import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('Lab6.csv', dtype='float', delimiter=',', unpack=True)
t = (data[:1])
yt = np.cos(4 * np.pi * t )
y = data[1:2]
plt.figure()
plt.plot(t,yt,t,y,'o')
plt.xlabel('Time, s')
plt.ylabel('Voltage, V')
plt.legend(('Signal 1', 'Signal 1'))
plt.show()
I imported my data from a cvs file. I am looking to have to waveforms with lines between each data point and two separate colors for each wave
Output from code
Related
In Pandas, I am trying to generate a Ridgeline plot for which the density values are shown (either as Y axis or color-ramp). I am using the Joyplot but any other alternative ways are fine.
So, first I created the Ridge plot to show the different distribution plot for each condition (you can reproduce it using this code):
import pandas as pd
import joypy
import matplotlib
import matplotlib.pyplot as plt
df1 = pd.DataFrame({'Category1':np.random.choice(['C1','C2','C3'],1000),'Category2':np.random.choice(['B1','B2','B3','B4','B5'],1000),
'year':np.arange(start=1900, stop=2900, step=1),
'Data':np.random.uniform(0,1,1000),"Period":np.random.choice(['AA','CC','BB','DD'],1000)})
data_pivot=df1.pivot_table('Data', ['Category1', 'Category2','year'], 'Period')
fig, axes = joypy.joyplot(data_pivot, column=['AA', 'BB', 'CC', 'DD'], by="Category1", ylim='own', figsize=(14,10), legend=True, alpha=0.4)
so it generates the figure but without my desired Y axis. So, based on this post, I could add a colorramp, which neither makes sense nor show the differences between the distribution plot of the different categories on each line :) ...
ar=df1['Data'].plot.kde().get_lines()[0].get_ydata() ## a workaround to get the probability values to set the colorramp max and min
norm = plt.Normalize(ar.min(), ar.max())
original_cmap = plt.cm.viridis
cmap = matplotlib.colors.ListedColormap(original_cmap(norm(ar)))
sm = matplotlib.cm.ScalarMappable(cmap=original_cmap, norm=norm)
sm.set_array([])
# plotting ....
fig, axes = joypy.joyplot(data_pivot,colormap = cmap , column=['AA', 'BB', 'CC', 'DD'], by="Category1", ylim='own', figsize=(14,10), legend=True, alpha=0.4)
fig.colorbar(sm, ax=axes, label="density")
But what I want is some thing like either of these figures (preferably with colorramp) :
I've made a bar chart in matplotlib. In that chart I managed to change the formatting of the number on the y-axes. The only problem is that the thousand seperator is now a comma (','). Since I live in The Netherlands I would very much like to convert that to a dot ('.'). It looks like a simple thing but I can't find anywhere how to do it. Please see code below.
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
%matplotlib inline
top = [72000, 73000, 78000, 71000]
base = [15000, 12000, 13000, 14000]
x = ['2017', '2018', '2019', '2020']
x_ax = pd.Series(x)
fig, ax = plt.subplots()
ax.bar(x, top, label='TOP CUSTOMERS')
ax.bar(x, base, bottom=top, label='BASE CUSTOMERS')
ax.set_title('TON YTD JULY')
ax.legend(loc='lower center')
ax.set_ylabel('Ton')
ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))
plt.savefig('C:/Users/MBRU/Box Sync/Data_Source/out/320/example.png', bbox_inches='tight')
plt.show()
Example of graph with code
I have a question about drawing a regression line and determining the slope of that line. I am doing research for water heights of inland lakes in Tibet with the help of satellite date. I have the data for one year of one lake in this script.
However I want to determine the annual rise of the lake for as well the reference height as for the total beams. Is there some one that could help me?
This is the link towards the excel file: https://drive.google.com/file/d/12wD2ByQC6ObNCWq_yIhkXiNsV3KfDpit/view?usp=sharing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# Graph in chronological order
heights = pd.read_excel ('Qinghai_dates_heights.xlsx')
dates = (heights.loc[:,'Date'])
strong_beams = (heights.loc[:,'Strong total'])
weak_beams = (heights.loc[:,'Weak total'])
total_beams = (heights.loc[:,'Total'])
# setting the reference data from Hydrolabs
reference_dates = (heights.loc[:,'Date.1'])
reference_heights = (heights.loc[:,'Hydrolabs'])
# Set the locator
locator = mdates.MonthLocator() # every month
# Specify the format - %b gives us Jan, Feb...
fmt = mdates.DateFormatter('%b')
#plt.plot(dates,strong_beams, label='Strong Beams', marker="o")
#plt.plot(dates,weak_beams, label='Weak Beams', marker="o")
plt.plot(dates, total_beams, label='Total Beams', marker="o")
plt.plot(reference_dates, reference_heights, label='Reference height (Hydrolabs)', marker="o")
X = plt.gca().xaxis
X.set_major_locator(locator)
# Specify formatter
X.set_major_formatter(fmt)
plt.xlabel('Date [months]')
plt.ylabel('elevation [m]')
plt.title("Water-Height Qinghai from November 2018 - November 2019 ")
plt.legend()
plt.show()
Does this help ? I usually use sklearn for this.
import numpy as np
from matplotlib import pyplot as plt
from sklearn import linear_model, datasets
Generate a set of data
X = np.linspace(0, 10)
line_X = X[:, np.newaxis]
Y = X + 0.2*np.random.normal(size=50)
Choose your regression model (there are plenty more, depending on your needs)
lr = linear_model.LinearRegression()
Here you really do the fit
lr.fit(line_X, Y)
Here u extract the parameters, since you seems to need it ;)
slope = lr.coef_[0]
intercept = lr.intercept_
And then you plot
plt.plot(X, slope*X + intercept, ls='-', marker=' ')
plt.plot(X, Y)
I've got the following data:
I'm interested in fitting a line on the 'middle bit' (intercept 0). How do I do that? It would be useful to get a figure for the gradient as well.
(FYI These are a list of cash transactions, in and out. The gradient would be the profit or loss).
Here's some of the data:
https://gist.github.com/chrism2671/1081c13b6760878b457a112d2041622f
You can use numpy.polyfit and numpy.poly1d to achieve that:
import matplotlib.pyplot as plt
import numpy as np
# Create data
ls = np.linspace(0, 100)
s = np.random.rand(len(ls))*100 + ls
# Fit the data
z = np.polyfit(ls, s, deg=1)
p = np.poly1d(z)
# Plotting
plt.figure(figsize=(16,4.5))
plt.plot(ls, s,
alpha=.75, label='signal')
plt.plot(ls, p(ls),
linewidth=1, linestyle='--', color='r', label='polyfit')
plt.legend(ncol=2)
Using the data you provided:
I have a data file exported from octave which include two vectors x and u0 . I want to plot u0 versus x in matplotlib with the following codes
import scipy.io
import matplotlib.pyplot as plt
data = scipy.io.loadmat('myfile.mat')
x = data['x']
u0 = data['u0']
plt.plot(x,u0)
plt.show()
The above codes gives just a blank figure
When I changed the line plt.plot(x,u0) with plt.plot(x,u0,'-bo') I got the following
Why solid line does not appear?
Here is the data myfile.mat
I strongly suspect that your data arrays have a shape of (N, 1) ie [[0], [0], ...] which matplotlib in broadcasting (correctly) to N 1-point lines.
Try:
fig, ax = plt.subplots(1, 1)
ax.plot(x.flatten(), u0.flatten())
plt.show()