Curtail | Simple & useful image compressor | Compression library

So I have a table from our time series sensor data for the plant. One of the sensor deals with movement of raw product on the belt (voltage / weight scale) before its processed into refinery. Whenever there is a delta (voltage of the belt less or more than normal / weight on belt (derived to every second) less or more than target for the 24 hour period (target ÷ 86,400 seconds ~ rounded to closest ton without decimal ) we capture it as a new event trigger and row in our warehouse database and move into data lake We need to find efficiency by work shift (day shift / grave shift) for time periods that cut across shift time

Considering a 2400 tons target on a normal day and day shift between 5:00 AM to 5:00 PM and night shift vice versa, we want the following dataframe:

starting dataframe row # event_start event_end operation_status tons_actual tons_target comment 1 2021-02-01 7:00 AM 2021-02-01 9:00 AM normal_run 197 200 2 2021-02-01 9:00 AM 2021-02-01 7:00 PM curtailed 700 1004 shift split here 3 2021-02-01 7:00 PM 2021-02-01 11:00 PM down_for_maintenance 0 301 4 2021-02-01 11:00 PM 2021-02-02 3:00 AM curtailed 320 402 5 2021-02-02 3:00 AM 2021-02-02 8:00 AM over_producing 600 502 shift split here 6 2021-02-02 8:00 AM 2021-02-02 11:00 AM normal_run 280 301 7 2021-02-02 11:00 AM 2021-02-04 4:00 PM broken_belt_unscheduled_loss 0 5323 multiple shift splits here

to split rows at shift change hours like this:

target dataframe row # event_start event_end operation_status tons_actual tons_target -------- 1 2021-02-01 7:00 AM 2021-02-01 9:00 AM normal_run 197 200 2.1 2021-02-01 9:00 AM 2021-02-01 5:00 PM curtailed 560 804 grave shift split 2.2 2021-02-01 5:00 PM 2021-02-01 7:00 PM curtailed 140 201 grave shift split 3 2021-02-01 7:00 PM 2021-02-01 11:00 PM down_for_maintenance 0 302 4 2021-02-01 11:00 PM 2021-02-02 3:00 AM curtailed 320 402 5.1 2021-02-02 3:00 AM 2021-02-02 5:00 AM over_producing 240 200 day shift split 5.2 2021-02-02 5:00 AM 2021-02-02 8:00 AM over_producing 360 302 day shift split 6 2021-02-02 8:00 AM 2021-02-02 11:00 AM normal_run 280 301 7.1 2021-02-02 11:00 AM 2021-02-02 5:00 PM broken_belt_unscheduled_loss 0 602 shift split 7.2 2021-02-02 5:00 PM 2021-02-03 5:00 AM broken_belt_unscheduled_loss 0 1205 shift split 7.3 2021-02-03 5:00 AM 2021-02-03 5:00 PM broken_belt_unscheduled_loss 0 1205 shift split 7.4 2021-02-03 5:00 PM 2021-02-04 5:00 AM broken_belt_unscheduled_loss 0 1205 shift split 7.5 2021-02-03 5:00 AM 2021-02-04 4:00 PM broken_belt_unscheduled_loss 0 1105 shift split

so the end result can be then be df.groupby(sum : tons) per shift

for a start, I know it needs some kind of array creating UDF inside an F.explode() function

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So the problem is, I now use Android studio to write a quite simple app, which has just one activity, but several views, which appear and get gone depending on what the user wants. It all looks smooth and fine, just the problem is all the views are written inside the same main activity layout XML file, which really becomes huge and difficult to navigate and keep track of. What I want to ask, if there are any means to curtail such a problem, like, say, split one huge activity XML file into several shorter ones and then "combine" them?

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I don't want to use numericInput(), so is there another way to get around this? Also, I tried limiting the number of characters, the error message works, but the updateTextInput() isn't working (it was supposed to curtail the original input to only 5 characters). Any help would be appreciated!

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I have an Arraylist with these attributes as shown below, and some of them have "," and "." and "?" after the words. I would like to replace the character with nothing, so as to remove them. I tried something like this:

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I am using a subroutine (stats) to calculate statistics for a list of numbers. These numbers may be big enough to lose precision if stored as normal perl numbers. I recieve such numbers as JSON formatted strings. To decode these strings without losing precision, I use a JSON::PP object with allow_nonref and allow_bignum activated. I send the list of such decoded numbers to stats subroutine (see in code shown below). This routine calculates some statistics. These statistics are then encoded to JSON and saved to file.

Most of the time the process seems to work correctly, but for some inputs (see code for examples) the calculated value of mean and variance statistics are either clearly wrong, or are encoded as JSON strings by the encoder, or both. I suspect this is due to interaction of Math::BigInt and Math::BigFloat objects created by JSON decode, and List::Util::sum0.

I am trying to figure out what causes this and a way to avoid/fix this, preferably without resorting to big non core modules. I am willing to accept imprecise calculation of mean and variance, but not entirely inaccurate results or numerical results encoded as string in JSON.

A script (stats.pl) to demonstrate the problem:

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I really want to disable pinch to zoom on my webpages (iframes) and use finger pinching events for another custom interaction. So while Apple in their documentation says that it supports the following clause:

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I have two plots in Matplotlib that I would like to merge. They have different axes and scales. Here is the code for each of them. Electrical Power Plot:

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Background

Traditionally I've used NREL SAM tool to estimate solar output. I've been experimenting with PVLIB which is great due to the open nature and flexibility, however I can't seem to reconcile the solar production estimates between PVLIB and NREL SAM.

What I've done

I'm modeling a hypothetical solar farm near Gympie QLD. I've gone to the climate.onebuiling website, and downloaded the zip folder / epw file for "AUS_QLD_Gympie.AP.945660_TMYx.2003-2017". I've then used that weather file in NREL's SAM tool using PVwatts, with the following specs;

• 200,000 KWdc
• Module Type = Standard
• 1.2 DC to AC Ratio
• 96% inverter efficiency
• 1 axis backtracking
• tilt = 26 degrees
• azimuth = 0 degrees
• GCR = 0.4
• losses, shading & curtailment = default

In NREL SAM i get an annual Energy Yield (AC GWh) of 415.96 GWh p.a.

I then took that same epw file and converted it to a csv, keeping just the columns for ghi, dni, dhi, temp_air & wind_speed (Google Drive link to CSV file). I've used this file as an import into PVLIB. I spec'd a PVLIB system the same specs above, with addition of albedo = 0.2 and max angle = 90 degrees (Code Below).

The result I get in PVLIB is 395.61 GWh.

Problem

The results I got are pretty different. PVLIB = ~395 GWh p.a. vs SAM = ~415 GWH p.a. I expected around a 1-2% difference, but not 5%.

Figures are even worse when I compare to a PVLIB system using clearsky.ineichen (adjusted with linke_turbidity) which yields ~475 GWh p.a.

Help Requested

Anyone know why my results are so different? is there anything I can do to narrow the gap?

PVLIB Code

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I'm trying to parse the value of some items from a dictionary and print them accordingly. I did parse them but when I tried to print them, the output becomes uglier considering the alignment. How can I get the printed result the way I've shown in expected output?

Items in the dictionary [curtailed portion]:

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