npTDMS | NumPy based Python module for reading TDMS files | File Utils library
kandi X-RAY | npTDMS Summary
kandi X-RAY | npTDMS Summary
NumPy based Python module for reading TDMS files produced by LabView
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Top functions reviewed by kandi - BETA
- Read data for a channel
- Build the index
- Compare two arrays
- Duplicate an array
- Create an HDF5 dataset from a TDMS file
- Convert Channel to DataFrame
- Convert data to HDF5 array
- Convert datetime value to string
- Read a tdms file
- Read a single data chunk
- Read metadata from the file
- Defragment a TdMS file
- Generator that yields raw data for a channel
- Display information about a TdMS file
- Calculate the temperature of the circuit
- Scale the circuit
- Reads raw data from the file
- The raw data for this channel
- Read a channel data chunk
- Export data from a given channel
- Create a DataFrame from a Channel object
- Channel data
- Return an iterator over the data chunks
- Data type of the channel
- Return the thermocouple pressure
- Data type of TDMS data
npTDMS Key Features
npTDMS Examples and Code Snippets
Community Discussions
Trending Discussions on npTDMS
QUESTION
from nptdms import TdmsFile as td
from matplotlib import pyplot as plt
import numpy as np
import skimage.color
import skimage.filters
import mplcursors
from skimage.feature import corner_harris,corner_peaks
file = 'sample.tdms'
with td.open(file) as tdms_file:
img = tdms_file.as_dataframe()
cropped_list = []
sel=cropped_list.append(img.iloc[700:1250,450:1550:])
coords=corner_peaks(corner_harris(sel),min_distance=10,threshold_rel=0.02)
fig, ax = plt.subplots()
ax.imshow(sel, cmap='gray')
plotted_points =ax.plot(coords[:, 1], coords[:, 0], color='cyan', marker='o',linestyle='None', markersize=2)
mplcursors.cursor(plotted_points, hover=True)
plt.show(
ANSWER
Answered 2021-Apr-11 at 00:59mplcursors automatically create annotations with the x and y positions. Only when you need extra information, you'd need to write a function to change the annotation depending on the selected point.
Here is some code, using a random image to show how it would work:
QUESTION
I have a TDMS file with a bunch of DateTime values with relevant instrumentation data.
The issue I am having is:
ANSWER
Answered 2021-Apr-22 at 12:23So I ended up contacting the author and he was helpful enough to send a response:
TDMS files internally store times in UTC. You don't say how you're getting the values in the "TDMS file" column but I assume this is from a program that converts times into your local timezone to display them. There is an example of how to convert UTC times from a TDMS file into your local timezone in the documentation.
If you are storing these times in a database though, I would strongly recommend you store times in UTC rather than your local timezone as local times can be ambiguous due to daylight savings changes, and UTC is easily understood from anywhere in the world without having to know the local timezone where the data originated.
If you still feel like making the change from UTC to EST then this should do it:
QUESTION
I have many binary files (.tdms format, similar to .wav) stored in S3 and I would like to read them with nptdms then process them in a distributed fashion with Dask on a cluster.
In PySpark there is pyspark.SparkContext.binaryFiles() which produces an RDD with a bytearray for each input file which is a simple solution to this problem.
I have not found an equivalent function in Dask - is there one? If not, how could the equivalent functionality be achieved in Dask?
I noticed there's dask.bytes.read_bytes() if it's necessary to involve this however nptdms can't read a chunk of a file - it needs the entire file to be available and I'm not sure how to accomplish that.
ANSWER
Answered 2021-Jan-06 at 14:05dask.bytes.read_bytes() will give you whole files if you use blocksize=None, i.e., exactly one block per file. The most common use case for that is compressed files (e.g., gzip) where you can't start mid-stream, but should work for your use case too. Note that the delayed objects you get each return bytes, not open files.
Alternatively, you can use fsspec.open_files. This returns OpenFile objects, which are safe to serialise and so you can use them in dask.delayed calls such as
QUESTION
I have been using the nptdms module to do analysis of TDMS files without issue for several years. Recently, I got an error when trying to read a TDMS file for the first time. I import TdmsFile from nptdms:
from nptdms import TdmsFile
I try to read it:
tdms_file = TdmsFile.read(path_to_my_tdms_file)
and then get the following error:
type object 'TdmsFile' has no attribute 'read'
I am using python v3.6.10, with Anaconda and the nptdms v0.12.0.
...ANSWER
Answered 2020-Sep-29 at 17:02The method you are referring to exists in the current documentation so it would make sense to
- Restart the virtual environment (close cmd line; start again;
conda activate).
If this does not help...
Reinstall the package:
conda remove nptdmsconda install nptdms
(the exact commands may differ depending on your environment).
If this does not help, create a fresh conda environment and install from scratch and check again.
QUESTION
During the project, I had to deal with a TDMS file. I'm asking because the file could not be read immediately.
My Goal: Perform analysis by converting TDMS file into DataFrame format
First attempt, -Perform TdmsFile open using npTdms package. -Converts to read_data() to execute pd.DataFrame command.
...ANSWER
Answered 2020-Jul-24 at 06:48Here you go =^..^=
QUESTION
I have multiple TDMS files in a source folder. I use below code to read the metadata out of those files, that works perfectly now. However, to improve my code I would like to merge all those files in 1 excel file and everytime I receive new TDMS files and run my code, the metadata will be added at the bottom of my Excel sheet.
This is the code I have right now:
...ANSWER
Answered 2020-Jun-23 at 11:36This is not using Python. But a simple batch script "type" can append all the file to txt oor csv file. Try this in command prompt pr save the code as filename.bat:
QUESTION
I'm trying to read just the metadata of my TDMS files but my code reads the whole file. Does someone has experience with this? Thank you.
This is my current code:
...ANSWER
Answered 2020-Jun-23 at 09:56As explained in the documentation, you can read the metadata directly using nptdms.TdmsFile.read_metadata
QUESTION
I have some data with very particular format (e.g., tdms files generated by NI systems) and I stored them in a S3 bucket. Typically, for reading this data in python if the data was stored in my local computer, I would use npTDMS package. But, how should is read this tdms files when they are stored in a S3 bucket? One solution is to download the data for instance to the EC2 instance and then use npTDMS package for reading the data into python. But it does not seem to be a perfect solution. Is there any way that I can read the data similar to reading CSV files from S3?
...ANSWER
Answered 2019-Dec-24 at 15:51Some Python packages (such as Pandas) support reading data directly from S3, as it is the most popular location for data. See this question for example on the way to do that with Pandas.
If the package (npTDMS) doesn't support reading directly from S3, you should copy the data to the local disk of the notebook instance.
The simplest way to copy is to run the AWS CLI in a cell in your notebook
QUESTION
I'm using the npTDMS Python module for reading TDMS files, and I'm having trouble getting all the channel properties. I can open my TDMS file using Scout or DIAdem or even the built-in LabVIEW viewer, and I can see that each channel in the file (Time and Pressure) has four properties: NI_ArrayColumn, NI_ChannelLength, NI_DataType, and name. However, the following code only outputs
...ANSWER
Answered 2017-Dec-27 at 21:15I've found out that the npTDMS module does not handle properties generated on-the-fly the same way the built-in LabVIEW viewer, Scout, DIAdem, and the Excel add-in do. This seems a bug to me, so I have an issue reported on the github.
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Install npTDMS
You can use npTDMS like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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