CODEWITHSUNDEEP
pythonpython-3.xnumpymatplotlibtime-series
user10553396
user10553396

Reputation:

How to merge different time-series plot into a 2D in Python

I have three different time-series data of the following format where the first column is timestamp and the second column is the value.

0.086206438,10
0.086425551,12
0.089227066,20
0.089262508,24
0.089744425,30
0.090036815,40
0.090054172,28
0.090377569,28
0.090514071,28
0.090762872,28
0.090912691,27

For reproduciability, I have shared the three time-series data I am using here.

From column 2, I wanted to read current row and compare it with the value of the previous row. If it is greater, I keep comparing. If the current value is smaller than the previous row's value continue, I take the difference. Let me make it clear. For example in the above sample record I provided, the seventh row (28) is smaller than the value in the sixth row (40) - so it will be (40-28 =12).

Here is my sample code.

import numpy as np
import pandas as pd
import csv
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
from statsmodels.graphics.tsaplots import plot_acf, acf


protocols = {}


types = {"data1": "data1.csv", "data2": "data2.csv", "data3": "data3.csv"}

for protname, fname in types.items():
    arr = []  
    arr1 = []  
    with open(fname, mode='r', encoding='utf-8-sig') as f:
        reader = csv.reader(f, delimiter=",")
        for i in reader:
            arr.append(int(i[1])) 
            arr1.append(float(i[0])) 

    arr, arr1 = np.array(arr), np.array(arr1)
    diffs = np.diff(arr)
    diffs1 = np.diff(arr1)
    diffs1 = diffs1[diffs > 0] 
    diffs = diffs[diffs > 0] # To keep only the increased values
    protocols[protname] = {
        "rtime": np.array(arr1),
        "rdata": np.array(arr),
        "data": diffs,
        "timediff": diffs,
    }

## change in time 
for protname, values in protocols.items():
    d = values["rdata"]
    t = values["rtime"]
    d = np.diff(d, 1) #/ d[:-1]
    t = np.diff(t, 1)
    plt.plot(t, d, ".", label=protname, alpha=0.5)
    plt.xlabel("Changes in time")
    plt.ylabel("differences")
    plt.legend()
    plt.show()

This gives me the following plots

enter image description here enter image description here enter image description here

How can we plot the differences versus the change in time (column onw) in a two-dimensitional (2D) graph of the three data I included separately?

Upvotes: 1

Views: 754

Answers (2)

William Miller
William Miller

Reputation: 10320

If you change the loop 

for protname, values in protocols.items():
    d = values["rdata"]
    t = values["rtime"]
    d = np.diff(d, 1) #/ d[:-1]
    t = np.diff(t, 1)
    plt.plot(t, d, ".", label=protname, alpha=0.5)
    plt.xlabel("Changes in time")
    plt.ylabel("differences")
    plt.legend()
    plt.show()

to 

for protname, values in protocols.items():
    d = values["rdata"]
    t = values["rtime"]
    d = np.diff(d, 1) #/ d[:-1]
    t = np.diff(t, 1)
    plt.plot(t[d < 0], d[d < 0], ".", label=protname, alpha=0.5)
    plt.xlabel("Changes in time")
    plt.ylabel("differences")
    plt.legend()

plt.show()

You will get a plot that looks like Filtered plot

I believe this is what you are looking for - if I understood you correctly.

Upvotes: 0

SpghttCd
SpghttCd

Reputation: 10860

All three plots into one: unindent plt.show()

Plot only the negative values of an array:

plt.plot(t[d<0], d[d<0], ...)

Upvotes: 1

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