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
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| UserExpertiseLevel
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⌀ |
|---|---|---|---|---|---|---|---|---|
77,653,011
| 12,304,000
|
send 50k post requests within a minute
|
<p>I want to load-test a system so I want to send around 50k POST requests within a minute. I want to do this for around 4 hours consecutively. The URL and payload is always the same. I just need to play around with the number of requests.</p>
<p>This is what I tried:</p>
<pre><code>import requests
import concurrent.futures
import time
....
events_per_minute_target = 50000
# Calculate the total number of events to send
total_events = events_per_minute_target * 60 * 2 * 2
# Set the duration for each minute
duration_per_minute = 60
start_time = time.time()
def send_request(_):
try:
response = requests.post(url, headers=headers, json=payload)
# Check if the request was successful (status code 2xx)
if response.ok:
print(f"Request successful. Response: {response.text}")
else:
print(f"Request failed. Status code: {response.status_code}, Response: {response.text}")
except requests.exceptions.RequestException as e:
print(f"An error occurred: {e}")
# Use a ThreadPoolExecutor to send requests concurrently for x minutes
with concurrent.futures.ThreadPoolExecutor() as executor:
for _ in range(duration_per_minute * 2 * 2):
# Submit tasks to the executor
futures = {executor.submit(send_request, None) for _ in range(events_per_minute_target)}
# Wait for all tasks to complete
concurrent.futures.wait(futures)
# Calculate the actual events per minute for the entire test duration
elapsed_time = time.time() - start_time
actual_events_per_minute = total_events / (elapsed_time / 60)
print(f"Load test completed. Actual events per minute: {actual_events_per_minute:.2f}")
</code></pre>
<p>But my Python script keeps running after 4 hours as well so I am not sure if this is behaving as intended or not. Since it's running after 2 hours as well, i get an impression that it failed to send 50k requests within a minute.</p>
<p>What could be an alternate approach?</p>
<p>Edit:</p>
<p>After running for more than 4 hours, I got this error:</p>
<pre><code>Traceback (most recent call last):
File "/Users/Desktop/snowplow/gtm_load_test/gtm.py", line 222, in <module>
for future in concurrent.futures.as_completed(futures, timeout=duration_per_minute * 4):
File "/opt/homebrew/Cellar/python@3.11/3.11.6/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py", line 239, in as_completed
raise TimeoutError(
TimeoutError: 45835 (of 50000) futures unfinished
</code></pre>
|
<python><python-3.x><multithreading><http><concurrency>
|
2023-12-13 10:58:36
| 0
| 3,522
|
x89
|
77,652,773
| 1,342,516
|
Matching elements in two equal-sized lists so that the pairs of elements have similar values
|
<p>For lists</p>
<pre><code>l1 = [2.5, 1.1, 3.6]
l2 = [3.4, 1.0, 2.2]
</code></pre>
<p>how to sort l2 efficiently to</p>
<pre><code>l2_sorted = [2.2, 1.0, 3.4]
</code></pre>
<p>so that Sum_i(l2_sorted[i] - l1[i])^2 is minimum?</p>
<p>There is a context for this question: the three complex roots, root_A, root_B, and root_C for the polynomial function x^3 + a x^2 + b x + c = 0 vary continuously with the parameters a, b, and c in the complex plane, and I want to keep track which root is which when varying the parameters. Simply sorting them each time according to their locations doesn't work because their trajectories (series of locations when continuously varying the parameters) can cross each other.</p>
<p>The actual problem is a much higher-order polynomial with a large number of roots, making it hard to track them manually.</p>
|
<python>
|
2023-12-13 10:24:51
| 1
| 539
|
user1342516
|
77,652,505
| 5,731,101
|
class inheritance in python as fields - side effects / incorrect handling
|
<p>Take the following simplified class structure which is used to clean and structure data from a legacy system.</p>
<ul>
<li>ProductModels contain fields</li>
<li>Fields are their own classes and types on which a bunch of cleaning is done.</li>
</ul>
<p>Overall the approach works like a charm in our use-case. But there is an issue where newly set products hold values from a previously generated product is the field is unused.</p>
<p>The minimal reproducible structure for Field / ProductModel looks like:</p>
<pre class="lang-py prettyprint-override"><code>class BaseManager:
database_settings = {}
def __init__(self, model_class):
self.model_class = model_class
def build_query(self):
pass
def connection(self):
pass
def run_query(self):
pass
def select(self):
pass
class MetaModel(type):
manager_class = BaseManager
def __new__(mcs, name, bases, attrs):
field_list = []
for k, v in attrs.items():
if isinstance(v, Field):
v.field_name = k
v.table_name = attrs.get('table_name')
field_list.append(k)
cls = type.__new__(mcs, name, bases, attrs)
cls.__field_list__ = field_list
return cls
def _get_manager(cls):
return cls.manager_class(model_class=cls)
@property
def objects(cls):
return cls._get_manager()
class Field:
def __init__(self, field_name, value=None):
self.field_name = field_name
self.value = value
def set_value(self, value):
self.value = value
class ProductModel(metaclass=MetaModel):
sku = Field('sku')
name = Field('name')
table_name = 'my_table'
def __init__(self, **field_data):
for field_name, value in field_data.items():
getattr(self, field_name).set_value(value)
def __str__(self):
return f"{self.sku.value=}, {self.name.value=}"
</code></pre>
<p>Now look at the first example:</p>
<pre class="lang-py prettyprint-override"><code> ...: prod = ProductModel(sku='124', name='Name')
...: print(prod)
self.sku.value='124', self.name.value='Name'
</code></pre>
<p>The value for the sku = 124, which is correct.
The value for the name = "Name", which is also correct.</p>
<p>But now, the second example:</p>
<pre><code> ...: prod_two = ProductModel(sku='789')
...: print(prod_two)
self.sku.value='789', self.name.value='Name'
</code></pre>
<p>The value for sku has changed to 789, correct.
BUT the value for name, has remained "Name" instead of being None</p>
<p>It seem that when I create a new product, the field values are somehow kept from the initial product instead of being re-initialised.</p>
<p>One way of handling it, would be to reset all the field values upon a new ProductModel.<strong>init</strong>(). But this feels like a poor solution. Instead I would rather understand better how to initialise the classes correctly.</p>
<p>Can you show me the right way?</p>
|
<python><python-3.x><class><composition>
|
2023-12-13 09:47:19
| 2
| 2,971
|
S.D.
|
77,652,464
| 11,801,298
|
How to add rows with missing dates in pandas dataframe?
|
<p>I have dataframe with empty dates.
How can I add new rows with absent dates?</p>
<pre><code> dt_object high
0 2000-01-03 27.490000
1 2000-01-04 27.448000
2 2000-01-05 27.597000
3 2000-01-06 27.597000
4 2000-01-07 27.174000
5 2000-01-10 28.090000
6 2000-01-11 29.250000
7 2000-01-12 28.850000
</code></pre>
<p>Expected output:</p>
<pre><code> dt_object high
0 2000-01-03 27.490000
1 2000-01-04 27.448000
2 2000-01-05 27.597000
3 2000-01-06 27.597000
4 2000-01-07 27.174000
5 2000-01-08 0
6 2000-01-09 0
7 2000-01-10 28.090000
8 2000-01-11 29.250000
9 2000-01-12 28.850000
</code></pre>
<p>I saw this answer</p>
<p>Add missing dates to pandas dataframe
<a href="https://stackoverflow.com/questions/19324453/add-missing-dates-to-pandas-dataframe">Add missing dates to pandas dataframe</a></p>
<p>but it doesn't describe my situation.
My dt_object is NOT index.</p>
<p>I need universal decision for any timeframes (15 minutes and hours too)</p>
<pre><code>15 minutes:
dt_object high
361980 2023-12-13 00:00:00 90.1216
361981 2023-12-13 00:15:00 90.1308
EMPTY
361983 2023-12-13 00:45:00 90.2750
EMPTY
361985 2023-12-13 01:15:00 90.3023
</code></pre>
|
<python><pandas>
|
2023-12-13 09:40:53
| 1
| 877
|
Igor K.
|
77,652,320
| 4,847,250
|
How to fix an vtk DLL import error when compiling with Pyinstaller?
|
<p>I'm having this error when I compile a code that uses the vtk module with pyinstaller.
<a href="https://i.sstatic.net/oKMnY.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/oKMnY.png" alt="enter image description here" /></a></p>
<p>usually I add some vtk hiddenimports to fix that kind of issue</p>
<pre><code>a = Analysis(
['EEG_Viewer_Converter.py'],
pathex=[],
binaries=[],
datas=data,
hiddenimports=['vtkmodules','vtkmodules.vtkWebCore', 'vtkmodules.all', 'vtkmodules.qt.QVTKRenderWindowInteractor', 'vtkmodules.util', 'vtkmodules.util', 'vtkmodules.numpy_interface', 'vtkmodules.numpy_interface.dataset_adapter',
</code></pre>
<p>in the .spec file generated with pyinstaller.
So I add <code>'vtkmodules.vtkWebCore'</code> in it but it didn't fix the issue this time.
How can I fix this issue when I execute the .exe?</p>
|
<python><import><dll><pyinstaller><vtk>
|
2023-12-13 09:20:33
| 0
| 5,207
|
ymmx
|
77,652,217
| 143,091
|
Allow optional parameter anywhere between subcommands
|
<p>I'm using argparse to create a tool with sub-commands. I have a flag, for example <code>-j</code> which gives the output as JSON, and want the user to be able to pass it anywhere on the command line:</p>
<pre><code>mytool courses list -j
mytool -j courses list
</code></pre>
<p>If I add the argument to my subparser, then I can only use the first version. If I add it to the main parser, then only the second version works.</p>
<p>I also tried using the <code>parents</code> argument, but that didn't work: If you pass the <code>-j</code> in the middle, then the last command doesn't see it and sets the variable to False.</p>
<pre><code>parser = argparse.ArgumentParser()
subparser = parser.add_subparsers(dest='command', required=True)
common_args = argparse.ArgumentParser(add_help=False)
common_args.add_argument('-j', '--json', action='store_true', help='Output as JSON', default=None)
course_parser = subparser.add_parser('course', parents=[common_args])
course_subparser = course_parser.add_subparsers(dest='subcommand', required=True)
course_list_parser = course_subparser.add_parser('list', parents=[common_args])
</code></pre>
|
<python><argparse>
|
2023-12-13 09:03:03
| 3
| 10,310
|
jdm
|
77,652,174
| 3,701,393
|
How to make the client download a static file from a custom module on clicking a button?
|
<p><strong>Odoo 14 Community Edition</strong></p>
<p>I have a custom module with a custom model inside it.</p>
<p>I created a custom view. It is working correctly.</p>
<p>Now that I need to add a button that let the client download a static file from the server.</p>
<p>The file should be put in the module folder, which I am still not sure where to put it, because of modularization.</p>
<p>Assume the button is created with a method for it correctly, how do I implement this and where should I put the file in order for it to work?</p>
<p><em>Note: It is just a normal PDF file. Think of it as a button to download a manual document.</em></p>
|
<python><odoo><odoo-14>
|
2023-12-13 08:55:05
| 1
| 6,768
|
holydragon
|
77,651,902
| 3,815,773
|
How can I figure out where a Python Segmentation fault comes from?
|
<p>I have a somewhat complex logging program, which I used to run for months in a row without crashes or problems. But recently I am getting - about once per day - crashes of this kind:</p>
<blockquote>
<p>Segmentation fault (core dumped)</p>
</blockquote>
<p>This is the sole error output I can see. It says "core dumped" but whereto? I found no dump file anywhere.</p>
<p>I tested all Python versions 3.8, 3.9, 3.10, 3.11, 3.12 - it may take a while to get this crash, but eventually it happens in all versions. I suspect PyQt5, but this is really gut feeling only.</p>
<p>What can I do about finding the cause of the Segmentation fault?</p>
|
<python><pyqt5><segmentation-fault>
|
2023-12-13 08:04:08
| 0
| 505
|
ullix
|
77,651,883
| 2,238,378
|
Getting/sending some information from Telegram at bot startup, after Application build() but before run_polling()?
|
<p>I'm a bit confused about this because Im new to python-telegram,-bot, and most examples online dont reflect the v20 changes for Updater and asyncio.</p>
<p>The relevant snippet of code is:</p>
<pre><code>def main() -> None:
persistence_object = PicklePersistence(filepath=persistent_data_file_path)
application = (
ApplicationBuilder()
.token(bot_token)
.persistence(persistence=persistence_object)
.post_init(post_init_handler)
.post_stop(post_stop_handler)
.build()
)
application.run_polling()
async def post_init_handler(application: Application) -> None:
print("init")
bot_id=application.bot.bot.id
# Needed: Code snippet here (details below)
# It produces a list, which is sent as a raw text private message to a static defined user_id
if __name__ == "__main__":
main()
</code></pre>
<p>The missing code snippet obtains a list of all chats the bot is in (private, group, supergroup), with some key information about each (see below) and then send that info to a specific user_id as a private message. It doesnt enter polling until that's succeeded. The info it collates and sends is:</p>
<pre><code>(chat id,
username/title of the chat,
chat type (private, group etc),
is the bot the owner of the chat? (if a group/channel),
bot's admin rights in the chat (if a group/channel and admin)
)
</code></pre>
<p>So a typical output sent would be:</p>
<pre><code>123456,@fred99,private,(ignored),(ignored)
872981,@technogeek_chat,supergroup,True,(rights data)
(etc)
</code></pre>
<p>I cant figure out the correct way to get and send this data at bot startup.</p>
<ul>
<li><p><strong>CORRECT API CALL/S TO USE?</strong><br/>All the examples I can find, assume their actions are done reactively within some callback handler as a result of a polled received update, or if done initially, the example uses older Updater/dispatcher techniques not applicable in v20.<br/>I * think * from the Transition guide to v20 that <code>application.create_task</code> could be the correct answer but if so I dont have a good example to rely on, how I'd use it for this.</p>
</li>
<li><p><strong>CORRECT PLACEMENT FOR API CALL/S?</strong><br/>I tried to move the code to between <code>application...build()</code> and before <code>run_polling()</code>, but the assignment <code>bot_id=application.bot.bot.id</code> failed and I couldnt get that to work. It wanted it initialized, I think, but I wasnt sure what was correct, or which was the more correct place of the two for this code snippet, and left it within <code>post_init_handler()</code> which at least * seemed * to work?</p>
</li>
<li><p><strong>GETTING DATA FOR THE CALL/S?</strong><br/>I tried to find code to list the chats the bot is in. The only links were to the example "chatmemberbot.py", and that code doesnt actually detect what chats the bot is in. It relies on adding and removing from a manually maintained list based on updates. Which could potentially desync from actual chats it's in, in some situations (bugs, persist store deleted/corrupted/old store restored, shutdown prior to final flush in the past, ...) and the bot would never know its list wasn't correct.</p>
</li>
</ul>
<p>What's the correct way to do this, please? Is it possible to have an actual example - simplified if it's easier - how it should be done, as I've scoured the docs and can't figure it from those?</p>
<p>Help much appreciated, thank you!!</p>
|
<python><telegram><python-telegram-bot>
|
2023-12-13 07:59:29
| 1
| 590
|
Stilez
|
77,651,841
| 8,037,521
|
Streamlit: buttons side-by-side in a sidebar
|
<p>I want to have two buttons side-by-side in the Streamlit sidebar. I search for this question and found this solution:</p>
<pre><code>col1, col2 = st.sidebar.columns(2)
with col1:
st.sidebar.button("Add")
with col2:
st.sidebar.button("Remove")
</code></pre>
<p>Unfortunately, it does absolutely nothing for me: the resulting buttons are located in separate rows, not in separate columns. Note that there is enough width of side bar for both buttons to be displayed side-by-side.</p>
<p>Why this does not work and is there some other solution? Streamlit version 1.29.0.</p>
|
<python><streamlit>
|
2023-12-13 07:51:52
| 1
| 1,277
|
Valeria
|
77,651,804
| 8,601,590
|
Python is not able to find libraries while accessing from shiny script
|
<p>I have a python script which is working fine while I'm running it from terminal. The same script I'm trying to invoke from shiny application with the arguments, but the shiny-server log says <code>File "/home/linuxadmin/Desktop/ADLS_test2.py", line 9, in <module> from azure.identity import DefaultAzureCredential ModuleNotFoundError: No module named 'azure.identity'</code> But <code>pip list</code> is displaying <code>azure-common 1.1.28 azure-core 1.29.5 azure-identity 1.15.0 azure-keyvault-secrets 4.7.0 azure-storage-blob 12.19.0 </code></p>
<p>I could understand that python is not able to recognize the libraries while the script is invoking from shiny server app. Below is the sample script from shiny-server app I'm trying to invoke python file. How do I set up the python libraries path to look for, in the Python file?</p>
<pre><code>server <- function(input, output, session) {
observeEvent(input$submitid,{
source <- renderText({ input$caption })
destination <- renderText({ input$caption2 })
system('python3 /home/linuxadmin/Desktop/ADLS_test2.py source destination')
output$info <- paste0('Source : ', source, ' | Destination : ', destination)
})
}
</code></pre>
|
<python><r><python-3.x><shiny><shiny-server>
|
2023-12-13 07:42:17
| 1
| 1,233
|
msr_003
|
77,651,663
| 2,444,251
|
Ffmpeg - transform mulaw 8000khz audio buffer data into valid bytes format
|
<p>I'm trying to read a bytes variable using ffmpeg, but the audio stream I listen to, sends me buffer data in <strong>mulaw encoded buffer</strong> like this:</p>
<p><a href="https://github.com/boblp/mulaw_buffer_data/blob/main/buffer_data" rel="nofollow noreferrer">https://github.com/boblp/mulaw_buffer_data/blob/main/buffer_data</a></p>
<p>I'm having trouble running the <strong>ffmpeg_read</strong> function from the transformers library found <a href="https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/audio_utils.py#L10" rel="nofollow noreferrer">here</a>:</p>
<pre><code>def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
"""
Helper function to read an audio file through ffmpeg.
"""
ar = f"{sampling_rate}"
ac = "1"
format_for_conversion = "f32le"
ffmpeg_command = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
try:
with subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) as ffmpeg_process:
output_stream = ffmpeg_process.communicate(bpayload)
except FileNotFoundError as error:
raise ValueError("ffmpeg was not found but is required to load audio files from filename") from error
out_bytes = output_stream[0]
audio = np.frombuffer(out_bytes, np.float32)
if audio.shape[0] == 0:
raise ValueError(
"Soundfile is either not in the correct format or is malformed. Ensure that the soundfile has "
"a valid audio file extension (e.g. wav, flac or mp3) and is not corrupted. If reading from a remote "
"URL, ensure that the URL is the full address to **download** the audio file."
)
return audio
</code></pre>
<p>But everytime I get:</p>
<pre><code>raise ValueError(
"Soundfile is either not in the correct format or is malformed. Ensure that the soundfile has "
"a valid audio file extension (e.g. wav, flac or mp3) and is not corrupted. If reading from a remote "
"URL, ensure that the URL is the full address to **download** the audio file."
)
</code></pre>
<p>If I grab any wav file I can do something like this:</p>
<pre><code>import wave
with open('./emma.wav', 'rb') as fd:
contents = fd.read()
print(contents)
</code></pre>
<p>And running it through the function does work!</p>
<p>So my question would be:</p>
<p>How can I transform my <strong>mulaw encoded buffer</strong> data into a valid bytes format that works with <code>ffmpeg_read()</code>?</p>
<p><strong>EDIT: I've found a way using pywav (<a href="https://pypi.org/project/pywav/" rel="nofollow noreferrer">https://pypi.org/project/pywav/</a>)</strong></p>
<pre><code># 1 stands for mono channel, 8000 sample rate, 8 bit, 7 stands
for MULAW encoding
wave_write = pywav.WavWrite("filename.wav", 1, 8000, 8, 7)
wave_write.write(mu_encoded_data)
wave_write.close()
</code></pre>
<p>This is the result: <a href="https://github.com/boblp/mulaw_buffer_data/blob/main/filename.wav" rel="nofollow noreferrer">https://github.com/boblp/mulaw_buffer_data/blob/main/filename.wav</a></p>
<p>the background noise is acceptable.</p>
<p>However, I want to use a FFMPEG instead to avoid creating a tmp file.</p>
|
<python><ffmpeg>
|
2023-12-13 07:08:41
| 2
| 592
|
Bob Lozano
|
77,651,471
| 16,853,253
|
AWS boto3 error 'IllegalLocationConstraintException'
|
<p>I'm getting this error - <code>'IllegalLocationConstraintException' 'The me-central-1' location constraint is incompatible for the region specific endpoint this request was sent to'</code> issue when trying to access object using presigned-url. I have created bucket to the location <code>('me-central-1')</code> and file gets uploaded then I try to access it using presigned-url it throws error, So I decided to test it again by creating another bucket in <code>('ap-south-1')</code> and generated presigned-url and tried to access it and it works. I still don't get what's the issue causing Location errors when accessing the object in bucket in middle east. The endpoint to be correct to <code>https://s3.me-central-1.amazonaws.com</code> .</p>
<p>Below is my python code:</p>
<pre><code>def save_to_s3_bucket(file, file_name):
try:
import base64,json
import requests
from io import BytesIO
from botocore.exceptions import ClientError
from botocore.config import Config
format, imgstr = file.split(';base64,')
s3 = boto3.client('s3', aws_access_key_id=settings.AWS_ACCESS_KEY_ID,
aws_secret_access_key=settings.AWS_SECRET_ACCESS_KEY, config=Config(signature_version='s3v4'),
region_name=settings.AWS_DEFAULT_REGION)
random_path = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(10))
s3.put_object(Bucket=settings.AWS_STORAGE_BUCKET_NAME, Key='pvt/doc/ln-app/'+random_path+'/'+file_name, Body=base64.b64decode(imgstr), StorageClass='REDUCED_REDUNDANCY')
img_url = settings.AWS_BUCKET_URL+'pvt/doc/ln-app/'+random_path+'/'+file_name
url = s3.generate_presigned_url('get_object', Params = {'Bucket': settings.AWS_STORAGE_BUCKET_NAME, 'Key': 'pvt/doc/ln-app/'+random_path+'/'+file_name}, ExpiresIn = 10000)
print('AWS-S3-URL--->>>>>>>>>', url)
res = {
'hasError': False,
'message': 'Success',
'response': {
'key': 'pvt/doc/ln-app/'+random_path+'/'+file_name,
'file_url': url
}
}
return res
except Exception as e:
res = {
'hasError': True,
'message': str(e),
'response': None
}
return res
</code></pre>
|
<python><amazon-web-services><amazon-s3><boto3>
|
2023-12-13 06:20:32
| 1
| 387
|
Sins97
|
77,651,418
| 2,052,889
|
How to use xfail in pytest in parameterized fixture when pytest.param is an elemenent in a tuple?
|
<p>This works</p>
<pre><code>import pytest
@pytest.mark.parametrize( "expected",
[
pytest.param(42, marks=pytest.mark.xfail(reason="some bug")),
],)
def test_func(expected):
assert 123 == expected
</code></pre>
<p>output is</p>
<pre><code>test_xfail_demo_1.py::test_func[42] XFAIL (some bug)
========== 1 xfailed in 0.07s ==========
</code></pre>
<p>but this one does not</p>
<pre><code>import pytest
def someFunc():
return 1
def someOtherFunc():
return 123
@pytest.mark.parametrize( "expected",
[
(1, pytest.param(42, marks=pytest.mark.xfail(reason="some bug"))),
],)
def test_func(expected):
assert someFunc() == expected[0]
assert someOtherFunc() == expected[1]
</code></pre>
<p>The error I got is</p>
<pre><code>> assert someOtherFunc() == expected[1]
E AssertionError: assert 123 == ParameterSet(values=(42,),
marks=(MarkDecorator(mark=Mark(name='xfail', args=(), kwargs={'reason': 'some bug'})),), id=None)
path/to/test_xfail_demo_2.py:10: AssertionError
==================== short test summary info ===============
FAILED path/to/test_xfail_demo_2.py::test_func[expected0] - AssertionError: assert 123 == ParameterSet(values=(42,), marks=(MarkDecorator(mark=Mark(name='xfail', args=(), kwargs={'reason': 'some bug'})),), id=None)
============== 1 failed in 0.09s =====
</code></pre>
<p>The reason I want expected to be a tuple is because I have multiple assert in a single test function, and I want to pass in all the expected values as a tuple or list, but mark only some of the list elements to be xfail or skip.</p>
|
<python><pytest><fixtures>
|
2023-12-13 06:08:17
| 0
| 4,192
|
Shuman
|
77,651,219
| 10,200,497
|
Finding the first row that meets conditions of a mask and selecting one row after it
|
<p>This is my dataframe:</p>
<pre><code>import pandas as pd
df = pd.DataFrame(
{
'a': [100, 1123, 123, 100, 1, 0, 1],
'b': [1000, 11123, 1123, 0, 55, 0, 1],
'c': ['a', 'b', 'c', 'd', 'e', 'f', 'g'],
}
)
</code></pre>
<p>And this is the output that I want. I want to create column <code>x</code>:</p>
<pre><code> a b c x
0 100 1000 a NaN
1 1123 11123 b NaN
2 123 1123 c NaN
3 100 0 d NaN
4 1 55 e e
5 0 0 f NaN
6 1 1 g NaN
</code></pre>
<p>By using a mask:</p>
<pre><code>mask = (
(df.a > df.b)
)
</code></pre>
<p>First of all I need to find the first occurrence of this mask which in my example is row number <code>3</code>. Then I want to move one row below it and use the value in column <code>c</code> to create column <code>x</code>.</p>
<p>So in my example, the first occurrence of <code>mask</code> is row 3. One row after it is row 4. That is why <code>e</code> is selected for column <code>x</code>.</p>
<p>Note that in row 4 which is one row after the <code>mask</code>, no condition is needed. For example for row 4, It is NOT necessary that <code>df.a > df.b</code>.</p>
<p>This is what I have tried:</p>
<pre><code>df.loc[mask.cumsum().eq(1) & mask, 'x'] = df.c.shift(-1)
</code></pre>
<p>I provide some additional <code>df</code>s for convenience to test whether the code works in other examples. For instance what if there are no cases that meet the conditions of <code>mask</code>. In that case I just want a column of <code>NaN</code> for <code>x</code>.</p>
<pre><code>df = pd.DataFrame({'a': [1000, 11230, 12300, 10000, 1000, 10000, 100000], 'b': [1000, 11123, 1123, 0, 55, 0, 1], 'c': ['a', 'b', 'c', 'd', 'e', 'f', 'g']})
df = pd.DataFrame({'a': [1, 1, 1, -1, -1, -1, -1], 'b': [1000, 11123, 1123, 0, 55, 0, 1], 'c': ['a', 'b', 'c', 'd', 'e', 'f', 'g']})
df = pd.DataFrame({'a': [-1, -1, -1, -1, -1, -1, 100000], 'b': [1000, 11123, 1123, 0, 55, 0, 1], 'c': ['a', 'b', 'c', 'd', 'e', 'f', 'g']})
</code></pre>
|
<python><pandas>
|
2023-12-13 05:07:12
| 2
| 2,679
|
AmirX
|
77,651,188
| 4,451,521
|
Visual Studio Code: How to run a python file with arguments
|
<p>I usually run and debug my programs from the console. Lately I have been started to run and debug from the vs code window. However I cannot do this when the program require arguments.</p>
<p>Searching on how to do this I found <a href="https://www.youtube.com/watch?v=zSljcz54pYQ" rel="nofollow noreferrer">this video</a> and yes somehow I managed to run (or more accurately debug) the script from vscode</p>
<p>However I have several doubts:</p>
<ol>
<li><p>To do this I had to press Debug, never Play because Play calls the script with no arguments. With debug I can set the arguments and run it but... is this always the case?</p>
</li>
<li><p>The <code>launch.json</code> file that is used is saved in the <code>.vscode</code> folder, so it is not ideal for git managing, right?</p>
</li>
<li><p>And more importantly, if I have two scripts Scritp1 and Script2 and they both use the <code>launch.json</code> file, how can I have two files of the same name? Do I have to create and rename files every time? That sounds highly impractical</p>
</li>
</ol>
|
<python><visual-studio-code><command-line-arguments>
|
2023-12-13 04:54:15
| 0
| 10,576
|
KansaiRobot
|
77,651,034
| 23,512,643
|
R: Making a Transparent Plot
|
<p>I am working with the R programming language.</p>
<p>Here is a transparent wire plot made in Python (<a href="https://www.geeksforgeeks.org/3d-wireframe-plotting-in-python-using-matplotlib/" rel="nofollow noreferrer">https://www.geeksforgeeks.org/3d-wireframe-plotting-in-python-using-matplotlib/</a>):</p>
<pre><code># importing modules
from mpl_toolkits import mplot3d
import numpy
from matplotlib import pyplot
# assigning coordinates
a = numpy.linspace(-5, 5, 25)
b = numpy.linspace(-5, 5, 25)
x, y = numpy.meshgrid(a, b)
z = numpy.sin(numpy.sqrt(x**2 + y**2))
# creating the visualization
fig = pyplot.figure()
wf = pyplot.axes(projection ='3d')
wf.plot_wireframe(x, y, z, color ='green')
# displaying the visualization
wf.set_title('Example 2')
pyplot.show()
</code></pre>
<p><a href="https://i.sstatic.net/IsyAp.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/IsyAp.png" alt="enter image description here" /></a></p>
<p><strong>My Question :</strong> I am trying to recreate this kind of plot in R for a 2 dimensional Normal Distribution</p>
<p>I know how to plot a regular 2d normal distribution in R:</p>
<pre><code>library(plotly)
library(MASS)
library(mvtnorm)
mu <- c(0, 0) # Mean
Sigma <- matrix(c(1, 0.8, 0.8, 1), nrow=2)
x <- seq(-3, 3, length.out = 100)
y <- seq(-3, 3, length.out = 100)
grid <- expand.grid(x=x, y=y)
z <- matrix(apply(grid, 1, function(x) dmvnorm(x, mean=mu, sigma=Sigma)), nrow=length(x), ncol=length(y))
plot_ly(x = x, y = y, z = z, type = "surface") %>%
add_trace(
type = "surface",
showscale = FALSE,
opacity = 0.5,
surfacecolor = matrix(rep(1, length(x) * length(y)), nrow=length(x), ncol=length(y)),
colorscale = list(c(0, 1), c("rgb(0,0,0)", "rgb(0,0,0)")),
lighting = list(ambient = 1, diffuse = 0, fresnel = 0, specular = 0, roughness = 1)
) %>%
layout(scene = list(xaxis = list(title = 'X1'),
yaxis = list(title = 'X2'),
zaxis = list(title = 'Density')))
</code></pre>
<p><a href="https://i.sstatic.net/BIaU7.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BIaU7.png" alt="enter image description here" /></a></p>
<p>But I don't know how to make this plot look "transparent wire mesh" style. The closest thing I could find was something like this: <a href="https://www.r-bloggers.com/2010/05/creating-surface-plots/" rel="nofollow noreferrer">https://www.r-bloggers.com/2010/05/creating-surface-plots/</a></p>
<p>Can someone please show me how to do this? Note: The plot does not need to be 3D. 2D or 3D is fine.</p>
<p>Thanks!</p>
<p><strong>EDIT:</strong> I think this is possible?</p>
<pre><code>library(plotly)
# Creating the data
x <- seq(-5, 5, length.out = 50)
y <- seq(-5, 5, length.out = 50)
grid <- expand.grid(x = x, y = y)
R <- sqrt(grid$x^2 + grid$y^2)
z <- sin(R)
fig <- plot_ly(x = ~grid$x, y = ~grid$y, z = ~z, type = 'scatter3d', mode = 'lines',
line = list(color = '#0066FF', width = 2)) %>%
layout(title = "Wireframe Plot",
scene = list(xaxis = list(gridcolor = 'rgb(255, 255, 255)',
zerolinecolor = 'rgb(255, 255, 255)',
showbackground = TRUE,
backgroundcolor = 'rgb(230, 230,230)'),
yaxis = list(gridcolor = 'rgb(255, 255, 255)',
zerolinecolor = 'rgb(255, 255, 255)',
showbackground = TRUE,
backgroundcolor = 'rgb(230, 230,230)'),
zaxis = list(gridcolor = 'rgb(255, 255, 255)',
zerolinecolor = 'rgb(255, 255, 255)',
showbackground = TRUE,
backgroundcolor = 'rgb(230, 230,230)')),
showlegend = FALSE)
# Print
fig
</code></pre>
<p><a href="https://i.sstatic.net/feHwu.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/feHwu.png" alt="enter image description here" /></a></p>
|
<python><r>
|
2023-12-13 03:57:02
| 1
| 6,799
|
stats_noob
|
77,649,919
| 23,002,898
|
In Tkinter, how to define two checkboxes from one file in the function of a function in another file?
|
<p>When i select <code>Tab2</code>, i would like to select the <code>Select all</code> checkbox which should automatically select the checkboxes <code>checkbox1</code> and <code>checkbox2</code>. The "Select all" Checkbox is located in the <code>x.py</code> file, so also the checkbox list <code>all_checkbox = [checkbox1, checkbox2]</code>. While the two checkboxes (checkbox1 and checkbox2) are found in the <code>page2.py</code> file. The code is reproducible.</p>
<p><a href="https://i.sstatic.net/uwrqS.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/uwrqS.png" alt="enter image description here" /></a></p>
<p><strong>PROBLEM:</strong> The problem is in the <code>all_checkbox = [checkbox1, checkbox2]</code> line of the<code> x.py</code> file, because i can't recover the two widgets from <code>page2.py</code>. I'm trying so many different ways, but I can't</p>
<p><strong>ERROR</strong>: Of course, the error I get is that they are not defined:</p>
<pre><code>NameError: name 'checkbox1' is not defined
NameError: name 'checkbox2' is not defined
</code></pre>
<p><strong>IMPORTANT:</strong> I know I could solve the problem by moving <code>all_checkbox = [checkbox1, checkbox2]</code> to another place, but i need checkbox list is created inside file <code>x.py</code>, because the code in my question is just a short reproduced example of a larger project, so I need to organize it the way how I did it.</p>
<p>How can I define <code>checkbox1</code> and <code>checkbox2</code>, in all_checkbox = [checkbox1, checkbox2], of <code>x.py </code>file?</p>
<p><strong>ATTEMPT AT A SOLUTION:</strong> What did I try to fix the problem? I tried using checkbox1, checkbox2 as parameter in <code>def myfunction</code>, like this <code>def myfunction(Verticalbar, checkbox1, checkbox2)</code>. So also in the <code>main.py </code>file in <code>if tab_text == "Tab2": myfunction(Verticalbar, checkbox1, checkbox2)</code>, but i get error:</p>
<pre><code>NameError: name 'checkbox1' is not defined
NameError: name 'checkbox2' is not defined
</code></pre>
<p>I can't define them in <code>main.py</code> either</p>
<p><strong>main.py</strong></p>
<pre><code>import tkinter as tk
from tkinter import Frame, Label, ttk
from page1 import Page1
from page2 import Page2
from x import *
root = tk.Tk()
root.geometry('475x325')
style = ttk.Style()
style.theme_use('default')
nb = ttk.Notebook(root)
nb.pack(fill='both', expand=1)
page1 = Page1(nb)
page2 = Page2(nb)
nb.add(page1, text='Tab1', compound='left')
nb.add(page2, text='Tab2', compound='left')
Verticalbar = Frame(root, width=200, height=300, background="white", highlightthickness=0)
Verticalbar.place(x=1, y=40)
def on_tab_selection_changed(event):
# First, get the selected tab id
tab_id = nb.select()
# Now we can get the tab text using the tab id
tab_text = nb.tab(tab_id, "text")
if tab_text == "Tab1":
newlabel1 = Label(Verticalbar, text='TAB 1 EXAMPLE ', background="white", fg="#78b130", font='Arial 10 bold')
newlabel1.place(x=2, y=3)
if tab_text == "Tab2":
myfunction(Verticalbar)
# Bind to a virtual event to detect when the tab selection changes
nb.bind("<<NotebookTabChanged>>", on_tab_selection_changed)
root.mainloop()
</code></pre>
<p><strong>page2.py</strong></p>
<pre><code>import tkinter as tk
class Page2(tk.Frame):
def __init__(self, master, **kw):
super().__init__(master, **kw)
#CHEXKBOX 1
def test_checkbox1():
print("Printed Checkbox 1")
checkbox1 = tk.Checkbutton(self, text="Checkbox 1",
variable= self.checkbox1,
onvalue=1, offvalue=0, height=1,
highlightthickness=0,
command= test_checkbox1)
checkbox1.place(x=250, y=30)
#CHEXKBOX 2
def test_checkbox2():
print("Printed Checkbox 2")
checkbox2 = tk.Checkbutton(self, text="Checkbox 2",
variable= self.checkbox2,
onvalue=1, offvalue=0, height=1,
highlightthickness=0,
command= test_checkbox2)
checkbox2.place(x=250, y=60)
</code></pre>
<p><strong>x.py</strong></p>
<pre><code>from tkinter import Label, Tk
import tkinter as tk
def myfunction(Verticalbar):
newlabel2 = Label(Verticalbar, text='TAB 2 EXAMPLE', background="white", fg="#78b130", font='Arial 10 bold')
newlabel2.place(x=2, y=3)
def select_first_and_second():
all_checkbox = [checkbox1, checkbox2]
for cb in all_checkbox:
cb.invoke()
### CHECKBOX SELECT ALL ###
ckbx = tk.Checkbutton(Verticalbar, text="Select all",
variable= ckbx_intvar,
onvalue=1, offvalue=0, height=1,
highlightthickness=0, command=select_first_and_second)
ckbx.place(x=0, y=30)
</code></pre>
<p>This not needed, but I add them for completeness
<strong>page1.py</strong></p>
<pre><code>import tkinter as tk
class Page1(tk.Frame):
def __init__(self, master, **kw):
super().__init__(master, **kw)
</code></pre>
|
<python><python-3.x><tkinter>
|
2023-12-13 02:27:35
| 1
| 307
|
Nodigap
|
77,649,812
| 825,227
|
Issues sending params dictionary to RESTful API
|
<p>Getting an error when trying to pass query params within a requests get call. Using the following in python:</p>
<pre><code>import requests as re
url = 'https://api.eia.gov/v2/natural-gas/pri/fut/data/?api_key=' + API_KEY
params = {
"frequency": "daily",
"data": [
"value"
],
"start": "2021-01-01",
"facets": {
"series": [
"RNGWHHD"
]
},
"offset": 0,
"length": 5000
}
x = re.get(url = url, params = params)
if x.status_code == 200:
print('Success')
else:
print(x.text)
{"error":"Invalid format for facet 'series'. Must provide which facet and an array of values. For example, to filter by location for VA and CA: facets[location][]=VA&facets[location][]=CA","code":400}
</code></pre>
<p>I'm able to return data using the <a href="https://www.eia.gov/opendata/browser/natural-gas/pri/fut?frequency=daily&data=value;&facets=duoarea;&duoarea=RGC;&start=2014-01-01&sortColumn=period;&sortDirection=desc;" rel="nofollow noreferrer">dashboard as reference</a>. And also by including params in the requested url as follows:</p>
<pre><code>'https://api.eia.gov/v2/natural-gas/pri/fut/data/?frequency=daily&data[0]=value&facets[series][]=RNGWHHD&start=2021-01-01&sort[0][column]=period&sort[0][direction]=desc&offset=0&length=5000&api_key=' + API_KEY
x = re.get(url = url)
</code></pre>
<p>But not by passing a parameter dict to <code>get</code>. What am I doing wrong?</p>
|
<python><python-requests>
|
2023-12-13 01:39:49
| 1
| 1,702
|
Chris
|
77,649,754
| 2,526,586
|
PyQt/Pyside: storing individual value in QPushButtons(or QWidgets in general)
|
<p>Suppose I have some <code>QPushButton</code>s displayed in a <code>QListWidget</code> or <code>QTableWidget</code>. The <code>QPushButtons</code> connect to the same slot function that carries out the same action for their relative list item or table row.</p>
<p>I am not sure if I am thinking in the right direction:</p>
<p>Suppose that the list items or table rows are created in a <code>for</code> loop, looping a list, for example. During the creation of the list item, or table row, along with the <code>QPushButton</code> in it, I am supposed to store some sort of data, or id, into the <code>QPushButton</code>, so that when the <code>QPushButton</code> is clicked, its connected slot will know which <code>QPushButton</code> is clicked and carry out some operation specifically to the <code>QPushButton</code>'s parent list item/table row. (assuming I am using the same one slot function for all the <code>QPushButton</code>s)</p>
<p>So my question is: Given that the list items or table rows are created in a <code>for</code> loop of a list, how do I store some sort of list-related value(s) into the <code>QPushButton</code> and how do I retrieve the value(s) inside the slot function?</p>
<p>I suppose my question isn't really bound to <code>QPushButton</code>s. The same question can go to <code>QCheckBox</code>es, <code>QLineEdit</code>s, or any <code>QWidget</code>s in general as long as they can send out signals.</p>
|
<python><pyqt><pyside><qpushbutton><slot>
|
2023-12-13 00:50:18
| 0
| 1,342
|
user2526586
|
77,649,610
| 9,661,990
|
Do I need to explicitely call con.close() after having done con.execute in SQLite?
|
<p>Suppose we have this code:</p>
<pre><code>def _te(tn: str, db_id: str) -> bool:
with _connect_db(db_id) as conn:
cur = conn.cursor()
cur.execute("SELECT count(*) FROM sqlite_master WHERE type='table' AND name=?", (tn,))
return cur.fetchone()[0] == 1
def _connect_db(db_id: str) -> sqlite3.Connection:
return sqlite3.connect(db_id)
</code></pre>
<p>Do I need to call close at the end of <code>_te</code>?</p>
<p>I found information that seems contradictory at my level of understanding:</p>
<p>On one hand, this accepted answer says (with a link to SQLite3 source code):
<a href="https://stackoverflow.com/a/25988110/9661990">https://stackoverflow.com/a/25988110/9661990</a></p>
<blockquote>
<p>You don't need to worry about closing the database. When you call prepare or execute, those calls are automatically call close when they are done. There is an internal rescue/ensure block that ensures the db is closed even if an error is raised. You can see this in the source code for SQLite3::Database.</p>
</blockquote>
<p>But on the other hand, this SQLite3 doc:
<a href="https://docs.python.org/3/library/sqlite3.html" rel="nofollow noreferrer">https://docs.python.org/3/library/sqlite3.html</a></p>
<p>has a snippet with <code>execute</code>, and yet in this snippet it specifically commands a manual <code>close</code>:</p>
<pre><code># Create and fill the table.
con = sqlite3.connect(":memory:")
con.execute("CREATE TABLE lang(name, first_appeared)")
data = [
("C++", 1985),
("Objective-C", 1984),
]
con.executemany("INSERT INTO lang(name, first_appeared) VALUES(?, ?)", data)
# Print the table contents
for row in con.execute("SELECT name, first_appeared FROM lang"):
print(row)
print("I just deleted", con.execute("DELETE FROM lang").rowcount, "rows")
# close() is not a shortcut method and it's not called automatically;
# the connection object should be closed manually
con.close()
</code></pre>
|
<python><sqlite>
|
2023-12-12 23:50:52
| 1
| 441
|
InfiniteLoop
|
77,649,601
| 2,821,077
|
Combining python "in" and "==" operator has confusing behavior
|
<p>A buddy of mine is learning python and I saw an odd thing he did with his code:</p>
<pre><code>if ch in input[index] == test[index])
</code></pre>
<p>Obviously there's some context missing for that fragment, but interestingly, that if statement gives him his desired behavior. Similarly, the below statement prints <code>True</code>:</p>
<pre><code>print("w" in "w" == "w")
</code></pre>
<p>I'm not a python expert, so I don't understand why this happens. I would assume some kind of precedence and/or associativity would bungle things up here and return <code>False</code> or throw an error.</p>
<p>From what I can tell <code>"w" in "w" == "w"</code> is the same as <code>"w" in "w" and "w"== "w"</code>, which is rather unintuitive. I'm hoping someone who understands python interpreting can explain why this kind of statement is evaluated in this way.</p>
|
<python><operators><operator-precedence>
|
2023-12-12 23:48:22
| 1
| 389
|
Christian Baker
|
77,649,536
| 4,019,495
|
Is there an easy way to reorder the coordinate levels in a DataArray?
|
<p>I have the following DataArray.</p>
<pre><code>data = xr.DataArray(
np.arange(24).reshape(2, 3, 4),
dims=['x', 'y', 'z'],
coords={
'x': ['a', 'b'],
'y': [10, 20, 30],
'z': [100, 200, 300, 400]
}
)
transposed_data = data.transpose('z', 'y', 'x')
</code></pre>
<p>When I print <code>transposed_data</code>, I get coordinates:</p>
<pre><code>>>> print(transposed_data)
...
Coordinates:
* x (x) <U1 'a' 'b'
* y (y) int64 10 20 30
* z (z) int64 100 200 300 400
</code></pre>
<p>that is, the coordinates are still in the order x, y, z. I would like to reorder them to be z, y, x (matching the dimension order). Specifically, I want to see</p>
<pre><code>>>> print(transposed_data)
...
Coordinates:
* z (z) int64 100 200 300 400
* y (y) int64 10 20 30
* x (x) <U1 'a' 'b'
</code></pre>
<p>Is there an easy way to do this?</p>
|
<python><python-xarray>
|
2023-12-12 23:24:58
| 1
| 835
|
extremeaxe5
|
77,649,492
| 1,458,688
|
Running a Python script in Node-RED results in [Errno 2] No such file or directory
|
<p>I am just starting out with Node-RED and trying to run a Python script but get the error "No such file or directory". Node-Red is running in docker on my Pi4.</p>
<pre><code>python: can't open file '/home/pi/docker/nodered/data/python/bins.py': [Errno 2] No such file or directory
</code></pre>
<p>In node red, I'm using an inject node to run an exec with the Python command then outputting to a debug node.</p>
<p>I can run the script from the command line using the same command so the path is correct. This is the output:</p>
<pre><code>pi@raspberrypi4:/ $ python /home/pi/docker/nodered/data/python/bins.py
I am bloody here: /home/pi/docker/nodered/data/python/bins.py
</code></pre>
<p>So the path is correct. What am I doing wrong that's stopping Node Red seeing the script? I've read several posts that say to the use the full path. The only other thing I can think of is that docker can't see outside of itself so would require a relative path but I've tried this as well.</p>
<p>Feeling a bit dim at the moment!</p>
<p>Many thanks,</p>
|
<python><node-red>
|
2023-12-12 23:06:11
| 1
| 319
|
Digital Essence
|
77,649,333
| 6,307,685
|
Substituting entire IndexedBase objects in Sympy
|
<p>I have an expression for the entropy <code>h</code> written in terms of the discrete distribution <code>p</code>, which is an IndexedBase.</p>
<pre class="lang-py prettyprint-override"><code>import sympy as sym
x = sym.Idx("x")
p = sym.IndexedBase("p")
N_X = sym.Symbol("N_X", positive=True)
h = -sym.summation(p[x] * sym.log(p[x]), (x, 1, N_X))
</code></pre>
<p>Pretty printing <code>h</code> outputs:</p>
<p><a href="https://i.sstatic.net/joEIn.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/joEIn.jpg" alt="enter image description here" /></a></p>
<p>I want to replace <code>p</code> by an expression in terms of the joint distribution <code>q</code>. Mathematically, I want to replace p by</p>
<p><a href="https://i.sstatic.net/NFzBO.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/NFzBO.jpg" alt="enter image description here" /></a></p>
<p>Here is my attempt:</p>
<pre class="lang-py prettyprint-override"><code>y = sym.Idx("y")
N_Y = sym.Symbol("N_Y, positive=True")
q = sym.IndexedBase("q")
h.subs(p, sym.summation(q[x,y], (y, 1, N_Y)))
</code></pre>
<p>This raises a type error:</p>
<pre class="lang-py prettyprint-override"><code>TypeError:
The base can only be replaced with a string, Symbol, IndexedBase or an
object with a method for getting items (i.e. an object with a
`__getitem__` method).
</code></pre>
<p>I get that the sum is not an <code>IndexedBase</code>, and that is an issue. But I do not know how to solve this: is there a way to create an <code>IndexedBase</code> that is represented in this way (as a sum of an <code>IndexedBase</code> <code>q</code>)?</p>
|
<python><sympy><symbolic-math>
|
2023-12-12 22:23:58
| 0
| 761
|
soap
|
77,649,233
| 1,601,580
|
How to install swarms; AssertionError: Error: Could not open 'optimum/version.py' due [Errno 2] No such file or directory: 'optimum/version.py'
|
<p>I'm trying to install swarms but I cannot and get this error:</p>
<pre class="lang-py prettyprint-override"><code>pip install swarms
Collecting swarms
Using cached swarms-2.7.7-py3-none-any.whl.metadata (15 kB)
Collecting Pillow (from swarms)
Using cached Pillow-10.1.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (9.5 kB)
Collecting PyPDF2 (from swarms)
Using cached pypdf2-3.0.1-py3-none-any.whl (232 kB)
Collecting accelerate (from swarms)
Using cached accelerate-0.25.0-py3-none-any.whl.metadata (18 kB)
Collecting asyncio (from swarms)
...
Using cached optimum-0.1.1.tar.gz (17 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Using cached optimum-0.1.0.tar.gz (16 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [24 lines of output]
Traceback (most recent call last):
File "<string>", line 7, in <module>
FileNotFoundError: [Errno 2] No such file or directory: 'optimum/version.py'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/ubuntu/miniconda/envs/tree_of_thoughts/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 353, in <module>
main()
File "/home/ubuntu/miniconda/envs/tree_of_thoughts/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "/home/ubuntu/miniconda/envs/tree_of_thoughts/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
File "/tmp/pip-build-env-rn01vqif/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 325, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=['wheel'])
File "/tmp/pip-build-env-rn01vqif/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 295, in _get_build_requires
self.run_setup()
File "/tmp/pip-build-env-rn01vqif/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 480, in run_setup
super(_BuildMetaLegacyBackend, self).run_setup(setup_script=setup_script)
File "/tmp/pip-build-env-rn01vqif/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 311, in run_setup
exec(code, locals())
File "<string>", line 10, in <module>
AssertionError: Error: Could not open 'optimum/version.py' due [Errno 2] No such file or directory: 'optimum/version.py'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
</code></pre>
<p>how to fix this issue?</p>
<p>cross: <a href="https://discord.com/channels/999382051935506503/1184252377356836954" rel="nofollow noreferrer">https://discord.com/channels/999382051935506503/1184252377356836954</a></p>
|
<python><machine-learning><deep-learning><nlp>
|
2023-12-12 21:58:14
| 0
| 6,126
|
Charlie Parker
|
77,649,157
| 9,251,158
|
How to attach files with space in filename when sending email
|
<p>I am developing a Python application to send email. I follow <a href="https://www.geeksforgeeks.org/send-mail-attachment-gmail-account-using-python/" rel="nofollow noreferrer">this tutorial</a> and attach with this code:</p>
<pre><code>import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email import encoders
def prepare_attachment(filepath):
filename = os.path.basename(filepath)
attachment = open(filepath, "rb")
# instance of MIMEBase and named as p
p = MIMEBase('application', 'octet-stream')
# To change the payload into encoded form.
p.set_payload((attachment).read())
# encode into base64
encoders.encode_base64(p)
p.add_header('Content-Disposition', "attachment; filename= %s" % filename)
return p
class Sender(object):
# other code...
def send(self):
msg = MIMEMultipart()
# other code...
# open the file to be sent
for attachment in self.attachments:
p = prepare_attachment(attachment)
# attach the instance 'p' to instance 'msg'
msg.attach(p)
# rest of code...
</code></pre>
<p>The code runs and send the email. When the user adds an attachment, such as <code>my attachment.pdf</code>, the receiver sees <code>my</code> as the name of the attachment and their client does not show a preview of the attachment.</p>
<p>When I replace spaces in the filename with <code>%20</code>, the receiver sees a preview of the file but also sees <code>%20</code> in the filename.</p>
<p>How can I attach files with spaces in the filename?</p>
|
<python><email><email-attachments><mime>
|
2023-12-12 21:37:21
| 1
| 4,642
|
ginjaemocoes
|
77,649,036
| 9,072,753
|
how to detect if input is stdin or not using python click?
|
<p>Consider the following code:</p>
<pre><code>import click
@click.command()
@click.argument("file", type=click.File())
def cli(file):
print(file)
if __name__ == "__main__":
cli()
</code></pre>
<p>Executing as:</p>
<pre><code>$ python ./cmd.py -
<_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>
$ touch '<stdin>'
$ python ./cmd.py '<stdin>'
<_io.TextIOWrapper name='<stdin>' mode='r' encoding='UTF-8'>
</code></pre>
<p>There is suprising difference in encoding.</p>
<p>How can I detect if the input was actual <code>-</code> and not anything else in python click?</p>
|
<python><python-3.x><python-click>
|
2023-12-12 21:06:28
| 1
| 145,478
|
KamilCuk
|
77,649,007
| 3,084,178
|
Python itertools all permutations of a list with repeated elements
|
<p>I'm wanting to generate all permutations of a list that contains repetitions.</p>
<p>A naive approach might be to use itertools.permutations, but permutations doesn't care about elements being repeated:</p>
<pre><code>for j in itertools.permutations([0,1,1]):
print(j)
(0, 1, 1)
(0, 1, 1)
(1, 0, 1)
(1, 1, 0)
(1, 0, 1)
(1, 1, 0)
</code></pre>
<p>So instead of 3 elements we get 6.</p>
<p>You could track the elements it's producing and discard repetitions. But it's easier to do it with product and discard ones that don't conform to the original shape:</p>
<pre><code>for j in itertools.product([0,1],repeat=3):
if sum(j)!=2: continue
print(j)
(0, 1, 1)
(1, 0, 1)
(1, 1, 0)
</code></pre>
<p>This is the desired output, but does itertools support this more naturally? Obviously, this could get computationally intense for bigger lists.</p>
|
<python><permutation><python-itertools>
|
2023-12-12 20:59:37
| 0
| 1,014
|
Dr Xorile
|
77,648,923
| 2,573,075
|
Can I render a graph template in flask and save to PDF?
|
<p>It is not very clear for me if I can save a generated chart graphics to PDF.</p>
<p>I have the following case, I have and endpoint that returns a json and i want to generate graphics and tables from that, and after than to make a PDF.</p>
<p>The source of everything (endpoint, webpage) are on the same server. I would like to avoid opening another page and print to pdf from there.</p>
<p>Here is the code that takes the json from endpoint and create what i want to save to PDF.</p>
<pre class="lang-html prettyprint-override"><code><!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Local request</title>
<meta name="viewport" content="width=297mm, initial-scale=1.0">
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<style>
.canvas {
max-width: 297mm;
max-height: 297mm;
margin-left: auto;
margin-right: auto;
display: block;
break-inside: avoid;
page-break-inside: avoid;
}
table, th, td {
text-align: center;
border-collapse: collapse;
border: 1px solid black;
}
th {
background-color: grey;
}
</style>
</head>
<body>
<h1 style="text-align: center">XPF REPORT</h1>
<p><br></p>
<p><br></p>
<div class="canvas">
<h2 style="text-align: left;">PRI par IC et par etape</h2>
<canvas id="PRIParICEtParEtape"></canvas>
</div>
<p><br></p>
<div class="canvas">
<center>
<h2 style="text-align: left;">PRI par IC et par etape / table</h2>
<table width="300px">
<thead>
<tr>
<th>IC</th>
<th>Total</th>
</tr>
</thead>
<tbody id="icTableBody">
</tbody>
<tfoot>
<tr>
<td>Total:</td>
<td id="totalSum"></td>
</tr>
</tfoot>
</table>
</center>
</div>
<div><p><br></p></div>
<div class="canvas">
<h2 style="text-align: left;">PRI par DSP et par etape</h2>
<canvas id="PRIParDSPEtParEtape"></canvas>
</div>
<script>
document.addEventListener('DOMContentLoaded', function (event) {
fetchData('http://localhost:8069/xpf_report')
.then(data => {
console.log(data);
PRIParDSPEtParEtape(data);
PRIParICEtParEtape(data);
createICTable(data);
})
.catch(error => console.error('Error fetching data:', error));
});
function createICTable(data) {
const icTableBody = document.getElementById('icTableBody');
const totalSumElement = document.getElementById('totalSum');
const icTotalSum = {};
data.forEach(lead => {
const icName = lead.user.name;
const expectedRevenue = lead.expected_revenue;
if (!icTotalSum[icName]) {
icTotalSum[icName] = 0
}
icTotalSum[icName] += expectedRevenue;
});
let totalSum = 0;
Object.keys(icTotalSum).forEach(icName => {
const row = document.createElement('tr');
const icCell = document.createElement('td');
const totalCell = document.createElement('td');
icCell.textContent = icName;
totalCell.textContent = icTotalSum[icName].toLocaleString('fr', {useGrouping: true});
row.append(icCell);
row.append(totalCell);
icTableBody.appendChild(row);
totalSum += icTotalSum[icName];
});
totalSumElement.textContent = totalSum.toLocaleString('fr', {useGrouping: true})
}
function PRIParICEtParEtape(data) {
const userStageSum = {};
data.forEach(lead => {
const userName = lead.user.name;
const stage = lead.stage.stage;
const expectedRevenue = lead.expected_revenue;
if (!userStageSum[userName]) {
userStageSum[userName] = {};
}
if (!userStageSum[userName][stage]) {
userStageSum[userName][stage] = 0;
}
userStageSum[userName][stage] += expectedRevenue;
});
const ctx = document.getElementById('PRIParICEtParEtape').getContext('2d');
const userNames = Object.keys(userStageSum);
const stages = [...new Set(data.map(lead => lead.stage.stage))];
const datasets = stages.map(stage => ({
label: stage,
data: userNames.map(userName => userStageSum[userName][stage] || 0),
borderWidth: 1
}));
new Chart(ctx, {
type: 'bar',
data: {
labels: userNames,
datasets: datasets
},
options: {
scales: {
x: {
type: 'category',
stacked: true,
ticks: {
color: 'black',
order: []
}
},
y: {
stacked: true,
beginAtZero: true,
ticks: {}
}
}
}
});
}
async function fetchData(apiUrl) {
const response = await fetch(apiUrl);
return await response.json();
}
</script>
</body>
</html>
</code></pre>
|
<javascript><python><html><flask><pdf>
|
2023-12-12 20:36:54
| 0
| 633
|
Claudiu
|
77,648,683
| 401,173
|
How reference a namespace at the root of my Python package?
|
<p>I am creating a local Python package to pip install into a series of applications.</p>
<p>I have a file in a subfolder of the package</p>
<p><code>\src\my_package\model\do_stuff_x.py</code></p>
<p>and a file at the root</p>
<p><code>\src\my_package\request_models.py</code></p>
<p>When I try to use <code>from request_models import MyModel</code> in <code>do_stuff_x.py</code>, I get the error</p>
<p><code>No module named 'request_models'</code></p>
<p>How do I properly reference a model at the root of my pip installed package?</p>
|
<python><pip><pyproject.toml>
|
2023-12-12 19:46:40
| 1
| 3,241
|
Josh Russo
|
77,648,668
| 8,112,003
|
setuptools dependency-management for different python versions
|
<p>Assume you have a giant continous integration to care about, there are tons of terabyte of docker-containers, and rebuilding containers takes more time somebody could ever pay for and whatever version you change will break all the depencies.</p>
<p>Conclusion: you have docker-containers with python x.y.z and for all of those I need to install a pypi-package</p>
<p>Therefore I tried something like this:</p>
<pre><code>install_requires = [
"jsonschema==2.6.0;python_version<='3.7'",
"jsonschema==4.19.1;python_version>'3.7'",
]
</code></pre>
<p>but still i get an error in python 3.7.9 (that could be ignored)</p>
<pre><code>ERROR: ecuci-github 1.0.19 has requirement jsonschema==4.19.1, but you'll have jsonschema 2.6.0 which is incompatible.
