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Find local maxima in data from dataframe

Is there a way to find the local maxima from data I get from CSV file and put its value on the plot?
The x and y values that are in a pandas dataframe look something like this
x = 1598.78, 1596.85, 1594.92, 1592.99, 1591.07, 1589.14, 1587.21, 1585.28, 1583.35, 1581.42, 1579.49, 1577.57, 1575.64, 1573.71, 1571.78, 1569.85, 1567.92, 1565.99, 1564.07, 1562.14, 1560.21, 1558.28, 1556.35, 1554.42, 1552.49, 1550.57, 1548.64, 1546.71, 1544.78, 1542.85, 1540.92, 1538.99, 1537.07, 1535.14, 1533.21, 1531.28, 1529.35, 1527.42, 1525.49, 1523.57, 1521.64, 1519.71, 1517.78, 1515.85, 1513.92, 1511.99, 1510.07, 1508.14, 1506.21, 1504.28, 1502.35, 1500.42, 1498.49, 1496.57, 1494.64, 1492.71, 1490.78, 1488.85, 1486.92, 1484.99, 1483.07, 1481.14, 1479.21, 1477.28, 1475.35, 1473.42, 1471.49, 1469.57, 1467.64, 1465.71, 1463.78, 1461.85, 1459.92, 1457.99, 1456.07, 1454.14, 1452.21, 1450.28, 1448.35, 1446.42, 1444.49, 1442.57, 1440.64, 1438.71, 1436.78, 1434.85, 1432.92, 1430.99, 1429.07, 1427.14, 1425.21, 1423.28, 1421.35, 1419.42, 1417.49, 1415.57, 1413.64, 1411.71, 1409.78, 1407.85, 1405.92, 1403.99, 1402.07, 1400.14
y = 0.640, 0.624, 0.609, 0.594, 0.581, 0.569, 0.558, 0.547, 0.537, 0.530, 0.523, 0.516, 0.508, 0.502, 0.497, 0.491, 0.487, 0.484, 0.481, 0.480, 0.479, 0.482, 0.490, 0.503, 0.520, 0.542, 0.566, 0.586, 0.600, 0.606, 0.593, 0.569, 0.557, 0.548, 0.538, 0.531, 0.527, 0.524, 0.522, 0.522, 0.523, 0.525, 0.526, 0.527, 0.530, 0.534, 0.536, 0.539, 0.547, 0.553, 0.557, 0.563, 0.573, 0.599, 0.654, 0.738, 0.852, 0.891, 0.810, 0.744, 0.711, 0.694, 0.686, 0.683, 0.683, 0.690, 0.700, 0.706, 0.713, 0.723, 0.731, 0.732, 0.737, 0.756, 0.779, 0.786, 0.790, 0.794, 0.802, 0.815, 0.827, 0.832, 0.831, 0.826, 0.823, 0.828, 0.834, 0.834, 0.832, 0.832, 0.831, 0.825, 0.816, 0.804, 0.798, 0.794, 0.786, 0.775, 0.764, 0.752, 0.739, 0.722, 0.708, 0.697
and I'm trying to get something like this.
P.S. Note that numeric values were added with the plt.text function just to exemplify what I want.

x = [1598.78, 1596.85, 1594.92, 1592.99, 1591.07, 1589.14, 1587.21, 1585.28, 1583.35, 1581.42, 1579.49, 1577.57, 1575.64, 1573.71, 1571.78, 1569.85, 1567.92, 1565.99, 1564.07, 1562.14, 1560.21, 1558.28, 1556.35, 1554.42, 1552.49, 1550.57, 1548.64, 1546.71, 1544.78, 1542.85, 1540.92, 1538.99, 1537.07, 1535.14, 1533.21, 1531.28, 1529.35, 1527.42, 1525.49, 1523.57, 1521.64, 1519.71, 1517.78, 1515.85, 1513.92, 1511.99, 1510.07, 1508.14, 1506.21, 1504.28, 1502.35, 1500.42, 1498.49, 1496.57, 1494.64, 1492.71, 1490.78, 1488.85, 1486.92, 1484.99, 1483.07, 1481.14, 1479.21, 1477.28, 1475.35, 1473.42, 1471.49, 1469.57, 1467.64, 1465.71, 1463.78, 1461.85, 1459.92, 1457.99, 1456.07, 1454.14, 1452.21, 1450.28, 1448.35, 1446.42, 1444.49, 1442.57, 1440.64, 1438.71, 1436.78, 1434.85, 1432.92, 1430.99, 1429.07, 1427.14, 1425.21, 1423.28, 1421.35, 1419.42, 1417.49, 1415.57, 1413.64, 1411.71, 1409.78, 1407.85, 1405.92, 1403.99, 1402.07, 1400.14]
y = [0.640, 0.624, 0.609, 0.594, 0.581, 0.569, 0.558, 0.547, 0.537, 0.530, 0.523, 0.516, 0.508, 0.502, 0.497, 0.491, 0.487, 0.484, 0.481, 0.480, 0.479, 0.482, 0.490, 0.503, 0.520, 0.542, 0.566, 0.586, 0.600, 0.606, 0.593, 0.569, 0.557, 0.548, 0.538, 0.531, 0.527, 0.524, 0.522, 0.522, 0.523, 0.525, 0.526, 0.527, 0.530, 0.534, 0.536, 0.539, 0.547, 0.553, 0.557, 0.563, 0.573, 0.599, 0.654, 0.738, 0.852, 0.891, 0.810, 0.744, 0.711, 0.694, 0.686, 0.683, 0.683, 0.690, 0.700, 0.706, 0.713, 0.723, 0.731, 0.732, 0.737, 0.756, 0.779, 0.786, 0.790, 0.794, 0.802, 0.815, 0.827, 0.832, 0.831, 0.826, 0.823, 0.828, 0.834, 0.834, 0.832, 0.832, 0.831, 0.825, 0.816, 0.804, 0.798, 0.794, 0.786, 0.775, 0.764, 0.752, 0.739, 0.722, 0.708, 0.697]
import matplotlib.pyplot as plt
# The slope of a line is a measure of its steepness. Mathematically, slope is calculated as "rise over run" (change in y divided by change in x).
slope = [np.sign((y[i] - y[i-1]) / (x[i] - x[i-1])) for i in range(1, len(y))]
x_prev = slope[0]
optima_dic={'minima':[], 'maxima':[]}
for i in range(1, len(slope)):
if slope[i] * x_prev == -1: #slope changed
if x_prev == 1: # slope changed from 1 to -1
optima_dic['maxima'].append(i)
else: # slope changed from -1 to 1
optima_dic['minima'].append(i)
x_prev=-x_prev
from matplotlib.pyplot import text
plt.rcParams["figure.figsize"] = (20,10)
ix = 0
for x_, y_ in zip(x, y):
plt.plot(x_, y_, 'o--', color='grey')
if(ix in optima_dic['minima']):
plt.text(x_, y_, s = x_, fontsize=10)
ix += 1

how can i smooth the graph values or extract main signals only

when i try to run the code below i get this graph
my code:
from numpy import nan
import json
import os
import numpy as np
import subprocess
import math
import matplotlib.pyplot as plt
from statistics import mean, stdev
def smooth(t):
new_t = []
for i, x in enumerate(t):
neighbourhood = t[max(i-2,0): i+3]
m = mean(neighbourhood)
s = stdev(neighbourhood, xbar=m)
if abs(x - m) > s:
x = ( t[i - 1 + (i==0)*2] + t[i + 1 - (i+1==len(t))*2] ) / 2
new_t.append(x)
return new_t
def outLiersFN(*U):
outliers=[] # after preprocessing list
#preprocessing Fc =| 2*LF1 prev by 1 - LF2 prev by 2 |
c0 = -2 #(previous) by 2 #from original
c1 =-1 #(previous) #from original
c2 =0 #(current) #from original
c3 = 1 #(next) #from original
preP = U[0] # original list
if c2 == 0:
outliers.append(preP[0])
c1+=1
c2+=1
c0+=1
c3+=1
oldlen = len(preP)
M_RangeOfMotion = 90
while oldlen > c2 :
if c3 == oldlen:
outliers.insert(c2, preP[c2]) #preP[c2] >> last element in old list
break
if (preP[c2] > M_RangeOfMotion and preP[c2] < (preP[c1] + preP[c3])/2) or (preP[c2] < M_RangeOfMotion and preP[c2] > (preP[c1] + preP[c3])/2): #Check Paper 3.3.1
Equ = (preP[c1] + preP[c3])/2 #fn of preprocessing # From third index # ==== inserting current frame
formatted_float = "{:.2f}".format(Equ) #with .2 number only
equu = float(formatted_float) #from string float to float
outliers.insert(c2,equu) # insert the preprocessed value to the List
c1+=1
c2+=1
c0+=1
c3+=1
else :
Equ = preP[c2] # fn of preprocessing #put same element (do nothing)
formatted_float = "{:.2f}".format(Equ) # with .2 number only
equu = float(formatted_float) # from string float to float
outliers.insert(c2, equu) # insert the preprocessed value to the List
c1 += 1
c2 += 1
c0 += 1
c3 += 1
return outliers
def remove_nan(list):
newlist = [x for x in list if math.isnan(x) == False]
return newlist
the_angel = [176.04, 173.82, 170.09, 165.3, 171.8, 178.3, 178.77, 179.24, 179.93, 180.0, 173.39, 166.78, 166.03, 165.28, 165.72, 166.17, 166.71, 167.26, 168.04, 167.22, 166.68, 166.13, 161.53, 165.81, 170.1, 170.05, 170.5, 173.01, 176.02, 174.53, 160.09, 146.33, 146.38, 146.71, 150.33, 153.95, 154.32, 154.69, 134.52, 114.34, 115.6, 116.86, 134.99, 153.12, 152.28, 151.43, 151.36, 152.32, 158.9, 166.52, 177.74, 178.61, 179.47, 167.44, 155.4, 161.54, 167.68, 163.96, 160.24, 137.45, 114.66, 117.78, 120.89, 139.95, 139.62, 125.51, 111.79, 112.07, 112.74, 110.22, 107.7, 107.3, 106.52, 105.73, 103.07, 101.35, 102.5, 104.59, 104.6, 104.49, 104.38, 102.81, 101.25, 100.62, 100.25, 100.15, 100.32, 99.84, 99.36, 100.04, 100.31, 99.14, 98.3, 97.92, 97.41, 96.9, 96.39, 95.88, 95.9, 95.9, 96.02, 96.14, 96.39, 95.2, 94.56, 94.02, 93.88, 93.8, 93.77, 93.88, 94.04, 93.77, 93.65, 93.53, 94.2, 94.88, 92.59, 90.29, 27.01, 32.9, 38.78, 50.19, 61.59, 61.95, 62.31, 97.46, 97.38, 97.04, 96.46, 96.02, 96.1, 96.33, 95.61, 89.47, 89.34, 89.22, 89.48, 89.75, 90.02, 90.28, 