├── .gitattributes ├── .gitignore ├── LICENSE ├── README.md ├── pastamarkers ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-311.pyc │ ├── __init__.cpython-313.pyc │ ├── markers.cpython-311.pyc │ ├── markers.cpython-313.pyc │ ├── markers_old.cpython-313.pyc │ ├── utils.cpython-311.pyc │ └── utils.cpython-313.pyc ├── add_marker.py ├── cupboard.py ├── markers.pkl ├── markers.py ├── pasta.pkl └── salsa.pkl ├── poetry.lock ├── pyproject.toml ├── source ├── generate_paths.py ├── markers.svg ├── markers_dev.svg ├── pastamarkers_logo.svg └── salsa.yaml └── test ├── data └── MPD │ ├── B.csv │ ├── B_curve.csv │ ├── IR.csv │ ├── IR_curve.csv │ ├── UV.csv │ └── UV_curve.csv ├── flowers-svgrepo-com.svg ├── test.ipynb ├── test.png ├── test.py └── test_v2.ipynb /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore Python build files 2 | __pycache__/ 3 | *.egg-info/ 4 | build/ 5 | dist/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # pastamarkers 2 | This is the amazing **pasta marker project**, which allows you to use the famous italian pasta types as matplotlib markers. 3 | 4 | pastamarkers logo 5 | 6 | 7 | ## Prerequisites: 8 | - Python 3.10 9 | 10 | ## Installation 11 | The code can be quikly installed from Pypi: 12 | ``` 13 | pip install pastamarkers 14 | ``` 15 | 16 | ## Usage 17 | Import the necessary modules: 18 | ``` 19 | import matplotlib.pyplot as plt 20 | from pastamarkers import pasta, salsa 21 | ``` 22 | Then, you can directly pass the desired pasta (i.e. marker) and salsa (i.e. colormaps) to the matplotlib argument: 23 | ``` 24 | plt.scatter(x, y, marker=pasta.penne, c=z, cmap=salsa.pesto, s=500, linewidth=0.2) 25 | ``` 26 | Note: play with `size` and `linewidth` parameters for a perfect pasta marker! 27 | 28 | You can also top your pastaplot with parmesan chees by adding ```_p``` to your colormaps. 29 | Alternatively, you may use the function ```add_parmesan``` to _sprinkle_ a bit of chees on your pastamarkers 30 | 31 | ``` 32 | from pastamarkers import cupboard 33 | 34 | fig, ax = plt.subplots(1,1) 35 | ax.scatter(x, y, s=500, c=z, marker=pasta.tagliatelle, cmap=salsa.ragu, linewidth=0.2) 36 | cupboard.add_parmesan(ax, amount = 10000) 37 | ``` 38 | 39 | Below you can see the supported data types. Please open an Issue if you would like new pasta types to be included! 40 | 41 | ## Examples 42 | See also the `\test` folder for working examples. 43 | 44 | 45 | 46 | 47 | ## Citation 48 | 49 | If you find this code useful in your research, please cite the following paper ([ADS](https://ui.adsabs.harvard.edu/abs/2024arXiv240320314P), [arXiv](https://arxiv.org/abs/2403.20314)): 50 | 51 | @ARTICLE{2024arXiv240320314P, 52 | author = {{PASTA Collaboration}}, 53 | title = "{pastamarkers: astrophysical data visualization with pasta-like markers}", 54 | journal = {arXiv e-prints}, 55 | keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, 56 | year = 2024, 57 | month = mar, 58 | eid = {arXiv:2403.20314}, 59 | pages = {arXiv:2403.20314}, 60 | doi = {10.48550/arXiv.2403.20314}, 61 | archivePrefix = {arXiv}, 62 | eprint = {2403.20314}, 63 | primaryClass = {astro-ph.IM}, 64 | adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240320314P}, 65 | adsnote = {Provided by the SAO/NASA Astrophysics Data System} 66 | } 67 | 68 | 69 | ## Currently supported pasta types 70 | 71 | CHIMERA 72 | 73 | -------------------------------------------------------------------------------- /pastamarkers/__init__.