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

# # QCoDeS Example with Lakeshore 325
# 
# Here provided is an example session with model 325 of the Lakeshore temperature controller 

# In[1]:


get_ipython().run_line_magic('matplotlib', 'notebook')


# In[2]:


import matplotlib.pyplot as plt
import numpy as np

from qcodes.instrument_drivers.Lakeshore import LakeshoreModel325


# In[3]:


lake = LakeshoreModel325("lake", "GPIB0::12::INSTR")


# ## Sensor commands

# In[4]:


# Check that the sensor is in the correct status 
lake.sensor_A.status()


# In[5]:


# What temperature is it reading? 
lake.sensor_A.temperature()


# In[6]:


lake.sensor_A.temperature.unit


# In[7]:


# We can access the sensor objects through the sensor list as well
assert lake.sensor_A is lake.sensor[0]


# ## Heater commands 

# In[8]:


# In a closed loop configuration, heater 1 reads from... 
lake.heater_1.input_channel()


# In[9]:


lake.heater_1.unit()


# In[10]:


# Get the PID values 
print("P = ", lake.heater_1.P())
print("I = ", lake.heater_1.I())
print("D = ", lake.heater_1.D())


# In[11]:


# Is the heater on? 
lake.heater_1.output_range()


# ## Loading and updating sensor calibration values

# In[12]:


curve = lake.sensor_A.curve


# In[13]:


curve_data = curve.get_data()


# In[14]:


curve_data.keys()


# In[15]:


fig, ax = plt.subplots()
ax.plot(curve_data["Temperature (K)"], curve_data['log Ohm'], '.')
plt.show()


# In[16]:


curve.curve_name()


# In[17]:


curve_x = lake.curve[23]


# In[18]:


curve_x_data = curve_x.get_data()


# In[19]:


curve_x_data.keys()


# In[20]:


temp = np.linspace(0, 100, 200)
new_data = {"Temperature (K)": temp, "log Ohm": 1/(temp+1)+2}

fig, ax = plt.subplots()
ax.plot(new_data["Temperature (K)"], new_data["log Ohm"], '.')
plt.show()


# In[21]:


curve_x.format("log Ohm/K")
curve_x.set_data(new_data)


# In[22]:


curve_x.format()


# In[24]:


curve_x_data = curve_x.get_data()


# In[25]:


fig, ax = plt.subplots()
ax.plot(curve_x_data["Temperature (K)"], curve_x_data['log Ohm'], '.')
plt.show()


# ## Go to a set point

# In[40]:


import time

import numpy
from IPython.display import display
from ipywidgets import interact, widgets
from matplotlib import pyplot as plt


def live_plot_temperature_reading(channel_to_read, read_period=0.2, n_reads=1000):
    """
    Live plot the temperature reading from a Lakeshore sensor channel
    
    Args:
        channel_to_read
            Lakeshore channel object to read the temperature from
        read_period
            time in seconds between two reads of the temperature
        n_reads
            total number of reads to perform
    """

    # Make a widget for a text display that is contantly being updated
    text = widgets.Text()
    display(text)

    fig, ax = plt.subplots(1)
    line, = ax.plot([], [], '*-')
    ax.set_xlabel('Time, s')
    ax.set_ylabel(f'Temperature, {channel_to_read.temperature.unit}')
    fig.show()
    plt.ion()

    for i in range(n_reads):
        time.sleep(read_period)

        # Update the text field
        text.value = f'T = {channel_to_read.temperature()}'

        # Add new point to the data that is being plotted
        line.set_ydata(numpy.append(line.get_ydata(), channel_to_read.temperature()))
        line.set_xdata(numpy.arange(0, len(line.get_ydata()), 1)*read_period)

        ax.relim()  # Recalculate limits
        ax.autoscale_view(True, True, True)  # Autoscale
        fig.canvas.draw()  # Redraw


# In[9]:


lake.heater_1.control_mode("Manual PID")
lake.heater_1.output_range("Low (2.5W)")
lake.heater_1.input_channel("A")
# The following seem to be good settings for our setup
lake.heater_1.P(400)
lake.heater_1.I(40)
lake.heater_1.D(10)


lake.heater_1.setpoint(15.0)  # <- temperature 
live_plot_temperature_reading(lake.sensor_a, n_reads=400)


# ## Querying the resistance and heater output 

# In[4]:


# to get the resistance of the system (25 or 50 Ohm)
lake.heater_1.resistance()


# In[5]:


# to set the resistance of the system (25 or 50 Ohm)
lake.heater_1.resistance(50)
lake.heater_1.resistance()


# In[6]:


# output in percent (%) of current or power, depending on setting, which can be queried by lake.heater_1.output_metric()
lake.heater_1.heater_output() # in %, 50 means 50%