Plot various charts.
def bar(
x,
height,
width=0.8,
bottom=0.0,
color = None,
**kwargs):
"""
x: The x coordinates of the bars or category
height: The height of the bars.
width: The width of the bars.
bottom: The y coordinate of the bottom side of the bars.
"""
def barh(
y,
width,
height=0.8,
left=0.0,
color = None,
**kwargs):
"""
y: The y coordinates of the bars or category
height: The height of the bars.
width: The width of the bars.
bottom: The y coordinate of the bottom side of the bars.
"""
import scisuitplot as plt
categ=["Q1", "Q2", "Q3", "Q4"]
A = [44, 55, 41, 67]
B = [13, 23, 8, 13]
plt.bar(x=categ, height=A, fc=(0,255,0), hatch="\\")
plt.bar(height=B, x=categ, lw=2, bottom=A)
plt.show()
import scisuitplot as plt
import numpy as np
Categ = ("Control", "MW", "IR")
Levels = {
'A': (18, 18, 14),
'B': (38, 48, 47),
'C': (189, 195, 217)}
x = np.arange(len(Categ)) # the label locations
width = 0.25 # the width of the bars
mult = 0
for key, measure in Levels.items():
offset = width * mult
plt.bar(x=x + offset, height=measure, width=width, label=key)
mult += 1
plt.set_xticks(x+width, Categ)
plt.show()
Plots a box-whisker chart.
def boxplot(
data:Iterable,
label:str|None = None,
**kwargs):
"""
data : Data to be plotted \n
label: Name of the series
"""
import scisuitplot as plt
x = [2, 1, 3, 6, 4]
y = [7, 7, 8, 4, 2]
plt.boxplot(data=x)
plt.boxplot(data=y)
plt.show()
Box-Whisker charts are interactive. Right-clicking on any of the box will show related context-menu options.
Plots a bubble chart.
def bubble(
x:Iterable,
y:Iterable,
s:Iterable,
scale:float=1.0,
color:str|tuple|list = (255, 0, 0),
label:str=None):
"""
x, y, s: x- and y- and size data
color: color
label: Name (currently not in use)
"""
"""
Data adapted from:
https://developers.google.com/chart/interactive/docs/gallery/bubblechart
"""
import scisuitplot as plt
L = [80.66, 79.84, 78.6, 72.73, 80.05, 72.49, 68.09, 81.55, 68.6, 78.09]
F = [1.67, 1.36, 1.84, 2.78, 2, 1.7, 4.77, 2.96, 1.54, 2.05]
P = [337399, 819023, 55230, 797162, 618015, 731371, 310907, 74856, 1418500, 3070070]
plt.bubble(x=L, y=F, s=P)
plt.show()
def canvas(
x:Iterable,
y:Iterable,
haxis = True,
vaxis = True,
hgrid = True,
vgrid = True,
scale = False):
"""
Shows a canvas (an empty chart with axes and gridlines)
## Input:
x: horizontal axis bounds
y: vertical axis bounds
haxis, vaxis: Show horizontal and vertical axes
hgrid, vgrid: Show horizontal and vertical gridlines
"""
import scisuitplot as plt
plt.layout(2,2)
#All shown
plt.subplot(0,0)
plt.canvas(x=[0,5], y=[0,5])
#Horizontal axis not shown
plt.subplot(0,1)
plt.canvas(x=[0,5], y=[0,5], haxis=False)
#Vertical axis not displayed
plt.subplot(1,0)
plt.canvas(x=[0,5], y=[0,5], vaxis=False)
#Gridlines are not displayed
plt.subplot(1,1)
plt.canvas(x=[0,5], y=[0,5], vgrid=False, hgrid=False)
plt.show()
Plots histogram.
def hist(
data:Iterable,
density = False, #by default frequency
cumulative = False,
breaks:int|Iterable = None,
**kwargs):
"""
data: Numeric data
density : density histogram if true otherwise frequency.
cumulative : True, cumulative distribution
breaks : Number of breaks or the break points
## Note
If density=True and cumulative=True, then the histogram is
normalized so that the cumulative end-value is 1.0
"""
import scisuitplot as plt
import scisuit.stats as st
x = st.rnorm(500)
plt.hist(x, fc="255 0 0", lw=2, ec=[0,255,0])
plt.show()
Plots Moody diagram.
def moody(
Re:Iterable = [1E3, 1E7],
friction:Iterable = [0.01, 0.09]):
"""
Re: Reynolds number range
friction: Friction factor range
"""
import scisuitplot as plt
plt.moody()
plt.show()
Plots psychrometric chart at given pressure, dry-bulb temperature range and relative humidities.
def psychrometry(
Tdb:Iterable=None,
RH:Iterable=None,
P:Real=101325.0):
"""
Plots psychromety chart.
Tdb: [min, max], minimum and maximum dry-bulb temperatures (Celcius)
RH: A list in increasing order containing the requested relative humidity (%) lines
P: Absolute pressure (Pa)
"""
import scisuitplot as plt
plt.psychrometry()
plt.show()
Please see the following video.
Plots quantile-quantile chart.
def qqnorm(
data:Iterable,
label:str=None,
show=True,
marker:str|Marker=None,
**kwargs):
"""
Normal Quantile-quantile chart
x-axis="Theoretical Quantiles"
y-axis="Sample Quantiles"
data: Data
show: Whether to show theoretical line or not
"""
def qqplot(
x:Iterable,
y:Iterable,
**kwargs):
"""
x, y: Data
"""
import scisuitplot as plt
import scisuit.stats as stat
x=stat.rnorm(100)
#full control on marker
plt.layout(1,2)
plt.subplot(0,0)
plt.qqnorm(x, lw=5, ec=(0, 255,0),
marker=plt.Marker(style="s", fc=(255,0,0), lw=2))
#marker with default Pen and Brush properties
plt.subplot(0, 1)
plt.qqnorm(x, lw=3, marker="s")
plt.show()
Plots quiver chart and direction field.
def quiver(
X:np.ndarray,
Y:np.ndarray,
U:np.ndarray,
V:np.ndarray,
scale=None,
**kwargs)->None:
""""
Plots quiver chart
x, y: (x,y) location, 2D ndarray \n
u, v: length in x and y directions, 2D ndarray \n
scale: if None automatic scaling takes place otherwise xu = x + u*scale is computed
"""
def dirfield(
x:np.ndarray,
y:np.ndarray,
slope:np.ndarray):
"""
Plots the direction field for a given function f=dy/dx \n
x, y: 2D numpy array (after using meshgrid) \n
slope: 2D array resulting from evaluation of f=dy/dx, first order ODE
"""
Let's visualize the following flow field: V= (u, v) = (0.5+0.8x)i + (1.5-0.8y)j
import scisuitplot as plt
import numpy as np
x = np.arange(-3.0, 3.0, 0.5)
y = np.arange(-1.0, 5.0, 0.5)
X, Y = np.meshgrid(x,y)
U = 0.5 + 0.8*X
V = 1.5 - 0.8*Y
plt.quiver(X,Y, U, V, scale=0.3, ec="#964B00")
plt.show()
Plots scatter chart with/without trendlines.
def scatter(
x:Iterable,
y:Iterable,
label:str = None,
smooth:bool = False,
marker:str|Marker = None,
**kwargs):
"""
x, y: x- and y-data
label: Label of the series
smooth: Uses smoothing algorith to smooth lines (instead of broken)
marker: Marker class to specify marker properties
- To plot lines set `lw` to an int > 0.
- If marker is None and lw is not set, by default markers will be shown.
- Customize line with (lw, ls and edgecolor properties)
- Customize marker with (line properties + facecolor, hatch)
"""
import scisuitplot as plt
import numpy as np
x = np.arange(0, 6, 0.5)
y = x**2
plt.layout(2,1)
#show line and marker with default pen and brush
plt.subplot(0,0)
plt.scatter(x=x, y=y, lw=3, ls=":", marker="s", markersize=10)
#customize marker properties
plt.subplot(1,0)
plt.scatter(x=x, y=y, marker=plt.Marker(fc="#00FF00"))
plt.show()