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pickle 3 | from collections import namedtuple 4 | from matplotlib.colors import LinearSegmentedColormap 5 | 6 | import sys 7 | 8 | __ROOT__ = os.path.dirname(__file__) 9 | sys.path.append(__ROOT__) 10 | 11 | from cupboard import * 12 | 13 | # dir_data = __ROOT__ + "/data/" 14 | dir_data = os.path.join(__ROOT__, "data") 15 | 16 | # loading _markers 17 | # pasta_file_path = __ROOT__ + r"\pasta.pkl" 18 | pasta_file_path = os.path.join(__ROOT__, "pasta.pkl") 19 | with open(pasta_file_path, "rb") as pkl_file: 20 | _markers = pickle.load(pkl_file) 21 | 22 | _Marker = namedtuple("Marker", [key for key in _markers.keys()]) 23 | pasta = _Marker(*_markers.values()) 24 | 25 | # loading salsa 26 | salsa_file_path = os.path.join(__ROOT__, "salsa.pkl") 27 | with open(salsa_file_path, "rb") as pkl_file: 28 | _cmaps = pickle.load(pkl_file)["colormaps"] 29 | 30 | _Cmaps = namedtuple("Cmaps", [key for key in _cmaps.keys()]) 31 | 32 | salsa = _Cmaps( 33 | *[LinearSegmentedColormap.from_list(key, val[::-1]) for key, val in _cmaps.items()] 34 | ) 35 | -------------------------------------------------------------------------------- /pastamarkers/__pycache__/__init__.cpython-311.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LR-inaf/pasta-marker/377a1bd6a44ef341201a3105016b72ce8f828242/pastamarkers/__pycache__/__init__.cpython-311.pyc -------------------------------------------------------------------------------- /pastamarkers/__pycache__/__init__.cpython-313.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /pastamarkers/add_marker.py: -------------------------------------------------------------------------------- 1 | import os 2 | import xml.etree.ElementTree as etree 3 | import pathlib 4 | import numpy as np 5 | from svgpath2mpl import parse_path 6 | 7 | SVG_PATH = "/home/luca/Scrivania/pastamarker/pasta-marker/test/flowers-svgrepo-com.svg" 8 | 9 | 10 | def main(SVG_PATH): 11 | # Parse the SVG file 12 | with open(SVG_PATH, "r") as file: 13 | tree = etree.parse(file) 14 | 15 | # Create a parent map 16 | parent_map = {c: p for p in tree.iter() for c in p} 17 | root = tree.getroot() 18 | 19 | # Find all path elements 20 | path_elems = root.findall(".//{http://www.w3.org/2000/svg}path") 21 | return path_elems 22 | 23 | 24 | if __name__ == "__main__": 25 | path = main(SVG_PATH) 26 | p = parse_path(path.attrib["d"]) 27 | print(p) 28 | -------------------------------------------------------------------------------- /pastamarkers/cupboard.py: -------------------------------------------------------------------------------- 1 | from matplotlib.pyplot import Axes 2 | import numpy as np 3 | 4 | # This cupboard contains the toppings for pasta. 5 | 6 | def add_parmesan(ax: Axes, amount: int = 10000) -> Axes: 7 | """Add parmesan to the Axes. 8 | Parameters: 9 | ax: Axes 10 | The Axes to which the parmesan will be added. 