In [161]:
plot([1, 2, 3], [2, 4, 6])
Out[161]:
[<matplotlib.lines.Line2D at 0x7fcafecce160>]
Simple plotting commands to look at data quickly
from matplotlib.pyplot import plot
plot([1, 2, 3], [2, 4, 6])
[<matplotlib.lines.Line2D at 0x7fcafecce160>]
import matplotlib.pyplot as plt
from matplotlib import style
plt.plot([1, 2, 3], [2, 4, 6])
[<matplotlib.lines.Line2D at 0x7fcb137fd580>]
plt.plot([1, 2, 3], [2, 4, 6])
plt.show()
x = [1, 2, 3]
y = [2, 4, 6]
plt.plot(x, y)
plt.show()
x = [1, 2, 3]
y = [2, 4, 6]
plt.plot(x, y)
plt.title('Straight Line')
plt.show()
x = [1, 2, 3]
y = [2, 4, 6]
plt.plot(x, y)
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.show()
x = [1, 2, 3]
y = [2, 4, 6]
plt.plot(x, y)
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.show()
x = [0, 1, 2, 3, 4]
y = [0, 2, 4, 6, 8]
plt.plot(x, y)
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xticks([0, 1, 2, 3, 4])
plt.yticks([0, 2, 4, 6, 8, 10])
plt.show()
x = [0, 1, 2, 3, 4]
y = [0, 2, 4, 6, 8]
plt.plot(x, y)
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xticks([0, 1, 2, 3, 3.5, 4])
plt.yticks([0, 2, 4, 6, 8, 8.5, 9, 10])
plt.show()
x = [0, 1, 2, 3, 4]
y = [0, 2, 4, 6, 8]
x1 = array([0, 1, 2, 3, 4])
y1 = array([0, 2, 4, 6, 8])
plt.plot(x, y, label = "$y = 2x$")
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xticks([0, 1, 2, 3, 3.5, 4])
plt.yticks([0, 2, 4, 6, 8, 8.5, 9, 10])
plt.legend()
plt.show()
x = [0, 1, 2, 3, 4]
y = [0, 2, 4, 6, 8]
plt.plot(x, y ** 2, label = "$y = x^2$")
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xticks([0, 1, 2, 3, 3.5, 4])
plt.yticks([0, 2, 4, 6, 8, 8.5, 9, 10])
plt.legend()
plt.show()
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) /var/folders/dw/p9lncvq57tv4px69pk911vl40000gn/T/ipykernel_901/2953554774.py in <module> 2 y = [0, 2, 4, 6, 8] 3 ----> 4 plt.plot(x, y ** 2, label = "$y = x^2$") 5 plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20}) 6 plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20}) TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int'
x1 = array([0, 1, 2, 3, 4])
y1 = array([0, 2, 4, 6, 8])
plt.plot(x1, y1 ** 2, label = "$y = x^2$")
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xticks([0, 1, 2, 3, 3.5, 4])
plt.yticks([0, 2, 4, 6, 8, 8.5, 9, 10])
plt.legend()
plt.show()
x1 = array([0, 1, 2, 3, 4])
y1 = array([0, 2, 4, 6, 8])
plt.plot(x1, y1 ** 2, label = "$y = x^2$")
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xticks([0, 1, 2, 3, 3.5, 4])
plt.yticks([0, 4, 16, 36, 64, 56.25, 81, 100])
plt.legend()
plt.show()
x = [0, 1, 2, 3, 4]
y = [0, 2, 4, 6, 8]
plt.plot(x, y, label = "$y = 2x$", color= '#2af6a5', marker = '.', linestyle = "dashdot" , markersize = 10, \
markeredgecolor="#dd3947")
plt.title('Straight Line', fontdict={"fontname": "Times New Roman", "fontsize":20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xlabel("X Asix", fontdict={"fontname": "Comic Sans MS", "fontsize": 20})
plt.xticks([0, 1, 2, 3, 3.5, 4])
plt.yticks([0, 2, 4, 6, 8, 8.5, 9, 10])
plt.legend()
plt.show()
labels = ['A', 'B', 'C']
values = [1, 4, 2]
plt.bar(labels, values)
plt.show()
labels = ['A', 'B', 'C']
values = [1, 4, 2]
plt.figure(figsize=(6,4))
bars = plt.bar(labels, values)
bars[0].set_hatch('/')
bars[1].set_hatch('o')
bars[2].set_hatch('*')
plt.show()
labels = ['A', 'B', 'C']
values = [1, 4, 2]
plt.figure(figsize=(6,4))
bars = plt.bar(labels, values)
paterns = ['/', 'o', '*']
for bar in bars:
bar.set_hatch(paterns.pop(0))
# bars[0].set_hatch('/')
# bars[1].set_hatch('o')
# bars[2].set_hatch('*')
plt.show()
labels = ['A', 'B', 'C']
values = [1, 4, 2]
plt.figure(figsize=(6,4))
bars = plt.bar(labels, values)
paterns = ['/', 'o', '*']
for bar in bars:
bar.set_hatch(paterns.pop())
# bars[0].set_hatch('/')
# bars[1].set_hatch('o')
# bars[2].set_hatch('*')
plt.show()
x = np.linspace(0,15,30)
y = np.sin(x) + 0.1*np.random.randn(len(x))
x
array([ 0. , 0.51724138, 1.03448276, 1.55172414, 2.06896552,
2.5862069 , 3.10344828, 3.62068966, 4.13793103, 4.65517241,
5.17241379, 5.68965517, 6.20689655, 6.72413793, 7.24137931,
7.75862069, 8.27586207, 8.79310345, 9.31034483, 9.82758621,
10.34482759, 10.86206897, 11.37931034, 11.89655172, 12.4137931 ,
12.93103448, 13.44827586, 13.96551724, 14.48275862, 15. ])
y
array([ 0.02981107, 0.55286175, 0.814338 , 0.96260674, 0.86756382,
0.52510829, -0.08335135, -0.52690874, -0.72357951, -0.99606741,
-0.71195337, -0.50130289, -0.17623514, 0.36274456, 0.69582955,
0.93660238, 0.97448074, 0.43590578, 0.05903551, -0.34580148,
-0.83620259, -0.92615519, -1.18514135, -0.34079546, -0.15822537,
0.50811401, 0.78945142, 0.91518241, 0.90104908, 0.80752386])
np.random.rand(len(x))
array([0.06460702, 0.42107375, 0.27613102, 0.77306782, 0.36642744,
0.62934932, 0.47778023, 0.18659001, 0.81884021, 0.9200216 ,
0.7648626 , 0.11784263, 0.15006604, 0.10967121, 0.62527486,
0.98538797, 0.4975219 , 0.07423203, 0.21770794, 0.75467839,
0.7095082 , 0.1356839 , 0.08203238, 0.76342986, 0.29198362,
0.80817945, 0.5909613 , 0.64050741, 0.30678636, 0.83130118])
len(x)
30
np.random.rand(1)
array([0.95514031])
np.random.rand(3)
array([0.27887584, 0.43397946, 0.10683499])
np.random.rand(30)
array([0.82548641, 0.74193505, 0.41554649, 0.45472931, 0.93463585,
0.12121709, 0.97314183, 0.59022656, 0.26035456, 0.65089384,
0.56376054, 0.44438056, 0.45718615, 0.40794219, 0.67726283,
0.78338543, 0.25311743, 0.23305277, 0.81471944, 0.79554439,
0.22916447, 0.13877519, 0.30641603, 0.98376895, 0.10030124,
0.67945965, 0.14550494, 0.36007792, 0.21213761, 0.02921576])
np.random.rand(len(x))
array([0.12773702, 0.42104591, 0.11871246, 0.35652773, 0.3946626 ,
0.47624334, 0.56938587, 0.20512452, 0.12015803, 0.71931095,
0.85032943, 0.19521646, 0.21198957, 0.29101547, 0.3958043 ,
0.04597919, 0.75948722, 0.90484102, 0.38966744, 0.82187083,
0.42049989, 0.82380348, 0.33116278, 0.45808617, 0.54382573,
0.62394608, 0.66134566, 0.63921516, 0.32245382, 0.90889006])
plt.style.use(['science', 'notebook', 'grid'])
print(plt.style.available)
['Solarize_Light2', '_classic_test_patch', 'bmh', 'bright', 'cjk-jp-font', 'cjk-kr-font', 'cjk-sc-font', 'cjk-tc-font', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'grid', 'high-contrast', 'high-vis', 'ieee', 'latex-sans', 'light', 'muted', 'nature', 'no-latex', 'notebook', 'pgf', 'retro', 'scatter', 'science', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'std-colors', 'tableau-colorblind10', 'vibrant']
plt.plot(x,y)
[<matplotlib.lines.Line2D at 0x7fcb036515b0>]
plt.style.use(['default'])
plt.plot(x,y)
[<matplotlib.lines.Line2D at 0x7fcb041dd100>]
plt.style.use(['science', 'notebook', 'grid'])
plt.plot(x,y)
[<matplotlib.lines.Line2D at 0x7fcb04279cd0>]
plt.plot(x,y, "-")
[<matplotlib.lines.Line2D at 0x7fcb044c2fd0>]
plt.plot(x,y, "--")
[<matplotlib.lines.Line2D at 0x7fcb040fefd0>]
plt.plot(x,y, "o-")
[<matplotlib.lines.Line2D at 0x7fcb01531760>]
plt.plot(x,y, "o")
[<matplotlib.lines.Line2D at 0x7fcb009fca00>]
plt.plot(x,y, "o--")
[<matplotlib.lines.Line2D at 0x7fcafa5789d0>]
plt.plot(x,y, "o--", color="purple")
[<matplotlib.lines.Line2D at 0x7fcb00e2e850>]
plt.plot(x,y, "o--", color="purple", lw=0.1)
[<matplotlib.lines.Line2D at 0x7fcaf532abe0>]
plt.plot(x,y, "o--", color="purple", lw=0.5)
[<matplotlib.lines.Line2D at 0x7fcb013b68e0>]
plt.plot(x,y, "o--", color="purple", lw=4)
[<matplotlib.lines.Line2D at 0x7fcb00d8dc70>]
plt.plot(x,y, "o--", color="purple", lw=0.7)
