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ملف:Bluesky Registered Users.svg

محتويات الصفحة غير مدعومة بلغات أخرى.
من ويكيبيديا، الموسوعة الحرة

الملف الأصلي(ملف SVG، أبعاده 960 × 576 بكسل، حجم الملف: 38 كيلوبايت)

ملخص

حُدِّث هذا الملف ليعكس معلومات معاصرة.
لو كنت تريد استعمال نسخةٍ مُحدَّدة لا تتضمن التَّحديثات، يُمكنك أن ترفعها بصفتها ملفَّاً مُستقِلَّاً.
الوصف
English: This chart depicts the growth of Bluesky, a social network. Data spans May 2023 until January 2024, and shows an increase from 55k to 3.0M users. The chart uses population data from Bluesky API requests rendered into SVG 1.1 using Matplotlib 3.7.2 via Python 3.11.5.
Disclaimer
InfoField
English: Due to the loss of vqv.app on 2023-11-07, a total of 68 Internet Archive captures across ~70 days from vqv.app/stats/chart, bsky.jazco.dev/stats, and twexit.nl were individually sorted by timestamp, then used to approximate the daily population totals as they would have appeared at the end of each day via linear interpolation. A spreadsheet containing all citations to the relevant Internet Archive captures are available here: Google Sheets. While efforts were made to ensure accuracy, some degree of estimation is inherent in the interpolation process.
التاريخ
المصدر Bluesky API. Daily population data until 2023-11-07 from https://vqv.app/stats/chart [dead link]; Internet Archive for snapshots of user totals from https://bsky.jazco.dev/stats and https://twexit.nl/.
المؤلف VintageNebula, with data gathered by Eddie Silva, Pedro Borracha, Jaz, and Adraianus
إصدارات أخرى
This file supersedes the file BlueSky user growth.png. It is recommended to use this file rather than the other one.

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minor quality
هذا رسمٌ مُعَدَّلٌ رقميَّاً من النسخة الأصليَّة. التعديلات هي: Generated with MatPlotLib with updated data and vectorized from source data. يُمكن الاطلاع على النسخة الأصليَّة هنا: BlueSky user growth.png. المُستخدم الذي أجرى التعديلات هو: VintageNebula.

SVG منشأ الملف
InfoField
 
الشيفرة المصدرية لهذا الرسم المتجه صالحة.
 
هذا الرسم المتجهي أُنشئ بواسطة Matplotlib
نص برمجي مصدري
InfoField

Python code

Created using Matplotlib 3.7.2 via Python 3.11.5
# Bluesky Registered Users v1.3 
# Created for Wikimedia Commons; last edited: 2024-01-15

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.ticker import FixedLocator
from datetime import datetime

