#!/usr/bin/env python
# coding: utf-8

# ![taller_python](https://github.com/fifabsas/talleresfifabsas/blob/master/python/2_Numerico/fig/logo_fifa.png?raw=true)

# # Taller de Python Capítulo 3:  Práctica
# [Link al notebook en Google Colaboratory](https://drive.google.com/file/d/12z9ZBxDxuaaYzFJ9UYS7Hwz0W5zixCg_/view?usp=sharing)

# ## Nuestra motivación para hoy
# 

# La idea de hoy es que aprendan un poquito sobre otro tipo de objeto que existe en Python: los `diccionarios` y sobre una función que puede serles útil en el futuro: `find_peaks`.
# 
# Una vez aprendido esto, ¡Ponemos las manos en la masa y nos peleamos mucho rato con ejercicios re divertidos!.
# 

# ## Diccionarios
# 
# Los diccionarios son otro tipo de objetos útiles, en cierta forma similares a las listas, que nos permiten vincular un valor con otro.
# 
# Estos se definen abriendo y cerrando **llaves**.
# 
# Supongamos que queremos guardar en una variable los nombres de personajes importantes en la ciencia y relacionarlos a los campos en los que trabajaron.

# In[ ]:


diccionario = {'Rosalind Franklin':'Química', 'Luis Caffarelli':'Matemática', 'Juan G. Roederer':'Física', 'Emmy Noether':'Matemática'}


# La parte que va a la _izquierda_ de los `:` se llama **llave**, mientras que lo que escribimos a la _derecha_ se llama **valor**.
# 
# Es decir, la llave `Rosalind Franklin` tiene el valor `Química`.
# 
# ¿Cómo accedo a esto?

# In[ ]:


print(diccionario["Rosalind Franklin"])


# Podemos acceder a todas las llaves y valores del diccionario.

# In[ ]:


llaves = diccionario.keys()
print(llaves)

print("\n")

valores = diccionario.values()
print(valores)


# Hay varias formas de recorrer estos objetos con un `for`, pero la más importante es la siguiente, utilizando el método `.items()`:

# In[ ]:


for llave, valor in diccionario.items():
    print(f"La persona {llave} se dedica a la {valor}")
    print()


# Para agregar un elemento al diccionario se hace de la siguiente manera:

# In[ ]:


diccionario['Ada Lovelace'] = 'Computación'

print(diccionario)


# Cada llave tiene un valor **único** y pueden tener adentro números, strings, listas o inclusive otro diccionario.

# In[ ]:


diccionario_raro = {"Milanesa": 42, "Almohada": "No tengo", "Precios boleto": [270,300,330,404, "Carísimo"]}


# In[ ]:


diccionario_raro[[12,13,14]] = "¿Podré hacer esto?"


# ## `find_peaks`: *como encontrar máximos y mínimos*

# In[ ]:


# Importamos las librerías que vamos a usar
import numpy as np
import matplotlib.pyplot as plt


# Supongamos que tenemos los datos de la siguiente figura y queremos saber cuales son los máximos de la señal.
# 
# ¿Cómo hacemos?

# In[ ]:


# Definimos el dominio y la imagen
x = np.linspace(0, 2*np.pi, 500)
y = np.abs(np.sin(x)*np.sin(2.5*x))

# Graficamos la función
plt.plot(x, y)
plt.xlabel('Eje de $\hat x$', fontsize = 14)
plt.ylabel('Eje de $\hat y$', fontsize = 14)

plt.grid()
plt.show()


# Para eso existe ```find_peaks```, una función de la librería llamada ```scipy``` que tiene un montón de cosas útiles para hacer ciencia.

# In[ ]:


# De esta manera importamos solo la función y no la librería entera, que puede ser pesada
from scipy.signal import find_peaks


# ```find_peaks``` recibe una lista/array de una dimensión y calcula los máximos locales.
# 
# Apliquemos la función a nuestra tira de datos ```y``` para ver que nos devuelve.

# In[ ]:


picos, diccionario = find_peaks(y) # Recordemos esta forma de nombrar varias variables

print(picos)

print("\n")

print(diccionario)


# ```find_peaks``` devuelve primero un array con los índices donde encontró máximos y luego un diccionario, que está vacío, pero más adelante vamos a ver que cosas puede contener.
# 
# De momento, graficamos los picos para ver que encontró ```find_peaks``` de base.

# In[ ]:


# Graficamos la función
plt.plot(x, y)

plt.plot(x[picos], y[picos],"o", color = "k")


plt.xlabel('Eje de $\hat x$', fontsize = 14)
plt.ylabel('Eje de $\hat y$', fontsize = 14)

plt.grid()
plt.show()


# ¡```find_peaks``` logró hallar todos los picos! ¡Es increíble!
# 
# Veamos ahora otro caso.

# In[ ]:


# Definimos el dominio y la imagen
x = np.linspace(0, 2*np.pi, 500)
y = np.abs(np.sin(x))+np.cos(5*x)/1.2

# Graficamos la función
plt.plot(x, y)
plt.xlabel('Eje de $\hat x$', fontsize = 14)
plt.ylabel('Eje de $\hat y$', fontsize = 14)

plt.grid()
plt.show()


