Data Science Portfolio

Plotting Current and Gain Distributions with Matplotlib

import csv
import time
import string
import requests
import json
import numpy
import pandas as pd
import matplotlib.pyplot as plt
import scrapy
from scrapy.crawler import CrawlerProcess

Power Data Cleaning

power = pd.read_csv('docs/B18U01_804A_Power_NC_power.csv')
power.head()

	Unnamed: 0	Test	Die	VDD (V)	Unnamed: 4	Unnamed: 5	Unnamed: 6	Id (mA)	Unnamed: 8	Freq (GHz)	...	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Unnamed: 21	Unnamed: 22	Unnamed: 23	Unnamed: 24	Unnamed: 25	Unnamed: 26
0	693862	POW1	AD10	5	#	#	#	58.76	#	0.5	...	#	#	#	#	#	#	#	#	#	#
1	693862	POW1	AD10	5	#	#	#	58.23	#	2.0	...	#	#	#	#	#	#	#	#	#	#
2	693862	POW1	AD10	5	#	#	#	58.30	#	6.0	...	#	#	#	#	#	#	#	#	#	#
3	693862	POW1	AD10	5	#	#	#	58.42	#	8.0	...	#	#	#	#	#	#	#	#	#	#
4	693862	POW1	AD10	5	#	#	#	58.84	#	12.0	...	#	#	#	#	#	#	#	#	#	#

5 rows × 27 columns

# Drop unnecessary columns
power = power.drop(columns=['Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 8', 
                            'Unnamed: 11', 'Unnamed: 13', 'Unnamed: 16', 'Unnamed: 17', 
                            'Unnamed: 18', 'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 
                            'Unnamed: 22', 'Unnamed: 23', 'Unnamed: 24', 'Unnamed: 25', 
                            'Unnamed: 26'])
power.head()

	Unnamed: 0	Test	Die	VDD (V)	Id (mA)	Freq (GHz)	Pin (dBm)	Pin (dBm).1	Pout (dBm)	Gain (dB)
0	693862	POW1	AD10	5	58.76	0.5	-9	-9.035750	7.550	16.550
1	693862	POW1	AD10	5	58.23	2.0	-9	-8.983687	7.952	16.952
2	693862	POW1	AD10	5	58.30	6.0	-9	-9.026563	7.698	16.698
3	693862	POW1	AD10	5	58.42	8.0	-9	-8.969000	8.024	17.024
4	693862	POW1	AD10	5	58.84	12.0	-9	-8.952312	9.724	18.724

# Rename columns
power.columns = ['ID', 'Test', 'Die', 'Vdd', 'Id', 'Freq', 'Pin', 'Pin_measured', 'Pout', 'Gain']
power.head()

	ID	Test	Die	Vdd	Id	Freq	Pin	Pin_measured	Pout	Gain
0	693862	POW1	AD10	5	58.76	0.5	-9	-9.035750	7.550	16.550
1	693862	POW1	AD10	5	58.23	2.0	-9	-8.983687	7.952	16.952
2	693862	POW1	AD10	5	58.30	6.0	-9	-9.026563	7.698	16.698
3	693862	POW1	AD10	5	58.42	8.0	-9	-8.969000	8.024	17.024
4	693862	POW1	AD10	5	58.84	12.0	-9	-8.952312	9.724	18.724

# Check datatypes
print(power.dtypes)

ID                int64
Test             object
Die              object
Vdd               int64
Id              float64
Freq            float64
Pin               int64
Pin_measured    float64
Pout            float64
Gain            float64
dtype: object

# Extract only the data for Pin=-9
power = power.loc[power['Pin'] == -9]
power.head()

	ID	Test	Die	Vdd	Id	Freq	Pin	Pin_measured	Pout	Gain
0	693862	POW1	AD10	5	58.76	0.5	-9	-9.035750	7.550	16.550
1	693862	POW1	AD10	5	58.23	2.0	-9	-8.983687	7.952	16.952
2	693862	POW1	AD10	5	58.30	6.0	-9	-9.026563	7.698	16.698
3	693862	POW1	AD10	5	58.42	8.0	-9	-8.969000	8.024	17.024
4	693862	POW1	AD10	5	58.84	12.0	-9	-8.952312	9.724	18.724

