Wikipedia

Datei:Bluesky Registered Users.svg

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Beschreibung
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.
Datum
Quelle Bluesky API. Daily population data until 2023-11-07 from https://vqv.app/stats/chart [toter Link]; Internet Archive for snapshots of user totals from https://bsky.jazco.dev/stats and https://twexit.nl/.
Urheber VintageNebula, with data gathered by Eddie Silva, Pedro Borracha, Jaz, and Adraianus
Andere Versionen
Die vorliegende Datei löst die Datei BlueSky user growth.png ab. Es wird empfohlen, die vorliegende Datei statt der verlinkten Datei zu verwenden.

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minor quality
Dieses Chart wurde digital nachbearbeitet. Folgende Änderungen wurden vorgenommen: Generated with MatPlotLib with updated data and vectorized from source data. Das Originalbild kann hier eingesehen werden: BlueSky user growth.png: . Bearbeitet von VintageNebula.

SVG‑Erstellung
InfoField
 
Der SVG-Code ist valide.
 
Dieses Chart wurde mit Matplotlib erstellt.
Quelltext
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()

Lizenz

Public domain
This work is based on a work in the public domain. It has been digitally enhanced and/or modified. This derivative work has been (or is hereby) released into the public domain by its author, VintageNebula. This applies worldwide.

In some countries this may not be legally possible; if so:

VintageNebula grants anyone the right to use this work for any purpose, without any conditions, unless such conditions are required by law.

Kurzbeschreibungen

Ergänze eine einzeilige Erklärung, was diese Datei darstellt.
Line chart depicting the growth of Bluesky, a social platform, from May 2023 to January 2024.

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image/svg+xml

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576 Pixel

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960 Pixel

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Version vomVorschaubildMaßeBenutzerKommentar
aktuell04:40, 15. Jan. 2024Vorschaubild der Version vom 04:40, 15. Jan. 2024960 × 576 (38 KB)VintageNebulaUpdate data to January 2024
03:50, 6. Nov. 2023Vorschaubild der Version vom 03:50, 6. Nov. 2023960 × 576 (37 KB)VintageNebulaUpdate data to November 2023
03:44, 9. Okt. 2023Vorschaubild der Version vom 03:44, 9. Okt. 2023900 × 540 (48 KB)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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