I'm a beginner in FloPy and I'm working with a unsatured model in MODFLOW-USG (using Richards equation), and I have the output files (.hds, .ddn, .cbb). I need to export saturation of unsatured cells (a parameter between 0 and 1) but I can't find how to do it. I've seen in some papers the parameter 'water content', 'water saturation', 'saturation' but I don't know how to get it...
I've been trying to get Saturation from output file .DDN postprocessing it in FloPy with the code:
flopy.utils.binaryfile.HeadFile(f"DRPA_TR_oc_.ddn",text="SATURATIONU", precision="auto")
and
flopy.utils.binaryfile.HeadFile(f"DRPA_TR_oc_.ddn",text="SATURATION", precision="auto")
but after ~1 hour computing following appears:
File "C:\Users\User1\Anaconda3\lib\site-packages\flopy\utils\binaryfile.py", line 369, in _get_header
return header[0]
IndexError: index 0 is out of bounds for axis 0 with size 0
when I ran CellBudgetFile with .cbb or HeadUFile with .hds it took few minutes. So, I don't understand very well where is the error and how to fix it, and why to compute .ddn file take a lot of time.
I hope you can help me to fix the error or another way to get saturation (maybe from .hds, or whatever) :)
I remain attentive to your comments!
Thanks in advance :)
Related
I have a file with saved modelling data like this
1
0
1
Trying to iterate over the file using readlines, which correctly reads the line as zero or one.
Error happens when I cast the string into 1 or 0 Value error my code is this
st1=myfile.readline()
try:
line=float(st1.strip(), base=10)
if line==0: zeros+=1
if line==1 or line==0: ones+=1
except ValueError
I am getting value error in each line. I have no clue what is causing this. I do not want to load whole file into a variable as it is quite large. I tried changing 1 to 100 still error.Though a trivial error, I am unable to fix it.I Searched online forums, stack overflow no help. I don't want Numpy, please don't give some solution using Array operation on NumPy. Can you please help?
I own a dataset of images with unknown label format, which is:
angry_actor_104.jpg 0 28 113 226 141 22.9362 0
It indicates an image as follows:
image_name face_id_in_image face_box_top face_box_left face_box_right face_box_bottom face_box_cofidence expression_label
My question is: How can this be converted into the yolov5 format?
I have been looking this up for a long time and hope someone can help.
Thank you very much in advance.
Since the format is unknown you are unlikely to find existing code to completely handle the transformation but I can share some tips to get started.
The annotations file does not have enough info to get converted to Yolo format. Because to convert to Yolo you also need to know the dimensions of the images. If all of your images are the same dimension then it easier but if all of the images are different then you will need additional code to extract the dimensions of the images. I will explain why below.
When you are done you will need to get the images and labels in a specific directly structure like this, with one txt file per image:
/images/actor1.jpg
/images/actor2.jpg
/labels/actor1.txt
/labels/actor2.txt
This is the shape that you want to get the annotation files into.
face_id_in_image x_center_image y_center_image width height
There is a clear description of what the values mean here https://stackoverflow.com/a/66563144/5183735.
Now you need to do some math to calculate the values.
width = (face_box_right - face_box_left)/image_width
height = (face_box_bottom - face_box_top)/image_height
x_center_image = face_box_left/image_width + (width/2)
y_center_image = face_box_top/image_height + (height/2)
I have some bits of code that may help you with reading the text file and saving the text files here.
https://github.com/pylabel-project/pylabel/blob/main/pylabel/exporter.py and https://github.com/pylabel-project/pylabel/blob/main/pylabel/importer.py.
If you are able to share your exact files I may be able to identify some shortcut to transform them.
I'm following the FastBert tutorial from huggingface https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384
The problem is this the code is not exactly reproducible. The main issue I'm facing is the dataset preparation. In the tutorial, this dataset is used https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data
But, if I set-up the folder structure according the tutorial, and place the dataset files in the folders I get errors with the databunch.
databunch = BertDataBunch(args['data_dir'], LABEL_PATH, args.model_name, train_file='train.csv', val_file='val.csv',
test_data='test.csv',
text_col="comment_text", label_col=label_cols,
batch_size_per_gpu=args['train_batch_size'], max_seq_length=args['max_seq_length'],
multi_gpu=args.multi_gpu, multi_label=True, model_type=args.model_type)
It complains about the file format being wrong. How should I format the dataset, labels for this dataset with fastbert?
First of all, you can use the notebook from GitHub for FastBert.
https://github.com/kaushaltrivedi/fast-bert/blob/master/sample_notebooks/new-toxic-multilabel.ipynb
There is a small tutorial in the FastBert README on how to process the dataset before using.
Create a DataBunch object
The databunch object takes training, validation and test csv files and converts the data into internal representation for BERT, RoBERTa, DistilBERT or XLNet. The object also instantiates the correct data-loaders based on device profile and batch_size and max_sequence_length.
from fast_bert.data_cls import BertDataBunch
databunch = BertDataBunch(DATA_PATH, LABEL_PATH,
tokenizer='bert-base-uncased',
train_file='train.csv',
val_file='val.csv',
label_file='labels.csv',
text_col='text',
label_col='label',
batch_size_per_gpu=16,
max_seq_length=512,
multi_gpu=True,
multi_label=False,
model_type='bert')
File format for train.csv and val.csv
index text label
0 Looking through the other comments, I'm amazed that there aren't any warnings to potential viewers of what they have to look forward to when renting this garbage. First off, I rented this thing with the understanding that it was a competently rendered Indiana Jones knock-off. neg
1 I've watched the first 17 episodes and this series is simply amazing! I haven't been this interested in an anime series since Neon Genesis Evangelion. This series is actually based off an h-game, which I'm not sure if it's been done before or not, I haven't played the game, but from what I've heard it follows it very well pos
2 his movie is nothing short of a dark, gritty masterpiece. I may be bias, as the Apartheid era is an area I've always felt for. pos
In case the column names are different than the usual text and labels, you will have to provide those names in the databunch text_col and label_col parameters.
labels.csv will contain a list of all unique labels. In this case the file will contain:
pos
neg
For multi-label classification, labels.csv will contain all possible labels:
severe_toxic
obscene
threat
insult
identity_hate
The file train.csv will then contain one column for each label, with each column value being either 0 or 1. Don't forget to change multi_label=True for multi-label classification in BertDataBunch.
id text toxic severe_toxic obscene threat insult identity_hate
0 Why the edits made under my username Hardcore Metallica Fan were reverted? 0 0 0 0 0 0
0 I will mess you up 1 0 0 1 0 0
label_col will be a list of label column names. In this case it will be:
['toxic','severe_toxic','obscene','threat','insult','identity_hate']
So, just keep the train.csv, val.csv (just make a copy of train.csv), and test.csv inside data/
In the labels folder, keep a labels.csv with the following contents.
severe_toxic
obscene
threat
insult
identity_hate
I recently ran across the PhotUtils package and am trying to use it to perform PSF Photometry on some images I have. However, when I try to run the code, I get very strange results. When I plot the image generated by get_residual_image(), the stars are not removed well. Some sample images are shown below.
The first image has sigma set to 2.05, as it is in one of the sample programs in the PhotUtils documentation:
However, the stars only appear to be removed in their center.
The second image has sigma set to 5.0. This one is especially strange. Some stars are way over-removed, some are under removed, some black squares are added to the image, etc.
Here is my code:
import photutils
from photutils.psf import DAOPhotPSFPhotometry as DAOP
from photutils.psf import IntegratedGaussianPRF as PRF
from photutils.background import MMMBackground
bkg = MMMBackground()
background = 2.5*bkg(img)
gaussian_prf = PRF(sigma=5.0)
gaussian_prf.sigma.fixed = False
photTester = DAOP(8,background,5,gaussian_prf,31)
photResults = photTester(imgStars)
finalImg = photTester.get_residual_image()
After this, I simply plot the original and final image in MatPlotLib. I use a greyscale colormap. The reason that the left images appear slightly darker is that they use a different color scaling.
Perhaps I have set one of the parameters incorrectly?
Could someone help me out with this? Thank you!
Looking at the residual image instantly told me that the background subtraction might be wrong. I could reproduce the result and wondered, if MMMBackground did not do the job correctly.
After taking a closer look at the documentation, Getting startet with Photutils finally gave the essential hint:
image -= np.median(image)
Ok, I am trying to upload a .csv file, get it into a spatial points data frame and set the projection system to WGS 84. I then want to determine the distance between each point This is what I have come up with but I
cluster<-read.csv(file = "cluster.csv", stringsAsFactors=FALSE)
coordinates(cluster)<- ~Latitude+Longitude
cluster<-CRS("+proj=longlat +datum=WGS84")
d<-dist2Line(cluster)
This returns an error that says
Error in .pointsToMatrix(p) :
points should be vectors of length 2, matrices with 2 columns, or inheriting from a SpatialPoints* object
But this isn't working and I will be honest that I don't fully comprehend importing and manipulating spatial data in R. Any help would be great. Thanks
I was able to determine the issue I was running into. With WGS 84, the longitude comes before the latitude. This is just backwards from how all the GPS data I download is formatted (e.g. lat-long). Hope this helps anyone else who runs into this issue!
thus the code should have been
cluster<-read.csv(file = "cluster.csv", stringsAsFactors=FALSE)
coordinates(cluster)<- ~Longitude+Latitude
cluster<-CRS("+proj=longlat +datum=WGS84")