Mary  Turcotte

Mary Turcotte

1635238800

How to Create an ECommerce Tag using Html5 & CSS3

CSS Creative Product Card UI Design | E commerce Card Using Html5 & CSS3

#css #html 

What is GEEK

Buddha Community

How to Create an ECommerce Tag using Html5 & CSS3
Easter  Deckow

Easter Deckow

1655630160

PyTumblr: A Python Tumblr API v2 Client

PyTumblr

Installation

Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install

Usage

Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
    '<consumer_key>',
    '<consumer_secret>',
    '<oauth_token>',
    '<oauth_secret>',
)

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

  1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
  2. The Tumblr API console at https://api.tumblr.com/console
  3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

  • state - a string, the state of the post. Supported types are published, draft, queue, private
  • tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
  • tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
  • date - a string, the customized GMT that you want
  • format - a string, the format that your post is in. Support types are html or markdown
  • slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",
                    data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

  • title - a string, the title of post that you want. Supports HTML entities.
  • url - a string, the url that you want to create a link post for.
  • description - a string, the desciption of the link that you have
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

Chloe  Butler

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.


pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl 

smm captain

1650364405

Best Instagram Hashtags for Reels, Giveaways, Travel, Fashion

Pick the right hash tags and enjoy likes and comments on the post.

Making engaging reels about the travels, fashion, fitness, contest, and more, the results are not satisfactory. All you get is a few likes, comments and nothing else. You need the engagement on your post to bring more business to you. How can you bring interaction to the content? Indeed you can buy real instagram likes uk to get high rates. But how can you make the Instagram world hit the likes button under the post? You need to boost the reach. You must present your content to the right audiences to get higher interaction rates. 

Your Instagram #tags are the power tool that works like magic for influencers and businesses. The blue text with # is the magical option that increases the viability of the posts. The Instagram algorithm keeps on changing, and now the engagement on the post is a must to place the content at a higher place in followers’ feed. For this, you require more likes and comments under the post. For this, you must lift the reach by using perfect tags.

Why are hashtags popular on Instagram?

Let me clear it for you. Do you know how many active users this digital handle has? It is about 2B and more, and the count is changing every day. Each of the followers must be posting something on the handles. Thousands of profit must be of a similar niche as yours. If you are the business and running the clothing brands, then many other companies deal with clothes. So, customers or followers have many choices to choose from. Why would they follow you or purchase from your companies?

Your reply must be that you offer quality material at the best rates. But how does anyone finds out about you? Indeed you can buy active instagram followers uk to bring more fans, but how can you boost the reach of your voices. All businesses must represent their product to the right audiences, but how?

Of course, hashtags.

Table of Contents

Not all Hashtags are for you

There are some basic tags that you can use, but if you are more specific about your approach, choose the relevant tags for your business. Your #tags game must be industry oriented. So in this part, you will learn about the famous tags as per various niches. 

Tags for Travel Niche

Indeed this niche is famous on Instagram, and influencers earn handsome amounts. These #tags are best for you if you possess a similar place. Use them smartly and rightly!

#TravelPhotography

#PicOfTheDay

#NaturePhotography

#TravelBlogger

#beautiful

#landscape

#adventure

#explore

#instatravel

#photo

#trip

#summer

#travelgram

#photography

#art

#travel

#wanderlust

#nature

#instagood

#PhotoOfTheDay

Tags for Fashion Industry

After thee travel next most famous niche is fashion. You can earn handsome amount form it. But for this you need to pick the right tags form the following:

  1. #bhfyp
  2. #smile
  3. #OutfitOfTheDay
  4. #FashionPhotography
  5. #FollowBack
  6. #ootd
  7. #FashionBlogger
  8. #WhatIWore
  9. #follow
  10. #fashionista
  11. #PhotoOfTheDay
  12. #StyleInspo
  13. #instastyle
  14. #love
  15. #CurrentlyWearing
  16. #FashionBlog
  17. #ShoppingAddict
  18. #LookGoodFeelGood
  19. #FashionAddict
  20. #FashionStyle
  21. #BeautyDoesntHaveToBePain
  22. #style
  23. #fashion
  24. #FollowForFollowBack
  25. #fashionable
  26. #l
  27. #PicOfTheDay
  28. #fashiongram