# Ahora quiero los picos más grandes nada más.
# 
# ¿Que parámetro los caracteriza como para que  ```find_peaks``` encuentre solo esos?
# 

#  ### Respuesta ***(¡Animense a contestar dale!)***

# **Hay varias cosas que podríamos usar:**
# 
# 
# *   La altura de los picos.
# *   La distancia entre los picos (en índices)
# 
# **Si utilizamos la altura de los picos obtenemos:**
# 

# In[ ]:


# Calculamos los picos
picos, diccionario = find_peaks(y, height = 1.5) # Acá agrego el parámetro

# Graficamos la función
plt.plot(x, y)
plt.plot(x[picos], y[picos],".", c = "k", ms = 10)
plt.xlabel('Eje de $\hat x$', fontsize = 14)
plt.ylabel('Eje de $\hat y$', fontsize = 14)

plt.grid()
plt.show()


# ¡Conseguimos lo que queremos! También podriamos haberle pasado la distancia u otro parámetro para que la función cuente con la mayor información posible.
# 
# Podemos ver ahora que cambio el diccionario, este contiene ahora la altura de los picos.

# In[ ]:


diccionario


# Veamos otro parámetro más: el ```distance```.
# 
# El ```distance``` define la cantidad de datos intermedios que debe haber entre picos. Hay que tener en cuenta que ```find_peaks``` recibe un array 1D, por lo que esta distancia es en índices y no en nuestra variable ```x```.

# In[ ]:


# Calculamos los picos
picos, diccionario = find_peaks(y, distance = 100) # Acá agrego el parámetro

# Graficamos la función
plt.plot(x, y)
plt.plot(x[picos], y[picos],".", c = "k", ms = 10)
plt.xlabel('Eje de $\hat x$', fontsize = 14)
plt.ylabel('Eje de $\hat y$', fontsize = 14)

plt.grid()
plt.show()


# Existen muchos otros parámetros opcionales más tales como:
# 
# 
# 
# *   **width**: Ancho de los picos (en índices)
# *   **threshold**: Delimita de cuanto puede ser el salto del máximo respecto a los otros puntos, verticalmente.
# *   **prominence**: Prominencia de los picos (que tan picudos son los picos)
# 
# 
# Si no, siempre conviene leer la [documentación](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html).
# 
# 

# ### Muy lindo todo esto... ¿Pero cómo puedo calcular los mínimos con ```find_peaks```?

# Muy fácil: ¡Invertimos los datos!
# 
# Así los mínimos pasan a ser los máximos y viceversa.

# In[ ]:


# Definimos el dominio y la imagen
x = np.linspace(0, 2*np.pi, 500)
y = np.abs(np.sin(x))+np.cos(5*x)/1.2

# Calculamos los picos
picos, diccionario = find_peaks(-y) # Acá agrego el parámetro

# Graficamos la función
plt.plot(x, y, label = "Datos originales")
plt.plot(x, -y, label = "Datos invertidos")

plt.xlabel('Eje de $\hat x$', fontsize = 14)
plt.ylabel('Eje de $\hat y$', fontsize = 14)
plt.legend(loc = "upper left", fontsize = 12)

plt.grid()
plt.show()


# In[ ]:


# Calculamos los picos
picos, diccionario = find_peaks(-y) # Le paso los datos invertidos

# Graficamos la función
plt.plot(x, y)
plt.plot(x[picos], y[picos],".", c = "k", ms = 10)
plt.xlabel('Eje de $\hat x$', fontsize = 14)
plt.ylabel('Eje de $\hat y$', fontsize = 14)

plt.grid()
plt.show()


# Con esto termina todo lo que teníamos para enseñarles hoy, ahora les toca aprender por su cuenta, llegó la hora de elegir un problema y pensar un rato largo.
# 
# ## ¡Manos a la obra!
# 
# ## Les pedimos por favor que **lean y respeten las consignas**.

# # _Problema integrador 1 (Física)_
# 
# En un laboratorio se armó el montaje que se muestra a continuación: un carrito sujeto a un resorte fijo en uno de sus extremos.
# 
# ![surface32779.png](data:image/png;base64,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)

# Se propuso estudiar como la masa del carrito (que se podía variar agregando pesas sobre el) afectaba su frecuencia de oscilación. Con este objetivo, se realizó el siguiente procedimiento 9 veces, comenzando con el carrito vacío y agregando una pesa en cada iteración:
# 
# 1. Se comenzaba a grabar el carrito con una cámara lateral al montaje.
# 2. El carrito se alejaba $5~\mathrm{cm}$ de su posición de equilibrio y se soltaba.
# 3. $10~\mathrm{s}$ después de soltar el carrito, se cortaba la grabación. Esta se guardaba como `[n].mp4`, con `[n]` el número de pesas sobre el carrito.
# 
# Finalmente se subieron todas las grabaciones a un software de trackeo, calibrado para poder medir la distancia del carrito a su posición de equilibrio cada $0.2~\mathrm{s}$ desde que se soltó. Los datos de cada grabación se guardaron en archivos nombrados `[n].csv`, con `[n]` el número de pesas correspondiente.
# 
# La idea es ahora hacer el análisis de las mediciones, que se pueden encontrar en este [link](https://github.com/fifabsas/talleresfifabsas/blob/master/python/3_Ejercicios/Datos_f%C3%ADsica/Datos%20Ejercicio%201%20-%20F%C3%ADsica.zip), en Python.

