With my code I get 2 equations in the legend that are the same. I don't how why it is so. I just want to correct this by making it only one equation. How can I do that? This equation is the line fit result of some of the data below.
Thanks in advance!
import matplotlib.pyplot as plt
import numpy as np
import plotly.plotly as py
import plotly.tools as tls
from sympy import S, symbols
import sympy
y = [2.7,2.3,1.9,1.5,1.3,1.0,0.8,0.6,0.5,0.4,0.2,0.1,0.0,0.0,-0.20,-0.2]
y = [i*10**(-16) for i in y]
x = [0,0.05,0.10,0.15,0.20,0.25,0.30,0.40,0.45,0.50,0.55,0.60,0.65,0.70,0.75,0.80]
e_y = [10**(-17)]* 16
e_x = [0.001] * 16
fig= plt.figure()
ax = fig.add_subplot(111)
ax.errorbar(x,y, yerr=e_y,xerr=0.001,fmt='-o')
ax.set_title('Current vs. Potential')
ax.set_xlabel('Retarding Potential')
ax.set_ylabel('Photocell Current')
x=x[:7]
y=y[:7]
e_y=e_y[:7]
e_x=e_x[:7]
#line fit:
fit=np.polyfit(x,y,1)
fit_fn = np.poly1d(fit)
a=symbols("x")
line = sum(S(format(v))*a**i for i, v in enumerate(fit[::-1]))
eq_latex = sympy.printing.latex(line)
plt.plot(x,y,x,fit_fn(x),label="${}$".format(eq_latex))
plt.legend(fontsize='small')
plt.show()
I solved this using the following:
#import matplotlib.patches as mpatches
plt.plot(x,y,x,fit_fn(x))
eqn = mpatches.Patch(color='green',label="${}$".format(eq_latex))
plt.legend(handles=[eqn])
instead of
plt.plot(x,y,x,fit_fn(x),label="${}$".format(eq_latex))
plt.legend(fontsize='small')
Related
is there a way to get TwoSlopeNorm in combination with base 2 ticks on the colorbar?
An example is something like this where you have normal linear scaling:
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
x = np.arange(-50,100,1)
y = x.copy()
c = x.copy()
scatter_plot = plt.scatter(x, y, c=c, cmap='bwr', norm=matplotlib.colors.TwoSlopeNorm(vmin=-50, vcenter=0, vmax=100))
cbar = plt.colorbar(scatter_plot)
plt.show()
I know based on a previous question of mine that SymLogNorm supports base2, but it looks like this is not the case for TwoSlopeNorm. Does anyone have a suggestion on how to do it?
Found this code here.
%matplotlib inline
from ipywidgets import interactive
import matplotlib.pyplot as plt
import numpy as np
def f(m, b):
plt.figure(2)
x = np.linspace(-10, 10, num=1000)
plt.plot(x, m * x + b)
plt.ylim(-5, 5)
plt.show()
interactive_plot = interactive(f, m=(-2.0, 2.0), b=(-3, 3, 0.5))
output = interactive_plot.children[-1]
output.layout.height = '350px'
interactive_plot
It creates a plot that can be interacted with and doesn't flicker. I looked in the documentation but I don't know how to add a box to control this plot? For instance how would I use this hbox to update the plot?
items = [widgets.FloatSlider(value=5, min=0, max=10, step=0.01,
orientation='vertical', layout=Layout(width="15px"), readout_format='.0f')
for i in range(5)]
hbox = widgets.HBox(items)
hbox
If you know the answer please elaborate and explain why it is done like that.
I tried to call the function f from observe and it simply created new unrelated plots.
When the same function is called from interactive it works amazingly well.
Here's a rough idea of what is happening with an interactive, perhaps you can adapt it to your situation?
