Compare commits
5 Commits
Author | SHA1 | Date | |
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![]() |
1dce636192 | ||
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2a998611bf | ||
0882dd06f3 | |||
ec60c85695 | |||
278d79f6d0 |
@@ -25,4 +25,8 @@ CC-BY-SA Olli Graf
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|18 | Generatoren und list comprehension|
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|19 | Webseiten (Flask)|
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|20 | virtuelle Umgebungen|
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|22 | Numpy|
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|21 | Matplotlib|
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|23 | Pandas|
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@@ -1,4 +1,4 @@
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#!/home/pi/git/pythonkurs/teil20/bin/python
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#!/home/pi/git/pythonkurs/teil20/bin/python3
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# -*- coding: utf-8 -*-
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import re
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import sys
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@@ -1,4 +1,4 @@
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#!/home/pi/git/pythonkurs/teil20/bin/python
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#!/home/pi/git/pythonkurs/teil20/bin/python3
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# -*- coding: utf-8 -*-
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import re
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import sys
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@@ -1,4 +1,4 @@
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#!/home/pi/git/pythonkurs/teil20/bin/python
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#!/home/pi/git/pythonkurs/teil20/bin/python3
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# -*- coding: utf-8 -*-
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import re
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import sys
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19
teil21/alarm.py
Executable file
19
teil21/alarm.py
Executable file
@@ -0,0 +1,19 @@
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#! /usr/bin/python3
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# encoding:utf-8
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import signal
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import time
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def handle_alarm(signum, frame):
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print(f'Alarm ausgelöst bei {time.ctime()}')
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signal.signal(signal.SIGALRM,handle_alarm)
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signal.alarm(3)
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print(f'aktuelle Zeit Start: {time.ctime()}')
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time.sleep(13)
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print(f'aktuelle Zeit Ende: {time.ctime()}')
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41
teil21/interrupts.py
Executable file
41
teil21/interrupts.py
Executable file
@@ -0,0 +1,41 @@
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#! /usr/bin/python3
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import time
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import signal
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import sys
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#Behandlung von SIGINT (CTRL-C)
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def handle_sigint(signum, frame) :
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print(f'Handling signal {signum} ({signal.Signals(signum).name}).')
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if signum == signal.SIGINT:
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print(f'SIGINT wird behandelt. {frame}')
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time.sleep(1)
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sys.exit(0)
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#Behandlung von SIGTSTP (CTRL-Z)
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def handle_sigtstp(signum,frame):
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print(f'Behandle signal {signum} ({signal.Signals(signum).name}).')
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print('Programm in Hintergrund')
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# Behandlung von SiGCONT
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def handle_sigcont(signum,frame):
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print(f'Behandle signal {signum} ({signal.Signals(signum).name}).')
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print('Programm im Vordergrund')
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if __name__ == '__main__':
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# Interrupt Handler registrieren
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signal.signal(signal.SIGINT, handle_sigint)
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signal.signal(signal.SIGTSTP, handle_sigtstp)
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signal.signal(signal.SIGCONT, handle_sigcont)
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for i in range(0,10000000):
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print(f'Schleife: {i}')
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time.sleep(0.5)
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print('Schleifenende')
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4
teil22/.gitignore
vendored
Normal file
4
teil22/.gitignore
vendored
Normal file
@@ -0,0 +1,4 @@
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# created by virtualenv automatically
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__pycache__
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bin
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lib
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18
teil22/StressFile.py
Normal file
18
teil22/StressFile.py
Normal file
@@ -0,0 +1,18 @@
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import io
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import numpy as np
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class StressFile:
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def readOutFile(self,filename):
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result = {
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'frequency': [],
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'temperatures': []
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}
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with open(filename,'r') as f:
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content = f.readlines()
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print(f'conent= {content}')
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sf = StressFile()
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sf.readOutFile('./kodos.out')
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42
teil22/calc_array.py
Normal file
42
teil22/calc_array.py
Normal file
@@ -0,0 +1,42 @@
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import numpy as np
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# Array initialisieren
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org = np.array([1,2,3,4,5,6])
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# Kopiert das int64 Array in ein float64 Array
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def copy_to_float64(source):
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liste = []
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for i in range(source.shape[1]):
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liste.append(float(source[i]))
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return np.array(liste,dtype=float64)
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# Ein paar Kopien anlegen
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copy_sum = org.copy()
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copy_mult = org.copy()
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copy_diff = org.copy()
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copy_div = org.astype(np.float64).copy()
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# Zu allen Elementen 2 dazuzählen
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copy_sum += 2