# ---
# 
# ### Ejercicio 1 (para ir entrando en calor)
# 
# Escribir una función llamada `masa_carrito(n)` que tome:
# - `n`: el número de pesas colocadas en el carro.
# 
# y que devuelva:
# - `m`: la masa total del carrito.
# 
# Considerar que la masa del carrito sin pesas era de $0.15~\mathrm{kg}$ y cada una de las pesas agregadas tenía una masa de $0.10~\mathrm{kg}$.
# 
# ---

# Cada `.csv` tiene 3 columnas: "T [s]" para los tiempos, "X [cm]" para la posición, y "X_err [cm]" para la incerteza en la posicion. Las mediciones de tiempo también tienen incerteza, pero mucho más chica y la vamos a despreciar en el análisis. También despreciariemos las incertezas en las mediciones de masa. (Si llegan a encontrarse con un experimento similar en un labo, registren todas las incertezas y hablen con sus profes antes de no tener en cuenta incertezas en algún cálculo o ajuste).
# 
# De cada archivo queremos obtener una frecuencia de oscilación. Para enfrentar esta tarea, vamos a empezar analizando solo un archivo, `0.csv`, y luego veremos como adaptar el código para poder analizar los otros .csv's sin hacer mucho más trabajo.

# ---
# ### Ejercicio 2: Abrir y graficar `0.csv`
# 
# Queremos empezar graficando la posición en función del tiempo del carrito sin pesitas, para observar las mediciones registradas que pueden encontrar [acá](https://github.com/fifabsas/talleresfifabsas/blob/4e61cb2c20365fd6c299ce317c963ff2b09d6201/python/3_Ejercicios/Datos%20Ejercicio%201%20-%20F%C3%ADsica/Datos%20Ejercicio%201%20-%20F%C3%ADsica.zip).
# 
# 1. Importar `numpy` y `matplotlib.pyplot` para poder acceder a las funciones de Numpy y del módulo `pyplot` de Matplotlib. Usar las abreviaciones estándar `np` y `plt` (ver clase pasada).
# 2. Cargar los datos del archivo `"0.csv"` subiendolo a Colab y usando la función `np.loadtxt`. Guardar:
#     - los _tiempos_ de las mediciones en la variable `ts`,
#     - las _posiciones_ medidas en `xs`,
#     - y las _incertezas_ de las posiciones en `xs_err`.
#     
#     _Para usar la función `np.loadtxt` tienen de referencia el material de la clase pasada o la documentación de Numpy_.
# 3. Usar la función `plt.errorbar` para gráficar `xs` en función de `ts` (`ts` corresponde al eje $\hat x$ del gráfico, mientras que `xs` corresponde al eje $\hat y$). Incluir las incertezas en la posición.
# 4. Etiquetar los ejes del gráfico "Tiempo [s]" (eje $\hat x$) y "Posición [cm]" (eje $\hat y$). De título poner "0 pesas sobre el carrito".
# 5. _(opcional)_ Embellecer.
# 
# ---

# ### UN PROBLEMA COMÚN
# 
# 

# Ahora que vimos que los datos tienen un comportamiento oscilatorio, como es de esperar por la naturaleza del experimento, nuestro objetivo es medir su frecuencia de oscilación. Nos gustaría hacer un ajuste sinusoidal, pero como vimos la vez pasada, `curve_fit` no siempre devuelve los parámetros más óptimos. A veces devuelve los parámetros correspondientes a algún otro mínimo local de la función RMSE. Veamoslo con los datos de `0.csv`, ajustandolos con un coseno:

# In[ ]:


#@title código secreto (solo abrir con doble-click **luego de haber intentado el Ejercicio 2**)
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

ts, xs, xs_err = np.loadtxt("0.csv", delimiter = ",", unpack = True, skiprows = 1)

def sinusoidal(t, A, w, phi):
    return A*np.cos(w*t + phi)

popt, pcov = curve_fit(sinusoidal, ts, xs)
A, w, phi = popt

ts_ajuste = np.linspace(min(ts), max(ts), 1000)
xs_ajuste = sinusoidal(ts_ajuste, A, w, phi)

plt.errorbar(ts, xs, yerr = xs_err, fmt = "s", ms = 2, capsize = 2, color = "k")
plt.plot(ts_ajuste, xs_ajuste, color = "r")
plt.xlabel("$t$ [s]")
plt.ylabel("$x$ [cm]")
plt.show()