ERROR: ecuci-jgod 1.0.22 has requirement jsonschema==4.19.1, but you'll have jsonschema 2.6.0 which is incompatible.
ERROR: ecuci-checks 1.0.8 has requirement jsonschema==4.19.1, but you'll have jsonschema 2.6.0 which is incompatible.
ERROR: ecuci-release-gui 1.0.75 has requirement jsonschema==4.19.1, but you'll have jsonschema 2.6.0 which is incompatible.
ERROR: ecuci-packaging 1.1.79 has requirement jsonschema==4.19.1, but you'll have jsonschema 2.6.0 which is incompatible.
Installing collected packages: ecuci-certs, ecuci-release, typing-extensions, annotated-types, pydantic-core, pydantic, peppercorn, ecuci-varparser, ecuci-buildgen, pywin32, packaging, urllib3, idna, certifi, charset-normalizer, requests, websocket-client, docker, jsonschema, appdirs, cached-property, lxml, pytz, six, isodate, defusedxml, requests-toolbelt, zipp, importlib-metadata, attrs, zeep, ecuci-github, ecuci-jgod, ecuci-flexcopy, ecuci-choco, ecuci-checks, ecuci-security, ecuci-engine, pycparser, cffi, cryptography, pyspnego, requests-ntlm, ecuci-flexclean, ecuci-release-gui, ecuci-quatelets, ecuci-validation, ecuci-packaging, ecuci-conan-install, ecuci-tools
Successfully installed annotated-types-0.5.0 appdirs-1.4.4 attrs-23.1.0 cached-property-1.5.2 certifi-2023.11.17 cffi-1.15.1 charset-normalizer-3.3.2 cryptography-41.0.7 defusedxml-0.7.1 docker-6.1.3 ecuci-buildgen-1.0.102 ecuci-certs-1.0.4 ecuci-checks-1.0.8 ecuci-choco-1.0.21 ecuci-conan-install-1.0.14 ecuci-engine-1.0.32 ecuci-flexclean-1.0.6 ecuci-flexcopy-1.0.6 ecuci-github-1.0.19 ecuci-jgod-1.0.22 ecuci-packaging-1.1.79 ecuci-quatelets-1.0.9 ecuci-release-1.0.26 ecuci-release-gui-1.0.75 ecuci-security-1.0.18 ecuci-tools-1.1.480 ecuci-validation-1.0.19 ecuci-varparser-1.0.22 idna-3.6 importlib-metadata-6.7.0 isodate-0.6.1 jsonschema-2.6.0 lxml-4.9.3 packaging-23.2 peppercorn-0.6 pycparser-2.21 pydantic-2.3.0 pydantic-core-2.6.3 pyspnego-0.9.2 pytz-2023.3.post1 pywin32-306 requests-2.30.0 requests-ntlm-1.2.0 requests-toolbelt-1.0.0 six-1.16.0 typing-extensions-4.7.1 urllib3-1.26.15 websocket-client-1.6.1 zeep-3.4.0 zipp-3.15.0
WARNING: You are using pip version 20.1.1; however, version 23.3.1 is available.
You should consider upgrading via the 'C:\Data\ecuci\pyenv37\Scripts\python.exe -m pip install --upgrade pip' command.
(pyenv37) C:\Data\ecuci>echo %errorlevel%
0
</code></pre>
<p>while its fine in version 3.10</p>
<p>So question is: how to get rid of those errors, I considered following webpages:
<a href="https://setuptools.pypa.io/en/latest/userguide/quickstart.html" rel="nofollow noreferrer">https://setuptools.pypa.io/en/latest/userguide/quickstart.html</a><br>
<a href="https://github.com/pypa/setuptools/issues/3313" rel="nofollow noreferrer">https://github.com/pypa/setuptools/issues/3313</a></p>
|
<python><pip><setuptools><pypi>
|
2023-12-12 19:44:01
| 0
| 752
|
Nikolai Ehrhardt
|
77,648,536
| 3,458,191
|
How to create a line graph from multiple columns in streamlit and python
|
<p>I am trying to create python script with help of streamlit and pandas from a csv file that has weather data with the below columns:
time,temperature,pressure,humidity,lux,Oxi-kO,Red-kO,NH3-kO,P1,P2,P3</p>
<p>What I am trying to do is to be able to select:</p>
<ol>
<li>Date range</li>
<li>Data set(s), a multiple selection of the columns temperature..P3</li>
</ol>
<p>Until now I have:</p>
<pre><code>import streamlit as st
import os
import pandas as pd
import plotly.express as px
st.set_page_config(
page_title = 'Real-Time Data Weather Dashboard',
page_icon = '🌤️',
layout = 'wide'
)
st.title(" :bar_chart: Real-Time / Weather Data from Enviro+")
st.markdown('<style>div.block-container{padding-top:1rem;}</style>', unsafe_allow_html=True)
fl = st.file_uploader(":file_folder: Upload a file", type=(["csv", "txt", "xlsx", "xls"]))
if fl is not None:
filename = fl.name
st.write(filename)
df = pd.read_csv(filename, encoding="ISO-8859-1")
else:
os.chdir(r"C:\WeatherData")
df = pd.read_csv("WeatherData.csv", encoding="ISO-8859-1")
list_of_column_names = list(df.columns)
col1, col2 = st.columns((2))
df["time"] = pd.to_datetime(df["time"])
# Getting the min and max date
startDate = pd.to_datetime(df["time"]).min()
endDate = pd.to_datetime(df["time"]).max()
with col1:
date1 = pd.to_datetime(st.date_input("Start Date", startDate))
with col2:
date2 = pd.to_datetime(st.date_input("End Date", endDate))
df = df[(df["time"] >= date1) & (df["time"] <= date2)].copy()
st.sidebar.header("Choose your filter: ")
# Create for Data sets
data_set = st.sidebar.multiselect("Pick your data set", df.columns[1:])
if not data_set:
df2 = df.copy()
else:
df2 = df[data_set]
</code></pre>
<p>But I can't figure out how create a line graph the data set of the selected date range and e.g. temperature. One step further if I could combine in the graph for the same date range also the other columns depending on what has the use selected.</p>
<p>How can I achieve this?</p>
<p>Example of csv data set (delimenter is ','):</p>
<pre><code>time,temperature,pressure,humidity,lux,Oxi-kO,Red-kO,NH3-kO,P1,P2,P3
2023-12-12 17:22:00.218,25.08,101330.98,33.52,18.62,11.32,266.51,263.17,11,15,15
2023-12-12 17:22:10.697,25.04,101329.69,33.58,18.62,12.90,269.93,338.87,11,13,13
2023-12-12 17:22:21.036,25.05,101327.94,33.52,18.62,14.48,269.93,374.77,13,15,15
2023-12-12 17:22:31.375,25.02,101330.41,33.65,18.62,15.71,268.21,393.64,13,15,16
2023-12-12 17:22:41.713,24.99,101331.40,33.75,18.62,16.73,266.51,403.70,12,15,16
2023-12-12 17:22:52.052,25.03,101329.48,33.70,18.62,17.60,266.51,414.23,12,15,16
2023-12-12 17:23:02.390,25.00,101329.97,33.77,18.62,18.31,266.51,417.85,11,14,14
</code></pre>
<p>What I have until now when running the streamlit app:
<a href="https://i.sstatic.net/PsnNt.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/PsnNt.png" alt="enter image description here" /></a></p>
<p>After the comment from <a href="https://stackoverflow.com/users/13107804/r-beginners">r-beginners</a> I updated some part:</p>
<pre><code># Create for Data sets
data_set = st.sidebar.multiselect("Pick your data set", df_cols)
if not data_set:
data_set = [df_cols[0]]
data_set.insert(0, "time")
df2 = df[data_set]
else:
data_set.insert(0, "time")
df2 = df[data_set]
linechart = df2
fig2 = px.line(linechart, x="time", y=data_set, height=500, width=1000, template="gridon")
st.plotly_chart(fig2, use_container_width=True)
</code></pre>
<p>In case the user does not select any of the data set by default 'temperature' is shown in the graph.</p>
|
<python><pandas><numpy><plotly><streamlit>
|
2023-12-12 19:17:01
| 1
| 1,187
|
FotisK
|
77,648,390
| 297,780
|
How to create Pandas Dataframe from Google API Response Object
|
<p>I'm trying to get a Google Analytics Admin API response into a Pandas dataframe, and I'm having trouble parsing the results. I'm working with the <a href="https://developers.google.com/analytics/devguides/config/admin/v1/rest/v1beta/properties.customDimensions/list" rel="nofollow noreferrer">list customDimensions method</a> within the admin_v1beta library. I can see the results, but I haven't yet figured out how to get them into a format that's usable within Pandas.</p>
<p>My function:</p>
<pre><code>def get_custom_dimensions(property_filter):
client = admin_v1beta.AnalyticsAdminServiceClient()
request = admin_v1beta.ListCustomDimensionsRequest(
parent=property_filter
)
return client.list_custom_dimensions(request=request)
</code></pre>
<p>And the output:</p>
<pre><code> ga4_custom_dimensions = get_custom_dimensions("properties/xxx")
print(type(ga4_custom_dimensions))
<class 'google.analytics.admin_v1beta.services.analytics_admin_service.pagers.ListCustomDimensionsPager'>
print(ga4_custom_dimensions)
ListCustomDimensionsPager<custom_dimensions {
name: "properties/xxx/customDimensions/yy1"
parameter_name: "custom_dimension_zz1"
display_name: "Custom Dimension ZZ1"
description: "The dimension ZZ1 useful for filtering."
scope: EVENT
}
custom_dimensions {
name: "properties/xxx/customDimensions/yy2"
parameter_name: "custom_dim...
</code></pre>
<p>The response has its own class, so before I can do anything with it I need to convert it and I haven't yet figured out how. My first thought was to convert to JSON:</p>
<pre><code>custom_dimensions_json_1 = json.dumps(ga4_custom_dimensions.__dict__)
custom_dimensions_json_2 = json.dumps(vars(ga4_custom_dimensions))
</code></pre>
<p>These produce the same error,</p>
<p><code>TypeError: Object of type _GapicCallable is not JSON serializable</code>.</p>
<p>My next attempt was via the Pandas <code>json_normalize</code> method:</p>
<pre><code>custom_dimension_df = pd.json_normalize(ga4_custom_dimensions)
</code></pre>
<p>The result is a dataframe with only an index.</p>
<p>Lastly, I tried parsing the results directly:</p>
<pre><code>temp_df = pd.DataFrame(ga4_custom_dimensions['custom_dimensions'])[['name',
'parameter_name',
'display_name',
'description',
'scope']]
</code></pre>
<p>This produces "TypeError:</p>
<p><code>ListCustomDimensionsPager' object is not subscriptable</code>.</p>
<p>Can anyone guide me on how to parse the API response within Pandas?</p>
|
<python><pandas><google-api-python-client>
|
2023-12-12 18:43:48
| 1
| 1,414
|
Lenwood
|
77,648,219
| 1,283,611
|
Issue with numpy in WSL2: libgcc_s.so.1: cannot open shared object file: No such file or directory
|
<p>I'm trying to get a python project running that uses numpy down the line and am getting an ImportError issue. I can't seem to resolve and unfortunately I haven't found a useful answer yet, so I'm hoping for help here. This is all running in Windows WSL2 Ubuntu. Here is the latest I can do to reproduce the issue. This is in an empty directory with no other code.</p>
<pre><code>If everything worked right, you would be out of a job.:~/src/pythontest$ python3 -m venv venv
If everything worked right, you would be out of a job.:~/src/pythontest$ source venv/bin/activate
(venv) If everything worked right, you would be out of a job.:~/src/pythontest$ pip install numpy
Collecting numpy
Using cached numpy-1.26.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)
Installing collected packages: numpy
Successfully installed numpy-1.26.2
WARNING: There was an error checking the latest version of pip.
(venv) If everything worked right, you would be out of a job.:~/src/pythontest$ python
Python 3.9.13 (main, May 17 2022, 14:19:07)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
Traceback (most recent call last):
File "/home/flamebaud/src/pythontest/venv/lib/python3.9/site-packages/numpy/core/__init__.py", line 24, in <module>
from . import multiarray
File "/home/flamebaud/src/pythontest/venv/lib/python3.9/site-packages/numpy/core/multiarray.py", line 10, in <module>
from . import overrides
File "/home/flamebaud/src/pythontest/venv/lib/python3.9/site-packages/numpy/core/overrides.py", line 8, in <module>
from numpy.core._multiarray_umath import (
ImportError: libgcc_s.so.1: cannot open shared object file: No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/flamebaud/src/pythontest/venv/lib/python3.9/site-packages/numpy/__init__.py", line 130, in <module>
from numpy.__config__ import show as show_config
File "/home/flamebaud/src/pythontest/venv/lib/python3.9/site-packages/numpy/__config__.py", line 4, in <module>
from numpy.core._multiarray_umath import (
File "/home/flamebaud/src/pythontest/venv/lib/python3.9/site-packages/numpy/core/__init__.py", line 50, in <module>
raise ImportError(msg)
ImportError:
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
* The Python version is: Python3.9 from "/home/flamebaud/src/pythontest/venv/bin/python"
* The NumPy version is: "1.26.2"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: libgcc_s.so.1: cannot open shared object file: No such file or directory
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/flamebaud/src/pythontest/venv/lib/python3.9/site-packages/numpy/__init__.py", line 135, in <module>
raise ImportError(msg) from e
ImportError: Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python interpreter from there.```
</code></pre>
|
<python><python-3.x><numpy><gcc><windows-subsystem-for-linux>
|
2023-12-12 18:07:14
| 1
| 998
|
flamebaud
|
77,648,138
| 8,167,752
|
Looking for an efficient way to find specific subfolders in a folder
|
<p>I'm trying to come up with a faster/more efficient way to scan a folder and find the subfolders of interest.</p>
<p>Below is what I have. It works fine for test cases, where I'm trying to find, say, a dozen folders of interest in a folder that contains, say, a hundred or so subfolders.</p>
<p>However, it chokes in my real-world case, where I'm trying to find around 100 subfolders of interest in a folder that contains around 300,000 subfolders plus a few text files.</p>
<p>I feel like there should be a faster/more efficient way to do this, because I have some MATLAB code that's doing the same thing (i.e. scanning a folder for subfolders of interest) and it works fine. However, for corporate reasons, I need to move from MATLAB to Python.</p>
<p>For what it's worth, I'm running this as a Jupyter notebook.</p>
<pre><code>import os
import re
def find_subfolders_of_interest(dir_of_interest, starting_string_of_interest):
all_subfolders = [item for item in os.listdir(dir_of_interest) if os.path.isdir(dir_of_interest, item))]
startWithPattern = starting_string_of_interest
regexp_pattern = re.compile(startsWithPattern)
all_subfolders_of_interest = list(filter(regexp_pattern.match, all_subfolders))
return all_subfolders_of_interest
def main():
all_subfolders_of_interest = find_subfolders_of_interest('test_folder', 'string_of_interest')
if __name__ == '__main__':
main()
</code></pre>
|
<python>
|
2023-12-12 17:53:05
| 1
| 477
|
BobInBaltimore
|
77,647,949
| 8,832,997
|
Python next() function fails while __next__ works on file?
|
<p>I was studying <em>Mark Lutz's</em> book on python called "<em>Learning Python (5th Ed)</em>". While perusing chapter 14 on <em>Iteration and Comprehensions</em> , I faced a problem in page 423.</p>
<p>There, we have created a file object returned by <code>os.popen()</code> call. It is an iterable object (has <code>__iter__</code> method) but not an iterator (no <code>__next__</code> method). So, this object should be called by neither <code>next()</code> built-in function nor <code>__next__</code> method call. But somehow this object returns values on next dunder method call but raise exception on next() builtin calls.</p>
<p>In the book, the author said it is unusual. But he didn't provide any explanation for that.</p>
<p>Can anyone explain me how <code>__next__</code> succeeds for that file-like object?</p>
<pre><code>import os
file = os.popen("ls -l")
print(dir(file)) # this shows __iter__ but no __next__/ next
# print(next(file)) # this fails if uncommented, expected
print(file.__next__()) # this does NOT fail even there is no __next__
</code></pre>
<p>Similar code for a list that behaves as I expect:</p>
<pre><code>file = [1]
print(dir(file)) # this shows __iter__ but no __next__/ next
# print(next(file)) # this fails if uncommented, expected
# print(file.__next__()) # this fails if uncommented, expected
</code></pre>
|
<python>
|
2023-12-12 17:20:10
| 1
| 1,357
|
Sabbir Ahmed
|
77,647,923
| 1,471,980
|
How do you update column value based on other column values in pandas
|
<p>I have this data frame:</p>
<pre><code>df
Server Port
Ser123 Ethernet3
Ser123 Ethernet4
Ser123 Ethernet12
Ser123 Ethernet567
Serabc Ethernet2
Serabc Ethernet34
Serabc Ethernet458
Serabc Ethernet5689
</code></pre>
<p>I need to create another column based on values on Port column. I need to check if the number single digit after Ethernet (fir example Ethernet2) then function_val column value be '5k', if the the number Ethernet is double digit like Ethernet34, then the function_val should be 10k etc. I need to be able check for three digits so forth.</p>
<p>Data frame needs to be like this:</p>
<pre><code>Server Port function_val
Ser123 Ethernet3 5k
Ser123 Ethernet4 5k
Ser123 Ethernet12 10k
Ser123 Ethernet567 20k
Serabc Ethernet2 5k
Serabc Ethernet34 10k
Serabc Ethernet458 20k
Serabc Ethernet5689 20k
</code></pre>
<p>I tried this, any ideas how I could do this better and most efficient way?</p>
<pre><code>df['function_val']=np.where((df.Port.str.contains("Ethernet[0-9]"),'5k', df['function_val'])
</code></pre>
|
<python><pandas><regex>
|
2023-12-12 17:14:26
| 1
| 10,714
|
user1471980
|
77,647,740
| 1,422,096
|
Copy a Numpy array to clipboard on Windows without win32 dependency?
|
<p>The main answer from <a href="https://stackoverflow.com/questions/22488566/how-to-paste-a-numpy-array-to-excel">How to paste a Numpy array to Excel</a> works to copy a Numpy array in the clipboard, ready to be pasted in Excel:</p>
<pre><code>import numpy as np
import win32clipboard as clipboard
def toClipboardForExcel(array):
array_string = "\r\n".join("\t".join(line.astype(str)).replace("\n","") for line in array)
clipboard.OpenClipboard()
clipboard.EmptyClipboard()
clipboard.SetClipboardText(array_string)
clipboard.CloseClipboard()
Y = np.arange(64).reshape((8, 8))
toClipboardForExcel(Y)
</code></pre>
<p><strong>Is it possible to do this without the extra <code>win32</code> package dependency?</strong> (I don't use it currently in my project and I'd like to avoid adding it only for clipboard, NB: I don't use <code>pandas</code> either)</p>
<p>I already tried with <code>os.system(`echo string | clip`)</code> but it doesn't work for multiline content (containing <code>\n</code>).</p>
<p>Or maybe is <code>OpenClipboard</code>, <code>SetClipboardText</code>, etc. accessible via <code>ctypes</code> (which I already use)?</p>
<p>NB: this is not a duplicate of <a href="https://stackoverflow.com/questions/11063458/python-script-to-copy-text-to-clipboard">Python script to copy text to clipboard</a> because the latter is general, and with extra dependencies, and here in my question, we would like to avoid new dependencies.</p>
|
<python><windows><numpy><ctypes><clipboard>
|
2023-12-12 16:45:21
| 2
| 47,388
|
Basj
|
77,647,692
| 4,537,160
|
Pytorch I3D Resnet model on a custom dataset
|
<p>This is a follow-up to a couple of questions I asked before...I want to fine-tune the I3D model for action recognition from Pytorch hub (which is pre-trained on Kinetics 400 classes) on a custom dataset, where I have 4 possible output classes.</p>
<p>I'm loading the model and modifying the last layer by:</p>
<pre><code>model = torch.hub.load("facebookresearch/pytorchvideo", "i3d_r50", pretrained=True)
num_classes = 4
model.blocks[6].proj = torch.nn.Linear(2048, num_classes)
</code></pre>
<p>I defined the <strong>getitem</strong> method of my Dataset to return:</p>
<pre><code>def __getitem__(self, ind):
[...]
return processed_images, target
</code></pre>
<p>where processed_images and target are Tensors, with shapes:</p>
<pre><code>>>processed_images.shape
torch.Size([5, 224, 224, 3])
>>target.shape
torch.Size([4])
</code></pre>
<p>Basically, processed_images is a sequence of 5 RGB images, each with shape (224, 224), while target is the one-hot encoding for the target classes.</p>
<p>In the training part, I have:</p>
<pre><code>model.train()
model.to(device)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=False,
persistent_workers=False,
timeout=0,
)
for epoch in range(number_of_epochs):
for batch_ind, batch_data in enumerate(train_dataloader):
# Extract data and label
datas, labels = batch_data
# move to device
datas_ = datas.to(device)
labels_ = labels.to(device)
weights_ = weights.to(device)
# permute axes (changing from [22, 5, 224, 224, 3] -> [22, 3, 5, 224, 224, 3]
datas_ = datas_.permute(0, 4, 1, 2, 3)
preds_ = model(datas_)
</code></pre>
<p>But I'm getting an error in the forward method of ResNetBasicHead:</p>
<pre><code>Exception has occurred: RuntimeError
input image (T: 2 H: 14 W: 14) smaller than kernel size (kT: 4 kH: 7 kW: 7)
File "/home/c.demasi/.cache/torch/hub/facebookresearch_pytorchvideo_main/pytorchvideo/models/head.py", line 374, in forward
x = self.pool(x)
File "/home/c.demasi/.cache/torch/hub/facebookresearch_pytorchvideo_main/pytorchvideo/models/net.py", line 43, in forward
x = block(x)
File "/home/c.demasi/work/projects/ball_shot_action_detection_dev_environment/src/train_torch.py", line 271, in train
preds_ = model(datas_)
File "/home/c.demasi/work/projects/ball_shot_action_detection_dev_environment/src/train_torch.py", line 571, in train_roi
train(training_parameters, train_from_existing_path=None, perform_tests=perform_tests, config=config)
File "/home/c.demasi/work/projects/ball_shot_action_detection_dev_environment/train.py", line 13, in <module>
train_roi(config=config, perform_tests=False)
RuntimeError: input image (T: 2 H: 14 W: 14) smaller than kernel size (kT: 4 kH: 7 kW: 7)
</code></pre>
<p>Any idea how to solve this?</p>
|
<python><pytorch><resnet>
|
2023-12-12 16:36:18
| 1
| 1,630
|
Carlo
|
77,647,564
| 7,563,454
|
Copy class instance, inheriting changes from old instance but allowing new changes without modifying it
|
<p>I'm looking for a way to clone a class instance, such that the new instance contains any variables changed at the time of copying but other changes can be done to it without retroactively modifying the instance it was copied from. I need to copy <code>y</code> from <code>x</code> instead of spawning a new <code>Item</code> class as the <code>x</code> instance contains properties that were modified elsewhere and I want <code>y</code> to inherit modified attributes then allow further changes to itself without also affecting <code>x</code>.</p>
<pre><code>class Item:
def __init__(self):
self.value = None
def change(self, value):
self.value = value
x = Item()
x.change(0)
print(str(x.value))
y = x
y.change(1)
print(str(x.value) + " - " + str(y.value))
</code></pre>
<p>The result I get:</p>
<pre><code>0
1 - 1
</code></pre>
<p>The desired result would look like:</p>
<pre><code>0
0 - 1
</code></pre>
<p>Obviously this happens because <code>y = x</code> only copies a reference to the existing instance, but what else should I do? All answers I found suggest <code>copy()</code> or <code>deepcopy()</code> but neither works: Whether I do <code>y = x.deepcopy()</code> or <code>y = deepcopy(x)</code> I'm told Python has no <code>copy</code> / <code>deepcopy</code> builtins.</p>
|
<python><python-3.x>
|
2023-12-12 16:15:59
| 1
| 1,161
|
MirceaKitsune
|
77,647,453
| 4,588,188
|
Alias field to existing key in Pydantic
|
<p>I am working with a legacy API and need to alias a response field to something that has an existing key. For example, the API response looks like this:</p>
<pre><code>[
{
"model_name": "Survey",
"logo": {
"url": "foo"
},
"uuid": "79bea0f3-d8d2-4b05-9ce5-84858f65ff4b",
"logo_url": "foo"
}
]
</code></pre>
<p>and I would like the string <code>"foo"</code> in the response to be in the field <code>logo</code> – not as a nested object but just as the string itself. I have the following schema:</p>
<pre><code>class Survey(UUIDIdentifiable):
logo_url: str = Field(None, alias="logo")
class Config:
allow_population_by_field_name = True
</code></pre>
<p>but it doesn't appear to be working properly (I assume because the aliased key still exists in the response). I could of course just iterate through the responses and delete the one <code>logo</code> key:</p>
<pre><code>for item in responses:
del item["logo"]
</code></pre>
<p>but that feels a bit hacky to me. Is there a better way to do this with Pydantic?</p>
|
<python><pydantic>
|
2023-12-12 16:00:56
| 1
| 618
|
AJwr
|
77,647,370
| 11,801,298
|
Make numbers less than 360 in pandas column
|
<p>This is my function</p>
<pre><code>def price_to_ephe(data):
converted = []
for i in data:
while i >= 360:
i = i - 360
converted.append (i)
return converted
</code></pre>
<p>it makes every number less than 360. I want to apply it to column in dataframe.</p>
<pre><code>2009-01-01, 886.0
2009-01-02, 884.2
2009-01-03, 882.1
2009-01-04, 882.6
2009-01-05, 883.4
2009-01-06, 889.1
2009-01-07, 887.6
2009-01-08, 882.5
2009-01-09, 879.7
2009-01-10, 878.3
2009-01-11, 876.6
2009-01-12, 875.2
</code></pre>
<p>Expected output:</p>
<pre><code>2009-01-01, 166.0
2009-01-02, 164.2
..............
</code></pre>
<p>...and so on. Numbers can be large and small: 10000 and 20.</p>
<p>Help me please to do it in most efficient way. DataFrame is very large. I need all speed of pandas!</p>
|
<python><pandas>
|
2023-12-12 15:48:39
| 2
| 877
|
Igor K.
|
77,647,255
| 11,267,783
|
White space using GridSpec and right colorbar with Matplotlib
|
<p>I would like to make a figure with 2 subplots.</p>
<p><a href="https://i.sstatic.net/UogI5.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/UogI5.png" alt="enter image description here" /></a></p>
<p>The problem is that on the right of the first figure, there is a whitespace (which belongs to the subplot below). Is is possible to make the first subplot to fit the entire space on the right ?</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
data1 = np.random.rand(10,10)
data2 = np.random.rand(10,10)
fig = plt.figure(constrained_layout=True)
gs = GridSpec(2,1,figure=fig)
ax = fig.add_subplot(gs[0,0])
img = plt.imshow(data1, aspect='auto')
plt.colorbar(location='bottom')
ax = fig.add_subplot(gs[1,0])
plt.imshow(data2)
plt.colorbar(location='right')
plt.axis("scaled")
plt.show()
</code></pre>
<p>Moreover, is it possible to have the right colorbar just next to the second plot ? As you can see here, the colorbar is at the far right.</p>
|
<python><matplotlib>
|
2023-12-12 15:31:43
| 1
| 322
|
Mo0nKizz
|
77,647,079
| 4,726,173
|
What's wrong with my subclass of pytorch's DataLoader? - Error: Too many open files
|
<p>What is wrong with my subclass of <code>torch.utils.data.DataLoader</code>? After using this instead of the original, I get an error. The error is solved by setting <code>pin_memory</code> (in the superclass) to <code>False</code>.</p>
<p>This is the class (the actual class has a few more parameters which are all not used, for compatibility with my code):</p>
<pre><code>class CustomDataLoader(torchdata.DataLoader):
def __init__(self, dataset, batch_size, shuffle,
collate_fn=None, pin_memory=False, num_workers=4, **kwargs):
''' Custom data loader to test how to subclass torchdata.DataLoader.
'''
super().__init__( dataset, batch_size=batch_size, shuffle=shuffle, collate_fn=collate_fn,
pin_memory=pin_memory, num_workers=num_workers, **kwargs)
def __iter__(self):
self.super_iter = super().__iter__()
return self
def __next__(self):
next_batch = self.super_iter.__next__()
return next_batch, None, None
</code></pre>
<p>And here the start of the 150 lines long error message:</p>
<pre><code>Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 300, in _run_finalizers
finalizer()
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/opt/conda/lib/python3.10/shutil.py", line 725, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/opt/conda/lib/python3.10/shutil.py", line 633, in _rmtree_safe_fd
onerror(os.scandir, path, sys.exc_info())
File "/opt/conda/lib/python3.10/shutil.py", line 629, in _rmtree_safe_fd
with os.scandir(topfd) as scandir_it:
OSError: [Errno 24] Too many open files: '/tmp/pymp-n1e35f4z'
</code></pre>
<p>The code that raises the error uses a dataset that is currently not public, so I cannot use the same code here. Just looping over the dataset fails to reproduce the issue, however.</p>
<p>I've seen a few questions here asking something related, but usually the answer does not require to subclass <code>DataLoader</code> (<a href="https://stackoverflow.com/questions/71744788/subclass-of-pytorch-dataloader-for-changing-batch-output">here</a> and <a href="https://stackoverflow.com/questions/69170854/pytorch-customized-dataloader">here</a>). More related could be this: <a href="https://discuss.pytorch.org/t/too-many-open-files-caused-by-persistent-workers-and-pin-memory/193372" rel="nofollow noreferrer">https://discuss.pytorch.org/t/too-many-open-files-caused-by-persistent-workers-and-pin-memory/193372</a>, because setting <code>pin_memory</code> to <code>False</code> also helped in my case.</p>
<p>Are there any issues with my class above, that could lead to pytorch-internal issues with data loading? Or does it look okay?</p>
<hr />
<p>Edit: Remainder of the error message:</p>
<pre><code>Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 300, in _run_finalizers
finalizer()
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 224, in __call__
s res = self._callback(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/opt/conda/lib/python3.10/shutil.py", line 725, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/opt/conda/lib/python3.10/shutil.py", line 633, in _rmtree_safe_fd
onerror(os.scandir, path, sys.exc_info())
File "/opt/conda/lib/python3.10/shutil.py", line 629, in _rmtree_safe_fd
with os.scandir(topfd) as scandir_it:
OSError: [Errno 24] Too many open files: '/tmp/pymp-n1e35f4z'
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 300, in _run_finalizers
finalizer()
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/opt/conda/lib/python3.10/shutil.py", line 725, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/opt/conda/lib/python3.10/shutil.py", line 633, in _rmtree_safe_fd
onerror(os.scandir, path, sys.exc_info())
File "/opt/conda/lib/python3.10/shutil.py", line 629, in _rmtree_safe_fd
with os.scandir(topfd) as scandir_it:
OSError: [Errno 24] Too many open files: '/tmp/pymp-u0_3n68n'
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 138, in _serve
File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 463, in accept
File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 609, in accept
File "/opt/conda/lib/python3.10/socket.py", line 293, in accept
fd, addr = self._accept()
OSError: [Errno 24] Too many open files
Exception in thread Thread-534 (_pin_memory_loop):
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
self.run()
File "/opt/conda/lib/python3.10/threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 51, in _pin_memory_loop
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 145, in _serve
send(conn, destination_pid)
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 50, in send
reduction.send_handle(conn, new_fd, pid)
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 183, in send_handle
with socket.fromfd(conn.fileno(), socket.AF_UNIX, socket.SOCK_STREAM) as s:
File "/opt/conda/lib/python3.10/socket.py", line 545, in fromfd
nfd = dup(fd)
OSError: [Errno 24] Too many open files
do_one_step()
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 28, in do_one_step
r = in_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
File "/opt/conda/lib/python3.10/multiprocessing/queues.py", line 122, in get
return _ForkingPickler.loads(res)
File "/opt/conda/lib/python3.10/site-packages/torch/multiprocessing/reductions.py", line 307, in rebuild_storage_fd
fd = df.detach()
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 58, in detach
return reduction.recv_handle(conn)
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 189, in recv_handle
return recvfds(s, 1)[0]
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 159, in recvfds
raise EOFError
EOFError
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/queues.py", line 244, in _feed
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 51, in dumps
cls(buf, protocol).dump(obj)
File "/opt/conda/lib/python3.10/site-packages/torch/multiprocessing/reductions.py", line 370, in reduce_storage
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 198, in DupFd
return resource_sharer.DupFd(fd)
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 48, in __init__
new_fd = os.dup(fd)
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 300, in _run_finalizers
finalizer()
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.10/multiprocessing/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/opt/conda/lib/python3.10/shutil.py", line 725, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/opt/conda/lib/python3.10/shutil.py", line 633, in _rmtree_safe_fd
onerror(os.scandir, path, sys.exc_info())
File "/opt/conda/lib/python3.10/shutil.py", line 629, in _rmtree_safe_fd
with os.scandir(topfd) as scandir_it:
OSError: [Errno 24] Too many open files: '/tmp/pymp-oyedwdyi'
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/queues.py", line 244, in _feed
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 51, in dumps
File "/opt/conda/lib/python3.10/site-packages/torch/multiprocessing/reductions.py", line 370, in reduce_storage
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 198, in DupFd
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 48, in __init__
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/multiprocessing/queues.py", line 244, in _feed
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 51, in dumps
File "/opt/conda/lib/python3.10/site-packages/torch/multiprocessing/reductions.py", line 370, in reduce_storage
File "/opt/conda/lib/python3.10/multiprocessing/reduction.py", line 198, in DupFd
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 48, in __init__
OSError: [Errno 24] Too many open files
</code></pre>
<hr />
<p>And then the very final part (raised within 'finally' after the error above):</p>
<pre><code>Exception in thread Thread-535 (_pin_memory_loop):
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
File "/opt/conda/lib/python3.10/threading.py", line 953, in run
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 51, in _pin_memory_loop
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 28, in do_one_step
File "/opt/conda/lib/python3.10/multiprocessing/queues.py", line 122, in get
File "/opt/conda/lib/python3.10/site-packages/torch/multiprocessing/reductions.py", line 307, in rebuild_storage_fd
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 57, in detach
File "/opt/conda/lib/python3.10/multiprocessing/resource_sharer.py", line 86, in get_connection
File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 502, in Client
File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 628, in SocketClient
File "/opt/conda/lib/python3.10/socket.py", line 232, in __init__
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File "[...]/train.py", line 545, in train
File "[...]/train.py", line 248, in train_or_eval_epoch
File "/src/group-orbit-cl/group_orbit_cl/data/sample_transformer.py", line 637, in __iter__
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 442, in __iter__
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 388, in _get_iterator
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1043, in __init__
File "/opt/conda/lib/python3.10/multiprocessing/process.py", line 121, in start
File "/opt/conda/lib/python3.10/multiprocessing/context.py", line 224, in _Popen
File "/opt/conda/lib/python3.10/multiprocessing/context.py", line 281, in _Popen
File "/opt/conda/lib/python3.10/multiprocessing/popen_fork.py", line 19, in __init__
File "/opt/conda/lib/python3.10/multiprocessing/popen_fork.py", line 65, in _launch
OSError: [Errno 24] Too many open files
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "[...]/train.py", line 1379, in <module>
File "[...]/train.py", line 1376, in main
File "[...]/train.py", line 639, in train
File "[...]/train.py", line 687, in maybe_store_model
File "/opt/conda/lib/python3.10/site-packages/torch/serialization.py", line 440, in save
File "/opt/conda/lib/python3.10/site-packages/torch/serialization.py", line 315, in _open_zipfile_writer
File "/opt/conda/lib/python3.10/site-packages/torch/serialization.py", line 288, in __init__
RuntimeError: File saved_models/4636315_0_mlp_ep-521.state_dict cannot be opened.
</code></pre>
|
<python><pytorch><pytorch-dataloader>
|
2023-12-12 15:05:53
| 1
| 627
|
dasWesen
|
77,647,014
| 1,914,781
|
bucket time duration dataframe with pandas
|
<p>I would like to bucket a datetime duration dataframe with pandas.
example:</p>
<pre><code>import pandas as pd
import itertools
import numpy as np
df = pd.DataFrame({
'ts': [1, 5, 10, 12],
'dur': [1, 2, 6, 6],
})
print(df)
ts dur
0 1 1
1 5 2
2 10 6
3 12 6
</code></pre>
<p>I need to bucket it with 3, so:</p>
<p><a href="https://i.sstatic.net/cUMtB.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/cUMtB.png" alt="enter image description here" /></a></p>
<pre><code>bin1, 1
bin2, 2
bin3, 0
bin4, 4
bin5, 6
bin6, 2
</code></pre>
<p>What's proper way to do it with pandas?</p>
<p>*Update
To follow up @Pieree D's answer, it's great for above example. One problem if I use <code>ns</code> as unit and data more bigger as below:</p>
<pre><code>import pandas as pd
import itertools
import numpy as np
import io
def bucket(df,ts,dur,span):
freq = 'ns' # could be 's' if preferred
dfs = pd.to_timedelta(df[ts], unit=freq) # start, inclusive
dfe = df[ts] + df[dur]
dfe = pd.to_timedelta(dfe, unit=freq) # end, exclusive
dfout = pd.concat([
pd.Series(1, index=dfs), # start: +1
pd.Series(-1, index=dfe), # end: -1
]).resample(freq).sum().cumsum().resample(
f'{span}{freq}', origin='start',
).sum().reset_index(drop=True).rename_axis('bin')
dfout = dfout.to_frame()
dfout.columns= ['sum']
dfout[ts] = df[ts].min() + dfout.index*span
return dfout
csvdata = '''ts,dur
19318744574,391823
21320087699,527291
23322650667,345208
25325015510,355729
27327401707,356354
29329792123,464531
31332296861,408802
32596494257,1131354
32738075298,416459'''
df = pd.read_csv(io.StringIO(csvdata))
df = bucket(df,'ts','dur',50)
print(df)
</code></pre>
<p>Then python report error:</p>
<pre><code>numpy.core._exceptions._ArrayMemoryError: Unable to allocate 100. GiB for an array with shape (13419747184,) and data type int64
</code></pre>
<p>Maybe need to consider to use sparse solution?</p>
|
<python><pandas>
|
2023-12-12 14:57:03
| 1
| 9,011
|
lucky1928
|
77,646,970
| 2,328,066
|
How can I pytest+monkeypatch a callable class to reusably specify the return of __call__?
|
<p>I am trying to write tests for classes that use Langchain's <code>LLMChain</code> class under the covers. The <code>LLMChain</code> class is callable.</p>
<pre class="lang-py prettyprint-override"><code>class MockLLMChain:
"""
Mock LLMChain class for testing
Example usage in a test:
monkeypatch.setattr(langchain.chains, "LLMChain",
MockLLMChain(desired_response='9'))
"""
def __init__(self, *args, **kwargs):
MockLLMChain.response = "default"
def __call__(self, *args, **kwargs):
return MockLLMChain.response
def test_yes_no_classifier(yes_no_classifier, monkeypatch):
MockLLMChain.response = {'text': '9'}
import src.query_helpers.yes_no_classifier
monkeypatch.setattr(
src.query_helpers.yes_no_classifier, "LLMChain", MockLLMChain
)
response = yes_no_classifier.classify(
conversation="1234", statement="The sky is blue."
)
assert response == 9
</code></pre>
<p>The above code results in a test error as the <code>MockLLMChain</code> response is not actually overridden, and inside the <code>YesNoClassifier</code> we are expecting <code>response</code> to be a dict.</p>
<pre class="lang-py prettyprint-override"><code>class MockLLMChain:
"""
Mock LLMChain class for testing
Example usage in a test:
monkeypatch.setattr(langchain.chains, "LLMChain",
MockLLMChain(desired_response='9'))
"""
def __init__(self, *args, **kwargs):
self.response = "default"
def __call__(self, *args, **kwargs):
return self.response
def test_yes_no_classifier(yes_no_classifier, monkeypatch):
ml = MockLLMChain()
ml.response = {'text':'default'}
import src.query_helpers.yes_no_classifier
monkeypatch.setattr(
src.query_helpers.yes_no_classifier, "LLMChain", ml
)
response = yes_no_classifier.classify(
conversation="1234", statement="The sky is blue."
)
assert response == 9
</code></pre>
<p>The above code results in an error:</p>
<pre><code> llm_chain = LLMChain(llm=bare_llm, prompt=prompt_instructions, verbose=verbose)
> response = llm_chain(inputs={"statement": statement})
E TypeError: 'dict' object is not callable
</code></pre>
<p>The error suggests to me that this monkeypatching is substituting the output of the callable class (the dict) and not the modified callable class (<code>ml</code>).</p>
<p>Can someone suggest a way to make this <code>MockLLMChain</code> class reusable across many tests without having to hard-code the entire mocked class definition in each test just to control what the mocked class returns when it is called?</p>
|
<python><mocking><pytest>
|
2023-12-12 14:48:48
| 1
| 901
|
Erik Jacobs
|
77,646,956
| 12,596,824
|
Using llama index but avoiding the tiktoken API call
|
<p>I want to use <code>llama_index</code> but when I import the package I get the following error</p>
<pre><code>ConnectionError: ('Connected aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None))
</code></pre>
<p>This is because <code>llama_index</code> makes a request via the package tiktoken by open ai. You can see this in line 55.
<a href="https://github.com/run-llama/llama_index/blob/main/llama_index/utils.py#L52" rel="nofollow noreferrer">https://github.com/run-llama/llama_index/blob/main/llama_index/utils.py#L52</a></p>
<p>Which at the end makes a call to this API (<a href="https://openaipublic.blob.core.windows.net/gpt-2/encodings/main/vocab.bpe" rel="nofollow noreferrer">https://openaipublic.blob.core.windows.net/gpt-2/encodings/main/vocab.bpe</a>)
<a href="https://github.com/openai/tiktoken/blob/main/tiktoken_ext/openai_public.py#L11" rel="nofollow noreferrer">https://github.com/openai/tiktoken/blob/main/tiktoken_ext/openai_public.py#L11</a></p>
<p>Is there a way to avoid this API call? And just maybe have it locally? I can't access the internet in the environment I'm working in so I can't make API calls.</p>
|
<python><nlp><large-language-model><llama-index><retrieval-augmented-generation>
|
2023-12-12 14:46:47
| 0
| 1,937
|
Eisen
|
77,646,924
| 12,390,973
|
How to add time limit to gurobi solver in Pypsa model?
|
<p>I have create a model using Pypsa and I am using gurobi solver to solve it. I am testing the time limit parameter of Gurobi. Here is how I am adding it to the model :</p>
<pre><code>solverOptions = {
'LogFile': "gurobiLog",
'MIPGap': model_inputs['model_data']['solverMipGap'], # 0.001
'BarConvTol': model_inputs['model_data']['BarConvTol'], # 0.01
'TimeLimit': 200, # 200 Seconds
}
network.lopf(network.snapshots, solver_name='gurobi', solver_options=solverOptions, extra_functionality=extra_functionality)
</code></pre>
<p>I have set the time limit of 200 seconds here and I can see that in the Gurobi log file these parameters are getting applied there:</p>
<pre><code>Gurobi 10.0.1 (win64) logging started Tue Dec 12 19:51:07 2023
Set parameter LogFile to value "gurobiLog"
Set parameter MIPGap to value 0.001
Set parameter BarConvTol to value 0.01
Set parameter TimeLimit to value 5
</code></pre>
<p>after 5 seconds in the log file I can see the solver is stopped and this message is printed :</p>
<pre><code>Stopped in 136184 iterations and 5.04 seconds (280.23 work units)
Time limit reached
</code></pre>
<p>But in the code, I see this error:</p>
<pre><code>network.lopf(network.snapshots, solver_name=solver, solver_options=solverOptions, extra_functionality=extra_functionality)
File "C:\Users\nvats\AppData\Local\Continuum\anaconda3\envs\production-cost-model_test\lib\site-packages\pypsa\components.py", line 769, in lopf
return network_lopf(self, **args)
File "C:\Users\nvats\AppData\Local\Continuum\anaconda3\envs\production-cost-model_test\lib\site-packages\pypsa\opf.py", line 2437, in network_lopf
extra_postprocessing=extra_postprocessing,
File "C:\Users\nvats\AppData\Local\Continuum\anaconda3\envs\production-cost-model_test\lib\site-packages\pypsa\opf.py", line 2296, in network_lopf_solve
options=solver_options
File "C:\Users\nvats\AppData\Local\Continuum\anaconda3\envs\production-cost-model_test\lib\site-packages\pyomo\opt\base\solvers.py", line 630, in solve
default_variable_value=self._default_variable_value)
File "C:\Users\nvats\AppData\Local\Continuum\anaconda3\envs\production-cost-model_test\lib\site-packages\pyomo\core\base\PyomoModel.py", line 228, in load_from
% str(results.solver.status))
ValueError: Cannot load a SolverResults object with bad status: aborted
</code></pre>
<p>I don't understand, it should return the solution that it found by that time but it's saying that it is aborted. Can anyone please help? Is there any other way of doing it?
I am also adding my code here:</p>
<pre><code>import pypsa
import numpy as np
import pandas as pd
from pyomo.environ import Constraint
from pyomo.environ import value
start_mt = 1
start_yr = 2022
end_mt = 12
end_yr = 2022
end_day = 31
frequency = 15
snapshots = pd.date_range("{}-{}-01".format(start_yr, start_mt), "{}-{}-{} 23:59".format(end_yr, end_mt, end_day),
freq=str(frequency) + "min")
np.random.seed(len(snapshots))
# Create a PyPSA network
network = pypsa.Network()
# Add a load bus
network.add("Bus", "Bus")
network.set_snapshots(snapshots)
load_profile = np.random.randint(2800, 3300, len(snapshots))
# Add the load to the network
network.add("Load", "Load profile", bus="Bus", p_set=load_profile)
# Define the generator data dictionary
generator_data = {
'coal1': {'capacity': 800, 'carrier': 'Coal', 'ramp up': 0.1, 'ramp down': 0.1, 'variable cost': 10, 'co2_emission_factor': 0.95},
'coal2': {'capacity': 600, 'carrier': 'Coal', 'ramp up': 0.1, 'ramp down': 0.1, 'variable cost': 11, 'co2_emission_factor': 0.95},
'coal3': {'capacity': 500, 'carrier': 'Coal', 'ramp up': 0.1, 'ramp down': 0.1, 'variable cost': 11, 'co2_emission_factor': 0.95},
'gas1': {'capacity': 600, 'carrier': 'Gas', 'ramp up': 0.05, 'ramp down': 0.05, 'variable cost': 12, 'co2_emission_factor': 0.45},
'gas2': {'capacity': 600, 'carrier': 'Gas', 'ramp up': 0.05, 'ramp down': 0.05, 'variable cost': 13, 'co2_emission_factor': 0.45},
'nuclear1': {'capacity': 300, 'carrier': 'Nuclear', 'ramp up': 0.01, 'ramp down': 0.01, 'variable cost': 4, 'co2_emission_factor': 0.03},
'nuclear2': {'capacity': 400, 'carrier': 'Nuclear', 'ramp up': 0.01, 'ramp down': 0.01, 'variable cost': 3, 'co2_emission_factor': 0.03},
'nuclear3': {'capacity': 250, 'carrier': 'Nuclear', 'ramp up': 0.01, 'ramp down': 0.01, 'variable cost': 3, 'co2_emission_factor': 0.03},
'solar1': {'capacity': 150, 'carrier': 'Solar', 'ramp up': 0.25, 'ramp down': 0.25, 'variable cost': 1, 'co2_emission_factor': 0.0},
'solar2': {'capacity': 200, 'carrier': 'Solar', 'ramp up': 0.25, 'ramp down': 0.25, 'variable cost': 2, 'co2_emission_factor': 0.0},
'backup': {'capacity': 1000, 'carrier': 'Import', 'ramp up': 0.25, 'ramp down': 0.25, 'variable cost': 2000, 'co2_emission_factor': 1.0},
}
# Add generators to the network
for name, data in generator_data.items():
network.add("Generator", name,
bus="Bus",
carrier=data['carrier'],
p_nom=data['capacity'],
marginal_cost=data['variable cost'],
ramp_limit_up=data['ramp up'],
ramp_limit_down=data['ramp down'],
)
print(network.generators.carrier.values)
network.add("Carrier", "Coal", co2_emissions=0.95)
network.add("Carrier", "Gas", co2_emissions=0.45)
network.add("Carrier", "Nuclear", co2_emissions=0.03)
network.add("Carrier", "Import", co2_emissions=1.0)
network.add("Carrier", "Solar", co2_emissions=0)
network.add(
"GlobalConstraint",
"CO2Limit",
carrier_attribute="co2_emissions",
sense="<=",
constant=50000000,
)
solver_name = "gurobi"
solverOptions = {
'LogFile': "gurobiLog",
'MIPGap': 0.001,
'BarConvTol': 0.01,
'TimeLimit': 5,
}
network.lopf(network.snapshots, solver_name=solver_name, solver_options=solverOptions)
csv_folder_name = 'model dump'
network.export_to_csv_folder(csv_folder_name)
dispatch = network.generators_t.p
total_gen = dispatch.sum()
co2 = sum([total_gen[gen] * data['co2_emission_factor'] for gen, data in generator_data.items()])
cost = sum([total_gen[gen] * data['variable cost'] for gen, data in generator_data.items()])
print('co2 emission = ', co2)
print('total cost = ', cost)
dispatch['load profile'] = load_profile
dispatch.to_excel('fuel wise dispatch.xlsx')
</code></pre>
|
<python><gurobi><pypsa>
|
2023-12-12 14:41:42
| 1
| 845
|
Vesper
|
77,646,884
| 5,401,672
|
How to invalidate a Cloudfront distribution using the AWS Python CDK?
|
<p>We have a static site which is hosted in S3 and I'm trying to create a stack which will automate uploading the files and invalidate the Cloudfront distribution.</p>
<p>I can upload the files but can't see any way to create the invalidation.</p>
<p>I can get the distribution like this:</p>
<pre class="lang-py prettyprint-override"><code>distribution = aws_cloudfront.Distribution.from_distribution_attributes(
self,
id="ImportedDistribution",
domain_name="https://hgfdh.cloudfront.net",
distribution_id="hgfdh",
)
</code></pre>
<p>but can't see how to create the invalidation.</p>
|
<python><amazon-cloudfront><aws-cdk>
|
2023-12-12 14:36:16
| 2
| 1,601
|
Mick
|
77,646,789
| 747,228
|
How to have the dialog box for choosing download location appeared in the frontend, before the file gets downloaded, using FastAPI?
|
<p>I have a <code>GET</code> endpoint that should return a <strong>huge file</strong> (500Mb).
