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Vectorizing functions
As mentioned several times by now, to get good performance we should try to avoid looping over elements in our vectors and matrices, and instead use vectorized algorithms. The first step in converting a scalar algorithm to a vectorized algorithm is to make sure that the functions we write work wit... | def theta(x):
"""
Scalar implemenation of the Heaviside step function.
"""
if x >= 0:
return 1
else:
return 0
try:
theta(np.array([-3,-2,-1,0,1,2,3]))
except Exception as e:
print(traceback.format_exc()) | notebooks/intro-numpy.ipynb | AlJohri/DAT-DC-12 | mit |
OK, that didn't work because we didn't write the Theta function so that it can handle a vector input...
To get a vectorized version of Theta we can use the Numpy function vectorize. In many cases it can automatically vectorize a function: | theta_vec = np.vectorize(theta)
%%time
theta_vec(np.array([-3,-2,-1,0,1,2,3])) | notebooks/intro-numpy.ipynb | AlJohri/DAT-DC-12 | mit |
We can also implement the function to accept a vector input from the beginning (requires more effort but might give better performance): | def theta(x):
"""
Vector-aware implemenation of the Heaviside step function.
"""
return 1 * (x >= 0)
%%time
theta(np.array([-3,-2,-1,0,1,2,3]))
# still works for scalars as well
theta(-1.2), theta(2.6) | notebooks/intro-numpy.ipynb | AlJohri/DAT-DC-12 | mit |
Using arrays in conditions
When using arrays in conditions,for example if statements and other boolean expressions, one needs to use any or all, which requires that any or all elements in the array evalutes to True: | M
if (M > 5).any():
print("at least one element in M is larger than 5")
else:
print("no element in M is larger than 5")
if (M > 5).all():
print("all elements in M are larger than 5")
else:
print("all elements in M are not larger than 5") | notebooks/intro-numpy.ipynb | AlJohri/DAT-DC-12 | mit |
Type casting
Since Numpy arrays are statically typed, the type of an array does not change once created. But we can explicitly cast an array of some type to another using the astype functions (see also the similar asarray function). This always create a new array of new type: | M.dtype
M2 = M.astype(float)
M2
M2.dtype
M3 = M.astype(bool)
M3 | notebooks/intro-numpy.ipynb | AlJohri/DAT-DC-12 | mit |
Further reading
http://numpy.scipy.org - Official Numpy Documentation
http://scipy.org/Tentative_NumPy_Tutorial - Official Numpy Quickstart Tutorial (highly recommended)
http://www.scipy-lectures.org/intro/numpy/index.html - Scipy Lectures: Lecture 1.3
Versions | %reload_ext version_information
%version_information numpy | notebooks/intro-numpy.ipynb | AlJohri/DAT-DC-12 | mit |
Chain Rule
考慮 $F = f(\mathbf{a},\mathbf{g}(\mathbf{b},\mathbf{h}(\mathbf{c}, \mathbf{i}))$
$\mathbf{a},\mathbf{b},\mathbf{c},$ 代表著權重 , $\mathbf{i}$ 是輸入
站在 \mathbf{g} 的角度，為了要更新權重，我們想算
$\frac{\partial F}{\partial b_i}$
我們需要什麼？ 由 chain rule 得知
$\frac{\partial F}{\partial b_i} =
\sum_j \frac{\partial F}{\partial g_j}\fra... | # 參考範例， 各種函數、微分
%run -i solutions/ff_funcs.py
# 參考範例， 計算 loss
%run -i solutions/ff_compute_loss2.py | Week11/DIY_AI/FeedForward-Backpropagation.ipynb | tjwei/HackNTU_Data_2017 | mit |
$ \frac{\partial L}{\partial d} = \sigma(CU+d)^T - p^T$
$ \frac{\partial L}{\partial C } = (\sigma(Z) - p) U^T$
$ \frac{\partial L}{\partial b_i }
= ((\sigma(Z) - p)^T C)_i f'(Ax+b)_i$
$ \frac{\partial L}{\partial A_{i,j} }
= ((\sigma(Z) - p)^T C)_i f'(Ax+b)_i x_j$ | # 計算 gradient
%run -i solutions/ff_compute_gradient.py
# 更新權重，計算新的 loss
%run -i solutions/ff_update.py | Week11/DIY_AI/FeedForward-Backpropagation.ipynb | tjwei/HackNTU_Data_2017 | mit |
練習：隨機訓練 20000 次 | %matplotlib inline
import matplotlib.pyplot as plt
# 參考範例
%run -i solutions/ff_train_mod3.py
plt.plot(L_history);
# 訓練結果測試
for i in range(16):
x = Vector(i%2, (i>>1)%2, (i>>2)%2, (i>>3)%2)
y = i%3
U = relu(A@x+b)
q = softmax(C@U+d)
print(q.argmax(), y) | Week11/DIY_AI/FeedForward-Backpropagation.ipynb | tjwei/HackNTU_Data_2017 | mit |
練習：井字棋的判定 | def truth(x):
x = x.reshape(3,3)
return int(x.all(axis=0).any() or
x.all(axis=1).any() or
x.diagonal().all() or
x[::-1].diagonal().all())
%run -i solutions/ff_train_ttt.py
plt.plot(accuracy_history); | Week11/DIY_AI/FeedForward-Backpropagation.ipynb | tjwei/HackNTU_Data_2017 | mit |
Introducing ReLU
The ReLu function is defined as $f(x) = \max(0, x),$ [1]
A smooth approximation to the rectifier is the analytic function: $f(x) = \ln(1 + e^x)$
which is called the softplus function.
The derivative of softplus is $f'(x) = e^x / (e^x + 1) = 1 / (1 + e^{-x})$, i.e. the logistic function.
[1] http://www.... | from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import SGD
nb_classes = 10
# FC@512+relu -> FC@512+relu -> FC@nb_classes+softmax
# ... your Code Here
# %load ../solutions/sol_321.py
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optim... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Data preparation (keras.dataset)
We will train our model on the MNIST dataset, which consists of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.
Since this dataset is provided with Keras, we just ask the keras.dataset model for training and test data.
We will:
download the dat... | from keras.datasets import mnist
from keras.utils import np_utils
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train.shape
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
# Put everything on grayscale
X_tra... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Split Training and Validation Data | from sklearn.model_selection import train_test_split
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train)
X_train[0].shape
plt.imshow(X_train[0].reshape(28, 28))
print(np.asarray(range(10)))
print(Y_train[0].astype('int'))
plt.imshow(X_val[0].reshape(28, 28))
print(np.asarray(range(10)))
print(Y_va... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Training
Having defined and compiled the model, it can be trained using the fit function. We also specify a validation dataset to monitor validation loss and accuracy. | network_history = model.fit(X_train, Y_train, batch_size=128,
epochs=2, verbose=1, validation_data=(X_val, Y_val)) | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Plotting Network Performance Trend
The return value of the fit function is a keras.callbacks.History object which contains the entire history of training/validation loss and accuracy, for each epoch. We can therefore plot the behaviour of loss and accuracy during the training phase. | import matplotlib.pyplot as plt
%matplotlib inline
def plot_history(network_history):
plt.figure()
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.plot(network_history.history['loss'])
plt.plot(network_history.history['val_loss'])
plt.legend(['Training', 'Validation'])
plt.figure()
plt.xla... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
After 2 epochs, we get a ~88% validation accuracy.
If you increase the number of epochs, you will get definitely better results.
Quick Exercise:
Try increasing the number of epochs (if you're hardware allows to) | # Your code here
model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.001),
metrics=['accuracy'])
network_history = model.fit(X_train, Y_train, batch_size=128,
epochs=2, verbose=1, validation_data=(X_val, Y_val)) | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Introducing the Dropout Layer
The dropout layers have the very specific function to drop out a random set of activations in that layers by setting them to zero in the forward pass. Simple as that.
It allows to avoid overfitting but has to be used only at training time and not at test time.
```python
keras.layers.core... | from keras.layers.core import Dropout
## Pls note **where** the `K.in_train_phase` is actually called!!
Dropout??
from keras import backend as K
K.in_train_phase? | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Exercise:
Try modifying the previous example network adding a Dropout layer: | from keras.layers.core import Dropout
# FC@512+relu -> DropOut(0.2) -> FC@512+relu -> DropOut(0.2) -> FC@nb_classes+softmax
# ... your Code Here
# %load ../solutions/sol_312.py
network_history = model.fit(X_train, Y_train, batch_size=128,
epochs=4, verbose=1, validation_data=(X_val, Y_va... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
If you continue training, at some point the validation loss will start to increase: that is when the model starts to overfit.
It is always necessary to monitor training and validation loss during the training of any kind of Neural Network, either to detect overfitting or to evaluate the behaviour of the model (any cl... | # %load solutions/sol23.py
from keras.callbacks import EarlyStopping
early_stop = EarlyStopping(monitor='val_loss', patience=4, verbose=1)
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.ad... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Inspecting Layers | # We already used `summary`
model.summary() | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
model.layers is iterable | print('Model Input Tensors: ', model.input, end='\n\n')
print('Layers - Network Configuration:', end='\n\n')
for layer in model.layers:
print(layer.name, layer.trainable)
print('Layer Configuration:')
print(layer.get_config(), end='\n{}\n'.format('----'*10))
print('Model Output Tensors: ', model.output) | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Extract hidden layer representation of the given data
One simple way to do it is to use the weights of your model to build a new model that's truncated at the layer you want to read.