# **¿Como solucionamos esto?** Como repaso, queremos que `curve_fit` empieze a buscar los parámetros óptimos _cerca de la solución que esperamos_, que se puede hacer pasandole parámetros iniciales a través del argumento `p0`:
# ```python
# popt, pcov = curve_fit(sinusoidal, t, x, p0 = [5, w_aprox, 0])
# ```
# De esta forma, la función va a buscar la frecuencia óptima cerca de la frecuencia aproximada `w_aprox`.
# 
# La pregunta es ahora, **¿de donde sacamos estos parámetros iniciales?** Algunas posibilidades:
# - **Usando más información:** Por ejemplo, ya sabemos que la amplitud de la curva debe ser aproximadamente 5 cm por el procedimiento llevado a cabo en el laboratorio. De la misma forma, sabemos que el defasaje `phi` del coseno debe estar cerca del 0 (si no les resulta intuitivo pregunten).
# - **A ojo:** Observamos el gráfico y elegímos parámetros razonables. Se puede aproximar la frecuencia estimando primero el período $T$ de las oscilaciones. $T$ está dado por el tiempo que tarda el carrito en alejarse de y volver a un máximo, como se puede ver en el siguiente gráfico:
# 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# La frecuencia aproximada se calcula entonces con $\omega_\text_{aprox} = 2\pi/T$.
# 
# - **Usando `find_peaks`:** Una forma de automatizar el método "a ojo" es midiendo el período de las oscilaciones usando `find_peaks`. Esto es lo que vamos a usar ahora, ya que tenemos muchos datos para analizar y el método "a ojo" puede ser tedioso.
# 

# ---
# ### Ejercicio 3: Estimar la frecuencia de las oscilaciones usando `find_peaks`
# 
# Lo que queremos hacer es medir el intervalo de tiempo entre los dos primeros picos de amplitud. Recordemos que `find_peaks` devuelve los indices de los picos del array que le damos y un diccionario con más información.
# 
# 1. Importar `find_peaks` de `scipy.signal` y aplicarlo al array de posiciónes `xs` de `0.csv`. Imprimir `picos` y el diccionario.
# 
# 2. Graficar los datos de `0.csv` nuevamente (podés copiar y pegar!!). Luego, graficar los picos encontrados por `find_peaks` con `plt.plot(ts[picos], xs[picos], "o")`. ¿Coinciden los picos con lo que esperan?
# 
# 3. Calcular el periódo `T` e imprimir el resultado (`f'Período estimado: {T} s'`). _Ayuda: ¿cuanto vale el tiempo en el segundo pico? ¿y en el primero? Restando estos valores van a poder obtener la respuesta!_
# 
# 4. Calcular la frecuencia `w0` usando que está dada por  $2\pi / T$. Imprimir el resultado (`f'Frecuencia estimada: {w0} 1/s'`).
# 
# _Nota: Estamos usando este método para estimar los parámetros iniciales, pero se podría usar para medir la frecuencia. El tema es que no estamos calculando la incerteza del valor del $\omega$ que obtenemos, y **una medición tiene incerteza**. Además, un ajuste usa todos los datos, no solo los picos, lo que está bueno para tener más información. Si queres conversar un poco más del tema, llama a un profe!_
# 
# ---

# ---
# ### Ejercicio 4: Hacer un ajuste sinusoidal usando la frecuencia estimada
# 
# 1. Definir una función llamada `sinusoidal` que tome la variable independiente `t` y los parámetros a determinar `A, w` y `phi` (amplitud, frecuencia, y fase), y que devuelva el resultado de $A\cos(\text{w}t + \text{phi})$.
# 2. Usando `curve_fit` (hay que importarlo), ajustar los datos de `xs` en función de `ts` y obtener la frecuencia óptima `w` de `popt` (los parámetros óptimos), y su error `w_err` de `pcov` (la matriz de covarianza). Imprimir el resultado usando `f'Frecuencia: ({w} ± {w_err}) 1/s'`. _Ayuda: ver clase pasada! Y configuren los parámetros iniciales (`p0`) en `curve_fit`._
# 3. Graficar la función ajustada sobre los datos.
# ---

# ---
# ### Ejercicio 5: Repetir para los demás archivos guardando los datos de la masa y la frecuencia.
# 
# **Acá**, _esto_ es porque programar es _super útil_. Ya trabajaste un montón para analizar `0.csv`. El solo pensamiento de repetirlo 10 veces te pone los pelos de punta. ¡Pero no lo tenés que hacer! Copiando el código que ya hiciste, ordenandolo un poco y usando la mágia de las iteraciones vas a haber analizado todo en un suspiro.
# 
# 1. Inicializar tres listas vacías, `[]`, guardandolas en las variables `ms`, `ws`, y `ws_err`. En estas listas vamos a guardar la masa y frecuencia correspondientes a cada archivo.
# 
# 2. Recorrer los archivos `"0.csv"` a `"8.csv"` _(recordar subirlos a Colab)_ con un `for`, obteniendo la frecuencia de los datos y la masa total del carrito en cada uno. Si les ayuda, pueden usar la siguiente planilla. _TIP: hagan un gráfico en cada ciclo del for para verificar que find_peaks está agarrando "bien" los picos y vayan probando si el for funciona mientras lo van armando._
# 
# ```python
# for n in range(9):
#     ts, xs, xs_err = np.loadtxt(f"{COMPLETAR}.csv", delimiter = COMPLETAR, skiprows = COMPLETAR, unpack = COMPLETAR)
# 
#     # USAR FIND_PEAKS PARA ESTIMAR LA FRECUENCIA (COPIAR CÓDIGO ANTERIOR, OJO CON LAS INDENTACIONES).
# 
#     # GRAFICAR SEÑAL Y PICOS
# 
#     plt.show() # para que los gráficos queden en figuras distintas
# 
#     # USAR CURVE_FIT PARA CONSEGUIR EL VALOR DE LA FRECUENCIA Y SU ERROR (COPIAR CÓDIGO ANTERIOR, OJO CON LAS INDENTACIONES).
# 
#     # AGREGAR FRECUENCIA OBTENIDA A LA LISTA ws
#     # AGREGAR ERROR DE LA FRECUENCIA OBTENIDA A LA LISTA ws_err
#     # AGREGAR MASA CORRESPONDIENTE A LA LISTA ms (usar función que hicieron al princípio)
# ```
# 
# ---