11 | amount: int 12 | The amount of parmesan to add. 13 | """ 14 | xlim = ax.get_xlim() 15 | ylim = ax.get_ylim() 16 | 17 | _ = ax.scatter( 18 | np.random.uniform(*xlim, amount), 19 | np.random.uniform(*ylim, amount), 20 | marker=".", 21 | color="white", 22 | s=3, 23 | ) 24 | 25 | ax.set(xlim=xlim, ylim=ylim) 26 | return ax 27 | -------------------------------------------------------------------------------- /pastamarkers/markers.pkl: 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numpy = "*" 526 | 527 | [metadata] 528 | lock-version = "2.0" 529 | python-versions = "^3.10" 530 | content-hash = "aa22bf1e35e07ed99fb036a83bfd2d16f06057ebea14c587dd62602767abdc7f" 531 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "pastamarkers" 3 | version = "1.0.2" 4 | description = "astrophysical data visualization with pasta-like markers" 5 | authors = ["Luca Rosignoli ", 6 | "Nicola Borghi ", 7 | "Giulia Papini ",] 8 | readme = "README.md" 9 | repository = "https://github.com/LR-inaf/pasta-marker" 10 | classifiers = [ 11 | "Development Status :: 5 - Production/Stable", 12 | "Intended Audience :: Science/Research", 13 | "Topic :: Scientific/Engineering :: Astronomy", 14 | "Topic :: Scientific/Engineering :: Visualization", 15 | "Programming Language :: Python :: 3", 16 | "Programming Language :: Python :: 3.8", 17 | "Programming Language :: Python :: 3.9", 18 | "Programming Language :: Python :: 3.10", 19 | "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", 20 | "Operating System :: OS Independent", 21 | ] 22 | packages = [ 23 | {include = "pastamarkers"}, 24 | ] 25 | include = [ 26 | { path = "source", format = "sdist" }, 27 | { path = "test", format = "sdist" }, 28 | ] 29 | 30 | [tool.poetry.dependencies] 31 | python = "^3.10" 32 | numpy = "^2.0.0" 33 | matplotlib = "^3.4.3" 34 | svgpath2mpl = "1.0.0" 35 | 36 | 37 | [build-system] 38 | requires = ["poetry-core"] 39 | build-backend = "poetry.core.masonry.api" -------------------------------------------------------------------------------- /source/generate_paths.py: -------------------------------------------------------------------------------- 1 | # Adapted from TC Markers. Credits to: abrammer 2 | # GitHub repo: https://github.com/abrammer/tc_markers/ 3 | 4 | # This script reads the SVG file containing the pasta markers and generates a Python file containing the paths for each marker. 5 | # The paths are then used in the main script to draw the markers. 6 | # This script should be run whenever the SVG file is updated and is only for developer use. 7 | # It requires the svgpath2mpl library to parse the SVG paths. 8 | 9 | import xml.etree.ElementTree as etree 10 | import pathlib 11 | import numpy as np 12 | from svgpath2mpl import parse_path 13 | from matplotlib.path import Path 14 | import shapely 15 | from itertools import combinations 16 | import pickle 17 | import yaml 18 | 19 | SVG_PATH = pathlib.Path(__file__).parent / "./markers_dev.svg" 20 | SALSA_PATH = pathlib.Path(__file__).parent / "./salsa.yaml" 21 | # SVG_PATH = pathlib.Path(__file__).parent / "./pasta-svgrepo-com.svg" 22 | 23 | 24 | def main(): 25 | # Parse the SVG file 26 | with open(SVG_PATH, "r") as file: 27 | tree = etree.parse(file) 28 | 29 | # Create a parent map 30 | parent_map = {c: p for p in tree.iter() for c in p} 31 | root = tree.getroot() 32 | 33 | # Find all path elements 34 | path_elems = root.findall(".//{http://www.w3.org/2000/svg}path") 35 | 36 | # Process each path element 37 | marks = { 38 | # path_elem.attrib["id"]: process_path(path_elem) 39 | path_elem.attrib["id"]: generate_filled_marker(process_path(path_elem)) 40 | for path_elem in path_elems 41 | } 42 | 43 | # Write the output 44 | # outpath = pathlib.Path(__file__).parent.parent / "pastamarkers/markers.py" 45 | outpath = pathlib.Path(__file__).parent.parent / "pastamarkers/markers.pkl" 46 | # with open(outpath, "wt") as outf: 47 | with open(outpath, "wb") as outf: 48 | # outf.write(generate_output(marks)) 49 | pickle.dump(marks, outf) 50 | 51 | # generate also salsa from yaml file 52 | # salsa_yaml_file = pathlib.Path(__file__).parent.parent / "pastamarkers/salsa.yaml" 53 | with open(SALSA_PATH, "r") as file: 54 | salsa = yaml.load(file, Loader=yaml.FullLoader) 55 | 56 | outpath = pathlib.Path(__file__).parent.parent / "pastamarkers/salsa.pkl" 57 | # with open(outpath, "wt") as outf: 58 | with open(outpath, "wb") as outf: 59 | # outf.write(generate_output(marks)) 60 | pickle.dump(salsa, outf) 61 | 62 | 63 | def generate_filled_marker(marker): 64 | exploded_polygons_marker = [shapely.Polygon(el) for el in marker.to_polygons()] 65 | 66 | new_polygons = [] 67 | new_codes = [] 68 | for poly in exploded_polygons_marker: 69 | for i, (x, y) in enumerate(np.array(poly.exterior.coords.xy).T): 70 | if i == 0: 71 | new_codes.append(1) 72 | else: 73 | new_codes.append(2) 74 | new_polygons.append([float(x), float(y)]) 75 | new_codes.pop() 76 | new_codes.append(79) 77 | 78 | for pol1, pol2 in combinations(exploded_polygons_marker, 2): 79 | if pol1.contains(pol2): 80 | for i, (x, y) in enumerate(np.array(pol2.exterior.coords.xy).T): 81 | if i == 0: 82 | new_codes.append(1) 83 | else: 84 | new_codes.append(2) 85 | new_polygons.append([float(x), float(y)]) 86 | new_codes.pop() 87 | new_codes.append(79) 88 | elif pol2.contains(pol1): 89 | for i, (x, y) in enumerate(np.array(poly.exterior.coords.xy).T): 90 | if i == 0: 91 | new_codes.append(1) 92 | else: 93 | new_codes.append(2) 94 | new_polygons.append([float(x), float(y)]) 95 | new_codes.pop() 96 | new_codes.append(79) 97 | 98 | new_marker = Path(new_polygons, new_codes) 99 | return new_marker 100 | 101 | 102 | def process_path(path_elem): 103 | p = parse_path(path_elem.attrib["d"]) 104 | verts = p.vertices 105 | offset = verts.max(axis=0) - (verts.max(axis=0) - verts.min(axis=0)) / 2 106 | for vert in p._vertices: 107 | vert[0] -= offset[0] 108 | vert[1] -= offset[1] 109 | vert[1] *= -1 110 | p._vertices = np.round(p.vertices, 2) # clean up the file 111 | return p.deepcopy() 112 | 113 | 114 | def generate_output(marks): 115 | output = """ 116 | # Automatically generated Paths for each pasta marker from the SVG file. 117 | # Do not edit manually. Edit SVG and regenerate this file. 118 | 119 | from matplotlib.path import Path 120 | from numpy import array, uint8 121 | 122 | """ 123 | for key, path in marks.items(): 124 | output += f"{key} = {path}\n\n" # relying on the matplotlib.path.Path repr 125 | return output 126 | 127 | 128 | if __name__ == "__main__": 129 | main() 130 | -------------------------------------------------------------------------------- /source/markers.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 7 | 12 | 46 | 74 | 100 | 113 | 128 | 156 | 169 | 192 | 212 | 227 | 243 | 263 | 282 | 292 | 310 | 327 | 343 | 358 | 374 | 384 | 404 | 421 | 442 | 458 | 468 | 490 | 513 | 532 | 556 | 582 | 607 | 632 | 647 | 662 | penne 663 | fusilli 664 | gramigna 665 | pipe 666 | tortellini 667 | tagliatelle 668 | stelline 669 | farfalline 670 | puntine 671 | tempestine 672 | tagliolini 673 | pappardelle 674 | mafaldine 675 | spaghetti 676 | cappelletti 677 | ravioli 678 | mezzelune 679 | soli 680 | lasagne 681 | farfalle 682 | conchiglie 683 | gnocchi 684 | creste 685 | maccheroni 686 | ruote 687 | fiori 688 | spighe 689 | fusilli2 690 | cavatappi 691 | gemelli 692 | radiatori 693 | rigatoni 694 | tortiglioni 695 | garganelli 696 | 697 | -------------------------------------------------------------------------------- /source/salsa.yaml: -------------------------------------------------------------------------------- 1 | colormaps: 2 | pomodoro: [ 3 | xkcd:dark red, 4 | xkcd:deep red, 5 | xkcd:scarlet, 6 | xkcd:tomato, 7 | xkcd:sand brown, 8 | xkcd:sand, 9 | xkcd:dull yellow, 10 | xkcd:gold, 11 | ] 12 | pesto: [ 13 | xkcd:forest, 14 | xkcd:emerald green, 15 | xkcd:kelly green, 16 | xkcd:apple green, 17 | xkcd:sand, 18 | xkcd:dull yellow, 19 | xkcd:dark yellow, 20 | ] 21 | cacio_e_pepe: [ 22 | black, 23 | xkcd:brown gray, 24 | xkcd:sand, 25 | xkcd:light tan, 26 | xkcd:off white, 27 | ] 28 | radicchio: [ 29 | xkcd:maroon, 30 | xkcd:red wine, 31 | xkcd:deep rose, 32 | xkcd:light rose, 33 | xkcd:very light pink, 34 | white, 35 | xkcd:very light pink, 36 | xkcd:buff, 37 | xkcd:dull yellow, 38 | xkcd:dark yellow, 39 | ] 40 | norma: [ 41 | xkcd:eggplant, 42 | xkcd:rich purple, 43 | xkcd:scarlet, 44 | xkcd:tomato, 45 | xkcd:blush, 46 | white, 47 | xkcd:light beige, 48 | xkcd:dull yellow, 49 | xkcd:dark yellow, 50 | xkcd:emerald green, 51 | xkcd:forest, 52 | ] 53 | ragu: [ 54 | xkcd:brown, 55 | xkcd:chestnut, 56 | xkcd:dark red, 57 | xkcd:deep red, 58 | xkcd:scarlet, 59 | xkcd:vermillion, 60 | xkcd:dark yellow, 61 | xkcd:dull yellow, 62 | xkcd:buff, 63 | ] 64 | carbonara: [ 65 | xkcd:cerise, 66 | xkcd:pink, 67 | xkcd:baby pink, 68 | xkcd:pale salmon, 69 | xkcd:light orange, 70 | xkcd:yellow orange, 71 | xkcd:goldenrod, 72 | xkcd:dull yellow, 73 | xkcd:buff, 74 | ] 75 | funghi: [ 76 | xkcd:chocolate brown, 77 | xkcd:brown, 78 | xkcd:earth, 79 | xkcd:dark yellow, 80 | xkcd:dull yellow, 81 | xkcd:buff, 82 | ] 83 | puttanesca: [ 84 | xkcd:dark maroon, 85 | xkcd:mahogany, 86 | xkcd:dark red, 87 | xkcd:deep red, 88 | xkcd:scarlet, 89 | xkcd:light maroon, 90 | xkcd:camo, 91 | xkcd:avocado, 92 | xkcd:dark yellow, 93 | xkcd:dull yellow, 94 | xkcd:buff, 95 | ] 96 | pomodoro_p: [ 97 | xkcd:gold, 98 | xkcd:dull yellow, 99 | xkcd:sand, 100 | xkcd:sand brown, 101 | xkcd:tomato, 102 | xkcd:scarlet, 103 | xkcd:deep red, 104 | xkcd:dark red, 105 | xkcd:off white 106 | ] 107 | pesto_p: [ 108 | xkcd:dark yellow, 109 | xkcd:dull yellow, 110 | xkcd:sand, 111 | xkcd:apple green, 112 | xkcd:kelly green, 113 | xkcd:emerald green, 114 | xkcd:forest, 115 | xkcd:off white 116 | ] 117 | ragu_p: [ 118 | xkcd:buff, 119 | xkcd:dull yellow, 120 | xkcd:dark yellow, 121 | xkcd:vermillion, 122 | xkcd:scarlet, 123 | xkcd:deep red, 124 | xkcd:dark red, 125 | xkcd:chestnut, 126 | xkcd:brown, 127 | xkcd:off white 128 | ] 129 | carbonara_p: [ 130 | xkcd:buff, 131 | xkcd:dull yellow, 132 | xkcd:goldenrod, 133 | xkcd:yellow orange, 134 | xkcd:light orange, 135 | xkcd:pale salmon, 136 | xkcd:baby pink, 137 | xkcd:pink, 138 | xkcd:cerise, 139 | xkcd:off white 140 | ] 141 | funghi_p: [ 142 | xkcd:buff, 143 | xkcd:dull yellow, 144 | xkcd:dark yellow, 145 | xkcd:earth, 146 | xkcd:brown, 147 | xkcd:chocolate brown, 148 | xkcd:off white 149 | ] 150 | puttanesca_p: [ 151 | xkcd:buff, 152 | xkcd:dull yellow, 153 | xkcd:dark yellow, 154 | xkcd:avocado, 155 | xkcd:camo, 156 | xkcd:light maroon, 157 | xkcd:scarlet, 158 | xkcd:deep red, 159 | xkcd:dark red, 160 | xkcd:mahogany, 161 | xkcd:dark maroon, 162 | xkcd:off white 163 | ] -------------------------------------------------------------------------------- /test/data/MPD/B.csv: -------------------------------------------------------------------------------- 1 | 0.004762722715845102, 26.48058839144896 2 | 0.07690626024766178, 26.524928559159264 3 | 0.25960742672434, 26.574880931932103 4 | 0.34775683567825877, 26.628084448539173 5 | 0.629616327550399, 26.850019519355392 6 | 0.7605521635253516, 27.030818719842596 7 | 0.8792298443136217, 27.045317697028374 8 | 1.2437733256296943, 27.11255172629179 9 | 1.7541654304408247, 27.027867393307204 10 | -------------------------------------------------------------------------------- /test/data/MPD/B_curve.csv: -------------------------------------------------------------------------------- 1 | 0.02592170396164839, 26.38547603803932 2 | 0.1293742875435282, 26.42081231749403 3 | 0.1777042114961196, 26.459282624650605 4 | 0.22579990318399745, 26.48884273645747 5 | 0.28232795640156794, 26.539169880853855 6 | 0.339012164462281, 26.595437155483378 7 | 0.4273958056809135, 26.657550867440154 8 | 0.48400193632005495, 26.71084807695311 9 | 0.5567700932244413, 26.778948765595967 10 | 0.6133762238635834, 26.83224597510892 11 | 0.6779462515029904, 26.88848982651197 12 | 0.7425162791423974, 26.94473367791502 13 | 0.8148940489389278, 26.99798404097503 14 | 0.9188931744718061, 27.054110776245725 15 | 1.0147722481612766, 27.101350739393183 16 | 1.14196036790081, 27.139586814285046 17 | 1.3242711472696325, 27.17468886147504 18 | 1.5132965848935807, 27.16521650869002 19 | 1.6544605630943638, 27.135094239447838 20 | 1.8584768656599882, 27.095874389044177 21 | 2.0071362763316105, 27.050878370992677 22 | 2.1245647183747405, 27.01785630631334 23 | 2.2417589281531565, 26.975924046284295 24 | 2.3509892409313067, 26.931045144365154 25 | 2.47622542513156, 26.895029591342777 26 | 2.5699183310170364, 26.859107731226285 27 | 2.7107699995315353, 26.817105201517826 28 | 2.8751229719389744, 26.769092271896813 29 | 2.992317181717391, 26.72716001186777 30 | 3.1016255719171126, 26.685251175065197 31 | 3.242477240431611, 26.643248645356735 32 | 3.446103155889379, 26.58917846937023 33 | 3.610299973453676, 26.535225409516077 34 | 3.7588813067037266, 26.487259326348006 35 | 3.9937381907899874, 26.421215196989337 36 | 4.251940223926045, 26.34322053748497 37 | 4.518106154062368, 26.268172519870703 38 | 4.72953981167726, 26.211108855541156 39 | 4.901856681084963, 26.16604256781024 40 | -------------------------------------------------------------------------------- /test/data/MPD/IR.csv: -------------------------------------------------------------------------------- 1 | -0.003981948500132448, 27.047941098393167 2 | 0.3562672746295228, 27.251821546245257 3 | 0.6428114117959369, 27.351960524055656 4 | 0.8833679476568963, 27.502731148206564 -------------------------------------------------------------------------------- /test/data/MPD/IR_curve.csv: -------------------------------------------------------------------------------- 1 | -0.05902653070784969, 27.054045191211607 2 | 0.07651587313980546, 27.110078233576417 3 | 0.20425053483033784, 27.169104764284263 4 | 0.3080154280985026, 27.216321304205252 5 | 0.41193647620980944, 27.269477974359376 6 | 0.5317853183216479, 27.328527928293695 7 | 0.6833335935914051, 27.393424319555272 8 | 0.8184856103312041, 27.434607036337233 9 | 0.8979684254907165, 27.458133325005075 10 | 1.0801230500163967, 27.48729524196193 11 | 1.1827167819609925, 27.489960805134373 12 | 1.324505379534346, 27.483599056824747 13 | 1.4185886725276782, 27.462527522291104 14 | 1.6224488202501601, 27.417367541654304 15 | 1.8019488124424177, 27.345547244647793 16 | 2.0834179172067016, 27.252631989881166 17 | 2.3411514858133318, 27.156816939677384 18 | 2.6299598682053116, 27.0430878058683 19 | 2.966083168069457, 26.929218132700385 20 | 3.2004715876262897, 26.845353612642295 21 | 3.4348600071831226, 26.761489092584206 22 | 3.6772904011617915, 26.683541279532786 23 | 3.9197207951404605, 26.605593466481363 24 | 4.1856524930120695, 26.52163525351739 25 | 4.420431299676759, 26.452621059042148 26 | 4.702368868970471, 26.377526194974937 27 | 4.913958681428504, 26.32640266087853 28 | -------------------------------------------------------------------------------- /test/data/MPD/UV.csv: -------------------------------------------------------------------------------- 1 | 0.3517387841783912, 25.879557769484222 2 | 0.6283670888052593, 26.20249847749028 3 | 0.8732959602742079, 26.519592748169085 4 | 0.7548525117506517, 26.514003966333018 5 | 1.2561095582379487, 26.681822014709788 6 | 1.758381611205671, 26.58825090960196 7 | 2.739736722934462, 26.418999359765145 8 | 3.9830415840347286, 26.0143162760193 9 | -------------------------------------------------------------------------------- /test/data/MPD/UV_curve.csv: -------------------------------------------------------------------------------- 1 | 0.032792517059916815, 25.746841768297443 2 | 0.10431143521916342, 25.76742141507519 3 | 0.1762988179078373, 25.80582145255235 4 | 0.23282687112540823, 25.856148596948735 5 | 0.28170333702899786, 25.9154093599213 6 | 0.3543934165118132, 25.98053998344759 7 | 0.4354377801027498, 26.063467574446822 8 | 0.5003201174284422, 26.131591686316153 9 | 0.5576289448617247, 26.21161948187823 10 | 0.631021721138681, 26.303480691453647 11 | 0.7116756976217617, 26.37155795687003 12 | 0.7527444213682286, 26.43381220818564 13 | 0.8255125782726154, 26.501912896828497 14 | 0.8903949155983071, 26.570037008697824 15 | 0.9869766860819187, 26.644007557894408 16 | 1.0755164821436938, 26.712061400084323 17 | 1.163587813676041, 26.762294851574822 18 | 1.2589203454145128, 26.788744358906293 19 | 1.3381689283093117, 26.803360452224425 20 | 1.4250690985180903, 26.80904292696638 21 | 1.4962757069910517, 26.817742313277847 22 | 1.575055825356423, 26.814538015896563 23 | 1.6772591701931634, 