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

# # Calliope Urban Scale Example Model 

# In[1]:


import calliope

# We increase logging verbosity
calliope.set_log_verbosity('INFO', include_solver_output=False)


# In[2]:


model = calliope.examples.urban_scale()


# In[3]:


# Model inputs can be viewed at `model.inputs`. 
# Variables are indexed over any combination of `techs`, `locs`, `carriers`, `costs` and `timesteps`, 
# although `techs`, `locs`, and `carriers` are often concatenated. 
# e.g. `chp`, `X1`, `heat` -> `X1::chp::heat` 
model.inputs


# In[4]:


# Individual data variables can be accessed easily, `to_pandas()` reformats the data to look nicer
model.inputs.resource.to_pandas()


# In[5]:


# To reformat the array, deconcatenating loc_techs / loc_tech_carriers, you can use model.get_formatted_array()
# You can then apply loc/tech/carrier only operations, like summing information over locations: 
model.get_formatted_array('resource').sum('locs').to_pandas()


# In[6]:


# Solve the model. Results are loaded into `model.results`. 
# By including logging (see package importing), we can see the timing of parts of the run, as well as the solver's log
model.run()


# In[7]:


# Model results are held in the same structure as model inputs. 
# The results consist of the optimal values for all decision variables, including capacities and carrier flow
# There are also results, like system capacity factor and levelised costs, which are calculated in postprocessing
# before being added to the results Dataset

model.results


# In[8]:


# We can sum heat output over all locations and turn the result into a pandas DataFrame
df_heat = model.get_formatted_array('carrier_prod').loc[{'carriers':'heat'}].sum('locs').to_pandas().T

#The information about the dataframe tells us about the amount of data it holds in the index and in each column
df_heat.info()


# In[9]:


# Using .head() to see the first few rows of heat generation and demand

# Note: carrier production in heat_pipes:N1 is heat received by the heat network at any location connected to `N1`

df_heat.head()


# In[10]:


# We can plot this by using the timeseries plotting functionality.
# The top-left dropdown gives us the chance to scroll through other timeseries data too.

model.plot.timeseries()


# In[11]:


# plot.capacities gives a graphical view of the non-timeseries variables, both input and output
model.plot.capacity()


# In[12]:


# We can also examine total technology costs
# notice all the NaN values which appear when seperating loc::techs to locs and techs. 
# Any NaN value means we never considered that combination of `loc` and `tech` for the variable

costs =  model.get_formatted_array('cost').loc[{'costs': 'monetary'}].to_pandas()
costs


# In[13]:


# We can examine levelized costs for each location and technology

lcoes = model.results.systemwide_levelised_cost.loc[{'carriers': 'electricity', 'costs':'monetary'}].to_pandas()
lcoes


# In[14]:


# We can just plot this directly using calliope analysis functionality
model.plot.capacity(array='systemwide_levelised_cost')


# In[15]:


# Plotting a map of locations and transmission lines is easily possible
# In our example, the system is purely hypothetical and the resulting map
# gives us little useful information...

model.plot.transmission()


# In[16]:


# Transmission plots give us a static picture of installed capacity along links. 
# If we want timeseries data on energy flows at locations and along links
# we can use energy flow plotting. We only show the first day here, to improve loading speed.

model.plot.flows(timestep_index_subset=[0, 24])


# ---

# See the [Calliope documentation](https://calliope.readthedocs.io/) for more details on setting up and running a Calliope model.