If None, the default depends on multiple. If True, fill in the area under univariate density curves or betweenīivariate contours. Plot will try to hook into the matplotlib property cycle. Single color specification for when hue mapping is not used. Or an object that will map from data units into a interval. hue_norm tuple or Įither a pair of values that set the normalization range in data units Specify the order of processing and plotting for categorical levels of the
Imply categorical mapping, while a colormap object implies numeric mapping. String values are passed to color_palette().
Method for choosing the colors to use when mapping the hue semantic. If provided, weight the kernel density estimation using these values. Semantic variable that is mapped to determine the color of plot elements. Variables that specify positions on the x and y axes. Either a long-form collection of vectors that can beĪssigned to named variables or a wide-form dataset that will be internally Parameters : data pandas.DataFrame, numpy.ndarray, mapping, or sequence Like a histogram, the quality of the representationĪlso depends on the selection of good smoothing parameters. Has the potential to introduce distortions if the underlying distribution isīounded or not smooth. More interpretable, especially when drawing multiple distributions. Relative to a histogram, KDE can produce a plot that is less cluttered and The approach is explained further in the user guide. Represents the data using a continuous probability density curve in one or Plot univariate or bivariate distributions using kernel density estimation.Ī kernel density estimate (KDE) plot is a method for visualizing theĭistribution of observations in a dataset, analogous to a histogram. kdeplot ( data = None, *, x = None, y = None, hue = None, weights = None, palette = None, hue_order = None, hue_norm = None, color = None, fill = None, multiple = 'layer', common_norm = True, common_grid = False, cumulative = False, bw_method = 'scott', bw_adjust = 1, warn_singular = True, log_scale = None, levels = 10, thresh = 0.05, gridsize = 200, cut = 3, clip = None, legend = True, cbar = False, cbar_ax = None, cbar_kws = None, ax = None, ** kwargs ) # Īs an example, a bullet 160 grains (10.4 g) in weight and a diameter of 0.284 in (7.2 mm), has a sectional density ( SD) of:ġ60 g/ = 0.283 lb m/in 2Īs another example, the M107 projectile mentioned above weighing 95.2 pounds (43.2 kg) and having a body diameter of 6.0909 inches (154.71 mm) has a sectional density of:ĩ5.2 lb/( 6.0909 in) 2 = 2.Seaborn.kdeplot # seaborn. In Europe the derivative unit g/cm 2 is also used in literature regarding small arms projectiles to get a number in front of the decimal separator. The sectional density defined this way is usually presented without units. d in is the diameter of the projectile in inches.SD is the sectional density in (mass) pounds per square inch.In a general physics context, sectional density is defined as: Röchling shells were tested in 19 against the Belgian Fort d'Aubin-Neufchâteau and saw very limited use during World War II. For illustration, a nail can penetrate a target medium with its pointed end first with less force than a coin of the same mass lying flat on the target medium.ĭuring World War II, bunker-busting Röchling shells were developed by German engineer August Cönders, based on the theory of increasing sectional density to improve penetration. It conveys how well an object's mass is distributed (by its shape) to overcome resistance along that axis. In this context, it is the ratio of a projectile's weight (often in either kilograms, grams, pounds or grains) to its transverse section (often in either square centimeters, square millimeters or square inches), with respect to the axis of motion. Sectional density is used in gun ballistics.
Sectional density (often abbreviated SD) is the ratio of an object's mass to its cross sectional area with respect to a given axis. Kilograms per square centimeter (kg/cm 2)
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