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print(f'copy_sum={copy_sum}')
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# Alle Elemente mit 2 multiplizieren
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copy_mult *= 2
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print(f'copy_mult={copy_mult}')
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# Von allen Elementen 1 abziehen
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copy_diff -= 1
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print(f'copy_diff={copy_diff}')
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copy_div /=2
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print(f'copy_div={copy_div}')
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20
teil22/copyarray.py
Normal file
20
teil22/copyarray.py
Normal file
@@ -0,0 +1,20 @@
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import numpy as np
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org = np.array([1,2,3,4,5,6])
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copy = org
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print(f'org= {org}')
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print(f'copy={copy}')
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copy[2]= 7
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print(f'org= {org}')
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print(f'copy={copy}')
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copy2 = org.copy()
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copy2[2]= 3
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print(f'org= {org}')
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print(f'copy2={copy2}')
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9
teil22/reshape.py
Normal file
9
teil22/reshape.py
Normal file
@@ -0,0 +1,9 @@
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import numpy as np
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a = np.array([1,2,3,4,5,6,7,8,9,10])
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print(f'orignal {a}')
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reshaped = a.reshape(2,5)
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print(f'reshaped {reshaped}')
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7
teil22/rndm.py
Normal file
7
teil22/rndm.py
Normal file
@@ -0,0 +1,7 @@
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import numpy as np
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#Erzeuge 1 Mio Zufallszahlen
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random_np = np.random(100000)
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random_list = list(random_np)
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6
teil22/shape.py
Normal file
6
teil22/shape.py
Normal file
@@ -0,0 +1,6 @@
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import numpy as np
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twodim_array = np.array([[1,2,3,4,5],[1,4,9,16,25]])
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print(f'shape is {twodim_array.shape}')
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9
teil22/simplearray.py
Normal file
9
teil22/simplearray.py
Normal file
@@ -0,0 +1,9 @@
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import numpy as np
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simple_array = np.array([1,2,3,4,5,6])
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print(f'simple_array={simple_array}')
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print(f'ndim={simple_array.ndim}')
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print(f'dtype={simple_array.dtype}')
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print(f'shape is {simple_array.shape}')
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9
teil22/twodimarray.py
Normal file
9
teil22/twodimarray.py
Normal file
@@ -0,0 +1,9 @@
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import numpy as np
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twodim_array = np.array([[1,2,3,4,5],[1,4,9,16,25]],dtype='int32')
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print(f'twodim_array={twodim_array}')
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print(f'ndim={twodim_array.ndim}')
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print(f'dtype={twodim_array.dtype}')
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print(f'shape is {twodim_array.shape}')
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6
teil23/.gitignore
vendored
Normal file
6
teil23/.gitignore
vendored
Normal file
@@ -0,0 +1,6 @@
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# created by virtualenv automatically
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__pycache__
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bin
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lib
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share
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*.csv
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20
teil23/iospeed_balken.py
Executable file
20
teil23/iospeed_balken.py
Executable file
@@ -0,0 +1,20 @@
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#! ./bin/python
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#encoding: utf-8
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import matplotlib.pyplot as plt
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import numpy as np
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labels = [ 'Pineberry Pi pcie1_gen=2','Pineberry Pi pcie1_gen=3', 'MicroSD Raspi 5', 'MicroSD Raspi 4']
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io_read= np.array([148.50,171.99,30.85,10.15])
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io_write = np.array([99.15,114.97,20.45,6.63])
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legend = ['Schreiben','Lesen']
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#Höhe der Balken
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__hoehe__ =0.6
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fig,ax = plt.subplots()
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plt.barh(labels,io_write,__hoehe__)
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plt.barh(labels,io_read,__hoehe__,left=io_write)
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plt.legend(legend)
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plt.show()
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30
teil23/normparabel_graph.py
Executable file
30
teil23/normparabel_graph.py
Executable file
@@ -0,0 +1,30 @@
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#! ./bin/python
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# encoding:utf-8
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import matplotlib.pyplot as plt
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import numpy as np
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def plot_normal_parabel():
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# Erzeuge Datenpunkte für x-Werte von -10 bis 10
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x = np.linspace(-3, 3, 100)
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# Berechne die y-Werte für die Normalparabel
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y = x**2
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# Erstelle das Diagramm
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plt.plot(x, y, label='Normalparabel: $y=x^2$')
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# Beschriftungen und Titel hinzufügen
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plt.xlabel('x-Achse')
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plt.ylabel('y-Achse')
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plt.title('Normalparabel')
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# Legende hinzufügen
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plt.legend()
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# Funktion aufrufen, um die Normalparabel zu zeichnen
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plot_normal_parabel()
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# Diagramm anzeigen
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plt.grid(True)
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plt.show()
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8
teil23/pyvenv.cfg
Normal file
8
teil23/pyvenv.cfg
Normal file
@@ -0,0 +1,8 @@
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home = /usr/bin
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implementation = CPython
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version_info = 3.11.2.final.0
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virtualenv = 20.17.1+ds
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include-system-site-packages = false
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base-prefix = /usr
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base-exec-prefix = /usr
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base-executable = /usr/bin/python3
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45
teil23/sinus_parabel_graph.py
Executable file
45
teil23/sinus_parabel_graph.py
Executable file
@@ -0,0 +1,45 @@
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#! ./bin/python
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# encoding:utf-8
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import matplotlib.pyplot as plt
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import numpy as np
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def plot_normal_parabel():
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# Erzeuge Datenpunkte für x-Werte von -10 bis 10
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x = np.linspace(-3, 3, 100)
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# Berechne die y-Werte für die Normalparabel
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y = x**2
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# Erstelle das Diagramm
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plt.plot(x, y, label='Normalparabel: $y=x^2$')
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# Beschriftungen und Titel hinzufügen
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plt.xlabel('x-Achse')
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plt.ylabel('y-Achse')
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plt.title('Normalparabel')
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# Legende hinzufügen
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plt.legend()
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def plot_sinus_function():
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# Erzeuge Datenpunkte für x-Werte von -2π bis 2π
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x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
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# Berechne die y-Werte für die Sinusfunktion
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y = np.sin(x)
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# Erstelle das Diagramm für die Sinusfunktion
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plt.plot(x, y, label='Sinusfunktion: $y = \sin(x)$', color='blue')
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# Legende hinzufügen
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plt.legend()
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# Funktion aufrufen, um die Normalparabel zu zeichnen
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plot_normal_parabel()
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plot_sinus_function()
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# Diagramm anzeigen
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plt.grid(True)
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plt.show()
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24
teil23/test.py
Normal file
24
teil23/test.py
Normal file
@@ -0,0 +1,24 @@
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# encoding: utf-8
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import numpy as np
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import matplotlib.pyplot as plt
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# Koordinaten
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x = np.arange(5, 10)
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y = np.arange(12, 17)
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print(f'x={x}')
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print(f'y={y}')
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# Plot
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plt.plot(x,y)
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# Titel hinzufügen
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plt.title("Matplotlib PLot NumPy Array")
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# Beschriftung für die Achsen
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plt.xlabel("x Achse")
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plt.ylabel("y Achse")
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# Fenster anzeigen
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plt.show()
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23
teil23/verkauf_stapel.py
Executable file
23
teil23/verkauf_stapel.py
Executable file
@@ -0,0 +1,23 @@
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#! ./bin/python
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#encoding: utf-8
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import matplotlib.pyplot as plt
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import numpy as np
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jahre= np.array(['2018','2019', '2020'])
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aepfel= np.array([1000,1500,2000])
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erdbeeren =np.array([500,950,900])
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bananen=np.array([400,800,750])
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breite = 0.6
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fig,ax = plt.subplots()
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#ax.legend('Verkäufe')
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ax.bar(jahre,bananen,color='yellow')
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ax.bar(jahre,erdbeeren,bottom=bananen,color='red')
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ax.bar(jahre,aepfel,bottom=erdbeeren,color='green')
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plt.legend(['Bananen','Erdbeeren','Äpfel'])
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plt.show()
|
47
teil23/vornamen_reader.py
Normal file
47
teil23/vornamen_reader.py
Normal file
@@ -0,0 +1,47 @@
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#encoding: utf-8
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def read_vornamen(filename):
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dict = {}
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# Der Zähler dient nur dazu, die erste Zeile zu überspringen
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count = 0
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dict['mädchen'] = 0
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dict['jungs'] = 0
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dict['divers'] = 0
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with open(filename,'r') as f:
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for zeile in f:
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if count >0:
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# einzelne Zeile in seine Bestandteile zerlegen
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splitted = zeile.strip().split(';')
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anzahl = splitted[0]
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vorname = splitted[1]
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geschlecht= splitted[2]
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position = splitted[3]
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if geschlecht == 'w':
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dict['mädchen'] += 1
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elif geschlecht == 'm':
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dict['jungs'] += 1
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else:
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dict['divers'] += 1
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data = {}
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data['vorname'] = vorname
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data['anzahl'] = int(anzahl)
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data['geschlecht'] = geschlecht
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data['position'] = position
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if vorname not in dict:
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dict[vorname] = data
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else:
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# doppelte Einträge summieren wir auf.
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e = dict[vorname]
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e['anzahl'] = int(anzahl) + (e['anzahl'])
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count +=1
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gesamt = dict['jungs'] + dict['mädchen'] + dict['divers']
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dict['gesamt'] = gesamt
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return dict
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|
42
teil23/vornamen_sauelen.py
Executable file
42
teil23/vornamen_sauelen.py
Executable file
@@ -0,0 +1,42 @@
|
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#! ./bin/python
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||||
#encondig: utf-8
|
||||
|
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import matplotlib.pyplot as plt
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from vornamen_reader import read_vornamen
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|
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|
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def plot_geschlecht(vornamen):
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|
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bez= ['Mädchen', 'Jungs','divers']
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geburten = [vornamen['mädchen'], vornamen['jungs'],vornamen['divers']]
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|
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print(f'geburten={geburten}')
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farben = ['red','blue','green']
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|
||||
fig, ax = plt.subplots()
|
||||
print(f'fig={fig}')
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print(f'ax={ax}')
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|
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ax.bar(bez,geburten,label=bez, color=farben)
|
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ax.set_ylabel('Geburten')
|
||||
ax.set_title('Geburten nach Geschlecht Wuppertal 2020')
|
||||
ax.legend(title='Geburten')
|
||||
|
||||
plt.show()
|
||||
|
||||
if __name__ == '__main__':
|
||||
vornamen = read_vornamen('./Vornamen_Wuppertal_2020.csv')
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||||
|
||||
jungs= vornamen['jungs']
|
||||
maedels = vornamen['mädchen']
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divers = vornamen['divers']
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|
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print(f'Jungs: {jungs}')
|
||||
print(f'Mädchen: {maedels}')
|
||||
print(f'divers: {divers}')
|
||||
# print(f'vornamen={vornamen}')
|
||||
|
||||
plot_geschlecht(vornamen)
|
||||
|
||||
|
43
teil23/vornamen_torte.py
Executable file
43
teil23/vornamen_torte.py
Executable file
@@ -0,0 +1,43 @@
|
||||
#! ./bin/python
|
||||
#encondig: utf-8
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import logging
|
||||
from vornamen_reader import read_vornamen
|
||||
|
||||
logging.basicConfig( format='%(asctime)-15s [%(levelname)s] %(funcName)s: %(message)s', level=logging.INFO)
|
||||
|
||||
def plot_geschlecht(vornamen):
|
||||
|
||||
bez= ['Mädchen', 'Jungs','divers']
|
||||
geburten = [vornamen['mädchen'], vornamen['jungs'],vornamen['divers']]
|
||||
|
||||
logging.info(f'geburten={geburten}')
|
||||
|
||||
proz_maedels = round(vornamen['mädchen'] / vornamen['gesamt'] *100,2)
|
||||
proz_jungs = round(vornamen['jungs'] / vornamen['gesamt'] *100,2)
|
||||
proz_divers = round(vornamen['divers'] / vornamen['gesamt'] *100,2)
|
||||
|
||||
farben = ['red','blue','green']
|
||||
sizes= [proz_maedels,proz_jungs,proz_divers]
|
||||
|
||||
logging.info(f'sizes={sizes}')
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
|
||||
ax.pie(sizes,explode=(0,0,0), labels=bez,autopct='%1.1f%%',shadow=True,startangle=90)
|
||||
ax.axis('equal')
|
||||
|
||||
plt.show()
|
||||
|
||||
if __name__ == '__main__':
|
||||
vornamen = read_vornamen('./Vornamen_Wuppertal_2020.csv')
|
||||
|
||||
jungs= vornamen['jungs']
|
||||
maedels = vornamen['mädchen']
|
||||
divers = vornamen['divers']
|
||||
|
||||
|
||||
plot_geschlecht(vornamen)
|
||||
|
||||
|
Reference in New Issue
Block a user