Metadata-Version: 2.1
Name: SecretColors
Version: 1.0.0
Summary: A small package for fantastic color palette
Home-page: https://github.com/secretBiology/SecretColors
Author: Rohit Suratekar
Author-email: rohitsuratekar@gmail.com
License: MIT License
Description: # Secret Colors
        
        [![PyPI version](https://badge.fury.io/py/SecretColors.svg)](https://badge.fury.io/py/SecretColors)
        
        Library generated for making plots with better color palette. It uses 
        famous color palettes 
        and adds few helpful functions. 
        
        Few sample plots and inspiration behind this library can be found in 
        [WeirdData blog](https://weirddata.github.io/2018/09/10/secret-colors.html). 
        
        
        ### Installation 
        
            pip install SecretColors
        
        ### Usage
        
        `Note: Following documentation is for older version (<1.0.0) of the library. 
        New documentation will be updated here soon.`
        
        Select different Palettes
            
            from SecretColors.palette import Palette
            ibm = Palette("ibm")  # IBM Palette
            material = Palette("material")  # Material Palette
        
        Get Common Colors
        
            ibm.red()  # Default Red color from IBM palette (#008673)
            ibm.cerulean()  # Default Cerulean color from IBM palette (#009bef)
        
        Shades of colors
            
            ibm.red(grade=10)  # Light Red with grade 10 (#fccec7)
            ibm.red(grade=80)  # Dark Red with grade 80 (#5c1f1b)
            ibm.red(grade=10000)  # Maximum/Minimum will be automatically adjusted (#3e1d1b)
        
        Number of colors
        
            reds = ibm.red(no_of_colors=5)  # List of 3 Red colors from IBM palette
            dark_reds = ibm.red(no_of_colors=5, start_from=40)  # List of 3 Red 
            colors from IBM palettes starting from grade 40
         
        Random Colors
        
            ibm.random()  # Random color from IBM Palette
            ibm.random(grade=60)  # Random color from grade 60
            ibm.random(no_of_colors=10)  # 10 Random colors
            ibm.random(no_of_colors=10, grade=30)  # Random colors with grade
        
        Gradients between colors
        
            ibm.gradient_between(ibm.red(), ibm.blue(), no_of_colors=5)
            # Gradient between your own custom colors
            ibm.gradient_between("#b73752", "#2d74da", no_of_colors=5)
        
        Palette output in other color-spaces
        
            ibm.change_color_mode("rgb")
            ibm.red()  # (0.90, 0.13, 0.14)
        
        General Conversion Functions
        
            from SecretColors.palette import hex_to_rgb, hex_to_hsv, rgb_to_hex
            hex_to_rgb("#b73752")  # (0.71, 0.21, 0.32)
            hex_to_hsv("#b73752")  # (0.96, 0.69, 183.0)
            rgb_to_hex((0.71, 0.21, 0.32))  # #b53551
        
        Text contrast on background color
        
            from SecretColors.palette import text_color
            text_color("#e62325")  # Returns #ffffff. This suggest white color text will
            # have good contrast on given color
            text_color("#eabbbc")  # Returns #000000. Suggesting black color text will have
            #  good contrast on given color
        
        Simple Usage with `matplotlib`
        
            import matplotlib.pylab as plt
            import numpy as np
            
            data = np.random.randint(10, 50, 5)
            plt.bar(range(len(data)), data, color=ibm.blue(no_of_colors=len(data), start_from=30))
            plt.show()
        
        Custom ColorMaps
        
            import matplotlib
            import matplotlib.pylab as plt
            from SecretColors.palette import ColorMap
            a = np.random.random((16, 16))
            cmap = ColorMap(matplotlib)
            plt.imshow(a, cmap=cmap.warm(), interpolation='nearest')
            plt.colorbar()
            plt.show()
            
            #Similarly
            plt.imshow(a, cmap=cmap.cool(), interpolation='nearest')
            plt.imshow(a, cmap=cmap.greens(), interpolation='nearest')
            plt.imshow(a, cmap=cmap.ibm(), interpolation='nearest')
            plt.imshow(a, cmap=cmap.material(), interpolation='nearest')
            
            # Qualitative maps
             plt.imshow(a, cmap=cmap.greens(is_qualitative=True), interpolation='nearest')
            
            # Definite Divisions
            plt.imshow(a, cmap=cmap.greens(is_qualitative=True, no_of_divisions=5), interpolation='nearest')
            
        More color maps will be added in next release !
            
        
        ### TODO
        
        - [x] IBM Color Palette
        - [x] Color gradients
        - [x] Google Material Design Palette
        - [x] Text contrast detection
        - [x] Matplotlib `cmap` helper functions
        - [ ] ColorBrewer Palette
        - [ ] VMware Palette
        
        ### Acknowledgments
        Colors used in this library are partly taken from [IBM Design Language](https://www.ibm.com/design/language/resources/color-library/) and [Google 
        Material Design](https://material.io/design/color/the-color-system.html)  
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
