0", legal_string) # 'text'
# TclError: Display column #0 cannot be set
tree.set(Id, 0, legal_string)
# Changes Count/Last Access Column (first column, not 'text' or "#0")
</code></pre>
| <python><tkinter><treeview> | 2023-07-16 03:44:02 | 1 | 2,005 | WinEunuuchs2Unix |
76,696,579 | 8,497,844 | Variables in decorators without syntactic sugar | <p>There is code:</p>
<pre><code>def bread(func):
def wrapper(*args, **kwargs):
print('</------\>')
func(*args, **kwargs)
print('<\______/>')
return wrapper
def ingredients(func):
def wrapper(*args, **kwargs):
print('tomato')
func(*args, **kwargs)
print('lettuce')
return wrapper
def burger(food='bacon'):
print(food)
burger = bread(ingredients(burger))
burger()
</code></pre>
<p>Output:</p>
<pre><code></------\>
tomato
bacon
lettuce
<\______/>
</code></pre>
<p>How you can understand there are present two decorators without syntactic sugar.</p>
<p><code>burger = bread(ingredients(burger))</code>
like</p>
<pre><code>@bread
@ingredients
def burger(...):
...
</code></pre>
<p>I want to push a some variable to the <code>burger()</code>. If I change <code>burger = bread(ingredients(burger))</code> to <code>burger = bread(ingredients(burger("cheese")))</code> I get an errors.</p>
<p><code>TypeError: 'NoneType' object is not callable</code></p>
<h1>My questions:</h1>
<p>Main question: I learn decorators in detail and I want to know how variables transfer in decorators without syntactic sugar?</p>
<h2>Subquestionals:</h2>
<ol>
<li>Why does not work <code>burger = bread(ingredients(burger("cheese")))</code> in my case?</li>
<li>How are variables actually passed to the <code>wrapper</code> sub function in <code>bread</code> and <code>ingredients</code> functions in without syntactic sugar?</li>
<li>Are there difference between work with variables passed to the decorator function and decorator sub function (Question #2) without syntactic sugar? Or the code will be the same - level add only.</li>
</ol>
<p>I mean this exanple in Q. 3:</p>
<pre><code>def bread(*args, **kwargs):
def wrapper(func):
def subwrapper(*args, **kwargs):
print('</------\>')
func(*args, **kwargs)
print('<\______/>')
return subwrapper
return wrapper
@bread('test')
def burger(food='bacon'):
print(food)
</code></pre>
| <python><python-decorators> | 2023-07-16 03:33:48 | 2 | 727 | Pro |
76,696,569 | 9,588,300 | Pyspark stuck and not processing. It shows more than 1K processes | <p>I have a <strong>for loop</strong> running in databricks and the first iterations run fast, then it <strong>gets slower and then it doesn't proceeds at all</strong>. While I know that is common in for loops if data size is increasing on each iteration and/or there's garbage variables not being deleted, <strong>at least I should see that either RAM/CPU/DISK/NETWORK are nearing 100%, right?</strong> but they are not, in fact they are not used at all. And also, at least I hoped to see spark Jobs being processed at SparkUI but there are none when the for loop gets stuck. <strong>Despite the notebook cell says the cell is still running.</strong></p>
<p>So do I have some resource that got clugged that is neither RAM/CPU/DISK/NETWORK?
And why spark shows no running jobs despite the for is still running?</p>
<p>Also, spark UI shows there's no job running, despite the notebook says the command is still running, and I know for sure it hasn't finished because I should see files getting created on AWS S3.</p>
<p>My case is that I am trying to generate some dummy data for testing. My goal is to create many csv files of the same dataframe schema that vary just in a datestamp column, so to emulate incremental load scenario. My goal is to have 400 csv that are essentially the same dataframe but with the datestamp column changing by 1 day, as if I received a file on Jan 01, another on Jan 02, another in Jan 03 and so on for 400 days.</p>
<p>For that, I have my "base" dataframe <code>input_df</code>, and I have a for loop in databricks that reads from it, increases the <code>id</code> column and <code>datestamp</code> column (as if they were new rows) and writes into S3 with a datestamp string. Here is the loop</p>
<pre><code>another=input_df
for i in range(400):
# I get the min and max values of column "id" to then add the ids for the new df
aggs=another.agg(max('id'),min('id'))
max_id=aggs.collect()[0][0]
min_id=aggs.collect()[0][1]
# Here I add the id column to emulate "new data" and the datestamp column
another=another.withColumn('id',col('id')+max_id-min_id+1).\
withColumn('created_time',date_add(col('created_time'),1)).\
withColumn('created_time',date_format(col("created_time"), "yyyy-MM-dd'T'HH:mm:ss.SSSZ"))
#here I create the file name by using the datestamp
date_procesed=datetime.strptime('20220112','%Y%m%d') + timedelta(days=i+1)
date_procesed=date_procesed.strftime('%Y%m%d')
print(date_procesed)
#And here I write it in a single csv file
another.coalesce(1).write.option('header','true').csv('dbfs:/tmp/wiki/transaction/'+date_procesed)
</code></pre>
<p>Now the cell of the notebook runs for about 11 files (that created and completed about 40 jobs) and then stops. I thought some resource was nearing capacity. But this is my problem</p>
<ol>
<li>At Spark UI no job is running. As if the notebook is not even creating more jobs</li>
<li>All the first 40 jobs created my the first 11 iterations of the loop are completed (and I see the files written on S3)</li>
<li>At GangliaUI I see only the driver is doing everything (which is expected cause my input_df was created using the <code>rand</code> library. But neither its CPU/RAM/NETWORK/DISK are full, and they are not even fluctuating for nearly an hour</li>
</ol>
<p>here's a picture of the driver at ganglia. you can see the resources (CPU,RAM,NETWORK) kind of do spike at some point in time (when the for loop was actually working, creating and completing spark jobs). But then they downsized and are stable, but the for loop stops and I know it shouldn't .</p>
<p>My ultimate questions are</p>
<ol>
<li>Do I have some resource that got clugged that is neither
RAM/CPU/DISK/NETWORK?</li>
<li>And why spark shows no running jobs despite the for is still
running? Why this for loop</li>
<li>doesn't works? I know they are not good practice in python, let
alone in spark. But I have no answer why it's not processing</li>
</ol>
<p><a href="https://i.sstatic.net/6BvfN.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/6BvfN.png" alt="Ganglia" /></a></p>
<p>Also as a side note, I noticed this on processes running on the driver (despite there's no spar job running) So I suspect spark is not closing some ports/connections or declaring processes as done</p>
<p><a href="https://i.sstatic.net/0atMe.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/0atMe.png" alt="Process running" /></a></p>
| <python><apache-spark><databricks><cluster-computing><ganglia> | 2023-07-16 03:28:22 | 2 | 462 | Eugenio.Gastelum96 |
76,696,552 | 17,275,588 | Most effective way to programmatically sort images into categories by color? | <p>I'm working on a project where I need to sort a large pool of images into a variety of color categories. It's pretty complicated because first, the sorting needs to happen at three levels:</p>
<ol>
<li><p>Very simple basic colors: "blue", "green", etc.</p>
</li>
<li><p>More specific colors: "steel blue", "crimson red", etc.</p>
</li>
<li><p>Very broad color groups: "vibrant and colorful", "warm colors", "neutral colors", etc.</p>
</li>
</ol>
<p>It's even MORE complicated because, how humans would sort the images kind of depends and might vary image by image. What I mean is, in some cases, the actual surface area covered by a specific color might be small, but if it's a really prominent focal point (for example, a bright red barn amid a black and white landscape scene), the human might sort it into the "red" category since the red is so pronounced. In other cases, there might be a fairly large surface area spanned by a certain color, but maybe the distribution of the color across the image actually makes it not really stand out or even "feel" like an image of that color. This mostly comes down to "surface area" vs. "color dominance/prominence".</p>
<p>While it felt like a simple task at first, the more I work on it, the more complex it seems to become.</p>
<p>Are there any standardized APIs, python libraries, or software tools that massively simplify this process by taking these different factors into consideration?</p>
<p>A few approaches I've tested so far are: Custom Python scripts that use kmeans to get the RGB color codes of the colors whose proportion in the image passes some specified threshold. I then tried taking that a step further, and mapping those more complex colors onto the closest match "simple colors", by mapping "steel blue" onto just "plain blue", etc. Really after lots of experimenting with this, the results simply weren't that good.</p>
<p>Others I've experimented with are: Using tools like Google Cloud Vision to get the "dominant colors." This is really a mixed bag. For example, sometimes an entirely black and white image with just like a small dash of another color will show the "another color" as the dominant color and totally miss the black and white. Another API-based tool, Ximilar, seems to output probably some of the best results I've seen -- but it appears to mostly be based entirely on surface area vs. color prominence.</p>
<p>Other ideas / other standard solutions to this problem would be greatly appreciated, as I've been working for hours on it and haven't seemed to hit upon the optimal solution yet.</p>
| <python><machine-learning><automation><colors><artificial-intelligence> | 2023-07-16 03:20:59 | 2 | 389 | king_anton |
76,696,379 | 9,588,300 | spark read file with string column as factor (or enum) | <p>I have seen in pyspark you can define a schema to a file that is going to be read. One very simple example is below:</p>
<pre><code>schema_input=StructType([StructField('some_string_column',StringType(),False)])
df=spark.read.schema(schema_input).option('header','true').csv(<path>)
</code></pre>
<p>Assuming I have a column that has only three possible string values (red,blue and yellow) but the dataframe is 1 billion rows long, isn't there a way to read as factors or enum to save some space/time?</p>
<p>I don't know if there's an equivalent <code>EnumType(<enum_list>)</code> to the <code>StringType()</code> data type. I have read the <a href="https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/data_types.html" rel="nofollow noreferrer">documentation</a> on pyspark datatypes and it only seems to understand strings as strings, there doesn't seems to be a something like reading as factors/enums to save space</p>
<p>One important note is that I am asking if it's possible to do this while reading time, not to encode the string column after it has been read.</p>
| <python><apache-spark><pyspark> | 2023-07-16 01:42:59 | 1 | 462 | Eugenio.Gastelum96 |
76,696,347 | 11,748,924 | Efficient code for screencap adb with python | <p>I have this code, it works like I expect:</p>
<pre><code>import subprocess
from time import sleep
# Run the command and capture the stdout
COMMAND = 'adb exec-out screencap -p'
FRAME_COUNT = 0
while 1:
with open(f'./frames/frame_{FRAME_COUNT}.png', 'wb') as file:
stdout, stderr = subprocess.Popen(COMMAND,
stdout=subprocess.PIPE).communicate()
# Store the byte stdout in a variable
file.write(stdout)
sleep(1)
FRAME_COUNT += 1
</code></pre>
<p>But I'm doubting about efficiency of code, mainly <code>Popen</code>. Every iteration, it's creating instance <code>Popen</code>, is it okay for memory?, Any idea for better solution?</p>
<p>Update, I added opencv:</p>
<pre><code>import subprocess
import cv2
import numpy as np
from time import time
COMMAND = 'adb exec-out screencap -p'
while True:
tLast = time()
# Capture the screen using adb
png_stdout_bytes = subprocess.check_output(COMMAND)
# Convert the stdout bytes to a numpy array
png_bytes = np.frombuffer(png_stdout_bytes, np.uint8)
# Decode the image from the numpy array
img = cv2.imdecode(png_bytes, cv2.IMREAD_COLOR)
# Resize the image to be 50% smaller
width = int(img.shape[1] * 0.45)
height = int(img.shape[0] * 0.45)
resized_img = cv2.resize(img, (width, height))
fps = 1 / (time() - tLast)
cv2.putText(resized_img, f'FPS: {fps:.2f}', (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# Display the resized image
cv2.imshow('Screen Capture', resized_img)
cv2.waitKey(1)
</code></pre>
| <python><subprocess><pipe><adb> | 2023-07-16 01:22:28 | 1 | 1,252 | Muhammad Ikhwan Perwira |
76,696,045 | 14,397,434 | How to return file names that don't have certain sentences? | <p>So I made some code that returns the names for each file in a directory.
I want to move on to <em>filtering</em> the files that my loop comes across whether that be during the loop itself or during the Path() function. <strong>Help?</strong></p>
<pre><code>from pathlib import Path
# grab all the pdf's, essentially
pdf_files = Path("C:\\Users\\ME\\Documents\\PretendFolder").glob("*.pdf")
for pdf in pdf_files:
print(pdf)
</code></pre>
<p>My Output:</p>
<p><code>C:\Users\ME\Documents\PretendFolder\Adjusted NO STORY.pdf C:\Users\ME\Documents\PretendFolder\Adjusted.pdf</code></p>
<p>My 1st Attempt:</p>
<pre><code>canvas = []
for pdf in pdf_files:
canvas.append(pdf =! 'NO STORY')
print(pdf)
</code></pre>
<p>My 2nd Attempt:
This second attempt doesn't print anything at all for some reason.</p>
<pre><code>pdf_files = Path("C:\\Users\\ME\\Documents\\PretendFolder").glob("*.pdf")
for pdf in pdf_files:
if not pdf.__contains__('NO STORY'):
print(pdf)
</code></pre>
| <python><for-loop><path> | 2023-07-15 22:47:42 | 1 | 407 | Antonio |
76,695,861 | 9,901,261 | How to get enum value | <pre><code>from pynput.keyboard import Key
from pynput.mouse import Button
from enum import Enum
value=Key.esc
def corresponding_event(key):
print(type(key))
corresponding_event(value)
</code></pre>
<p>produces <enum 'Key'></p>
<p>How would I obtain the value for the enum 'Key'and also check if it's an enum the function can pass different type of values as well.
<enum 'Button'> ,<enum 'Key'> ,<class 'str'> ,<class 'int'></p>
<p>I know you can check for enums with</p>
<pre><code>if isinstance(key,Enum):
print(key.name,key.value)
</code></pre>
<p>I would like to do different actions if it's a key or button.</p>
| <python><enums><pynput> | 2023-07-15 21:34:11 | 1 | 9,989 | Arundeep Chohan |
76,695,759 | 1,056,563 | Permission denied on creating a file after os.makedirs even after setting umask(0) | <p>A popular question <a href="https://stackoverflow.com/questions/5231901/permission-problems-when-creating-a-dir-with-os-makedirs-in-python">Permission problems when creating a dir with os.makedirs in Python</a>, as well as a number of other resources mention setting</p>
<blockquote>
<p>os.umask(0)</p>
</blockquote>
<p>before calling <code>os.makedirs()</code>. I have done this, but still have permission denied on creating files in the new directory.</p>
<pre><code>BASEDIR="./cdm"
import os
from pathlib import Path
try:
original_umask = os.umask(0)
print("original_umask", original_umask)
os.makedirs(BASEDIR, mode = 0o777, exist_ok = True)
print("files: ",[f.name for f in Path(BASEDIR).glob("*")])
print('perms on dir', hex(os.stat(BASEDIR).st_mode))
finally:
os.umask(original_umask)
with open(f"{BASEDIR}/xx.yml",'w') as f:
f.write("hi")
</code></pre>
<p>Here is the result:</p>
<pre><code>original_umask 18
files: []
perms on dir 0x4177
PermissionError: [Errno 13] Permission denied: './cdm/xx.yml
</code></pre>
<p>So the perms are not open to the world (<em>4177</em>) even after setting <code>umask(0)</code>. But that should not matter here since the code creating the new files is in the same process/owner.</p>
<p>I am on <code>macOS</code> <code>Ventura 13.4.1</code> using <code>python3.10</code></p>
| <python> | 2023-07-15 20:59:46 | 0 | 63,891 | WestCoastProjects |
76,695,216 | 6,779,049 | Python string to formatted text in Apple Notes? | <p>I've written a Python script to extract notes and highlights from my kindle. Now I would like to create a formatted string that I can write from Python to my clipboard, and then ideally paste into Apple Notes with formatting (headers, font style, etc.). I've looked around by can't really find anything on this. Is this possible? I'm open to using additional tools (shortcuts, applescript, etc.) if absolutely necessary, but my preference would be to have a self-contained python script.</p>
| <python><applescript><richtext> | 2023-07-15 18:27:42 | 1 | 398 | Nick-H |
76,695,194 | 6,380,992 | Why setting index_col to zero shifts the first column heading by one row? | <p>I have the following code:</p>
<pre><code>import pandas as pd
df = pd.read_csv('data.csv', index_col=0)
print(df)
</code></pre>
<p>The <em>data.csv</em> file contents are:</p>
<pre><code>Reg. No, Score
234, 78
467, 98
876, 23
675, 49
123, 56
</code></pre>
<p>This gives me the following weird output:</p>
<pre><code> Score
Reg. No
234 78
467 98
876 23
675 49
123 56
</code></pre>
<p>If I remove the <code>index_col</code> parameter or set it to <code>None</code> then both <strong>Reg. No</strong> and <strong>Score</strong> are aligned to the same row. But, for further processing of this data, I need the <code>index_col=0</code>.
