Contatto da ISS con scuola Rita Sidoti 24 gennaio 2024 ore 15:37 Firenze

 

Impronte

 Impronte sul M.Ferrato

Impronta di Lupo

Impronta di Tasso

ho trovato anche questo osso che mostra tracce di predazione

Rimozione trend da serie tempo

Una prova per rimuovere la componente stagionale da una serie tempo usando la libreria StatsModels di Python

In alto dati originali. al centro trend, seguend dati periodici stimati ed in basso calcolo dei residui

i

#!/usr/bin/env python

# coding: utf-8

# In[1]:

from IPython.display import display

import numpy as np

import pandas as pd

pd.set_option('display.max_rows', 15)

pd.set_option('display.max_columns', 500)

pd.set_option('display.width', 1000)

import matplotlib.pyplot as plt

from datetime import datetime

from datetime import timedelta

from pandas.plotting import register_matplotlib_converters

from mpl_toolkits.mplot3d import Axes3D

from statsmodels.tsa.stattools import acf, pacf

from statsmodels.tsa.statespace.sarimax import SARIMAX

import statsmodels.api as sm

import statsmodels.formula.api as smf

register_matplotlib_converters()

from time import time

import seaborn as sns

sns.set(style="whitegrid")

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import PCA

from sklearn.cluster import KMeans

from sklearn.covariance import EllipticEnvelope

import warnings

warnings.filterwarnings('ignore')

RANDOM_SEED = np.random.seed(0)

# # Carica i dati

# In[2]:

def parser(s):

return datetime.strptime(s, '%Y-%m-%d %H:%M:%S')

# In[3]:

#read data

est = pd.read_csv('/home/luca/luca/liscione.csv',sep=";",parse_dates=[0], index_col=0, date_parser=parser,header=0)

print(est)

# In[4]:

est = est.asfreq(freq='1H', method='ffill')

# ### Introduce an Anomaly

# In[5]:

idx = pd.IndexSlice

# In[6]:

start_date = datetime(2023,10,5,0,0,0)

end_date = datetime(2023,12,31,23,59,0)

lim_est = est[start_date:end_date]

# In[7]:

lim_est

# In[8]:

del lim_est['Temp']

# In[9]:

plt.figure(figsize=(10,4))

plt.plot(lim_est)

plt.title('Est', fontsize=20)

plt.ylabel('Est(mm)', fontsize=16)

for year in range(start_date.year,end_date.year):

plt.axvline(pd.to_datetime(str(year)+'-01-01'), color='k', linestyle='--', alpha=0.2)

# ## Seasonal Decompose

# In[10]:

from statsmodels.tsa.seasonal import seasonal_decompose

import matplotlib.dates as mdates

# In[18]:

plt.rc('figure',figsize=(12,8))

plt.rc('font',size=15)

result = seasonal_decompose(lim_est,model='additive')

print(result.trend)

plt.savefig('trend')

fig = result.plot()

fig.savefig('trend.png') # save the figure to file

result.trend.to_csv('/home/luca/tempo.csv', date_format='%Y-%m-%d_%H',sep=";")

# In[12]:

plt.rc('figure',figsize=(12,6))

plt.rc('font',size=15)

fig, ax = plt.subplots()

x = result.resid.index

y = result.resid.values

print(x.size)

ax.plot_date(x, y, color='black',linestyle='--')

ax.set_ylim([-0.7, 0.7])

fig.autofmt_xdate()

plt.savefig('residual')

plt.show()

# In[13]:

temp = est[start_date:end_date]

del temp['Est']

print(temp.index)

temp.index.to_csv('tempo2.csv', date_format='%Y-%m-%d_%H',delimiter=";")

#np.savetxt('tempo.csv',temp.index,delimiter=";")

# In[ ]:

# In[ ]:

fig, ax1 = plt.subplots()

color = 'tab:red'

ax1.set_xlabel('time')

ax1.set_ylabel('Temp', color=color)

ax1.plot(temp, color=color)

ax1.tick_params(axis='y', labelcolor=color)

ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis

color = 'tab:blue'

ax2.set_ylabel('Est', color=color) # we already handled the x-label with ax1

ax2.plot(result.trend, color=color)

ax2.tick_params(axis='y', labelcolor=color)

plt.savefig('sovrapposto')

plt.show()

# In[ ]:

np.savetxt('trend.csv',result.trend.values,delimiter=";")

np.savetxt('temp.csv',temp.values,delimiter=";")

# In[ ]:

import matplotlib.animation as animation

fig,ax = plt.subplots()

scat = ax.scatter(result.trend.values, temp.values)

plt.gca().invert_xaxis()

ax.set(xlabel='Estensimetro (mm)')

ax.set(ylabel='Temperatura (C)')

def update(i):

scat.set_offsets((result.trend.values[i],temp.values[i]))

return scat,

ani = animation.FuncAnimation(fig,update, repeat=True, frames=len(temp)-1, interval=10)

#writer = animation.PillowWriter(fps=15)

#ani.save('animation.gif',writer=writer)

#plt.savefig('scatterplot')

plt.show()

Animazione di grafico con Matplotlib

 

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import pandas as pd
df = pd.read_csv("tempo.csv", dtype=str,sep=";")
print(df)
my_data = np.genfromtxt('./scatterplot.csv', delimiter=';')
x = my_data[:,1]
y = my_data[:,0]
fig = plt.figure()
ax = fig.add_subplot()
plt.ylim(-5, 22)
plt.ylabel ('Temperatura (C)')
plt.xlim(-59, -53)
plt.xlabel ('Estensimetro (mm)')
tempo = ax.text(0.8, 0.9, '', horizontalalignment='center',verticalalignment='center', transform=ax.transAxes)
graph, = plt.plot([], [], 'o', markersize=1)
def animate(i):
    graph.set_data(x[:i+1], y[:i+1])
    tempo.set_text(df.Data[i])
    return graph,
ani = FuncAnimation(fig, animate, frames=2000, interval=50)
#FFwriter = animation.FFMpegWriter(fps=10)
#ani.save('animation.mp4')
plt.show()

Le GEPAN n'a rien a cacher

 

ARISS 40th Anniversary SSTV Event

 

Docker Compose esempio

 In ufficio stanno spingendo per utilizzare docker per ogni applicazione

Per rendere lo sviluppo piu' tracciabile viene richiesto che oltre al docker file vi sia anche il file yaml di docker compose (in pratica le impostazioni del container che si dovrebbero passare sulla linea di comando sono scritti nel file compose.yaml) 

con build . (punto) viene indicato di usare il dockerfile compreso nello stesso folder

Per lanciare si usa

docker compose up -d

alla fine della costruzione dell'immagine viene fatto in automatico lo start

Si accede quindi con

docker exec -it Omnia bash

si termina con

docker compose stop

version: '3'

services:

omnia_apuane:

build: .

container_name: Omnia

ports:

- "9192:80"

dns:

- "10.100.1.3"

environment:

- HOST=omnia

- VIRTUAL_HOST=omnia.aaaa.aaaa.it

- VIRTUAL_PROTO=http

- VIRTUAL_PORT=80

- TZ=Europe/Rome

restart: always

network_mode: bridge