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Fikk vi med alle rader og kolonner?
Ta en kikk i Excel-filen din og se etter antall rader og antall kolonner og sammenlikn med tallet du med bruk av df.shape. | df.shape | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Det første tallet er rader, det andre er kolonner. Vi har altså over 61.757 rader. Og det stemmer med rader i Excel-filen. Så langt alt fint!
Ta en sjekk for å se at dataene ser ok ut
Vi sjekker topp og bunn | df.head(n=3)
df.tail(n=3) | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Hvilke kolonner har vi og hva slags datatype har de? | df.dtypes | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Forklaring: int64 betyr heltall, object betyr som regel at det er tekst, float64 betyr et tall med desimaler.
Fjern kolonner du ikke trenger
Gjør det det mer oversiktlig å jobbe med. Her kan vi fjerne lat og lon kolonnene som angir kartreferanse. | df = df.drop(['Lat', 'Lon'], axis='columns') | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Forklaring: Her lager vi en ny DataFrame med samme navnet, der vi dropper kolonnene Lat og Lon.
axis=columns betyr at det er kolonner vi skal droppe, ikke rader.
Endre kolonnenavn
Noen ganger har kolonnene rare og lange navn, la oss lage dem kortere.
Vi lager et objekt som viser hvilke kolonner vi vil endre navn på o... | df = df.rename(columns={'Voksne hunnlus': 'hunnlus', 'Sjøtemperatur': 'sjotemp'}) | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Har vi manglende data?
Har vi rader uten data, eller kolonner uten data?
La oss først se på de 5 første radene. | df.head(n=5) | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Allerede her ser vi gjengangeren NaN som betyr Not a Number. Altså at denne cellen ikke har en numerisk verdi (slik som de andre i kolonnen)
La oss se hvor mange rader som mangler verdi (isnull) i hunnlus-feltet | df['hunnlus'].isnull().sum() | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Oi, nokså mange uten verdi. Trolig har ikke de rapportert lusetall den uka. Vi skal se på det senere.
Fyll inn manglende data
Vi ser på et nytt eksempel, en Excel-fil der det er manglende data i mange celler. | df2 = pd.read_excel('data/bord4_20171028_kommunedummy.xlsx')
df2 | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Her ser vi et typisk mønster der kun første raden i hvert fylke har verdi i fylkekolonnen. For å kunne behandle disse dataene i Pandas må alle ha verdi. Så la oss fylle celler med tomme verdier (fillna) nedover, en såkalt Forward Fill eller ffill | df2['Fylke'] = df2['Fylke'].fillna(method='ffill')
df2 | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Normaliser dataene
Er det mennesker som har punchet dataene du har?
Da er det garantert ord som er skrevet NESTEN likt og som roter til data-analysen din.
Vi bruker her et datasett fra UiBs medborgerpanel hvor velgernes holdninger til andre partier er angitt.
Datasettet er satt sammen av flere Excel-filer, laget på ul... | df = pd.read_csv('data/uib_medborgerpanelet_20170601_partiomparti.csv')
df.head() | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Det er ofte lurt å se hva slags unike verdier som finnes i en kolonne. Det kan du gjøre slik. | df['omtaler_parti'].value_counts().to_frame().sort_index() | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Her var det mye forskjellig! Legg merke til at partiene er skrevet på forskjellige måter. Det betyr trøbbel om vi skal gruppere senere.
Vi må normalisere disse verdiene, dvs samle oss om en måte å skrive på partiene på.
En måte å gjøre det på er å lage en fra-til liste med verdier. | partimapping = {
'FRP': 'Frp',
'FrP': 'Frp',
'AP': 'Ap',
'Høyre': 'H',
'SP': 'Sp',
'Venstre': 'V',
'KRF': 'KrF'
} | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Så må vi fortelle Pandas at vi vil bytte ut innholdet i de tre kolonnene som inneholder partinavn med den riktige formen av partinavn. | df = df.replace({
'parti_valgt_2013': partimapping,
'omtaler_parti': partimapping}
) | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Så tar vi igjen en sjekk på hvilke unike verdier vi har | df['omtaler_parti'].value_counts().to_frame().sort_index() | notebooks/02 Vasking av data.ipynb | BergensTidende/dataskup-2017-notebooks | mit |
Making a HTTP Connection | import requests
req = requests.get('http://google.com')
print(req.text)
def connect(prot='http', **q):
"""
Makes a connection with CAPE.
Required that at least one query is made.
Parameters
----------
:params prot: Either HTTP or HTTPS
:params q: Query Dictionary
Returns
----... | notebooks/cape.ipynb | jjangsangy/GraphUCSD | apache-2.0 |
Running the Code
**q is a variable set of keyword arguments that it will apply to the URL
```python
connect(department=CHEM)
```
Will make a request to http://cape.ucsd.edu/responses/Results.aspx?department=CHEM and return the result. | # URL: http://cape.com/responses/Results.aspx?
req = connect(department="CHEM")
print(req.text) | notebooks/cape.ipynb | jjangsangy/GraphUCSD | apache-2.0 |
Cleaning up the result using BeautifulSoup4
BeautifulSoup is a HTML Parser
Let's grab all the class listings within
html
<option value="">Select a Department</option>
<option value="ANTH">ANTH - Anthropology</option>
<option value="BENG">BENG - Bioengineering</option>
... | from bs4 import BeautifulSoup
# Grab the HTML
req = connect(department="CHEM")
# Shove it into BeautifulSoup
soup = BeautifulSoup(req.text, 'lxml')
# Find all Option Tags
options = soup.find_all('option')
# Returns a list of options
options
# Grab the `value= ` Attribute
for option in options:
print(option.at... | notebooks/cape.ipynb | jjangsangy/GraphUCSD | apache-2.0 |
Now Grab all the Departments
Kind of..... | def departments():
"""
Gets a mapping of all the deparments by key.
"""
logging.info('Grabbing a list of Departments')
prototype = connect("http", department="CHEM")
soup = BeautifulSoup(prototype.content, 'lxml')
options = list(reversed(soup.find_all('option')))
options.pop()
... | notebooks/cape.ipynb | jjangsangy/GraphUCSD | apache-2.0 |
Data Munging | def create_table(courses):
"""
Generates a pandas DataFrame by querying UCSD Cape Website.
Parameters
==========
:params courses: Either Course or Path to HTML File
Returns
=======
:returns df: Query Results
:rtype: pandas.DataFrame
"""
header = [
'inst... | notebooks/cape.ipynb | jjangsangy/GraphUCSD | apache-2.0 |
Make it Go Fast with Multi Threading | def main(threads=6):
"""
Get all departments
"""
logging.info('Program is Starting')
# Get Departments
deps = departments()
keys = [department.strip() for department in deps.keys()]
# Run Scraper Concurrently Using ThreadPool
pool = ThreadPool(threads)
logging.info('Initiali... | notebooks/cape.ipynb | jjangsangy/GraphUCSD | apache-2.0 |
Target Configuration | # Setup a target configuration
my_target_conf = {
# Target platform and board
"platform" : 'linux',
"board" : 'aboard',
# Target board IP/MAC address
"host" : '192.168.0.1',
# Login credentials
"username" : 'root',
"password" : 'test0000',
} | ipynb/tutorial/04_ExecutorUsage.ipynb | JaviMerino/lisa | apache-2.0 |
Tests Configuration | my_tests_conf = {
# Folder where all the results will be collected
"results_dir" : "ExecutorExample",
# Platform configurations to test
"confs" : [
{
"tag" : "base",
"flags" : "ftrace", # Enable FTrace events
"sched_features" : ... | ipynb/tutorial/04_ExecutorUsage.ipynb | JaviMerino/lisa | apache-2.0 |
Tests execution | from executor import Executor
executor = Executor(my_target_conf, my_tests_conf)
executor.run()
!tree {executor.te.res_dir} | ipynb/tutorial/04_ExecutorUsage.ipynb | JaviMerino/lisa | apache-2.0 |
Exercice 2 : json
Un premier essai. | obj = dict(a=[50, "r"], gg=(5, 't'))
import jsonpickle
frozen = jsonpickle.encode(obj)
frozen | _doc/notebooks/td2a/td2a_correction_session_2E.ipynb | sdpython/ensae_teaching_cs | mit |
Ce module est équivalent au module json sur les types standard du langage Python (liste, dictionnaires, nombres, ...). Mais le module json ne fonctionne pas sur les dataframe. | frozen = jsonpickle.encode(df)
len(frozen), type(frozen), frozen[:55] | _doc/notebooks/td2a/td2a_correction_session_2E.ipynb | sdpython/ensae_teaching_cs | mit |
La methode to_json donnera un résultat statisfaisant également mais ne pourra s'appliquer à un modèle de machine learning produit par scikit-learn. | def to_json(obj, filename):
frozen = jsonpickle.encode(obj)
with open(filename, "w", encoding="utf-8") as f:
f.write(frozen)
def read_json(filename):
with open(filename, "r", encoding="utf-8") as f:
enc = f.read()
return jsonpickle.decode(enc)
to_json(df, "df_text.json")
try:
... | _doc/notebooks/td2a/td2a_correction_session_2E.ipynb | sdpython/ensae_teaching_cs | mit |
Visiblement, cela ne fonctionne pas sur les DataFrame. Il faudra s'inspirer du module numpyson.