[<matplotlib.lines.Line2D at 0x7fcb01299d90>]
plt.plot(x,y, 'o--', color='purple', lw=2, ms=10)
[<matplotlib.lines.Line2D at 0x7fcafa3fddc0>]
plt.plot(x,y, 'o--', color='purple', lw=2, ms=0.1)
[<matplotlib.lines.Line2D at 0x7fcb0077ba60>]
plt.plot(x,y, 'o--', color='purple', lw=2, ms=5)
[<matplotlib.lines.Line2D at 0x7fcae5c262e0>]
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='purple', lw=2, ms=5)
[<matplotlib.lines.Line2D at 0x7fcae5cc5730>]
plt.figure(figsize=(4,3))
plt.plot(x,y, 'o--', color='purple', lw=2, ms=5)
[<matplotlib.lines.Line2D at 0x7fcae5f55730>]
plt.figure(figsize=(10,3))
plt.plot(x,y, 'o--', color='purple', lw=2, ms=5)
[<matplotlib.lines.Line2D at 0x7fcae687d6a0>]
plt.style.use(['science', 'notebook', 'grid'])
plt.figure(figsize=(10,3))
plt.plot(x,y, 'o--', color='purple', lw=2, ms=5)
plt.xlabel('Time(s)')
plt.ylabel('Voltage(V)')
Text(0, 0.5, 'Voltage(V)')
plt.figure(figsize=(10,3))
plt.plot(x,y, 'o--', color='purple', lw=2, ms=5)
plt.xlabel('Time(s)', fontsize=10)
plt.ylabel('Voltage(V)', fontsize=10)
Text(0, 0.5, 'Voltage(V)')
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='purple', lw=2, ms=5)
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode')
Text(0.5, 1.0, 'Voltage in 3rd Electrode')
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='gold', lw=2, ms=5)
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
Text(0.5, 1.0, 'Voltage in 3rd Electrode')
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='gold', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend()
<matplotlib.legend.Legend at 0x7fcb00748dc0>
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='gold', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10)
<matplotlib.legend.Legend at 0x7fcae7cba4c0>
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='red', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc="upper left")
<matplotlib.legend.Legend at 0x7fcae793e2b0>
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='red', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc="lower right")
<matplotlib.legend.Legend at 0x7fcae81c5730>
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='red', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc="center")
<matplotlib.legend.Legend at 0x7fcb004cf7f0>
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='red', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0,0))
<matplotlib.legend.Legend at 0x7fcae84b3670>
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='red', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(1,1))
<matplotlib.legend.Legend at 0x7fcae86b2850>
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='red', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
<matplotlib.legend.Legend at 0x7fcae88b9940>
x2 = np.linspace(0, 15, 100)
y2 = np.sin(x2)
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o--', color='red', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
plt.plot(x2,y2, label='Numerical Curve 2')
plt.legend(loc='lower right', fontsize=12)
plt.show()
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o', color='red', lw=2, ms=5, label='Numerical curve')
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
plt.plot(x2,y2, label='Numerical Curve 2')
plt.legend(loc='lower right', fontsize=12)
plt.show()
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o', color='red', lw=2, ms=5, label='Numerical curve', zorder = 1) # adding zorder
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
plt.plot(x2,y2, label='Numerical Curve 2', zorder = 0) # adding zoder
plt.legend(loc='lower right', fontsize=12)
plt.show()
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o', color='red', lw=2, ms=5, label='Numerical curve', zorder = 0) # adding zorder
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
plt.plot(x2,y2, label='Numerical Curve 2', zorder = 1) # adding zoder
plt.legend(loc='lower right', fontsize=12)
plt.show()
## Legend in 2 column an 1 row
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o', color='red', lw=2, ms=5, label='Numerical curve', zorder = 1) # adding zorder
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
plt.plot(x2,y2, label='Numerical Curve 2', zorder = 0) # adding zoder
plt.legend(loc='lower right', fontsize=12, ncol=2)
plt.show()
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o', color='red', lw=2, ms=5, label='Numerical curve', zorder = 1) # adding zorder
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
plt.plot(x2,y2, label='Numerical Curve 2', zorder = 0) # adding zoder
plt.legend(loc='lower right', fontsize=12, ncol = 2)
plt.ylim(bottom = - 2)
(-2.0, 1.1089340254494993)
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o', color='red', lw=2, ms=5, label='Numerical curve', zorder = 1) # adding zorder
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
plt.plot(x2,y2, label='Numerical Curve 2', zorder = 0) # adding zoder
plt.legend(loc='lower right', fontsize=12, ncol = 2)
plt.ylim(bottom = - 2)
plt.ylim(top = 2)
(-2.0, 2.0)
plt.figure(figsize=(8,3))
plt.plot(x,y, 'o', color='red', lw=2, ms=5, label='Numerical curve', zorder = 1) # adding zorder
plt.xlabel('Time(s)', fontsize=16)
plt.ylabel('Voltage(V)', fontsize=16)
plt.title('Voltage in 3rd Electrode', fontsize=20)
plt.legend(fontsize=10, loc=(0.2, 0.4))
plt.plot(x2,y2, label='Numerical Curve 2', zorder = 0) # adding zoder
plt.legend(loc='lower right', fontsize=12, ncol = 2)
plt.ylim(bottom = - 2)
plt.ylim(top = 2)
plt.xlim(left = - 1)
plt.xlim(right = 16)
(-1.0, 16.0)
res = np.random.randn(1000)*0.2 + 0.4
res
array([ 5.37955235e-01, 9.63451698e-02, 3.04462388e-02, 7.22901636e-01,
1.97477962e-01, 2.28331311e-01, 1.88601040e-01, 3.03974964e-01,
6.61342709e-01, 5.05026047e-01, 5.23552944e-01, 2.69813715e-01,
5.31953609e-01, 3.80274217e-01, 6.40778053e-01, 3.46193741e-01,
3.10332311e-01, 4.87320207e-01, 2.97444251e-01, 5.72153725e-01,
5.75476219e-01, 3.92910178e-01, 4.43344751e-01, 2.27889124e-01,
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3.70276085e-01, 6.74722892e-01, 1.51791629e-01, 2.97190647e-01,
5.87930854e-01, 6.02048533e-01, 1.95835199e-01, 4.85124191e-01,
3.28750161e-01, 2.69533194e-01, 1.83202622e-01, 5.74830905e-01,
3.08784065e-01, 3.04422404e-01, 6.45447613e-01, 2.41987049e-01,
4.00159613e-01, 6.38575410e-01, 4.37321663e-01, 6.34578659e-01,
1.07236492e-01, 4.50917702e-01, 6.06205056e-01, 4.74612129e-01,
1.55971632e-01, 6.78258032e-01, 4.28830310e-01, 3.36377709e-01,
6.37299021e-01, 2.51693843e-01, 4.70864346e-01, 3.01361536e-01,
5.00906592e-01, 6.88036455e-01, 3.16375231e-01, 6.12411250e-01,
3.12855910e-01, 3.04815870e-01, 4.71945716e-01, 3.95436754e-02,
6.68406794e-01, 2.89708740e-01, 7.60536742e-01, 5.91932089e-01,
3.43720779e-01, -1.52189227e-01, 6.21231037e-01, 3.95858287e-01,
8.06952241e-01, 3.67164462e-01, 5.28300295e-01, 5.53907600e-01,
7.94923084e-01, 4.76201399e-01, 1.82139937e-01, 2.97019535e-01,
2.33751605e-01, 5.24674197e-01, 3.43705011e-01, 2.62697935e-01,
3.82698507e-01, 7.41474155e-01, 1.42372528e-01, 5.80079567e-01,
2.02567469e-01, 4.96327868e-01, 3.61260047e-01, 2.70944781e-01,
2.26158386e-01, 4.87114616e-01, 4.44822841e-01, 3.40248242e-01,
4.25253165e-01, 4.64761013e-01, 3.51793115e-01, 6.90738053e-01,
3.76836224e-01, 4.30421197e-01, 5.30352183e-01, 4.44564687e-01,
3.72114482e-01, 3.05191677e-01, 3.49832970e-01, 3.90899667e-01,
3.12178793e-01, 6.01148740e-01, 6.69939274e-01, 3.76127575e-01,
4.17484961e-01, 5.44075227e-01, 3.95880211e-01, 5.62845569e-01,
3.47859407e-01, 6.62758367e-01, 5.22426720e-01, 4.53479446e-01,
4.65075042e-01, 4.53900981e-01, -1.01657509e-01, 5.57254860e-01,
3.55560944e-01, 4.38527288e-01, 3.47810344e-01, 4.03104574e-01,
4.37793654e-01, 4.25172599e-01, 3.13301350e-01, 3.57487945e-01,
6.10482739e-01, 4.70420238e-01, 6.14024623e-01, 2.94362576e-01,
3.20913135e-01, 5.11657745e-01, 2.21821345e-01, 5.96613868e-01,
3.85096498e-01, 4.89048438e-01, -2.21229100e-01, 6.85253597e-01,
6.16936896e-01, 1.97885062e-01, 6.59220211e-01, 5.55065493e-01,
3.57267503e-01, 1.08446393e-01, 4.38533767e-01, 4.69073016e-01,
4.04083422e-01, 3.36719654e-01, 4.89408649e-01, 2.35142130e-01,
5.27982537e-01, 2.85332256e-01, 2.46120563e-01, 2.02140138e-01,
5.61641144e-01, 2.50402694e-01, 5.04093500e-01, 5.44387031e-01,
1.80213387e-02, 4.71900515e-01, 5.65142309e-01, 5.23171739e-01,
4.18356455e-01, 5.70672629e-01, 2.44603280e-01, 4.45923194e-01,
6.06338610e-01, 4.48853886e-01, 6.55244357e-01, 2.05566182e-01,
4.09445106e-01, 2.21427810e-01, 2.67729571e-01, 7.70835035e-02,
4.50739972e-01, 4.03566697e-01, 3.58126651e-01, 1.78777585e-01,
1.20008367e-01, 3.95031194e-01, 1.05815169e-01, 3.51734249e-01,
5.41820244e-01, 5.17234688e-01, 2.51713134e-01, 2.21864556e-01])
plt.hist(res)
(array([ 8., 20., 61., 138., 231., 240., 183., 82., 28., 9.]),
array([-0.2212291 , -0.10023671, 0.02075567, 0.14174806, 0.26274045,
0.38373284, 0.50472522, 0.62571761, 0.74671 , 0.86770238,
0.98869477]),
<BarContainer object of 10 artists>)
z = plt.hist(res)
z
(array([ 8., 20., 61., 138., 231., 240., 183., 82., 28., 9.]),
array([-0.2212291 , -0.10023671, 0.02075567, 0.14174806, 0.26274045,
0.38373284, 0.50472522, 0.62571761, 0.74671 , 0.86770238,
0.98869477]),
<BarContainer object of 10 artists>)
plt.hist(res, bins=2)
(array([458., 542.]), array([-0.2212291 , 0.38373284, 0.98869477]), <BarContainer object of 2 artists>)
plt.hist(res, bins=150)
(array([ 1., 0., 1., 0., 0., 0., 2., 0., 2., 0., 0., 1., 0.,
0., 1., 0., 0., 0., 0., 0., 3., 0., 1., 1., 3., 4.,
1., 2., 1., 4., 2., 2., 3., 2., 4., 1., 3., 7., 3.,
5., 7., 4., 5., 5., 8., 3., 9., 7., 6., 14., 10., 11.,
9., 10., 12., 8., 7., 8., 11., 13., 5., 15., 17., 13., 17.,
10., 21., 12., 10., 16., 21., 19., 12., 19., 24., 15., 22., 17.,
23., 14., 11., 20., 23., 19., 11., 18., 11., 7., 14., 15., 17.,
12., 21., 11., 15., 10., 17., 12., 12., 8., 10., 13., 7., 10.,
8., 3., 8., 8., 2., 5., 9., 5., 4., 11., 5., 1., 6.,
7., 5., 3., 4., 4., 2., 3., 5., 2., 1., 3., 2., 2.,
0., 0., 0., 0., 0., 1., 1., 2., 1., 1., 0., 1., 0.,
0., 0., 0., 1., 0., 0., 1.]),
array([-0.2212291 , -0.21316294, -0.20509678, -0.19703062, -0.18896446,
-0.1808983 , -0.17283214, -0.16476599, -0.15669983, -0.14863367,
-0.14056751, -0.13250135, -0.12443519, -0.11636903, -0.10830287,
-0.10023671, -0.09217055, -0.08410439, -0.07603824, -0.06797208,
-0.05990592, -0.05183976, -0.0437736 , -0.03570744, -0.02764128,
-0.01957512, -0.01150896, -0.0034428 , 0.00462336, 0.01268952,
0.02075567, 0.02882183, 0.03688799, 0.04495415, 0.05302031,
0.06108647, 0.06915263, 0.07721879, 0.08528495, 0.09335111,
0.10141727, 0.10948342, 0.11754958, 0.12561574, 0.1336819 ,
0.14174806, 0.14981422, 0.15788038, 0.16594654, 0.1740127 ,
0.18207886, 0.19014502, 0.19821118, 0.20627733, 0.21434349,
0.22240965, 0.23047581, 0.23854197, 0.24660813, 0.25467429,
0.26274045, 0.27080661, 0.27887277, 0.28693893, 0.29500508,
0.30307124, 0.3111374 , 0.31920356, 0.32726972, 0.33533588,
0.34340204, 0.3514682 , 0.35953436, 0.36760052, 0.37566668,
0.38373284, 0.39179899, 0.39986515, 0.40793131, 0.41599747,
0.42406363, 0.43212979, 0.44019595, 0.44826211, 0.45632827,
0.46439443, 0.47246059, 0.48052674, 0.4885929 , 0.49665906,
0.50472522, 0.51279138, 0.52085754, 0.5289237 , 0.53698986,
0.54505602, 0.55312218, 0.56118834, 0.5692545 , 0.57732065,
0.58538681, 0.59345297, 0.60151913, 0.60958529, 0.61765145,
0.62571761, 0.63378377, 0.64184993, 0.64991609, 0.65798225,
0.6660484 , 0.67411456, 0.68218072, 0.69024688, 0.69831304,
0.7063792 , 0.71444536, 0.72251152, 0.73057768, 0.73864384,
0.74671 , 0.75477616, 0.76284231, 0.77090847, 0.77897463,
0.78704079, 0.79510695, 0.80317311, 0.81123927, 0.81930543,
0.82737159, 0.83543775, 0.84350391, 0.85157006, 0.85963622,
0.86770238, 0.87576854, 0.8838347 , 0.89190086, 0.89996702,
0.90803318, 0.91609934, 0.9241655 , 0.93223166, 0.94029782,
0.94836397, 0.95643013, 0.96449629, 0.97256245, 0.98062861,
0.98869477]),
<BarContainer object of 150 artists>)
plt.hist(res, bins=150)
show()
plt.hist(res, bins=20)
(array([ 4., 4., 3., 17., 20., 41., 64., 74., 103., 128., 132.,
108., 109., 74., 42., 40., 24., 4., 7., 2.]),
array([-0.2212291 , -0.16073291, -0.10023671, -0.03974052, 0.02075567,
0.08125187, 0.14174806, 0.20224425, 0.26274045, 0.32323664,
0.38373284, 0.44422903, 0.50472522, 0.56522142, 0.62571761,
0.6862138 , 0.74671 , 0.80720619, 0.86770238, 0.92819858,
0.98869477]),
<BarContainer object of 20 artists>)
plt.figure(figsize=(8,3))
plt.hist(res, bins = 20)
(array([ 4., 4., 3., 17., 20., 41., 64., 74., 103., 128., 132.,
108., 109., 74., 42., 40., 24., 4., 7., 2.]),
array([-0.2212291 , -0.16073291, -0.10023671, -0.03974052, 0.02075567,
0.08125187, 0.14174806, 0.20224425, 0.26274045, 0.32323664,
0.38373284, 0.44422903, 0.50472522, 0.56522142, 0.62571761,
0.6862138 , 0.74671 , 0.80720619, 0.86770238, 0.92819858,
0.98869477]),
<BarContainer object of 20 artists>)
plt.figure(figsize=(8,3))
plt.hist(res, bins = 20, density = True) # Area under the histogram is now equal to one
(array([0.06611986, 0.06611986, 0.0495899 , 0.28100942, 0.33059931,
0.67772859, 1.0579178 , 1.22321746, 1.70258646, 2.1158356 ,
2.18195547, 1.78523629, 1.80176626, 1.22321746, 0.69425856,
0.66119863, 0.39671918, 0.06611986, 0.11570976, 0.03305993]),
array([-0.2212291 , -0.16073291, -0.10023671, -0.03974052, 0.02075567,
0.08125187, 0.14174806, 0.20224425, 0.26274045, 0.32323664,
0.38373284, 0.44422903, 0.50472522, 0.56522142, 0.62571761,
0.6862138 , 0.74671 , 0.80720619, 0.86770238, 0.92819858,
0.98869477]),
<BarContainer object of 20 artists>)
res2 = np.random.randn(1000)*0.2 + 0.4
plt.figure(figsize=(8,3))
plt.hist(res, bins = 20, density = True)
plt.hist(res2, bins = 20, density = True)
(array([0.03141243, 0.12564974, 0.25129948, 0.40836165, 0.65966113,
1.083729 , 1.31932226, 1.99468961, 1.83762744, 1.63344661,
1.85333366, 1.53920931, 1.24079118, 0.84813574, 0.47118652,
0.20418083, 0.0942373 , 0.03141243, 0.03141243, 0.04711865]),
array([-0.18536151, -0.12169245, -0.0580234 , 0.00564566, 0.06931471,
0.13298376, 0.19665282, 0.26032187, 0.32399092, 0.38765998,
0.45132903, 0.51499809, 0.57866714, 0.64233619, 0.70600525,
0.7696743 , 0.83334335, 0.89701241, 0.96068146, 1.02435051,
1.08801957]),
<BarContainer object of 20 artists>)
plt.figure(figsize=(8,3))
plt.hist(res, bins = 20, density = True, histtype="step")
plt.hist(res2, bins = 20, density = True, histtype="step")
(array([0.03141243, 0.12564974, 0.25129948, 0.40836165, 0.65966113,
1.083729 , 1.31932226, 1.99468961, 1.83762744, 1.63344661,
1.85333366, 1.53920931, 1.24079118, 0.84813574, 0.47118652,
0.20418083, 0.0942373 , 0.03141243, 0.03141243, 0.04711865]),
array([-0.18536151, -0.12169245, -0.0580234 , 0.00564566, 0.06931471,
0.13298376, 0.19665282, 0.26032187, 0.32399092, 0.38765998,
0.45132903, 0.51499809, 0.57866714, 0.64233619, 0.70600525,
