Regular Expressions 101

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An explanation of your regex will be automatically generated as you type.
Detailed match information will be displayed here automatically.
  • All Tokens
  • Common Tokens
  • General Tokens
  • Anchors
  • Meta Sequences
  • Quantifiers
  • Group Constructs
  • Character Classes
  • Flags/Modifiers
  • Substitution
  • A single character of: a, b or c
    [abc]
  • A character except: a, b or c
    [^abc]
  • A character in the range: a-z
    [a-z]
  • A character not in the range: a-z
    [^a-z]
  • A character in the range: a-z or A-Z
    [a-zA-Z]
  • Any single character
    .
  • Alternate - match either a or b
    a|b
  • Any whitespace character
    \s
  • Any non-whitespace character
    \S
  • Any digit
    \d
  • Any non-digit
    \D
  • Any word character
    \w
  • Any non-word character
    \W
  • Non-capturing group
    (?:...)
  • Capturing group
    (...)
  • Zero or one of a
    a?
  • Zero or more of a
    a*
  • One or more of a
    a+
  • Exactly 3 of a
    a{3}
  • 3 or more of a
    a{3,}
  • Between 3 and 6 of a
    a{3,6}
  • Start of string
    ^
  • End of string
    $
  • A word boundary
    \b
  • Non-word boundary
    \B

Regular Expression
Processing...

Test String

Code Generator

Generated Code

import re regex = re.compile(r"(?<=\W)\$[a-zA-Z_\\\{\}= +']+\$", flags=re.MULTILINE) test_str = ("We try to quantitatively capture these characteristics by defining a set of indexes, which can be computed using the mosaic image and the corresponding ground truth:\n" "\\begin{itemize}\n" " \\item $\\mu_{A_T}$ and $\\sigma_{A_T}$, the mean and standard deviation of the tiles area $A_T$, respectively;\n" " \\item $\\rho_\\text{filler}$, the ratio between the filler area and the overall mosaic are, computed as $\\rho_\\text{filler}=\\frac{\\sum_{T \\in \\mathcal{T} A_T}}{A}$, being $A$ the area of the mosaic;\n" " \\item \\todo{does it worth?};\n" " \\item \\todo{does it worth?};\n" " \\item $\\mu_{C_T}$, the mean of the tiles \\emph{color dispersion} $C_T$, being $C_T = \\sigma_R+\\sigma_G+\\sigma_B$, where $\\sigma_R$, $\\sigma_G$ and $\\sigma_B$ are the standard deviation of the red, green and blue channel values of the pixels within the tile $T$.\n" "After applying a method to an image, we compare the segmented image (i.e., the result) against the ground truth and assess the performance according to the following three metrics:\n" "\\begin{itemize}\n" " \\item average tile precision $P$\n" " \\item average tile recall $R$\n" " \\item tile count error $C$\n" "\\end{itemize}\n" "Let $T$ be a tile on the ground truth $\\mathcal{T}$ with area $A_T$.\n" "Let $T'$ be the tile in the segmented image which mostly overlaps $T$ and let $A_{T'}$ be the area of $T$; let $A_{T \\cap T'}$ be the overlapping area between $T$ and $T'$.\n" "Let $n$ and $n'$ the number of tiles respectively in the ground truth and in the segmented image.\n" "Metrics are defined as:\n" "\\begin{align}\n" " P &amp;= \\frac{1}{n} \\sum_{T \\in \\mathcal{T}} \\frac{A_{T \\cap T'}}{A_{T'}} \\\\\n" " R &amp;= \\frac{1}{n} \\sum_{T \\in \\mathcal{T}} \\frac{A_{T \\cap T'}}{A_T} \\\\\n" " C &amp;= \\frac{|n-n'|}{n}\n" "\\end{align}\n" "We try to quantitatively capture these characteristics by defining a set of indexes, which can be computed using the mosaic image and the corresponding ground truth:\n" "\\begin{itemize}\n" " \\item $\\mu_{A_T}$ and $\\sigma_{A_T}$, the mean and standard deviation of the tiles area $A_T$, respectively;\n" " \\item $\\rho_\\text{filler}$, the ratio between the filler area and the overall mosaic are, computed as $\\rho_\\text{filler}=\\frac{\\sum_{T \\in \\mathcal{T} A_T}}{A}$, being $A$ the area of the mosaic;\n" " \\item \\todo{does it worth?};\n" " \\item \\todo{does it worth?};\n" " \\item $\\mu_{C_T}$, the mean of the tiles \\emph{color dispersion} $C_T$, being $C_T = \\sigma_R+\\sigma_G+\\sigma_B$, where $\\sigma_R$, $\\sigma_G$ and $\\sigma_B$ are the standard deviation of the red, green and blue channel values of the pixels within the tile $T$.\n" "After applying a method to an image, we compare the segmented image (i.e., the result) against the ground truth and assess the performance according to the following three metrics:\n" "\\begin{itemize}\n" " \\item average tile precision $P$\n" " \\item average tile recall $R$\n" " \\item tile count error $C$\n" "\\end{itemize}\n" "Let $T$ be a tile on the ground truth $\\mathcal{T}$ with area $A_T$.\n" "Let $T'$ be the tile in the segmented image which mostly overlaps $T$ and let $A_{T'}$ be the area of $T$; let $A_{T \\cap T'}$ be the overlapping area between $T$ and $T'$.\n" "Let $n$ and $n'$ the number of tiles respectively in the ground truth and in the segmented image.\n" "Metrics are defined as:\n" "\\begin{align}\n" " P &amp;= \\frac{1}{n} \\sum_{T \\in \\mathcal{T}} \\frac{A_{T \\cap T'}}{A_{T'}} \\\\\n" " R &amp;= \\frac{1}{n} \\sum_{T \\in \\mathcal{T}} \\frac{A_{T \\cap T'}}{A_T} \\\\\n" " C &amp;= \\frac{|n-n'|}{n}\n" "\\end{align}") matches = regex.finditer(test_str) for match_num, match in enumerate(matches, start=1): print(f"Match {match_num} was found at {match.start()}-{match.end()}: {match.group()}") for group_num, group in enumerate(match.groups(), start=1): print(f"Group {group_num} found at {match.start(group_num)}-{match.end(group_num)}: {group}")

Please keep in mind that these code samples are automatically generated and are not guaranteed to work. If you find any syntax errors, feel free to submit a bug report. For a full regex reference for Python, please visit: https://docs.python.org/3/library/re.html