Tags for fitness Influencers

So, what to boost your fitness business then uses these tags and enjoys likes:

  1. #exercise
  2. #bodybuilding
  3. #life
  4. #gymlife
  5. #motivation
  6. #healthy
  7. #lifestyle
  8. #health
  9. #gym
  10. #sport
  11. #training
  12. #workout
  13. #HealthyLifestyle
  14. #muscle
  15. #fit
  16. #CrossFit
  17. #fitness
  18. #FitFam
  19. #goals
  20. #PersonalTrainer
  21. #FitnessMotivation

Best Tags for Giveaway

So, are you arranging the giveaway and want a maximum number of people to participate? If so, then it is time to boost the reach vis using these tags

  1. #giveaway
  2. #sweepstakes
  3. #WinItWednesday
  4. #freebie
  5. #ContestAlert
  6. #ContestEntry
  7. #instacontest
  8. #instagiveaway
  9. #WinIt
  10. #contest
  11. #GiveawayAlert
  12. #giveaway

The popular #tags for Reels

Are you the reels queen, or do you want to become the one? Then these below mentioned tags are for you. But don’t go for all of them because you can use only thirty of them. Pick it smartly!

  1. #ReelsInstagram
  2. #VideoOfTheDay
  3. #ReelsIndia
  4. #ReelSteady
  5. #disney
  6. #ForYouPage
  7. #InstagramReels
  8. #bhfyp
  9. #instareels
  10. #reelsinsta
  11. #fyp
  12. #ReelsOfInstagram
  13. #TikTokIndia
  14. #HolaReels
  15. #reels
  16. #ReelsBrasil
  17. #k
  18. #ReelsVideo
  19. #instareel
  20. #music

#tags for foodie

Do you love to eat and what to share your experience with another foodie on Instagram? If you are visiting any cafe, then before uploading, always add one of the following tags!

  1. #instafood
  2. #FoodBlogger
  3. #lunch
  4. #PicOfTheDay
  5. #instadaily
  6. #FoodPhotography
  7. #PhotoOfTheDay
  8. #food
  9. #healthy
  10. #foodie
  11. #FoodLover
  12. #bhfyp
  13. #instagood
  14. #tasty
  15. #delicious
  16. #foodstagram
  17. #homemade
  18. #cooking
  19. #FoodPorn
  20. #love
  21. #foodgasm
  22. #foodies
  23. #HealthyFood
  24. #dinner
  25. #yummy
  26. #restaurant

How to Pick the proper tags or find the best one for you?

There is a long list of each niche, and you can use all of them. If you are confused about what to pick and whatnot, here is the guide to choosing the perfect tag.

  1. Use the search function. Just mentions a keyword applicable to your content and choose the Tags tab. This handle will then provide you with a hashtags list. Search for relevant #tags with fair usage ( 50K)
  2. Use the tags that others use in your sector.

Study your competition. Review their post and study the tags they are using.

Tamale  Moses

Tamale Moses

1669003576

Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python. 

Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

  • Lists
  • Sets
  • Dictionaries
  • User-Defined Classes (It purely depends upon the user to define the characteristics) 

Objects of built-in type that are immutable are:

  • Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
  • Strings
  • Tuples
  • Frozen Sets
  • User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

  • Identity – This refers to the address that the object refers to in the computer’s memory.
  • Type – This refers to the kind of object that is created. For example- integer, list, string etc. 
  • Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

Check out this free python certificate course to get started with Python.

Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities  

cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]

# Printing the elements from the list cities, separated by a comma & space

for city in cities:
		print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [2]: 0x1691d7de8c8

#Adding a new city to the list cities

cities.append(‘Chennai’)

#Printing the elements from the list cities, separated by a comma & space 

for city in cities:
	print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [4]: 0x1691d7de8c8

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0. 

#Creating a Tuple with variable name ‘foo’

foo = (1, 2)

#Changing the index[0] value from 1 to 3

foo[0] = 3
	
TypeError: 'tuple' object does not support item assignment 

Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’

# Printing the elements of tuple weekdays

print(weekdays)

Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [2]: 0x1691cc35090

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

weekdays  +=  ‘Pythonday’,

#Printing the elements of tuple weekdays

print(weekdays)

Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [4]: 0x1691cc8ad68

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it.  Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs:  Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender 

person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the tuple

print(person)

Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [2]: 0x1691ef47f88

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

person[0][1] = 4

#printing the updated tuple

print(person)

Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [4]: 0x1691ef47f88

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Also Read: Real-Time Object Detection Using TensorFlow

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

list1 = [(1, 2, 3), (4, 5, 6)]

#printing the list

print(list1)

Output [1]: [(1, 2, 3), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [2]: 0x1691d5b13c8	

#changing object reference at index 0

list1[0] = (7, 8, 9)

#printing the list

Output [3]: [(7, 8, 9), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [4]: 0x1691d5b13c8

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’ 

x = 10
 

#printing the value of ‘x’

print(x)

Output [1]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(x)))

Output [2]: 0x538fb560

#creating an object of integer type with value 10 and reference variable name ‘y’

y = 10

#printing the value of ‘y’

print(y)

Output [3]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(y)))

Output [4]: 0x538fb560

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick checkPython Data Structures

Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.

FAQs

1. Difference between mutable vs immutable in Python?

Mutable ObjectImmutable Object
State of the object can be modified after it is created.State of the object can’t be modified once it is created.
They are not thread safe.They are thread safe
Mutable classes are not final.It is important to make the class final before creating an immutable object.

2. What are the mutable and immutable data types in Python?

  • Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

  • Some immutable data types are: 

int, float, decimal, bool, string, tuple, range.

3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

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Original article source at: https://www.mygreatlearning.com

#python 

Nat  Grady

Nat Grady

1670915040

How to Create a Live Dashboards with Airtable and React

Reporting and visualizing data is crucial to businesses of all sizes. Dashboards allow users to efficiently access and use this data for a range of business operations. In this article, Toptal Full-stack Engineer Dylan Golow demonstrates how he created a powerful dashboard for telemedicine using Airtable, Typeform, and React.

Whether a company is a large enterprise or a budding startup, collecting data from users and customers, and reporting on or visualizing that data is crucial to the business.

I recently worked with a telemedicine startup based in Brazil. Its mission is to provide remote care and monitoring by connecting patients to medical professionals and health coaches. The core need was to create an interface for the coaches and health professionals to easily review a patient’s information and most important metrics related to their particular situation: a dashboard.

Enter Typeform and Airtable.

Typeform

Typeform is one of the go-to data collection tools that enables responsive web experiences for users completing a survey. It also comes with several features that make surveys more intelligent, especially when combined:

  • Logic Jumps
  • Hidden Fields

Surveys can be shared via URLs that can be pre-seeded with values for the hidden fields, which can then be used to implement logic jumps and alter the behavior of the survey for the user with the link.

Airtable Uses

Airtable is a spreadsheet-database hybrid and a collaborative cloud platform. Its focus on point and click functionality means that non-technical users can configure it without coding. Airtable has a multitude of use cases in any business or project.

You can use an Airtable Base for:

  • CRM (Client Relationship Management)
  • HRIS (Human Resources Information System)
  • Project Management
  • Content Planning
  • Event Planning
  • User Feedback

There are many more potential use cases. You can explore Airtable case studies here.

If you are not familiar with Airtable, the conceptual data model breaks down like this:

  • Workspace - Composed of Bases
  • Base - Composed of Tables
  • Table - Composed of Fields (columns) and rows
  • View - A perspective on Table data with optional filters and reduced Fields
  • Field - A column of a Table with a Field Type; see here for more information on Field Types

Apart from providing a cloud-hosted database with familiar spreadsheet features, here are some of the reasons the platform is so powerful:

 

Depiction of technical and non-technical users working with Airtable.