Gain Distribution (Pin=-9dBm, Freq=0.5GHz)

# Extract all rows for Freq=0.5
power_500MHz = power.loc[power['Freq'] == 0.5].drop(['ID', 'Test', 'Die', 'Vdd', 'Pin_measured'], axis=1)
power_500MHz.head()

	Id	Freq	Pin	Pout	Gain
0	58.76	0.5	-9	7.550	16.550
80	58.51	0.5	-9	7.654	16.654
160	58.64	0.5	-9	7.606	16.606
240	58.38	0.5	-9	7.280	16.280
281	59.05	0.5	-9	7.632	16.632

# Export data to csv
power_500MHz.to_csv('docs/B18U01_804A_Power_NC_power_500MHz.csv')

# Plot gain distribution at 0.5GHz
power_500MHz.Gain.hist(bins=20)
plt.title('Gain Distribution @500MHz')
plt.xlabel('Gain @500MHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (Pin=-9dBm, Freq=0.5GHz)

power_500MHz.describe()

	Id	Freq	Pin	Pout	Gain
count	199.000000	199.0	199.0	199.000000	199.000000
mean	58.790352	0.5	-9.0	7.442050	16.442050
std	0.287915	0.0	0.0	0.091517	0.091517
min	58.160000	0.5	-9.0	7.249000	16.249000
25%	58.610000	0.5	-9.0	7.370500	16.370500
50%	58.780000	0.5	-9.0	7.436000	16.436000
75%	58.990000	0.5	-9.0	7.502500	16.502500
max	59.420000	0.5	-9.0	7.673000	16.673000

Gain Distribution (Pin=-9dBm, Freq=2GHz)

# Extract all rows for Freq=2
power_2GHz = power.loc[power['Freq'] == 2].drop(['ID', 'Test', 'Die', 'Vdd', 'Pin_measured'], axis=1)
power_2GHz.head()

	Id	Freq	Pin	Pout	Gain
1	58.23	2.0	-9	7.952	16.952
81	57.89	2.0	-9	8.044	17.044
161	58.09	2.0	-9	7.994	16.994
241	57.74	2.0	-9	7.836	16.836
282	58.45	2.0	-9	7.999	16.999

# Export data to csv
power_2GHz.to_csv('docs/B18U01_804A_Power_NC_power_2GHz.csv')

# Plot gain distribution at 2GHz
power_2GHz.Gain.hist(bins=20)
plt.title('Gain Distribution @2GHz')
plt.xlabel('Gain @2GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (Pin=-9dBm, Freq=2GHz)

power_2GHz.describe()

	Id	Freq	Pin	Pout	Gain
count	199.000000	199.0	199.0	199.000000	199.000000
mean	58.081508	2.0	-9.0	7.889543	16.889543
std	0.311156	0.0	0.0	0.079377	0.079377
min	57.450000	2.0	-9.0	7.667000	16.667000
25%	57.850000	2.0	-9.0	7.831000	16.831000
50%	58.070000	2.0	-9.0	7.888000	16.888000
75%	58.285000	2.0	-9.0	7.949500	16.949500
max	58.870000	2.0	-9.0	8.074000	17.074000

Gain Distribution (Pin=-9dBm, Freq=6GHz)

# Extract all rows for Freq=6
power_6GHz = power.loc[power['Freq'] == 6].drop(['ID', 'Test', 'Die', 'Vdd', 'Pin_measured'], axis=1)
power_6GHz.head()

	Id	Freq	Pin	Pout	Gain
2	58.30	6.0	-9	7.698	16.698
82	57.97	6.0	-9	7.756	16.756
162	58.17	6.0	-9	7.732	16.732
242	57.90	6.0	-9	6.752	15.752
283	58.59	6.0	-9	7.728	16.728