I am using <code>FileResponse</code> to do that (the code is simplified for clarity reasons):</p>
<pre><code> async def get_file()
headers = {"Content-Disposition": f"attachment; filename={filename}"}
return FileResponse(file_path, headers=headers)
</code></pre>
<p>The problem is that I have to wait on the frontend until that file is <strong>completely</strong> downloaded, in order for the dialog box that allows choosing a download location to be shown:
<a href="https://i.sstatic.net/lA6ER.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/lA6ER.png" alt="enter image description here" /></a></p>
<p>And then, this file is saved instantly.</p>
<p>So, for example, I have a file with size 500 MB, when I click download on UI I have to wait a minute or something till the "Save dialog" is displayed. Then, when I click "Save" the file is saved instantly. Obviously, the frontend was waiting for the file to be downloaded.</p>
<p><strong>What I need is</strong>: The dialog to be shown instantly and then the user waiting for the file downloading to finish, <strong>after</strong> the user clicks 'Save' to choose the download location. So, how can I achieve that?</p>
|
<javascript><python><html><download><fastapi>
|
2023-12-12 14:20:31
| 1
| 2,028
|
unresolved_external
|
77,646,624
| 9,284,651
|
Data Frame- remove special character
|
<p>My DF looks like below:</p>
<pre><code>id date
1 ' : 07/01/2020 23:25'
2 ': 07/02/2020'
3 ' 07/03/2020 23:25 1'
4 '07/04/2020'
5 '23:50 07/05/2020'
6 '07 06 2023'
7 '00:00 07 07 2023'
</code></pre>
<p>I need to remove all special characters and numbers that are between ':' so DF should looks like below</p>
<pre><code>id date
1 07/01/2020
2 07/02/2020
3 07/03/2020
4 07/04/2020
5 07/05/2020
</code></pre>
<p>I can't use a simple:
<code>df['date'].str.split(':').str[0]</code> or <code>df['date'].str.replace(":",'')</code> beacuse I will lose correct values. Do you have idea how I could solve this?</p>
<p>Regards</p>
|
<python><pandas><dataframe><replace><data-cleaning>
|
2023-12-12 13:53:21
| 1
| 403
|
Tmiskiewicz
|
77,646,583
| 10,595,871
|
Pandas shift dataframe if date is found in string column
|
<p>I have a pandas dataframe that should have 7 columns. It has 9 columns instead, that's because some rows are shifted to the right to one or two columns.</p>
<p>I need to make some order in my df. I have this elements:</p>
<ul>
<li>the first two columns are always fine, but the second one could be null (that is also fine);</li>
<li>the third column is a date, could be null due to the shift to the next column or because it's actually null</li>
<li>the fourth column is a name of a city, and sometimes it contains the date from column 3. Sometimes is null</li>
<li>fifth column has the same behaviour as the fourth</li>
<li>last two columns are fine (even if some rows are shifted)</li>
</ul>
<p>Now, the approach that I was thinking is to check in column 4 for values that could be converted into date and, if found, shift that row from that column of one space to the left (not the entire row, because the first two columns are fine). Then I would repeat with a check in the fifth column.</p>
<p>code up until now (provided by chatGPT)</p>
<pre><code>df['names'], df['dates'] = df['fourth column'].str.split(' ', 1).str
</code></pre>
<p>It successfully create a column with only the names, but it fails in creating the dates one. all of the results are null.</p>
<p>Other approach with no results:</p>
<pre><code>des_col= 'fourth'
col_index= df.columns.get_loc(des_col)
for index, row in df.iterrows():
if pd.api.types.is_datetime64_any_dtype(row[des_col]):
df.iloc[index, col_index:] = row.iloc[col_index:].shift(-1)
</code></pre>
<p>sample: (tt is some text)
In this case rows 0 and 2 are good, while 1 and 3 should be shifted to the left from column 3:</p>
<pre><code>1 2 3 4 5 6 7 na1 na2
A E 2026-02-27 Torino tt tt tt NaN NaN
Z G NaN 1964-06-22 tt tt tt tt NaN
Z G NaN NaT tt tt tt NaN NaN
Z F NaN 1961-04-26 tt tt tt NaN NaN
</code></pre>
<p>I made some progress by doing so:</p>
<pre><code># first I've created a column with only the dates from column 4
df['date'] = pd.to_datetime(df['4], errors='coerce')
#Then I assign the value at column 3, if it's not null, and then I shift
for index, row in df.iterrows():
if pd.notnull(row['date']):
df.iloc[index, df.columns.get_loc('3')] = row['date']
df.iloc[index, df.columns.get_loc('3'):] = row.iloc[df.columns.get_loc('3'):].shift(-1)
</code></pre>
<p>The problem here is that now the df is shifted, but there are still some rows not shifted due to the fact that they don't belong to the previous case.</p>
<p>So the question now is how do I shift to the left the rows that still have column 'na1' populated, until I find an empty column?</p>
<pre><code>1 2 3 4 5 6 7 na1 na2
Z G 1964-06-22 NaN tt tt tt tt NaN
#expected output:
1 2 3 4 5 6 7 na1 na2
Z G 1964-06-22 tt tt tt tt NaN NaN
</code></pre>
<p>Thanks!</p>
|
<python><pandas>
|
2023-12-12 13:46:45
| 0
| 691
|
Federicofkt
|
77,646,470
| 4,537,160
|
Pytorch dataloader, returned shape of targets
|
<p>I'm running a Pytorch model training, where I defined the <strong>getitem</strong> method of the dataset to return:</p>
<pre><code>def __getitem__(self, ind):
[...]
return processed_images, target
</code></pre>
<p>processed_images is a sequence of 5 RGB images with shape (224,224), while target is a 4-dim vector with one-hot encoding for the classes targets.</p>
<p>So, each call of <strong>getitem</strong> is returning, for example:</p>
<pre><code>>> processed_images.shape
(5, 224, 224, 3)
>> target
[0.0, 1.0, 0.0, 0.0]
</code></pre>
<p>In the training script, I'm extracting batch using:</p>
<pre><code>train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=False,
persistent_workers=False,
timeout=0,
)
for epoch in range(number_of_epochs):
for batch_ind, batch_data in enumerate(train_dataloader):
datas, targets = batch_data
</code></pre>
<p>The problem is datas has the correct shape, i.e. a stack of 22 sequences of images:</p>
<pre><code>datas.shape
torch.Size([22, 5, 224, 224, 3])
</code></pre>
<p>However, targets are stacked in a weird way:</p>
<pre><code>len(targets) = 4
len(targets[0]) = 22
</code></pre>
<p>while I would expect the opposite (a list of 22 elements, each of len=4).
Am I doing something wrong?</p>
|
<python><pytorch><pytorch-dataloader>
|
2023-12-12 13:27:38
| 1
| 1,630
|
Carlo
|
77,646,466
| 13,178,529
|
Function that returns pointer, when imported in separate file, return number instead of object
|
<p>I have a file called, runner.py, which has a lot of functions that make calls to a market DLL.</p>
<pre><code>#Imports para execução da DLL
import time
import gc
from ctypes import *
from ctypes.wintypes import UINT
import struct
from datetime import*
import sys
import functions
from threading import Thread
rotPassword = ''
accountID = ''
corretora = ''
profit_dll = WinDLL('./ProfitDLL64.dll')
profit_dll.argtypes = None
def printPosition():
global profit_dll
global corretora, accountID
asset = 'asset'
bolsa = 'f'
thisCorretora = corretora
acc_id = accountID
priceToReturn = 0
result = profit_dll.GetPosition(c_wchar_p(str(acc_id)), c_wchar_p(str(thisCorretora)), c_wchar_p(asset), c_wchar_p(bolsa))
print(f'here {result}')
return result
n_qtd = result[0]
if (n_qtd == 0):
print('Nao ha posicao para esse ativo')
return priceToReturn
else:
n_tam = result[1]
arr = cast(result, POINTER(c_char))
frame = bytearray()
for i in range(n_tam):
c = arr[i]
frame.append(c[0])
start = 8
for i in range(n_qtd):
corretora_id = struct.unpack('i', frame[start:start+4])[0]
start += 4
acc_id_length = struct.unpack('h', frame[start:start+2])[0]
start += 2
account_id = frame[start:start+acc_id_length]
start += acc_id_length
titular_length = struct.unpack('h', frame[start:start+2])[0]
start += 2
titular = frame[start:start+titular_length]
start += titular_length
ticker_length = struct.unpack('h', frame[start:start+2])[0]
start += 2
ticker = frame[start:start+ticker_length]
start += ticker_length
intraday_pos = struct.unpack('i', frame[start:start+4])[0]
start += 4
price = struct.unpack('d', frame[start:start + 8])[0]
priceToReturn = str(price)
start += 8
avg_sell_price = struct.unpack('d', frame[start:start + 8])[0]
start += 8
sell_qtd = struct.unpack('i', frame[start:start+4])[0]
start += 4
avg_buy_price = struct.unpack('d', frame[start:start + 8])[0]
start += 8
buy_qtd = struct.unpack('i', frame[start:start+4])[0]
start += 4
custody_d1 = struct.unpack('i', frame[start:start+4])[0]
start += 4
custody_d2 = struct.unpack('i', frame[start:start+4])[0]
start += 4
custody_d3 = struct.unpack('i', frame[start:start+4])[0]
start += 4
blocked = struct.unpack('i', frame[start:start+4])[0]
start += 4
pending = struct.unpack('i', frame[start:start+4])[0]
start += 4
allocated = struct.unpack('i', frame[start:start+4])[0]
start += 4
provisioned = struct.unpack('i', frame[start:start+4])[0]
start += 4
qtd_position = struct.unpack('i', frame[start:start+4])[0]
start += 4
available = struct.unpack('i', frame[start:start+4])[0]
start += 4
print(f"Corretora: {corretora_id}, Titular: {str(titular)}, Ticker: {str(ticker)}, Price: {price}, AvgSellPrice: {avg_sell_price}, AvgBuyPrice: {avg_buy_price}, SellQtd: {sell_qtd}, BuyQtd: {buy_qtd}")
return priceToReturn
def dllStart():
try:
global profit_dll
key = input("Chave de acesso: ")
user = input("Usuário: ") # preencher com usuário da conta (email ou documento)
password = input("Senha: ") # preencher com senha da conta
bRoteamento = True
if bRoteamento :
result = profit_dll.DLLInitializeLogin(c_wchar_p(key), c_wchar_p(user), c_wchar_p(password), stateCallback, historyCallBack, orderChangeCallBack, accountCallback,
newTradeCallback, newDailyCallback, priceBookCallback,
offerBookCallback, newHistoryCallback, progressCallBack, newTinyBookCallBack)
else :
result = profit_dll.DLLInitializeMarketLogin(c_wchar_p(key), c_wchar_p(user), c_wchar_p(password), stateCallback, newTradeCallback, newDailyCallback, priceBookCallback,
offerBookCallback, newHistoryCallback, progressCallBack, newTinyBookCallBack)
profit_dll.SendSellOrder.restype = c_longlong
profit_dll.SendBuyOrder.restype = c_longlong
profit_dll.SendStopBuyOrder.restype = c_longlong
profit_dll.SendStopSellOrder.restype = c_longlong
profit_dll.SendZeroPosition.restype = c_longlong
profit_dll.GetAgentNameByID.restype = c_wchar_p
profit_dll.GetAgentShortNameByID.restype = c_wchar_p
profit_dll.GetPosition.restype = POINTER(c_int)
profit_dll.SendMarketSellOrder.restype = c_longlong
profit_dll.SendMarketBuyOrder.restype = c_longlong
print('DLLInitialize: ' + str(result))
wait_login()
except Exception as e:
print(str(e))
if __name__ == '__main__':
try:
dllStart()
strInput = ''
while strInput != "exit":
strInput = input('Insira o comando: ')
if strInput == 'subscribe':
subscribeTicker()
elif strInput == 'unsubscribe':
unsubscribeTicker()
elif strInput == 'offerbook':
subscribeOffer()
elif strInput == 'position':
printPosition()
elif strInput == 'lastAdjusted':
printLastAdjusted()
elif strInput == 'buystop' :
buyStopOrder()
elif strInput == 'sellstop':
sellStopOrder()
elif strInput == 'cancel':
cancelOrder()
elif strInput == 'changeOrder':
changeOrder()
elif strInput == 'cancelAllOrders':
cancelAllOrders()
elif strInput == 'getOrders':
getOrders()
elif strInput == 'getOrder':
getOrder()
elif strInput == 'selectOrder':
selectOrder()
elif strInput == 'cancelOrder':
cancelOrder()
elif strInput == 'getOrderProfitID':
getOrderProfitID()
elif strInput == 'getAllTickersByStock':
getAllTickersByStock()
elif strInput == 'getOld':
getSerieHistory()
elif strInput == 'account':
getAccount()
elif strInput == 'myBuy':
sendBuyOrder()
except KeyboardInterrupt:
pass
</code></pre>
<p>The correct log that I get when calling:</p>
<pre><code><ctypes.wintypes.LP_c_long object at 0x000001714E9CABC0>
</code></pre>
<p>When running directly the <code>runner.py</code> and calling <code>printPosition()</code>, everything works fine and it returns what I need (the object above).<br>
Although, I need to import this printPosition in a separate file to make a call for it. When performing so, I received a number only and not the object I needed.</p>
<pre><code>import socket, base64
import re
import sys
from operator import neg
from datetime import datetime
import profitrunner
import time
import gc
from ctypes import *
from ctypes.wintypes import UINT
import struct
def calculate_pos():
memory_address = profitrunner.printPosition()
print(heapPosition)
# I though on doing but did not work:
arr = cast(memory_address, POINTER(c_char))
</code></pre>
<p>The logs from this:</p>
<pre><code>2015042512
2015042656
2015042800
2015042944
2015043088
2015043232
</code></pre>
<p>The documentation from <code>GetPosition</code> gives is the follwing:</p>
<pre><code>Function that returns the position for a given ticker. Returns a data structure specified below. With full size (90 + N + T + K) bytes:
function GetPosition(
pwcIDAccount : PWideChar;
pwcIDCorretora : PWideChar;
pwcTicker : PWideChar;
pwcBolsa : PWideChar) : Pointer; stdcall;
</code></pre>
<p><a href="https://i.sstatic.net/woKNa.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/woKNa.png" alt="enter image description here" /></a></p>
|
<python><python-3.x><pointers>
|
2023-12-12 13:26:45
| 1
| 1,200
|
Nilton Schumacher F
|
77,646,455
| 1,075,996
|
PyMySQL TypeError when connecting
|
<p>I'm working with the following (very simple) MariaDB connection:</p>
<pre><code>>>> from dbconfig import db_host, db_user, db_pass, db_name, system_number
>>> import pymysql as mdb
>>> print(db_host, db_user, db_pass, db_name)
some.server.co.uk my_username my_password my_db_name
>>> db = mdb.connect(db_host, db_user, db_pass, db_name)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: __init__() takes 1 positional argument but 5 were given
</code></pre>
<p>I feel like I have done this many times before but for some reason it is throwing this error and I'm unable to connect. I've confirmed that the details are correct and I'm able to use them to connect in a MySQL client on the same machine.</p>
<p>What am I missing?</p>
|
<python><mysql><mariadb><pymysql>
|
2023-12-12 13:25:08
| 1
| 453
|
btongeorge
|
77,646,431
| 6,372,189
|
Why is shutil.rmtree takes hours to delete a folder sometimes? Any alternatives?
|
<p>It seems like a trivial issue but I've noticed lately that the <code>shutil.rmtree</code> function is either taking a ridiculously long time (like 6 hours) or is simply blocking the execution of the remaining script.</p>
<p>I have a script that does a bunch of operations but before starting everything else, it deletes this folder called "temp" and waits for 5 minutes, then it does a bunch of operations. I've noticed that recently the script is taking over 6 hours to run instead of the usual 10 minutes. After troubleshooting the issue to looks like the script is getting stuck on the <code>shutil.rmtree</code> function which basically blocks further execution of the script. I know the script is logically and programmatically fine but this delay is simply causing a lot of trouble. Below is the sample of the script.</p>
<pre><code>import shutil
from pathlib import Path
import os
def delFolder(folderPath):
try:
shutil.rmtree(folderPath)
except:
pass
def main_func(modulePath):
modulePath = Path(modulePath)
# Variable declarations and other stuff
temp_folder = Path.joinpath(modulePath, "tempFolder")
print("Deleting temp folder....")
delFolder(temp_folder )
print("Sleeping for 5 min....")
time.sleep(5*60)
# Other script functionality to be executed.
if __name__ == "__main__":
this_modulePath = os.getcwd()
main_func(this_modulePath)
</code></pre>
<p>It takes forever(like over 6 hours) to reach from <code>Deleting temp folder....</code> to <code>Sleeping for 5 min....</code>. I don't why this is happening with shutil. The folder that I am trying to delete is around 4 GB in size and has many files and subfolders in it and no other program is using any files from the temp folder (except onedrive sync, which shouldn't be an issue)</p>
|
<python><python-3.x><shutil>
|
2023-12-12 13:20:44
| 0
| 701
|
Prashant Kumar
|
77,646,352
| 14,695,308
|
Test-dependent teardown fixture
|
<p>I'm using teardown fixtures as follow</p>
<pre><code>@pytest.fixture
def clean_after_test()
yield
function_to_clean()
def test_object(clean_after_test)
<TESTING CODE>
</code></pre>
<p>But in specific test-case I need to create object, do something with it and after testing done remove it by ID. ID I can get only when test is in progress so I cannot use teardown fixture as usually since I cannot know ID in advance.
I can add object-removal code line at the end of test, but it will be executed only in case of passed status, so it's a bad option</p>
<p>So what will be the best practice for cleaning-up after test based on data received during test?</p>
|
<python><pytest>
|
2023-12-12 13:10:05
| 0
| 720
|
DonnyFlaw
|
77,646,307
| 6,836,950
|
Correlation matrix like DataFrame in Polars
|
<p>I have Polars dataframe</p>
<pre class="lang-py prettyprint-override"><code>data = {
"col1": ["a", "b", "c", "d"],
"col2": [[-0.06066, 0.072485, 0.548874, 0.158507],
[-0.536674, 0.10478, 0.926022, -0.083722],
[-0.21311, -0.030623, 0.300583, 0.261814],
[-0.308025, 0.006694, 0.176335, 0.533835]],
}
df = pl.DataFrame(data)
</code></pre>
<p>I want to calculate cosine similarity for each combination of column <code>col1</code></p>
<p>The desired output should be the following:</p>
<pre><code>┌─────────────────┬──────┬──────┬──────┬──────┐
│ col1_col2 ┆ a ┆ b ┆ c ┆ d │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════╪══════╪══════╪══════╪══════╡
│ a ┆ 1.0 ┆ 0.86 ┆ 0.83 ┆ 0.54 │
│ b ┆ 0.86 ┆ 1.0 ┆ 0.75 ┆ 0.41 │
│ c ┆ 0.83 ┆ 0.75 ┆ 1.0 ┆ 0.89 │
│ d ┆ 0.54 ┆ 0.41 ┆ 0.89 ┆ 1.0 │
└─────────────────┴──────┴──────┴──────┴──────┘
</code></pre>
<p>Where each value represents cosine similarity between respective column values.</p>
<p>I'm using following cosine similarity function</p>
<pre class="lang-py prettyprint-override"><code>from numpy.linalg import norm
cosine_similarity = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
</code></pre>
<p>I tried to use it with <code>pivot</code> method</p>
<pre class="lang-py prettyprint-override"><code>df.pivot(on="col1", values="col2", index="col1", aggregate_function=cosine_similarity)
</code></pre>
<p>However I'm getting the following error</p>
<pre><code>AttributeError: 'function' object has no attribute '_pyexpr'
</code></pre>
|
<python><python-polars>
|
2023-12-12 13:02:00
| 3
| 4,179
|
Okroshiashvili
|
77,646,165
| 235,472
|
How to get the current directory from the whole path?
|
<p>With:</p>
<pre><code>currentPath = pathlib.Path.cwd()
</code></pre>
<p>I get the current path:</p>
<pre><code>/path/to/my/location/
</code></pre>
<p>How can I get the directory <code>location</code> without the need to manually process the complete path?</p>
|
<python><python-3.x>
|
2023-12-12 12:36:41
| 2
| 13,528
|
Pietro
|
77,645,765
| 12,571,870
|
Custom Python module not found when placed in Docker container (with setup.py and entry_points, console_scripts)
|
<p>I have the following structure of my project:</p>
<pre><code>|--Dockerfile
|--requirements.txt
|--setup.py
|--mydummyproject
|--|--__init__.py
|--|--__main__.py
|--|--dummy.py
</code></pre>
<p>The <code>dummy.py</code> has one simple function:</p>
<pre><code>import numpy as np
def return_output(*args):
for a in args:
print(f"Hello {a}")
print("Phew!")
print("We've got numpy:",np.__version__)
</code></pre>
<p>The <code>__init__.py</code> file is there <em>pro forma</em> and in <code>__main__.py</code> I have a simple <code>argparse</code>interface:</p>
<pre><code>import argparse
from .dummy import return_output
def command_line_target():
parser = argparse.ArgumentParser()
parser.add_argument('-v','--value', nargs='+',default=["John","Jane"])
args = parser.parse_args()
return_output(*args.value)
</code></pre>
<p>My <code>setup.py</code> has the following:</p>
<pre><code>from setuptools import setup
with open("requirements.txt", "r") as _f:
install_reqs = _f.read().strip().split("\n")
setup(name="dummy",
version="0.0.1",
package_dir={"mydummyproject": "mydummyproject"},
install_requires=install_reqs,
python_requires=">=3.8",
entry_points={
'console_scripts': [
'hello = mydummyproject.__main__:command_line_target'
]}
)
</code></pre>
<p>So basically once I install it and run something like <code>hello Mark Johnny</code> it will run the <code>return_output</code> and print out the above.</p>
<p>I've tested this in a test conda environment, and it works. Next step is dockerizing my application. I'm running WSL and Docker Desktop. My <code>Dockerfile</code> is:</p>
<pre><code>FROM python:3.8
WORKDIR /app
COPY . /app
RUN pip install --no-cache-dir -r requirements.txt
RUN python setup.py install
CMD ["hello"]
</code></pre>
<p>I'm using <code>docker build -t dummytest</code> in WSL and it builds it just fine. But when I run it via <code>docker run dummytest</code> I get the following error:</p>
<pre><code> Traceback (most recent call last):
File "/usr/local/bin/hello", line 33, in <module>
sys.exit(load_entry_point('dummy==0.0.1', 'console_scripts', 'hello')())
File "/usr/local/bin/hello", line 25, in importlib_load_entry_point
return next(matches).load()
File "/usr/local/lib/python3.8/importlib/metadata.py", line 77, in load
module = import_module(match.group('module'))
File "/usr/local/lib/python3.8/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 961, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 973, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'mydummyproject'
</code></pre>
<p>This would imply that my custom module (mydummyproject) was not installed correctly. Is there an error in my Dockerfile?</p>
<p>EDIT1:
I've modified <code>Dockerfile</code>:</p>
<pre><code>FROM python:3.8
WORKDIR /app
COPY . /app
RUN pip install .
CMD ["hello"]
</code></pre>
<p>I've used command <code>docker build -t dummy2 .</code> and command <code>docker run dummy2</code>. The error was now:</p>
<pre><code>Traceback (most recent call last):
File "/usr/local/bin/hello", line 5, in <module>
from mydummyproject.__main__ import command_line_target
ModuleNotFoundError: No module named 'mydummyproject'
</code></pre>
<p>I've changed <code>setup.py</code> and directory (replaced "mydummyproject" with "dummy") and the error was still:</p>
<pre><code>Traceback (most recent call last):
File "/usr/local/bin/hello", line 5, in <module>
from dummy.__main__ import command_line_target
ModuleNotFoundError: No module named 'dummy'
</code></pre>
<p>EDIT2:
The solution by Teemu Risku works. The solution is to modify the <code>setup.py</code> via <code>find_packages</code>:</p>
<pre><code>from setuptools import setup, find_packages
with open("requirements.txt", "r") as _f:
install_reqs = _f.read().strip().split("\n")
setup(name="dummy",
version="0.0.1",
packages=find_packages(),
install_requires=install_reqs,
python_requires=">=3.8",
entry_points={
'console_scripts': [
'hello = mydummyproject.__main__:command_line_target'
]}
)
</code></pre>
|
<python><docker>
|
2023-12-12 11:33:33
| 1
| 438
|
ivan199415
|
77,645,742
| 6,060,982
|
pyright and imports from shared libraries
|
<p>Can pyright or any other linter resolve imports from <code>.so</code> libraries?
In my case I have a c++ shared library with pybind11 used to generate the python bindings.
I can successfully import and use the <code>FourierSeries</code> from the <code>.so</code> library</p>
<pre class="lang-py prettyprint-override"><code>from .c_lib.c_fourier import FourierSeries as FourierSeries_c
</code></pre>
<p>but at the same time pyright complains that the import cannot be resolved.</p>
<blockquote>
<p>Import ".c_lib.c_fourier" could not be resolved</p>
</blockquote>
<p>Any ideas as to how this can be fixed, or at least how to silence this warning?</p>
|
<python><pybind11><pyright>
|
2023-12-12 11:29:56
| 0
| 700
|
zap
|
77,645,552
| 5,973,911
|
Is it possible to reformat python __all__ statement with ruff to make it multiline?
|
<p>I'm using <code>ruff</code> (<a href="https://docs.astral.sh/ruff/" rel="nofollow noreferrer">https://docs.astral.sh/ruff/</a>). Is it possible to format the code from this:</p>
<pre><code>__all__ = ["Model", "User", "Account"]
</code></pre>
<p>Into this?</p>
<pre><code>__all__ = [
"Model",
"User",
"Account"
]
</code></pre>
|
<python><ruff>
|
2023-12-12 11:00:05
| 1
| 853
|
Михаил Павлов
|
77,645,520
| 6,759,459
|
Dockerized FastAPI SQLAlchemy PostgreSQL 'NoneType' object has no attribute 'execute'
|
<p>This is a sudden error, whose root cause I haven't been able to figure out.
I successfully built 2 Docker images that are connected over the same network:</p>
<ol>
<li>FastAPI</li>
<li>PostgreSQL</li>
</ol>
<p>I am not able to use the SQLAlchemy dependency injection to connect to my DB.
However, I was able to seed my DB successfully many times over.
Only made one additional field addition to my SQLAlchemy model and then this failure occurred.</p>
<pre><code>backend-web-1 | ERROR:services.utils:Health check failed in 0.00 seconds: 'NoneType' object has no attribute 'execute'
backend-web-1 | ERROR:services.utils:Traceback (most recent call last):
backend-web-1 | File "/app/main.py", line 27, in read_root
backend-web-1 | db.execute('SELECT 1')
backend-web-1 | ^^^^^^^^^^
backend-web-1 | AttributeError: 'NoneType' object has no attribute 'execute'
</code></pre>
<p>Code failure above happens here</p>
<pre><code>from fastapi import FastAPI, Request, Depends, HTTPException
from sqlalchemy.orm import Session
import psutil
import threading
import time
import traceback
# Import your application modules
from database.database_config import get_db
from database.models import *
from database.crud import *
from api.interview import complete_interview
from api.chat_handling import *
from api.user_registration import register_user
from api.stripe import *
from services.utils import *
from config.env_var import *
app = FastAPI()
@app.get("/")
def read_root(db: Session = Depends(get_db)):
start_time = time.time()
try:
# Check database connectivity
db.execute('SELECT 1')
db_status = "Connected"
# Memory and Thread Count
memory_usage = psutil.virtual_memory().percent
thread_count = threading.active_count()
response_time = time.time() - start_time
logger.info(f"Health check passed in {response_time:.2f} seconds, Memory Usage: {memory_usage}%, Thread Count: {thread_count}, DB Status: {db_status}")
return {"message": "Hello, World!"}
except Exception as e:
response_time = time.time() - start_time
logger.error(f"Health check failed in {response_time:.2f} seconds: {e}")
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
</code></pre>
<p>For reference, here's how I define my dependency injection:</p>
<pre><code>from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from services.utils import logger
import traceback
from config.env_var import *
DB_USER = os.getenv('DB_USER')
DB_PASSWORD = os.getenv('DB_PASSWORD')
DB_HOST = os.getenv('DB_HOST')
DB_NAME = os.getenv('DB_NAME')
Base = declarative_base()
db_url = f'postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:5432/{DB_NAME}'
try:
engine = create_engine(db_url)
except Exception as e:
logger.info(f"Error creating database engine: {e}")
logger.info(traceback.format_exc())
raise
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
def get_db():
db = SessionLocal()
try:
yield db
except Exception as e:
logger.info(f"Database session error: {e}")
logger.info(traceback.format_exc())
raise
finally:
db.close()
</code></pre>
<p>I am able to successfully connect via my FastAPI Docker instance to my PostgreSQL instance via this script though:</p>
<pre><code>from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
import os
DB_USER = os.getenv('DB_USER')
DB_PASSWORD = os.getenv('DB_PASSWORD')
DB_HOST = os.getenv('DB_HOST')
DB_NAME = os.getenv('DB_NAME')
db_url = f'postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:5432/{DB_NAME}'
engine = create_engine(db_url)
SessionLocal = sessionmaker(bind=engine)
def test_session_local():
session = SessionLocal()
print(f"Session: {session}")
session.close()
def test_db_connection():
try:
db = SessionLocal()
# Perform a simple query
result = db.execute('SELECT 1')
for row in result:
print(row)
db.close()
print("Connection successful.")
except Exception as e:
print(f"Connection failed: {e}")
if __name__ == "__main__":
test_session_local()
test_db_connection()
</code></pre>
<p>I don't see where I made a critical failure.</p>
|
<python><postgresql><docker><sqlalchemy><fastapi>
|
2023-12-12 10:56:18
| 1
| 926
|
Ari
|
77,645,510
| 16,674,436
|
Simple seaborn distribution plot not working
|
<p>I’m trying to plot the distribution of scores on reddit posts, but can’t figure it out.</p>
<p>My data frame is something like that</p>
<pre><code>df = pd.DataFrame({"score": [12, 19, 25987, 887, 887, 1],
"author": ["xxx", "x", "xxx", "xx", "xxxx", "x"]})
</code></pre>
<p>With a lot more data points (about 300,000).</p>
<p>I have attempted the following two things:</p>
<pre><code>plt.figure(figsize=(10, 6))
sns.displot(data=df, x="score", bins=30, kde=True)
plt.title('Distribution of Post Scores')
plt.xlabel('Score')
plt.ylabel('Frequency')
plt.show()
</code></pre>
<p>But it gives me the following: </p>
<p><a href="https://i.sstatic.net/l6Cph.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/l6Cph.png" alt="enter image description here" /></a></p>
<p>And I’ve tried this:</p>
<pre><code>score_freq = df['score'].value_counts()
plt.figure(figsize=(10, 6))
sns.displot(score_freq, kind="kde", bw_adjust=30)
plt.title('Distribution of Post Scores')
plt.xlabel('Score')
plt.ylabel('Frequency')
plt.show()
</code></pre>
<p>Which gives me this:</p>
<p><a href="https://i.sstatic.net/FnRWw.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/FnRWw.png" alt="enter image description here" /></a></p>
<p>So the second option seems a bit better, though still wrong. I don’t have scores that go all the way to -100,000, and I <strong>do</strong> have scores that go all the way to 299,489.</p>
<p>I don’t really get what I’m doing wrong.</p>
<h2>Update</h2>
<p>This is what the real data looks like when I do <code>df['score'].sort_values()</code>:</p>
<p><a href="https://i.sstatic.net/N5sRE.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/N5sRE.png" alt="enter image description here" /></a></p>
<p>And this is what it looks like with <code>df['score'].value_counts()</code>:
<a href="https://i.sstatic.net/ijimS.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ijimS.png" alt="enter image description here" /></a></p>
|
<python><plot><statistics><seaborn><distribution>
|
2023-12-12 10:55:25
| 1
| 341
|
Louis
|
77,645,454
| 4,752,738
|
PyCharm removes unused imports when moving a file
|
<p>I'm using Pycharm for moving files inside my project and PyCharm updates the imports in the project to the new path which is great.</p>
<p>The problem is that it also <em>removes unused imports</em> from those files. This is a very bad behavior for me and I can't find a way to stop this.</p>
<p>I can't check whether it did that every time because sometimes its hundreds of files.</p>
|
<python><pycharm>
|
2023-12-12 10:45:59
| 2
| 943
|
idan ahal
|
77,645,290
| 9,488,023
|
How can I choose to not change the image size when reprojecting with Python rioxarray?
|
<p>I have an image in tif-format of size 18346x10218 in coordinate system EPSG:4326 that I want to convert to a new coordinate system, which I do with the following method with rioxarray in Python.</p>
<pre><code>import rioxarray
rast_path = "old_projection.tif"
rds = rioxarray.open_rasterio(rast_path)
rds_3996 = rds.rio.reproject("EPSG:3996")
rds_3996.rio.to_raster("new_projection.tif")
</code></pre>
<p>This saves the tif file in the new projection, but I noticed that it increases the image size to 20198x20198. When I open the file, the new pixels are not visible, so I assume they do not contain any data, but the size of the file has more than doubled. Is there a way to re-project data in this manner without saving the empty pixels? Thanks for any help!</p>
|
<python><raster><python-xarray><projection><epsg>
|
2023-12-12 10:21:43
| 1
| 423
|
Marcus K.
|
77,645,267
| 8,580,469
|
pcolormesh: artefacts / overlapping points when using ec='face'
|
<p>I am plotting maps with pcolormesh and cartopy. When I don't use the option <code>edgecolor</code> or set it to None, results are like expected. But when I use <code>ec='face'</code>, I get really strange artefacts and/or overlapping points - depending on the selected output format.