# ---
# ### Ejercicio 6: El Ajuste Final
# 
# El modelo de un oscilador sin amortiguamiento cuya fuerza restitutiva está dada por:
# $$
# F_\text{resorte} = -kx\qquad \text{(Ley de Hooke)}
# $$
# con $k$ una constante y $x$ el desplazamiento del oscilador del equilibrio, tiene como frecuencia natural:
# $$
# \omega(m) = \sqrt{\frac{k}{m}}
# $$
# 1. Graficar `ws` en función de `ms`, mostrando el error de `ws`. (`ws`en el eje $\hat y$, `ms` en el eje $\hat x$)
# 2. Ajustar la función $\omega(m) = \sqrt{k/m}$ a los datos de $\omega$ y $m$ que se guardaron en el item anterior, obteniendo el parámetro óptimo para $k$ y su error. Graficar la función ajustada encima de los datos y juzgar si el modelo es apropiado. _Nota: En los labos van a ver herramientas útiles para realizar estas evaluaciones de forma sistemática._
# 
# AUTOCORRECCIÓN: Les debería dar $k = (2.000 \pm 0.002) ~\text{N/m}$
# 
# ---

# # _Problema integrador 2 (Estadística)_
# 
# Vamos a resolver probabilísticamente un problema simple.
# 
# Si tiramos 100 monedas y las ordenamos en una secuencia como la siguiente:
# 
# *"... Cara Cruz Cara Cara Cruz ..."*
# 
# ¿Qué es más probable? ¿Encontrar la secuencia "Cara Cara" o encontrar la secuencia "Cruz Cara"? ¿Son equiprobables?
# 
# Rompamos nuestra cabeza.

# Vamos a hacer todo de a pasos.
# 
# ### **1)** Función ```generar_secuencia(largo)```
# Lo primero que tenemos que poder hacer es generar una secuencia de caras y cruces. Como tanto cara como cruz empiezan con la misma letra, las vamos a cambiar por 0 y 1, es decir, estudiaremos cuantas veces aparece la secuencia 00 y cuantas aparece la secuencia 01.
# 
# *Creá una función que reciba el largo de la secuencia y devuelva una lista llena de ceros y unos. Para esto, realizá un bucle ```for``` en el genere con cada iteración un 0 o un 1.*
# 
# **AYUDA**: La función ```np.random.randint(0,n)``` genera un número aleatorio entre ```0``` y ```n-1```, es decir, no incluye a ```n```, al igual que el ```range```.
# 
# ### **2)** Función ```calcular_puntajes(secuencia)```
# 
# Ahora tenemos que calcular los puntajes de las secuencias que generamos, es decir, hay que recorrer la secuencia y si encuentro un 0 y en el siguiente elemento de la lista otro 0 sumo un punto para la secuencia 00 mientras que si encuentro un 1 y luego un 0 sumo un punto para la secuencia 01.
# 
# *Creá una función que reciba una secuencia y devuelva el puntaje obtenido para la secuencia 00 y para la secuencia 01.*
# 
# **AYUDITA**: Recorrer la secuencia y fijarse si estoy parado en un 0 ¿Cómo debe ser el siguiente elemento de la lista para que sume un punto a la secuencia 00?
# 
# ### **3)** *¡A simular!*
# 
# Ahora necesitamos hacer estadística, queremos ver que secuencia es más probable que la otra.
# 
# Vamos a estudiar secuencias de distintos largos, desde 2 hasta 100 números. ¿Por qué empezamos en 2?
# 
# *Para **cada largo**, realizá 1000 secuencias utilizando la función ```generar_secuencia(largo)``` y calculá los puntajes de cada secuencia con  ```calcular_puntajes(secuencia)```. Guardá en variables la cantidad de victorias de cada secuencia **¡y acordate de los empates!**, transformalas en probabilidad (dividilas por 1000, cof cof) y **guardalas en tres listas (POR FAVOR TE LO PIDO)**, una para 00, otra para 01 y otra para los empates.*
# 
# No hagan más de 1000 secuencias porque sino el código va a empezar a tardar en ejecutarse y acá queremos las cosas ¡YA!. Si tienen ganas de esperar más tiempo pasen a 10000, pero solo pueden hacerlo una vez saben que funciona el código, sino la compu **explota**.
# 
# ¿Que secuencia crees que va a ser más probable que la otra?
# 
# 
# ### **4)** *¡Es hora de graficar!*
# 
# Llegó el momento de responder la tan esperada pregunta y vamos a hacerlo utilizando un gráfico.
# 
# *Graficá las probabilidades de cada secuencia y los empates, en función del largo de la secuencia.*
# 
# Es decir, en el eje $\hat{x}$ tendremos el largo de la secuencia y en el eje $\hat{y}$ la probabilidad de cada secuencia.
# 
# ¡Hacé el gráfico lo más hermoso posible! Ponele grilla, leyenda, nombres a los ejes y labels a cada curva. Si tenés ganas jugá con los colores y preguntanos que tan lindo te quedó el gráfico *(por contrato tenemos que responder que quedó hermoso)*, *(mentira no hay contrato)*.
# 
# ### **5)** *¡¿Qué está pasando?!*
# 
# Respondimos la pregunta de que secuencia tiene más chances de ganar, pero el gráfico no responde el porqué ocurre esto, así que vamos a ahondar más profundo.
# 
# *Generá 100000 secuencias de largo 4, calculá la diferencia de puntaje entre 00 y 01 y guardalas en una lista. Hacé un histograma de esa lista utilizando ```plt.hist(lista)``` y hacé el gráfico lo más bello que puedas. ¿Hay alguna tendencia? ¿Con cuanto puntaje máximo gana una secuencia y con cuanto gana la otra?*
# 
# Luego, **LUEGO** de hacer esto. Abrí el siguiente enlace. ***¡¡LUEGO ES LUEGO!!***
# 
# [¡SOLO LUEGO DE RESOLVER EL INCISO!](https://drive.google.com/file/d/1E6mq9gDdjaWj0hhhsFbvDU-Bb1glLmP0/view?usp=sharing)
# 
# Si querés jugá a cambiar el largo de la secuencia para ver como cambian los histogramas.
# 
# ### **6)** *¡Felicitaciones!*
# 
# Con esto hemos podido responder la pregunta, o al menos iluminar bastante la respuesta. Sin embargo podemos seguir aprendiendo cosas.
# 
# *Calculá el valor medio y la desviación estándar del histograma que calculaste recién. Utilizá las funciones de numpy ```np.mean(lista)``` y ```np.std(lista)``` y aprendé inglés para saber cual es cual. Anotate en el Laboratorio de idiomas de la UBA y... bueno paro.*
# 
# ¿Tenes ganas de charlar un rato? Preguntanos que significa ```ddof = 1```.
# 

