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

# #GLACINDIA Workshop
# ##Part 6: Numpy arrays

# Nikolay Koldunov
# 
# koldunovn@gmail.com

# -    a powerful N-dimensional array object
# -    sophisticated (broadcasting) functions
# -    tools for integrating C/C++ and Fortran code
# -    useful linear algebra, Fourier transform, and random number capabilities
# 

# In[1]:


import numpy as np
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


np.set_printoptions(precision=3 , suppress= True) # this is just to make the output look better


# ## Load data

# We going to work with data from [GHCN (Global Historical Climatology Network)-Daily](http://www.ncdc.noaa.gov/oa/climate/ghcn-daily/) data.
# 
# Convinient way to select data from there is to use [KNMI Climatological Service](http://climexp.knmi.nl/selectdailyseries.cgi?id)
# 
# Load data in to a variable (Delhi daily air temperatures):

# In[3]:


ls


# In[4]:


temp = np.loadtxt('DelhiTmax.txt')


# We load data in to the spetiall variable called `numpy array`. This is homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type. Numpy arrays are basic elements of almost all python based scientific software.

# In[5]:


type(temp)


# In[6]:


temp


# Shape of the array can be viewed as a `size of the table` that contain data:

# In[7]:


temp.shape


# <img  height="300" src="files/anatomyarray.png" >

# However this `tables` can have 3 and more dimentions.

# So it's a *row-major* order. Matlab and Fortran use *column-major* order for arrays.

# Numpy arrays are statically typed, which allow faster operations

# In[8]:


temp.dtype


# You can't assign value of different type to element of the numpy array:

# In[9]:


temp[0,0] = 'Year'


# Slicing works similarly to Matlab:

# In[10]:


temp[0:5,:]


# In[11]:


temp[-5:-1,:]


# One can look at the data. This is done by matplotlib module:

# In[12]:


import matplotlib.pylab as plt
plt.plot(temp[:,3])


# ## Index slicing

# In general it is similar to Matlab

# First 12 elements of **second** column (months). Remember that indexing starts with 0:

# In[13]:


temp[0:12,1]


# First raw:

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temp[:10,:]


# ##Exercise
# 
#  - Plot only first 1000 values
#  - Plot last 1000 values
# 

# We can create mask, selecting all raws where values in third raw (days) equals 10:

# In[15]:


mask = (temp[:,2]==10)


# Here we apply this mask and show only first 5 raws of the array:

# In[16]:


temp[mask][:20,:]


# You don't have to create separate variable for mask, but apply it directly. Here instead of first five rows I show five last rows:

# In[17]:


temp[temp[:,2]==10][-5:,:]


# You can combine conditions. In this case we select days from 10 to 12 (only first 10 elements are shown):

# In[18]:


temp[(temp[:,2]>=10)&(temp[:,2]<=12)][0:10,:]


# ##Exercise
# 
#     Select only summer months
#     Select only first half of the year
# 

# ## Basic operations

# Create example array from first 12 values of second column and perform some basic operations:

# In[19]:


days = temp[0:12,2]
days


# In[20]:


days+10


# In[21]:


days*20


# In[22]:


days*days


# In[23]:


np.sin(days)


# ##Exercise
# 
# - Create new array that will contain only temperatures
# 
# - Convert all temperatures to deg F
# 

# 

# ## Basic statistics

# Create *temp_values* that will contain only data values:

# In[24]:


temp_values = temp[:,3]
temp_values


# Simple statistics:

# In[25]:


temp_values.min()


# In[26]:


temp_values.max()


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temp_values.mean()


# In[28]:


temp_values.std()


# In[29]:


temp_values.sum()


# You can also use *sum* function:

# In[30]:


np.sum(temp_values)


# One can make operations on the subsets:

# ##Exercise
# 
#     Calculate mean for first 1000 values of temperature
# 

# ## Saving data

# You can save your data as a text file

# In[31]:


np.savetxt('temp_only_values.csv',temp[:, 3], fmt='%.4f')


# ##Exercises
# 
# * Select and plot only data for October
# * Calculate monthly means for years from 1990 to 1999 and plot them

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