I have tried with <code>snap</code> and <code>antialiased</code> but those don't help unfortunately.
When I zoom into the interactive plot, it looks not that bad but my output file (pdf or png) looks like a mess.</p>
<p><strong>How can I activate the edgecolor without having this weird behaviour?</strong></p>
<p>MWE below. Using
python 3.10.12,
matplotlib 3.5.1,
Cartopy 0.22.0.</p>
<pre><code>import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
proj1 = ccrs.PlateCarree()
proj2 = ccrs.LambertAzimuthalEqualArea()
fig = plt.figure(figsize=(16,8), layout='constrained')
# generate coordinates
xx = np.linspace(-10,30,300)
yy = np.linspace( 35,75,300)
xxx,yyy = np.meshgrid(xx,yy)
rng = np.random.default_rng(6666)
# create artificial data
zzz = xxx + yyy
noise = rng.normal(0,np.nanmax(zzz)*0.1,zzz.shape)
zzz += noise
# create some gaps in data
zdim = zzz.size
idx = rng.choice(range(zdim), size=zdim//3)
zshape = zzz.shape
zz = zzz.flatten()
zz[idx] = np.nan
zzz = zz.reshape(zshape)
ax = fig.add_subplot(1,2,1, projection=proj2)
ax.add_feature(cfeature.BORDERS, lw=0.5, ec='k')
ax.add_feature(cfeature.COASTLINE, lw=0.5, ec='k')
# looks ok
ax.pcolormesh(xxx, yyy, zzz,
transform=proj1)
ax = fig.add_subplot(1,2,2, projection=proj2)
ax.add_feature(cfeature.BORDERS, lw=0.5, ec='k')
ax.add_feature(cfeature.COASTLINE, lw=0.5, ec='k')
# looks not ok
ax.pcolormesh(xxx, yyy, zzz,
#snap=True,
#antialiased=True,
ec='face', # this seems to have a problem here
transform=proj1)
plt.show()
#plt.savefig('plot_test.pdf')
</code></pre>
<p><a href="https://i.sstatic.net/I9tQN.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/I9tQN.jpg" alt="enter image description here" /></a></p>
|
<python><matplotlib><cartopy>
|
2023-12-12 10:17:43
| 1
| 377
|
Waterkant
|
77,645,115
| 1,897,839
|
Bokeh legend breaks on Python callback
|
<p>I have implemented a Bokeh figure with</p>
<ol>
<li>a scatter plot using <code>circle()</code> with a <code>legend_group</code> for colouring and for creating an interactive legend.</li>
<li>a range slider with a Python callback that filters the data</li>
</ol>
<p>I have set <code>click_policy=hide</code> for the legend so that points (circles) from a specific group are hidden when I click on a legend item.</p>
<p>All of that works fine initially. When I move the slider, however, the legend "breaks":</p>
<ol>
<li>Legend items disappear randomly, even though multiple groups from above are still visible</li>
<li>The legend items that remain behave erroneously. With few initial total groups, only one item remains. With many initial groups, several tend to remain, but when I click on one of them, multiple are greyed out, while only one of the corresponding circle groups are hidden.</li>
</ol>
<p>This is what the initial plot looks like:</p>
<p><a href="https://i.sstatic.net/7kUfF.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/7kUfF.png" alt="Plot with full range" /></a></p>
<p>When I move the slider (at the bottom of the plot), some groups are correctly filtered out from the plot, but only one legend item remains, while multiple groups are still visible in the plot.</p>
<p><a href="https://i.sstatic.net/ybdVD.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ybdVD.png" alt="Plot after moving the slider" /></a></p>
<p>The code is embedded into an object-oriented implementation.
This is the method that adds the circles to the figure:</p>
<pre class="lang-py prettyprint-override"><code> def _add_circles(self):
palette = self._select_palette()
labels = self._data[self._LABEL_FIELD].unique().tolist()
for cluster in self._clusters:
glyph = self._figure.circle(
source=self._source,
x="x",
y="y",
color=factor_cmap(self._LABEL_FIELD, palette, labels),
legend_group=self._LABEL_FIELD,
view=CDSView(
filter=GroupFilter(
column_name=self._LABEL_FIELD, group=cluster.label
),
),
)
if cluster.label == OUTLIERS_LABEL:
glyph.visible = False
</code></pre>
<p>This method sets up the legend:</p>
<pre class="lang-py prettyprint-override"><code> def _setup_legend(self, legend_location: str = "right", click_policy: str = "hide"):
legend = self._figure.legend[0]
legend.label_text_font_size = "6px"
legend.spacing = 0
legend.location = legend_location
legend.click_policy = click_policy
</code></pre>
<p>The slider with callback is added like this:</p>
<pre class="lang-py prettyprint-override"><code> def _year_slider(self) -> RangeSlider:
def callback(attr, old, new): # noqa: unused-argument
self._source.data = self._data.loc[
self._data.year.between(new[0], new[1])
].to_dict(orient="list")
min_year: int = self._data[self._YEAR_COLUMN].min()
max_year: int = self._data[self._YEAR_COLUMN].max()
slider = RangeSlider(
start=min_year,
end=max_year,
value=(min_year, max_year),
width=self._figure.frame_width,
)
slider.on_change("value_throttled", callback)
return slider
</code></pre>
<p>When I move the slider back to its original span, all legend items are displayed correctly again.</p>
<p>The <code>Legend</code> object still contains all the <code>LegendItem</code> objects after moving the slider. All of them still have the <code>visible</code> property set to <code>true</code>.</p>
<p>The question is: why the legend does not display all its items? How is this related to the slider and/or the callback the slider uses?</p>
|
<python><slider><filtering><legend><bokeh>
|
2023-12-12 09:53:46
| 1
| 2,092
|
Carsten
|
77,645,110
| 1,799,528
|
Iterate on langchain document items
|
<p>I loaded pdf files from a directory and I need to split them to smaller chunks to make a summary. The problem is that I can't iterate on documents object in a for loop and I get an error like this: <strong>AttributeError: 'tuple' object has no attribute 'page_content'</strong></p>
<p>How can I iterate on my document items to call the summary function for each of them?
Here is my code:</p>
<pre><code># Load the documents
from langchain.document_loaders import DirectoryLoader
document_directory = "pdf_files"
loader = DirectoryLoader(document_directory)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=50)
# Iterate on long pdf documents to make chunks (2 pdf files here)
for doc in documents:
# it fails on this line
texts = text_splitter.split_documents(doc)
chain = load_summarize_chain(llm, chain_type="map_reduce", map_prompt=prompt, combine_prompt=prompt)
</code></pre>
<p><a href="https://i.sstatic.net/BfR1e.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BfR1e.png" alt="enter image description here" /></a></p>
|
<python><openai-api><langchain>
|
2023-12-12 09:53:10
| 3
| 1,376
|
gabi
|
77,645,015
| 4,537,160
|
Fine-tune Pytorch I3D model on a custom dataset
|
<p>I want to fine-tune the I3D model from torch hub, which is pre-trained on Kinetics 400 classes, on a custom dataset, where I have 4 possible output classes.</p>
<p>I'm loading the model by:</p>
<pre><code>model = torch.hub.load("facebookresearch/pytorchvideo", i3d_r50, pretrained=True)
</code></pre>
<p>I printed it, and saw this layer:</p>
<pre><code>(6): ResNetBasicHead(
(pool): AvgPool3d(kernel_size=(4, 7, 7), stride=(1, 1, 1), padding=(0, 0, 0))
(dropout): Dropout(p=0.5, inplace=False)
(proj): Linear(in_features=2048, out_features=400, bias=True)
(output_pool): AdaptiveAvgPool3d(output_size=1)
</code></pre>
<p>So, I tried:</p>
<pre><code>model = torch.hub.load("facebookresearch/pytorchvideo", i3d_r50, pretrained=True)
num_classes = 4
model.ResNetBasicHead.proj = torch.nn.Linear(model.ResNetBasicHead.proj.in_features, num_classes)
</code></pre>
<p>but I'm getting the error:</p>
<pre><code>AttributeError: 'Net' object has no attribute 'ResNetBasicHead'
</code></pre>
<p>What's the proper way to do this?</p>
|
<python><pytorch>
|
2023-12-12 09:39:49
| 1
| 1,630
|
Carlo
|
77,644,943
| 11,942,776
|
asynch.errors.UnexpectedPacketFromServerError: Code: 102. Unexpected packet from server <host:port> (expected Hello or Exception, got Unknown packet)
|
<p>I'm trying to connect to clickhouse with sqlalchemy. I'm using:</p>
<ul>
<li>python3.11</li>
<li>clickhouse-driver == 0.2.6</li>
<li>sqlalchemy == 2.0.23</li>
<li>clickhouse-sqlalchemy == 0.3.0</li>
<li>asynch == 0.2.3</li>
<li>asyncio == 3.4.3</li>
</ul>
<p>Here is my script I used:</p>
<pre class="lang-py prettyprint-override"><code>import asyncio
import contextlib
import pydantic
import traceback
import typing
from sqlalchemy import text, TextClause, engine
from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, create_async_engine
from sqlalchemy.pool import Pool, QueuePool
class AsyncDatabase:
def __init__(self):
self.ch_uri: str = "clickhouse+asynch://admin:Password123@host:31123/db"
self.ch_engine: AsyncEngine = create_async_engine(
url=self.ch_uri,
echo=False,
pool_size=100,
max_overflow=20,
poolclass=QueuePool,
)
self.ch_session: AsyncSession = AsyncSession(bind=self.ch_engine)
self.ch_pool: Pool = self.ch_engine.pool
async_db: AsyncDatabase = AsyncDatabase()
@contextlib.asynccontextmanager
async def get_ch_session() -> typing.AsyncGenerator[AsyncSession, None]:
try:
yield async_db.ch_session
except Exception as e:
print(traceback.print_exc())
await async_db.ch_session.rollback()
finally:
await async_db.ch_session.close()
async def hello() -> str:
session: AsyncSession = None
async with get_ch_session() as session:
stmt: TextClause = text("SELECT * FROM table_name LIMIT 1")
result: engine.Result = await session.execute(stmt)
print(result.all())
return "ok"
if __name__ == "__main__":
asyncio.run(hello())
</code></pre>
<p>I did try without async but still got the same error
<code>asynch.errors.UnexpectedPacketFromServerError: Code: 102. Unexpected packet from server host:31123 (expected Hello or Exception, got Unknown packet)</code></p>
<p>when I use DataGrid to connect with above creds, it works fine. so I think it's not about the <code>31123</code> port</p>
<p>I stuck on this for 5 hrs, all answers from the web doesn't help me at all.</p>
|
<python><sqlalchemy><python-asyncio><clickhouse>
|
2023-12-12 09:28:53
| 1
| 331
|
Nguyễn Đức Huy
|
77,644,803
| 936,269
|
h5py: how save a scalar attribute as array with shape=(1,)
|
<p>I have some data that I need to write into an HDF5 file which will be read by a data processing application.
This I do with <a href="https://www.h5py.org/" rel="nofollow noreferrer">h5py</a>.
However, the data processing application seems to only accept attributes with an array size of one, but when I create my attributes with h5py I get an array size named scalar. Below picture is from <a href="https://www.hdfgroup.org/downloads/hdfview/" rel="nofollow noreferrer">HDFView</a>.</p>
<p><a href="https://i.sstatic.net/nIFBb.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/nIFBb.png" alt="enter image description here" /></a></p>
<p>I understand that when I call <code>dataset.attrs.create(key, value)</code> that I can set a dtype and shape <a href="https://docs.h5py.org/en/3.2.1/high/attr.html#h5py.AttributeManager.create" rel="nofollow noreferrer">h5py documentation for attributes</a>. But I do not understand what I need to provide to always have the array size as 1 and not scalar. In particular when as shown above, some of them seem to just be set to one out of the box.</p>
<p>I create the attributes in the following way:</p>
<pre class="lang-py prettyprint-override"><code>import h5py
if __name__ == '__main__':
out_file = h5py.File('tmp.h5')
data = [0, 1, 2, 3, 4]
attributes = {'sample': 1, 'option': 'tmp'}
out_file.create_dataset('tmp_dataset', data=data)
for attr_key, attr_value in attributes.items():
out_file['tmp_dataset'].attrs.create(attr_key, attr_value)
out_file.close()
</code></pre>
<p>I have tried to specify the dtype to <code>float</code> and so on. But it did not seem to have an effect.</p>
|
<python><h5py>
|
2023-12-12 09:04:15
| 1
| 2,208
|
Lars Kakavandi-Nielsen
|
77,644,752
| 6,215,597
|
How to improve training performance of model.fit() with generators?
|
<p>Normally when I train models with tensorflow, I just feed my entire data set to the model training (Scenario 1). However, from time to time this data set is to large to fit into my GPU. For that reason I wanted to use generators and just load batch after batch into the GPU (Scenario 2). Both scenarios run perfectly fine but the normal fit without generators produces significantly better results. How can I use generators and yield the "same" result as when I'm loading the whole data set?</p>
<p><strong>Scenario 1:</strong> parsing whole data set</p>
<pre><code>from tensorflow.keras.models import Model
import h5py
# creating a model and stuff
model = Model(...)
model.compile(...)
with h5py.File("db.h5py", 'r') as db_:
model.fit(db_["X_train"][...],
db_["Y_train"][...],
epochs=60,
batch_size=256,
shuffle=True,
validation_data=(db_["X_val"][...], db_["Y_val"][...])
)
</code></pre>
<p><strong>Scenario 2:</strong> Using generators</p>
<pre><code>import tensorflow as tf
from tensorflow.keras.models import Model
import h5py
with h5py.File("db.h5py", 'r') as db_:
x = db_["X_train"][...]
y = db_["Y_train"][...]
x_val = db_["X_val"][...]
y_val = db_["Y_val"][...]
class DataGen(tf.keras.utils.Sequence):
def __init__(self, index_map, batch_size, args):
if args == "training":
self.X = x
self.y = y
else:
self.X = x_val
self.y = y_val
self.index_map = index_map
self.batch_size = batch_size
def __getitem__(self, index):
X_batch = self.X[self.index_map[
index * self.batch_size: (index + 1) * self.batch_size
]]
y_batch = self.y[self.index_map[
index * self.batch_size: (index + 1) * self.batch_size
]]
return X_batch, y_batch
def __len__(self):
return len(self.index_map) // self.batch_size
train_gen = DataGen(np.arange(x.shape[0]), 256, "training")
val_gen = DataGen(np.arange(x_val.shape[0]), 256, "validation")
# creating a model and stuff
model = Model(...)
model.compile(...)
model.fit(train_gen,
epochs=60,
steps_per_epoch=x.shape[0] // 256,
shuffle=True,
validation_data=val_gen
)
</code></pre>
<p><strong>Generator case 4:</strong> Same as Scenario 2 + <code>on_epoch_end()</code> method</p>
<pre><code>import tensorflow as tf
from tensorflow.keras.models import Model
import h5py
with h5py.File("db.h5py", 'r') as db_:
x = db_["X_train"][...]
y = db_["Y_train"][...]
x_val = db_["X_val"][...]
y_val = db_["Y_val"][...]
class DataGen(tf.keras.utils.Sequence):
def __init__(self, index_map, batch_size, args):
if args == "training":
self.X = x
self.y = y
else:
self.X = x_val
self.y = y_val
self.index_map = index_map
self.batch_size = batch_size
def __getitem__(self, index):
X_batch = self.X[self.index_map[
index * self.batch_size: (index + 1) * self.batch_size
]]
y_batch = self.y[self.index_map[
index * self.batch_size: (index + 1) * self.batch_size
]]
return X_batch, y_batch
def __len__(self):
return len(self.index_map) // self.batch_size
def on_epoch_end(self):
'Updates indexes after each epoch'
np.random.shuffle(self.index_map)
train_gen = DataGen(np.arange(x.shape[0]), 256, "training")
val_gen = DataGen(np.arange(x_val.shape[0]), 256, "validation")
# creating a model and stuff
model = Model(...)
model.compile(...)
model.fit(train_gen,
epochs=60,
steps_per_epoch=x.shape[0] // 256,
shuffle=True,
validation_data=val_gen
)
</code></pre>
<h3>Performance results</h3>
<p>This plot shows predicting performance of the test dataset. Each boxplot is comprised of the evaluation metrics of 10 model runs with different hyperparameter configurations.
<a href="https://i.sstatic.net/XdSsK.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/XdSsK.png" alt="Performance" /></a></p>
<ul>
<li>Generator case 1: model.fit() with <code>batch_size</code> argument</li>
<li>Generator case 2: model.fit() without <code>batch_size</code> argument</li>
<li>Generator case 3: model.fit() without <code>batch_size</code> argument, with <code>steps_per_epoch</code> argument</li>
<li>Generator case 4: Same as "Generator case 3" + shuffle module <code>on_epoch_end()</code> within <code>DataGen</code> class</li>
</ul>
<h3>UPDATE: It seems like 'Generator case 4' solves the specified problem. However its unclear what the <code>shuffle</code> argument does when using generators.</h3>
|
<python><tensorflow><generator><tf.keras>
|
2023-12-12 08:55:18
| 0
| 433
|
Max2603
|
77,644,650
| 1,487,336
|
How to change pandas MultiIndex name?
|
<p>I would like to change the pandas multi-index name as follows and encountered this strange issue. Is this a bug? My pandas version is 1.5.3.</p>
<p><a href="https://i.sstatic.net/J19Wf.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/J19Wf.png" alt="enter image description here" /></a></p>
|
<python><pandas><dataframe>
|
2023-12-12 08:35:51
| 0
| 809
|
Lei Hao
|
77,644,631
| 17,347,824
|
Connecting Alpaca to Python in AWS
|
<p>I am creating a program to simulate stock trading with an Alpaca paper trade account but I'm not able to get my program to work with Alpaca.</p>
<p>I have installed the module the documentation says to install using <code>pip3 install alpaca_py</code> it says it is successful, but then when I try to import it and use my API keys it gives an error that the module is not found.</p>
<p>Here is the code I'm trying to use (only with my actual keys):</p>
<pre><code>import alpaca_py as tradeapi
# Set your Alpaca API key and secret
api_key= "api_key_here"
api_secret = "api_secret_here"
# Set the base URL for paper trading
base_url = "https://paper-api.alpaca.markets"
# Create an Alpaca API connection
api = tradeapi.REST(api_key, api_secret, base_url=base_url, api_version='v2')
</code></pre>
<p>This is the error I'm getting:</p>
<pre><code>Traceback (most recent call last):
File "/home/ubuntu/environment/final_play.py", line 2, in <module>
import alpaca_py as tradeapi
ModuleNotFoundError: No module named 'alpaca_py'
</code></pre>
<p>I found a couple other articles about this, but they both mentioned it being an issue with a file sharing the name of the module. I do not have a file named alpaca.py, just final_play.py</p>
<p><code>pip install alpaca-py</code> doesn't work to install. the output when I use this is below:</p>
<pre><code>ubuntu:~/environment $ pip install alpaca-py
DEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. pip 21.0 will drop support for Python 2.7 in January 2021. More details about Python 2 support in pip can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support pip 21.0 will remove support for this functionality.
Defaulting to user installation because normal site-packages is not writeable
ERROR: Could not find a version that satisfies the requirement alpaca-py (from versions: none)
ERROR: No matching distribution found for alpaca-py
</code></pre>
<p>If I use <code>pip3 install alpaca-py</code> I get the following:</p>
<pre><code>ubuntu:~/environment $ pip3 install alpaca-py
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: alpaca-py in /home/ubuntu/.local/lib/python3.10/site-packages (0.13.4)
Requirement already satisfied: sseclient-py<2.0.0,>=1.7.2 in /home/ubuntu/.local/lib/python3.10/site-packages (from alpaca-py) (1.8.0)
Requirement already satisfied: pandas>=1.5.3 in /home/ubuntu/.local/lib/python3.10/site-packages (from alpaca-py) (2.1.4)
Requirement already satisfied: websockets<12.0.0,>=11.0.3 in /home/ubuntu/.local/lib/python3.10/site-packages (from alpaca-py) (11.0.3)
Requirement already satisfied: msgpack<2.0.0,>=1.0.3 in /home/ubuntu/.local/lib/python3.10/site-packages (from alpaca-py) (1.0.7)
Requirement already satisfied: pydantic<3.0.0,>=2.0.3 in /home/ubuntu/.local/lib/python3.10/site-packages (from alpaca-py) (2.5.2)
Requirement already satisfied: requests<3.0.0,>=2.30.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from alpaca-py) (2.31.0)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5.3->alpaca-py) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3/dist-packages (from pandas>=1.5.3->alpaca-py) (2022.1)
Requirement already satisfied: tzdata>=2022.1 in /home/ubuntu/.local/lib/python3.10/site-packages (from pandas>=1.5.3->alpaca-py) (2023.3)
Requirement already satisfied: numpy<2,>=1.22.4 in /home/ubuntu/.local/lib/python3.10/site-packages (from pandas>=1.5.3->alpaca-py) (1.26.2)
Requirement already satisfied: annotated-types>=0.4.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.0.3->alpaca-py) (0.6.0)
Requirement already satisfied: pydantic-core==2.14.5 in /home/ubuntu/.local/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.0.3->alpaca-py) (2.14.5)
Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.0.3->alpaca-py) (4.7.1)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/lib/python3/dist-packages (from requests<3.0.0,>=2.30.0->alpaca-py) (1.26.5)
Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests<3.0.0,>=2.30.0->alpaca-py) (2020.6.20)
Requirement already satisfied: charset-normalizer<4,>=2 in /home/ubuntu/.local/lib/python3.10/site-packages (from requests<3.0.0,>=2.30.0->alpaca-py) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests<3.0.0,>=2.30.0->alpaca-py) (3.3)
Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.3->alpaca-py) (1.16.0)
</code></pre>
|
<python><alpaca>
|
2023-12-12 08:31:37
| 2
| 409
|
data_life
|
77,644,599
| 18,125,313
|
Why does adding a break statement significantly slow down the Numba function?
|
<p>I have the following Numba function:</p>
<pre class="lang-py prettyprint-override"><code>@numba.njit
def count_in_range(arr, min_value, max_value):
count = 0
for a in arr:
if min_value < a < max_value:
count += 1
return count
</code></pre>
<p>It counts how many values are in the range in the array.</p>
<p>However, I realized that I only needed to determine if they existed.
So I modified it as follows:</p>
<pre class="lang-py prettyprint-override"><code>@numba.njit
def count_in_range2(arr, min_value, max_value):
count = 0
for a in arr:
if min_value < a < max_value:
count += 1
break # <---- break here
return count
</code></pre>
<p>Then, this function becomes <strong>slower</strong> than before the change.
Under certain conditions, it can be surprisingly more than 10 times slower.</p>
<p>Benchmark code:</p>
<pre class="lang-py prettyprint-override"><code>from timeit import timeit
rng = np.random.default_rng(0)
arr = rng.random(10 * 1000 * 1000)
# To compare on even conditions, choose the condition that does not terminate early.
min_value = 0.5
max_value = min_value - 1e-10
assert not np.any(np.logical_and(min_value <= arr, arr <= max_value))
n = 100
for f in (count_in_range, count_in_range2):
f(arr, min_value, max_value)
elapsed = timeit(lambda: f(arr, min_value, max_value), number=n) / n
print(f"{f.__name__}: {elapsed * 1000:.3f} ms")
</code></pre>
<p>Result:</p>
<pre><code>count_in_range: 3.351 ms
count_in_range2: 42.312 ms
</code></pre>
<p>Further experimenting, I found that the speed varies greatly depending on the search range (i.e. <code>min_value</code> and <code>max_value</code>).</p>
<p>At various search ranges:</p>
<pre><code>count_in_range2: 5.802 ms, range: (0.0, -1e-10)
count_in_range2: 15.408 ms, range: (0.1, 0.09999999990000001)
count_in_range2: 29.571 ms, range: (0.25, 0.2499999999)
count_in_range2: 42.514 ms, range: (0.5, 0.4999999999)
count_in_range2: 24.427 ms, range: (0.75, 0.7499999999)
count_in_range2: 12.547 ms, range: (0.9, 0.8999999999)
count_in_range2: 5.747 ms, range: (1.0, 0.9999999999)
</code></pre>
<p>Can someone explain to me what is going on?</p>
<hr />
<p>I am using Numba 0.58.1 under Python 3.10.11.
Confirmed on both Windows 10 and Ubuntu 22.04.</p>
<hr />
<h1>EDIT:</h1>
<p>As an appendix to Jérôme Richard's answer:</p>
<p>As he pointed out in the comments, the performance difference that depends on a search range is likely due to branch prediction.</p>
<p>For example, when <code>min_value</code> is <code>0.1</code>, <code>min_value < a</code> has a 90% chance of being true, and <code>a < max_value</code> has a 90% chance of being false. So mathematically it can be predicted correctly with 81% accuracy. I have no idea how the CPU does this, but I have come up with a way to check if this logic is correct.</p>
<p>First, by partitioning the array with values above and below the threshold, and second, by mixing it with a certain probability of error. When the array is partitioned, the number of branch prediction misses should be unaffected by the threshold. When we include errors in it, the number of misses should increase depending on the errors.</p>
<p>Here is the updated benchmark code:</p>
<pre class="lang-py prettyprint-override"><code>from timeit import timeit
import numba
import numpy as np
@numba.njit
def count_in_range(arr, min_value, max_value):
count = 0
for a in arr:
if min_value < a < max_value:
count += 1
return count
@numba.njit
def count_in_range2(arr, min_value, max_value):
count = 0
for a in arr:
if min_value < a < max_value:
count += 1
break # <---- break here
return count
def partition(arr, threshold):
"""Place the elements smaller than the threshold in the front and the elements larger than the threshold in the back."""
less = arr[arr < threshold]
more = arr[~(arr < threshold)]
return np.concatenate((less, more))
def partition_with_error(arr, threshold, error_rate):
"""Same as partition, but includes errors with a certain probability."""
less = arr[arr < threshold]
more = arr[~(arr < threshold)]
less_error, less_correct = np.split(less, [int(len(less) * error_rate)])
more_error, more_correct = np.split(more, [int(len(more) * error_rate)])
mostly_less = np.concatenate((less_correct, more_error))
mostly_more = np.concatenate((more_correct, less_error))
rng = np.random.default_rng(0)
rng.shuffle(mostly_less)
rng.shuffle(mostly_more)
out = np.concatenate((mostly_less, mostly_more))
assert np.array_equal(np.sort(out), np.sort(arr))
return out
def bench(f, arr, min_value, max_value, n=10, info=""):
f(arr, min_value, max_value)
elapsed = timeit(lambda: f(arr, min_value, max_value), number=n) / n
print(f"{f.__name__}: {elapsed * 1000:.3f} ms, min_value: {min_value:.1f}, {info}")
def main():
rng = np.random.default_rng(0)
arr = rng.random(10 * 1000 * 1000)
thresholds = np.linspace(0, 1, 11)
print("#", "-" * 10, "As for comparison", "-" * 10)
bench(
count_in_range,
arr,
min_value=0.5,
max_value=0.5 - 1e-10,
)
print("\n#", "-" * 10, "Random Data", "-" * 10)
for min_value in thresholds:
bench(
count_in_range2,
arr,
min_value=min_value,
max_value=min_value - 1e-10,
)
print("\n#", "-" * 10, "Partitioned (Yet Still Random) Data", "-" * 10)
for min_value in thresholds:
bench(
count_in_range2,
partition(arr, threshold=min_value),
min_value=min_value,
max_value=min_value - 1e-10,
)
print("\n#", "-" * 10, "Partitioned Data with Probabilistic Errors", "-" * 10)
for ratio in thresholds:
bench(
count_in_range2,
partition_with_error(arr, threshold=0.5, error_rate=ratio),
min_value=0.5,
max_value=0.5 - 1e-10,
info=f"error: {ratio:.0%}",
)
if __name__ == "__main__":
main()
</code></pre>
<p>Result:</p>
<pre><code># ---------- As for comparison ----------
count_in_range: 3.518 ms, min_value: 0.5,
# ---------- Random Data ----------
count_in_range2: 5.958 ms, min_value: 0.0,
count_in_range2: 15.390 ms, min_value: 0.1,
count_in_range2: 24.715 ms, min_value: 0.2,
count_in_range2: 33.749 ms, min_value: 0.3,
count_in_range2: 40.007 ms, min_value: 0.4,
count_in_range2: 42.168 ms, min_value: 0.5,
count_in_range2: 37.427 ms, min_value: 0.6,
count_in_range2: 28.763 ms, min_value: 0.7,
count_in_range2: 20.089 ms, min_value: 0.8,
count_in_range2: 12.638 ms, min_value: 0.9,
count_in_range2: 5.876 ms, min_value: 1.0,
# ---------- Partitioned (Yet Still Random) Data ----------
count_in_range2: 6.006 ms, min_value: 0.0,
count_in_range2: 5.999 ms, min_value: 0.1,
count_in_range2: 5.953 ms, min_value: 0.2,
count_in_range2: 5.952 ms, min_value: 0.3,
count_in_range2: 5.940 ms, min_value: 0.4,
count_in_range2: 6.870 ms, min_value: 0.5,
count_in_range2: 5.939 ms, min_value: 0.6,
count_in_range2: 5.896 ms, min_value: 0.7,
count_in_range2: 5.899 ms, min_value: 0.8,
count_in_range2: 5.880 ms, min_value: 0.9,
count_in_range2: 5.884 ms, min_value: 1.0,
# ---------- Partitioned Data with Probabilistic Errors ----------
# Note that min_value = 0.5 in all the following.
count_in_range2: 5.939 ms, min_value: 0.5, error: 0%
count_in_range2: 14.015 ms, min_value: 0.5, error: 10%
count_in_range2: 22.599 ms, min_value: 0.5, error: 20%
count_in_range2: 31.763 ms, min_value: 0.5, error: 30%
count_in_range2: 39.391 ms, min_value: 0.5, error: 40%
count_in_range2: 42.227 ms, min_value: 0.5, error: 50%
count_in_range2: 38.748 ms, min_value: 0.5, error: 60%
count_in_range2: 31.758 ms, min_value: 0.5, error: 70%
count_in_range2: 22.600 ms, min_value: 0.5, error: 80%
count_in_range2: 14.090 ms, min_value: 0.5, error: 90%
count_in_range2: 6.027 ms, min_value: 0.5, error: 100%
</code></pre>
<p>I am satisfied with this result.</p>
|
<python><performance><numba>
|
2023-12-12 08:24:15
| 1
| 3,446
|
ken
|
77,644,510
| 8,963,682
|
FastAPI with Azure AD: TypeError on OAuth2 Authentication
|
<p>I'm trying to implement Azure AD OAuth2 authentication in a FastAPI application. I have set up the environment variables correctly and configured the OAuth client with Azure AD's endpoints. However, I'm encountering a TypeError: Invalid type for url. Expected str or httpx.URL, got <class 'NoneType'>: None when trying to authenticate a user.</p>
<p>Here's the relevant part of my code:</p>
<p><strong>FastAPI:</strong></p>
<pre><code>app.add_middleware(SessionMiddleware, secret_key="q803pJMcx6KNkIlBGi_mPQSYiOP0IPze")
#app.add_middleware(TokenVerificationMiddleware)
@app.get("/")
async def health():
return JSONResponse(content={"status": "healthy"}, status_code=200)
# Redirect to login URL
@app.get("/login")
async def login(request: Request):
redirect_uri = request.url_for('auth')
return await oauth.azure.authorize_redirect(request, redirect_uri)
# Callback endpoint
@app.get("/auth")
async def auth(request: Request):
token = await oauth.azure.authorize_access_token(request)
user = token.get('userinfo')
if not user:
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail='Authentication failed')
return user
</code></pre>
<p><strong>OAuth2:</strong></p>
<pre><code>from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2AuthorizationCodeBearer
from authlib.integrations.starlette_client import OAuth
from starlette.requests import Request
import os
# Load environment variables
CLIENT_ID = os.getenv("ASPEN_APP_AUTH_CLIENT_ID")
TENANT_ID = os.getenv("ASPEN_APP_AUTH_TENANT_ID")
CLIENT_SECRET = os.getenv("ASPEN_APP_AUTH_SECRET")
# Initialize OAuth2
oauth = OAuth()
oauth.register(
name='azure',
client_id=CLIENT_ID,
client_secret=CLIENT_SECRET,
authorize_url=f'https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/authorize',
token_url=f'https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/token',
client_kwargs={'scope': 'openid email profile'}
)
oauth2_scheme = OAuth2AuthorizationCodeBearer(
authorizationUrl=f'https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/authorize',
tokenUrl=f'https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/token'
)
async def get_current_user(request: Request, token: str = Depends(oauth2_scheme)):
try:
response = await oauth.azure.parse_id_token(request, token)
return response
except Exception as e:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail=str(e)
)
</code></pre>
<p>The error occurs at the line token = await oauth.azure.authorize_access_token(request). I have verified that the environment variables are set correctly and that the redirect URL in Azure matches exactly.</p>
<p><strong>My dependencies are as follows:</strong></p>
<pre><code>Python 3.12
uvicorn~=0.23.2
fastapi~=0.103.1
pydantic~=2.4.2
pandas~=2.1.0
requests~=2.31.0
beautifulsoup4~=4.12.2
aiohttp~=3.9.1
pyproj~=3.6.0
python-dateutil~=2.8.2
python-dotenv~=1.0.0
python-jose~=3.3.0
mangum~=0.17.0
SQLAlchemy~=2.0.21
starlette~=0.27.0
bs4~=0.0.1
httpx~=0.23.0
Authlib~=1.2.1
itsdangerous~=2.1.2
boto3~=1.33.12
botocore~=1.33.12
respx>=0.20.1
</code></pre>
<p>Traceback:</p>
<pre><code>#I add some print statements to fetch access token
redirect_uri: http://localhost:8000/auth
metadata: {'token_url': 'https://login.microsoftonline.com/TENANT_ID/oauth2/v2.0/token'}
token_endpoint: None
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\uvicorn\protocols\http\h11_impl.py", line 408, in run_asgi
result = await app( # type: ignore[func-returns-value]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\uvicorn\middleware\proxy_headers.py", line 84, in __call__
return await self.app(scope, receive, send)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\fastapi\applications.py", line 292, in __call__
await super().__call__(scope, receive, send)
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\applications.py", line 122, in __call__
await self.middleware_stack(scope, receive, send)
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\middleware\errors.py", line 184, in __call__
raise exc
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\middleware\errors.py", line 162, in __call__
await self.app(scope, receive, _send)
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\middleware\sessions.py", line 86, in __call__
await self.app(scope, receive, send_wrapper)
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\middleware\exceptions.py", line 79, in __call__
raise exc
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\middleware\exceptions.py", line 68, in __call__
await self.app(scope, receive, sender)
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\fastapi\middleware\asyncexitstack.py", line 20, in __call__
raise e
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\fastapi\middleware\asyncexitstack.py", line 17, in __call__
await self.app(scope, receive, send)
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\routing.py", line 718, in __call__
await route.handle(scope, receive, send)
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\routing.py", line 276, in handle
await self.app(scope, receive, send)
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\starlette\routing.py", line 66, in app
response = await func(request)
^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\fastapi\routing.py", line 273, in app
raw_response = await run_endpoint_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\fastapi\routing.py", line 190, in run_endpoint_function
return await dependant.call(**values)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\AspenAPI\fast_api.py", line 56, in auth
token = await oauth.azure.authorize_access_token(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\authlib\integrations\starlette_client\apps.py", line 82, in authorize_access_token
token = await self.fetch_access_token(**params, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\authlib\integrations\base_client\async_app.py", line 128, in fetch_access_token
token = await client.fetch_token(token_endpoint, **params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\authlib\integrations\httpx_client\oauth2_client.py", line 125, in _fetch_token
resp = await self.post(
^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\httpx\_client.py", line 1842, in post
return await self.request(
^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\authlib\integrations\httpx_client\oauth2_client.py", line 90, in request
return await super(AsyncOAuth2Client, self).request(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\httpx\_client.py", line 1514, in request
request = self.build_request(
^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\httpx\_client.py", line 344, in build_request
url = self._merge_url(url)
^^^^^^^^^^^^^^^^^^^^
File "C:\Users\X\PycharmProjects\ServicesAPI\venv\Lib\site-packages\httpx\_client.py", line 374, in _merge_url
merge_url = URL(url)
^^^^^^^^
File "C:\Users\X\PycharmProjects\AspenServicesAPI\venv\Lib\site-packages\httpx\_urls.py", line 102, in __init__
raise TypeError(
TypeError: Invalid type for url. Expected str or httpx.URL, got <class 'NoneType'>: None
</code></pre>
<p>I suspect there might be an issue with the dependencies, specifically httpx and authlib, but I'm not sure how to resolve this. Any insights or suggestions on what might be causing this error and how to fix it would be greatly appreciated.</p>
<p><strong>Update : 12/12/2023</strong></p>
<p>Using python 3.11 or 3.9 didn't work. However, I discovered that renaming 'toke_url' to 'token_endpoint' eliminates the initial error. Additionally, incorporating 'jwks_uri' seems to resolve a subsequent error. But now, I'm encountering a new issue: a 'KeyError' associated with 'id_token'. I have updated my post with more details for further insight.</p>
<pre><code> JWKS_URI = "https://login.microsoftonline.com/{TENANT_ID}/discovery/v2.0/keys".format(TENANT_ID=ASPEN_APP_AUTH_TENANT_ID)
# Initialize OAuth2
oauth = OAuth()
oauth.register(
name='azure',
client_id=ASPEN_APP_AUTH_CLIENT_ID,
client_secret=ASPEN_APP_AUTH_SECRET,
authorize_url=f'https://login.microsoftonline.com/{ASPEN_APP_AUTH_TENANT_ID}/oauth2/v2.0/authorize',
token_endpoint=f'https://login.microsoftonline.com/{ASPEN_APP_AUTH_TENANT_ID}/oauth2/v2.0/token',
token_url=f'https://login.microsoftonline.com/{ASPEN_APP_AUTH_TENANT_ID}/oauth2/v2.0/token',
client_kwargs={'scope': 'openid email profile'},
jwks_uri=JWKS_URI
)
</code></pre>
<p><strong>Fixed</strong></p>
<p>Replacing 'toke_url' to 'token_endpoint' <strong>fixed</strong> Expected str or httpx.URL, got <class 'NoneType'></p>
<p>than issue with token_id was fixed like this :</p>
<pre><code>@app.get("/auth")
async def auth(request: Request):
try:
token = await oauth.azure.authorize_access_token(request)
nonce = token.get('userinfo').get('nonce')
# Parse the ID token
user_info = await oauth.azure.parse_id_token(token=token, nonce=nonce)
return {"user_info": user_info}
except HTTPException as e:
raise e
except Exception as e:
print("Error during authentication:", str(e))
raise HTTPException(status_code=500, detail="Authentication failed.")
</code></pre>
<p>I'm uncertain if this is the correct approach, but it seems to be functioning as intended for me. If there are any errors or security issues in what I've done, please let me know.</p>
|
<python><azure><oauth-2.0><oauth><fastapi>
|
2023-12-12 08:06:34
| 1
| 617
|
NoNam4
|
77,644,469
| 4,451,521
|
Counting the inequalities in a dataframe column
|
<p>I have two dataframes that are basically the same.</p>
<p>However in a particular column there are some rows where the values (floats) are different.</p>
<p>I would like to calculate the number of rows where the values are different.</p>
<p>This task seem pretty simple if not for the fact of the following two things</p>
<ul>
<li>They are floats so a normal equality sometimes gives different to two rows that are basically the same</li>
</ul>
<p>And the most important thing</p>
<ul>
<li>The column has Nans . When I compare Nans they are deemed different. I don't want to count two rows with Nans as unequal but as equal. They should not be considered in the count</li>
</ul>
<p>How can I compare a series of floats when there are Nans in it</p>
|
<python><pandas>
|
2023-12-12 07:59:44
| 2
| 10,576
|
KansaiRobot
|
77,644,424
| 2,859,206
|
SQL code works in Azure, but Sqlalchemy throws ProgrammingErrors: Invalid column name OR multi-part identifier could not be bound
|
<p>I'm trying to run an sql query that works as native sql code in a program like Azure, but throws programming errors when I try to use the exact code in python using SQLAlchemy with ODBC and pandas.</p>
<p>SQL code:</p>
<pre><code>Select RecordNumbers -- ID des Falles
, Min(T.Created) As FirstRecord -- erste
From Data.Table1 V
Left Outer Join Data.Table2Version TV
On V.VersionNumber=TV.VersionNumber
Left Outer Join Data.Table3 T
On TV.IdT=T.IdT
And T.Type=11 -- nur Export
Where RType=1 -- nur was
And Doob1=1 -- nur was auch
And Doob2=1 -- nur anderes
Group By RecordNumber
</code></pre>
<p>This returns the two selected columns.</p>
<p>In SQLAlchemy, after establishing and testing a connection - dbConnection - I store query as text and use pandas to read the query:</p>
<pre><code>from sqlalchemy import text
query = text("Select RecordNumbers, \
Min(T.Created) As FirstRecord -- erste \
From Data.Table1 V
\
Left Outer Join Data.Table2Version TV On V.VersionNumber=TV.VersionNumber \
Left Outer Join Data.Table3 T \
ON TV.IdT=T.IdT \
AND T.Type=11 -- nur Export \
Where (RType=1 -- nur was \
And Doob=1 -- nur was auch \
And Doob2=1 ) -- nur anderes \
Group By IdRecord \
Order By 1 Desc")
out = pd.read_sql_query(query, dBConnection)
</code></pre>
<p>This throws two errors for both variables in the select part of the query.</p>
<ol>
<li>ProgrammingError: (pyodbc.ProgrammingError) ('42S22', '[42S22] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]Invalid column name "FirstRecord".</li>
<li>The multi-part identifier "T.CreatedAt" could not be bound.</li>
</ol>
<p>Replacing the selected variables with "Select * From..." solves the problem, but the output is too large to be functional solution.</p>
<p>Trying to solve invalid column name, if I explicitly define the location of V.FirstRecord, it throws the same multi-part identifier error for this varible.</p>
<p>I've read about explicit and implicit queries, but think everything here is pretty explicit... maybe.</p>
<p>SQLAlchemy says: This error is a DBAPI Error and originates from the database driver (DBAPI), not SQLAlchemy itself.</p>
<p>I'm not very good at SQL, so would greatly appreciate being pointed in the right direction.</p>
|
<python><sql><sql-server><sqlalchemy>
|
2023-12-12 07:50:32
| 1
| 2,490
|
DrWhat
|
77,644,270
| 17,721,722
|
Creating Django Models Without Rounding off Decimal Fields
|
<p>For now, when I save an amount like 5400.5789, it is being saved as 5400.58. However, I want it to be saved as 5400.57 only. I don't want to round off the decimal places. How can I achieve that?</p>
<pre><code>class PerTransaction(models.Model):
amount = models.DecimalField(default=0, max_digits=10,decimal_places=2, verbose_name = "Transaction Amount")
</code></pre>
<p>Database: PostgreSQL</p>
|
<python><django><postgresql><django-models>
|
2023-12-12 07:16:21
| 1
| 501
|
Purushottam Nawale
|
77,644,117
| 12,780,274
|
Decode protobuf without proto files in python
|
<p>I would like to decode protobuf.</p>
<p>Example of the protobuf data: <code>0a06282c0241057a10011805220d080510bea3f493062a03010c1628f1a6f493063002382b4001481482010f383634333233303532343736343839</code></p>
<p>I can Decoding online (e.g. via <a href="https://protobuf-decoder.netlify.app/" rel="nofollow noreferrer">https://protobuf-decoder.netlify.app/</a>) works fine.</p>
<p>The website decode Protobuf without having the original .proto files. And all decoding is done locally via JavaScript (Contribute on <a href="https://github.com/pawitp/protobuf-decoder" rel="nofollow noreferrer">GitHub</a>).</p>
<p>How can I do this in python?</p>
<p>I try <a href="https://stackoverflow.com/a/55275458/12780274">this solution</a> but not exist .proto file.</p>
|
<python><protocol-buffers><protobuf.js><protobuf-python>
|
2023-12-12 06:42:10
| 1
| 643
|
henrry
|
77,644,026
| 11,082,866
|
Django middleware is executed at every API call
|
<p>I created a middleware to record the activity log for user login but it executes at every API call. The <code>ActivityLog</code> is to record the entries.</p>
<p>Middleware.py</p>
<pre><code>class UserActivityMiddleware:
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
# Set a flag in the session to indicate that the user has been logged in during this session
request.session['user_logged_in'] = request.session.get('user_logged_in', False)
response = self.get_response(request)
# Check if the user is authenticated, has just logged in, and hasn't been logged during this session
if (
request.user.is_authenticated
and request.user.last_login is not None
and request.session.get('user_logged_in', False) is False
and not request.path.startswith('/static/')
and not request.path.startswith('/media/')
):
print("reached middleware")
print(f"User: {request.user.username}")
print(f"Last Login: {request.user.last_login}")
print(f"Session Flag: {request.session.get('user_logged_in')}")
print(f"Request Path: {request.path}")
# Log the user login activity
ActivityLog.objects.create(
user=request.user,
action="Login",
target_model="User",
target_object_id=request.user.id,
details=f"User {request.user.username} logged in at {timezone.now()}."
)
# Set a flag in the session to indicate that the user has been logged in during this session
request.session['user_logged_in'] = True
return response
</code></pre>
<p>What should I do to reduce it to a single execution i.e. when the user logs in?</p>
|
<python><django>
|
2023-12-12 06:13:16
| 0
| 2,506
|
Rahul Sharma
|
77,643,953
| 4,620,616
|
PySimpleGUI appends Plots in Canvas
|
<p>I have the issue that if I try to update a canvas in PySimpleGUI the application instead appends the new plot below the old one. Trigering the 'Generate Plot' from the code below leads to the output at the bottom. Instead of Appending the plot I expected it to replace it.</p>
<pre><code>import PySimpleGUI as sg
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
def draw_figure(canvas, figure):
figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
figure_canvas_agg.draw()
figure_canvas_agg.get_tk_widget().pack(side='top', fill='both')
return figure_canvas_agg
# Define the layout of the GUI
layout = [
[sg.Button('Generate Plot')],
[sg.Canvas(size=(100, 100),key='canvas')],
[sg.Button('Exit')]
]
window = sg.Window('Time Series Plot', layout, finalize=True)
# Event loop
while True:
event, values = window.read()
x = np.linspace(1, 50)
y = np.random.randn(50)
if event == sg.WIN_CLOSED or event == 'Exit':
break
if event in ['Generate Plot']:
fig, ax =plt.subplots()
# Create a time series plot
ax.plot(x, y)
# Embed the Matplotlib plot into the PySimpleGUI window
draw_figure(window['canvas'].TKCanvas, fig)
# Close the window
window.close()
</code></pre>
<p><a href="https://i.sstatic.net/EI4Cy.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/EI4Cy.png" alt="enter image description here" /></a></p>
|
<python><matplotlib><tkinter><pysimplegui>
|
2023-12-12 05:55:35
| 3
| 490
|
mhwh
|
77,643,828
| 10,964,685
|
Insert word at beginning of row - python
|
<p>I want to insert a word at the beginning of a string for each row in a column where the first word is not <em>x</em>. If it is <em>x</em>, then move on. For below, <em>x</em> = <code>BP</code>. So if first word in cat is not <code>BP</code>, then insert it.</p>
<pre><code>df = pd.DataFrame({
'cat': ['BP STATION', 'STATION', 'BP OLD', 'OLD OLD'],
})
df['cat'] = df['cat'].str.replace(r'^\w+', 'BP')
</code></pre>
<p>intent:</p>
<pre><code> cat
0 BP STATION
1 BP STATION
2 BP OLD
3 BP OLD OLD
</code></pre>
|
<python>
|
2023-12-12 05:17:10
| 2
| 392
|
jonboy
|
77,643,527
| 20,803,947
|
Docker-compose: ModuleNotFoundError: No module named 'src'
|
<p>I have a problem trying to run my flask, python, celery, mongodb and rabbitmq app via docker.</p>
<p>When I run the docker build command and then compose-up
I get the following problem:</p>
<p><a href="https://i.sstatic.net/15kLr.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/15kLr.png" alt="enter image description here" /></a></p>
<p>The problem appears to be when running the command: docker-compose up.</p>
<p>Dockerfile:</p>
<pre><code>FROM python:3.11-alpine AS builder
RUN pip install poetry
WORKDIR /backend
COPY pyproject.toml poetry.lock ./
RUN poetry config virtualenvs.create false && poetry install --no-root
FROM python:3.11-alpine
WORKDIR /backend
COPY --from=builder /usr/local/lib/python3.11/site-packages/ /usr/local/lib/python3.11/site-packages/
COPY --from=builder /usr/local/bin/ /usr/local/bin/
COPY . .
</code></pre>
<p>docker-compose.yml :</p>
<pre><code>version: '3.9'
services:
mongodb:
image: mongo:latest
container_name: mongodb
environment:
- MONGO_INITDB_ROOT_USERNAME=admin
- MONGO_INITDB_ROOT_PASSWORD=admin
restart: always
ports:
- 27017:27017
volumes:
- mongodb_data:/data/db
rabbitmq:
image: rabbitmq:3-management
container_name: rabbitmq
restart: always
environment:
- RABBITMQ_DEFAULT_USER=admin
- RABBITMQ_DEFAULT_PASS=admin
- RABBITMQ_DEFAULT_VHOST=/
ports:
- 5672:5672
- 15672:15672
volumes:
- rabbitmq_data:/var/lib/rabbitmq
celery_worker:
build: .
container_name: celery_worker
environment:
- CELERY_BROKER_URL=amqp://admin:admin@rabbitmq:5672
restart: always
command: celery --app src.task worker --loglevel=info -O fair --queues default
depends_on:
- mongodb
- rabbitmq
flask_app:
build: .
container_name: flask_app
command: python3 src/app.py
restart: always
tty: true
environment:
- SERVER_HOST=0.0.0.0
- SERVER_PORT=5000
- FLASK_APP=src/app.py
ports:
- 5000:5000
depends_on:
- mongodb
- rabbitmq
- celery_worker
volumes:
mongodb_data: # Volume para persistência dos dados do MongoDB
rabbitmq_data: # Volume para persistência dos dados do RabbitMQ
</code></pre>
<p>The folders structure:</p>
<p><a href="https://i.sstatic.net/d4MNU.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/d4MNU.png" alt="enter image description here" /></a></p>
<p>src/app.py:</p>
<pre><code>import os
from flask import Flask
from src.task import add
app = Flask(__name__)
@app.route('/')
def hello():
add.delay(1, 2)
return "Docker compose working!"
if __name__ == '__main__':
host = os.environ.get('SERVER_HOST', 'localhost')
port = os.environ.get('SERVER_PORT', '8001')
print(f"Server listenning {host}:{port}")
app.run(host=host, port=port, debug=True)
</code></pre>
<p>src/task.py:</p>
<pre><code>import os
from celery import Celery
broker = os.environ.get('CELERY_BROKER_URL', 'amqp://guest@localhost//')
celery = Celery('myapp', include=['src.task'])
@celery.task(queue='default')
def add(x, y):
print(f'Adding {x} + {y}')
return x + y
</code></pre>
<p>pyproject.toml:</p>
<pre><code>[tool.poetry]
name = ""
version = "0.1.0"
description = ""
authors = ""
[tool.poetry.dependencies]
python = "^3.11"
Flask = "^3.0.0"
celery = "^5.3.6"
pymongo = "^4.6.1"
[tool.poetry.dev-dependencies]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
</code></pre>
<p>i can't perform the up using docker-compose up
because of the errors, this is the exact code and folder structure I currently have. Can anyone tell me how to fix this and if I'm doing something wrong?</p>
|
<python><docker><docker-compose><dockerfile>
|
2023-12-12 03:25:29
| 1
| 309
|
Louis
|
77,643,512
| 10,964,685
|
Split column by last delimiter AND uppercase values - python
|
<p>I'm trying to split a column by the last <code>' - '</code> <strong>AND</strong> being followed by all uppercase strings letters.</p>
<p><strong>It may not necessarily be the last delimiter in isolation. But it will be the last before all uppercase strings</strong>.</p>
<p>I can find separate questions that separate based on first/last delimiter. But not with a combination.</p>
<p>Below, I have a df with <code>Value</code> containing various combinations. I want to split the col into two individuals columns, whereby, everything before the last <code>' - '</code> and uppercase letters.</p>
<p>I've got <code>Last</code> column correct but not <code>First</code> column.</p>
<pre><code>df = pd.DataFrame({
'Value': ['Juan-Diva - HOLLS', 'Carlos - George - ESTE BAN - BOM', 'Javier Plain - Hotham Ham - ALPINE', 'Yul - KONJ KOL MON'],
})
</code></pre>
<p>option 1)</p>
<pre><code>df[['First', 'l']] = df['Value'].str.split(' - ', n=1, expand=True)
df['Last'] = df['Value'].str.split('- ').str[-1]
</code></pre>
<p>option 2)</p>
<pre><code># Regular expression pattern
pattern = r'^(.*) - ([A-Z\s]+)$'
# Extract groups into two new columns
df[['First', 'Last']] = df['Value'].str.extract(pattern)
</code></pre>
<p>option 3)</p>
<pre><code>df[["First", "Last"]] = df["Value"].str.rsplit(" - ", n=1, expand=True)
</code></pre>
<p>None of these options return the intended output.</p>
<p>intended output:</p>
<pre class="lang-none prettyprint-override"><code> First Last
0 Juan-Diva HOLLS
1 Carlos - George ESTE BAN - BOM
2 Javier Plain - Hotham Ham ALPINE
3 Yul KONJ KOL MON
</code></pre>
<p>regex:</p>
<pre><code>df[["First", "Last"]] = df["Value"].str.extract(r'(.*?)\s*-\s*([A-Z]+(?:\s*-?\s*[A-Z]+)*)')
</code></pre>
<pre><code> Value First Last
0 Juan-Diva - HOLLS Juan D
1 Carlos - George - ESTE BAN - BOM Carlos G
2 Javier Plain - Hotham Ham - ALPINE Javier Plain H
3 Yul - KONJ KOL MON Yul KONJ KOL MON
</code></pre>
|
<python><pandas><split>
|
2023-12-12 03:17:03
| 1
| 392
|
jonboy
|
77,643,373
| 188,740
|
Attach debugger to Firebase Python Functions
|
<p>I'm using the emulator to run cloud functions with Python. I'd like to attach a debugger using VS Code. This is easy when using Node, I just need to pass the <code>--inspect-functions</code> flag in when starting the emulator:</p>
<pre><code>firebase emulators:start --project demo-project --inspect-functions
</code></pre>
<p>Here is my vscode launch.json:</p>
<pre><code>{
"version": "0.2.0",
"configurations": [
{
"type": "node",
"request": "attach",
"name": "Firebase Cloud Functions",
"port": 9229
}
]
}
</code></pre>
<p>Now clicking <kbd>F5</kbd> is vscode will attach the debugger.</p>
<p>When running Python functions, I get this warning in emulator logs:</p>
<pre><code>functions --inspect-functions only supported for Node.js runtimes.
functions --inspect-functions not supported for Python functions. Ignored.
