算法艺术创作
algorithmic-art
通过代码和可调参数创作生成艺术与交互式视觉效果。
render-publication-graphic【设计与创意】Produce publication-ready 2D graphics with proper DPI, color profiles, typography, and export formats for print and digital media. Use when preparing figures for academic journal submission, creating graphics for print publications, ensuring graphics meet publisher technical specifications, exporting visualizations for web with proper optimization, o…
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生發備二維圖含正 DPI、色檔、字、出格為印與數媒。
| Input | Type | Description | Example |
|---|---|---|---|
| Source graphic | File/Data | Original visualization or artwork | SVG, R ggplot, Python matplotlib, Blender render |
| Publication target | Specification | Journal, web, print, presentation | Nature journal, IEEE paper, website |
| Technical requirements | Parameters | DPI, dimensions, color space, format | 300 DPI, 180mm width, CMYK, TIFF |
| Style guide | Document | Publisher typography and formatting rules | Font families, line widths, color palette |
| Metadata | Information | Title, author, date, copyright, description | Figure caption, license info |
識標發之技規:
# Common publication requirements
academic_journal:
dpi: 300-600
format: TIFF, EPS, PDF
color_space: RGB or CMYK (check guidelines)
max_width: 180mm (single column) or 390mm (double column)
fonts: Embed or outline
resolution_minimums:
line_art: 1000 DPI
halftone: 300 DPI
combination: 600 DPI
web_publication:
dpi: 72-96 (retina: 144-192)
format: PNG, WebP, SVG
color_space: sRGB
max_file_size: 200KB-500KB
optimization: Compress, progressive loading
presentation:
dpi: 96-150
format: PNG, PDF, SVG
color_space: RGB
dimensions: 16:9 or 4:3 aspect ratio
contrast: High contrast for projectors
print_book:
dpi: 300-600
format: TIFF, PDF/X
color_space: CMYK
bleed: 3-5mm beyond trim
fonts: Embedded
得:標需明 敗:問發者特導、用保守默
按出媒設解:
from PIL import Image
def set_dpi_pillow(image_path, output_path, target_dpi=300):
"""Set DPI metadata for PNG/TIFF."""
img = Image.open(image_path)
# Save with DPI metadata
img.save(output_path, dpi=(target_dpi, target_dpi))
print(f"Saved with {target_dpi} DPI: {output_path}")
def calculate_dimensions(width_mm, height_mm, dpi=300):
"""Calculate pixel dimensions from physical size."""
# Convert mm to inches
width_inches = width_mm / 25.4
height_inches = height_mm / 25.4
# Calculate pixels
width_px = int(width_inches * dpi)
height_px = int(height_inches * dpi)
return width_px, height_px
# Example: 180mm wide figure at 300 DPI
width, height = calculate_dimensions(180, 120, dpi=300)
print(f"Required resolution: {width}x{height} pixels")
# Output: Required resolution: 2126x1417 pixels
# R ggplot2 export with proper DPI
library(ggplot2)
# Create plot
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
theme_minimal(base_size = 12)
# Save for publication (300 DPI)
ggsave(
filename = "figure1.png",
plot = p,
width = 180,
height = 120,
units = "mm",
dpi = 300
)
# Save as vector for flexibility
ggsave(
filename = "figure1.pdf",
plot = p,
width = 180,
height = 120,
units = "mm",
device = cairo_pdf # Better text rendering
)
得:圖渲於正解為印質 敗:驗 DPI 屬正存、察檔大應
設應色檔:
from PIL import Image, ImageCms
def convert_to_cmyk(rgb_image_path, cmyk_output_path):
"""Convert RGB to CMYK for print."""
img = Image.open(rgb_image_path)
if img.mode != 'RGB':
img = img.convert('RGB')
# Convert to CMYK
cmyk_img = img.convert('CMYK')
cmyk_img.save(cmyk_output_path, format='TIFF', compression='tiff_lzw')
print(f"Converted to CMYK: {cmyk_output_path}")
def apply_srgb_profile(image_path, output_path):
"""Apply sRGB profile for web."""
img = Image.open(image_path)
# sRGB profile (embedded in Pillow)
srgb_profile = ImageCms.createProfile('sRGB')
# Convert to sRGB
img_srgb = ImageCms.profileToProfile(
img,
srgb_profile,
srgb_profile,
renderingIntent=ImageCms.Intent.PERCEPTUAL
)
img_srgb.save(output_path)
# ImageMagick for color space conversion
convert input.png -colorspace sRGB output_srgb.png
convert input.png -colorspace CMYK output_cmyk.tiff
# Check color profile
identify -verbose image.png | grep -i colorspace
得:色空合發需 敗:驗色檔嵌、測印預
確文易讀正格:
from PIL import ImageFont
def get_publication_fonts():
"""Load fonts appropriate for publication."""
# Common publication-safe fonts
fonts = {
'serif': 'Times New Roman',
'sans': 'Arial',
'mono': 'Courier New'
}
try:
# Load with proper size for DPI
# At 300 DPI, 12pt = 12 * 300/72 = 50 pixels
base_size_300dpi = 50
font_regular = ImageFont.truetype(f"{fonts['sans']}.ttf", base_size_300dpi)
font_bold = ImageFont.truetype(f"{fonts['sans']} Bold.ttf", base_size_300dpi)
return {'regular': font_regular, 'bold': font_bold}
except:
return {'regular': ImageFont.load_default(), 'bold': ImageFont.load_default()}
# Typography guidelines
typography_specs = {
'minimum_font_size': '8pt', # Readable when printed
'line_width_min': 0.5, # Points, for print clarity
'panel_labels': {
'font': 'Arial Bold',
'size': '12pt',
'position': 'top-left',
'style': 'A, B, C' # Or (a), (b), (c)
},
'axis_labels': {
'font': 'Arial',
'size': '10pt'
},
'legend': {
'font': 'Arial',
'size': '9pt',
'position': 'outside plot area'
}
}
# R publication-quality typography
library(ggplot2)
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(size = 2) +
labs(
title = "Fuel Efficiency vs Weight",
x = "Weight (1000 lbs)",
y = "Miles per Gallon"
) +
theme_bw(base_size = 12, base_family = "Arial") +
theme(
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
legend.text = element_text(size = 10),
panel.grid.minor = element_blank(),
# Ensure text is black for print
text = element_text(color = "black")
)
得:文於發大可讀、字正嵌 敗:增字大、察字權、化文為線
按用擇格:
def export_multi_format(source_path, output_base, formats=['png', 'pdf', 'tiff']):
"""Export graphic in multiple formats."""