88.16, 88.22, 88.29, 88.17, 88.17, 94.98, 94.84, 94.69, 94.94, 94.74, 94.54, 94.69, 94.71, 94.64, 94.58, 94.19, 94.52, 94.85, 87.7, 87.54, 87.38, 95.71, 96.57, 97.11, 97.05, 96.56, 96.07, 95.76, 95.56, 95.35, 95.28, 95.74, 96.2, 96.32, 96.33, 96.2, 96.14, 96.07, 96.07, 96.12, 96.17, 96.28, 96.31, 96.33, 96.16, 96.05, 95.94, 95.33, 88.96, 95.0, 95.78, 88.19, 88.19, 88.19, 87.92, 87.93, 88.03, 87.94, 87.86, 87.85, 87.89, 88.08, 88.01, 87.88, 88.02, 88.15, 88.15, 88.66, 88.73, 88.81, 88.41, 88.55, 88.68, 88.69, 88.02, 87.35, 95.19, 95.39, 95.38, 95.37, 95.27, 95.17, 95.33, 95.32, 95.31, 95.37, 95.42, 95.34, 95.44, 95.53, 95.47, 95.41, 95.13, 94.15, 94.78, 97.64, 97.1, 96.87, 97.03, 96.76, 35.44, 23.63, 23.27, 24.71, 26.16, 96.36, 113.13, 129.9, 96.82, 63.74, 34.25, 33.42, 32.6, 30.69, 31.06, 31.43, 97.14, 97.51, 97.23, 98.54, 100.13, 100.95, 28.82, 33.81, 66.81, 99.82, 102.63, 101.9, 101.44, 102.19, 103.22, 103.67, 104.13, 104.07, 104.73, 105.46, 103.74, 102.02, 103.32, 102.59, 29.54, 28.08, 28.76, 29.79, 30.82, 113.51, 129.34, 145.16, 143.18, 148.29, 153.67, 166.14, 161.16, 151.64, 149.27, 146.9, 151.67, 153.02, 149.28, 145.53, 149.1, 152.67, 158.78, 164.89, 164.84, 164.8, 162.11, 159.42, 156.73, 156.28, 155.83, 156.4, 161.0, 165.59, 164.44, 159.73, 155.76, 156.97, 158.92, 159.15, 159.39, 159.99, 160.44, 160.88, 163.89, 166.9, 167.71, 167.11, 167.0, 167.44, 168.38, 153.16, 137.94, 137.65, 152.09, 169.49, 171.36, 173.22, 174.01, 174.0, 174.2, 174.41, 157.74, 141.09, 149.32, 157.57, 156.4, 148.4, 140.78, 141.06, 141.73, 143.05, 143.91, 156.59, 169.29, 172.17, 175.05, 175.29, 175.27, 175.15, 175.02, 174.81, 174.59, 174.76, 174.94, 175.18, 175.41, 175.23, 174.51, 174.64, 174.77, 174.56, 173.25, 172.38, 174.17, 176.4, 177.27, 177.29, 177.33, 178.64, 179.98, 179.99, 176.0, 172.88, 173.77, 173.8, 173.97, 174.72, 175.24, 176.89, 179.07, 179.27, 178.78, 178.29, 175.61, 174.21, 172.8, 173.05, 173.41, 173.77, 174.65, 175.52, 175.58, 176.15, 176.71, 159.12, 141.54, 141.12, 155.62, 170.53, 165.54, 160.71, 158.22, 156.35, 156.82, 158.55, 160.27, 161.33, 162.39, 162.37, 159.48, 156.59, 156.77, 158.05, 159.32, 158.49, 157.66, 157.7, 157.74, 158.44, 159.14, 150.13, 143.06, 136.0, 125.7, 115.41, 111.19, 106.97, 107.1, 107.24, 107.45, 107.67, 113.34, 119.01, 144.87, 170.73, 174.31, 177.89, 174.78, 171.67, 163.26, 134.58, 105.9, 102.98, 100.77, 101.05, 101.39, 101.73, 99.79, 98.71, 97.64, 97.8, 97.89, 96.67, 95.45, 94.33, 93.38, 92.44, 48.53, 91.4, 91.35, 91.34, 91.33, 90.92, 90.51, 88.63, 87.0, 86.74, 86.48, 96.79, 96.09, 95.46, 95.39, 94.32, 93.25, 93.31, 93.37, 93.11, 92.57, 93.41, 94.25, 96.48, 92.71, 88.94, 90.07, 90.43, 78.06, 77.69, 77.32, 90.1, 89.15, 89.14, 88.85, 88.38, 87.63, 121.2, 120.66, 86.89, 86.42, 85.69, 84.86, 84.86, 85.34, 85.82, 86.07, 86.32, 85.82, 85.32, 86.23, 86.69, 87.15, 87.04, 86.87, 86.58, 86.0, 85.41, 85.41, 85.53, 85.66, 85.7, 85.72, 85.75, 85.92, 86.09, 85.77, 85.45, 84.94, 85.55, 86.16, 86.21, 86.1, 85.77, 85.27, 84.56, 84.99, 85.38, 85.42, 85.98, 86.54, 86.5, 86.45, 86.56, 86.63, 86.35, 86.08, 85.82, 85.51, 85.21, 84.6, 84.84, 84.97, 85.1, 86.12, 86.88, 86.8, 86.46, 86.47, 87.23, 87.8, 88.0, 88.08, 88.16, 87.72, 87.63, 87.37, 86.42, 86.48, 87.24, 87.97, 88.09, 88.19, 88.32, 88.44, 87.82, 87.2, 86.03, 85.78, 91.5, 93.0, 88.2, 88.52, 88.42, 87.28, 85.73, 85.62, 85.5, 85.5, 87.06, 87.6, 88.1, 88.31, 88.53, 88.77, 89.14, 89.52, 89.46, 89.4, 90.28, 89.74, 91.28, 92.17, 92.16, 92.15, 93.08, 94.0, 94.66, 95.32, 94.13, 93.7, 93.32, 93.69, 94.58, 95.47, 97.25, 99.03, 99.63, 99.67, 99.71, 100.33, 101.58, 103.36, 103.49, 103.41, 106.31, 109.34, 109.28, 109.21, 107.76, 106.31, 105.43, 104.94, 104.44, 111.19, 117.93, 115.59, 113.24, 116.15, 119.06, 125.43, 140.72, 156.0, 161.7, 143.52, 135.33, 127.13, 127.68, 148.68, 169.68, 172.2, 174.72, 174.75, 174.66, 158.57, 142.63, 145.13, 153.29, 161.45, 163.34, 165.24, 162.25, 159.89, 159.07, 156.39, 155.21, 156.04, 159.29, 160.07, 160.85, 163.45, 162.93, 161.71, 160.06, 158.4, 144.74, 132.64, 134.57, 150.22, 165.86, 172.95, 174.12, 175.3, 175.5, 176.31, 177.71, 179.72, 168.13, 156.55, 146.24, 155.75, 176.0, 175.99, 175.98, 176.0, 176.02, 176.25, 175.13, 174.26, 173.38, 173.37, 173.46, 176.34, 174.55, 172.77, 168.45, 166.35, 166.47, 168.81, 167.43, 166.79, 167.35, 168.65, 168.51, 168.37, 168.88, 169.74, 171.19, 171.33, 169.91, 168.49, 167.11, 166.83, 167.01, 168.68, 170.34, 170.43, 172.15, 173.86, 177.62, 177.61, 175.34, 173.06, 176.47, 179.87, 179.9, 177.67, 175.67, 175.39, 175.36, 177.03, 176.0, 174.98, 174.96, 174.94, 175.76, 176.57, 169.05, 162.99, 164.97, 168.74, 172.51, 167.38, 165.08, 163.03, 163.81, 164.83, 164.81, 164.8, 165.88, 165.36, 159.61, 153.86, 153.57, 153.61, 153.65, 154.62, 155.58, 157.97, 156.35, 155.66, 154.98, 156.11, 157.24, 159.25, 159.6, 160.43, 161.26, 164.71, 168.17, 147.46, 126.92, 106.38, 105.23, 104.4, 105.37, 106.65, 109.21, 107.44, 104.65, 101.86, 102.35, 102.84, 102.79, 102.19, 101.59, 100.98, 100.38, 98.72, 97.73, 97.32, 96.9, 95.11, 93.97, 94.12, 94.12, 93.1, 92.08, 89.29, 90.35, 90.35, 90.35, 90.35, 86.95, 86.37, 86.06, 85.74, 94.56, 93.16, 92.46, 91.76, 88.55, 85.33, 87.52, 92.18, 93.68, 95.18, 94.4, 92.17, 89.94, 89.4, 89.37, 99.44, 100.98, 102.52, 103.18, 88.96, 88.23, 87.5, 85.2, 85.19, 86.87, 121.42, 155.96, 155.97, 155.97, 86.2, 86.5, 86.8, 87.22, 87.36, 87.34, 87.03, 87.04, 87.05, 86.36, 85.68, 85.71, 85.84, 85.93, 86.01, 86.04, 86.08, 85.92, 86.05, 86.18, 86.17, 86.19, 86.23, 86.22, 86.09, 85.92, 85.66, 85.69, 85.69, 85.31, 84.91, 84.93, 84.95, 84.93, 84.91, 84.9, 84.9, 84.9, 84.9, 85.38, 85.52, 85.66, 85.66, 85.4, 85.14, 85.47, 85.8, 85.72, 85.64, 86.09, 85.84, 85.27, 85.47, 85.66, 85.59, 85.52, 85.38, 85.39, 85.28, 85.17, 85.39, 85.7, 85.98, 86.26, 86.61, 92.97, 93.15, 86.58, 86.58, 86.53, 86.47, 98.55, 99.41, 100.16, 100.9, 89.19, 90.28, 91.38, 91.39, 91.4, 91.44, 92.05, 131.05, 170.63, 170.13, 162.43, 125.64, 88.85, 88.85, 99.08, 100.38, 101.69, 100.74, 99.79, 96.33, 93.31, 93.73, 94.87, 96.01, 96.93, 97.85, 98.97, 97.85, 98.14, 99.37, 102.01, 103.8, 105.58, 108.52, 108.12, 107.72, 106.75, 106.82, 109.08, 112.37, 112.52, 112.66, 112.97, 114.12, 115.64, 117.1, 118.57, 126.13, 133.69, 149.27, 163.96, 166.62, 169.27, 164.94, 160.61, 149.35, 141.18, 143.41, 143.57, 149.26, 157.49, 159.94, 151.93, 147.47, 145.97, 145.56, 145.15, 143.85, 142.54, 142.18, 142.43, 143.12, 144.41, 144.38, 151.99, 159.59, 174.81, 174.94, 175.84, 176.87, 162.41, 152.94, 151.59, 155.24, 155.22, 155.19, 155.04]
p0 = outLiersFN(smooth(remove_nan(the_angel)))
the_angel = p0
plt.plot(the_angel) #list(filter(fun, L1))
plt.show()
print((the_angel))
how can i smooth the values in (the_angel) to get graph like this (red line)
i mean ignoring all unnecessary and noisy values and get only main line instead
you can edit my code or suggest me new filter or algorithm

pandas has a rolling() method for dataframes that you can use to calculate the mean over a window of values, e.g. the 70 closest ones:
import pandas as pd
import matplotlib.pyplot as plt
WINDOW_SIZE = 70
the_angel = [176.04, 173.82, 170.09, 165.3, 171.8, # ...