json + scikit-learn
Il faut lire l'issue 147 pour saisir l'intérêt des deux lignes suivantes. | import jsonpickle.ext.numpy as jsonpickle_numpy
jsonpickle_numpy.register_handlers()
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
y = iris.target
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X,y)
clf.pr... | _doc/notebooks/td2a/td2a_correction_session_2E.ipynb | sdpython/ensae_teaching_cs | mit |
Donc on essaye d'une essaye d'une autre façon. Si le code précédent ne fonctionne pas et le suivant si, c'est un bug de jsonpickle. | class EncapsulateLogisticRegression:
def __init__(self, obj):
self.obj = obj
def __getstate__(self):
return {k: v for k, v in sorted(self.obj.__getstate__().items())}
def __setstate__(self, data):
self.obj = LogisticRegression()
self.obj.__setstate__(data)
enc = Enca... | _doc/notebooks/td2a/td2a_correction_session_2E.ipynb | sdpython/ensae_teaching_cs | mit |
Fit_Transform:
1) Fits the model and learns the vocabulary
2) transoforms the data into feature vectors | #using only the "Text Feed" column to build the features
features = vector_data.fit_transform(anomaly_data.TextFeed.tolist())
#converting the data into the array
features = features.toarray()
features.shape
#printing the words in the vocabulary
vocab = vector_data.get_feature_names()
print (vocab)
# Sum up the counts... | AnomaliesTwitterText/anomalies_in_tweets.ipynb | manojkumar-github/NLP-TextAnalytics | mit |
Analytic I
Within the classic PowerShell log, event ID 400 indicates when a new PowerShell host process has started. Excluding PowerShell.exe is a good way to find alternate PowerShell hosts
| Data source | Event Provider | Relationship | Event |
|:------------|:---------------|--------------|-------|
| Powershell | Wi... | df = spark.sql(
'''
SELECT `@timestamp`, Hostname, Channel
FROM sdTable
WHERE (Channel = "Microsoft-Windows-PowerShell/Operational" OR Channel = "Windows PowerShell")
AND (EventID = 400 OR EventID = 4103)
AND NOT Message LIKE "%Host Application%powershell%"
'''
)
df.show(10,False) | docs/notebooks/windows/02_execution/WIN-190610201010.ipynb | VVard0g/ThreatHunter-Playbook | mit |
Analytic II
Looking for processes loading a specific PowerShell DLL is a very effective way to document the use of PowerShell in your environment
| Data source | Event Provider | Relationship | Event |
|:------------|:---------------|--------------|-------|
| Module | Microsoft-Windows-Sysmon/Operational | Process load... | df = spark.sql(
'''
SELECT `@timestamp`, Hostname, Image, Description
FROM sdTable
WHERE Channel = "Microsoft-Windows-Sysmon/Operational"
AND EventID = 7
AND (lower(Description) = "system.management.automation" OR lower(ImageLoaded) LIKE "%system.management.automation%")
AND NOT Image LIKE "%powershell.exe"... | docs/notebooks/windows/02_execution/WIN-190610201010.ipynb | VVard0g/ThreatHunter-Playbook | mit |
Analytic III
Monitoring for PSHost* pipes is another interesting way to find other alternate PowerShell hosts in your environment.
| Data source | Event Provider | Relationship | Event |
|:------------|:---------------|--------------|-------|
| Named pipe | Microsoft-Windows-Sysmon/Operational | Process created Pipe | ... | df = spark.sql(
'''
SELECT `@timestamp`, Hostname, Image, PipeName
FROM sdTable
WHERE Channel = "Microsoft-Windows-Sysmon/Operational"
AND EventID = 17
AND lower(PipeName) LIKE "\\\pshost%"
AND NOT Image LIKE "%powershell.exe"
'''
)
df.show(10,False) | docs/notebooks/windows/02_execution/WIN-190610201010.ipynb | VVard0g/ThreatHunter-Playbook | mit |
reference for LaTeX commands in MathJax
http://www.onemathematicalcat.org/MathJaxDocumentation/TeXSyntax.htm
http://oeis.org/wiki/List_of_LaTeX_mathematical_symbols | # define symbol
x = sympy.symbols('x')
print(type(x))
x
# define fuction
f = x**2 + 4*x
f
# differentiation
sympy.diff(f)
# simplify function
sympy.simplify(f)
# solving equation
from sympy import solve
solve(f)
# factorize
from sympy import factor
sympy.factor(f)
# partial differentiation
x, y = sympy.symbols('... | scripts/[HYStudy 14th] SymPy, Matplotlib 1.ipynb | Lattecom/HYStudy | mit |
Draw function graph | # draw 2nd degree function
def f2(x):
return x**3 + 2*x**2 - 20
x = np.linspace(-21, 21, 500)
y = f2(x)
plt.plot(x, y)
plt.show() | scripts/[HYStudy 14th] SymPy, Matplotlib 1.ipynb | Lattecom/HYStudy | mit |
Gradient vector, quiver & contour plot | import numpy as np
import matplotlib as mpl
import matplotlib.pylab as plt
# function definition
def f(x, y):
return 3*x**2 + 4*x*y + 4*y**2 - 50*x - 20*y + 100
# coordinate range
xx = np.linspace(-11, 16, 500)
yy = np.linspace(-11, 16, 500)
# make coordinate point
X, Y = np.meshgrid(xx, yy)
# dependent variabl... | scripts/[HYStudy 14th] SymPy, Matplotlib 1.ipynb | Lattecom/HYStudy | mit |
2. Read data
The data are read from numpy npy files and wrapped as Datasets. Features (vertices) are normalized to have unit variance. | dss_train = []
dss_test = []
subjects = ['rid000005', 'rid000011', 'rid000014']
for subj in subjects:
ds = Dataset(np.load('raiders/{subj}_run00_lh.npy'.format(subj=subj)))
ds.fa['node_indices'] = np.arange(ds.shape[1], dtype=int)
zscore(ds, chunks_attr=None)
dss_train.append(ds)
ds = Dataset(np.lo... | Tutorials/hyperalignment/hyperalignment_tutorial.ipynb | Summer-MIND/mind_2017 | mit |
3. Create SearchlightHyperalignment instance
The QueryEngine is used to find voxel/vertices within a searchlight. This SurfaceQueryEngine use a searchlight radius of 5 mm based on the fsaverage surface. | sl_radius = 5.0
qe = SurfaceQueryEngine(read_surface('fsaverage.lh.surf.gii'), radius=sl_radius)
hyper = SearchlightHyperalignment(
queryengine=qe,
compute_recon=False, # We don't need to project back from common space to subject space
nproc=1, # Number of processes to use. Change "Docker - Preferences - A... | Tutorials/hyperalignment/hyperalignment_tutorial.ipynb | Summer-MIND/mind_2017 | mit |
4. Create common template space with training data
This step may take a long time. In my case it's 10 minutes with nproc=1. | # mappers = hyper(dss_train)
# h5save('mappers.hdf5.gz', mappers, compression=9)
mappers = h5load('mappers.hdf5.gz') # load pre-computed mappers | Tutorials/hyperalignment/hyperalignment_tutorial.ipynb | Summer-MIND/mind_2017 | mit |
5. Project testing data to the common space | dss_aligned = [mapper.forward(ds) for ds, mapper in zip(dss_test, mappers)]
_ = [zscore(ds, chunks_attr=None) for ds in dss_aligned] | Tutorials/hyperalignment/hyperalignment_tutorial.ipynb | Summer-MIND/mind_2017 | mit |
6. Benchmark inter-subject correlations | def compute_average_similarity(dss, metric='correlation'):
"""
Returns
=======
sim : ndarray
A 1-D array with n_features elements, each element is the average
pairwise correlation similarity on the corresponding feature.
"""
n_features = dss[0].shape[1]
sim = np.zeros((n_feat... | Tutorials/hyperalignment/hyperalignment_tutorial.ipynb | Summer-MIND/mind_2017 | mit |
7. Benchmark movie segment classifications | def movie_segment_classification_no_overlap(dss, window_size=6, dist_metric='correlation'):
"""
Parameters
==========
dss : list of ndarray or Datasets
window_size : int, optional
dist_metric : str, optional
Returns
=======
cv_results : ndarray
An n_subjects x n_segments boo... | Tutorials/hyperalignment/hyperalignment_tutorial.ipynb | Summer-MIND/mind_2017 | mit |
<h3> Simulate some time-series data </h3>
Essentially a set of sinusoids with random amplitudes and frequencies. | import tensorflow as tf
print(tf.__version__)
import numpy as np
import seaborn as sns
def create_time_series():
freq = (np.random.random()*0.5) + 0.1 # 0.1 to 0.6
ampl = np.random.random() + 0.5 # 0.5 to 1.5
noise = [np.random.random()*0.3 for i in range(SEQ_LEN)] # -0.3 to +0.3 uniformly distributed
... | courses/machine_learning/deepdive/09_sequence_keras/sinewaves.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
<h3> Train model locally </h3>
Make sure the code works as intended. | %%bash
DATADIR=$(pwd)/data/sines
OUTDIR=$(pwd)/trained/sines
rm -rf $OUTDIR
gcloud ml-engine local train \
--module-name=sinemodel.task \
--package-path=${PWD}/sinemodel \
-- \
--train_data_path="${DATADIR}/train-1.csv" \
--eval_data_path="${DATADIR}/valid-1.csv" \
--output_dir=${OUTDIR} \
... | courses/machine_learning/deepdive/09_sequence_keras/sinewaves.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
<h3> Cloud ML Engine </h3>
Now to train on Cloud ML Engine with more data. | import shutil
shutil.rmtree(path = "data/sines", ignore_errors = True)
os.makedirs("data/sines/")
np.random.seed(1) # makes data generation reproducible
for i in range(0,10):
to_csv("data/sines/train-{}.csv".format(i), 1000) # 1000 sequences
to_csv("data/sines/valid-{}.csv".format(i), 250)
%%bash
gsutil -m rm... | courses/machine_learning/deepdive/09_sequence_keras/sinewaves.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Ejercicio
Crea codigo para una iteración mas con estos mismos parametros y despliega el resultado. | x3 = # Escribe el codigo de tus calculos aqui
from pruebas_2 import prueba_2_1
prueba_2_1(x0, x1, x2, x3, _) | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Momento... que esta pasando? Resulta que este $\Delta t$ es demasiado grande, intentemos con 20 iteraciones:
$$
\begin{align}
\Delta t &= 0.5 \
x(0) &= 1
\end{align}
$$ | x0 = 1
n = 20
Δt = 10/n
F = lambda x : -x
x1 = x0 + F(x0)*Δt
x1
x2 = x1 + F(x1)*Δt
x2
x3 = x2 + F(x2)*Δt
x3 | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Esto va a ser tardado, mejor digamosle a Python que es lo que tenemos que hacer, y que no nos moleste hasta que acabe, podemos usar un ciclo for y una lista para guardar todos los valores de la trayectoria: | xs = [x0]
for t in range(20):
xs.append(xs[-1] + F(xs[-1])*Δt)
xs | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Ahora que tenemos estos valores, podemos graficar el comportamiento de este sistema, primero importamos la libreria matplotlib: | %matplotlib inline
from matplotlib.pyplot import plot | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Mandamos a llamar la función plot: | plot(xs); | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Sin embargo debido a que el periodo de integración que utilizamos es demasiado grande, la solución es bastante inexacta, podemos verlo al graficar contra la que sabemos es la solución de nuestro problema: | from numpy import linspace, exp
ts = linspace(0, 10, 20)
plot(xs)
plot(exp(-ts)); | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Si ahora utilizamos un numero de pedazos muy grande, podemos mejorar nuestra aproximación: | xs = [x0]
n = 100
Δt = 10/n
for t in range(100):
xs.append(xs[-1] + F(xs[-1])*Δt)
ts = linspace(0, 10, 100)
plot(xs)
plot(exp(-ts)); | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
odeint
Este método funciona tan bien, que ya viene programado dentro de la libreria scipy, por lo que solo tenemos que importar esta librería para utilizar este método.
Sin embargo debemos de tener cuidado al declarar la función $F(x, t)$. El primer argumento de la función se debe de referir al estado de la función, es... | from scipy.integrate import odeint
F = lambda x, t : -x
x0 = 1
ts = linspace(0, 10, 100)
xs = odeint(func=F, y0=x0, t=ts)
plot(ts, xs); | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Ejercicio
Grafica el comportamiento de la siguiente ecuación diferencial.
$$
\dot{x} = x^2 - 5 x + \frac{1}{2} \sin{x} - 2
$$
Nota: Asegurate de impotar todas las librerias que puedas necesitar | ts = # Escribe aqui el codigo que genera un arreglo de puntos equidistantes (linspace)
x0 = # Escribe el valor de la condicion inicial
# Importa las funciones de librerias que necesites aqui
G = lambda x, t: # Escribe aqui el codigo que describe los calculos que debe hacer la funcion
xs = # Escribe aqui el comando n... | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Sympy
Y por ultimo, hay veces en las que incluso podemos obtener una solución analítica de una ecuación diferencial, siempre y cuando cumpla ciertas condiciones de simplicidad. | from sympy import var, Function, dsolve
from sympy.physics.mechanics import mlatex, mechanics_printing
mechanics_printing()
var("t")
x = Function("x")(t)
x, x.diff(t)
solucion = dsolve(x.diff(t) + x, x)
solucion | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Ejercicio
Implementa el codigo necesario para obtener la solución analítica de la siguiente ecuación diferencial:
$$
\dot{x} = x^2 - 5x
$$ | # Declara la variable independiente de la ecuación diferencial
var("")
# Declara la variable dependiente de la ecuación diferencial
= Function("")()
# Escribe la ecuación diferencial con el formato necesario (Ecuacion = 0)
# adentro de la función dsolve
sol = dsolve()
sol
from pruebas_2 import prueba_2_3
prueba_2_3... | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Solución a ecuaciones diferenciales de orden superior
Si ahora queremos obtener el comportamiento de una ecuacion diferencial de orden superior, como:
$$
\ddot{x} = -\dot{x} - x + 1
$$
Tenemos que convertirla en una ecuación diferencial de primer orden para poder resolverla numericamente, por lo que necesitaremos conve... | from numpy import matrix, array
def F(X, t):
A = matrix([[0, 1], [-1, -1]])
B = matrix([[0], [1]])
return array((A*matrix(X).T + B).T).tolist()[0]
ts = linspace(0, 10, 100)
xs = odeint(func=F, y0=[0, 0], t=ts)
plot(xs); | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Ejercicio
Implementa la solución de la siguiente ecuación diferencial, por medio de un modelo en representación de espacio de estados:
$$
\ddot{x} = -8\dot{x} - 15x + 1
$$
Nota: Tomalo con calma y paso a paso
* Empieza anotando la ecuación diferencial en tu cuaderno, junto a la misma identidad del ejemplo
* Extrae la ... | def G(X, t):
A = # Escribe aqui el codigo para la matriz A
B = # Escribe aqui el codigo para el vector B
return array((A*matrix(X).T + B).T).tolist()[0]
ts = linspace(0, 10, 100)
xs = odeint(func=G, y0=[0, 0], t=ts)
plot(xs);
from pruebas_2 import prueba_2_4
prueba_2_4(xs) | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Funciones de transferencia
Sin embargo, no es la manera mas facil de obtener la solución, tambien podemos aplicar una transformada de Laplace, y aplicar las funciones de la libreria de control para simular la función de transferencia de esta ecuación; al aplicar la transformada de Laplace, obtendremos:
$$
G(s) = \frac{... | from control import tf, step
F = tf([0, 0, 1], [1, 1, 1])
xs, ts = step(F)
plot(ts, xs); | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Ejercicio
Modela matematicamente la ecuación diferencial del ejercicio anterior, usando una representación de función de transferencia.
Nota: De nuevo, no desesperes, escribe tu ecuación diferencial y aplica la transformada de Laplaca tal como te enseñaron tus abuelos hace tantos años... | G = tf([], []) # Escribe los coeficientes de la función de transferencia
xs, ts = step(G)
plot(ts, xs);
from pruebas_2 import prueba_2_5
prueba_2_5(ts, xs) | Practicas/.ipynb_checkpoints/Practica 2 - Solucion de ecuaciones diferenciales-checkpoint.ipynb | robblack007/clase-dinamica-robot | mit |
Introduction to Divide-and-Conquer Algorithms
The subfamily of Divide-and-Conquer algorithms is one of the main paradigms of algorithmic problem solving next to Dynamic Programming and Greedy Algorithms. The main goal behind greedy algorithms is to implement an efficient procedure for often computationally more complex... | def linear_search(lst, item):
for i in range(len(lst)):
if lst[i] == item:
return i
return -1
lst = [1, 5, 8, 12, 13]
for k in [8, 1, 23, 11]:
print(linear_search(lst=lst, item=k)) | ipython_nbs/essentials/divide-and-conquer-algorithm-intro.ipynb | rasbt/algorithms_in_ipython_notebooks | gpl-3.0 |
The runtime of linear search is obviously $O(n)$ since we are checking each element in the array -- remember that big-Oh is our upper bound. Now, a cleverer way of implementing a search algorithm would be binary search, which is a simple, yet nice example of a divide-and-conquer algorithm.
The idea behind divide-and-co... | def binary_search(lst, item):
first = 0
last = len(lst) - 1
found = False
while first <= last and not found:
midpoint = (first + last) // 2
if lst[midpoint] == item:
found = True
else:
if item < lst[midpoint]:
last = midpoint - 1
... | ipython_nbs/essentials/divide-and-conquer-algorithm-intro.ipynb | rasbt/algorithms_in_ipython_notebooks | gpl-3.0 |
Example 2 -- Finding the Majority Element
"Finding the Majority Element" is a problem where we want to find an element in an array positive integers with length n that occurs more than n/2 in that array. For example, if we have an array $a = [1, 2, 3, 3, 3]$, $3$ would be the majority element. In another array, b = [1,... | def majority_ele_lin(lst):
cnt = {}
for ele in lst:
if ele not in cnt:
cnt[ele] = 1
else:
cnt[ele] += 1
for ele, c in cnt.items():
if c > (len(lst) // 2):
return (ele, c, cnt)
return (-1, -1, cnt)
############################################... | ipython_nbs/essentials/divide-and-conquer-algorithm-intro.ipynb | rasbt/algorithms_in_ipython_notebooks | gpl-3.0 |
Now, "finding the majority element" is a nice task for a Divide and Conquer algorithm. Here, we use the fact that if a list has a majority element it is also the majority element of one of its two sublists, if we split it into 2 halves.
More concretely, what we do is:
Split the array into 2 halves
Run the majority el... | def majority_ele_dac(lst):
n = len(lst)
left = lst[:n // 2]
right = lst[n // 2:]
l_maj = majority_ele_lin(left)
r_maj = majority_ele_lin(right)
# case 3A
if l_maj[0] == -1 and r_maj[0] == -1:
return -1
# case 3B
elif l_maj[0] == -1 and r_maj[0] > -1:
... | ipython_nbs/essentials/divide-and-conquer-algorithm-intro.ipynb | rasbt/algorithms_in_ipython_notebooks | gpl-3.0 |
In algorithms such as binary search that we saw at the beginning of this notebook, we recursively break down our problem into smaller subproblems. Thus, we have a recurrence problem with time complexity
$T(n) = T(\frac{2}{n}) + O(1) \rightarrow T(n) = O(\log n).$
In this example, finding the majority element, we break ... | import multiprocessing as mp
def majority_ele_dac_mp(lst):
n = len(lst)
left = lst[:n // 2]
right = lst[n // 2:]
results = (pool.apply_async(majority_ele_lin, args=(x,))
for x in (left, right))
l_maj, r_maj = [p.get() for p in results]
if l_maj[0] == -1 and r_ma... | ipython_nbs/essentials/divide-and-conquer-algorithm-intro.ipynb | rasbt/algorithms_in_ipython_notebooks | gpl-3.0 |
SQLAlchemy
SQLAlchemy is a commonly used database toolkit. Unlike many database libraries it not only provides an ORM (Object-relational mapping) layer but also a generalized API for writing database-agnostic code without SQL.
$ pip install sqlalchemy
Example | from sqlalchemy import create_engine, ForeignKey
from sqlalchemy import Column, Date, Integer, String
from sqlalchemy.ext.declarative import declarative_base
# engine.dispose()
engine = create_engine('sqlite:///userlist.db', echo=True)
Base = declarative_base()
class User(Base):
__tablename__ = 'users'
id =... | Section 2 - Advance Python/Chapter S2.04 - Database/Databases.ipynb | mayankjohri/LetsExplorePython | gpl-3.0 |
Records
Records is minimalist SQL library, designed for sending raw SQL queries to various databases. Data can be used programmatically, or exported to a number of useful data formats.
$ pip install records
Also included is a command-line tool for exporting SQL data. | import json # https://docs.python.org/3/library/json.html
import requests # https://github.com/kennethreitz/requests
import records # https://github.com/kennethreitz/records
# randomuser.me generates random 'user' data (name, email, addr, phone number, etc)
r = requests.get('http://api.randomuser.me/0.6/?nat=us&result... | Section 2 - Advance Python/Chapter S2.04 - Database/Databases.ipynb | mayankjohri/LetsExplorePython | gpl-3.0 |
SQLObject
SQLObject is yet another ORM. It supports a wide variety of databases: Common database systems MySQL, Postgres and SQLite and more exotic systems like SAP DB, SyBase and MSSQL.
SQLObject is a popular Object Relational Manager for providing an object interface to your database, with tables as classes, rows as... | import sqlobject
from sqlobject.sqlite import builder
conn = builder()('sqlobject_demo.db')
class PhoneNumber(sqlobject.SQLObject):
_connection = conn
number = sqlobject.StringCol(length=14, unique=True)
owner = sqlobject.StringCol(length=255)
lastCall = sqlobject.DateTimeCol(default=None)
Phone... | Section 2 - Advance Python/Chapter S2.04 - Database/Databases.ipynb | mayankjohri/LetsExplorePython | gpl-3.0 |
Defining relationships among tables
SQLObject lets you define relationships among tables as foreign keys | import sqlobject
from sqlobject.sqlite import builder
conn = builder()('sqlobject_demo_relationships.db')
class PhoneNumber(sqlobject.SQLObject):
_connection = conn
number = sqlobject.StringCol(length=14, unique=True)
owner = sqlobject.ForeignKey('Person')
lastCall = sqlobject.DateTimeCol(default=No... | Section 2 - Advance Python/Chapter S2.04 - Database/Databases.ipynb | mayankjohri/LetsExplorePython | gpl-3.0 |
现在查询机构很多，我们可以根据不同的查询机构和查询方式，来通过继承的方式实现其对应的股票查询器类。例如，WebA和WebB的查询器类可以构造如下： | class WebAStockQueryDevice(StockQueryDevice):
def login(self,usr,pwd):
if usr=="myStockA" and pwd=="myPwdA":
print ("Web A:Login OK... user:%s pwd:%s"%(usr,pwd))
return True
else:
print ("Web A:Login ERROR... user:%s pwd:%s"%(usr,pwd))
return False
... | DesignPattern/TemplatePattern.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
在场景中，想要在网站A上查询股票，需要进行如下操作： | web_a_query_dev=WebAStockQueryDevice()
web_a_query_dev.login("myStockA","myPwdA")
web_a_query_dev.setCode("12345")
web_a_query_dev.queryPrice()
web_a_query_dev.showPrice() | DesignPattern/TemplatePattern.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
每次操作，都会调用登录，设置代码，查询，展示这几步，是不是有些繁琐？既然有些繁琐，何不将这几步过程封装成一个接口。由于各个子类中的操作过程基本满足这个流程，所以这个方法可以写在父类中： | class StockQueryDevice():
stock_code="0"
stock_price=0.0
def login(self,usr,pwd):
pass
def setCode(self,code):
self.stock_code=code
def queryPrice(self):
pass
def showPrice(self):
pass
def operateQuery(self,usr,pwd,code):
self.login(usr,pwd)
se... | DesignPattern/TemplatePattern.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
Setting up the properties of time-space and create the domain: | t = 27 / 365
dx = 0.2
L = 40
phi = 0.8
dt = 1e-4
ftc = Column(L, dx, t, dt) | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
To make things interesting lets create not simple inital conditions for iron: | x = np.linspace(0, L, int(L / dx) + 1)
Fe3_init = np.zeros(x.size)
Fe3_init[x > 5] = 75
Fe3_init[x > 15] = 0
Fe3_init[x > 25] = 75
Fe3_init[x > 35] = 0 | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
Adding species with names, diffusion coefficients, initial concentrations and boundary top and bottom conditions: | ftc.add_species(theta=phi, name='O2', D=368, init_conc=0, bc_top_value=0.231, bc_top_type='dirichlet', bc_bot_value=0, bc_bot_type='flux')
ftc.add_species(theta=phi, name='TIC', D=320, init_conc=0, bc_top_value=0, bc_top_type='flux', bc_bot_value=0, bc_bot_type='flux')
ftc.add_species(theta=phi, name='Fe2', D=127, init... | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
Specify the constants used in the rates: | ftc.constants['k_OM'] = 1
ftc.constants['Km_O2'] = 1e-3
ftc.constants['Km_FeOH3'] = 2
ftc.constants['k8'] = 1.4e+5
ftc.constants['Q10'] = 4 ### added
ftc.constants['CF'] = (1-phi)/phi ### conversion factor | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
Simulate Temperature with thermal diffusivity coefficient 281000 and init and boundary temperature 5C: | ftc.add_species(theta=0.99, name='Temperature', D=281000, init_conc=5, bc_top_value=5., bc_top_type='constant', bc_bot_value=0, bc_bot_type='flux') | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
Add Q10 factor: | ftc.rates['R1'] = 'Q10**((Temperature-5)/10) * k_OM * OM * O2 / (Km_O2 + O2)'
ftc.rates['R2'] = 'Q10**((Temperature-5)/10) * k_OM * OM * FeOH3 / (Km_FeOH3 + FeOH3) * Km_O2 / (Km_O2 + O2)'
ftc.rates['R8'] = 'k8 * O2 * Fe2' | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
ODEs for specific species: | ftc.dcdt['OM'] = '-R1-R2'
ftc.dcdt['O2'] = '-R1-R8'
ftc.dcdt['FeOH3'] = '-4*R2+R8/CF'
ftc.dcdt['Fe2'] = '-R8+4*R2*CF'
ftc.dcdt['TIC'] = 'R1+R2*CF' | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
Because we are changing the boundary conditions for temperature and Oxygen (when T < 0 => no oxygen at the top), then we need to have a time loop: | # %pdb
for i in range(1, len(ftc.time)):
day_of_bi_week = (ftc.time[i]*365) % 14
if day_of_bi_week < 7:
ftc.Temperature.bc_top_value = 5 + 5 * np.sin(np.pi * 2 * ftc.time[i] * 365)
else:
ftc.Temperature.bc_top_value = -10 + 5 * np.sin(np.pi * 2 * ftc.time[i] * 365)
# when T ... | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
What we did with temperature | ftc.plot_depths("Temperature",[0,1,3,7,10,40]) | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
Concentrations of different species during the whole period of simulation: | ftc.plot_contourplots() | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
The rates of consumption and production of species: | ftc.reconstruct_rates()
ftc.plot_contourplots_of_rates()
ftc.plot_contourplots_of_deltas() | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
Profiles at the end of the simulation | Fx = ftc.estimate_flux_at_top('CO2g')
ftc.custom_plot(ftc.time*365, 1e+3*Fx*1e+4/365/24/60/60,x_lbl='Days, [day]' , y_lbl='$F_{CO_2}$, $[\mu mol$ $m^{-2}$ $s^{-1}]$')
Fxco2 = 1e+3*Fx*1e+4/365/24/60/60
Fxco2nz = (ftc.time*365<7)*Fxco2 + ((ftc.time*365>14) & (ftc.time*365<21))*Fxco2
import seaborn as sns
fig, ax1 = pl... | examples/Column - Freeze-Thaw.ipynb | biogeochemistry/PorousMediaLab | mit |
Collocations between two data arrays
Let's try out the simplest case: You have two xarray datasets with
temporal-spatial data and you want to find collocations between them.