0.7696743 , 0.83334335, 0.89701241, 0.96068146, 1.02435051,
1.08801957]),
[<matplotlib.patches.Polygon at 0x7fcaeb29a9a0>])
plt.figure(figsize=(8,3))
plt.hist(res, bins=30, density=True, histtype='step')
plt.hist(res2, bins=30, density=True, histtype='step')
plt.xlabel('$\hat{E}-E$', fontsize=20)
plt.ylabel('Frequency')
plt.show()
fig, ax = plt.subplots(1, 1, figsize=(12,4))
fig, ax = plt.subplots(1, 2, figsize=(12,4))
fig, ax = plt.subplots(1, 1, figsize=(12,4))
ax.plot(x, y, "o--")
[<matplotlib.lines.Line2D at 0x7fcae9344f70>]
fig, ax = plt.subplots(1, 1, figsize=(12,4))
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
Text(0, 0.5, 'Voltage(V)')
fig, ax = plt.subplots(3, 2, figsize=(12,12))
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /var/folders/dw/p9lncvq57tv4px69pk911vl40000gn/T/ipykernel_901/79967231.py in <module> 1 fig, ax = plt.subplots(3, 2, figsize=(12,12)) ----> 2 ax.plot(x, y, "o--") 3 ax.set_xlabel("Time(s)") 4 ax.set_ylabel("Voltage(V)") AttributeError: 'numpy.ndarray' object has no attribute 'plot'
fig, axes = plt.subplots(3, 2, figsize=(12,12))
# ax.plot(x, y, "o--")
# ax.set_xlabel("Time(s)")
# ax.set_ylabel("Voltage(V)")
axes
array([[<AxesSubplot:>, <AxesSubplot:>],
[<AxesSubplot:>, <AxesSubplot:>],
[<AxesSubplot:>, <AxesSubplot:>]], dtype=object)
fig, axes = plt.subplots(3, 2, figsize=(12,12))
ax = axes[0][0]
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
Text(0, 0.5, 'Voltage(V)')
fig, axes = plt.subplots(3, 2, figsize=(12,12))
ax = axes[0][0]
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax = axes[1][1]
ax.hist(res2, bins=30, density=True, histtype='step')
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
Text(0, 0.5, 'Voltage(V)')
fig, axes = plt.subplots(3, 2, figsize=(12,12))
ax = axes[0][0]
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax = axes[1][1]
ax.hist(res2, bins=30, density=True, histtype='step', label = "component 1") # Adding legend
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.legend() # adding legend
<matplotlib.legend.Legend at 0x7fcaee1febe0>
fig, axes = plt.subplots(3, 2, figsize=(12,12))
ax = axes[0][0]
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax = axes[1][1]
ax.hist(res2, bins=30, density=True, histtype='step', label = "component 1")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.set_title("Histogram") # Adding title
ax.legend()
<matplotlib.legend.Legend at 0x7fcaeb147610>
fig, axes = plt.subplots(3, 2, figsize=(12,12))
ax = axes[0][0]
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.text(0.1, 0.1, "Text") # adding text
ax = axes[1][1]
ax.hist(res2, bins=30, density=True, histtype='step', label = "component 1")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.set_title("Histogram")
ax.legend()
<matplotlib.legend.Legend at 0x7fcae9a9a4c0>
fig, axes = plt.subplots(3, 2, figsize=(12,12))
ax = axes[0][0]
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.text(0.1, 0.1, "Text", transform=ax.transAxes) # rescaling axes
ax = axes[1][1]
ax.hist(res2, bins=30, density=True, histtype='step', label = "component 1")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.set_title("Histogram")
ax.legend()
<matplotlib.legend.Legend at 0x7fcafeb81460>
fig, axes = plt.subplots(3, 2, figsize=(8,8))
ax = axes[0][0]
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.text(0.1, 0.1, "Text", transform=ax.transAxes) # rescaling axes
ax = axes[1][1]
ax.hist(res2, bins=30, density=True, histtype='step', label = "component 1")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.set_title("Histogram")
ax.legend()
fig.suptitle('Title of All Plots', y=0.95, fontsize=25)
Text(0.5, 0.95, 'Title of All Plots')
fig, axes = plt.subplots(3, 2, figsize=(12,12))
ax = axes[0][0]
ax.plot(x, y, "o--")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.text(0.1, 0.1, "Text", transform=ax.transAxes)
ax.tick_params(axis="both", labelsize=10) # resizing numbers of y-axes
ax = axes[1][1]
ax.hist(res2, bins=30, density=True, histtype='step', label = "component 1")
ax.set_xlabel("Time(s)")
ax.set_ylabel("Voltage(V)")
ax.set_title("Histogram")
ax.legend()
<matplotlib.legend.Legend at 0x7fcae9cd9130>
plt.style.use(['science', 'notebook', 'grid'])
fig, ax = plt.subplots(1, 1, figsize=(8,3))
ax.plot(x, y, 'o--', color='r', lw=1, ms=5)
ax.text(0.1, 0.1, 'text here', transform=ax.transAxes)
ax.set_xlabel('Time(s)')
ax.set_ylabel(r'$\frac{d}{dx} f(x)$', fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=10)
ax.grid('minor')
plt.show()
plt.style.use(['science', 'notebook', 'grid'])
fig, ax = plt.subplots(1, 1, figsize=(8,3))
ax.plot(x, y, 'o--', color='r', lw=1, ms=5)
ax.text(0.1, 0.1, 'text here', transform=ax.transAxes)
ax.set_xlabel('Time(s)')
ax.set_ylabel(r'$\frac{d}{dx} f(x)$', fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=10)
ax.grid('major')
plt.show()
plt.style.use(['science', 'notebook', 'grid'])
fig, ax = plt.subplots(1, 1, figsize=(8,3))
ax.plot(x, y, 'o--', color='r', lw=1, ms=5)
ax.text(0.1, 0.1, 'text here', transform=ax.transAxes)
ax.set_xlabel('Time(s)')
ax.set_ylabel(r'$\frac{d}{dx} f(x)$', fontsize=15)
ax.tick_params(axis='both', which='minor', labelsize=10)
ax.grid('minor')
plt.show()
plt.style.use(['science', 'notebook', 'grid'])
fig, ax = plt.subplots(1, 1, figsize=(8,3))
ax.plot(x, y, 'o--', color='r', lw=1, ms=5)
ax.text(0.1, 0.1, 'text here', transform=ax.transAxes)
ax.set_xlabel('Time(s)')
ax.set_ylabel(r'$\frac{d}{dx} f(x)$', fontsize=15)
ax.tick_params(axis='both', which='minor', labelsize=10)
ax.grid('major')
plt.show()
Creating the following plot
res_a1 = 0.2*np.random.randn(1000)+0.4
res_b1 = 0.25*np.random.randn(1000)+0.4
res_a2 = 0.21*np.random.randn(1000)+0.3
res_b2 = 0.22*np.random.randn(1000)+0.3
res_a1
array([ 0.53706106, 0.09092345, 0.44297261, 0.3971587 , 0.58147618,
0.38043837, 0.42944279, 0.12042916, 0.10805351, 0.49859688,
0.58734148, 0.44958496, 0.74113972, 0.06017804, 0.13065208,
0.25616705, 0.51275934, 0.69715773, 0.78283581, 0.20306139,
0.33423718, 0.42265135, 0.41676025, 0.46942775, 0.1788309 ,
0.59455238, 0.46837013, 0.25047649, 0.19254628, 0.58279778,
0.34557362, 0.23345237, 0.19861986, 0.37696096, 0.63422154,
0.46752813, 0.35311792, 0.50162699, -0.14111933, 0.29561268,
0.66184121, 0.08311108, 0.21762748, 0.47997024, 0.62247507,
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0.60589462, 0.3395431 , 0.57982169, 0.32161073, 0.15368509,
0.4303693 , 0.36866465, 1.04320698, 0.26741686, 0.63945706,
0.47290826, 0.48122212, 0.29606462, 0.19516069, 0.60116129,
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0.12606133, 0.63794884, 0.86346221, 0.3060493 , 0.69867298,
0.81598633, 0.67552313, 0.28269139, 0.4800447 , 0.3901144 ,
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0.37434515, 0.9123973 , 0.10163482, 0.44053124, 0.24207608,
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0.37525564, 0.64853901, 0.61907674, 0.27881105, 0.59091557,
0.24973802, 0.51033384, 0.29042087, 0.30502273, 0.28423302,
0.16752387, 0.53965133, 0.55622157, 0.49832931, 0.48354617,
0.59132802, 0.51522835, 0.39463862, 0.31309059, 0.24020676,
0.60331474, 0.59451954, 0.54610528, 0.75079076, 0.97464365,
0.56245551, 0.47951529, 0.31136982, 0.34936368, 0.45017654,
0.07337879, 0.52353277, 0.44125774, 0.33514513, 0.61886712,
0.89046076, 0.76005863, 0.69808606, 0.29352252, 0.57121157,
0.21759234, 0.51633736, 0.42173877, 0.71756686, 0.18744601,
0.51798308, 0.22617763, 0.43748957, 0.3750329 , 0.23928971,
0.40522179, 0.6555432 , 0.45354627, 0.17136074, 0.35896685,
0.79347806, 0.38110182, 0.19066957, 0.40642546, 0.23597648,
0.34103748, 0.35439248, 0.36569009, 0.32467558, 0.69850378,
0.26783702, 0.31476667, 0.21606791, 0.31595746, 0.51814625,
0.21889941, 0.26384637, 0.29222135, 0.41117872, 0.32209812,
0.38389296, 0.25172852, 0.73826154, 0.4393454 , 0.43661754,
0.40673486, 0.23085188, 0.50108468, 0.38838532, 0.50422726,
0.70296556, 0.11898721, 0.56952127, 0.70900784, 0.79096198,
0.31952477, 0.74648007, 0.81802361, 0.21558634, 0.40573148,
0.64495574, 0.24917173, 0.54280916, 0.79093196, 0.1897866 ,
0.25119304, 0.13646469, 0.70581046, 0.23971912, 0.47413577,
0.43045974, 0.14984479, 0.32130526, 0.30500973, 0.5950111 ,
0.49669649, 0.49055595, 0.15817314, 0.7642534 , 0.65838364,
0.36127436, 0.41264183, 0.52592281, 0.54901071, 0.60845023,
0.3265467 , 0.18018592, 0.26612474, 0.48775529, 0.25154653,
0.10837653, 0.46975273, 0.02213782, 0.36440435, 0.4415768 ,
0.5115908 , 0.72165038, -0.03064654, 0.40869872, 0.82036483,
0.06094523, 0.6907146 , 0.54539563, 0.49951625, 0.3066197 ,
0.49270166, 0.08583813, 0.64541769, 0.40926793, 0.1271723 ,
0.71702155, 0.31268347, 0.36418443, 0.45164501, 0.45915833,
0.40693911, 0.36453735, 0.43537192, 0.63054625, 0.48888852,
0.31803704, 0.55611469, 0.16623417, 0.32941849, 0.27432522,
0.2415684 , 0.29549426, 0.61555108, 0.19290471, 0.16562977,
0.40870775, 0.20061046, 0.50738513, 0.17709507, 0.30527158,
0.41540161, 0.20185262, 0.71233194, 0.32973541, 0.54721805,
0.41464807, 0.42978664, 0.51979717, 0.38874083, 0.39511642,
0.25114755, 0.24210138, 0.4211319 , 0.4668018 , 0.48033483,
0.71445456, 0.42744067, 0.2301105 , 0.29496634, 0.03606382,
0.20343229, 0.57189349, 0.41305289, 0.43572621, 0.47182038,
0.53085083, 0.51259647, 0.67896971, 0.35879025, 0.48566729,
0.20926115, 0.41998676, 0.32451667, 0.40462824, 0.68549266,
0.44272109, 0.2008751 , 0.54738573, 0.4099816 , 0.58392715,
0.56676407, 0.53115491, 0.70296636, 0.63172447, 0.24096615,
0.3104594 , 0.4465337 , 0.67213235, 0.36523163, 0.58601136,
0.26471279, 0.2735722 , 0.27017111, 0.61565459, 0.22573843,
0.3149361 , 0.03139641, 0.83695086, 0.29492184, 0.33125782,
0.50831843, 0.37649229, 0.99364035, 0.31878133, 0.45280027,
0.27422573, 0.5629657 , 0.1519791 , 0.20159338, 0.57309421,
0.62303219, 0.50562353, 0.08406881, 0.39528841, 0.53762722,
0.11395186, 0.44074834, 0.63989816, 0.43956433, 0.71526115,
0.39573902, 0.41782816, 0.59478289, 0.2913705 , 0.54665639,
0.50059882, 0.20746656, 0.21633617, 0.29328564, 0.15619529,
0.41335674, 0.76328746, 0.50240231, 0.43861013, 0.35273726,
0.00755818, 0.47220177, 0.6597174 , 0.31175062, 0.48439515,
0.62300631, 0.59800583, 0.19636319, 0.32261234, 0.44392486,
0.33283424, 0.25883772, 0.35478153, 0.62236345, 0.18976177,
0.31518237, 0.52404478, 0.37406333, 0.59333411, 0.67787243,
0.78380865, 0.69137397, 0.76119752, 0.27879103, 0.2616703 ,
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0.43771017, 0.56445339, 0.38725428, 0.24851678, 0.75862287,
0.31913373, 0.31327049, 0.51263807, 0.2121433 , 0.19122743,
0.68582598, 0.64236385, 0.25424705, -0.06948703, 0.23693527,
0.35675618, 0.40305212, 0.44858131, 0.1085405 , 0.0485327 ,
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0.35822463, 0.15862452, 1.04877901, 0.43733498, 0.34434906,
0.28852837, 0.60736446, 0.24949967, 0.05253564, 0.60547227,
0.27568976, 0.05990747, 0.38493553, 0.72633565, 0.06801505,
0.42688689, 0.51783805, 0.27514716, 0.6375917 , 0.85587494,
0.64038931, 0.39898869, 0.61102383, 0.29922253, 0.36146005,
0.35745988, 0.56994965, 0.63562327, 0.40021203, 0.41897492,
0.29154193, 0.15184405, 0.32508217, 0.69622386, 0.55762874,
0.32133247, 0.39654576, 0.30668072, 0.05539188, 0.34208426,
0.05035552, 0.52047232, 0.33394013, 0.40866204, 0.24699611,
0.15455289, 0.20173499, 0.39435303, 0.25680014, 0.23222286,
0.76314552, 0.25000879, 0.28669324, 0.03448195, 0.32115141,
0.41993736, -0.01269274, 0.50046868, 0.31618087, 0.76832951,
0.09038824, 0.3623926 , 0.84803844, 0.44151117, 0.3309744 ,
0.1941798 , 0.35586646, 0.52895421, 0.10091044, 0.7192503 ,
0.61105438, 0.19953192, 0.37448455, 0.46602826, 0.44244401,
0.14911991, 0.23518901, 0.31831118, -0.02492618, 0.04750118,
0.19797959, 0.2937718 , 0.79316289, 0.16679284, 0.45187957,
0.34714305, 0.60169608, 0.49665535, 0.13514808, 0.31002687,
0.51396632, 0.29700791, 0.57258524, 0.17680513, 0.20276026,
0.36096625, 0.70704322, 0.3882481 , 0.53745731, 0.16552276,
0.75884996, 0.25771637, 0.00329108, 0.43462086, 0.17449285,
0.54416731, 0.18283135, 0.54690997, 0.32812587, 0.24015561,
0.37558362, 0.34586164, 0.69393136, 0.52983292, 0.58802719,
0.62542637, 0.20633755, 0.19011204, 0.3556355 , 0.10200103,
0.31726844, 0.61310512, 0.39079466, 0.42853281, 0.29370677,
0.48836401, 0.38260905, 0.18372893, 0.25068912, 0.46113199,
0.31695097, 0.73754567, 0.50122901, 0.63293453, 0.44584116,
0.22062183, 0.58560865, 0.19508677, 0.35622414, 0.27786473,
0.22699135, 0.49945855, 0.65765553, 0.54775442, 0.12442254,
0.21241512, 0.47999275, 0.42232787, 0.40215866, 0.36564083,
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0.21641912, 0.59559132, 0.50227093, 0.57160491, 0.12528199,
0.68997867, 0.2865068 , 0.4164978 , 0.42767826, 0.35512801,
0.36390191, 0.41785117, 0.52702263, 0.3080539 , 0.5263498 ,
0.53573064, 0.70537264, 0.22800016, 0.40695776, 0.62088481,
0.37428016, 0.41068326, 0.13443902, 0.23906211, 0.37833239,
0.21670347, 0.66327668, 0.50144957, 0.10437929, 0.41401852,
0.55657579, 0.35697018, 0.21544493, 0.61017347, 0.44236555,
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0.41606887, 0.17485845, 0.40639353, 0.35558444, 0.50066452,
0.15089541, 0.59786823, 0.57719568, -0.0520356 , 0.47328334,
0.38903309, 0.27040992, 0.52279614, 0.55772948, 0.42206545,
0.28216158, 0.27611526, 0.22157539, 0.71226067, 0.28612107,
0.72959143, 0.47327523, 0.63675215, 0.69937582, 0.72199167,
0.21581056, 0.51950946, 0.58474083, 0.35445355, 0.47990627,
0.25248002, 0.41070003, 0.15679805, 0.65289619, 0.25966885,
0.78961118, 0.53296325, 0.39637903, 0.45127791, 0.01291762,
0.16834696, 0.42241846, 0.1865833 , 0.26101549, 0.51575762,
0.43864767, 0.39945639, 0.62178326, 0.48183734, 0.34869202,
0.50230143, 0.53656778, 0.37098257, 0.40097434, -0.00238873,
0.12068945, 0.42077725, 0.42070854, 0.3086665 , 0.70570599,
0.70529542, 0.57381846, 0.30537764, 0.49306069, 0.27211591,
0.10454611, 0.42661194, 0.3172947 , 0.19384414, 0.50056088,
0.60220131, 0.74610239, 0.57092488, 0.34759184, 0.33447558,
0.3327017 , 0.15434263, 0.44617971, 0.06328989, 0.18858527,
0.34168822, 0.26251875, 0.39052853, 0.48274465, 0.46618939,
0.36364427, 0.52183114, 0.21668654, 0.75012335, 0.45488271,
-0.03708733, 0.71411961, 0.16641778, 0.09623113, 0.31844729,
0.22691516, 0.20903377, 0.2880195 , 0.23519612, 0.34110407,
0.32528228, 0.33336618, 0.5263205 , 0.53832761, 0.63380296,
0.29619873, 0.62291481, 0.08306306, 0.5602393 , 0.69400063,
0.51310035, 0.64007037, 0.2908748 , 0.46532484, 0.28017934,
0.15887301, 0.46007129, 0.59969441, 0.31944609, 0.14972578,
0.86848083, 1.01642221, 0.22781806, 0.56914399, 0.19341163,
0.03495728, 0.37780626, 0.43282426, 0.65546641, 0.01105678,
0.33634145, -0.06522817, 0.29078921, 0.12685667, 0.09418529,
0.32845855, 0.54836528, 0.28316277, 0.60630214, 0.31685782,
0.46951277, 0.31121956, 0.08412447, 0.81418917, 0.57557904,
0.25675403, 0.55048414, 0.62336164, 0.30742059, 0.29195192,
0.53820437, 0.70098784, 0.89314065, 0.38131319, 0.2122529 ,
0.24615024, 0.30683815, 0.67936619, 0.38393316, 0.50477405,
0.06045513, 0.57304504, 0.04599019, 0.36620769, 0.66001193,
0.6518558 , 0.31866347, 0.51058982, 0.59441024, 0.22415831,
0.00368503, 0.05683916, 0.67538678, 0.45622787, 0.33411598,
0.24920846, 0.1460187 , 0.47383282, 0.53777718, 0.71216099,
0.5968254 , 0.40780337, 0.78069369, 0.28365968, 0.30840939,
0.53492108, 0.33403532, 0.38337356, 0.32040221, 0.61210291,
0.17109873, 0.30375487, 0.81439533, 0.39029692, 0.28705937,
0.47095551, 0.40789496, 0.33391859, 0.60440121, 0.50176842,
0.29580303, 0.26009877, 0.22549962, 0.63412979, 0.37887241,
0.84948255, 0.24710319, 0.26711935, 0.15776808, 0.21022164,
-0.081536 , 0.33406272, 0.61634587, 0.32035635, 0.48767223,
0.48734256, 0.37613021, 0.34718424, 0.1378584 , 0.63801655,
0.12046489, 0.53453419, 0.19939647, 0.57589545, 0.19814196,
0.23627396, 0.5813684 , 0.58574669, 0.30709989, 0.31997441,
0.42211153, 0.65180428, 0.39285915, 0.67826672, 0.51894605,
0.49244356, 0.74020828, 0.47311913, 0.12340451, 0.74180547,
0.7332458 , 0.33136837, 0.49522722, 0.45922086, 0.13884125,
0.35016458, 0.52065434, 0.4657211 , 0.55021432, 0.02264143,
0.61543146, 0.45474847, 0.08776027, 0.475851 , 0.14452061,
0.35655663, 0.53077753, 0.35585434, 0.49911491, 0.41342734,
0.29128983, 0.32897911, 0.59315778, 0.12244428, 0.53926604,
0.34700098, 0.45860988, 0.12077627, 0.39265804, 0.49242012,
0.61122506, 0.38420468, 0.33508903, 0.2130077 , 0.18405961,
0.37196236, 0.95111188, 0.34807336, 0.46231081, 0.8164078 ,
0.35748156, 0.34129369, 0.28342419, 0.32054981, 0.30818 ,
0.4197477 , 0.3637979 , 0.55422332, -0.14762074, 0.2114099 ,
0.57801204, 0.15979879, 0.11380273, 0.35272662, 0.05685052,
0.32220455, 0.27739922, 0.79976711, 0.44587783, 0.68608816,
-0.18593972, 0.14854989, 0.61397232, 0.54105513, 0.27544158,
0.47196382, 0.15998342, 0.60573303, 0.37642765, 0.62778249,
0.4722535 , 0.28171367, 0.53846221, 0.60248715, 0.48928109,
0.57889994, 0.82867085, 0.33826874, 0.24412692, 0.77498555,
0.22786846, 0.14303555, 0.35484073, 0.39661093, 0.02476493])
plt.hist(res_a1, bins=100)
(array([ 1., 0., 0., 2., 1., 1., 0., 0., 1., 2., 1., 0., 2.,
2., 3., 5., 5., 5., 3., 9., 3., 5., 7., 8., 10., 9.,
8., 17., 14., 11., 16., 17., 22., 19., 16., 22., 17., 18., 27.,
20., 31., 26., 23., 28., 24., 21., 21., 20., 29., 21., 24., 20.,
16., 25., 20., 24., 15., 19., 21., 17., 11., 17., 14., 18., 18.,
15., 16., 9., 8., 7., 6., 15., 13., 6., 4., 7., 7., 2.,
4., 6., 1., 6., 2., 2., 2., 1., 0., 2., 1., 0., 0.,
0., 1., 1., 0., 1., 0., 1., 0., 2.]),
array([-1.85939717e-01, -1.73592530e-01, -1.61245343e-01, -1.48898155e-01,
-1.36550968e-01, -1.24203781e-01, -1.11856593e-01, -9.95094062e-02,
-8.71622189e-02, -7.48150315e-02, -6.24678442e-02, -5.01206569e-02,
-3.77734696e-02, -2.54262823e-02, -1.30790950e-02, -7.31907666e-04,
1.16152796e-02, 2.39624670e-02, 3.63096543e-02, 4.86568416e-02,
6.10040289e-02, 7.33512162e-02, 8.56984035e-02, 9.80455908e-02,
1.10392778e-01, 1.22739965e-01, 1.35087153e-01, 1.47434340e-01,
1.59781527e-01, 1.72128715e-01, 1.84475902e-01, 1.96823089e-01,
2.09170277e-01, 2.21517464e-01, 2.33864651e-01, 2.46211839e-01,
2.58559026e-01, 2.70906213e-01, 2.83253401e-01, 2.95600588e-01,
3.07947775e-01, 3.20294962e-01, 3.32642150e-01, 3.44989337e-01,
3.57336524e-01, 3.69683712e-01, 3.82030899e-01, 3.94378086e-01,
4.06725274e-01, 4.19072461e-01, 4.31419648e-01, 4.43766836e-01,
4.56114023e-01, 4.68461210e-01, 4.80808398e-01, 4.93155585e-01,
5.05502772e-01, 5.17849959e-01, 5.30197147e-01, 5.42544334e-01,
5.54891521e-01, 5.67238709e-01, 5.79585896e-01, 5.91933083e-01,
6.04280271e-01, 6.16627458e-01, 6.28974645e-01, 6.41321833e-01,
6.53669020e-01, 6.66016207e-01, 6.78363395e-01, 6.90710582e-01,
7.03057769e-01, 7.15404957e-01, 7.27752144e-01, 7.40099331e-01,
7.52446518e-01, 7.64793706e-01, 7.77140893e-01, 7.89488080e-01,
8.01835268e-01, 8.14182455e-01, 8.26529642e-01, 8.38876830e-01,
8.51224017e-01, 8.63571204e-01, 8.75918392e-01, 8.88265579e-01,
9.00612766e-01, 9.12959954e-01, 9.25307141e-01, 9.37654328e-01,
9.50001515e-01, 9.62348703e-01, 9.74695890e-01, 9.87043077e-01,
9.99390265e-01, 1.01173745e+00, 1.02408464e+00, 1.03643183e+00,
1.04877901e+00]),
<BarContainer object of 100 artists>)
plt.hist(res_a1, bins=100)
plt.hist(res_b1, bins=100)
plt.show()
Good habit to come up with complicated strings beforehand
textstr1 = '\n'.join((
r'$\sigma_a=%.4f$' % (np.std(res_a1)),
r'$\sigma_b=%.4f$' % (np.std(res_b1))))
textstr2 = '\n'.join((
r'$\sigma_a=%.4f$' % (np.std(res_a2)),
r'$\sigma_b=%.4f$' % (np.std(res_b2))))
textstr1
'$\\sigma_a=0.2005$\n$\\sigma_b=0.2491$'
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1)
(array([ 8., 35., 92., 194., 244., 201., 133., 70., 17., 6.]),
array([-0.18593972, -0.06246784, 0.06100403, 0.1844759 , 0.30794778,
0.43141965, 0.55489152, 0.67836339, 0.80183527, 0.92530714,
1.04877901]),
<BarContainer object of 10 artists>)
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1)
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step')
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step')
ax.hist(res_b1, bins = 30, density = True, histtype='step')
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right")
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes)
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$")
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center")
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
ax = axes[0]
ax.hist(res_a2, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b2, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr2, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
ax = axes[1]
ax.hist(res_a2, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b2, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr2, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a", color = "blue")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b", color = "red")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
ax = axes[1]
ax.hist(res_a2, bins = 30, density = True, histtype='step', label = "a")
ax.hist(res_b2, bins = 30, density = True, histtype='step', label = "b")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr2, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a", color = "blue")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b", color = "red")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
ax = axes[1]
ax.hist(res_a2, bins = 30, density = True, histtype='step', label = "a", color = "cyan")
ax.hist(res_b2, bins = 30, density = True, histtype='step', label = "b", color = "gold")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr2, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
plt.show()
fig, axes = plt.subplots(1, 2, figsize = (10, 3.5))
ax = axes[0]
ax.hist(res_a1, bins = 30, density = True, histtype='step', label = "a", color = "blue")
ax.hist(res_b1, bins = 30, density = True, histtype='step', label = "b", color = "red")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr1, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
ax.set_ylabel("Density")
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
ax.set_title("First Title")
ax = axes[1]
ax.hist(res_a2, bins = 30, density = True, histtype='step', label = "a", color = "cyan")