]
df = pd.DataFrame({'the angel': the_angel})
df[f'mean of {WINDOW_SIZE}'] = df['the angel'].rolling(
window=WINDOW_SIZE, center=True).mean()
df.plot(color=['blue', 'red']);

How to visualize high-dimension vectors as points in 2D plane?

For example, there are three vectors as below.
[ 0.0377, 0.1808, 0.0807, -0.0703, 0.2427, -0.1957, -0.0712, -0.2137,
-0.0754, -0.1200, 0.1919, 0.0373, 0.0536, 0.0887, -0.1916, -0.1268,
-0.1910, -0.1411, -0.1282, 0.0274, -0.0781, 0.0138, -0.0654, 0.0491,
0.0398, 0.1696, 0.0365, 0.2266, 0.1241, 0.0176, 0.0881, 0.2993,
-0.1425, -0.2535, 0.1801, -0.1188, 0.1251, 0.1840, 0.1112, 0.3172,
0.0844, -0.1142, 0.0662, 0.0910, 0.0416, 0.2104, 0.0781, -0.0348,
-0.1488, 0.0129],
[-0.1302, 0.1581, -0.0897, 0.1024, -0.1133, 0.1076, 0.1595, -0.1047,
0.0760, 0.1092, 0.0062, -0.1567, -0.1448, -0.0548, -0.1275, -0.0689,
-0.1293, 0.1024, 0.1615, 0.0869, 0.2906, -0.2056, 0.0442, -0.0595,
-0.1448, 0.0167, -0.1259, -0.0989, 0.0651, -0.0424, 0.0795, -0.1546,
0.1330, -0.2284, 0.1672, 0.1847, 0.0841, 0.1771, -0.0101, -0.0681,
0.1497, 0.1226, 0.1146, -0.2090, 0.3275, 0.0981, -0.3295, 0.0590,
0.1130, -0.0650],
[-0.1745, -0.1940, -0.1529, -0.0964, 0.2657, -0.0979, 0.1510, -0.1248,
-0.1541, 0.1782, -0.1769, -0.2335, 0.2011, 0.1906, -0.1918, 0.1896,
-0.2183, -0.1543, 0.1816, 0.1684, -0.1318, 0.2285, 0.1784, 0.2260,
-0.2331, 0.0523, 0.1882, 0.1764, -0.1686, 0.2292]
How to plot them as three points in the same 2D plane like this picture below? Thanks!

I use PCA from sklearn, maybe this code help you:
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
usa = [ 0.0377, 0.1808, 0.0807, -0.0703, 0.2427, -0.1957, -0.0712, -0.2137,
-0.0754, -0.1200, 0.1919, 0.0373, 0.0536, 0.0887, -0.1916, -0.1268,
-0.1910, -0.1411, -0.1282, 0.0274, -0.0781, 0.0138, -0.0654, 0.0491,
0.0398, 0.1696, 0.0365, 0.2266, 0.1241, 0.0176, 0.0881, 0.2993,
-0.1425, -0.2535, 0.1801, -0.1188, 0.1251, 0.1840, 0.1112, 0.3172,
0.0844, -0.1142, 0.0662, 0.0910, 0.0416, 0.2104, 0.0781, -0.0348,
-0.1488, 0.0129]
obama = [-0.1302, 0.1581, -0.0897, 0.1024, -0.1133, 0.1076, 0.1595, -0.1047,
0.0760, 0.1092, 0.0062, -0.1567, -0.1448, -0.0548, -0.1275, -0.0689,
-0.1293, 0.1024, 0.1615, 0.0869, 0.2906, -0.2056, 0.0442, -0.0595,
-0.1448, 0.0167, -0.1259, -0.0989, 0.0651, -0.0424, 0.0795, -0.1546,
0.1330, -0.2284, 0.1672, 0.1847, 0.0841, 0.1771, -0.0101, -0.0681,
0.1497, 0.1226, 0.1146, -0.2090, 0.3275, 0.0981, -0.3295, 0.0590,
0.1130, -0.0650]
nationality = [-0.1745, -0.1940, -0.1529, -0.0964, 0.2657, -0.0979, 0.1510, -0.1248,
-0.1541, 0.1782, -0.1769, -0.2335, 0.2011, 0.1906, -0.1918, 0.1896,
-0.2183, -0.1543, 0.1816, 0.1684, -0.1318, 0.2285, 0.1784, 0.2260,
-0.2331, 0.0523, 0.1882, 0.1764, -0.1686, 0.2292]
pca = PCA(n_components=1)
X = np.array(usa).reshape(2,len(usa)//2)
X = pca.fit_transform(X)
Y = np.array(obama).reshape(2,len(obama)//2)
Y = pca.fit_transform(Y)
Z = np.array(nationality).reshape(2,len(nationality)//2)
Z = pca.fit_transform(Z)
x_coordinates = [X[0][0], Y[0][0], Z[0][0]]
y_coordinates = [X[1][0], Y[1][0], Z[1][0]]
colors = ['r','g','b']
annotations=["U.S.A","Obama","Nationality"]
plt.figure(figsize=(8,6))
plt.scatter(x_coordinates, y_coordinates, marker=",", color=colors,s=300)
for i, label in enumerate(annotations):
plt.annotate(label, (x_coordinates[i], y_coordinates[i]))
plt.show()
output:

Matplotlib: contour plot with data interpolation

I use scipy.interpolate.griddata to interpolate my data for contour plot. The data have different scale on x and y axes:
from scipy.interpolate import griddata