</code></pre>
<p>How do I attach the debugger to Python cloud functions with Firebase?</p>
|
<python><google-cloud-functions><vscode-debugger><firebase-tools>
|
2023-12-12 02:34:22
| 0
| 57,942
|
Johnny Oshika
|
77,643,232
| 6,533,037
|
Output 256-bit using Argon2 hasher
|
<p>I am trying to generate a 256-bit output using Argon2 password hasher. The function take s a <code>hash_len</code> parameter which I set to 32. Thinking that 32 bytes equals 256 bits.</p>
<p>Why does Argon2 output the length of 43, and not 32?</p>
<pre><code>from argon2 import PasswordHasher
password = "abc123"
salt = b'b8b17dbde0a2c67707342c459f6225ed'
hasher = PasswordHasher(
salt_len=len(salt),
hash_len=32,
)
hasherOutput = hasher.hash(password, salt = salt)
hash = hasherOutput.split('$')[-1]
print(len(hash))
# Output: 43
# Expected: 32
</code></pre>
|
<python><argon2-cffi>
|
2023-12-12 01:33:34
| 1
| 1,683
|
O'Niel
|
77,643,184
| 10,266,059
|
Why does pydoc say "any" is a package? I thought any() was a function
|
<p>I am learning Python and I thought I would try using pydoc to give me a description of each built-in function. But when I run <code>pydoc any</code>, it returns:</p>
<pre class="lang-none prettyprint-override"><code>Help on package any:
NAME
any
PACKAGE CONTENTS
FILE
(built-in)
</code></pre>
<p>However, <code>pydoc builtins</code> has a FUNCTIONS section which has what I was looking for:</p>
<pre class="lang-none prettyprint-override"><code>any(iterable, /)
Return True if bool(x) is True for any x in the iterable.
If the iterable is empty, return False.
</code></pre>
<p>In perl, you can run e.g., <code>perldoc -tf open</code> to see the documentation for the <code>open()</code> function.</p>
<p>I was expecting similar functionality in Python, especially given this text from pydoc help:</p>
<pre class="lang-none prettyprint-override"><code>pydoc <name> ...
Show text documentation on something. <name> may be the name of a
Python keyword, topic, function, module, or package, or a dotted
reference to a class or function within a module or module in a
package. If <name> contains a '/', it is used as the path to a
Python source file to document. If name is 'keywords', 'topics',
or 'modules', a listing of these things is displayed.
</code></pre>
<p>To recap, <code>any()</code> is a function but <code>pydoc any</code> doesn't show me the documentation for the <code>any()</code> function. Instead I have to dig inside the <code>pydoc builtins</code> output which is quite large.</p>
<p>Is there a Python equivalent to <code>perldoc -tf</code> where I can pull out documentation for a single function?</p>
|
<python>
|
2023-12-12 01:12:06
| 1
| 1,676
|
Aleksey Tsalolikhin
|
77,643,183
| 10,200,497
|
groupby streak of numbers and one row after it then check the first value of a column for each group
|
<p>This is an extension to this <a href="https://stackoverflow.com/questions/77372530/groupby-streak-of-numbers-and-one-row-after-it">post</a>.</p>
<p>This is my dataframe:</p>
<pre><code>import pandas as pd
df = pd.DataFrame(
{
'a': [ 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0],
'b': [-1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1]
}
)
</code></pre>
<p>And my desired outcome which is about grouping them is:</p>
<pre><code> a b
4 1 1
5 0 -1
10 1 1
11 1 1
12 0 -1
</code></pre>
<p>Basically, I want to group them by streak of 1 and one row after where streak ends in column <code>a</code>.
This <a href="https://stackoverflow.com/a/77372543/10200497">answer</a> does that:</p>
<pre><code>g = df.loc[::-1, 'a'].eq(0).cumsum()
out = [g for _,g in df.groupby(g, sort=False) if len(g)>1]
</code></pre>
<p>But now what I want is check if the first value in <code>b</code> for each group is 1.</p>
<p>I don't know what is the best approach to check the first value of <code>b</code>.
This is what I have tried but I am not sure if it works in every case.</p>
<pre><code>groups = df.groupby(g).filter(lambda x: x.b.iloc[0] == 1)
</code></pre>
<p>I have experienced some situations where the code works in an example but it does not work in every situation with different conditions so I want to double check my code.</p>
|
<python><pandas>
|
2023-12-12 01:11:37
| 2
| 2,679
|
AmirX
|
77,643,130
| 10,964,685
|
Split col by any delimiter and Uppercase values
|
<p>I'm trying to split a column by the last <code>' - '</code> that is followed by all uppercase strings letters.</p>
<p>Below, I have a df with <code>Value</code> containing various combinations. I want to split the col into two individuals columns, whereby, everything before the last <code>' - '</code> and uppercase letters.</p>
<p>I've got <code>Last</code> column correct but not <code>First</code> column.</p>
<pre><code>df = pd.DataFrame({
'Value': [
'Juan-Diva - HOLLS',
'Carlos - George - ESTE BAN - BOM',
'Javier Plain - Hotham Ham - ALPINE',
'Yul - KONJ KOL MON'],
})
</code></pre>
<p>option 1)</p>
<pre><code>df[['First', 'l']] = df['Value'].str.split(' - ', n=1, expand=True)
df['Last'] = df['Value'].str.split('- ').str[-1]
</code></pre>
<p>option 2)</p>
<pre><code># Regular expression pattern
pattern = r'^(.*) - ([A-Z\s]+)$'
# Extract groups into two new columns
df[['First', 'Last']] = df['Value'].str.extract(pattern)
</code></pre>
<p>option 3)</p>
<pre><code>df[["First", "Last"]] = df["Value"].str.rsplit(" - ", n=1, expand=True)
</code></pre>
<p>Option 4 works but is incredibly slow. Use case datasets are only a few hundred rows and it's still computing after hours.</p>
<p>Option 4)</p>
<pre><code>df[["First", "Last"]] = df["Value"].str.extract(r'(.*?)\s*-\s*([A-Z]+(?:\s*-?\s*[A-Z]+)*)')
</code></pre>
<p>intended output:</p>
<pre class="lang-none prettyprint-override"><code> First Last
0 Juan-Diva HOLLS
1 Carlos - George ESTE BAN - BOM
2 Javier Plain - Hotham Ham ALPINE
3 Yul KONJ KOL MON
</code></pre>
|
<python><pandas><split>
|
2023-12-12 00:57:17
| 2
| 392
|
jonboy
|
77,643,104
| 232,798
|
AWS Lambda, Snowflake connector and "module 'cryptography.hazmat.bindings._rust.openssl' has no attribute 'hashes'"
|
<p>I am creating a layer for the snowflake connector.
I downloaded amazon linux image, installed the snowflake connector in the image:
<code>pip install snowflake-connector-python</code>;</p>
<p>after that I uploaded the layer and lambda through SAM installation.
Lambda is still giving me this issue though</p>
<pre><code> " File \"/var/lang/lib/python3.9/importlib/__init__.py\", line 127, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n",
" File \"<frozen importlib._bootstrap>\", line 1030, in _gcd_import\n",
" File \"<frozen importlib._bootstrap>\", line 1007, in _find_and_load\n",
" File \"<frozen importlib._bootstrap>\", line 986, in _find_and_load_unlocked\n",
" File \"<frozen importlib._bootstrap>\", line 680, in _load_unlocked\n",
" File \"<frozen importlib._bootstrap_external>\", line 850, in exec_module\n",
" File \"<frozen importlib._bootstrap>\", line 228, in _call_with_frames_removed\n",
" File \"/var/task/app.py\", line 2, in <module>\n import snowflake.connector\n",
</code></pre>
<p>any ideas appreciated.</p>
<p>python 3.9.16</p>
<p>snowflake-connector-python==3.6.0
cryptography==41.0.7</p>
<p>amazonlinux: 2023.2.20231113.0</p>
|
<python><aws-lambda><snowflake-cloud-data-platform>
|
2023-12-12 00:44:46
| 0
| 10,613
|
Danail
|
77,643,086
| 11,124,121
|
How to solve the error:ufunc 'isfinite' not supported for the input types in the sm.models?
|
<p>I tried to follow the code in this link:
<a href="https://jbhender.github.io/Stats506/F17/Projects/Poisson_Regression.html" rel="nofollow noreferrer">https://jbhender.github.io/Stats506/F17/Projects/Poisson_Regression.html</a></p>
<p>But the error message popped up:</p>
<pre><code>TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could
not be safely coerced to any supported types according to the casting rule ''safe''
</code></pre>
<p>And I checked the code and found that the dummy variables did not cast as integers, it was still a factor/character. I am not sure whether the error was due to the data type or something that I don't know. Please give me some suggestions. Thank you!</p>
|
<python><pandas><numpy>
|
2023-12-12 00:36:05
| 0
| 853
|
doraemon
|
77,642,881
| 9,263,783
|
Python 3.11 Typing for multiple inheritance models
|
<p>I want to implement typing for a group of related classes that share a common metaclass (<code>AMeta</code>), there are two 'de facto' abstract parent classes:</p>
<ul>
<li><code>A</code> (:type[AMeta])</li>
<li><code>ADerived</code> - that also inherits from another class <code>C</code>.</li>
</ul>
<p>Finally there are actual implementation models of <code>ADerived</code> (<code>D1</code>, <code>D2</code>, ... these are dynamically created) and <code>A</code> (<code>E</code>, <code>F</code>, ...) parent classes.</p>
<p>The implementations of <code>A</code> (<code>E</code>, <code>F</code>) have also a class variable of type <code>ADerived</code> (<code>_DerivedModel</code>) and the problem I have is trying to make <code>mypy</code> infer the correct type for them.</p>
<p>I know this piece of code might not make sense, but the actual implementation is more complex and the derived models are dynamically created. Here's a simplification of it:</p>
<pre><code>from __future__ import annotations
from typing import TypeVar, Type, ClassVar
_BModel = TypeVar("_BModel", bound="ADerived")
class C:
pass
class AMeta(type):
@property
def BModel(cls: Type[A]) -> Type[_BModel]:
return cls._DerivedModel
# Abstract Models
class A(metaclass=AMeta):
_DerivedModel: ClassVar[Type[_BModel]]
class ADerived(A, C):
pass
# Derived Models (this models are dynamically created)
class D1(ADerived):
pass
class D2(ADerived):
pass
# Implementations
class E(A):
_DerivedModel = D1
class F(A):
_DerivedModel = D2
MyDerived1 = E.BModel # Should be infered by mypy as type[D1]
MyDerived2 = F.BModel # Should be infered by mypy as type[D2]
</code></pre>
|
<python><mypy><typing>
|
2023-12-11 23:23:50
| 1
| 341
|
Bravhek
|
77,642,836
| 4,398,966
|
Simplify .format printing in Python
|
<p>I'm trying to print out the following:</p>
<pre><code>-----------------------------
| | |
| | P |
| | Y |
| | T |
| PYTHON! | H |
| | O |
| | N |
| | ! |
| | |
-----------------------------
| | |
| | |
| | |
| | |
|PYTHON! | PYTHON!|
| | |
| | |
| | |
| | |
-----------------------------
</code></pre>
<p>Here's what I have:</p>
<pre><code>rowBorder = '-' * 29
col = '|'
space = ' '
emptyColRow4 = (col + space * 13 + col + space * 13 + col + "\n") * 4
text = 'PYTHON!'
emptyRow = col+space*13+col+space*13+col
print(rowBorder)
print(emptyRow)
for l in text:
if l != 'H':
verticalLetter = '{}{}{}'.format(col + space*13 + col + space*6,l,space*6+col)
else:
verticalLetter = '{}{:^13}{}{}{}'.format(col,text, col + space*6,l,space*6+col)
print(verticalLetter)
print(emptyRow)
print(rowBorder)
print(emptyColRow4,end='')
print('{}{:<13}{}{:>13}{}'.format(col,text,col,text,col))
print(emptyColRow4,end='')
print(rowBorder)
</code></pre>
<p>Is there some way we could embed the <em>for</em> loop directly into the <em>print</em> statement?</p>
|
<python><python-3.x><for-loop><format>
|
2023-12-11 23:07:15
| 3
| 15,782
|
DCR
|
77,642,795
| 13,135,901
|
Pandas change values in pandas dataframe based on a slice with selected indexes and shift()
|
<p>Let's say I have a dataframe <code>df</code>. First I filter it, then I need to find all rows that satisfy a criteria and then mark all these rows plus 2 previous rows for each in a specific bool column in the original dataframe:</p>
<pre><code>df = pd.DataFrame({{'A': {0: nan, 1: nan, 2: 1944.09, 3: nan, 4: nan, 5: 1926.0, 6: nan, 7: 1930.31, 8: nan, 9: nan, 10: nan, 11: nan, 12: nan, 13: nan, 14: nan, 15: 1917.66, 16: 1920.43, 17: nan, 18: 1909.04, 19: nan, 20: nan, 21: nan, 22: nan, 23: nan, 24: 1920.05, 25: nan, 26: 1915.4, 27: 1921.87, 28: nan, 29: nan, 30: nan, 31: 1912.42, 32: 1920.08, 33: 1915.8, 34: nan, 35: nan, 36: nan, 37: nan, 38: 1919.71, 39: 1916.2, 40: nan, 41: 1926.79, 42: nan, 43: 1918.66, 44: nan, 45: 1925.5, 46: 1922.22, 47: nan, 48: nan, 49: 1927.87, 50: 1923.24, 51: nan, 52: 1929.53, 53: nan, 54: nan, 55: nan, 56: nan, 57: nan, 58: nan, 59: nan, 60: nan, 61: 1918.37, 62: nan, 63: nan, 64: 1923.61, 65: nan, 66: 1917.1, 67: nan, 68: nan, 69: nan, 70: nan, 71: nan, 72: nan, 73: nan, 74: nan, 75: nan, 76: nan, 77: nan, 78: nan, 79: nan, 80: nan, 81: 1924.48, 82: nan, 83: nan, 84: 1923.03, 85: nan, 86: nan, 87: nan, 88: nan, 89: 1926.87, 90: nan, 91: nan, 92: nan, 93: 1921.79, 94: nan, 95: 1925.27, 96: nan, 97: 1919.0, 98: nan, 99: nan, 100: 1923.74, 101: nan, 102: nan, 103: nan, 104: nan, 105: 1911.61, 106: nan, 107: 1923.33, 108: nan, 109: nan, 110: nan, 111: 1912.0, 112: nan, 113: 1915.8, 114: nan, 115: 1913.05, 116: nan, 117: nan, 118: nan, 119: nan, 120: nan, 121: nan, 122: 1916.93, 123: nan, 124: 1913.69, 125: nan, 126: nan, 127: nan, 128: nan, 129: 1918.38, 130: 1913.7, 131: nan, 132: nan, 133: nan, 134: nan, 135: nan, 136: 1919.5, 137: nan, 138: 1916.14, 139: nan, 140: nan, 141: nan, 142: nan, 143: nan, 144: 1921.28, 145: nan, 146: nan, 147: nan, 148: nan, 149: nan, 150: 1915.0, 151: nan, 152: nan, 153: nan, 154: nan, 155: nan, 156: 1927.48, 157: 1889.17, 158: nan, 159: 1921.91, 160: 1917.67, 161: 1923.23, 162: nan, 163: nan, 164: nan, 165: 1909.88, 166: nan, 167: 1913.82, 168: 1902.51, 169: nan, 170: nan, 171: nan, 172: nan, 173: nan, 174: nan, 175: nan, 176: nan, 177: nan, 178: nan, 179: 1920.15}, 'C': {0: False, 1: False, 2: True, 3: False, 4: False, 5: False, 6: False, 7: False, 8: False, 9: False, 10: False, 11: False, 12: False, 13: False, 14: False, 15: False, 16: False, 17: False, 18: False, 19: False, 20: False, 21: False, 22: False, 23: False, 24: False, 25: False, 26: False, 27: True, 28: False, 29: False, 30: False, 31: False, 32: True, 33: False, 34: False, 35: False, 36: False, 37: False, 38: False, 39: False, 40: False, 41: True, 42: False, 43: False, 44: False, 45: False, 46: False, 47: False, 48: False, 49: False, 50: False, 51: False, 52: True, 53: False, 54: False, 55: False, 56: False, 57: False, 58: False, 59: False, 60: False, 61: False, 62: False, 63: False, 64: False, 65: False, 66: False, 67: False, 68: False, 69: False, 70: False, 71: False, 72: False, 73: False, 74: False, 75: False, 76: False, 77: False, 78: False, 79: False, 80: False, 81: False, 82: False, 83: False, 84: False, 85: False, 86: False, 87: False, 88: False, 89: True, 90: False, 91: False, 92: False, 93: False, 94: False, 95: False, 96: False, 97: False, 98: False, 99: False, 100: False, 101: False, 102: False, 103: False, 104: False, 105: False, 106: False, 107: True, 108: False, 109: False, 110: False, 111: False, 112: False, 113: False, 114: False, 115: False, 116: False, 117: False, 118: False, 119: False, 120: False, 121: False, 122: False, 123: False, 124: False, 125: False, 126: False, 127: False, 128: False, 129: False, 130: False, 131: False, 132: False, 133: False, 134: False, 135: False, 136: False, 137: False, 138: False, 139: False, 140: False, 141: False, 142: False, 143: False, 144: False, 145: False, 146: False, 147: False, 148: False, 149: False, 150: False, 151: False, 152: False, 153: False, 154: False, 155: False, 156: True, 157: False, 158: False, 159: False, 160: False, 161: True, 162: False, 163: False, 164: False, 165: False, 166: False, 167: False, 168: False, 169: False, 170: False, 171: False, 172: False, 173: False, 174: False, 175: False, 176: False, 177: False, 178: False, 179: False}})
df2 = df[df.C]
print(df2)
2 1944.09
27 1921.87
32 1920.08
41 1926.79
52 1929.53
89 1926.87
107 1923.33
156 1927.48
161 1923.23
Name: A, dtype: float64
df3 = df2[(df2.A > df2.A.shift(1)) & (df2.A.shift(1) > df2.A.shift(2))]
print(df3)
52 1929.53
Name: A, dtype: float64
df[FROM_2nd_PREVIOUS_TO_EVERY_ROW_OF_df3_IN_df2:TO_EVERY_ROW_IN_df3, 'B'] = True
print(df)
A B
0 NaN False
.. ... ...
31 1234.56 False
32 1920.08 True
33 1234.56 True
.. ... ...
41 1926.79 True
40 1234.56 True
.. ... ...
51 1234.56 True
52 1929.53 True
52 1234.56 False
.. ... ...
176 NaN False
177 NaN False
178 NaN False
179 1920.15 False
</code></pre>
<p>What is the correct way to do it?</p>
|
<python><pandas><dataframe>
|
2023-12-11 22:55:13
| 1
| 491
|
Viktor
|
77,642,777
| 329,365
|
inconsistent "Invalid JSON from piped input" error from python/powershell
|
<p>I have a Jenkins instance running on a windows server. There is a specific job that runs a python script and part of that python script is calling powershell:</p>
<pre><code>generate_password = f"""Invoke-Expression $('{PASSWORD}' | op signin --account {ACCOUNT} )
op item create --vault shared title={TITLE} --generate-password='letters,digits,16' --category Password"""
result = subprocess.Popen(["powershell","-Command", generate_password", capture_output=True, text=True, encoding="utf-8")
</code></pre>
<p>The variables are passed from the Jenkins environment, but I have also tested this where they are hardcoded into the script.</p>
<p>Now I can run the script fine on my local machine with no problems, i can run it on the jenkins server AS the jenkins user with no problems, but the jenkins job itself consistently gets the error "Invalid JSON in piped input".</p>
<p>I've even tried manually creating the script and storing it on the server, then instead of passing the script directly, I call powershell and tell it to run the saved file - this resutls in exactly the same error.</p>
<p>I'm assuming the issue must relate to the Jenkins environment that is spawning the powershell process, but I'm unsure of what I can try to resolve it.</p>
<p>I currently run a variety of python jobs via jenkins (80 or so) and this is the only time I've run into an issue like this.</p>
<p><em>edit</em>:
All of my jenkins pipelines are very simple, they pull a git repo and run a script in that repo. I'm using jenkins for orchestration only as it has brilliant features for monitoring and scheduling at a price that can't be beat.</p>
<p><em>edit</em>:
I've come up with a work around, I created a scheduled task on the Jenkins server that runs a specific powershell script in a specific location. In my python job I now create that script, call the task, read the output file, then delete the script and the output file. It's not ideal, but it works.</p>
<p><em>edit</em>:
Interestingly, this very similar code runs fine:</p>
<pre><code>share_password = f"""Invoke-Expression $('{PASSWORD}' | op signin --account {ACCOUNT} )
op item share {final_password_name} --vault shared --emails {email_list}"""
result = subprocess.Popen(["powershell","-Command", share_password , capture_output=True, text=True, encoding="utf-8")
</code></pre>
|
<python><powershell><jenkins>
|
2023-12-11 22:50:06
| 0
| 3,447
|
danspants
|
77,642,702
| 3,361,462
|
How does iterated application of the `+=` operator to a string variable manage to avoid quadratic complexity?
|
<p>I am kinda confused. I was sure that due do immutability of <code>str</code> operation like <code>s += "abc"</code> need to copy whole <code>s</code> content to another place in memory so effectively adding character to very long string will consume much time.</p>
<p>I wrote snippet to prove my theory:</p>
<pre><code>import timeit
def foo(i):
res = ""
for _ in range(i):
res += "a"
return res
def foo2(i):
res = []
for _ in range(i):
res.append("a")
return "".join(res)
iterations = 100000
print(timeit.timeit('foo(iterations)', globals=globals(), number=100))
print(timeit.timeit('foo2(iterations)', globals=globals(), number=100))
</code></pre>
<p>However it looks like</p>
<ol>
<li><code>foo</code> execution time grows <em>linearly</em> based on <code>iterations</code></li>
<li><code>foo2</code> is barely two times faster than <code>foo</code></li>
</ol>
<p>I tried to inspect bytecode searching for some hidden optimizations. I tried to change constant string to randomized one with proper length to deny interning as well but couldn't find any explanation of that behaviour.</p>
<p>Was I wrong then? Does <code>+=</code> depend on string length or not? If so, how can I prove that?</p>
|
<python><string><string-concatenation><cpython>
|
2023-12-11 22:28:47
| 1
| 7,278
|
kosciej16
|
77,642,635
| 7,563,454
|
Convert list index to a 3D position based on width and height
|
<p>I'm working on a CPU voxel raytracer in Python. With the amount of costly calculations involved everything must be as efficient as possible especially storing and fetching point data in space. I started by storing 2D and 3D data in dictionaries indexed by string positions, eg: <code>data["4,16"] == "solid"</code>. Since converting positions to and from string is costly while dictionaries are slower than lists, I'm in the process of moving to the more efficient system of storing everything in an ordered array and using index to determine which position an entry refers to. I already have this working successfully in 2D:</p>
<pre><code># Returns x, y position from index i based on width
def index_vec2(i: int, width: int):
return math.floor(i % width), math.floor(i / width)
</code></pre>
<p>Say you have a 4x4 square: If the array contains 16 entries, you easily know index 3 represents position <code>x == 3, y == 0</code> then index 4 is position <code>x == 0, y == 1</code>. The function only needs the rectangle width to deduce this, height isn't necessary as there's no need to ensure the whole area is filled or extra entries don't spill out.</p>
<p>I'm trying to expand the same concept to 3D: You pass an <code>i</code> index to the function, in this case it needs to know both width and height to also calculate the depth. I'm struggling a bit as the math is more complex with a third axis... if I force my mind on it for a few more hours I might figure it out, but it also seems beneficial to have an answer here for others attempting the same thing.</p>
<pre><code># Returns x, y, z position from index i based on width and height
def index_vec3(i: int, width: int, height: int):
return math.floor(i % width), math.floor(i / width), math.floor(i / (width * height))
</code></pre>
<p>Currently this seems to report X and Z accurately, but I can't figure out what to do with Y. Initially it starts out well, increasing once per number of times <code>i</code> exceeds the width. Problem is that once we pass into the next Z layer, Y doesn't return back to 0 and start all over again, it keeps climbing all the way up to 15. Using <code>for i in range(0, 64): index_vec3(i, 4, 4)</code> (simulates iterating a 4x4x4 cube) I get the following result:</p>
<pre><code>0,0,0
1,0,0
2,0,0
3,0,0
0,1,0
1,1,0
2,1,0
3,1,0
0,2,0
1,2,0
2,2,0
3,2,0
0,3,0
1,3,0
2,3,0
3,3,0
0,4,1
1,4,1
2,4,1
3,4,1
0,5,1
1,5,1
2,5,1
3,5,1
0,6,1
1,6,1
2,6,1
3,6,1
0,7,1
1,7,1
2,7,1
3,7,1
0,8,2
1,8,2
2,8,2
3,8,2
0,9,2
1,9,2
2,9,2
3,9,2
0,10,2
1,10,2
2,10,2
3,10,2
0,11,2
1,11,2
2,11,2
3,11,2
0,12,3
1,12,3
2,12,3
3,12,3
0,13,3
1,13,3
2,13,3
3,13,3
0,14,3
1,14,3
2,14,3
3,14,3
0,15,3
1,15,3
2,15,3
3,15,3
</code></pre>
<p>How can I improve my math and what's the simplest form to achieve a third axis? Only rule is no loops or increments which would cheat the optimization and negate performance benefits, each axis must be determined by transforming <code>i</code> with simple math based on the width and height. Hopefully it's simple enough to remain a one-liner but that's not mandatory.</p>
|
<python><python-3.x><math><vector><3d>
|
2023-12-11 22:06:11
| 1
| 1,161
|
MirceaKitsune
|
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