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# -*- coding: utf-8 -*-
"""PyRGG params."""
from textwrap import dedent, fill
import os
PYRGG_VERSION = "2.0"
os.environ["PYRGG_TEST_MODE"] = "0"
MENU_ITEMS = {
1: ["engine", dedent(
"""\
- Select generation engine :
1- PyRGG
2- Erdos-Renyi-Gilbert - G(n, p)
3- Erdos-Renyi - G(n, m)
4- Stochastic Block Model - G(C, P)
5- Barabasi-Albert - G(n, k)
6- Watts-Strogatz - G(n, k, p)
7- Random Geometric Graph - G(d, r)
"""
)],
2: ["file_name", "- File Name (Not Empty) : "],
3: ["number_of_files", "- Number of Files (>=0) : "],
4: ["output_format", dedent(
"""\
- Graph Formats :
1- DIMACS(.gr)
2- JSON(.json)
3- CSV(.csv)
4- YAML(.yaml)
5- WEL(.wel)
6- ASP(.lp)
7- Pickle(.p)
8- UCINET DL(.dl)
9- TGF(.tgf)
10- TSV(.tsv)
11- Matrix Market(.mtx)
12- Graph Line(.gl)
13- GDF(.gdf)
14- GML(.gml)
15- GEXF(.gexf)
16- DOT(.gv)
"""
)],
5: ["config", "- Save Config[1] or Not[0]"],
}
SUFFIX_MENU = {
1: ".gr",
2: ".json",
3: ".csv",
4: ".yaml",
5: ".wel",
6: ".lp",
7: ".p",
8: ".dl",
9: ".tgf",
10: ".tsv",
11: ".mtx",
12: ".gl",
13: ".gdf",
14: ".gml",
15: ".gexf",
16: ".gv"
}
ENGINE_MENU = {
1: "pyrgg",
2: "erg",
3: "er",
4: "sbm",
5: "ba",
6: "ws",
7: "gg",
}
ENGINE_MENU_INV = {v: k for k, v in ENGINE_MENU.items()}
PYRGG_ENGINE_PARAMS = {
1: ["vertices", "- Vertices Number (n >= 0) : "],
2: ["min_edges", "- Min Edge Number - Connected to Each Vertex (>=0) : "],
3: ["max_edges", "- Max Edge Number - Connected to Each Vertex (>=0) : "],
4: ["weight", "- Unweighted[0] or Weighted[1]"],
5: ["min_weight", "- Min Weight : "],
6: ["max_weight", "- Max Weight : "],
7: ["sign", "- Unsigned[0] or Signed[1]"],
8: ["direct", "- Undirected[0] or Directed[1]"],
9: ["self_loop", "- No Self Loop[0] or Self Loop[1]"],
10: ["multigraph", "- Simple[0] or Multigraph[1]"],
}
ERG_ENGINE_PARAMS = {
1: ["vertices", "- Vertices Number (n >= 0) : "],
2: ["probability", "- Probability (0 <= p <= 1) : "],
3: ["direct", "- Undirected[0] or Directed[1]"],
}
ER_ENGINE_PARAMS = {
1: ["vertices", "- Vertices Number (n >= 0) : "],
2: ["edge_number", "- Edge Number (m >= 0) : "],
3: ["direct", "- Undirected[0] or Directed[1]"],
}
SBM_ENGINE_PARAMS = {
1: ["vertices", "- Vertices Number (n >= 0) : "],
2: ["blocks", "- Blocks Number (k >= 0) : "],
3: ["intra_probability", "- Within Block Probability (0 <= p <= 1): "],
4: ["inter_probability", "- Between Block Probability (0 <= p <= 1): "],
5: ["direct", "- Undirected[0] or Directed[1]"],
6: ["self_loop", "- No Self Loop[0] or Self Loop[1]"],
}
BA_ENGINE_PARAMS = {
1: ["vertices", "- Vertices Number (n >= 0) : "],
2: ["attaching_edge_number", "- Number of Edges to Attach to a New Node (0 < k < n) : "],
}
WS_ENGINE_PARAMS = {
1: ["vertices", "- Vertices Number (n >= 0) : "],
2: ["mean_degree", "- Mean Degree (should be a positive even number) : "],
3: ["rewiring_probability", "- Rewiring Probability (0 <= p <= 1) : "],
}
GG_ENGINE_PARAMS = {
1: ["vertices", "- Vertices Number (n >= 0) : "],
2: ["space_dimension", "- The space dimension (d > 0) : "],
3: ["cutoff_threshold", "- Cutoff threshold distance (0 <= r <= 1) : "],
}
ENGINE_PARAM_MAP = {
1: PYRGG_ENGINE_PARAMS,
2: ERG_ENGINE_PARAMS,
3: ER_ENGINE_PARAMS,
4: SBM_ENGINE_PARAMS,
5: BA_ENGINE_PARAMS,
6: WS_ENGINE_PARAMS,
7: GG_ENGINE_PARAMS,
}
OUTPUT_FORMAT = {i: output_format[1:].upper()
for i, output_format in SUFFIX_MENU.items()}
OUTPUT_FORMAT_INV = {v: k for k, v in OUTPUT_FORMAT.items()}
SOURCE_DIR = os.getcwd()
CONFIG_FILE_FORMAT = "{file_name}.pyrgg.config.json"
PYRGG_LINKS = """
Webpage : https://www.pyrgg.site
Repository : https://github.com/sepandhaghighi/pyrgg
Paper : https://doi.org/10.21105/joss.00331
* If you use PyRGG in your research, please cite our paper
"""
_description = """\
PyRGG is a user-friendly synthetic random graph generator that is written in Python and supports multiple graph file formats, such as DIMACS-Graph files.
It can generate graphs of various sizes and is specifically designed to create input files for a wide range of graph-based research applications, including testing,
benchmarking, and performance analysis of graph processing frameworks.
PyRGG is aimed at computer scientists who are studying graph algorithms and graph processing frameworks.
"""
PYRGG_DESCRIPTION = fill(_description, width=113)
PYRGG_INPUT_ERROR_MESSAGE = "[Error] Bad input!"
PYRGG_FILE_ERROR_MESSAGE = "[Error] Bad input file!"
PYRGG_INVALID_ENGINE_ERROR_MESSAGE = "[Error] Invalid engine!"
PYRGG_YAML_ERROR_MESSAGE = "[Error] Failed to generate YAML file!"
PYRGG_PICKLE_ERROR_MESSAGE = "[Error] Failed to generate Pickle file!"
PYRGG_LOGGER_ERROR_MESSAGE = "[Error] Logger failed!"
PYRGG_CONFIG_LOAD_ERROR_MESSAGE = "[Error] Failed to load config file!"
PYRGG_CONFIG_SAVE_ERROR_MESSAGE = "[Error] Failed to save config file!"
PYRGG_UNDIVISIBLE_WARNING_MESSAGE = "[Warning] Number of vertices is not divisible by the number of blocks. The last block would contain the remainder."
PYRGG_SBM_WARNING_MESSAGE = "[Warning] Stochastic block model gets the number of blocks and between/within block probabilities. To get more detailed configuration, please save and edit the config file."
PYRGG_CONFIG_LIST_MESSAGE = "Config files detected in the current directory are listed below:"
PYRGG_CONFIG_LOAD_MESSAGE = "Press the config index to load or any other keys to start with the new one:"
DIMACS_FIX = dedent(
"""\
c FILE :{file_name}.gr
c No. of vertices :{vertices_number}
c No. of edges :{edge_number}
c Max. weight :{max_weight}
c Min. weight :{min_weight}
p sp {vertices_number} {edge_number}
"""
)
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