'''
The 'data_array' variable holds the population dataset. Each day and value is paired in a list of strings,
formatted as "YYYY-MM-DD, ######", where YYYY-MM-DD is the ISO 8601 date, and ###### is the number of 
existing users at the end of the day listed. Note that values after 2023-11-07 were calculated using
linear interpolation using website captures on the Internet Archive, and are rounded to the nearest 500.
'''
data_array = [
    "2023-05-02, 55570", "2023-05-03, 58119", "2023-05-04, 60909", "2023-05-05, 63362",
    "2023-05-06, 65035", "2023-05-07, 66194", "2023-05-08, 67477", "2023-05-09, 69053",
    "2023-05-10, 70898", "2023-05-11, 72458", "2023-05-12, 74236", "2023-05-13, 76326",
    "2023-05-14, 77472", "2023-05-15, 78972", "2023-05-16, 80328", "2023-05-17, 81682",
    "2023-05-18, 83411", "2023-05-19, 84718", "2023-05-20, 85805", "2023-05-21, 86699",
    "2023-05-22, 87761", "2023-05-23, 89034", "2023-05-24, 90565", "2023-05-25, 92559",
    "2023-05-26, 94625", "2023-05-27, 96395", "2023-05-28, 97927", "2023-05-29, 99408",
    "2023-05-30, 101215", "2023-05-31, 102578", "2023-06-01, 103919", "2023-06-02, 105437",
    "2023-06-03, 106591", "2023-06-04, 107638", "2023-06-05, 108875", "2023-06-06, 110169",
    "2023-06-07, 111989", "2023-06-08, 113673", "2023-06-09, 115868", "2023-06-10, 118019",
    "2023-06-11, 120206", "2023-06-12, 122911", "2023-06-13, 124731", "2023-06-14, 128350",
    "2023-06-15, 131274", "2023-06-16, 133937", "2023-06-17, 136152", "2023-06-18, 137423",
    "2023-06-19, 138780", "2023-06-20, 140016", "2023-06-21, 143093", "2023-06-22, 153451",
    "2023-06-23, 157707", "2023-06-24, 159933", "2023-06-25, 161913", "2023-06-26, 164419",
    "2023-06-27, 169343", "2023-06-28, 174342", "2023-06-29, 179043", "2023-06-30, 184094",
    "2023-07-01, 203365", "2023-07-02, 222026", "2023-07-03, 240687", "2023-07-04, 254189",
    "2023-07-05, 262135", "2023-07-06, 271292", "2023-07-07, 278898", "2023-07-08, 284516",
    "2023-07-09, 287745", "2023-07-10, 291393", "2023-07-11, 294869", "2023-07-12, 299667",
    "2023-07-13, 304946", "2023-07-14, 309949", "2023-07-15, 313683", "2023-07-16, 317209",
    "2023-07-17, 321258", "2023-07-18, 324959", "2023-07-19, 328269", "2023-07-20, 331240",
    "2023-07-21, 335652", "2023-07-22, 340658", "2023-07-23, 347321", "2023-07-24, 370048",
    "2023-07-25, 386014", "2023-07-26, 394662", "2023-07-27, 404693", "2023-07-28, 414773",
    "2023-07-29, 423934", "2023-07-30, 433895", "2023-07-31, 442390", "2023-08-01, 450486",
    "2023-08-02, 458146", "2023-08-03, 472748", "2023-08-04, 487476", "2023-08-05, 499582",
    "2023-08-06, 511605", "2023-08-07, 525141", "2023-08-08, 535769", "2023-08-09, 543286",
    "2023-08-10, 550163", "2023-08-11, 556915", "2023-08-12, 563776", "2023-08-13, 571517",
    "2023-08-14, 581620", "2023-08-15, 591309", "2023-08-16, 601562", "2023-08-17, 611424",
    "2023-08-18, 639033", "2023-08-19, 659173", "2023-08-20, 670671", "2023-08-21, 682208",
    "2023-08-22, 692512", "2023-08-23, 703097", "2023-08-24, 723279", "2023-08-25, 742913",
    "2023-08-26, 758806", "2023-08-27, 768190", "2023-08-28, 777341", "2023-08-29, 788438",
    "2023-08-30, 807753", "2023-08-31, 826957", "2023-09-01, 845340", "2023-09-02, 855268",