ax.hist(res_b2, bins = 30, density = True, histtype='step', label = "b", color = "gold")
ax.legend(loc="upper right", fancybox=False, edgecolor="black")
ax.text(0.05, 0.81, textstr2, transform=ax.transAxes, bbox=dict(facecolor="white", edgecolor="black"))
fig.text(0.5, -0.04, "$\Delta E(eV)$", ha = "center", fontsize = 20)
ax.set_title("Second Title", color="red")
plt.show()
Generate some data
plt.style.use(['default'])
_ = np.linspace(-1, 1, 100)
x, y = np.meshgrid(_,_)
z = x**2+x*y
z
array([[ 2.00000000e+00, 1.93980206e+00, 1.88042037e+00, ...,
-3.87715539e-02, -1.97938986e-02, 0.00000000e+00],
[ 1.97979798e+00, 1.92000816e+00, 1.86103459e+00, ...,
-1.93857770e-02, 1.11022302e-16, 2.02020202e-02],
[ 1.95959596e+00, 1.90021426e+00, 1.84164881e+00, ...,
2.22044605e-16, 1.97938986e-02, 4.04040404e-02],
...,
[ 4.04040404e-02, 1.97938986e-02, -2.22044605e-16, ...,
1.84164881e+00, 1.90021426e+00, 1.95959596e+00],
[ 2.02020202e-02, -1.11022302e-16, -1.93857770e-02, ...,
1.86103459e+00, 1.92000816e+00, 1.97979798e+00],
[ 0.00000000e+00, -1.97938986e-02, -3.87715539e-02, ...,
1.88042037e+00, 1.93980206e+00, 2.00000000e+00]])
plt.contourf(x,y,z)
<matplotlib.contour.QuadContourSet at 0x7fcad6d8be80>
plt.contourf(x,y,z, levels=30)
<matplotlib.contour.QuadContourSet at 0x7fcad7178c70>
plt.contourf(x,y,z, levels=30)
plt.colorbar()
<matplotlib.colorbar.Colorbar at 0x7fcad73e5d00>
plt.contourf(x,y,z, levels=30)
plt.colorbar(label = "Temperature($\degree$C)")
<matplotlib.colorbar.Colorbar at 0x7fcad763b5e0>
plt.contourf(x,y,z, levels=30, vmin = 0.7)
plt.colorbar(label = "Temperature($\degree$C)")
<matplotlib.colorbar.Colorbar at 0x7fcad7986bb0>
plt.contourf(x,y,z, levels=30, vmax = 1.68)
plt.colorbar(label = "Temperature($\degree$C)")
<matplotlib.colorbar.Colorbar at 0x7fcad7bd2e50>
plt.contourf(x,y,z, levels=30, vmax = 1.68, cmap = "plasma")
plt.colorbar(label = "Temperature($\degree$C)")
<matplotlib.colorbar.Colorbar at 0x7fcad7e3ae80>
plt.contourf(x,y,z, levels=30, vmax = 1.68, cmap = "plasma")
plt.colorbar(label = "Temperature($\degree$C)")
plt.xlabel('Horizontal Position(m)')
plt.ylabel('Vertical Position(m)')
Text(0, 0.5, 'Vertical Position(m)')
plt.contour(x,y,z)
<matplotlib.contour.QuadContourSet at 0x7fcad83847f0>
cs = plt.contour(x,y,z)
plt.clabel(cs, fontsize=8)
<a list of 14 text.Text objects>
cs = plt.contour(x,y,z, levels = 20)
plt.clabel(cs, fontsize=8)
<a list of 28 text.Text objects>
3D Surface Plot (Generally not preferred). However, they tend to work well in animations (we'll get to this later)
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.plot_surface(x, y, z, cmap="coolwarm")
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7fcad90dc6a0>
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.plot_surface(x,y,z, cmap='coolwarm', linewidth=0)
ax.view_init(elev=10, azim=50)
import numpy as np
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
plt.style.use('seaborn-poster')
fig = plt.figure(figsize = (10,10))
ax = plt.axes(projection='3d')
plt.show()
fig = plt.figure(figsize = (8,8))
ax = plt.axes(projection='3d')
ax.grid()
t = np.arange(0, 10*np.pi, np.pi/50)
x = np.sin(t)
y = np.cos(t)
ax.plot3D(x, y, t)
ax.set_title('3D Parametric Plot')
# Set axes label
ax.set_xlabel('x', labelpad=20)
ax.set_ylabel('y', labelpad=20)
ax.set_zlabel('t', labelpad=20)
plt.show()
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
# syntax for 3-D projection
ax = plt.axes(projection ='3d')
# defining all 3 axes
z = np.linspace(0, 1, 100)
x = z * np.sin(25 * z)
y = z * np.cos(25 * z)
# plotting
ax.plot3D(x, y, z, 'green')
ax.set_title('3D line plot geeks for geeks')
plt.show()
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
# syntax for 3-D projection
ax = plt.axes(projection ='3d')
# defining axes
z = np.linspace(0, 1, 100)
x = z * np.sin(25 * z)
y = z * np.cos(25 * z)
c = x + y
ax.scatter(x, y, z, c = c)
# syntax for plotting
ax.set_title('3d Scatter plot geeks for geeks')
plt.show()
x = np.random.random(50)
y = np.random.random(50)
z = np.random.random(50)
fig = plt.figure(figsize = (10,10))
ax = plt.axes(projection='3d')
ax.grid()
ax.scatter(x, y, z, c = 'r', s = 50)
ax.set_title('3D Scatter Plot')
# Set axes label
ax.set_xlabel('x', labelpad=20)
ax.set_ylabel('y', labelpad=20)
ax.set_zlabel('z', labelpad=20)
plt.show()
x = [1, 2, 3, 4]
y = [3, 4, 5]
X, Y = np.meshgrid(x, y)
print(X)
[[1 2 3 4] [1 2 3 4] [1 2 3 4]]
print(Y)
[[3 3 3 3] [4 4 4 4] [5 5 5 5]]
fig = plt.figure(figsize = (12,10))
ax = plt.axes(projection='3d')
x = np.arange(-5, 5.1, 0.2)
y = np.arange(-5, 5.1, 0.2)
X, Y = np.meshgrid(x, y)
Z = np.sin(X)*np.cos(Y)
surf = ax.plot_surface(X, Y, Z, cmap = plt.cm.cividis)
# Set axes label
ax.set_xlabel('x', labelpad=20)
ax.set_ylabel('y', labelpad=20)
ax.set_zlabel('z', labelpad=20)
fig.colorbar(surf, shrink=0.5, aspect=8)
plt.show()
fig = plt.figure(figsize=(12,6))
ax = fig.add_subplot(1, 2, 1, projection='3d')
ax.plot_wireframe(X,Y,Z)
ax.set_title('Wireframe plot')
ax = fig.add_subplot(1, 2, 2, projection='3d')
ax.plot_surface(X,Y,Z)
ax.set_title('Surface plot')
plt.tight_layout()
plt.show()
For plotting vector fields
plt.style.use(['science', 'notebook', 'grid'])
w = 3
_ = np.linspace(-3,3,100)
X, Y = np.meshgrid(_,_)
U = -1 - X**2 + Y
V = 1 + X - Y**2
speed = np.sqrt(U**2 + V**2)
fig, axes = plt.subplots(2,2,figsize=(5,5))
fig, axes = plt.subplots(2,2,figsize=(7,7))
fig, axes = plt.subplots(2,2,figsize=(6,6))
ax = axes[0][0]
ax.streamplot(X, Y, U, V)
<matplotlib.streamplot.StreamplotSet at 0x7fcaf488fdf0>
fig, axes = plt.subplots(2,2,figsize=(7,7))
ax = axes[0][0]
ax.streamplot(X, Y, U, V)
ax = axes[0][1]
ax.streamplot(X, Y, U, V, color=speed)
<matplotlib.streamplot.StreamplotSet at 0x7fcaf3613c10>
fig, axes = plt.subplots(2,2,figsize=(7,7))
ax = axes[0][0]
ax.streamplot(X, Y, U, V)
ax = axes[0][1]
ax.streamplot(X, Y, U, V, color=speed)
ax = axes[1][0]
lw = 5*speed / speed.max()
ax.streamplot(X, Y, U, V, color=speed, linewidth=lw)
<matplotlib.streamplot.StreamplotSet at 0x7fcae979aaf0>
fig, axes = plt.subplots(2,2,figsize=(7,7))
ax = axes[0][0]
ax.streamplot(X, Y, U, V)
ax = axes[0][1]
ax.streamplot(X, Y, U, V, color=speed)
ax = axes[1][0]
lw = 5*speed / speed.max()
ax.streamplot(X, Y, U, V, color=speed, linewidth=lw)
ax = axes[1][1]
# ax.streamplot(X, Y, U, V, color=speed, linewidth=2)
seed_points = np.array([[0,1], [1,0]])
ax.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn', start_points=seed_points)
plt.savefig('figs/test1.jpeg', dpi=100)
im = plt.imread('figs/flower.png')
im
array([[[0.2784314 , 0.49411765, 0.81960785, 1. ],
[0.2784314 , 0.49411765, 0.81960785, 1. ],
[0.2784314 , 0.49411765, 0.81960785, 1. ],
...,
[0.36862746, 0.49803922, 0.30980393, 1. ],
[0.37254903, 0.49803922, 0.30588236, 1. ],
[0.37254903, 0.49803922, 0.29803923, 1. ]],
[[0.2784314 , 0.49411765, 0.81960785, 1. ],
[0.2784314 , 0.49411765, 0.81960785, 1. ],
[0.2784314 , 0.49411765, 0.81960785, 1. ],
...,
[0.37254903, 0.5019608 , 0.2901961 , 1. ],
[0.37254903, 0.5019608 , 0.28627452, 1. ],
[0.37254903, 0.5019608 , 0.28235295, 1. ]],
[[0.2784314 , 0.49411765, 0.81960785, 1. ],
[0.2784314 , 0.49411765, 0.81960785, 1. ],
[0.2784314 , 0.49411765, 0.81960785, 1. ],
...,
[0.37254903, 0.5019608 , 0.27058825, 1. ],
[0.37254903, 0.5019608 , 0.27058825, 1. ],
[0.37254903, 0.5019608 , 0.27058825, 1. ]],
...,
[[0.48235294, 0.60784316, 0.34117648, 1. ],
