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

# <a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/main/tutorials/intro.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> &nbsp; <a href="https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/tutorials/intro.ipynb" target="_parent"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open in Kaggle"/></a>

# # Introduction
# 
# Welcome to the Neuromatch computational neuroscience course!

# <p align='center'><img src='https://github.com/NeuromatchAcademy/widgets/blob/master/sponsors.png?raw=True'/></p>

# ## Orientation Video

# In[ ]:


# @markdown
from ipywidgets import widgets
from IPython.display import YouTubeVideo
from IPython.display import IFrame
from IPython.display import display


class PlayVideo(IFrame):
  def __init__(self, id, source, page=1, width=400, height=300, **kwargs):
    self.id = id
    if source == 'Bilibili':
      src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'
    elif source == 'Osf':
      src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'
    super(PlayVideo, self).__init__(src, width, height, **kwargs)


def display_videos(video_ids, W=400, H=300, fs=1):
  tab_contents = []
  for i, video_id in enumerate(video_ids):
    out = widgets.Output()
    with out:
      if video_ids[i][0] == 'Youtube':
        video = YouTubeVideo(id=video_ids[i][1], width=W,
                             height=H, fs=fs, rel=0)
        print(f'Video available at https://youtube.com/watch?v={video.id}')
      else:
        video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,
                          height=H, fs=fs, autoplay=False)
        if video_ids[i][0] == 'Bilibili':
          print(f'Video available at https://www.bilibili.com/video/{video.id}')
        elif video_ids[i][0] == 'Osf':
          print(f'Video available at https://osf.io/{video.id}')
      display(video)
    tab_contents.append(out)
  return tab_contents


video_ids = [('Youtube', 'cV2q-vpdKUA'), ('Bilibili', 'BV1Zg4y1A7tJ')]
tab_contents = display_videos(video_ids, W=854, H=480)
tabs = widgets.Tab()
tabs.children = tab_contents
for i in range(len(tab_contents)):
  tabs.set_title(i, video_ids[i][0])
display(tabs)


# ## Concepts map
# 
# <img src="https://github.com/NeuromatchAcademy/course-content/blob/main/tutorials/static/ConceptMap.png?raw=1" alt="Concept map overview of curriculum" class="bg-primary" width="100%">
# 
# *Image made by John Butler, with expert color advice from Isabelle Butler*

# ## Curriculum overview
# 
# Welcome to the comp neuro course!
# 
# We have curated a curriculum that spans most areas of computational neuroscience (a hard task in an increasingly big field!).
# We will expose you to both theoretical modeling and more data-driven analyses. This section will overview the curriculum.
# 
# We will start with several optional pre-reqs refreshers.
# 
# The **Neuro Video Series** is a series of 12 videos that covers basic neuroscience concepts and neuroscience methods. These videos
# are completely optional and do not need to be watched in a fixed order so you can pick and choose which videos will help you
#  brush up on your knowledge.
# 
# The pre-reqs refresher days are asynchronous, so you can go through the material on your own time.
# You will learn how to code in Python from scratch using a simple neural model, the leaky integrate-and-fire model, as a motivation.
# Then, you will cover linear algebra, calculus and probability & statistics.
# The topics covered on these days were carefully chosen based on what you need for the comp neuro course.
# 
# After this, it’s the start of the proper course! You will start out with the module on **Intro to Modeling**. On the first day,
# you’ll learn all about the broad types of questions we can ask with models in neuroscience (*Model Types*). You will learn
# that we can use models to ask what happens, how it happens, and why it happens in the brain. Importantly, we classify models
# into ‘what’, ‘how’, and ‘why’ models, not based on the toolkit used, but on the questions asked!
# 
# After this solid grounding in what questions you can ask with models and the process to start doing so, you will move to the module on **Machine Learning**.
#  This module covers fitting models to data and using them to ask and answer questions in neuroscience. We can pose all sorts of questions
#  (including what, how, and why questions) using machine learning — we focus especially on more data-driven analyses that often result
#   in asking what is happening in the brain. You will learn about key principles behind fitting models to data (*Model Fitting*),
#   how to use generalized linear models to fit encoding and decoding models (*Generalized Linear Models*), how to uncover underlying
#   lower dimensional structure in data (*Dimensionality Reduction*), and how to use deep learning to build more complex encoding models,
#    including comparing deep networks to the visual system (*Deep Learning*). You’ll then dive into projects and
# learn about the process of modeling using a step-by-step guide to modeling that you will apply to your own projects (*Modeling Practice*).
# 
# Next, you’ll move to the module on **Dynamical Systems**. In this module, you will learn all about dynamical systems and
# how to apply them to build more biologically plausible models of neurons and networks of neurons. In *Linear Systems*,
# you will cover a lot of the really foundational knowledge on dynamical systems that you will use throughout the rest of the course,
# including a brief dive into stochastic systems which will underlie the next module.  During *Biological Neuron Models*,
# you will start to use this knowledge to build models of individual neurons that are more rooted in biology. In *Dynamic Networks*,
#  you will extend upon the previous day to start building and analyzing networks of neurons. We will often ask ‘how’ questions
#  using these models: how are things in the brain happening mechanistically? These models are often not directly fit to data
#  (in contrast to the machine learning models), but instead are built based on bottom up knowledge of the system.
# 
# Next, you will move to the module on **Stochastic Processes**. We start with a day learning about Bayesian inference, within the context
# of making decisions (*Bayesian Decisions*). Specifically, we are learning about how to estimate a state of the world from measurements.
# In the next day, we extend this to include time: the state of the world is now changing over time (*Hidden Dynamics*). Next, we look
# at how we can take actions to affect the state of the world (*Optimal Control* and *Reinforcement Learning*). Once again, these models
#  can be used as ‘what’, ‘how’, or ‘why’ models but we focus on asking ‘why’ questions (why should the brain compute this?).
# 
# Finally, we end with learning all about causality (*Network Causality*). This covers one of the most important science questions:
# when can we determine if something is causally related vs. just correlated?