I am writing an app, which takes a STL file as input. I want to get volume of the stl object without saving the stl file and use the volume to calculate a quote and post it back to browser. Right now I am using numpy-stl package, but I am stuck on how to create a mesh object for numpy-stl from the file I get with request.files['file'].read(). Any help is appreciated.
mycode:
what I get for filedata
what I get for error
You can try the following code:
import io
filedata = request.files['file'].read()
data = io.BytesIO(filedata)
tmp_mesh = mesh.Mesh.from_file("tmp.stl", fh=data)
You can use tmp_mesh object to do you interesting operation
suggestion to add error handle on something not expected
if request.files not contain 'file' keys
Related
I created an API using FastAPI that returned a JSON. First, I used to turn the Dataframe to JSON using the Pandas .to_json() method, which allowed me to choose the correct "orient" parameter. This saved a .json file and then opened it to make fastAPI return it as it follows:
DATA2.to_json("json_records.json",orient="records")
with open('json_records.json', 'r') as f:
data = json.load(f)
return(data)
This worked perfectly, but i was told that my script shouldn't save any files since this script would be running on my company's server, so I had to directly turn the dataframe into JSON and return it. I tried doing this:
data = DATA2.to_json(orient="records")
return(data)
But now the API's output is a JSON full of "\". I guess there is a problem with the parsing but i can't really find a way to do it properly.
The output now looks like this:
"[{\"ExtraccionHora\":\"12:53:00\",\"MiembroCompensadorCodigo\":117,\"MiembroCompensadorDescripcion\":\"OMEGA CAPITAL S.A.\",\"CuentaCompensacionCodigo\":\"1143517\",\"CuentaNeteoCodigo\":\"160234117\",\"CuentaNeteoDescripcion\":\"UNION FERRO SRA A\",\"ActivoDescripcion\":\"X17F3\",\"ActivoID\":8,\"FinalidadID\":2,\"FinalidadDescripcion\":\"Margenes\",\"Cantidad\":11441952,\"Monto\":-16924935.3999999985,\"Saldo\":-11379200.0,\"IngresosVerificados\":11538288.0,\"IngresosNoVerificado\":0.0,\"MargenDelDia\":0.0,\"SaldoConsolidadoFinal\":-16765847.3999999985,\"CuentaCompensacionCodigoPropia\":\"80500\",\"SaldoCuentaPropia\":-7411284.3200000003,\"Resultado\":\"0\",\"MiembroCompensadorID\":859,\"CuentaCompensacionID\":15161,\"CuentaNeteoID\":7315285}.....
What would be a proper way of turning my dataframe into a JSON using the "records" orient, and then returning it as the FastAPI output?
Thanks!
update: i changed the to_json() method to to_dict() using the same parameters and seems to work... don't know if its correct.
data = DATA2.to_dict(orient="records")
return(data)
I am reading a large csv file using Pandas and then serving it using Flask. I am getting the error 'TextFileReader' object has no attribute 'to_html'. I think chunk size is causing the issue, but I can't open a file above 4GB without it.
from flask import Flask, session, request, json,Response,stream_with_context,send_from_directory,render_template
import pandas as pd
app = Flask(__name__)
#app.route('/readcsv')
def host_data():
csvname=request.args.get('csvname')
df=pd.read_csv(csvname,chunksize=5000)
return df.to_html(header="true")
When using chunksize you will get a generator of chunks. You should concatenate them for example using the following:
df = pd.concat((chunk for chunk in pd.read_csv(csvname,chunksize=5000)))
Serving a big file like this without implementing some sort of pagination, will create a total blocking response from your server, that will lead the user to wait till the file is opened and properly rendered as html.
I have this json file which has the image name(medical images-around 7500) and its corresponding medical reports.
It is of the format:
{"IMAGE_NAME1.png": "MEDICAL_REPORT1_IN_TEXT", "IMAGE_NAME2.png": "MEDICAL_REPORT2_IN_TEXT", ...and so on for all images ...}
What I want is all the image names in the JSON file so that I can take all the images(From a database of images which is a super set of the image names in the JSON file) and make its own folder. Can someone help me with this?
I think you're looking for the json library. I haven't tested this, but I am pretty confident it will work.
import json
with open("path/to/json.json", 'r') as file:
json_data = json.load(file)
to get image names from what you described the data to look like.
image_names = list(json_data.keys())
I am practicing github machine learning contest using Python. I start from other's submission, but stuck at the first step: use pandas to read CSV file:
import pandas as pd
import numpy as np
filename = './facies_vectors.csv'
training_data = pd.read_csv(filename)
print(set(training_data["Well Name"]))
[enter image description here][1]training_data.head()
This gave me the following error message:
pandas.io.common.CParserError: Error tokenizing data. C error: Expected 1 fields in line 104, saw 3
I could not understand that the .csv file describe itself as html DOCTYPE. Please help.
The representing segments of the csv data content are attached. Thanks
It turns out I download the csv file following the convention of regular web operation: right click and save as. The right way is open the item from github, and then open it from the github desktop. I got the tables now. But the way to work with html files from python is definite something I would learn more about. Thanks.
I'm working with Django and need to read the sheets and cells of an uploaded xlsx file. It should be possible with xlrd but because the file has to stay in memory and may not be saved to a location I'm not sure how to continue.
The start point in this case is a web page with an upload input and a submit button. When submitted the file is caught with request.FILES['xlsx_file'].file and send to a processing class that would have to extract all the important data for further processing.
The type of request.FILES['xlsx_file'].file is BytesIO and xlrd is not able to read that type because of no getitem methode.
After converting the BytesIO to StringIO the error messages seems to stay the same '_io.StringIO' object has no attribute '__getitem__'
file_enc = chardet.detect(xlsx_file.read(8))['encoding']
xlsx_file.seek(0)
sio = io.StringIO(xlsx_file.read().decode(encoding=file_enc, errors='replace'))
workbook = xlrd.open_workbook(file_contents=sio)
I'm moving my comment into an answer of it's own. It related to the example code (which includes decoding) given in the updated question:
Ok, thanks for your pointers. I downloaded xlrd and tested it locally. It seems the best way to go here is to pass it a string ie. open_workbook(file_contents=xlsx_file.read().decode(encoding=file_enc, errors='replace')). I misunderstood the docs, but I'm positive that file_contents= will work with a string.
Try xlrd.open_workbook(file_contents=request.FILES['xlsx_file'].read())
I had a similar problem but in my case I needed to unit test a Djano app with download by the user of an xls file.
The basic code using StringIO worked for me.
class myTest(TestCase):
def test_download(self):
response = self.client('...')
f = StringIO.StringIO(response.content)
book = xlrd.open_workbook(file_contents = f.getvalue() )
...
#unit-tests here