Is there anyway to generate this output from the Stanford CoreNLP server?
https://drive.google.com/drive/folders/1K2g7nBzHgOpiBQZFRQBNWbylIvCANsdQ?usp=sharing
I have tried running the server on sample sentences with the following annotators:
'tokenize','ssplit','pos','lemma','depparse','natlog','openie', 'ner', 'parse'
and get similar data, just in a different format.
I am assuming that the format I am trying to get the output into is the default output from an older version of CoreNLP. Is there any way to get the output in the format needed?
Output format is fixed and it cannot be configured out of the box, however you can de-serialize the output and convert it in required format. You can use #dataclasses based design pattern to achieve the operation optimally!
Related
I am trying to use EECodes in python to get various weather information, such as temperature, humidity, etc out of grib2 files. I am using the GFS files. I would like to be able to extract the data as (lat,lon,alt,$data_point), and as a 2d array for each altitude.
I have tried the example programs located here: https://confluence.ecmwf.int/display/ECC/grib_iterator_bitmap
I can't figure out what I am looking in the output of that program. When I load the messages using their keys, it is not obvious how to make a grid. When I load the grid, the data doesn't have labels I understand.
#craeft have a look to https://github.com/ecmwf/cfgrib. cfgrib is the new standard for python and grib file handling. It is easy to install and easy to access files. Please install the latest version because it supports GFS files.
Unable to convert tensorflow to VINO format
Followed documentation
In case I wish to fix the graph using the Point 97 in the Mo_FAQ.html document in the VINO documentation docs,
which nodes do I include in the first command -
python3 mo.py --input_model model/frozen_inference_graph.pb --tensorflow_subgraph_pattern ""FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/FusedBatchNorm, FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6, FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise, FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/FusedBatchNorm, FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6,..
Which of the nodes do I put above to offload a sub-graph of operations?
(Actual .pbtxt file had about 100 nodes)
What model you are trying to convert? Not sure what you need to use param --tensorflow_subgraph_pattern to convert MobileNet, it supported out of the box.
I am having weather data in grib2 format and i want to convert it into json format or csv. I know the languages PHP , node.js. Is it possible to conversion using these technologies?
Using Nodejs try this package may help you
https://www.npmjs.com/package/grib2json
I'm trying to make simple Python 3 program to read weather information from XML web source, convert it into Python-readable object (maybe dictionary) and process it (for example visualize multiple observations into graph).
Source of data is national weather service's (direct translation) xml file at link provided in code.
What's different from typical XML parsing related question in Stack Overflow is that there are repetitive tags without in-tag identificator (<station> tags in my example) and some with (1st line, <observations timestamp="14568.....">). Also I would like to try parse it straight from website, not local file. Of course, I could create local temporary file too.
What I have so far, is simply loading script, that gives string containing xml code for both forecast and latest weather observations.
from urllib.request import urlopen
#Read 4-day forecast
forecast= urlopen("http://www.ilmateenistus.ee/ilma_andmed/xml/forecast.php").read().decode("iso-8859-1")
#Get current weather
observ=urlopen("http://www.ilmateenistus.ee/ilma_andmed/xml/observations.php").read().decode("iso-8859-1")
Shortly, I'm looking for as universal as possible way to parse XML to Python-readable object (such as dictionary/JSON or list) while preserving all of the information in XML-file.
P.S I prefer standard Python 3 module such as xml, which I didn't understand.
Try xmltodict package for simple conversion of XML structure to Python dict: https://github.com/martinblech/xmltodict
I'm trying to receive some data in csv format, what I read is that StrongLoop only works with json data. So can I receive csv and transform to json to process the data?
Thanks.
This isn't a StrongLoop specific question. It is a general Node.js and data question. As such, I will answer in a generic fashion, but it is applicable to StrongLoop.
You will need to use a library to convert the delimited file into a JavaScript object. There are many packages on npm for reading/parsing/transforming/etc. CSV files: search npm.
The package that I have used extensively is David's CSV parser.
These libraries will allow you to parse and transform CSV into JavaScript objects (JSON).
Beware, however, that most CSV that I have dealt with does not conform to well formatted CSV. They don't properly escape quotes, quote strings with delimiters, etc.