# # _Problema integrador 3 (Biología)_
# 
# Vamos a estudiar la dinámica de poblaciones según un modelo clásico de depredador-presa.
# 
# ¡Lotka-Volterra!
# 
# Tanto Lotka como Volterra propusieron de manera independiente un sistema de ecuaciones diferenciales para describir la dinámica de las poblaciones de dpredadores y presas, para modelar como interactúan entre sí.
# 

# ### **1)** *Primero de todo, los datos.com (**No es necesario entrar a esta página**).*
# 
# Para arrancar a trabajar necesitamos los datos, sino no podemos hacer nada.
# 
# *Utilizando la función ```np.loadtxt()``` (recordar la clase pasada) cargá los datos del archivo  ```"datos_poblaciones.csv"```. A cada columna asignale el nombre apropiado, es decir, definí las variables ```tiempo```, ```pob_presas``` y ```pob_depredadores```.*
# 
# Los datos pueden encontrarse dentro de una carpeta en el siguiente enlace:
# [Mercado Pago de la FIFA](https://github.com/fifabsas/talleresfifabsas/tree/master/python/3_Ejercicios)
# 
# ### **2)** *¡El momento de graficar!*
# 
# Lo primero que tenemos que hacer cuando nos dan unos datos, a parte de cargarlos, es graficarlos, así vemos cual es su pinta y podemos pensar en que análisis realizar.
# 
# *Utilizando lo que vimos la clase pasada de ```matplotlib```, graficá las poblaciones de presas y depredadores en función del tiempo y hacé que el gráfico sea lo más canchero posible, ponele grilla, leyenda, los colores que vos quieras, etc.*
# 
# ¿Son las poblaciones como esperabas? *¿No?* $\;$ **Enseñanos algo de biología... ¡Por favor!**
# 
# ### **3)** *Aprendiendo un poco sobre las poblaciones*
# 
# Estaría bueno tener cierto conocimiento general de las poblaciones, como su promedio y cuanto se desvían las poblaciones de este.
# 
# *Calculá el promedio de la población de presas utilizando la función de numpy ```np.mean(pob_presas)```.*
# 
# ¿Es un valor representativo de la población?
# 
# *Graficá este promedio en la figura anterior que hiciste, utilizando ```plt.axhline(valor_medio, c = "g", linestyle = "--")```. Probá cambiando los parámetros opcionales.*
# 
# *Calculá también la desviación estandar, utilizando ```np.std(pob_presas)```.*
# 
# ¿Este valor te dice algo más respecto al promedio?
# ### **4)** *El momento de utilizar ```find_peaks```*
# 
# Observamos que las poblaciones oscilan y en particular notamos varios picos.
# 
# *Utilizar la función ```find_peaks``` aplicada a la población de las presas, para determinar la posición de los máximos. Grafica los picos en un nuevo gráfico junto a la población de las presas en función del tiempo.*
# 
# ¿Cuanta población de presas hay en estos picos?
# 
# ### **5)** *Ciclos poblacionales: ¿¡El espacio de fases!?*
# 
# Vamos a tomarnos el laburo de hacer un gráfico bello, pero que les puede resultar complicado, así que vamos de a poco.
# 
# 
# *Graficá la población de depredadores en función de la población de presas. ¿Qué se observa? ¿¿Alguna especie de ciclo?? Guiño guiño.*
# 
# 
# Si lo pensas, en este gráfico perdemos la noción del tiempo, no sabemos de donde parte la población ni donde termina (aunque quizá sí te des cuenta), así que vamos a tomarnos el trabajo de agregarle esto.
# 
# *Copiá las siguientes líneas (no hace falta que las entiendas del todo):*
# 
# ```python
# from matplotlib import colors
# colormap = plt.cm.plasma
# norm = colors.Normalize(vmin=min(tiempo), vmax= max(tiempo))
# scalar_map = plt.cm.ScalarMappable(norm=norm, cmap=colormap)
# ```
# 
# *Buscá en el navegador: matplotlib colormaps y cambia donde dice ```plasma``` por el colormap que más te guste. Puede ser que el que más te guste sea ```plasma```, a mí me gusta...*
# 
# Ahora se viene lo lindo.
# 
# *Realizá un ciclo ```for``` que recorra las listas de las poblaciones y graficá para cada ```i``` (esto si debería entenderse):*
# 
# ```python
# plt.plot([pob_presa[i], pob_presa[i + 1]], [pob_depredador[i], pob_depredador[i + 1]], color = colormap(tiempo[i]/max(tiempo)))
# ```
# 
# ¿Que tal el gráfico ahora? Para agregar la barra de colores del tiempo, copiá:
# 
# ```python
# cbar = plt.colorbar(scalar_map)
# cbar.set_label("Tiempo")
# ```
# 
# ¿Te molesta como quedaron los números del eje x?
# 
# *Agregale al gráfico la línea ```plt.xticks(rotation = 45)```, mirá a los ojos a algún docente y decile que pensas que no queda mucho mejor.*
# 
# 
# ### **6)** *¿Qué te pareció?*
# 
# Este fue un ejercicio de biología pensado por una persona que no sabe de biología, así que nos re sirve tener tu opinión!
# 
# Te dejamos tu última misión.
# 
# *Aprendé programación por tu cuenta el tiempo que sea necesario para formar parte del plantel docente del taller de Python de la FIFA, cambiá este ejercicio y lentamente añadí gente de Biología al taller hasta que pase a ser un taller de estudiantes de Biología **para** estudiantes de Biología. **El taller de Python de la FIBA**...*
# 
# ### **Desafío:** *Integración numérica*
# 
# El modelo de Lotka-Volterra con el que se generaron numéricamente los datos *(te pido disculpas no fuimos a medir durante 5 años las poblaciones)*, sigue las siguientes ecuaciones diferenciales, donde $u$ es la población de presas y $v$ la de depredadores:
# 
# $$ \frac{du}{dt} =   au - buv $$
# 
# $$ \frac{dv}{dt} = - cv + duv $$
# 
# El resto de parámetros son:
# * $a$ es la tasa de nacimiento de presas
# * $c$ es la tasa de muerte de predadores
# * $b$ y $d$ son los acoplamientos entre especies, que define como interactúan
# 
# Estas ecuaciones se pueden resolver numéricamente para jugar con los distintos parámetros y entender como evolucionan las poblaciones. Para esto utilizaremos la siguiente función de Scipy:
# 
# ```python
# from scipy.integrate import odeint
# ```
# 
# #### **a)** *Intervalo de tiempo*
# *Definí un array de tiempos como vimos la clase pasada, que vaya de 0 a 5 y tenga longitud 5000. Si querés probar otro intervalo de tiempo o cantidad de puntos, hacelo!*
# 
# #### **b)** ¡*Definiendo los parámetros! Acá está la joda*
# 
# *Definí $a$, $b$, $c$ y $d$ con ávida intuición, para que pase lo que vos querés.*
# 
# Por ejemplo, si $a$ es muy grande, la población de presas crecerá muy rápido. Si $d$ es muy chico, la población de depredadores crecerá muy poco al alimentarse de las presas.
# 
# *Definí las poblaciones iniciales de las presas y depredadores en una lista que se llame X0 y tenga en cada posición, la población inicial de cada uno.*
# 
# #### **c)** *Ecuaciones diferenciales en código*
# 
# *Definí una función, llamada `lotka_volterra`, que tome de `input` X (que ahora te digo que es, aunque es parecido a lo de arriba), el array de tiempos que hiciste, y los parámetros $a$, $b$, $c$ y $d$.*
# 
# *En la primera línea definí u, v = X (ya te dije que es, ponele) (si, hay que escribirlo en un solo parámetro, no podés poner u y v como parámetros de la función, así es la vida).*
# 
# *La función debe devolver, como `output`, una lista donde cada elemento es la parte derecha de las ecuaciones diferenciales de más arriba, es decir, al derivada temporal de cada población.*
# 
# #### **d)** *Magia*
# 
# *Utilizá la siguiente línea para integrar numéricamente las ecuaciones*
# 
# ```python
# u, v = odeint(lotka_volterra,X0, tiempo, args = (a,b,c,d)).T
# ```
# 
# El .T del final es para transponer la matriz con la que nos dan los datos, probá si hace falta o nó.
# 
# 
# #### **e)** *¿¿Funciona o no funciona??*
# 
# *Graficá las poblaciones en función del tiempo, probá todos los parámetros que te gusten y sé feliz el resto de tu vida.* **La última no es opcional.**
# 
# Si querés chequear que funciona bien, probá que las interacciones sean nulas y que evolucione cada población por separado. *¿El crecimiento debería ser?* **¡Expone-**
# 
# ¡Ahora ya sabés como resolver ecuaciones diferenciales y podés modelar lo que vos quieras! *¿Supongo?*
# 
# 
# 
# 
# 