xx = [0.0493, 0.0458, 0.0425, 0.0394, 0.0365, 0.0337, 0.0311, 0.0286, 0.0262, 0.024, 0.0219, 0.0198, 0.0179, 0.016, 0.0143, 0.0126, 0.0109, 0.0094, 0.0079, 0.0064, 0.005, 0.0037, 0.0024, 0.0012, 0.0, 0.0663, 0.0637, 0.0613, 0.059, 0.0567, 0.0546, 0.0525, 0.0506, 0.0487, 0.0469, 0.0451, 0.0434, 0.0418, 0.0402, 0.0387, 0.0373, 0.0359, 0.0345, 0.0332, 0.0319, 0.0307, 0.0295, 0.0283, 0.0272, 0.0261, 0.0792, 0.0774, 0.0756, 0.0739, 0.0722, 0.0706, 0.0691, 0.0676, 0.0661, 0.0647, 0.0633, 0.062, 0.0607, 0.0594, 0.0582, 0.057, 0.0559, 0.0547, 0.0536, 0.0526, 0.0515, 0.0505, 0.0495, 0.0486, 0.0477, 0.0919, 0.0905, 0.0891, 0.0878, 0.0865, 0.0852, 0.084, 0.0828, 0.0816, 0.0805, 0.0794, 0.0783, 0.0772, 0.0762, 0.0752, 0.0742, 0.0732, 0.0723, 0.0714, 0.0705, 0.0696, 0.0688, 0.0679, 0.0671, 0.0663, 0.1044, 0.1033, 0.1022, 0.1011, 0.1, 0.099, 0.098, 0.097, 0.096, 0.0951, 0.0942, 0.0933, 0.0924, 0.0915, 0.0907, 0.0898, 0.089, 0.0882, 0.0874, 0.0867, 0.0859, 0.0852, 0.0845, 0.0837, 0.083, 0.1168, 0.1159, 0.1149, 0.114, 0.1132, 0.1123, 0.1114, 0.1106, 0.1098, 0.109, 0.1082, 0.1074, 0.1066, 0.1059, 0.1052, 0.1045, 0.1037, 0.1031, 0.1024, 0.1017, 0.1011, 0.1004, 0.0998, 0.0992, 0.0985, 0.1291, 0.1283, 0.1275, 0.1267, 0.126, 0.1252, 0.1245, 0.1238, 0.123, 0.1223, 0.1217, 0.121, 0.1203, 0.1197, 0.119, 0.1184, 0.1178, 0.1172, 0.1166, 0.116, 0.1154, 0.1148, 0.1143, 0.1137, 0.1132]
yy = [0.6137, 0.8211, 1.0277, 1.2338, 1.4393, 1.6444, 1.8489, 2.053, 2.2567, 2.4601, 2.6631, 2.8658, 3.0682, 3.2703, 3.4722, 3.6738, 3.8752, 4.0763, 4.2773, 4.4781, 4.6787, 4.8791, 5.0794, 5.2795, 5.4795, 0.3217, 0.5059, 0.694, 0.8859, 1.0812, 1.2799, 1.4816, 1.6861, 1.8934, 2.1033, 2.3155, 2.5301, 2.7467, 2.9655, 3.1861, 3.4086, 3.6328, 3.8586, 4.0861, 4.315, 4.5453, 4.777, 5.01, 5.2442, 5.4795, 0.2447, 0.4154, 0.5919, 0.7737, 0.9606, 1.1524, 1.3488, 1.5496, 1.7547, 1.9638, 2.1767, 2.3932, 2.6133, 2.8367, 3.0633, 3.293, 3.5257, 3.7611, 3.9993, 4.2401, 4.4833, 4.729, 4.977, 5.2272, 5.4795, 0.1814, 0.3467, 0.5184, 0.696, 0.8795, 1.0685, 1.2629, 1.4624, 1.6668, 1.876, 2.0897, 2.3079, 2.5302, 2.7567, 2.987, 3.2212, 3.4591, 3.7004, 3.9452, 4.1933, 4.4446, 4.6989, 4.9563, 5.2165, 5.4795, 0.1202, 0.2837, 0.4538, 0.6303, 0.8128, 1.0012, 1.1953, 1.3949, 1.5998, 1.8099, 2.0249, 2.2448, 2.4693, 2.6983, 2.9318, 3.1694, 3.4112, 3.657, 3.9066, 4.16, 4.417, 4.6776, 4.9416, 5.2089, 5.4795, 0.0598, 0.2232, 0.3932, 0.5697, 0.7525, 0.9413, 1.136, 1.3365, 1.5425, 1.754, 1.9706, 2.1924, 2.4191, 2.6506, 2.8867, 3.1275, 3.3726, 3.6221, 3.8757, 4.1334, 4.3951, 4.6606, 4.93, 5.203, 5.4795, 0.0, 0.1638, 0.3344, 0.5115, 0.695, 0.8848, 1.0806, 1.2823, 1.4897, 1.7028, 1.9212, 2.145, 2.3739, 2.6078, 2.8466, 3.0903, 3.3385, 3.5914, 3.8486, 4.1102, 4.376, 4.646, 4.9199, 5.1978, 5.4795]
vv = [0.4829, 0.5196, 0.5541, 0.5866, 0.6173, 0.6463, 0.6738, 0.6998, 0.7246, 0.7481, 0.7706, 0.7919, 0.8123, 0.8318, 0.8504, 0.8683, 0.8854, 0.9017, 0.9175, 0.9326, 0.9471, 0.9611, 0.9745, 0.9875, 1.0, 0.4229, 0.4512, 0.4782, 0.5041, 0.5288, 0.5525, 0.5752, 0.597, 0.618, 0.6381, 0.6575, 0.6761, 0.6941, 0.7114, 0.7282, 0.7443, 0.7599, 0.775, 0.7895, 0.8036, 0.8173, 0.8305, 0.8433, 0.8557, 0.8678, 0.4044, 0.4259, 0.4467, 0.4668, 0.4862, 0.505, 0.5231, 0.5407, 0.5578, 0.5743, 0.5903, 0.6059, 0.621, 0.6356, 0.6498, 0.6637, 0.6771, 0.6902, 0.703, 0.7154, 0.7274, 0.7392, 0.7507, 0.7618, 0.7727, 0.3883, 0.4056, 0.4225, 0.4388, 0.4548, 0.4703, 0.4854, 0.5001, 0.5144, 0.5283, 0.5419, 0.5552, 0.5681, 0.5808, 0.5931, 0.6051, 0.6169, 0.6284, 0.6396, 0.6506, 0.6613, 0.6718, 0.6821, 0.6921, 0.7019, 0.3725, 0.3871, 0.4014, 0.4153, 0.4289, 0.4422, 0.4551, 0.4678, 0.4802, 0.4924, 0.5042, 0.5159, 0.5272, 0.5384, 0.5493, 0.56, 0.5704, 0.5807, 0.5907, 0.6006, 0.6102, 0.6197, 0.629, 0.6381, 0.6471, 0.3569, 0.3697, 0.3821, 0.3943, 0.4063, 0.418, 0.4295, 0.4407, 0.4518, 0.4626, 0.4732, 0.4836, 0.4938, 0.5038, 0.5137, 0.5233, 0.5328, 0.5421, 0.5513, 0.5603, 0.5691, 0.5778, 0.5863, 0.5947, 0.6029, 0.3415, 0.3529, 0.3641, 0.375, 0.3858, 0.3964, 0.4068, 0.417, 0.427, 0.4368, 0.4465, 0.456, 0.4654, 0.4745, 0.4836, 0.4925, 0.5012, 0.5098, 0.5182, 0.5265, 0.5347, 0.5428, 0.5507, 0.5585, 0.5662]
N=500
data_points = (xx, yy)
grid_points = (np.linspace(min(xx), max(xx), N), np.linspace(min(yy), max(yy), N))
vi = griddata(data_points, vv,
(grid_points[0][None, :], grid_points[1][:, None]), method='cubic')
plot(xx,yy, '.k')
contour(grid_points[0], grid_points[1], vi)
But I got ugly sharped contours:
However, if I scale for example the y axis, like this yy = [v/50. for v in yy], I got the smoothed plot:
How to get the smoothed contours with original axes scales?

cv2.fillConvexPoly not drawing entirely polygon

My code reads csv from annotations made in VIA (VGG IMAGE ANOTATOR) and draw its regions.
This is the code that draws the regions.
df = csv_file[csv_file['region_shape_attributes'] != '{}']
images = []
NAMES = []
p,r,point = 0,0,0
for index, img in enumerate(tqdm(img_list)):
if int(csv_file.region_count[index]) == 0:
continue
lista = csv_file[csv_file.img == img] #Recebe os dados encontrados dentro do csv para a imagem em questão
tamanho = lista.shape
if tamanho[0] > 1:
imagem = cv2.imread(os.path.join(img_dir, img)) #Carrega a imagem em questão
img_last = img
# Importa a mascara do indice de vegetacao:
msk = np.zeros(imagem.shape[:2], dtype = 'uint8') #Carrega as informações do tamanho da imagem
msk_name = os.path.join(img_dir, img.replace('.JPG', '_msk.png')) #Faz a junção do diretório com o nome da imagem, alterando seu formato
#print(msk_name) #Apresenta o nome da máscara com o diretório a ser salvo
for i in range(tamanho[0]):
line = lista.iloc[i,:] #Recebe todas as marcações realizadas dentro daquela imagem
region_shape = line.region_shape_attributes #Informa a posição onde o ponto se encontra
region_attributes = (line.region_attributes) #Informa a classe do ponto
region_attributes = re.findall('"([^"]*)"', region_attributes)
if 'polygon' in region_shape:
p+=1
line = region_shape.split("all_points")
x_coords = [float(s) for s in re.findall(r'-?\d+\.?\d*', line[1])]
y_coords = [float(s) for s in re.findall(r'-?\d+\.?\d*', line[2])]
coords = []
for x, y in zip(x_coords, y_coords):
coords.append([x,y])
pts = np.array(coords, dtype=np.int32)
cv2.fillConvexPoly(msk, pts, 1)
elif 'rect' in region_shape:
continue
r+=1
coords = [float(s) for s in re.findall(r'-?\d+\.?\d*', region_shape)] #Encontrando valores de x e y
cx = int(coords[0]) #Coordenadas no eixo X
cy = int(coords[1]) #Coordenadas no eixo y
width = int(coords[2])
height = int(coords[3])
cv2.rectangle(msk, (cx-width,cy-height), (cx+width, cy+height), 1, -1)
elif "\"point\"" in region_shape:
continue
point+=1
coords = [float(s) for s in re.findall(r'-?\d+\.?\d*', region_shape)] #Encontrando valores de x e y
cx = int(coords[0]) #Coordenadas no eixo X
cy = int(coords[1]) #Coordenadas no eixo y
width = 10
height = 10
cv2.rectangle(msk, (cx-width,cy-height), (cx+width, cy+height), 1, -1)
#cv2.imwrite(msk_name, msk.astype('uint8')) #Realiza o salvamento do background
im_l = [imagem, msk]
NAMES.append(img)
images.append(im_l)
break
print(p,r,point)
The problem is happening when why try to pass this image:
Original image
Instead of getting something like this:
Image in VIA
I get this:
Output
The points are:
array([[2148, 687],
[2120, 658],
[2100, 650],
[2062, 631],
