_Now here's something you can work on_ - I recently refactored this to be in a single place - now it's time to be on no place - replace me the iewin control with webview. This will bin the comtypes dependency that crashes big time on linux (and WINE) and anyway is another dependency - I intend to move on wx 3.02 classic

Another thing is - are you using non English loacale ?


@locutus2 Wouldn't it be a good candidate for a combo patch with DiscanX's idea of ignoring pawns on the second row ?

````
     const Bitboard mask = rank3 + rank 4 + rank5;
     ...

     if (mask & ((pos.pieces(PAWN) ^ ei.pe->passed_pawns(Them)) & (s + pawn_push(Us)))
         score += MinorBehindPawn;
````


dah sip dah tu


@andrewfb you're right.  The problem is with the zlib in freetype.  In freetype's [zutil.h](https://github.com/cinder/Cinder/blob/master/src/freetype/gzip/zutil.h#L185) (and in the zlib 1.2.8 version of [zutil.h),](https://github.com/cinder/Cinder/blob/master/src/zlib/zutil.h#L219) `z_error` and `z_verbose` are bracketed with `DEBUG` instead of the newer `ZLIB_DEBUG` in zlib 1.2.11.

The real problem, however, is that `z_error` and `z_verbose` are mysteriously not included in freetype's version of [zutil.c](https://github.com/cinder/Cinder/blob/master/src/freetype/gzip/zutil.c) like they are in zlib's [zutil.c](https://github.com/cinder/Cinder/blob/master/src/zlib/zutil.c#L123).  When I searched the source code in the terminal (not all of the freetype files show up in Xcode), it doesn't seem like `z_error` and `z_verbose` are implemented anywhere in freetype's version of zlib.  I'm not sure why this is except that freetype's zlib is old and seemingly erroneous (there is no version number, but the copyright is dated 2002).  Whatever the reason, they have apparently been being implemented only by the actual zlib.

The bottom line is that when I added `ZLIB_DEBUG=1` to Xcode's debug configuration preprocessor macros, the link errors went away.  I believe that this will solve the problem, but I've only tested it with my personal development app.  So, you should check this more thoroughly before you decide to re-update to zlib 1.2.11.

What are your thoughts on a longer term solution?  I personally don't like the idea of having very old, very out-dated code embedded in freetype.  From their [changelog](http://zlib.net/ChangeLog.txt), I'm guessing that the version of zlib in freetype is 1.1.4.  The notes for zlib 1.1.4 start about 25% from the bottom of the page, so there have been A LOT of changes and bug fixes since then.


Also update solver.cpp to format grid


Thanks - interesting. For one I do not want to have `if os == ...` all over - this must be taken care of somewhere centrally - that's why I created the env module. Put this to sleep for now (keep using it to run Bash ofc) but address the points noted.

I appreciate you addressed me with a patch (on something I care about btw) - rather than blah blah 👍

Have a look at beta release issue #260 for some really pressing stuff


thx pak


@gkhanna79 @chcosta , I assume this is going to be brought back pretty quickly?


Sorry if this affected you - I guess that's why you changed it. But I wanted to apply this font size to all my documentation pages - how would I do that?


Сделал Бзаров Е.К.
Коэффициент корреляции для Переносье и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -11.0739, df = 1620, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3099462 -0.2194369
sample estimates:
       cor
-0.2652758

Коэффициент корреляции для Нос_слева и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -12.725, df = 1616, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3454403 -0.2568331
sample estimates:
       cor
-0.3017882

Коэффициент корреляции для Нос_справа и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -12.7901, df = 1617, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3467027 -0.2582004
sample estimates:
      cor
-0.303105

Коэффициент корреляции для Щитовидная_железа и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -10.4775, df = 1563, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3018957 -0.2092826
sample estimates:
      cor
-0.256177

Коэффициент корреляции для Яремная_область и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -7.8926, df = 625, p-value = 1.331e-14
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3706199 -0.2281328
sample estimates:
       cor
-0.3010558

Коэффициент корреляции для Сердце и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -11.575, df = 1594, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3231064 -0.2325641
sample estimates:
       cor
-0.2784538

Коэффициент корреляции для Эпигастрий_1 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -10.0707, df = 1597, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2899131 -0.1977121
sample estimates:
       cor
-0.2443649

Коэффициент корреляции для Эпигастрий_2 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 0.9075, df = 1016, p-value = 0.3644
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.03304157  0.08974394
sample estimates:
       cor
0.02845854

Коэффициент корреляции для Тонкий_кишечник,_лев. и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -8.5717, df = 1615, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2547591 -0.1614970
sample estimates:
       cor
-0.2086022

Коэффициент корреляции для Тонкий_кишечник,_прав. и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 1.1991, df = 1001, p-value = 0.2308
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.02408528  0.09953836
sample estimates:
       cor
0.03787145

Коэффициент корреляции для Толстый_кишечник,_лев. и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 2.3607, df = 996, p-value = 0.01843
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.01259773 0.13602018
sample estimates:
       cor
0.07459462

Коэффициент корреляции для Толстый_кишечник,_прав. и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 1.887, df = 995, p-value = 0.05945
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.002379065  0.121352480
sample estimates:
       cor
0.05971608

Коэффициент корреляции для Надлонная_область,_лев. и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -9.4748, df = 1591, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2770771 -0.1840847
sample estimates:
       cor
-0.2311087

Коэффициент корреляции для Надлонная_область,_середина и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -10.1743, df = 1549, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2963656 -0.2030388
sample estimates:
       cor
-0.2502835

Коэффициент корреляции для Надлонная_область,_прав. и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -10.7556, df = 1590, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3056449 -0.2140362
sample estimates:
       cor
-0.2604266

Коэффициент корреляции для Мочевой_пузырь и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 1.7426, df = 377, p-value = 0.08222
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.01144817  0.18842740
sample estimates:
       cor
0.08938952

Коэффициент корреляции для Селезенка и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -10.3769, df = 1585, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2977312 -0.2055652
sample estimates:
       cor
-0.2522201

Коэффициент корреляции для Печень и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -11.8606, df = 1619, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3269280 -0.2373209
sample estimates:
       cor
-0.2827412

Коэффициент корреляции для Желчный_пузырь и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -11.1947, df = 1615, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3130004 -0.2225106
sample estimates:
       cor
-0.2683474

Коэффициент корреляции для Дуоденом и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 0.5918, df = 986, p-value = 0.5541
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.04357772  0.08111535
sample estimates:
       cor
0.01884208

Коэффициент корреляции для Поджелудочная_железа_1 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -0.339, df = 1008, p-value = 0.7347
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.07231456  0.05104201
sample estimates:
       cor
-0.0106769

Коэффициент корреляции для Поджелудочная_железа_2 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 0.7084, df = 975, p-value = 0.4789
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.04009559  0.08527787
sample estimates:
       cor
0.02268031

Коэффициент корреляции для Легкие_1 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -8.1772, df = 1620, p-value = 5.8e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2453921 -0.1518975
sample estimates:
       cor
-0.1990978

Коэффициент корреляции для Легкие_2 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 0.7446, df = 1005, p-value = 0.4567
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.03835281  0.08513362
sample estimates:
       cor
0.02347996

Коэффициент корреляции для Легкие_3 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 1.3033, df = 1008, p-value = 0.1928
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.02072203  0.10244179
sample estimates:
       cor
0.04101568

Коэффициент корреляции для Легкие_4 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -8.7193, df = 1611, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2584155 -0.1651888
sample estimates:
       cor
-0.2122851

Коэффициент корреляции для Легкие_5 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 0.421, df = 1005, p-value = 0.6739
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.04853927  0.07499332
sample estimates:
       cor
0.01327769

Коэффициент корреляции для Легкие_6 и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 1.0933, df = 1000, p-value = 0.2745
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.02743775  0.09627725
sample estimates:
       cor
0.03455212

Коэффициент корреляции для Почка_левая и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -7.9527, df = 1618, p-value = 3.397e-15
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2403859 -0.1466372
sample estimates:
       cor
-0.1939543

Коэффициент корреляции для Почка_правая и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -8.7845, df = 1618, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2593670 -0.1663864
sample estimates:
       cor
-0.2133598

Коэффициент корреляции для Позвоночник_(Шея) и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -9.6029, df = 1610, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2784132 -0.1860451
sample estimates:
      cor
-0.232754

Коэффициент корреляции для Позвоночник_(Грудь) и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -8.9889, df = 1610, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2646051 -0.1716148
sample estimates:
       cor
-0.2186062