%matplotlib inline
import ipywidgets as ipwy
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
m_widg = ipwy.FloatSlider(min=-2, max=2, step=0.5)
b_widg = ipwy.FloatSlider(min=-3, max=3, step=0.5)
out = ipwy.Output(layout = ipwy.Layout(height='350px'))
display(ipwy.VBox(children=(m_widg, b_widg, out)))
def f(*args):
out.clear_output()
m = m_widg.value
b = b_widg.value
plt.figure(2)
x = np.linspace(-10, 10, num=1000)
plt.plot(x, m * x + b)
plt.ylim(-5, 5)
with out:
clear_output()
plt.show()
m_widg.observe(f, names=['value'])
b_widg.observe(f, names=['value'])
I am trying to use ScalarFormatter to convert my y-axis ticks into standard form, but after much experimenting I'm experiencing no success and I can't figure out why. MWE:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import scipy.constants as const
k = 1/(4*np.pi*const.epsilon_0)
def force_e(q_1,q_2,x):
return k*q_1*q_2/x**2
d = 10
delta = 0.1*d
x = np.linspace(0+delta,d-delta,1000)
q1 = 2e-6
q2 = -2e-6
q = 1e-9
F = force_e(q1,q,x) - force_e(q2,q,d-x)
fig,ax1 = plt.subplots(1, figsize=(6,6))
ax1.plot(x, F)
ax1.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter(useMathText=True))
ax1.set_xlabel("Separation $r$ (m)")
ax1.set_ylabel("Force $F$ (N)")
plt.tight_layout()
plt.show()
It produces a figure as so:
where as I would like it to display 1.75x10^5 etc.
I am using scientific notation in a colorbar within a 2D plot. I want to write 10^{-3} instead of e-3. I tried to change that (see code below) but it does not work...
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)*0.001
x=x.reshape((10,10))
y=y.reshape((10,10))
z=z.reshape((10,10))
fig, ax = plt.subplots(figsize=(8,6))
cs = ax.contourf(x,y,z, 10)
plt.xticks(fontsize=16,rotation=0)
plt.yticks(fontsize=16,rotation=0)
cbar = plt.colorbar(cs,)
cbar.set_label("test",fontsize = 22)
cbar.formatter.set_scientific(True)
cbar.formatter.set_powerlimits((0, 0))
cbar.ax.tick_params(labelsize=16)
cbar.ax.yaxis.get_offset_text().set_fontsize(22)
cbar.ax.xaxis.major.formatter._useMathText = True
cbar.update_ticks()
plt.savefig("test.png")
It seems you want a ScalarFormatter with mathtext in use.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker
x = np.tile(np.arange(10), 10).reshape((10,10))
y = np.repeat(np.arange(10),10).reshape((10,10))
z = np.sort(np.random.rand(100)*0.001).reshape((10,10))
fig, ax = plt.subplots(figsize=(8,6))
cs = ax.contourf(x,y,z, 10)
fmt = matplotlib.ticker.ScalarFormatter(useMathText=True)
fmt.set_powerlimits((0, 0))
cbar = plt.colorbar(cs,format=fmt)
plt.show()
I want to draw a scatter trend line on matplot. How can I do that?
Python
import pandas as pd
import matplotlib.pyplot as plt
csv = pd.read_csv('/tmp/test.csv')
data = csv[['fee', 'time']]
x = data['fee']
y = data['time']
plt.scatter(x, y)
plt.show()
CSV
fee,time
100,650
90,700
80,860
70,800
60,1000
50,1200
time is integer value.
Scatter chart
I'm sorry I found the answer by myself.
How to add trendline in python matplotlib dot (scatter) graphs?
Python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
csv = pd.read_csv('/tmp/test.csv')
data = csv[['fee', 'time']]
x = data['fee']
y = data['time']
plt.scatter(x, y)
z = np.polyfit(x, y, 1)
p = np.poly1d(z)
plt.plot(x,p(x),"r--")
plt.show()
Chart
With text:
from sklearn.metrics import r2_score
plt.plot(x,y,"+", ms=10, mec="k")
z = np.polyfit(x, y, 1)
y_hat = np.poly1d(z)(x)
plt.plot(x, y_hat, "r--", lw=1)
text = f"$y={z[0]:0.3f}\;x{z[1]:+0.3f}$\n$R^2 = {r2_score(y,y_hat):0.3f}$"
plt.gca().text(0.05, 0.95, text,transform=plt.gca().transAxes,
fontsize=14, verticalalignment='top')
You also can use Seaborn lmplot:
import seaborn as sns
import pandas as pd
from io import StringIO
textfile = StringIO("""fee,time
100,650
90,700
80,860
70,800
60,1000
50,1200""")
df = pd.read_csv(textfile)
_ = sns.lmplot(x='fee', y='time', data=df, ci=None)
Output: