I am new to swift but I have made an android app where a string array is selected from an xml file. This is a large xml file that contains a lot of string arrays and the app gets the relevant string array based on a user selection.
I am now trying to develop the same app for iOS using swift. I would like to use the same xml file but I can not see and easy way to get the correct array. For example, part of the xml looks like this
<string-array name="OCR_Businessstudies_A_Topics">
<item>1. Business objectives and strategic decisions</item>
<item>2. External influences facing businesses</item>
<item>3. Marketing and marketing strategies</item>
<item>4. Operational strategy</item>
<item>5. Human resources</item>
<item>6. Accounting and financial considerations</item>
<item>7. The global environment of business</item>
</string-array>
<string-array name="OCR_Businessstudies_AS_Topics">
<item>1. Business objectives and strategic decisions</item>
<item>2. External influences facing businesses</item>
<item>3. Marketing and marketing strategies</item>
<item>4. Operational strategy</item>
<item>5. Human resources</item>
<item>6. Accounting and financial considerations</item>
</string-array>
If I have the string "OCR_Businessstudies_A_Topics" how do i get the "OCR_Businessstudies_A_Topics" array from the xml file.
This is very straight forward in android and although I have used online tutorials for swift it seems like I have to parse the xml file but do not seem to be getting anywhere.
Is there a better approach than trying to parse the whole xml fie?
Thanks
Barry
You can write your own XML parser, conforming to NSXMLParser or use a library like HTMLReader:
let fileURL = NSBundle.mainBundle().URLForResource("data", withExtension: "xml")!
let xmlData = NSData(contentsOfURL: fileURL)!
let topic = "OCR_Businessstudies_A_Topics"
let document = HTMLDocument(data: xmlData, contentTypeHeader: "text/xml")
for item in document.nodesMatchingSelector("string-array[name='\(topic)'] item") {
print(item.textContent)
}
Related
I'm very new to TFX, but have an apparently-working ML Pipeline which is to be used via BulkInferrer. That seems to produce output exclusively in Protobuf format, but since I'm running bulk inference I want to pipe the results to a database instead. (DB output seems like it should be the default for bulk inference, since both Bulk Inference & DB access take advantage of parallelization... but Protobuf is a per-record, serialized format.)
I assume I could use something like Parquet-Avro-Protobuf to do the conversion (though that's in Java and the rest of the pipeline's in Python), or I could write something myself to consume all the protobuf messages one-by-one, convert them into JSON, deserialize the JSON into a list of dicts, and load the dict into a Pandas DataFrame, or store it as a bunch of key-value pairs which I treat like a single-use DB... but that sounds like a lot of work and pain involving parallelization and optimization for a very common use case. The top-level Protobuf message definition is Tensorflow's PredictionLog.
This must be a common use case, because TensorFlowModelAnalytics functions like this one consume Pandas DataFrames. I'd rather be able to write directly to a DB (preferably Google BigQuery), or a Parquet file (since Parquet / Spark seems to parallelize better than Pandas), and again, those seem like they should be common use cases, but I haven't found any examples. Maybe I'm using the wrong search terms?
I also looked at the PredictExtractor, since "extracting predictions" sounds close to what I want... but the official documentation appears silent on how that class is supposed to be used. I thought TFTransformOutput sounded like a promising verb, but instead it's a noun.
I'm clearly missing something fundamental here. Is there a reason no one wants to store BulkInferrer results in a database? Is there a configuration option that allows me to write the results to a DB? Maybe I want to add a ParquetIO or BigQueryIO instance to the TFX pipeline? (TFX docs say it uses Beam "under the hood" but that doesn't say much about how I should use them together.) But the syntax in those documents looks sufficiently different from my TFX code that I'm not sure if they're compatible?
Help?
(Copied from the related issue for greater visibility)
After some digging, here is an alternative approach, which assumes no knowledge of the feature_spec before-hand. Do the following:
Set the BulkInferrer to write to output_examples rather than inference_result by adding a output_example_spec to the component construction.
Add a StatisticsGen and a SchemaGen component in the main pipeline right after the BulkInferrer to generate a schema for the aforementioned output_examples
Use the artifacts from SchemaGen and BulkInferrer to read the TFRecords and do whatever is neccessary.
bulk_inferrer = BulkInferrer(
....
output_example_spec=bulk_inferrer_pb2.OutputExampleSpec(
output_columns_spec=[bulk_inferrer_pb2.OutputColumnsSpec(
predict_output=bulk_inferrer_pb2.PredictOutput(
output_columns=[bulk_inferrer_pb2.PredictOutputCol(
output_key='original_label_name',
output_column='output_label_column_name', )]))]
))
statistics = StatisticsGen(
examples=bulk_inferrer.outputs.output_examples
)
schema = SchemaGen(
statistics=statistics.outputs.output,
)
After that, one can do the following:
import tensorflow as tf
from tfx.utils import io_utils
from tensorflow_transform.tf_metadata import schema_utils
# read schema from SchemaGen
schema_path = '/path/to/schemagen/schema.pbtxt'
schema_proto = io_utils.SchemaReader().read(schema_path)
spec = schema_utils.schema_as_feature_spec(schema_proto).feature_spec
# read inferred results
data_files = ['/path/to/bulkinferrer/output_examples/examples/examples-00000-of-00001.gz']
dataset = tf.data.TFRecordDataset(data_files, compression_type='GZIP')
# parse dataset with spec
def parse(raw_record):
return tf.io.parse_example(raw_record, spec)
dataset = dataset.map(parse)
At this point, the dataset is like any other parsed dataset, so its trivial to write a CSV, or to a BigQuery table or whatever from there. It certainly helped us in ZenML with our BatchInferencePipeline.
Answering my own question here to document what we did, even though I think #Hamza Tahir's answer below is objectively better. This may provide an option for other situations where it's necessary to change the operation of an out-of-the-box TFX component. It's hacky though:
We copied and edited the file tfx/components/bulk_inferrer/executor.py, replacing this transform in the _run_model_inference() method's internal pipeline:
| 'WritePredictionLogs' >> beam.io.WriteToTFRecord(
os.path.join(inference_result.uri, _PREDICTION_LOGS_FILE_NAME),
file_name_suffix='.gz',
coder=beam.coders.ProtoCoder(prediction_log_pb2.PredictionLog)))
with this one:
| 'WritePredictionLogsBigquery' >> beam.io.WriteToBigQuery(
'our_project:namespace.TableName',
schema='SCHEMA_AUTODETECT',
write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND,
create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
custom_gcs_temp_location='gs://our-storage-bucket/tmp',
temp_file_format='NEWLINE_DELIMITED_JSON',
ignore_insert_ids=True,
)
(This works because when you import the BulkInferrer component, the per-node work gets farmed out to these executors running on the worker nodes, and TFX copies its own library onto those nodes. It doesn't copy everything from user-space libaries, though, which is why we couldn't just subclass BulkInferrer and import our custom version.)
We had to make sure the table at 'our_project:namespace.TableName' had a schema compatible with the model's output, but didn't have to translate that schema into JSON / AVRO.
In theory, my group would like to make a pull request with TFX built around this, but for now we're hard-coding a couple key parameters, and don't have the time to get this to a real public / production state.
I'm a little late to this party but this is some code I use for this task:
import tensorflow as tf
from tensorflow_serving.apis import prediction_log_pb2
import pandas as pd
def parse_prediction_logs(inference_filenames: List[Text]): -> pd.DataFrame
"""
Args:
inference files: tf.io.gfile.glob(Inferrer artifact uri)
Returns:
a dataframe of userids, predictions, and features
"""
def parse_log(pbuf):
# parse the protobuf
message = prediction_log_pb2.PredictionLog()
message.ParseFromString(pbuf)
# my model produces scores and classes and I extract the topK classes
predictions = [x.decode() for x in (message
.predict_log
.response
.outputs['output_2']
.string_val
)[:10]]
# here I parse the input tf.train.Example proto
inputs = tf.train.Example()
inputs.ParseFromString(message
.predict_log
.request
.inputs['input_1'].string_val[0]
)
# you can pull out individual features like this
uid = inputs.features.feature["userId"].bytes_list.value[0].decode()
feature1 = [
x.decode() for x in inputs.features.feature["feature1"].bytes_list.value
]
feature2 = [
x.decode() for x in inputs.features.feature["feature2"].bytes_list.value
]
return (uid, predictions, feature1, feature2)
return pd.DataFrame(
[parse_log(x) for x in
tf.data.TFRecordDataset(inference_filenames, compression_type="GZIP").as_numpy_iterator()
], columns = ["userId", "predictions", "feature1", "feature2"]
)
I want to save small images in my room database and I have two issues:
How do I save an image in my database?
How do I save multiple images in my database?
I tried saving a bitmap in the way recommended by the developers page (https://developer.android.com/training/data-storage/room/defining-data)
#Parcelize
#Entity(tableName = "image_table")
data class ImgMod(
#PrimaryKey(autoGenerate = true)
var invoiceId: Long = 0L,
#ColumnInfo(name = "image")
var single_img: Bitmap?
): Parcelable
However, I receive the following error:
Cannot figure out how to save this field into database. You can consider adding a type converter for it.
Secondly, I would like to save multiple images in one database entry. But I receive the same error with the following snippets:
#ColumnInfo(name = "imageList")
var img_list: ArrayList<BitMap>
or decoded bitmap as a String
#ColumnInfo(name = "imageList")
var decoded_img_list: ArrayList<String>
I am sorry if this is a very basic question. But how do I have to configure the database/process the data to an image list?
Thank you in advance,
rot8
A very simple way to get images into the database (although I personally discourage it) would be to base-64 encode the bitmaps into a String and put it into a row of the database.
Take in account that bitmaps are very memory heavy; and base64 encoding something increases it's size some more so be careful when loading a bunch of images... I do also think Room and SQLite supports binary data as blobs, so you could just declare a column as ByteArray and it should just work.
What I've been doing in my projects is to write them into the internal or external storage of my app and then store the reference Uri as a String to later be able to retrieve the image from disk.
Something that I discourage even more is to stuff more than one value per row; having a list of stuff inside a coulmn in SQL is definetely not a pattern you should follow. Creating a "join" table should be simple enough or simply an extra column you could use to group them by should be easy enough, right?
i´m currently building a Flutter app that includes a larger set of data (like 2000-10000 pieces of text).
I´m relatively new to Flutter delevopment so i have no idea what databases are the best for this case.
The app needs no connection to the internet a all data is on the device after downloading.
You only need to query this data extensively and build now datasets out of it.
I researched a bit, but the most common used database (hive) seems not to be suitable for my needs.
If anyone could help, I´d appreciate it.
It seems ObjectBox is best For Large Data ,it Performs very Well
[Check this package best suits for your data][1]
import 'package:objectbox/objectbox.dart';
class Note {
// Each "Entity" needs a unique integer ID property.
// Add `#Id()` annotation if its name isn't "id" (case insensitive).
int id = 0;
String? text;
DateTime date;
#Transient() // Make this field ignored, not stored in the database.
int? notPersisted;
// An empty default constructor is needed but you can use optional args.
Note({this.text, DateTime? date}) : date = date ?? DateTime.now();
// Note: just for logs in the examples below(), not needed by ObjectBox.
toString() => 'Note{id: $id, text: $text}';
}```
I am dealing with json objects containing geo coordinate points. I would like to run these points against a postgis server I have locally to assess point in polygon matching.
I'm hoping to do this with preexisting processors - I am successfully extracting the lat/lon coordinates into attributes with an "EvaluateJsonPath" processor, and successfully issuing queries to my local postgis datastore with "ExecuteSQL". This leaves me with avro responses, which I can then convert to JSON with the "ConvertAvroToJSON" processor.
I'm having conceptual trouble with how to merge the results of the query back together with the original JSON object. As it is, I've got two flow files with the same fragment ID, which I could theoretically merge together with "mergecontent", but that gets me:
{"my":"original json", "coordinates":[47.38, 179.22]}{"polygon_match":"a123"}
Are there any suggested strategies for merging the results of the SQL query into the original json structure, so my result would be something like this instead:
{"my":"original json", "coordinates":[47.38, 179.22], "polygon_match":"a123"}
I am running nifi 6.0, postgres 9.5.2, and postgis 2.2.1.
I saw some reference to using replaceText processor in https://community.hortonworks.com/questions/22090/issue-merging-content-in-nifi.html - but this seems to be merging content from an attribute into the body of the content. I'm missing the point of merging the content of the original and either the content of the SQL response, or attributes extracted from the SQL response without the content.
Edit:
Groovy script following appears to do what is needed. I am not a groovy coder, so any improvements are welcome.
import org.apache.commons.io.IOUtils
import java.nio.charset.*
import groovy.json.JsonSlurper
def flowFile = session.get();
if (flowFile == null) {
return;
}
def slurper = new JsonSlurper()
flowFile = session.write(flowFile,
{ inputStream, outputStream ->
def text = IOUtils.toString(inputStream, StandardCharsets.UTF_8)
def obj = slurper.parseText(text)
def originaljsontext = flowFile.getAttribute('original.json')
def originaljson = slurper.parseText(originaljsontext)
originaljson.put("point_polygon_info", obj)
outputStream.write(groovy.json.JsonOutput.toJson(originaljson).getBytes(StandardCharsets.UTF_8))
} as StreamCallback)
session.transfer(flowFile, ExecuteScript.REL_SUCCESS)
If your original JSON is relatively small, a possible approach might be the following...
Use ExtractText before getting to ExecuteSQL to copy the original JSON into an attribute.
After ExecuteSQL, and after ConvertAvroToJSON, use an ExecuteScript processor to create a new JSON document that combines the original from the attribute with the results in the content.
I'm not exactly sure what needs to be done in the script, but I know others have had success using Groovy and JsonSlurper through the ExecuteScript processor.
http://groovy-lang.org/json.html
http://docs.groovy-lang.org/latest/html/gapi/groovy/json/JsonSlurper.html
I'm trying to figure out how to populate a table from a JSON array. So far, I can populate my table cells perfectly fine by using the following code:
self.countries = [[NSArray alloc]initWithObjects:#"Argentina",#"China",#"Russia",nil];
Concerning the JSON, I can successfully retrieve one line of text at a time and display it in a label. My goal is to populate an entire table view from a JSON array. I tried using the following code, but it still won't populate my table. Obviously I'm doing something wrong, but I searched everywhere and still can't figure it out:
NSURL *url = [NSURL URLWithString:#"http://BlahBlahBlah.com/CountryList"];
NSURLRequest *request = [NSURLRequest requestWithURL:url];
AFJSONRequestOperation *operation = [AFJSONRequestOperation JSONRequestOperationWithRequest:request success:^(NSURLRequest *request, NSHTTPURLResponse *response, id JSON)
{
NSLog(#"%#",[JSON objectForKey:#"COUNTRIES"]);
self.countries = [JSON objectForKey:#"COUNTRIES"];
}
failure:nil];
[operation start];
I am positive that the data is being retrieved, because the NSLog outputs the text perfectly fine. But when I try setting my array equal to the JSON array, nothing happens. I know the code is probably wrong, but I think I'm on the right track. Your help would be much appreciated.
EDIT:
This is the text in the JSON file I'm using:
{
"COUNTRIES": ["Argentina", "China", "Russia",]
}
-Miles
It seems that you need some basic JSON parsing. If you only target iOS 5.0 and above devices, then you should use NSJSONSerialization. If you need to support earlier iOS versions, then I really recommend the open source JSONKit framework.
Having recommended the above, I myself almost always use the Sensible TableView framework to fetch all data from my web service and automatically display it on a table view. Saves me a ton of manual labor and makes app maintenance a breeze, so it's probably something to consider too. Good luck!