    "2023-09-03, 865253", "2023-09-04, 875942", "2023-09-05, 887544", "2023-09-06, 898496",
    "2023-09-07, 913032", "2023-09-08, 933157", "2023-09-09, 952217", "2023-09-10, 968247",
    "2023-09-11, 980472", "2023-09-12, 1001758", "2023-09-13, 1017594", "2023-09-14, 1028614",
    "2023-09-15, 1038289", "2023-09-16, 1046587", "2023-09-17, 1055012", "2023-09-18, 1071682",
    "2023-09-19, 1125267", "2023-09-20, 1157089", "2023-09-21, 1179696", "2023-09-22, 1197915",
    "2023-09-23, 1211413", "2023-09-24, 1223934", "2023-09-25, 1237273", "2023-09-26, 1249557",
    "2023-09-27, 1261156", "2023-09-28, 1272657", "2023-09-29, 1294216", "2023-09-30, 1308941",
    "2023-10-01, 1324469", "2023-10-02, 1343949", "2023-10-03, 1361891", "2023-10-04, 1377805",
    "2023-10-05, 1393473", "2023-10-06, 1410649", "2023-10-07, 1425252", "2023-10-08, 1438074",
    "2023-10-09, 1452503", "2023-10-10, 1470776", "2023-10-11, 1489064", "2023-10-12, 1508389",
    "2023-10-13, 1529182", "2023-10-14, 1543696", "2023-10-15, 1557027", "2023-10-16, 1570787",
    "2023-10-17, 1583493", "2023-10-18, 1618452", "2023-10-19, 1646361", "2023-10-20, 1666281",
    "2023-10-21, 1681299", "2023-10-22, 1694865", "2023-10-23, 1709734", "2023-10-24, 1725184",
    "2023-10-25, 1738664", "2023-10-26, 1750386", "2023-10-27, 1761839", "2023-10-28, 1772651",
    "2023-10-29, 1785693", "2023-10-30, 1799713", "2023-10-31, 1811867", "2023-11-01, 1823445",
    "2023-11-02, 1836704", "2023-11-03, 1850723", "2023-11-04, 1863205", "2023-11-05, 1876044",
    "2023-11-06, 1890622", "2023-11-07, 1902887", "2023-11-08, 1918000", "2023-11-09, 1935000",
    "2023-11-10, 1953000", "2023-11-11, 1973500", "2023-11-12, 1994000", "2023-11-13, 2010500",
    "2023-11-14, 2023500", "2023-11-15, 2038000", "2023-11-16, 2052000", "2023-11-17, 2067500",
    "2023-11-18, 2085000", "2023-11-19, 2103500", "2023-11-20, 2124000", "2023-11-21, 2144500",
    "2023-11-22, 2156000", "2023-11-23, 2170500", "2023-11-24, 2189500", "2023-11-25, 2202500",
    "2023-11-26, 2217000", "2023-11-27, 2229000", "2023-11-28, 2252500", "2023-11-29, 2274500",
    "2023-11-30, 2287000", "2023-12-01, 2318000", "2023-12-02, 2339500", "2023-12-03, 2364000",
    "2023-12-04, 2383500", "2023-12-05, 2402000", "2023-12-06, 2418500", "2023-12-07, 2443500",
    "2023-12-08, 2463500", "2023-12-09, 2484000", "2023-12-10, 2493500", "2023-12-11, 2503000",
    "2023-12-12, 2510500", "2023-12-13, 2523500", "2023-12-14, 2538000", "2023-12-15, 2552500",
    "2023-12-16, 2568000", "2023-12-17, 2585000", "2023-12-18, 2611500", "2023-12-19, 2635000",
    "2023-12-20, 2658500", "2023-12-21, 2690000", "2023-12-22, 2712500", "2023-12-23, 2734500",
    "2023-12-24, 2755500", "2023-12-25, 2773500", "2023-12-26, 2791000", "2023-12-27, 2805000",
    "2023-12-28, 2821500", "2023-12-29, 2838000", "2023-12-30, 2854500", "2023-12-31, 2872000",
    "2024-01-01, 2890000", "2024-01-02, 2904500", "2024-01-03, 2917500", "2024-01-04, 2927500",
    "2024-01-05, 2935500", "2024-01-06, 2943000", "2024-01-07, 2961000", "2024-01-08, 2988500",
    "2024-01-09, 3009500", "2024-01-10, 3017000", "2024-01-11, 3024000", "2024-01-12, 3031500",
    "2024-01-13, 3039000", "2024-01-14, 3044000"
]