At first, we create two example xarray datasets with faked measurements. Let's
assume, these data arrays represent measurements from two different... | # Create the data
primary = xr.Dataset(
coords={
"lat": (('along_track'), 30.*np.sin(np.linspace(-3.14, 3.14, 24))+20),
"lon": (('along_track'), np.linspace(0, 90, 24)),
"time": (('along_track'), np.arange("2018-01-01", "2018-01-02", dtype="datetime64[h]")),
},
data_vars={
"T... | doc/tutorials/collocations.ipynb | atmtools/typhon | mit |
Now, let’s find all measurements of primary that have a maximum distance of 300 kilometers to the measurements of secondary: | collocator = Collocator(name='primary_secondary_collocator')
collocations = collocator.collocate(
primary=('primary', primary),
secondary=('secondary', secondary),
max_distance=600, # collocation radius in km
)
print(f'Found collocations are {collocations["Collocations/distance"].values} km apart')
colloca... | doc/tutorials/collocations.ipynb | atmtools/typhon | mit |
The obtained collocations dataset contains variables of 3 groups: primary, secondary and Collocations.
The first two correspond to the variables of the two respective input datasets and contain only the matched
data points. The Collocations group adds some new variables containing information about the collocations, e... | def collocations_wmap(collocations):
fig = plt.figure(figsize=(10, 10))
# Plot the collocations
wmap = worldmap(
collocations['primary/lat'],
collocations['primary/lon'],
facecolor="r", s=128, marker='x', bg=True
)
worldmap(
collocations['secondary/lat'],
col... | doc/tutorials/collocations.ipynb | atmtools/typhon | mit |
We can also add a temporal filter that filters out all points which difference in time is bigger than a time interval. We are doing this by using max_interval. Note that our testdata is sampled very sparsely in time. | collocations = collocator.collocate(
primary=('primary', primary),
secondary=('secondary', secondary),
max_distance=300, # collocation radius in km
max_interval=timedelta(hours=1), # temporal collocation interval as timedelta
)
print(
f'Found collocations are {collocations["Collocations/distance"].v... | doc/tutorials/collocations.ipynb | atmtools/typhon | mit |
As mentioned in :func:collocate, the collocations are returned in compact format, e.g. an efficient way to store the collocated data. When several data points in the secondary group collocate with a single observation of the primary group, it is not obvious how this should be handled. The compact format accounts for th... | expand(collocations) | doc/tutorials/collocations.ipynb | atmtools/typhon | mit |
Applying collapse to the collocations will calculate some generic statistics (mean, std, count) over the datapoints that match with a single data point of the other dataset. | collapse(collocations) | doc/tutorials/collocations.ipynb | atmtools/typhon | mit |
Purely temporal collocations are not implemented yet and attempts will raise a NotImplementedError.
Find collocations between two filesets
Normally, one has the data stored in a set of many files. typhon provides an object to handle those filesets (see the typhon doc). It is very simple to find collocations between the... | fh = NetCDF4()
fh.write(secondary, 'testdata/secondary/2018/01/01/000000-235959.nc')
# Create the filesets objects and point them to the input files
a_fileset = FileSet(
name="primary",
path="testdata/primary/{year}/{month}/{day}/"
"{hour}{minute}{second}-{end_hour}{end_minute}{end_second}.nc",
# ... | doc/tutorials/collocations.ipynb | atmtools/typhon | mit |
Now, we can search for collocations between a_dataset and b_dataset and store them to ab_collocations. | # Create the output dataset:
ab_collocations = Collocations(
name="ab_collocations",
path="testdata/ab_collocations/{year}/{month}/{day}/"
"{hour}{minute}{second}-{end_hour}{end_minute}{end_second}.nc",
)
ab_collocations.search(
[a_fileset, b_fileset], start="2018", end="2018-01-02",
max_inter... | doc/tutorials/collocations.ipynb | atmtools/typhon | mit |
Exercise
Try out these commands to see what they return:
data.head()
data.tail(3)
data.shape | data.shape | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
Its important to note that the Series returned when a DataFrame is indexted is merely a view on the DataFrame, and not a copy of the data itself. So you must be cautious when manipulating this data: | vals = data.value
vals
vals[5] = 0
vals
data | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
If we plan on modifying an extracted Series, its a good idea to make a copy. | vals = data.value.copy()
vals[5] = 1000
data | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
Exercise
From the data table above, create an index to return all rows for which the phylum name ends in "bacteria" and the value is greater than 1000. | # Write your answer here | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
Importing data
A key, but often under-appreciated, step in data analysis is importing the data that we wish to analyze. Though it is easy to load basic data structures into Python using built-in tools or those provided by packages like NumPy, it is non-trivial to import structured data well, and to easily convert this ... | !cat ../data/microbiome.csv | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
This table can be read into a DataFrame using read_csv: | mb = pd.read_csv("../data/microbiome.csv")
mb | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
Notice that read_csv automatically considered the first row in the file to be a header row.
We can override default behavior by customizing some the arguments, like header, names or index_col. | pd.read_csv("../data/microbiome.csv", header=None).head() | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
read_csv is just a convenience function for read_table, since csv is such a common format: | mb = pd.read_table("../data/microbiome.csv", sep=',') | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
The sep argument can be customized as needed to accomodate arbitrary separators. For example, we can use a regular expression to define a variable amount of whitespace, which is unfortunately very common in some data formats:
sep='\s+'
For a more useful index, we can specify the first two columns, which together prov... | mb = pd.read_csv("../data/microbiome.csv", index_col=['Patient','Taxon'])
mb.head() | notebooks/Introduction to Pandas.ipynb | fonnesbeck/scientific-python-workshop | cc0-1.0 |
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