Then you can run the ._predict(X_batch) method to get the activations for a batch of inputs. | model_truncated = Sequential()
model_truncated.add(Dense(512, activation='relu', input_shape=(784,)))
model_truncated.add(Dropout(0.2))
model_truncated.add(Dense(512, activation='relu'))
for i, layer in enumerate(model_truncated.layers):
layer.set_weights(model.layers[i].get_weights())
model_truncated.compile(los... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Hint: Alternative Method to get activations
(Using keras.backend function on Tensors)
python
def get_activations(model, layer, X_batch):
activations_f = K.function([model.layers[0].input, K.learning_phase()], [layer.output,])
activations = activations_f((X_batch, False))
return activations
Generate the Emb... | from sklearn.manifold import TSNE
tsne = TSNE(n_components=2)
X_tsne = tsne.fit_transform(hidden_features[:1000]) ## Reduced for computational issues
colors_map = np.argmax(Y_train, axis=1)
X_tsne.shape
nb_classes
np.where(colors_map==6)
colors = np.array([x for x in 'b-g-r-c-m-y-k-purple-coral-lime'.split('-')])... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Using Bokeh (Interactive Chart) | from bokeh.plotting import figure, output_notebook, show
output_notebook()
p = figure(plot_width=600, plot_height=600)
colors = [x for x in 'blue-green-red-cyan-magenta-yellow-black-purple-coral-lime'.split('-')]
colors_map = colors_map[:1000]
for cl in range(nb_classes):
indices = np.where(colors_map==cl)
p... | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Note: We used default TSNE parameters. Better results can be achieved by tuning TSNE Hyper-parameters
Exercise 1:
Try with a different algorithm to create the manifold | from sklearn.manifold import MDS
## Your code here | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Exercise 2:
Try extracting the Hidden features of the First and the Last layer of the model | ## Your code here
## Try using the `get_activations` function relying on keras backend
def get_activations(model, layer, X_batch):
activations_f = K.function([model.layers[0].input, K.learning_phase()], [layer.output,])
activations = activations_f((X_batch, False))
return activations | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | infilect/ml-course1 | mit |
Example. Rolling a fair n-sided die (with n=6). | n = 6
die = list(range(1, n+1))
P = BoxModel(die)
RV(P).sim(10000).plot() | docs/common_cards_coins_dice.ipynb | dlsun/symbulate | mit |
Example. Flipping a fair coin twice and recording the results in sequence. | P = BoxModel(['H', 'T'], size=2, order_matters=True)
P.sim(10000).tabulate(normalize=True) | docs/common_cards_coins_dice.ipynb | dlsun/symbulate | mit |
Example. Unequally likely outcomes on a colored "spinner". | P = BoxModel(['orange', 'brown', 'yellow'], probs=[0.5, 0.25, 0.25])
P.sim(10000).tabulate(normalize = True) | docs/common_cards_coins_dice.ipynb | dlsun/symbulate | mit |
DeckOfCards() is a special case of BoxModel for drawing from a standard deck of 52 cards. By default replace=False.
Example. Simulated hands of 5 cards each. | DeckOfCards(size=5).sim(3) | docs/common_cards_coins_dice.ipynb | dlsun/symbulate | mit |
G-force Control
As a first test, we'll start from the launchpad, thrust at full throttle till we hit $altitude_{goal} > 100\ \text{meters}$ and then use a proportional gain of $Kp = 0.05$ to keep $gforce \sim 1.0$.
LOCK dthrott_p TO Kp * (1.0 - gforce).
LOCK dthrott TO dthrott_p. | data = loadData('collected_data\\gforce.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
Pretty cool! Once it passes 100m altitude the controller starts, the throttle controls for gforce, bringing it oscillating down around 1g. This zeros our acceleration but not our existing velocity, so the position continues to increase. We could add some derivative gain to damp down the gforce overshoot, but it won't s... | data = loadData('collected_data\\vspeed.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
Awesome! The controller drops the velocity to a stable oscillation around 0 m/s, and the position seems to flatten off, but it isn't perfect. Maybe it's because of the oscillations? In the game I can see the engine spurt on and off rythmically. It seems to try and stay at roughly 0 m/s, but the position is not 100m and... | # To run in kOS console: RUN hover3(pos0.txt,20,0.05,0).
data = loadData('collected_data\\pos0.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
Well, we crashed.
Turns out an ideal stable oscillation (which Proportional only controllers tend to do) starting from ground level (around 76m from where the accelerometer is located on the landed craft) would necessarily come back to that point...
Lets try adding some derivative gain to damp that out, the derivative ... | # To run in kOS console: RUN hover3(pos1.txt,20,0.05,0).
data = loadData('collected_data\\pos1.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
Great! The controller burned us about 100m and then tried staying there, but there is quite a lot of bounce, maybe if we tweak our gains some.
Let's try gains of $Kp = 0.08,\ \ Kd = 0.04$. | # To run in kOS console: RUN hover3(pos2.txt,20,0.08,0.04).
data = loadData('collected_data\\pos2.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
Hmm, after trying a few other combinations, it seems like there's a conceptual error here keeping us from getting to a smooth point.
We've been trying to build our controller with $thrott = thrott + \Delta thrott$$ which means it takes time to overcome our previous throttle, introducing this lag between our current po... | # To run in kOS console: RUN hover4(hover0.txt,60,0.01,0.001).
data = loadData('collected_data\\hover0.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
It's stably oscillating! This is a good sign, showing our hover setpoint is doing it's job, the proportional gain is there, and there's barely any derivative gain.
Let's bump up the derivative gain to $Kd = 0.01$. | # To run in kOS console: RUN hover4(hover1.txt,60,0.01,0.01).
data = loadData('collected_data\\hover1.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
Woohoo! It overshoots a little but stablizes smoothly at 100m! Great to see this going in the game, looks a bit like the SpaceX grasshopper.
The kOS script used is hover4.ks and these tests are run by calling RUN hover4(hoverN.txt,20,Kp,Kd).
Some datasets:
hover0.txt
hover1.txt
hover2.txt
hover3.txt
Tweaking gains
N... | # To run in kOS console: RUN hover4(hover2.txt,10,0.1,0.1).
data = loadData('collected_data\\hover2.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
Much faster! What happens if we change the altitude to say 300m? | # To run in kOS console: RUN hover5(hover3.txt,10,0.1,0.1,300).
data = loadData('collected_data\\hover3.txt')
plotData(data) | KSP_pid_tuning.ipynb | Elucidation/KSP-rocket-hover-controller | mit |
Backtest SquareMathLevels
Cíl
Ověření hypotézy, že SquareMath Levels fungují jako S/R úrovně, tzn. trh má tendenci se od nich odrážet.
Ověření na statistice ZN 1min, SML - 30min SQUARE 16
Příprava dat
Nastavení pro kalkulaci SquareMath | SQUARE = 128
SQUARE_MULTIPLIER = 1.5
# how many
BARS_BACK_TO_REFERENCE = np.int(np.ceil(SQUARE * SQUARE_MULTIPLIER))
# set higher timeframe for getting SquareMathLevels
MINUTES = 30 # range 0-59
PD_RESAMPLE_RULE = f'{MINUTES}Min'
# set the period of PD_RESAMPLE_RULE will be started. E.g. PD_RESAMPLE_RULE == '30min'... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Data, která se budou analyzovat | TICK_SIZE_STR = f'{1/32*0.5}'
TICK_SIZE = float(TICK_SIZE_STR)
#SYMBOL = 'ZN'
TICK_SIZE_STR
DATA_FILE = '../../Data/ZN-1s.csv'
read_cols = ['Date', 'Time', 'Open', 'High', 'Low', 'Last']
data = pd.read_csv(DATA_FILE, index_col=0, skipinitialspace=True, usecols=read_cols, parse_dates={'Datetime': [0, 1]})
data.rename(... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Maximální high a low za posledních BARS_BACK_TO_REFERENCE svíček z vyššího timeframu.
High | # calculate max high for actual record from higher tiframe his period
df_helper_gr = df[['High']].groupby(pd.Grouper(freq=PD_RESAMPLE_RULE, base=PD_GROUPER_BASE))
df_helper = df_helper_gr.rolling(PD_RESAMPLE_RULE, min_periods=1).max().dropna() # cummax() with new index
df_helper['bigCumMaxHigh'] = df_helper.assign(l=df... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Low | # calculate min low for actual record from higher tiframe his period
df_helper_gr = df[['Low']].groupby(pd.Grouper(freq=PD_RESAMPLE_RULE, base=PD_GROUPER_BASE))
df_helper = df_helper_gr.rolling(PD_RESAMPLE_RULE, min_periods=1).min().dropna() # cummin() with new index
df_helper['bigCumMinLow'] = df_helper.assign(l=df_he... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Zahození nepotřebných prostředků a záznamů NaN, které nemůžu analyzovat | del df_helper
del df_helper_gr
df.dropna(inplace=True)
df | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Výpočet SMLevels pro každý záznam | from vhat.squaremath.funcs import calculate_octave
SML_INDEXES = np.arange(-2, 10+1, dtype=np.int) # from -2/8 to +2/8
def round_to_tick_size(values, tick_size):
return np.round(values / tick_size) * tick_size
def get_smlines(r):
tick_size = TICK_SIZE
lowLimit = r.SMLLowLimit
highLimit = r.SMLHighLim... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Výpočet dotyku SML
Musím vypočítat dotyk předchozího průrazu kvůli frame-shift. | df['prevSML'] = df.SML.shift()
df.dropna(inplace=True)
df
df['SMLTouch'] = df.apply(lambda r: np.bitwise_and(r.Low<=r.prevSML, r.prevSML<=r.High), axis=1)
df['SMLTouchCount'] = df.SMLTouch.apply(lambda v: sum(v))
df
from dataclasses import dataclass
from typing import List
@dataclass
class Trade:
tId: int
# ... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Statistika výsledků
Backtest základní info | print('Od:', df.iloc[0].name)
print('Do', df.iloc[-1].name)
print('Časové období:', df.iloc[-1].name - df.iloc[0].name)
print('Počet obchodních dnů:', df.Close.resample('1D').ohlc().shape[0])
print('Počet záznamů jemného tf:', df.shape[0]) | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Validita nízkého timeframe pro backtest - možná zasenesená chyba
Zjištění, zda je zvolený SQUARE na vyšším timeframu dostatečný pro backtest na tomto nízkém timeframu. Tzn. pokud mám Square=32 z vyššího timeframe='30min', mohu zjistit jestli jsou záznamy timeframe='1min' vhodné pro backtest.
Pokud by byla vysoká chyba ... | touchCounts = df.SMLTouchCount.value_counts().to_frame(name='Occurences')
touchCounts['Occ%'] = touchCounts / df.shape[0]*100
print(f'Počet protnutích více něž jedné SML v jednom záznamu: v {(df.SMLTouchCount>1).sum()} případech ({(df.SMLTouchCount>1).sum()/df.shape[0]*100:.3f}%) z {df.shape[0]} celkem\n')
touchCounts | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Velmi nízký SML spread | spread_stats = df.spread.value_counts().to_frame(name='Occurences')
spread_stats['Occ%'] = spread_stats / df.shape[0]*100
spread_stats['Ticks'] = spread_stats.index / TICK_SIZE # index musím
print(f'Počet spredu SML menších než 2 ticky v jednom záznamu: v {(df.spread/TICK_SIZE<2).sum()} případech ({(df.spread/TICK_SIZE... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Výsledná možná chybovost na nízkém TF pro backtest | chybovost = df.spread[(df.spread/TICK_SIZE<2) | (df.SMLTouchCount>1)].shape[0]
print(f'Celková chybovost v nízkém timeframe může být v {chybovost} případech ({chybovost/df.shape[0]*100:.3f}%) z {df.shape[0]} celkem') | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Validita výsledků obchodů | finishedCount = stats.shape[0]
print('Total finished trades:', finishedCount)
# pokud je opravdu hodně "unrecognizableTrade", mám moc nízké rozlišení SquareMath levels (malý square)
unrec_trades = stats.unrecognizableTrade.sum()
print('Unrecognizable trades:', unrec_trades, f'({unrec_trades/finishedCount *100:.3f}%)')
... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Dál nebudu potřebovat unrecognized trades | stats.drop(stats[stats.unrecognizableTrade].index, inplace=True)
shorts_mask = stats.lots<0
longs_mask = stats.lots>0
stats.loc[shorts_mask, 'PnL'] = ((stats[shorts_mask].entryPrice - stats[shorts_mask].exitPrice) / TICK_SIZE).round()
stats.loc[longs_mask, 'PnL'] = ((stats[longs_mask].exitPrice - stats[longs_mask].en... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Celkové výsledky | # masks
shorts_mask = stats.lots<0
longs_mask = stats.lots>0
profit_mask = stats.PnL>0
loss_mask = stats.PnL<0
breakeven_mask = stats.PnL==0
total_trades = stats.shape[0]
profit_trades_count = stats.PnL[profit_mask].shape[0]
loss_trades_count = stats.PnL[loss_mask].shape[0]
breakeven_trades_count = stats.PnL[breakeven... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Ztrátové obchody | selected_stats = stats[loss_mask]
selected_pnl_stats = selected_stats.PnL.value_counts().to_frame(name='PnLOccurences')
selected_pnl_stats['Occ%'] = selected_pnl_stats / selected_stats.shape[0]*100
selected_pnl_stats['Ticks'] = selected_pnl_stats.index / TICK_SIZE
selected_pnl_stats | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Max pohyb v zisku ve ztrátových obchodech | sns.distplot(selected_stats.runPTicks, color="g"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Poměrově pohyb v zisku k nastavenému PT u ztrátových obchodů. | sns.distplot(selected_stats.runPTicks/selected_stats.ptTicks, color="g"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Max pohyb ve ztrátě ve ztrátových obchodech | sns.distplot(selected_stats.runLTicks, color="r");
sns.distplot(selected_stats.runLTicks/selected_stats.slTicks, color="r"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Ziskové obchody | selected_stats = stats[profit_mask]
selected_pnl_stats = selected_stats.PnL.value_counts().to_frame(name='PnLOccurences')
selected_pnl_stats['Occ%'] = selected_pnl_stats / selected_stats.shape[0]*100
selected_pnl_stats['Ticks'] = selected_pnl_stats.index / TICK_SIZE
selected_pnl_stats | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
PT adjustment ve ziskových obchodech - Max pohyb v zisku | sns.distplot(selected_stats.runPTicks, color="g"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Poměrově pohyb v zisku k PT u ziskových obchodů. | sns.distplot(selected_stats.runPTicks/selected_stats.ptTicks, color="g"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Max pohyb ve ztrátě ve ziskových obchodech | sns.distplot(selected_stats.runLTicks, color="r"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
poměr vývoje ztráty k zadanému SL v ziskových obchodech | sns.distplot(selected_stats.runLTicks/selected_stats.slTicks, color="r"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Long obchody | selected_stats = stats[longs_mask]
print('Počet obchodů:', selected_stats.shape[0], f'({selected_stats.shape[0]/stats.shape[0]*100:.2f}%) z {stats.shape[0]}')
print('Počet win:', selected_stats[selected_stats.PnL>0].shape[0], f'({selected_stats[selected_stats.PnL>0].shape[0]/selected_stats.shape[0]*100:.2f}%) z {select... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
PT adjustment ve ztrátových long obchodech - Max pohyb v zisku | sns.distplot(selected_stats[selected_stats.PnL<0].runPTicks, color="g"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Poměrově pohyb v zisku k nastavenému PT u ztrátových obchodů. | sns.distplot(selected_stats[selected_stats.PnL<0].runPTicks/selected_stats[selected_stats.PnL<0].ptTicks, color="g"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
SL djustment ve ztrátových obchodech - max pohyb v zisku | sns.distplot(selected_stats[selected_stats.PnL<0].runLTicks, color="r");
sns.distplot(selected_stats[selected_stats.PnL<0].runLTicks/selected_stats[selected_stats.PnL<0].slTicks, color="r"); # kontrola | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
PT adjustment v ziskových long obchodech - Max pohyb v zisku | sns.distplot(selected_stats[selected_stats.PnL>0].runPTicks, color="g"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Poměrově pohyb v zisku k nastavenému PT u ziskových obchodů. | sns.distplot(selected_stats[selected_stats.PnL>0].runPTicks/selected_stats[selected_stats.PnL>0].ptTicks, color="g"); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
SL djustment v ziskových obchodech - max pohyb ve ztrátě | sns.distplot(selected_stats[selected_stats.PnL>0].runLTicks, color="r");
sns.distplot(selected_stats[selected_stats.PnL>0].runLTicks/selected_stats[selected_stats.PnL>0].slTicks, color="r"); # kontrola | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
SML analýza
Celkový počet vstupů na jednotlivých SML | #smlvl_stats = stats.entrySmLvl.value_counts().to_frame(name='entrySmLvlOcc')
smlvl_stats = stats[['entrySmLvl', 'lots']].groupby(['entrySmLvl']).count()
smlvl_stats.sort_values(by='lots', ascending=False, inplace=True)
smlvl_stats.rename(columns={'lots':'entrySmLvlOcc'}, inplace=True)
smlvl_stats['Occ%'] = smlvl_stats... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Vstupy na jednotlivých levelech | sns.barplot(x=smlvl_stats.entrySmLvlOcc.sort_index().index, y=smlvl_stats.entrySmLvlOcc.sort_index()); | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Počet vstupů Buy nebo Sell na SML | stats.lots.replace({1: 'Long', -1: 'Short'}, inplace=True)
smlvl_stats_buy_sell = stats[['entrySmLvl', 'PnL', 'lots']].groupby(['entrySmLvl', 'lots']).count()
smlvl_stats_buy_sell.sort_index(ascending=False, inplace=True)
smlvl_stats_buy_sell.rename(columns={'PnL':'LongShortCount'}, inplace=True)
smlvl_stats_buy_sell
... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Úspěšnost Long obchodů na SML | stats['Win']=profit_mask
stats['Win'] = stats['Win'].mask(~profit_mask) # groupby bude počítat jen výhry
smlvl_stats_buy_sell['WinCount'] = stats[['entrySmLvl', 'PnL', 'lots', 'Win']].groupby(['entrySmLvl', 'lots', 'Win']).count().droplevel(2)
smlvl_stats_buy_sell['Win%'] = smlvl_stats_buy_sell.WinCount / smlvl_stats_... | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Jen pro kontrolu. Win == True, Loss == False | # stats['Win'] = profit_mask
# smlvl_stats_buy_sell2 = stats[['entrySmLvl', 'PnL', 'lots', 'Win']].groupby(['entrySmLvl', 'lots', 'Win']).sum()
# smlvl_stats_buy_sell2.sort_index(ascending=False, inplace=True)
# smlvl_stats_buy_sell2.rename(columns={'PnL':'WinLossCount'}, inplace=True)
# smlvl_stats_buy_sell2 | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
Seřazeny výsledky dle úspěsnosti: | smlvl_stats_buy_sell.sort_values('Win%', ascending=False) | SquareMath/2020-04-10-SquareMathLevels-Backtest-example-ZN-1min-30M-128.ipynb | vanheck/blog-notes | mit |
1.Searching and Printing a List of 50 'Lil' Musicians
With "Lil Wayne" and "Lil Kim" there are a lot of "Lil" musicians. Do a search and print a list of 50 that are playable in the USA (or the country of your choice), along with their popularity score. | #With "Lil Wayne" and "Lil Kim" there are a lot of "Lil" musicians. Do a search and print a list of 50
#that are playable in the USA (or the country of your choice), along with their popularity score.
count =0
for artist in Lil_artists:
count += 1
print(count,".", artist['name'],"has the popularity of", artis... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
2 Genres Most Represented in the Search Results
What genres are most represented in the search results? Edit your previous printout to also display a list of their genres in the format "GENRE_1, GENRE_2, GENRE_3". If there are no genres, print "No genres listed". | # What genres are most represented in the search results? Edit your previous printout to also display a list of their genres
#in the format "GENRE_1, GENRE_2, GENRE_3". If there are no genres, print "No genres listed".
#Tip: "how to join a list Python" might be a helpful search
# if len(artist['genres']) == 0 )
# prin... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
More Spotify - LIL' GRAPHICS
Use Excel, Illustrator or something like https://infogr.am/ to make a graphic about the Lil's, or the Lil's vs. the Biggies.
Just a simple bar graph of their various popularities sounds good to me.
Link to the Line Graph of Lil's Popularity chart
Lil Popularity Graph | Lil_response = requests.get('https://api.spotify.com/v1/search?query=Lil&type=artist&limit=50&country=US')
Lil_data = Lil_response.json()
#Lil_data | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
The Second Highest Popular Artist
Use a for loop to determine who BESIDES Lil Wayne has the highest popularity rating. Is it the same artist who has the largest number of followers? | #Use a for loop to determine who BESIDES Lil Wayne has the highest popularity rating.
#Is it the same artist who has the largest number of followers?
name_highest = ""
name_follow =""
second_high_pop = 0
highest_pop = 0
high_follow = 0
for artist in Lil_artists:
if (highest_pop < artist['popularity']) & (artist['n... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
4. List of Lil's Popular Than Lil' Kim |
Lil_artists = Lil_data['artists']['items']
#Print a list of Lil's that are more popular than Lil' Kim.
count = 0
for artist in Lil_artists:
if artist['popularity'] > 62:
count+=1
print(count, artist['name'],"has the popularity of", artist['popularity'])
#else:
#print(artist['na... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
5.Two Favorite Lils and Their Top Tracks | response = requests.get("https://api.spotify.com/v1/search?query=Lil&type=artist&limit=2&country=US")
data = response.json()
for artist in Lil_artists:
#print(artist['name'],artist['id'])
if artist['name'] == "Lil Wayne":
wayne = artist['id']
print(artist['name'], "id is",wayne)
if... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
6. Average Popularity of My Fav Musicians (Above) for Their explicit songs vs. their non-explicit songs
Will the world explode if a musicians swears? Get an average popularity for their explicit songs vs. their non-explicit songs. How many minutes of explicit songs do they have? Non-explicit? | response = requests.get("https://api.spotify.com/v1/artists/" +yachty+ "/top-tracks?country=US")
data = response.json()
tracks = data['tracks']
#print(tracks)
#for track in tracks:
#print(track.keys())
#Get an average popularity for their explicit songs vs. their non-explicit songs.
#How many minutes of explicit... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
7a. Number of Biggies and Lils
Since we're talking about Lils, what about Biggies? How many total "Biggie" artists are there? How many total "Lil"s? If you made 1 request every 5 seconds, how long would it take to download information on all the Lils vs the Biggies? | #How many total "Biggie" artists are there? How many total "Lil"s?
#If you made 1 request every 5 seconds, how long would it take to download information on all the Lils vs the Biggies?
biggie_response = requests.get('https://api.spotify.com/v1/search?query=biggie&type=artist&country=US')
biggie_data = biggie_respons... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
7b. Time to Download All Information on Lil and Biggies | #If you made 1 request every 5 seconds, how long would it take to download information on all the Lils vs the Biggies?
limit_download = 50
biggie_artists = biggie_data['artists']['total']
Lil_artist = Lil_data['artists']['total']
#1n 5 sec = 50
#in 1 sec = 50 / 5 req = 10 no, for 1 no, 1/10 sec
# for 4501 = 4501/10 s... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
8. Highest Average Popular Lils and Biggies Out of The Top 50 | #Out of the top 50 "Lil"s and the top 50 "Biggie"s, who is more popular on average?
biggie_response = requests.get('https://api.spotify.com/v1/search?query=biggie&type=artist&limit=50&country=US')
biggie_data = biggie_response.json()
biggie_artists = biggie_data['artists']['items']
big_count_pop = 0
for artist in big... | homework05/Homework05_Spotify_radhika.ipynb | radhikapc/foundation-homework | mit |
Getting attendance records from datatracker
When attendees register for a meeting, the report their name, email address, and affiliation.
While this is noisy data (any human-entered data is!), we will use this information to associate domains with affilations. E.g. the email domain apple.com is associated with the comp... | datatracker = DataTracker()
meetings = datatracker.meetings(meeting_type = datatracker.meeting_type(MeetingTypeURI('/api/v1/name/meetingtypename/ietf/')))
full_ietf_meetings = list(meetings)
ietf_meetings = []
for meeting in full_ietf_meetings:
meetingd = dataclasses.asdict(meeting)
meetingd['meeting_obj'] = ... | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Individual Affiliations | dt = DataTrackerExt() # initialize, for all meeting registration downloads | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
This will construct a dataframe of every attendee's registration at every specified meeting. (Downloading this data takes a while!) | ietf_meetings[110]['date']
meeting_attendees_df = pd.DataFrame()
for meeting in ietf_meetings:
if meeting['num'] in [104,105,106,107,108,109]: # can filter here by the meetings to analyze
registrations = dt.meeting_registrations(meeting=meeting['meeting_obj'])
df = pd.DataFrame.from_records([datacl... | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Filter by those who actually attended the meeting (checked in, didn't just register). | ind_affiliation = meeting_attendees_df[['full_name', 'affiliation', 'email', 'domain','date']] | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
This format of data -- with name, email, affiliation, and a timestamp -- can also be extracted from other IETF data, such as the RFC submission metadata. Later, we will use data of this form to infer duration of affilation for IETF attendees. | ind_affiliation[:10]
ind_affiliation['affiliation'].dropna().value_counts() | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Matching affiliations with domains | affil_domain = ind_affiliation[['affiliation', 'domain', 'email']].pivot_table(
index='affiliation',columns='domain', values='email', aggfunc = 'count') | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Drop both known generic and known personal email domains. | ddf = domains.load_data()
generics = ddf[ddf['category'] == 'generic'].index
personals = ddf[ddf['category'] == 'personal'].index
generic_email_domains = set(affil_domain.columns).intersection(generics)
affil_domain.drop(generic_email_domains, axis = 1, inplace = True)
personal_email_domains = set(affil_domain.colum... | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Duration of affiliation
The current data we have for individual affiliations is "point" data, reflecting the affiliation of an individual on a particular date.
For many kinds of analysis, we may want to understand the full duration for which an individual has been associated with an organization. This requires an infer... | affil_dates = ind_affiliation.pivot_table(
index="date",
columns="full_name",
values="affiliation",
aggfunc="first"
).fillna(method='ffill').fillna(method='bfill')
top_attendees = ind_affiliation.groupby('full_name')['date'].count().sort_values(ascending=False)[:40].index
top_attendees
affil_dates[to... | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Linking to Organization lists | import bigbang.analysis.process as process
# drop subsidiary organizations
org_cats = org_cats[org_cats['subsidiary of / alias of'].isna()]
org_cats | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Normalize/resolve the names from the IETF attedence records. | org_names = ad_stats['sum']
org_names = org_names.append(
pd.Series(index = org_cats['name'], data = 1)
)
org_names = org_names.sort_values(ascending = False)
org_names = org_names[~org_names.index.duplicated(keep="first")]
ents = process.resolve_entities(
org_names,
process.containment_distance,
thres... | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Export the graph of relations
Getting the affiliation data relations extracted from the attendance tables.
Final form: Three tables:
- Name - Email, earliest and latest date
- Name - Affiliation, earliest and latest date
- Email - Affiliation, earliest and latest date
These can be combined into a tripartite graph, w... | meeting_range = [106,107,108]
a, b, c = attendance.name_email_affil_relations_from_IETF_attendance(meeting_range, threshold = 0.17)
a
b
b['affiliation'].value_counts()['cisco']
c | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Match to a mailing list | from bigbang.archive import Archive
arx = Archive("httpbisa") | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
From the archive data: From -> email address, Date
Match with table B: email,. min_date, max_date, to get Affiliation
Add Affiliation to the archive data. | arx.add_affiliation(b)
arx.data[['From','Date','affiliation']].dropna() | examples/attendance/Extracting Org-Domain and Person-Org-Duration Information From Attendance Data.ipynb | datactive/bigbang | mit |
Ejemplo 2. Determine los desplazamientos nodales y rotaciones, fuerzas nodales globales, y fuerzas en elementos para la viga mostrada en la figura. Se ha discretizado la viga como se indica en la numeración nodal. La viga está fija en los nodos 1 y 5, y tiene un soporte de rodillo en el nodo 3. Las cargas verticales de... | """
Logan, D. (2007). A first course in the finite element analysis.
Example 4.2 , pp. 166.
"""
from nusa.core import *
from nusa.model import *
from nusa.element import *
# Input data
E = 30e6
I = 500.0
P = 10e3
L = 10*(12.0) # ft -> in
# Model
m1 = BeamModel("Beam Model")
# Nodes
n1 = Node((0,0))
n2 = Node((10*12,... | docs/nusa-info/es/beam-element.ipynb | JorgeDeLosSantos/nusa | mit |
Ejemplo 3. | """
Beer & Johnston. (2012) Mechanics of materials.
Problem 9.13 , pp. 568.
"""
# Input data
E = 29e6
I = 291 # W14x30
P = 35e3
L1 = 5*12 # in
L2 = 10*12 #in
# Model
m1 = BeamModel("Beam Model")
# Nodes
n1 = Node((0,0))
n2 = Node((L1,0))
n3 = Node((L1+L2,0))
# Elements
e1 = Beam((n1,n2),E,I)
e2 = Beam((n2,n3),E,I)
... | docs/nusa-info/es/beam-element.ipynb | JorgeDeLosSantos/nusa | mit |
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