How to get around this?</p>
| <python><pandas> | 2023-07-15 18:21:55 | 1 | 1,232 | Seshadri R |
76,694,986 | 19,366,064 | Python poetry set up | <pre><code>[tool.poetry]
name = "webapp"
version = "0.1.0"
description = ""
authors = []
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.11"
[tool.poetry.group.dev.dependencies]
pytest = "^7.4.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
</code></pre>
<p>I have a pyproject.toml file. What would be the command line to change dependency versions? How do I add depdencies to .group.dev.depdencies only?</p>
| <python><python-poetry> | 2023-07-15 17:21:10 | 1 | 544 | Michael Xia |
76,694,813 | 10,179,854 | Application bundles uploaded using Google Developer API do not appear anywhere, despite successful request execution | <p>Here is the Python code I am using, building on the <code>googleapiclient</code> module:</p>
<pre><code>from googleapiclient.discovery import build, MediaFileUpload
from oauth2client.service_account import ServiceAccountCredentials
app_id = "com.myapp.myapp.app"
aab_file_path = "H:/packages/0.0.18/com.myapp.myapp.app.aab"
service_account_file_path = "service-account-key.json"
# Create the service account credentials object.
credentials = ServiceAccountCredentials.from_json_keyfile_name(
service_account_file_path, ["https://www.googleapis.com/auth/androidpublisher"]
)
# Create the API client object.
service = build("androidpublisher", "v3", credentials=credentials)
# Get an edit ID.
request = service.edits().insert(packageName=app_id)
response = request.execute()
editId = response['id']
media_body = MediaFileUpload(aab_file_path, mimetype='application/octet-stream', resumable=True)
request = service.edits().bundles().upload(
packageName=app_id,
editId=editId,
media_body=media_body,
)
response = request.execute()
# Check that the response contains a versionCode.
versionCode = response.get("versionCode")
if response.get("versionCode") is not None:
print("Bundle successfully uploaded.")
else:
print("Failed to upload bundle.")
</code></pre>
<p>The script runs through, the <code>versionCode</code> retrieved is correct, but the bundle is not actually visible in the <code>App bundle explorer</code> in the Google Play Console, neither is it useable to update a track, or returned by <code>edits.bundles.list</code>.</p>
<p>Could someone please explain to me what I am doing wrong?</p>
| <python><android><google-api><google-play-console> | 2023-07-15 16:43:01 | 1 | 399 | Julien Debache |
76,694,633 | 6,357,916 | Giving user input to interactive command line tool from jupyter notebook | <p>Many command line utilities do certain things and then wait for user input before proceeding further. I was trying one such utility but inside jupyter notebook. But let me give an example of reading variable command. I typed <code>! read var</code> in jupyter notebook cell, it simply hanged awaiting (with * in front of it) for user input:</p>
<p><a href="https://i.sstatic.net/OjmQz.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/OjmQz.png" alt="enter image description here" /></a></p>
<p>The utility I am trying to use in jupyter notebook exhibits same behavior. It executes and then simply hangs for user input with no way to provide a user input to it.</p>
<p>Is it possible to give user input to interactive command line tool when used jupyter notebook? And if yes, how?</p>
<p>PS: I want to do this because its great way to document in jupyter notebook (even full command line utility output).</p>
<p>PS: More common use case will be command line utilities that ask for y/n input. But more complex scenario will be: command line tools / scripts that themselves ask for user input "programatically"? By "programatically", I mean, in some environments, they may ask for one input while in other environment, it might ask for different input.</p>
| <python><jupyter-notebook><jupyter><jupyter-lab> | 2023-07-15 16:03:19 | 0 | 3,029 | MsA |
76,694,599 | 4,451,315 | Print source of lambda without surrounding call | <p>If I define a function</p>
<pre class="lang-py prettyprint-override"><code>def foo(function):
import inspect
return inspect.getsource(function)
</code></pre>
<p>and then call it, I get:</p>
<pre class="lang-py prettyprint-override"><code>In [11]: foo(lambda x: x[0] + x[1]*2)
Out[11]: 'foo(lambda x: x[0] + x[1]*2)\n'
</code></pre>
<p>Note how it printed the entire line, rather than just the lambda function.</p>
<p>Is there a way to get it to output just the lambda?</p>
<p>Desired output:</p>
<pre class="lang-py prettyprint-override"><code>In [11]: foo(lambda x: x[0] + x[1]*2)
lambda x: x[0] + x[1]*2'
</code></pre>
<p>Is there a way to do this that doesn't involve using a regular expression?</p>
<p>EDIT:</p>
<p>Example of how <code>ast.parse(inspect.getsource(function))</code> may fail:</p>
<pre class="lang-py prettyprint-override"><code>ast.parse(foo(
lambda x: x+1))
</code></pre>
| <python><lambda><abstract-syntax-tree><python-ast> | 2023-07-15 15:56:13 | 1 | 11,062 | ignoring_gravity |
76,694,467 | 11,168,443 | Is it possible to access SQL Trace Data (same as what SQL Profiler can see) through python? | <p>Overview: I need to monitor traffic to the SQL server, and be able to extract some of the parameters, and determine which user sent the request. This all needs to be done in python.</p>
<p>I'm going to lay out how I need this to work, because I may be looking at it wrong.</p>
<p>I have User A, User B, and User C. All three of these users are using an inventory software (very large program, made by a big company) to communicate with the Server. The server is running SQL Server 17. I have a python application each user has installed on their computer and they are using alongside their inventory management software. This program gives the users the ability to do things the inventory software just simply doesn't offer. My python program uses PYODBC to connect to the SQL server and run queries to the same SQL database the inventory management server is already connected to.</p>
<p>I need my python software to be able to monitor the SQL queries sent between the inventory software on each users computer and the SQL server. I can already do this by logging into the SQL server VM and opening up SQL Server Profiler and starting a trace. But now I need to access that information inside my python application on each of the users computers.</p>
<p>The end goal here is for the python application on user A computer to be monitoring the SQL queries, and then a specific query is ran to the server (IE: exec StoredProc1 4, 500, 100) I need to be able to extract the parameters, and call functions to be able to display additional data in my python application.</p>
<p>The python program installed on User A's computer only needs to monitor traffic between user A and the server. I do not care about any traffic between any other computer and the server.</p>
<p>I tried to create an Extended Event Session, and log the data to a file, and just access that file with my python program to read from it, but the problem is this needs to happen in real time, and I could not access the .xel file while the SQL server is writing to it.</p>
<p>One "idea" I had was to instead of logging the data to a .xel file, I could instead insert it into a table, and then just select from the table with my python application every 1-2 seconds, but I am not sure how to set the Extended Event session to log the data into a table. Another "idea" I had was to just monitor the packets sent from the users computer to the specific server IP address and port and try and dissect that, but I am not exactly sure this will work either.</p>
<p>If anyone has ever done anything like this and has any input on how to best make this work, without taxing the server too much, it would be greatly appreciated.</p>
| <python><sql><python-3.x><sql-server> | 2023-07-15 15:26:17 | 0 | 965 | Lzypenguin |
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