You’ll notice that top brands have the habit of picking bold and eye-catching colors associated with good characteristics. On the other hand, the least used colors are violet and pink. The next most popular logo colors are red and black in the following spots. Designs that rely on shape and texture rather than lots of colors to bring their brand across usually work better when scaled up or down on devices and in contexts where only black and white distribution is possible, like on some packaging.Īs for the most-used colors, blue took center stage with 35% of logos predominantly in this hue. This type of design is particularly useful for creating both strong brand awareness and name recognition as well as giving it a strong visual presence with purpose-built images.Ĭompanies like to keep it simple with their colors, with 76% of designs using a selection of only one or two colors in their logo which is considered as a logo design best practice. We found that the majority of logos sampled are combination marks, meaning they have both a text and graphic design component. Brands that do this tend to think about how their logo will look when put in different applications and assets. Before we head onto the list, take a look at the interesting trends we noticed in big brand logos:Īt the most basic level, we can already tell successful companies tend to opt for versatile but simple designs. We will give you insight into how these brands managed to make their design concept work so you can do the same for your own brand identity. To help with that, we've rounded up the 100 most valuable brands of the world according to Forbes. So if you're in the market for a custom-designed logo for your fledgling company, taking a look at the big movers and shakers of the world can really help clarify what you need to achieve in branding. One of the most crucial elements of any brand, whether new or already making millions, is its logo design. Play your cards right and you’ll find yourself setting the wheels in motion to turn an idea into a money-making success.Īnd what better way to aim for the moon than by taking a magnifying glass to the stars? The big brands have made it all the way to the top for good reason. The clipping itself is made in the lines 48 and 49.It's always a good time to start thinking seriously about that company you've wanted to set up.This assumes the polygon coordinates to be the ones used in the data (longitudes and latitudes in our case). Note the trasnform=ax.transData attribute. The path is created using the Path function, and then added to a PathPatch, to be able to use it as a closed polygon.The prt array is for managing multipolygons, which is not the case, but the code will create correct clipping for multipolygons.Of course, only the polygon for Andorra has to be used.In our case, only straight lines have to be used, so there will be a MOVETO to indicate the beginning of the polygon, many LINETO to create the segments and one CLOSEPOLY for closing it.One with the points (called vertices in the script), and the other with the functions for every point (called codes). A Matplotlib path is made by two arrays.The hightlighted lines in the code are the ones that do the job. To clip the image, a Basemap path is needed. contourf ( xx, yy, data, range ( 0, 3600, 200 )) for contour in cs. transData ) m = Basemap ( llcrnrlon = 1.4, llcrnrlat = 42.4, urcrnrlon = 1.77, urcrnrlat = 42.7, resolution = None, projection = 'cyl' ) ds = gdal. append (( pts, pts )) codes += codes += * ( prt - prt - 2 ) codes += clip = Path ( vertices, codes ) clip = PathPatch ( clip, transform = ax. parts ) + for i in range ( len ( prt ) - 1 ): for j in range ( prt, prt ): vertices. record = 'Andorra' : vertices = codes = pts = shape_rec. Reader ( "ne_10m_admin_0_countries" ) for shape_rec in sf. From mpl_toolkits.basemap import Basemap from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.pyplot as plt from osgeo import gdal import numpy import shapefile fig = plt.
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