# Export data to csv
power_6GHz.to_csv('docs/B18U01_804A_Power_NC_power_6GHz.csv')

# Plot gain distribution at 6GHz
power_6GHz.Gain.hist(bins=20)
plt.title('Gain Distribution @6GHz')
plt.xlabel('Gain @6GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (Pin=-9dBm, Freq=6GHz)

power_6GHz.describe()

	Id	Freq	Pin	Pout	Gain
count	199.000000	199.0	199.0	199.000000	199.000000
mean	58.196935	6.0	-9.0	7.668819	16.668819
std	0.303582	0.0	0.0	0.103030	0.103030
min	57.550000	6.0	-9.0	6.752000	15.752000
25%	57.970000	6.0	-9.0	7.618000	16.618000
50%	58.190000	6.0	-9.0	7.667000	16.667000
75%	58.395000	6.0	-9.0	7.734500	16.734500
max	58.980000	6.0	-9.0	7.845000	16.845000

Gain Distribution (Pin=-9dBm, Freq=8GHz)

# Extract all rows for Freq=8
power_8GHz = power.loc[power['Freq'] == 8].drop(['ID', 'Test', 'Die', 'Vdd', 'Pin_measured'], axis=1)
power_8GHz.head()

	Id	Freq	Pin	Pout	Gain
3	58.42	8.0	-9	8.024	17.024
83	58.23	8.0	-9	8.183	17.183
163	58.33	8.0	-9	8.048	17.048
284	58.77	8.0	-9	8.075	17.075
364	57.95	8.0	-9	8.056	17.056

# Export data to csv
power_8GHz.to_csv('docs/B18U01_804A_Power_NC_power_8GHz.csv')

# Plot gain distribution at 8GHz
power_8GHz.Gain.hist(bins=20)
plt.title('Gain Distribution @8GHz')
plt.xlabel('Gain @8GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (Pin=-9dBm, Freq=8GHz)

power_8GHz.describe()

	Id	Freq	Pin	Pout	Gain
count	198.000000	198.0	198.0	198.000000	198.000000
mean	58.442980	8.0	-9.0	7.998076	16.998076
std	0.293867	0.0	0.0	0.093862	0.093862
min	57.770000	8.0	-9.0	7.690000	16.690000
25%	58.262500	8.0	-9.0	7.952500	16.952500
50%	58.430000	8.0	-9.0	7.988000	16.988000
75%	58.640000	8.0	-9.0	8.056000	17.056000
max	59.110000	8.0	-9.0	8.200000	17.200000

Gain Distribution (Pin=-9dBm, Freq=12GHz)

# Extract all rows for Freq=12
power_12GHz = power.loc[power['Freq'] == 12].drop(['ID', 'Test', 'Die', 'Vdd', 'Pin_measured'], axis=1)
power_12GHz.head()

	Id	Freq	Pin	Pout	Gain
4	58.84	12.0	-9	9.724	18.724
84	58.58	12.0	-9	9.591	18.591
164	58.72	12.0	-9	9.601	18.601
285	59.16	12.0	-9	9.757	18.757
365	58.37	12.0	-9	9.717	18.717

# Export data to csv
power_12GHz.to_csv('docs/B18U01_804A_Power_NC_power_12GHz.csv')

# Plot gain distribution at 12GHz
power_12GHz.Gain.hist(bins=20)
plt.title('Gain Distribution @12GHz')
plt.xlabel('Gain @12GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (Pin=-9dBm, Freq=12GHz)

power_12GHz.describe()

	Id	Freq	Pin	Pout	Gain
count	198.000000	198.0	198.0	198.000000	198.000000
mean	58.881515	12.0	-9.0	9.699510	18.699510
std	0.298056	0.0	0.0	0.158162	0.158162
min	58.170000	12.0	-9.0	9.052000	18.052000
25%	58.692500	12.0	-9.0	9.648250	18.648250
50%	58.880000	12.0	-9.0	9.716500	18.716500
75%	59.080000	12.0	-9.0	9.775750	18.775750
max	59.610000	12.0	-9.0	10.029000	19.029000

Vg1 Distribution (DC)

dc = pd.read_csv('docs/B18U01_804A_Power_NC_DC.csv')
dc.head()

	Unnamed: 0	Test	Die	VDD (V)	Unnamed: 4	Unnamed: 5	Unnamed: 6	Unnamed: 7	Unnamed: 8	Unnamed: 9	...	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Unnamed: 21	Unnamed: 22	Unnamed: 23	Unnamed: 24	Unnamed: 25	Unnamed: 26
0	693862.0	TCC2	AD10	5.0	#	#	#	#	#	#	...	#	#	#	#	#	#	#	#	#	#
1	693862.0	TCC2	AD15	5.0	#	#	#	#	#	#	...	#	#	#	#	#	#	#	#	#	#
2	693862.0	TCC2	AD16	5.0	#	#	#	#	#	#	...	#	#	#	#	#	#	#	#	#	#
3	693862.0	TCC2	AD17	5.0	#	#	#	#	#	#	...	#	#	#	#	#	#	#	#	#	#
4	693862.0	TCC2	AD18	5.0	#	#	#	#	#	#	...	#	#	#	#	#	#	#	#	#	#

5 rows × 27 columns

# Drop unnecessary columns
dc = dc.drop(columns=['Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 12', 
                      'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17',  'Unnamed: 18', 
                      'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'Unnamed: 24', 
                      'Unnamed: 25', 'Unnamed: 26']).dropna()

dc.columns = ['ID', 'Test', 'Die', 'Vdd', 'Vg1', 'Idd']
dc.head()

	ID	Test	Die	Vdd	Vg1	Idd
0	693862.0	TCC2	AD10	5.0	-0.426875	0.05789
1	693862.0	TCC2	AD15	5.0	-0.399375	0.05774
2	693862.0	TCC2	AD16	5.0	-0.402813	0.05783
3	693862.0	TCC2	AD17	5.0	-0.399375	0.05754
4	693862.0	TCC2	AD18	5.0	-0.402813	0.05822

# Plot Vg1 distribution
dc.Vg1.hist(bins=20)
plt.title('Vg1 Distribution')
plt.xlabel('Vg1')
plt.ylabel('Count')
plt.show()

png

Key Statistics (DC)

dc.describe()

	ID	Vdd	Vg1	Idd
count	199.0	199.0	199.000000	199.000000
mean	693862.0	5.0	-0.389460	0.058003
std	0.0	0.0	0.026801	0.000294
min	693862.0	5.0	-0.461250	0.057430
25%	693862.0	5.0	-0.406250	0.057820
50%	693862.0	5.0	-0.392500	0.058010
75%	693862.0	5.0	-0.366719	0.058210
max	693862.0	5.0	-0.344375	0.058560

Compression Data Cleaning

compression = pd.read_csv('docs/B18U01_804A_Power_NC_compression.csv')
compression.head()

	Unnamed: 0	Test	Die	VDD (V)	Unnamed: 4	Freq (GHz)	Comp. level (dB)	Pin_comp(dBm)	Pout_comp (dBm)	Linear Gain	...	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Unnamed: 21	Unnamed: 22	Unnamed: 23	Unnamed: 24	Unnamed: 25	Unnamed: 26
0	693862	PDB2	AD10	5	#	0.5	1	1.33	16.88	16.55	...	#	#	#	#	#	#	PASS	#	#	#
1	693862	PDB2	AD10	5	#	2.0	1	0.11	16.06	16.95	...	#	#	#	#	#	#	PASS	#	#	#
2	693862	PDB2	AD10	5	#	6.0	1	0.10	15.79	16.69	...	#	#	#	#	#	#	PASS	#	#	#
3	693862	PDB2	AD10	5	#	8.0	1	-0.35	15.67	17.02	...	#	#	#	#	#	#	PASS	#	#	#
4	693862	PDB2	AD10	5	#	12.0	1	-4.62	13.09	18.72	...	#	#	#	#	#	#	PASS	#	#	#

5 rows × 27 columns

# Drop unnecessary columns
compression = compression.drop(columns=['Unnamed: 4','Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 
                                        'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 
                                        'Unnamed: 18', 'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 
                                        'Unnamed: 22', 'Unnamed: 23', 'Unnamed: 24', 'Unnamed: 25', 
                                        'Unnamed: 26'])
compression.head()

	Unnamed: 0	Test	Die	VDD (V)	Freq (GHz)	Comp. level (dB)	Pin_comp(dBm)	Pout_comp (dBm)	Linear Gain	Id (mA)
0	693862	PDB2	AD10	5	0.5	1	1.33	16.88	16.55	66.390
1	693862	PDB2	AD10	5	2.0	1	0.11	16.06	16.95	64.855
2	693862	PDB2	AD10	5	6.0	1	0.10	15.79	16.69	64.274
3	693862	PDB2	AD10	5	8.0	1	-0.35	15.67	17.02	62.335
4	693862	PDB2	AD10	5	12.0	1	-4.62	13.09	18.72	60.268

print(compression.dtypes)

Unnamed: 0            int64
Test                 object
Die                  object
VDD (V)               int64
Freq (GHz)          float64
Comp. level (dB)      int64
Pin_comp(dBm)       float64
Pout_comp (dBm)     float64
Linear Gain         float64
Id (mA)             float64
dtype: object

# Rename columns
compression.columns = ['ID', 'Test', 'Die', 'Vdd', 'Freq', 'Compression Level', 'Pin_comp', 'Pout_comp', 'Linear Gain', 'Id']
compression.head()

	ID	Test	Die	Vdd	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
0	693862	PDB2	AD10	5	0.5	1	1.33	16.88	16.55	66.390
1	693862	PDB2	AD10	5	2.0	1	0.11	16.06	16.95	64.855
2	693862	PDB2	AD10	5	6.0	1	0.10	15.79	16.69	64.274
3	693862	PDB2	AD10	5	8.0	1	-0.35	15.67	17.02	62.335
4	693862	PDB2	AD10	5	12.0	1	-4.62	13.09	18.72	60.268

# Extract P-1dB data
compression_level_1dB = compression.loc[compression['Compression Level'] == 1].dropna()
compression_level_1dB.head()

	ID	Test	Die	Vdd	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
0	693862	PDB2	AD10	5	0.5	1	1.33	16.88	16.55	66.390
1	693862	PDB2	AD10	5	2.0	1	0.11	16.06	16.95	64.855
2	693862	PDB2	AD10	5	6.0	1	0.10	15.79	16.69	64.274
3	693862	PDB2	AD10	5	8.0	1	-0.35	15.67	17.02	62.335
4	693862	PDB2	AD10	5	12.0	1	-4.62	13.09	18.72	60.268

# Extract P-3dB data
compression_level_3dB = compression.loc[compression['Compression Level'] == 3].dropna()
compression_level_3dB.head()

	ID	Test	Die	Vdd	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
5	693862	PDB2	AD10	5	0.5	3	5.16	18.71	16.55	70.883
6	693862	PDB2	AD10	5	2.0	3	3.90	17.86	16.95	70.517
7	693862	PDB2	AD10	5	6.0	3	4.02	17.71	16.69	69.217
8	693862	PDB2	AD10	5	8.0	3	3.80	17.82	17.02	65.642
9	693862	PDB2	AD10	5	12.0	3	0.06	15.78	18.72	62.861

P1dB Distribution (Freq=0.5GHz)

compression_level_1dB_500MHz = compression_level_1dB.loc[compression_level_1dB['Freq'] == 0.5].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1).dropna()
compression_level_1dB_500MHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
0	0.5	1	1.33	16.88	16.55	66.390
10	0.5	1	0.97	16.63	16.65	64.983
20	0.5	1	1.11	16.72	16.60	65.554
30	0.5	1	1.16	16.79	16.63	66.031
40	0.5	1	1.08	16.66	16.58	65.069

# Export data to csv
compression_level_1dB_500MHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_1dB_500MHz.csv')

# Plot P1dB distribution at 0.5GHz
compression_level_1dB_500MHz.Pout_comp.hist(bins=20)
plt.title('P1dB Distribution @0.5GHz')
plt.xlabel('P1dB @0.5GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P1dB, Freq=0.5GHz)

compression_level_1dB_500MHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	0.5	1.0	0.944040	16.386970	16.438687	65.134843
std	0.0	0.0	0.314287	0.325064	0.090897	1.044362
min	0.5	1.0	-0.220000	15.290000	16.240000	62.016000
25%	0.5	1.0	0.732500	16.160000	16.370000	64.329750
50%	0.5	1.0	1.015000	16.420000	16.430000	65.237000
75%	0.5	1.0	1.130000	16.617500	16.500000	65.848250
max	0.5	1.0	1.760000	17.080000	16.670000	67.889000

P1dB Distribution (Freq=2GHz)

compression_level_1dB_2GHz = compression_level_1dB.loc[compression_level_1dB['Freq'] == 2].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_1dB_2GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
1	2.0	1	0.11	16.06	16.95	64.855
11	2.0	1	-0.25	15.79	17.04	63.663
21	2.0	1	-0.09	15.89	16.99	64.271
31	2.0	1	0.00	15.99	16.99	64.685
41	2.0	1	-0.16	15.81	16.97	63.773

# Export data to csv
compression_level_1dB_2GHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_1dB_2GHz.csv')

# Plot P1dB distribution at 2GHz
compression_level_1dB_2GHz.Pout_comp.hist(bins=20)
plt.title('P1dB Distribution @2GHz')
plt.xlabel('P1dB @2GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P1dB, Freq=2GHz)

compression_level_1dB_2GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.00000
mean	2.0	1.0	-0.186414	15.695253	16.885404	63.91949
std	0.0	0.0	0.255454	0.249539	0.079501	0.78086
min	2.0	1.0	-1.270000	14.710000	16.660000	61.58200
25%	2.0	1.0	-0.337500	15.512500	16.830000	63.38425
50%	2.0	1.0	-0.145000	15.725000	16.880000	63.96500
75%	2.0	1.0	0.000000	15.870000	16.947500	64.43750
max	2.0	1.0	0.440000	16.300000	17.070000	66.03100

P1dB Distribution (Freq=6GHz)

compression_level_1dB_6GHz = compression_level_1dB.loc[compression_level_1dB['Freq'] == 6].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_1dB_6GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
2	6.0	1	0.10	15.79	16.69	64.274
12	6.0	1	-0.18	15.57	16.75	63.153
22	6.0	1	-0.07	15.66	16.73	63.745
32	6.0	1	-0.12	15.60	16.72	63.931
42	6.0	1	-0.06	15.68	16.75	63.356

# Export data to csv
compression_level_1dB_6GHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_1dB_6GHz.csv')

# Plot P1dB distribution at 6GHz
compression_level_1dB_6GHz.Pout_comp.hist(bins=20)
plt.title('P1dB Distribution @6GHz')
plt.xlabel('P1dB @6GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P1dB, Freq=6GHz)

compression_level_1dB_6GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	6.0	1.0	-0.060960	15.607778	16.669040	63.588475
std	0.0	0.0	0.242344	0.242546	0.079775	0.708473
min	6.0	1.0	-1.180000	14.530000	16.380000	61.390000
25%	6.0	1.0	-0.217500	15.440000	16.612500	63.072250
50%	6.0	1.0	-0.005000	15.650000	16.665000	63.644000
75%	6.0	1.0	0.110000	15.780000	16.730000	64.051500
max	6.0	1.0	0.710000	16.100000	16.840000	65.804000