26.802353253486157 24 | 1.7636908758725145, 26.790215337528693 25 | 1.8343509423944782, 26.778124268024172 26 | 1.920470338387545, 26.75410609160043 27 | 2.0381330126953885, 26.729994222270804 28 | 2.155483377316947, 26.6940020924749 29 | 2.4374209466106587, 26.61890722840769 30 | 2.7743250206905166, 26.53473820640547 31 | 3.063914177298208, 26.45070972376208 32 | 3.4551601367916427, 26.3337060228923 33 | 3.784100313871234, 26.246590358999985 34 | 4.136229485157481, 26.141584034728837 35 | 4.5198238573369345, 26.033513952435236 36 | 4.887958900045284, 25.937370977060855 37 | -------------------------------------------------------------------------------- /test/flowers-svgrepo-com.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | flowers 5 | 6 | -------------------------------------------------------------------------------- /test/test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LR-inaf/pasta-marker/377a1bd6a44ef341201a3105016b72ce8f828242/test/test.png -------------------------------------------------------------------------------- /test/test.py: -------------------------------------------------------------------------------- 1 | # %% 2 | 3 | class Test(): 4 | def __init__(self): 5 | self.test_import() 6 | self.test_data() 7 | self.test_plot() 8 | 9 | def test_import(self): 10 | import pastamarkers 11 | from pastamarkers import markers 12 | 13 | print("imported") 14 | 15 | def test_data(self): 16 | import os 17 | import pastamarkers 18 | 19 | print(pastamarkers.dir_data) 20 | print(os.path.dirname(pastamarkers.__file__)) 21 | 22 | def test_plot(self): 23 | 24 | import pastamarkers 25 | from pastamarkers import markers 26 | import numpy as np 27 | import matplotlib.pyplot as plt 28 | 29 | dir_data = pastamarkers.dir_data + "MPD/" 30 | 31 | xUV,yUV = np.genfromtxt(dir_data+"UV.csv", delimiter=",", unpack=1) 32 | xB,yB = np.genfromtxt(dir_data+"B.csv", delimiter=",", unpack=1) 33 | xIR,yIR = np.genfromtxt(dir_data+"IR.csv", delimiter=",", unpack=1) 34 | xIR_curve,yIR_curve = np.genfromtxt(dir_data+"IR_curve.csv", delimiter=",", unpack=1) 35 | xB_curve,yB_curve = np.genfromtxt(dir_data+"B_curve.csv", delimiter=",", unpack=1) 36 | xUV_curve,yUV_curve = np.genfromtxt(dir_data+"UV_curve.csv", delimiter=",", unpack=1) 37 | fig, ax = plt.subplots(dpi=300, figsize=(5,4)) 38 | ax.scatter(xIR, yIR, marker=markers.fusilli2, s=700, color="darkred", linewidth=0.2, label="IR-band") 39 | ax.scatter(xB, yB, marker=markers.tortellini, s=300, color="dodgerblue", linewidth=0.2, label="B-band") 40 | ax.scatter(xUV, yUV, marker=markers.farfalle, s=500, color="purple", linewidth=0.1, label="UV-band") 41 | ax.plot(xIR_curve,yIR_curve, color="darkred", lw=0.8, ls="--") 42 | ax.plot(xB_curve,yB_curve, color="dodgerblue", lw=0.8, ls="--") 43 | ax.plot(xUV_curve,yUV_curve, color="purple", lw=0.8, ls="--") 44 | plt.legend() 45 | plt.title("Madau, Pozzetti & Dickinson (1998)") 46 | plt.xlabel("redshift") 47 | plt.ylabel(r"$\log \rho_\nu \, (\mathrm{erg \, s^{-1} \, Hz^{-1} \, Mpc^{-3}})$") 48 | plt.plot() 49 | 50 | plt.tight_layout() 51 | plt.savefig("test.png") 52 | 53 | # %% 54 | 55 | if __name__ == "__main__": 56 | Test() 57 | 58 | 59 | # %% 60 | --------------------------------------------------------------------------------