import – How do I read reliably from stdin with wolframscript?

I'm trying to route a data stream to a wls Script in a command-line environment. Wolframscript delivers the $ScriptInputString Symbol for reading stdin, However, if the input is large, no input appears to be read.

How do I read from standard input for large formats?


Minimal (not working) example:

for i in $(seq 0 $SIZE); do echo $i; done | wolframscript -print -c $ScriptInputString

When $SIZE=100000 (one hundred thousand), outputs all integers 0 to 1000000, When $SIZE=1000000 (one million) is nothing.

$Version is 11.3.0 for Linux x86 (64-bit) (March 7, 2018)

Open, read, count rows in the Google spreadsheet, and update the number in emails with the c # winform application

I'm doing an automation. Details are given below:

  1. Open the Google spreadsheet. The table contains 2 columns and n rows. Each line has the value 1, 2 and 3.

  2. Number of 1,2 and 3 must be updated in the table

  3. The table must be sent by e-mail

So far, I have completed email tasks and other design work.

I am completely new to this area and I need a lot of help in completing this project.

I need code to work and count values ​​1,2 and 3 and update them in a table

I am ready for any kind of input

Ease of Use – How easy is it to read small caps versus lowercase letters?

There is definitely a qualitative and quantitative difference between the reading experience in upper and lower case.
This has to do with reading speed, familiarity, shape and eye movement. I myself am a fast reader (moderate: I've written just over 1200 words per minute), so most of what's below is experience.

Lowercase letters are simply smaller, so I can capture more words at a glance (again I was tested for the ability to read 3-4 words at once, depending on their length).
Basically, reading at any reasonable speed requires reading more than one letter at a time. Usually it is at least one word, but owners of quick-reading records can read entire pages at once.

As I read a lot, loud interpolation is also important, and though it is not covered by the name, capital letters are a part of it.
The brain also requires a lot of reading for the accepted use of upper and lower case.

So putting it all together will cause you all sorts of trouble if you use uppercase letters. A small upper has the advantage of being small, but that's it too.

Miles Tinker also mentions it:

Capitalized text covers about 35 percent more printing area than the same material in lowercase letters. This would tend to extend the reading time. When combined with the difficulty of reading uppercase words as units, the barrier to read quickly becomes apparent. In the eye movement study of Tinker and Patterson, the main difference in the oculomotor patterns between lowercase letters and all uppercase letters was the very large increase in the number of fixation pauses for reading the all-capital impression.

If I remember correctly a TV program about the development of Transport font for UK Motorways, all sorts of fonts, variants and methods were tested during this development and results were achieved where the lowercase letters were clearly superior. I think they mentioned explicitly that they also tested the Smallcap variant, but it was still problematic.

Just for your information, I find it very painful to read ALL CAPS. No joke here. When I see something in the internet written in CAPITALS, I just skip it (I mean here whole sentences, posts or messages). I'm not shy when I write, but try to limit myself to as few ALL CAP words as possible.

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Encryption – Can Google and Apple read the text of the message alerts?

Of course, if you use E2E (secret chat) encryption, the answer is no (I hope), but I'm talking about NOT E2E encrypted messages stored in the cloud.

Of course I am aware that the messages are transmitted over SSL encrypted connections. However, are the messages encrypted in any way when they reach Google / Firebase Cloud Message, APN, and other third-party push notification services?

In other words, when I send a message to Bob via a non-secret chat, can Google, Apple, and so on read the text of the message I sent to him when it was sent through the push notification server, or is it encrypted?

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How do I read a CSV file in Python? "," Is a delimiter, but text fields contain more commas

I have a file like this

"#AltasHoras https://t.co/fuEeAPSr9M"",""2019-08-05 12:26 +0000"",""4113.0"",""73.0"",""0.01774860199367858"",""0.0"",""1.0"",""22.0"",""5.0"",""6.0"",""0.0"",""2.0"",""0.0"",""0"",""0"",""0"",""0"",""0"",""37"",""37"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"""     
"1158346916901609472,""https://twitter.com/gshow/status/1158346916901609472"",""Fátima Bernardes, se despede das férias ao lado de Túlio Gadêlha: 'Hora de voltar pra casa' ? https://t.co/QgVMt78nmp #GshowFamosos https://t.co/gNuOawbrFI"",""2019-08-05 12:00 +0000"",""20144.0"",""487.0"",""0.024175933280381257"",""7.0"",""6.0"",""125.0"",""15.0"",""26.0"",""5.0"",""15.0"",""0.0"",""0"",""0"",""0"",""0"",""0"",""288"",""288"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"",""-"""     
"1158339367238287361,""https://twitter.com/gshow/status/1158339367238287361"","""

and a code

import pandas as pd
import numpy as np
import pytz
from datetime import timedelta, date #, timezone, datetime
from google.cloud import storage
import re
import io
import gcsfs

def tz_convert(dt, tz1, tz2):
    from_tz = pytz.timezone(tz1)
    to_tz = pytz.timezone(tz2)
    return from_tz.localize(dt).astimezone(to_tz)


def bq_date(x):
    if len(str(x.day)) == 1:
        day = "0" + str(x.day)
    else:
        day = str(x.day)

    if len(str(x.month)) == 1:
        month = "0" + str(x.month)
    else:
        month = str(x.month)

    return "{0}{1}{2}".format(x.year, month, day)


def list_gcs_objs(bucket, prefix):
    storage_client = storage.Client()
    bucket_check = storage_client.get_bucket(bucket)
    blob_list = list(bucket_check.list_blobs(prefix=prefix))
    obj_paths = list()
    if len(blob_list) <= 1:
        print("Folder emptyn")
        return obj_paths
    else:
        count = 1
    while count < len(blob_list):
        obj_paths.append(blob_list(count).name)
        count += 1
    return obj_paths


def upload_to_gcs(bucket, object_key, data):
    storage_client = storage.Client(bucket)
    bucket_up = storage_client.get_bucket(bucket)
    blob_up = bucket_up.blob(object_key)
    response = blob_up.upload_from_string(data)
    return(response)



def tw_scrapper(request):
    global ndf
    since_date = date.today() - timedelta(1)
    until_date = date.today()

    mybucket = "gdata-dn-gshow-sandbox"
    rawpostprefix = "AD/RAW_TWITTER/TWEETS/"
    rawvideoprefix = "AD/RAW_TWITTER/VIDEOS/"
    mainprefix = "AD/TW/"

    query_tags = "SELECT * FROM `globoid.AD_gshow_hashtags`"

    dtags = pd.read_gbq(query_tags, dialect='standard', index_col="Hashtag")

    # Pegar as tags
    tags = dtags('Produto').to_dict()

    # List Twitter Raw Data
    rawpostsdata = list_gcs_objs(mybucket, rawpostprefix)
    rawvideodata = list_gcs_objs(mybucket, rawvideoprefix)

    maindata = list_gcs_objs(mybucket, mainprefix)
    tw_dates = (x(-12:-4) for x in maindata)

    # Posts
    ctr = 0
    for obj in rawpostsdata:
        path = "gs://{0}/{1}".format(mybucket, obj)
        page_name = re.findall(r"^.*_metrics_((A-Za-z_)+)_(d)", obj)(0)

        if ctr == 0:
            #try:
            df = pd.read_csv(path, sep=",", encoding="utf-8", low_memory=False)
            df('page_name') = page_name
            ndf = df.copy()
            ctr += 1
        #except:
        #ctr += 0

        else:
            #try:
            df = pd.read_csv(path, sep=",", encoding="utf-8", low_memory=False)
            df('page_name') = page_name
            ndf = pd.concat((ndf, df), sort=False)
            ctr += 1
        #except:
        #ctr += 0


    print(ndf.head())

    tw_mapper = {'id do Tweet': 'tweet_id', 'link permanente do Tweet': 'tweet_link',
                 'texto do Tweet': 'tweet_text', 'horário': 'tweet_date', 'impressões': 'impressions',
                 'interações': 'interactions', 'taxa de envolvimento': 'engagement_rate'}

    ndf.rename(tw_mapper, axis=1, inplace=True)

    ndf = ndf(("tweet_id", "tweet_link", "tweet_text", "tweet_date",
               "impressions", "interactions", "engagement_rate",
               "retweets", "page_name"))

    ndf('tweet_date') = pd.to_datetime(ndf.tweet_date, infer_datetime_format=True)
    ndf('tweet_date') = ndf.tweet_date.dt.tz_localize('UTC')
    ndf('tweet_date') = ndf.tweet_date.dt.tz_convert('America/Sao_Paulo')

    # ndf('tweet_date') = ndf('tweet_date').apply(tz_convert)

    sdf = ndf.copy()

WITH MISTAKES
Error: Function has crashed. details:
Error while token of data. C error: Expected 1 fields in line 26, saw 38