# # _Ejercicios adicionales sueltos_

# La idea de estos ejercicios es que son cortos pero requieren conceptos que son importantes a la hora de programar o son problemas que se van a encontrar en algún momento en el futuro.
# 
# Algunos son más divertidos o fáciles que otros, pero no por eso son menos importantes.

# ### Histogramas
# 
# 
# 

# Queremos generar numeros aleatorios con una cierta distribución de probabilidad, en este caso una Gaussiana, que es una distribución muy **normal** *(jajá que buen juego de palabras jajajajajajajajajajajajajajajajajajajajajajaja)* que aparece en todas las ciencias.
# 
# Utilizando la función de `numpy` llamada `np.random.normal()` *(si, a la Gaussiana también se la llama la normal, ahora entendiste el chiste de arriba)* podemos generar números aleatorios según una distribución Gaussiana.
# 
# **1)** *Generá 100 números aleatorios utilizando `np.random.normal()` y guardalos en una `lista`. Realizá un histograma utilizando `plt.hist(lista)` y hacé que el gráfico sea lo más canchero posible (recomendamos utilizar el parámetro opcional `edgecolor = "k"` para ponerle color negro a los bordes).*
# 
# **2)** *Calcula `np.mean(lista)` y `np.std(lista)`. Estos devuelven el valor medio y la desviación estándar de los datos, respectivamente.*
# 
# **3)** *Si conoces a la Gaussiana, sabés que esta tiene un valor medio, donde está centrada la campana, y una desviación estandar, que determina que tan dispersos están los datos. En particular `np.random.normal()` genera números aleatorios con valor medio 0 y desviación estándar 1. Probá cambiando los valores de la siguiente manera `np.random.normal(valor_medio, desviacion_estandar)` y ponele los valores que quieras. También jugá a cambiar la cantidad de números aleatorios que generas (100 por 1000 y así) y fijate como van cambiando `np.mean(lista)` y `np.std(lista)`.*
# 
# **4)** *Si te pareció que hay demasiados chistes, dejalo en las encuestas, aprendé programación todo un cuatrimestre, vení la siguiente edición y modificá este ejercicio.*
# 
# 
# 
# 

# ### Diccionarios
# 

# Tenemos la siguiente lista de nombres y carreras y queremos armar un diccionario que asocie cada nombre con su respectiva carrera.
# 
# ```python
# nombres = ['Rosalind Franklin', 'Juan G. Roederer', 'Albert-László Barabási', 'Emmy Noether', 'Phyllis Nicolson',
#            'Ada Lovelace', 'Luis Caffarelli', 'Miguel Ángel Virasoro', 'Luis Federico Leloir']
# 
# carreras = ['Química', 'Física', 'Física', 'Matemática', 'Física',
#             'Computación', 'Matemática', 'Física', 'Química']
# ```
# 
# *Realizá un ciclo `for` para recorrer las listas. Ayuda: Al nombre i-ésimo le corresponde la carrera i-ésima.*
# 
# *Una vez hayas hecho el diccionario, recorrelo e imprimí en pantalla los nombres de las personas cuya carrera sea "Física" (si así no sale ya no se que más hacer, te pido perdón).*

# ### Funciones
# 
# 

# #### Factorial

# *Definir la función `factorial(n)`, que recibe un número entero y devuelve el factorial dicho número.*
# 
# Expresión del factorial:
# 
# $$n! = n \cdot (n-1) \cdot (n-2) \cdot \; ... \cdot 1$$
# 
# `math` ya tiene una función para realizar esto, llamada `math.factorial()`. Comparar los resultados.

# #### Valor absoluto

# Definir la función `absoluto(x)` que recibe un número real (`float`) `x` y devuelve su módulo.
# *texto en cursiva*
# Si $x < 0$ debería devolver $-x$ y si $x \geq 0$ debería devolver $x$.
# 
# En Python ya existe una función que hace esto, llamada `abs()`. Comparar los resultados.

# ### Listas

# La idea de este ejercicio es familiarizarse con funciones super útiles aplicables a listas.
# 
# ```python
# numeros = [20,12,10,3,8,14,1,42]
# ```
# 
# *Copie la lista de arriba y aplique las funciones `max()` y `min()`.
# ¿Qué devuelven?*
# 
# Ahora aplicaremos `métodos` a la lista de números.
# 
# *Aplique el método `pop()` a la lista de números, de la siguiente manera `numeros.pop()` (recuerde el `append()` de la primera clase).*
# 
# Por último, aplicamos otro método.
# 
# *Aplique el método `insert()` a la lista de números, de la siguiente manera `numeros.insert(0,77)`.*
# 
# ¿Qué hace cada método?