from PIL import Image
import cairosvg
import os
base, ext = os.path.splitext(output_base)
if ext.lower() in ['.svg']:
# SVG source - convert to rasters
for fmt in formats:
output = f"{base}.{fmt}"
if fmt == 'png':
cairosvg.svg2png(
url=source_path,
write_to=output,
output_width=2126, # 180mm @ 300 DPI
output_height=1417 # 120mm @ 300 DPI
)
elif fmt == 'pdf':
cairosvg.svg2pdf(url=source_path, write_to=output)
elif fmt == 'tiff':
# Convert via PNG intermediate
temp_png = f"{base}_temp.png"
cairosvg.svg2png(url=source_path, write_to=temp_png)
img = Image.open(temp_png)
img.save(output, format='TIFF', compression='tiff_lzw')
os.remove(temp_png)
else:
# Raster source
img = Image.open(source_path)
for fmt in formats:
output = f"{base}.{fmt}"
if fmt == 'png':
img.save(output, format='PNG', dpi=(300, 300), optimize=True)
elif fmt == 'tiff':
img.save(output, format='TIFF', compression='tiff_lzw', dpi=(300, 300))
elif fmt == 'pdf':
# Use img2pdf or similar for raster-to-PDF
img.save(output, format='PDF', resolution=300.0)
print(f"Exported in formats: {', '.join(formats)}")
# Format selection guide
format_guide = {
'TIFF': {
'use_for': 'Journal submission, archival',
'benefits': 'Lossless, supports CMYK, high quality',
'compression': 'LZW or ZIP (lossless)'
},
'PDF': {
'use_for': 'Submission, print, archival',
'benefits': 'Vector or raster, text searchable, widely accepted',
'variants': 'PDF/A (archival), PDF/X (print)'
},
'PNG': {
'use_for': 'Web, presentations, digital',
'benefits': 'Lossless, transparency, good compression',
'limitation': 'RGB only, larger than JPEG'
},
'SVG': {
'use_for': 'Web, further editing, scalable graphics',
'benefits': 'Vector, infinitely scalable, small file size',
'limitation': 'Not always accepted by journals'
},
'EPS': {
'use_for': 'Legacy journal requirements',
'benefits': 'Vector format accepted by older systems',
'limitation': 'Being phased out, use PDF instead'
}
}
得:應發道之格 敗:察發者需、供多格
建網優版:
def optimize_for_web(input_path, output_path, max_width=1200, quality=85):
"""Optimize image for web publication."""
from PIL import Image
img = Image.open(input_path)
# Resize if too large
if img.width > max_width:
ratio = max_width / img.width
new_height = int(img.height * ratio)
img = img.resize((max_width, new_height), Image.LANCZOS)
# Convert to RGB if needed
if img.mode in ('RGBA', 'LA', 'P'):
background = Image.new('RGB', img.size, (255, 255, 255))
if img.mode == 'P':
img = img.convert('RGBA')
background.paste(img, mask=img.split()[-1] if 'A' in img.mode else None)
img = background
# Save optimized
img.save(output_path, format='JPEG', quality=quality, optimize=True, progressive=True)
# Check file size
import os
file_size_kb = os.path.getsize(output_path) / 1024
print(f"Optimized: {file_size_kb:.1f} KB")
def create_responsive_set(input_path, output_base):
"""Create multiple resolutions for responsive web."""
from PIL import Image
img = Image.open(input_path)
sizes = [
(640, '640w'),
(1024, '1024w'),
(1920, '1920w')
]
for width, suffix in sizes:
if img.width >= width:
ratio = width / img.width
height = int(img.height * ratio)
resized = img.resize((width, height), Image.LANCZOS)
output = f"{output_base}_{suffix}.jpg"
resized.save(output, format='JPEG', quality=85, optimize=True)
得:網優圖 <500KB、響應大已生 敗:減質、續減大、考 WebP 格
加描述屬為存:
from PIL import Image
from PIL.PngImagePlugin import PngInfo
def embed_metadata(image_path, output_path, metadata):
"""Embed metadata in PNG."""
img = Image.open(image_path)
# Create metadata
png_info = PngInfo()
for key, value in metadata.items():
png_info.add_text(key, str(value))
# Save with metadata
img.save(output_path, format='PNG', pnginfo=png_info)
# Example metadata
metadata = {
'Title': 'Figure 1: Relationship between weight and fuel efficiency',
'Author': 'Jane Doe',
'Description': 'Scatter plot showing negative correlation',
'Copyright': 'CC-BY 4.0',
'Software': 'R 4.3.0, ggplot2 3.4.0',
'Creation Date': '2026-02-16',
'Source': 'mtcars dataset'
}
embed_metadata('figure1.png', 'figure1_with_metadata.png', metadata)
得:屬已嵌可取 敗:察格支屬(PNG、TIFF、PDF 是;JPEG 限)