# Convert the data point strings to datetime objects.
date_rng = [datetime.strptime(row.split(',')[0].strip('"'), "%Y-%m-%d") for row in data_array]
y_values = [int(row.split(',')[1].strip()) for row in data_array]

# Create an initial plot.
fig, ax = plt.subplots(figsize=(10, 6))

# Plot the main data line and specify its z-order for layering.
ax.plot(date_rng, y_values, zorder=2)

# Add a grid and fill the area under the data line.
ax.grid(True, linestyle="-", linewidth=0.4, alpha=0.4)
ax.fill_between(date_rng, y_values, color="skyblue", alpha=0.4, zorder=1)

# Translators - edit these strings to translate into the desired language.
plt.title("Bluesky - Registered Users")
plt.xlabel("Date (YYYY-MM)")
plt.ylabel("Total Registered Users")

# Set the format for the x-axis to display only the first day of each month (ie. YYYY-MM).
ax.xaxis.set_major_locator(mdates.MonthLocator(bymonthday=1))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))

# Make sure y-axis starts at 0.
ax.set_ylim(bottom=0)

# Format y-axis labels with commas and set tick locations.
y_ticks = ax.get_yticks()
ax.yaxis.set_major_locator(FixedLocator(y_ticks))
ax.set_yticklabels([f'{int(label):,}' for label in y_ticks])

# Hide y-axis offset number (1e6, etc.)
ax.yaxis.offsetText.set_visible(False)

'''
Logic to highlight and label the first day of each month:
     1. Create an empty set to keep track of processed months.
     2. Loop through each date-value pair.
     3. Check if the day is the 1st of the month or if the month hasn't been processed yet.
     4. Place a scatter dot on the first day of the unprocessed month.
     5. Annotate the scatter dot with the corresponding value.
     6. Add the month to the set to avoid processing it again.
'''
months = set()
for date, value in zip(date_rng, y_values):
     if date.day == 1 or date.strftime("%Y-%m") not in months:
          ax.scatter(date, value, color='C0', marker='o', alpha=1, zorder=3)
          ax.annotate(f'{value:,}', (date, value), textcoords="offset points", xytext=(0, 10), ha='center')
          months.add(date.strftime("%Y-%m"))

# Optimize for visibility, then display.
plt.tight_layout()
plt.show()

ترخيص

Public domain

يرتكز هذا العمل المُشتق على أصلٍ مَوجُودٍ في النِّطاق العام، وقد حُسِّن الأصل أو عُدِّل رقميَّاً. إنَّ العمل المُشتق في النِّطاق العام أيضاً بناءً على رغبة مؤلفه. VintageNebula. يسري هذا الإجراء عالمياً.
قد لا يكُون هذا مُمكناً قانونيَّاً في بعض البلدان، لذلك:
يمنح VintageNebula أيَّاً كان، الحقَّ في استعمال هذا العمل لأي غرضٍ ودون أيِّ شروطٍ ما لم يكن هناك قيود أخرى مُحدَدة بالقانون.


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الشروحات

أضف شرحاً من سطر واحد لما يُمثِّله هذا الملف
Line chart depicting the growth of Bluesky, a social platform, from May 2023 to January 2024.

٦ أكتوبر 2023

e36046a4f02e617eb799fc69f1922accf91930fe

٣٧٬٦٧٢ بايت

٥٧٦ بكسل

٩٦٠ بكسل

تاريخ الملف

اضغط على زمن/تاريخ لرؤية الملف كما بدا في هذا الزمن.

زمن/تاريخصورة مصغرةالأبعادمستخدمتعليق
حالي02:40، 15 يناير 2024تصغير للنسخة بتاريخ 02:40، 15 يناير 2024960 × 576 (38 كيلوبايت)VintageNebulaUpdate data to January 2024
01:50، 6 نوفمبر 2023تصغير للنسخة بتاريخ 01:50، 6 نوفمبر 2023960 × 576 (37 كيلوبايت)VintageNebulaUpdate data to November 2023
01:44، 9 أكتوبر 2023تصغير للنسخة بتاريخ 01:44، 9 أكتوبر 2023900 × 540 (48 كيلوبايت)VintageNebulaUploaded a work by Data compiled by m3ta.uk (Pedro) and vqv.app (Eddie). Chart created by VintageNebula (myself). from Data was obtained from requests to the public-facing Bluesky API, which were archived and rendered for display at https://vqv.app/stats/chart. Chart generated using the archived Bluesky API data created by VintageNebula (myself) using Matplotlib. with UploadWizard

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