[0.47843137, 0.6039216 , 0.3372549 , 1. ],
[0.47843137, 0.6039216 , 0.3372549 , 1. ],
...,
[0.41568628, 0.5294118 , 0.20784314, 1. ],
[0.42352942, 0.5411765 , 0.21568628, 1. ],
[0.4117647 , 0.5254902 , 0.2 , 1. ]],
[[0.48235294, 0.60784316, 0.34117648, 1. ],
[0.47843137, 0.6039216 , 0.3372549 , 1. ],
[0.4745098 , 0.6039216 , 0.3372549 , 1. ],
...,
[0.43529412, 0.54901963, 0.22745098, 1. ],
[0.4392157 , 0.5529412 , 0.22745098, 1. ],
[0.42352942, 0.5411765 , 0.21568628, 1. ]],
[[0.48235294, 0.60784316, 0.34117648, 1. ],
[0.4745098 , 0.6039216 , 0.3372549 , 1. ],
[0.4745098 , 0.6 , 0.33333334, 1. ],
...,
[0.4392157 , 0.5529412 , 0.23137255, 1. ],
[0.4392157 , 0.5529412 , 0.22745098, 1. ],
[0.42352942, 0.5411765 , 0.21568628, 1. ]]], dtype=float32)
plt.imshow(im)
<matplotlib.image.AxesImage at 0x7fcad9538fd0>
Animations require relating the time of your problem to the frame of the animation, then specifying the frames per second
def f(x,t):
return np.sin(x-3*t)
x = np.linspace(0, 10*np.pi, 1000)
plt.plot(x, f(x, 0))
[<matplotlib.lines.Line2D at 0x7fcada92c220>]
plt.plot(x, f(x, 0))
plt.plot(x, f(x, 0.1))
[<matplotlib.lines.Line2D at 0x7fcadae0b790>]
plt.plot(x, f(x, 0))
plt.plot(x, f(x, 0.1))
plt.plot(x, f(x, 0.2))
plt.plot(x, f(x, 1))
plt.plot(x, f(x, 1.2))
[<matplotlib.lines.Line2D at 0x7fcadb55e9d0>]
from matplotlib import animation
from matplotlib.animation import PillowWriter
fig, ax = plt.subplots(1,1, figsize=(8,4))
fig, ax = plt.subplots(1,1, figsize=(8,4))
ln1, = plt.plot([], [])
fig, ax = plt.subplots(1,1, figsize=(8,4))
ln1, = plt.plot([], [])
time_text = ax.text(0.65, 0.95, '', fontsize=15, bbox=dict(facecolor='white', edgecolor='black'),
transform=ax.transAxes)
fig, ax = plt.subplots(1,1, figsize=(8,4))
ln1, = plt.plot([], [])
time_text = ax.text(0.65, 0.95, '', fontsize=15, bbox=dict(facecolor='white', edgecolor='black'),
transform=ax.transAxes)
fig, ax = plt.subplots(1,1, figsize=(8,4))
ln1, = plt.plot([], [])
time_text = ax.text(0.65, 0.95, '', fontsize=15, bbox=dict(facecolor='white', edgecolor='black'),
transform=ax.transAxes)
ax.set_xlim(0,10*np.pi)
ax.set_ylim(-1.5, 1.5)
(-1.5, 1.5)
fig, ax = plt.subplots(1,1, figsize=(8,4))
ln1, = plt.plot([], [])
time_text = ax.text(0.65, 0.95, '', fontsize=15, bbox=dict(facecolor='white', edgecolor='black'),
transform=ax.transAxes)
ax.set_xlim(0,10*np.pi)
ax.set_ylim(-1.5, 1.5)
def animate(i):
ln1.set_data(x, f(x, 1/50*i))
time_text.set_text('t={:.2f}'.format(i/60))
fig, ax = plt.subplots(1,1, figsize=(8,4))
ln1, = plt.plot([], [])
time_text = ax.text(0.65, 0.95, '', fontsize=15, bbox=dict(facecolor='white', edgecolor='black'),
transform=ax.transAxes)
ax.set_xlim(0,10*np.pi)
ax.set_ylim(-1.5, 1.5)
def animate(i):
ln1.set_data(x, f(x, 1/50*i))
time_text.set_text('t={:.2f}'.format(i/50))
ani = animation.FuncAnimation(fig, animate, frames=240, interval=50)
fig, ax = plt.subplots(1,1, figsize=(8,4))
ln1, = plt.plot([], [])
time_text = ax.text(0.65, 0.95, '', fontsize=15, bbox=dict(facecolor='white', edgecolor='black'),
transform=ax.transAxes)
ax.set_xlim(0,10*np.pi)
ax.set_ylim(-1.5, 1.5)
def animate(i):
ln1.set_data(x, f(x, 1/50*i))
time_text.set_text('t={:.2f}'.format(i/50))
ani = animation.FuncAnimation(fig, animate, frames=240, interval=50)
ani.save('figs/ani.gif', writer='pillow', fps=50, dpi=100)
/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/matplotlib/animation.py:973: UserWarning: Animation was deleted without rendering anything. This is most likely unintended. To prevent deletion, assign the Animation to a variable that exists for as long as you need the Animation. warnings.warn(
The animation function can be used in other ways too. One can create 3D rotating gifs of surfaces.
_ = np.linspace(-1, 1, 100)
x, y = np.meshgrid(_,_)
z = x**2+x*y
plt.style.use(["default"])
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.plot_surface(x,y,z, cmap='coolwarm', linewidth=0)
ax.view_init(elev=10, azim=0)
def animate(i):
ax.view_init(elev=10, azim=3*i )
ani = animation.FuncAnimation(fig, animate, frames=140, interval=50)
ani.save('figs/ani2.gif', writer='pillow', fps=30, dpi=100)
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 10, 0.2)
y = np.sin(x)
fig, axes = plt.subplots(2,3, gridspec_kw = {'width_ratios':[3, 1, 2]})
axes[0, 0].plot(x,y)
axes[1, 1].plot(y,x)
plt.show()
The 'gridspec_kw' argument is simple. Notie that we pass in an array of length three; this is the number of columns in the plot grid. This allows us to determine the ratio of the width of the columns. The same thing can be accomplished with the rows.
x = np.arange(0, 10, 0.2)
y = np.cos(x)
fig, axes = plt.subplots(2,3, gridspec_kw = {'height_ratios':[2, 1]})
axes[0, 0].plot(x,y)
axes[1, 1].plot(y,x)
plt.show()
Sometimes we want might two small subplots on the top and one small one on the bottom. This can be accomplished simply as follows.
fig = plt.figure()
ax1 = plt.subplot(221)
ax2 = plt.subplot(223)
ax3 = plt.subplot(122)
plt.show()
The numerical arguments work as follows. The first two numbers 22 or 12 represent a 2x2 or 1x2 grid. The third number 1 or 3 or 2 represents the position in that grid that the plot should go.
Another way of accomplishing this is using the subplot2grid feature.
The first tuple breaks the figure apart into a 3x3 grid. The second tuple places the top left corner of the 'ax' in that location on the grid. The colspan and rowspan then stretch out the plot so that it covers more area of the grid.
fig = plt.figure(figsize=(8,5))
ax1 = plt.subplot2grid((3, 3), (0, 0))
ax2 = plt.subplot2grid((3, 3), (0, 1), colspan=2)
ax3 = plt.subplot2grid((3, 3), (1, 0), colspan=2, rowspan=2)
ax4 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
fig, (ax1, ax2) = plt.subplots(1, 2, subplot_kw=dict(projection='polar'))
ax1.plot(x, y)
ax2.plot(x, y ** 2)
plt.show()
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, (ax1, ax2) = plt.subplots(2)
fig.suptitle('Axes values are scaled individually by default')
ax1.plot(x, y)
ax2.plot(x + 1, -y)
[<matplotlib.lines.Line2D at 0x7fcad4e5b370>]
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
fig.suptitle('Aligning x-axis using sharex')
ax1.plot(x, y)
ax2.plot(x + 1, -y)
[<matplotlib.lines.Line2D at 0x7fcad86ba580>]
fig, axs = plt.subplots(3, sharex=True, sharey=True)
fig.suptitle('Sharing both axes')
axs[0].plot(x, y ** 2)
axs[1].plot(x, 0.3 * y, 'o')
axs[2].plot(x, y, '+')
[<matplotlib.lines.Line2D at 0x7fcaf26c36a0>]
fig = plt.figure()
gs = fig.add_gridspec(3, hspace=0)
axs = gs.subplots(sharex=True, sharey=True)
fig.suptitle('Sharing both axes')
axs[0].plot(x, y ** 2)
axs[1].plot(x, 0.3 * y, 'o')
axs[2].plot(x, y, '+')
# Hide x labels and tick labels for all but bottom plot.
for ax in axs:
ax.label_outer()
fig = plt.figure()
gs = fig.add_gridspec(2, 2, hspace=0, wspace=0)
(ax1, ax2), (ax3, ax4) = gs.subplots(sharex='col', sharey='row')
fig.suptitle('Sharing x per column, y per row')
ax1.plot(x, y)
ax2.plot(x, y**2, 'tab:orange')
ax3.plot(x + 1, -y, 'tab:green')
ax4.plot(x + 2, -y**2, 'tab:red')
for ax in axs.flat:
ax.label_outer()
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(x, y)
axs[0, 0].set_title("main")
axs[1, 0].plot(x, y**2)
axs[1, 0].set_title("shares x with main")
axs[1, 0].sharex(axs[0, 0])
axs[0, 1].plot(x + 1, y + 1)
axs[0, 1].set_title("unrelated")
axs[1, 1].plot(x + 2, y + 2)
axs[1, 1].set_title("also unrelated")
fig.tight_layout()