Коэффициент корреляции для Позвоночник_(Поясница) и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -7.0859, df = 1610, p-value = 2.058e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2208532 -0.1261534
sample estimates:
       cor
-0.1739053

Коэффициент корреляции для Позвоночник_(Крестец) и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -10.446, df = 1608, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2972794 -0.2057679
sample estimates:
       cor
-0.2520871

Коэффициент корреляции для Грудь_левая и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = 0.4546, df = 908, p-value = 0.6495
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.04995066  0.07999551
sample estimates:
       cor
0.01508612

Коэффициент корреляции для Грудь_правая и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -0.6434, df = 908, p-value = 0.5201
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.08621556  0.04370108
sample estimates:
        cor
-0.02134736

Коэффициент корреляции для Ладонь_левая и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -10.9502, df = 605, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.4710423 -0.3380678
sample estimates:
      cor
-0.406707

Коэффициент корреляции для Ладонь_правая и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -11.5155, df = 605, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.4871477 -0.3564507
sample estimates:
       cor
-0.4240042

Коэффициент корреляции для Над_левым_коленом и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -5.5764, df = 602, p-value = 3.712e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2961656 -0.1443977
sample estimates:
       cor
-0.2216234

Коэффициент корреляции для Над_правым_коленом и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -4.2774, df = 602, p-value = 2.199e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.24812186 -0.09324164
sample estimates:
       cor
-0.1717428

Коэффициент корреляции для Подколенная_левая и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -5.4197, df = 602, p-value = 8.645e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2904732 -0.1382939
sample estimates:
       cor
-0.2156929

Коэффициент корреляции для Подколенная_правая и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -5.934, df = 602, p-value = 4.992e-09
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3090560 -0.1582626
sample estimates:
       cor
-0.2350733

Коэффициент корреляции для Предплечие_лев.,_верх и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -2.6742, df = 606, p-value = 0.007694
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.18591422 -0.02872611
sample estimates:
       cor
-0.1079951

Коэффициент корреляции для Предплечие_прв.,_верх и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -2.6765, df = 606, p-value = 0.007641
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.18600514 -0.02882021
sample estimates:
       cor
-0.1080882

Коэффициент корреляции для Предплечие_лев.,_низ и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -4.6846, df = 606, p-value = 3.465e-06
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2625585 -0.1090512
sample estimates:
       cor
-0.1869458

Коэффициент корреляции для Предплечие_прв.,_низ и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -5.004, df = 606, p-value = 7.367e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2743705 -0.1216114
sample estimates:
       cor
-0.1992008

Коэффициент корреляции для Стопа_левая,_верх и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -4.924, df = 604, p-value = 1.096e-06
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2718444 -0.1186609
sample estimates:
       cor
-0.1964511

Коэффициент корреляции для Стопа_правая,_верх и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -4.6569, df = 605, p-value = 3.948e-06
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2617330 -0.1080449
sample estimates:
       cor
-0.1860266

Коэффициент корреляции для Стопа_левая,_низ и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -9.5191, df = 604, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.4285005 -0.2898722
sample estimates:
       cor
-0.3611803

Коэффициент корреляции для Стопа_правая,_низ и Возраст

	Pearson's product-moment correlation

data:  as.numeric(whole[, i]) and as.numeric(whole[, n])
t = -9.9364, df = 605, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.4410003 -0.3040466
sample estimates:
       cor
-0.3745646


Are you serious?
"Establish expiration time" to NOW? But where is
.minus(_retainDays, ChronoUnit.DAYS)?
Now removeOldFiles() destroys all the log files in directory, thanks.


@OtherCrashOverride
I have the other drive I think

**0x0bc2:0x3321**

here is a full lsusb -v for it
https://pastebin.com/1bdjhjq8

Let me know if you need me to run any other commands to get what you need for the 2nd patch :)


Could you compare with `===`?


Took me a sec to see what this one was doing, but then I realized. Good catch!!


Is this actually cancelled when the event gets cancelled?


Congrats!


Can you please convert these GDAL script for linux into window.
=========================================
# CREATE FULL-RES BROWSE Sentinel 1 data re sample
# FOR BAND 5 TRANSLATE TO 20 METERS SINCE BANDS 8A AND 11 ARE 20 METERS
# GET THE QUANTIFICATION VALUE FROM THE DATASTRIP XML FILE

QUANTIFICATION=`/usr/bin/ggrep -i 'quantification' ${DATASTRIP_XML} | /usr/bin/gsed "s/ *//g" | /usr/bin/gsed "s/[\t]//g" | /usr/bin/gcut -d' >' -f2 | /usr/bin/gcut -d'<' -f1`

/pkg/S2/gdal/bin/gdal_translate --config GDAL_CACHEMAX 4096 -of GTiff -ot Byte -scale 0 ${QUANTIFICATION} 0 255 -exponent 0.5 -tr 20 20 ${BAND04_FILE} tile_band04.tif

/pkg/S2/gdal/bin/gdal_translate --config GDAL_CACHEMAX 4096 -of GTiff -ot Byte -scale 0 ${QUANTIFICATION} 0 255 -exponent 0.5 ${BAND8A_FILE} tile_band8a.tif

/pkg/S2/gdal/bin/gdal_translate --config GDAL_CACHEMAX 4096 -of GTiff -ot Byte -scale 0 ${QUANTIFICATION} 0 255 -exponent 0.5 ${BAND11_FILE} tile_band11.tif

/pkg/S2/gdal/bin/gdal_merge.py --config GDAL_CACHEMAX 4096 -o tile_band118a04.tif -separate tile_band11.tif tile_band8a.tif tile_band04.tif

/pkg/S2/gdal/bin/gdal_translate --config GDAL_CACHEMAX 4096 -of GTiff -co COMPRESS=JPEG -co TILED=YES -co PHOTOMETRIC=YCBCR -co "JPEG_QUALITY=75" -mo European_Commission_Terms_and_Conditions=https://lta.cr.usgs.gov/sites/default/files/Sentinel_Data_Terms_and_Conditions.pdf -a_nodata 0 tile_band118a04.tif ${FRB_BROWSE_FILE}

/pkg/S2/gdal/bin/gdaladdo --config GDAL_CACHEMAX 4096 -r average --config COMPRESS_OVERVIEW JPEG --config USE_RRD NO --config JPEG_QUALITY 75 --config TILED YES --config PHOTOMETRIC_OVERVIEW YCBCR ${FRB_BROWSE_FILE} 2 4 8 16 32

Thanks
Akshay Gore


Why don't you initialise `i`?

Since you are accessing `$scope.pinnedDialogs[i]` repeatedly, assign it to a variable.


I'd go for curly brackets for the block to be executed.


Same question here. Also, why is the 5 on the next line hardcoded?


The `-` is barely noticable.


## Global

| Changed    |   Status |
|:-----------|---------:|
| `coverage` |    -0.07 |

## Words

| Changed   |   Status |
|:----------|---------:|
| `eng`     |  +126820 |
| `lat`     |   -82560 |

*[Hook UI build recap](http://ci.perseids.org/repo/PerseusDL/canonical-latinLit/202)*


Set your text editor to use 2-space tabs instead of 4-space tabs and to “convert indentations to spaces”; ask me if you have any trouble. This is key for readability, especially when you checked your code into a central repo that other devs will work off of.


Yes


Remove commented out code if you're done with it.


Why would `ng-show="dialogMessage.pinned"` not work here?


Hello there, I just watched your talk at BHTP last august, and I am very interested in working on this project. I have the skeleton of a similar project in Portland, OR; do you know about the houseguest art residency? I submitted a unsuccessful proposal doing community based bioremediation with fun take home 'flower arrangement' components as bioremediative agents. But I would love to continue this, in and out of my city.

look forward to hearing from you soon, we can also talk on another platform if that would be convenient


Why is this line commented in now?


@elzbth let me know!


Why would `ng-class="{active: selectedPeers[dialogMessage.peerID]}"` not work here?


When there is a test with a new strategic term, I simply play through the games in the pgn at fast pace and try to see if the new evaluation term can explain some of the wins of the patched version against master: is it possible that the patch version has followed this particular pv because of the new term? Multi-attacking some pieces, or breaking the pawn structure, or separating the kings, stuff like that are easily spotted.

So just human monitoring at depth 0. It sometimes gives new ideas for refining the implementation :-)

Doesn't work at all with search-related patches, obviously, nor with most tuning patches when the tuned values are just +/- 2.


Could you compare with `===`?


Finish writing this test.


Add a few more tests that test the functionality of your new feature. Especially test against non-happy paths and edge cases. For instance, “it correctly generates sum when the column includes non-number inputs”.