[2028, 596],
[1994, 580],
[1978, 580],
[1938, 557],
[1914, 519],
[1877, 491],
[1845, 485],
[1825, 468],
[1785, 466],
[1747, 470],
[1716, 481],
[1687, 494],
[1648, 535],
[1626, 573],
[1598, 604],
[1597, 640],
[1597, 687],
[1574, 727],
[1578, 782],
[1582, 816],
[1593, 849],
[1597, 866],
[1605, 895],
[1598, 947],
[1589, 978],
[1566, 1043],
[1546, 1067],
[1518, 1080],
[1506, 1104],
[1482, 1148],
[1481, 1227],
[1484, 1251],
[1498, 1271],
[1498, 1289],
[1514, 1310],
[1544, 1331],
[1554, 1350],
[1546, 1433],
[1536, 1481],
[1521, 1504],
[1518, 1548],
[1510, 1579],
[1508, 1606],
[1512, 1647],
[1493, 1698],
[1493, 1739],
[1504, 1752],
[1525, 1784],
[1548, 1836],
[1557, 1853],
[1574, 1872],
[1567, 1889],
[1581, 1917],
[1563, 1946],
[1566, 1971],
[1577, 1978],
[1577, 1998],
[1585, 2014],
[1602, 2032],
[1621, 2112],
[1631, 2147],
[1642, 2184],
[1647, 2213],
[1663, 2271],
[1688, 2305],
[1720, 2339],
[1763, 2366],
[1821, 2394],
[1846, 2399],
[1888, 2376],
[1930, 2185],
[1946, 2136],
[1951, 2117],
[1974, 1993],
[1805, 1662],
[2010, 1562],
[1910, 1425],
[1993, 1387],
[1933, 1255],
[1951, 970],
[2035, 1215],
[2163, 1273],
[2230, 1109],
[2468, 1104],
[2581, 1306],
[2675, 1226],
[2609, 1118],
[2588, 1040],
[2561, 1000],
[2528, 976],
[2484, 976],
[2456, 984],
[2384, 960],
[2347, 834],
[2306, 824],
[2269, 794],
[2242, 789],
[2198, 744],
[2176, 736],
[2173, 731]], dtype=int32)
I already checked if the points I read are correct, and they are.
If I call cv2.polylines(msk, [pts], True, 1) instead of cv2.fillConvexPoly(msk, pts, 1, 10) I get the desired output, but unfilled:
with polylines
So I'd like to know if you guys can help me finding out why fillConvexPoly isn't filling properly. I didn't find anything about limit of points I can pass throug the function and even when I slice my array to decrease it the output is the same (cv2.fillConvexPoly(msk, pts[int(pts.shape[0]/2):], 1)):
The same problem using half the array

You could use cv2.drawContours() functions to achieve that. If you have your points:
points = [[2148, 687],
[2120, 658],
[2100, 650],
[2062, 631],
[2028, 596],
[1994, 580],
[1978, 580],
[1938, 557],
[1914, 519],
[1877, 491],
[1845, 485],
[1825, 468],
[1785, 466],
[1747, 470],
[1716, 481],
[1687, 494],
[1648, 535],
[1626, 573],
[1598, 604],
[1597, 640],
[1597, 687],
[1574, 727],
[1578, 782],
[1582, 816],
[1593, 849],
[1597, 866],
[1605, 895],
[1598, 947],
[1589, 978],
[1566, 1043],
[1546, 1067],
[1518, 1080],
[1506, 1104],
[1482, 1148],
[1481, 1227],
[1484, 1251],
[1498, 1271],
[1498, 1289],
[1514, 1310],
[1544, 1331],
[1554, 1350],
[1546, 1433],
[1536, 1481],
[1521, 1504],
[1518, 1548],
[1510, 1579],
[1508, 1606],
[1512, 1647],
[1493, 1698],
[1493, 1739],
[1504, 1752],
[1525, 1784],
[1548, 1836],
[1557, 1853],
[1574, 1872],
[1567, 1889],
[1581, 1917],
[1563, 1946],
[1566, 1971],
[1577, 1978],
[1577, 1998],
[1585, 2014],
[1602, 2032],
[1621, 2112],
[1631, 2147],
[1642, 2184],
[1647, 2213],
[1663, 2271],
[1688, 2305],
[1720, 2339],
[1763, 2366],
[1821, 2394],
[1846, 2399],
[1888, 2376],
[1930, 2185],
[1946, 2136],
[1951, 2117],
[1974, 1993],
[1805, 1662],
[2010, 1562],
[1910, 1425],
[1993, 1387],
[1933, 1255],
[1951, 970],
[2035, 1215],
[2163, 1273],
[2230, 1109],
[2468, 1104],
[2581, 1306],
[2675, 1226],
[2609, 1118],
[2588, 1040],
[2561, 1000],
[2528, 976],
[2484, 976],
[2456, 984],
[2384, 960],
[2347, 834],
[2306, 824],
[2269, 794],
[2242, 789],
[2198, 744],
[2176, 736],
[2173, 731]]
Then you can do:
cv2.drawContours(image, np.array([points]), -1, (1), thickness=-1)
Note that thickness=-1 means to fill the contour. For more information on drawing contours go here
Outputs: