FPO | Supports | Functional Programming library
kandi X-RAY | FPO Summary
kandi X-RAY | FPO Summary
FPO (/ˈefpō/) is an FP Library for JavaScript. The main aesthetic difference is that the FPO.* core API methods are all styled to use named-arguments (object parameter destructuring) instead of individual positional arguments. Not only do named-arguments eliminate having to remember a method signature's parameter order -- named arguments can be provided in any order! -- they also make skipping optional parameters (to apply defaults) simple.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of FPO
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Community Discussions
Trending Discussions on FPO
QUESTION
How can i strip the string below?(I just want to create a column to show city and state,you can see the city and state's name after \n)
...ANSWER
Answered 2021-May-23 at 15:26You can use regex expressions to get the city name, in your case it would look something like this:
QUESTION
I have a spark job that writes data to parquet files with snappy compression. One of the columns in parquet is a repeated INT64.
When upgrading from spark 2.2 with parquet 1.8.2 to spark 3.1.1 with parquet 1.10.1, I witnessed a severe degradation in compression ratio.
For this file for example (saved with spark 2.2) I have the following metadata:
...ANSWER
Answered 2021-May-09 at 08:32So as updated above, snappy-java 1.1.2.6 resolved my issue. Any version higher than this, results in degraded compression. Also tried the purejava flag, but this results in Exception reading parquet. Will open tickets for snappy-java
QUESTION
I am trying to create a portfolio of users for the stock market. I have custom users model like this:
...ANSWER
Answered 2021-Mar-10 at 12:38You can override the to_representation method in your serializer
QUESTION
Thank you once again in advance for your assistance.
Trying to get market order to execute at first profitable open
As recommended, tried several 'process_orders_on_close'. Set to true, fixes original problem of exiting one bar late (perfect!) But, it breaks the entry, first image below, in that entry is on condition as opposed to bar after condition is met. Image One below.
[![Image One][1]][1]
For image two, the intent was to toggle 'process_orders_on_close' from "na" to "true". Fixed entry but original problem exiting one bar late returns. Results and code in Image Two below.
[![Image Two][2]][2]
Thank you once again.
...ANSWER
Answered 2020-May-20 at 10:03You can use ``` before and after your code for monospace.
You can use process_orders_on_close=true with your strategy() declaration statement. See Why are my orders executed on the bar following my triggers?.
QUESTION
I have a program which uses fixed point numbers, because the CPU I'm using doesn't support IEEE754 floats.
I've been doing fine with first converting standard IEEE754s into fixed points by finding the exponent, then shifting the number and so on by manually accessing the bits of the said IEE754 float inside the memory. After conversion, I'm able to do fixed-point calculations just fine.
However, is it possible to reconstruct a fixed point (say a Q15.16 integer) back to a IEE754 floating point without FPO so that CPUs with IEEE754/FPO support would be able to read it as their native float type? Is anywhere code or examples of how the CPU's FPO unit actually does this conversion in raw byte manipulation, or is it just some black magic that cannot be done in software? Obviously, I'm not looking for super-precise conversion.
All the answers I've seen until now use FPO. for example, by first calculating 2^(-num_fraction_bits_in_fixed), which already needs FPO, and then scaling the fixed point to that scaling factor.
Edit: By using EOF's answer as a baseline, I was able to create the following code snippet for reconstructing the IEEE754 float from a fixed point integer (In this example the fixed point is a Q31.32, stored inside a INT64). In the end, I just handled the case of 0 manually, since without it the code would actually return a really small, but still a non-zero value.
Here's the code:
...ANSWER
Answered 2020-May-03 at 21:02Without loss of generality, consider unsigned fixed-point number x, assuming (loss of generality here) that every number in your fixed-point format is (representable by) a normalized float of the floating-point format:
1) Find the number of leading zeros n (there may be special CPU instructions to do this quickly and without a (software) loop).
2) Shift the number left (y = x << n+1) (to produce a normalized float mantissa), then right (m = y >> (signbit+exponentbits)), this is the mantissa of the float.
3) Take your n, subtract the number of non-fractional bits of the fixed-point format, add the exponent bias of the floating-point format. Shift the biased exponent to the exponent bit-position of the fixed-point result.
4) If the original number was not unsigned, set the sign-bit in the result iff the number was negative.
a) If the fixed-point number is signed v, then convert to unsgined u, and keep the sign s separately (you can copy it to the sign bit of the floating-point number directly). The unsigned input to the above algorithm will be x = v < 0 ? -u : u.
b) exponentbits depends on the floating-point number format. For ieee754 32-bit float, it is 8.
c) A fixed-point format typically represents number by an integer of n bits, which is (conceptually) divided by a constant of 2^m. The non-fractional bits (if any exist) are the bits n - m if n > m.
d) exponent bias is again described by the floating-point format. For ieee754 32-bit float, the bias is 127.
QUESTION
In Woocommerce, I'm adding a footnote to a specific checkout field using this code:
...ANSWER
Answered 2019-Mar-08 at 20:00First the hook you are using is a filter hook not an action hook, so should use add_filter() and global $woocommerce; is not needed.
Try this code version (without a div tag):
QUESTION
I am using BigQuery to query an external data source (also known as a federated table), where the source data is a hive-partitioned parquet table stored in google cloud storage. I used this guide to define the table.
My first query to test this table looks like the following
...ANSWER
Answered 2020-Apr-13 at 15:53Note that, the schema of the external table is inferred from the last file sorted by the file names lexicographically among the list of all files that match the source URI of the table. So any chance that particular Parquet file in your case has a different schema than the one you described, e.g., a INT32 column with DATE logical type for the "visitor_partition" field -- which BigQuery would infer as DATE type.
QUESTION
I am running a spark job to write to parquet. I want to enable dictionary encoding for the files written. When I check the files, I see they are 'plain dictionary'. However, I do not see any stats for these columns
Let me know if I am missing anything
...ANSWER
Answered 2020-Mar-27 at 23:18Got the answer. The parquet tools version I was using was 1.6. Upgrading to 1.10 solved the issue
QUESTION
We have an Azure Time Series Insights Preview instance connected to an event hub. The incoming events are written to the related cold storage data account as parquet files. When I try to open the parquet file with various readers (like the parquet-[head|cat|etc] cmd tools) I get errors.
Output of parquet-head
org.apache.parquet.io.ParquetDecodingException: Can not read value at 0 in block -1 in file file:20200123140854700_c8876d10_01.parquet
Here is a sample of the issue in more detail. This is the output of parquet-dump
$ parquet-dump 20200123140854700_c8876d10_01.parquet
row group 0 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- timestamp: INT64 SNAPPY DO:0 FPO:4 SZ:100/850/8.50 VC:100 ENC:PLAIN,RLE ST:[min: 2020-01-23T14:08:52.583+0000, max: 2020-01-23T14:08:52.583+0000, num_nulls: 0] id_string: BINARY SNAPPY DO:167 FPO:194 SZ:80/76/0.95 VC:100 ENC:PLAIN_DICTIONARY,PLAIN,RLE ST:[min: dabas96, max: dabas96, num_nulls: 0] dabasuploader_time_string: BINARY SNAPPY DO:313 FPO:855 SZ:705/2177/3.09 VC:100 ENC:PLAIN_DICTIONARY,PLAIN,RLE ST:[num_nulls: 0, min/max not defined] dabasuploader_prod_kwh_string: BINARY SNAPPY DO:1118 FPO:1139 SZ:62/58/0.94 VC:100 ENC:PLAIN_DICTIONARY,PLAIN,RLE ST:[min: 0, max: 0, num_nulls: 0] dabasuploader_pred_nxd_kwh_string: BINARY SNAPPY DO:1252 FPO:1488 SZ:319/390/1.22 VC:100 ENC:PLAIN_DICTIONARY,PLAIN,RLE ST:[num_nulls: 0, min/max not defined] dabasuploader_pred_today_kwh_string: BINARY SNAPPY DO:1650 FPO:1903 SZ:336/404/1.20 VC:100 ENC:PLAIN_DICTIONARY,PLAIN,RLE ST:[num_nulls: 0, min/max not defined] java.lang.IllegalArgumentException: [solpos_altitude_double] optional double solpos_altitude_double is not in the store: [[dabasuploader_time_string] optional binary dabasuploader_time_string (STRING), [dabasuploader_pred_nxd_kwh_string] optional binary dabasuploader_pred_nxd_kwh_string (STRING), [id_string] optional binary id_string (STRING), [timestamp] optional int64 timestamp (TIMESTAMP(MILLIS,true)), [dabasuploader_pred_today_kwh_string] optional binary dabasuploader_pred_today_kwh_string (STRING), [dabasuploader_prod_kwh_string] optional binary dabasuploader_prod_kwh_string (STRING)] 100
The solpos_altitude_double is coming from the events we upload to the eventhub. I mean, we call that solpos_altitude. The _double postfix is coming from TSI, according to the docs.
According to all MS Azure documentations I could find, reading the parquet file should be possible without issues.
Does anybody know what went wrong? If more info is needed, I am more than happy to provide.
...ANSWER
Answered 2020-Feb-24 at 22:35I believe this is a known issue caused by changing the schema of the data (drifting schema). We're currently working on a fix for it.
QUESTION
CREATE OR REPLACE FUNCTION file_compare()
RETURNS text LANGUAGE 'plpgsql'
COST 100 VOLATILE AS $BODY$
DECLARE
filedata text[];
fpo_data jsonb;
inddata jsonb;
f_cardholderid text;
f_call_receipt text;
i INT;
BEGIN
SELECT json_agg((fpdata))::jsonb
FROM (SELECT fo_data AS fpdata
FROM fpo
LIMIT 100
) t INTO fpo_data;
i=0;
FOR inddata IN SELECT * FROM jsonb_array_elements(fpo_data) LOOP
f_cardholderid := (inddata->>0)::JSONB->'cardholder_id'->>'value';
f_call_receipt := (inddata->>0)::JSONB->'call_receipt_date'->>'value';
f_primary_key := f_cardholderid || f_auth_clm_number;
filedata[i] := jsonb_build_object(
'fc_primary_key',f_primary_key
);
i := i+1;
END LOOP;
RAISE NOTICE 'PRINTING DATA %', filedata;
END;
$BODY$;
ANSWER
Answered 2020-Jan-30 at 20:56I was able to get the result you describe with the code below. By unnesting the data, you are able to take advantage of regular SQL syntax (offset, grouping, counting) which are the crux of the problem you described.
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