 

For non-technical users, Airtable provides:

  • An easy to use front-end interface
  • Automations that can be created with point-and-click configuration to send emails, process rows of data, schedule appointments in calendars, and more
  • Multiple types of views that allow teams to collaborate on the same Base and tables
  • Airtable Apps that can be installed from the marketplace to supercharge a Base

For developers, Airtable provides:

  • A well-documented back-end API
  • A scripting environment that allows developers to automate actions within a Base
  • Automations that can also trigger custom developed scripts that run within the Airtable environment, extending the capabilities of automations

You can learn more on Airtable here.

Getting Started: Typeform to Airtable

Typeform surveys were already configured by the client, and the next step was to plan how that data would land in Airtable and then be turned into a dashboard. There are many questions to consider when creating dashboards on top of any database: How should we structure the data? What data will need to be processed prior to visualization? Should we sync the Base with Google Sheets and use Google Data Studio? Should we export and find another third-party tool?

Fortunately for developers, not only does Airtable provide automations and scripting to handle the data processing steps, but it has also made it possible to build custom applications and interfaces on top of an Airtable Base with Airtable Apps.

Custom Apps in Airtable

Custom Apps in Airtable have been around since the Airtable Blocks SDK was released at the beginning of 2018, and were recently renamed to Apps. The release of Blocks was huge in that it meant that creators now had the ability to develop, as Airtable puts it, “An infinitely recombinable Lego kit.”

More recently with the change to apps, the Airtable Marketplace made it possible to share apps publicly, as well.

Airtable Apps provide businesses with an infinitely recombinable Lego kit they can tailor to their needs.

In order to build a custom app in Airtable, a JavaScript developer must know how to use React, one of the most popular JavaScript libraries for building user interfaces. Airtable provides a component library of functional React components and hooks, which are a huge help for rapidly building a consistent UI and determining how you will manage state within the app and its components.

Check out Airtable’s Getting Started article for more information and Airtable on GitHub for examples of apps.

Airtable Dashboard Requirements

After reviewing the dashboard mockups with the client team, the types of data to be used were clear. We would need a series of dashboard components that would display as text on the dashboard and charts of different metrics that could be tracked over time.

Coaches and medical professionals needed to be able to build a custom dashboard for each patient, so we needed a flexible way to add and remove charts. Other static data relative to each patient would be displayed no matter the patient selected.

In this case, the dashboard sections boiled down to:

  • General Information - Patient Name, Email, Phone Number, Contact Preference, Date of Birth, Age
  • Objectives - Goals the patient has based on survey results
  • Some Stats - BMI, Height, and Weight
  • Medicine Use - Listing all prescription drugs already used by a patient
  • Family History of Conditions - Helpful in diagnosing certain conditions
  • Charts - A section where the Airtable dashboard user could add a chart and configure which metric it would visualize over time

 

Image showing an Airtable Dashboard mockup.

 

One way to approach all of the sections except for charts would be to hard-code all the columns for objectives, medicine use, and family history into the dashboard. However, that would not allow the client team to add new questions to a Typeform survey nor add a new column to an Airtable table to present that data on the dashboard without having a developer update the custom app.

A more elegant and extensible solution to this challenge was finding a way to tag columns as relevant to a particular dashboard section and retrieve those columns using the metadata that Airtable exposes when using the Table and Field models.

This was achieved using Field Descriptions as a place to tag a column from the Table as relevant to a dashboard section to be displayed to the user. Then, we could ensure only those with the Creator role (the administrators) for the Base had the ability to modify these Field Descriptions to alter what appears on the dashboard. To illustrate this solution, we will focus mostly on the items in General Information and how to present Charts.

Creating a #TAG# System

Given the dashboard sections, it made sense to make reusable tags for some sections and specific tags for certain columns. For items like patient name, email, and phone number, #NAME#, #EMAIL#, and #PHONE# were added to each Field’s description, respectively. That would allow that information to be retrieved via the Table metadata like this:

const name = table ? table.fields.filter(field => field.description?.includes("#NAME#"))

For areas of the dashboard that would need to draw from many tagged columns we would have the following tags for each dashboard section:

  • OBJ - Objectives
  • FAM - Family History
  • MED - Medicine Usage
  • CAN - Family History specific to cancer
  • CHART - Any column that should be sourced for adding charts; must be a quantity

In addition, it was important to separate the name of a column in a Table from the label it would receive on the dashboard, so anything that received a #TAG# would also have the ability to receive two #LABEL# tags in its Field Description. A Field Description would look like this:

 

Screenshot showcasing the use of tags in a field description.

 

In case the #LABEL# tags are missing, we will display the column name from the Table.

We can parse out the label set in the description with a simple function like this after retrieving the field with the previous code example:

// utils.js

export const setLabel = (field, labelTag = "#LABEL#") => {
   const labelTags = (field.description?.match(new RegExp(labelTag, "g")) || []).length;
   let label;
   if (labelTags === 2) label = field.description?.split(`${labelTag}`)[1];
   if (!label || label?.trim() === '') label = field.name;
   return {...field, label, name: field.name, description: field.description};
}

With this #TAG# system, we achieve three main things:

  • Column names (fields) in the Table can be changed as desired.
  • Labels for data in the dashboard can be distinct from column names.
  • Dashboard sections for Objectives, Medicine Usage, Family History, and Charts can be updated by the client team without touching a line of code.

Persisting State in Airtable

In React, we use state and pass it to components as props in order to re-render that component if its state changes. Normally this is tied to an API call that fuels a dashboard component, but in Airtable we already have all the data and simply need to filter what we are displaying based on which patient we are viewing. In addition, if we use state, it will not persist the data past a refresh in the dashboard itself.

So, how can we persist a value past refresh to keep a dashboard filtered? Fortunately, Airtable provides a hook for this called useGlobalConfig in which it maintains a key-value store for an app installation on a dashboard. We simply need to implement the logic of retrieving values from this key-value store when the app loads to fuel our dashboard components.

What is even more useful about using the useGlobalConfig hook is that when its values are set, the dashboard component and its child components re-render, so you can use the Global Config like you would use a state variable in a typical React implementation.

Introducing Charts

Airtable provides examples of charts with its Simple Chart App, which uses React Charts, a React wrapper on Chart.js (chart-ception).

In the Simple Chart App, we have one chart for the whole app, but in our Dashboard App, we need the ability for the user to add and remove their own charts from their own dashboard. What’s more, in discussion with the client team, it seems that certain metrics would be better viewed on the same chart (like readings for diastolic and systolic blood pressure).

With this we have the following items to tackle:

  • Persisting state for each user’s chart (or even better using Global Config)
  • Allowing multiple metrics per chart

This is where the power of the Global Config comes in handy, as we can use the key-value store to maintain the selected metrics and anything else about our list of charts. As we configure a chart in the UI, the chart component itself will be re-rendered due to updates to the Global Config. For the charting section of the dashboard, here is a gist with the components for reference, focusing on dashboard charts.js and single chart.js.

The table passed to each chart is what is used for its metadata to find the fields, whereas the records passed have already been filtered by the patient selected at the top-level dashboard component that imports dashboard_charts/index.js.

Note that the fields listed as options in the dropdown for a chart are pulled using the #CHART# tag we mentioned before, with this line in a useEffect hook:

// single_chart/index.js

…
useEffect(() => {
  (async () => {

...

    if (table) {
      const tempFieldOptions = table.fields.filter(field =>    
        field.description?.includes('#CHART#')).map(field => {
          return {
            ...setLabel(field),
            value: field.id
          }
       });
       setFieldSelectOptions([...tempFieldOptions]);
    }
  })();
}, [table, records, fields]);


...

The code above shows how the setLabel function referenced earlier is used with the #TAG# to add anything provided in the #LABEL# tags and display it for the option in the field dropdown.

Our chart component takes advantage of the multi-axis capabilities provided by Chart.js, which is shown with React Charts. We just extended it via the UI with the user’s ability to add a dataset and a chart type (line or bar).

The key to using Global Config, in this case, is to know that each key can only hold a string | boolean | number | null | GlobalConfigArray | GlobalConfigObject (see Global Config Value reference).

We have the following items to maintain per chart:

  • chartTitle which is autogenerated and can be renamed by the user
  • fields array in which each item has:
    • field as fieldId from Airtable
    • chartOption as one line | bar as the Chart.js docs indicate
    • color as the Airtable color from the colorUtils
    • hex as the hex code relating to the Airtable color

To manage this, I found it most convenient to stringify this data as an object instead of setting Global Config keys and values all the way down. See the example below (globalConfig.json in the gist), which includes Global Config values to filter records by the patient and some related variables used to support a typeahead filtering component (thanks to react-bootstrap-typeahead):

{
 "xCharts": {
   "chart-1605425876029": "{\"fields\":[{\"field\":\"fldxLfpjdmYeDOhXT\",\"chartOption\":\"line\",\"color\":\"blueBright\",\"hex\":\"#2d7ff9\"},{\"field\":\"fldqwG8iFazZD5CLH\",\"chartOption\":\"line\",\"color\":\"blueLight1\",\"hex\":\"#9cc7ff\"}],\"chartTitle\":\"Gráfico criado em 11/15/2020, 2:37:56 AM\"}",
   "chart-1605425876288": "{\"fields\":[{\"field\":\"fldGJZIdRlq3V3cKu\",\"chartOption\":\"line\",\"color\":\"blue\",\"hex\":\"#1283da\"}],\"chartTitle\":\"Gráfico criado em 11/15/2020, 2:37:56 AM\"}",
   "chart-1605425876615": "{\"fields\":[{\"field\":\"fld1AnNcfvXm8DiNs\",\"chartOption\":\"line\",\"color\":\"blueLight1\",\"hex\":\"#9cc7ff\"},{\"field\":\"fldryX5N6vUYWbdzy\",\"chartOption\":\"line\",\"color\":\"blueDark1\",\"hex\":\"#2750ae\"}],\"chartTitle\":\"Gráfico criado em 11/15/2020, 2:37:56 AM\"}",
   "chart-1605425994036": "{\"fields\":[{\"field\":\"fld9ak8Ja6DPweMdJ\",\"chartOption\":\"line\",\"color\":\"blueLight2\",\"hex\":\"#cfdfff\"},{\"field\":\"fldxVgXdZSECMVEj6\",\"chartOption\":\"line\",\"color\":\"blue\",\"hex\":\"#1283da\"}],\"chartTitle\":\"Gráfico criado em 11/15/2020, 2:39:54 AM\"}",
   "chart-1605430015978": "{\"fields\":[{\"field\":\"fldwdMJkmEGFFSqMy\",\"chartOption\":\"line\",\"color\":\"blue\",\"hex\":\"#1283da\"},{\"field\":\"fldqwG8iFazZD5CLH\",\"chartOption\":\"line\",\"color\":\"blueLight1\",\"hex\":\"#9cc7ff\"}],\"chartTitle\":\"New Chart\"}",
   "chart-1605430916029": "{\"fields\":[{\"field\":\"fldCuf3I2V027YAWL\",\"chartOption\":\"line\",\"color\":\"blueLight1\",\"hex\":\"#9cc7ff\"},{\"field\":\"fldBJjtRkWUTuUf60\",\"chartOption\":\"line\",\"color\":\"blueDark1\",\"hex\":\"#2750ae\"}],\"chartTitle\":\"Gráfico criado em 11/15/2020, 4:01:56 AM\"}",
   "chart-1605431704374": "{\"fields\":[{\"field\":\"fld7oBtl3iiHNHqoJ\",\"chartOption\":\"line\",\"color\":\"blue\",\"hex\":\"#1283da\"}],\"chartTitle\":\"Gráfico criado em 11/15/2020, 4:15:04 AM\"}"
 },
 "xPatientEmail": "elle@gmail.com",
 "xTypeaheadValue": "Elle Gold (elle@gmail.com)",
 "xSelectedValue": "[{\"label\":\"Elle Gold (elle@gmail.com)\",\"id\":\"elle@gmail.com\",\"name\":\"Elle Gold\",\"email\":\"elle@gmail.com\"}]"
}

Note: All data contained above, and the data included in the animations below, are not real patient data.

Here’s a look at the final result:

 

Animated display of Airtable dashboard UI.

 

What About the Typeahead?

In order to filter by patient, we needed a way to select a patient and then filter the records based on this patient. In this section, we review how this was achieved.

For the typeahead, react-bootstrap-typeahead was an easy choice, as the only steps left were preparing the options for the typeahead, mixing it with an Airtable input for styling and loading bootstrap, and some other styles for our menu. Dropping components from your favorite component libraries into an Airtable app is not as straightforward as in typical React web development; however, there are only a few extra steps to get everything to look the way you would expect.

Here is the final result:

 

Animated GIF showcasing the filter-by-patient functionality.

 

To render the Airtable input and keep all our styles consistent, react-bootstrap-typeahead comes with a renderInput prop. See more on how to modify the rendering of the component here.

For the bootstrap styles and to override our menu items, the following two utils were used from Airtable:

See frontend.js in the gist for an excerpt of the typeahead implementation.

This line was used to load bootstrap globally:

// frontend/index.js

loadCSSFromURLAsync('https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css');

You will notice some added logic for things like handling style changes on hover or restyling links (<a></a>) to get the familiar bootstrap look and feel. This also includes the handling of setting the Global Config values for the typeahead and filtering of records so that if a user leaves their dashboard, refreshes their page, or would like to share this dashboard with others, the app persists the selected patient in the Dashboard App. This also allows the users to install multiple copies of this same app side by side in the same Airtable Dashboard with different patients selected or with different charts.

Keep in mind that a dashboard in Airtable is also available to all users of the Base, so these custom app installations on a dashboard will be filtered to the same patients and charts no matter which users are looking at the dashboard at the same time.

Let’s recap what we’ve covered for far:

  1. Airtable allows both non-technical users and technical users to collaborate in Airtable.
  2. Typeform comes with an Airtable integration that allows non-technical users to map Typeform results to Airtable.
  3. Airtable Apps provide a powerful way to supercharge its Airtable Base, whether selecting from the marketplace or building a custom app.
  4. Developers can extend Airtable rapidly in nearly any way imaginable with these apps. Our example above took only three weeks to design and implement (with enormous help from existing libraries, of course).
  5. A #TAG# system can be used to modify the dashboard without requiring code changes by developers. There are better and worse use cases for this. Be sure to limit permissions to the Creator role if using this strategy.
  6. Using Global Config allows developers to persist data within an app installation. Mix this into your state management strategy to seed data for your components.
  7. Don’t expect to drag and drop components from other libraries and projects directly into your Airtable App. Styles can be loaded using the loadCSSFromString and loadCSSFromURLAsync utils provided by Airtable.

Future-proofing

Use a more sophisticated middleware

With Typeform and Airtable, it’s easy and cost-effective to configure the mapping of questions to columns.

However, there is one big drawback: If you have a survey of more than 100 questions mapped to Airtable and you need to modify a mapping, you must delete the entire mapping and start again. This is clearly not ideal, but for a free integration, we can deal with this.

Other options would be having a Zapier (or similar) integration manage the data between Typeform and Airtable.Then you could modify the mapping of any question to any column without starting from scratch. This would have its own cost considerations to factor in as well.

Hopefully, some of the lessons learned and communicated here will help others who are looking to build solutions with Airtable.

Finally, you can check out the gist with the files discussed in this article.

Original article source at: https://www.toptal.com/

#react #dashboards