P1dB Distribution (Freq=8GHz)

compression_level_1dB_8GHz = compression_level_1dB.loc[compression_level_1dB['Freq'] == 8].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_1dB_8GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
3	8.0	1	-0.35	15.67	17.02	62.335
13	8.0	1	-1.08	15.10	17.18	61.188
23	8.0	1	-0.70	15.34	17.04	61.786
33	8.0	1	-0.73	15.33	17.07	62.192
43	8.0	1	-0.75	15.30	17.05	61.328

# Export data to csv
compression_level_1dB_8GHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_1dB_8GHz.csv')

# Plot P1dB distribution at 8GHz
compression_level_1dB_8GHz.Pout_comp.hist(bins=20)
plt.title('P1dB Distribution @8GHz')
plt.xlabel('P1dB @8GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P1dB, Freq=8GHz)

compression_level_1dB_8GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	8.0	1.0	-0.667222	15.320909	16.993788	61.711783
std	0.0	0.0	0.308224	0.298023	0.094010	0.580499
min	8.0	1.0	-2.000000	14.040000	16.690000	60.051000
25%	8.0	1.0	-0.867500	15.112500	16.950000	61.278000
50%	8.0	1.0	-0.620000	15.340000	16.980000	61.745000
75%	8.0	1.0	-0.462500	15.530000	17.050000	62.108000
max	8.0	1.0	0.060000	16.020000	17.200000	63.266000

P1dB Distribution (Freq=12GHz)

compression_level_1dB_12GHz = compression_level_1dB.loc[compression_level_1dB['Freq'] == 12].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_1dB_12GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
4	12.0	1	-4.62	13.09	18.72	60.268
14	12.0	1	-4.19	13.39	18.59	60.130
24	12.0	1	-4.54	13.05	18.60	60.112
34	12.0	1	-4.62	13.12	18.75	60.482
44	12.0	1	-4.69	13.02	18.71	59.567

# Export data to csv
compression_level_1dB_12GHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_1dB_12GHz.csv')

# Plot P1dB distribution at 12GHz
compression_level_1dB_12GHz.Pout_comp.hist(bins=20)
plt.title('P1dB Distribution @12GHz')
plt.xlabel('P1dB @12GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P1dB, Freq=12GHz)

compression_level_1dB_12GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	12.0	1.0	-4.733586	12.955758	18.695404	60.020495
std	0.0	0.0	0.411589	0.344755	0.157910	0.428966
min	12.0	1.0	-6.100000	11.610000	18.050000	59.078000
25%	12.0	1.0	-4.975000	12.760000	18.640000	59.734000
50%	12.0	1.0	-4.720000	12.960000	18.710000	60.020000
75%	12.0	1.0	-4.552500	13.177500	18.770000	60.288750
max	12.0	1.0	-3.320000	13.730000	19.020000	61.358000

P3dB Distribution (Freq=0.5GHz)

compression_level_3dB_500MHz = compression_level_3dB.loc[compression_level_3dB['Freq'] == 0.5].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_3dB_500MHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
5	0.5	3	5.16	18.71	16.55	70.883
15	0.5	3	4.77	18.42	16.65	68.855
25	0.5	3	4.92	18.53	16.60	69.615
35	0.5	3	4.96	18.59	16.63	70.068
45	0.5	3	4.91	18.49	16.58	69.164

# Export data to csv
compression_level_3dB_500MHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_3dB_500MHz.csv')

# Plot P1dB distribution at 6GHz
compression_level_3dB_500MHz.Pout_comp.hist(bins=20)
plt.title('P3dB Distribution @0.5GHz')
plt.xlabel('P3dB @0.5GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P3dB, Freq=0.5GHz)

compression_level_3dB_500MHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	0.5	3.0	4.761263	18.203990	16.438687	68.790288
std	0.0	0.0	0.308463	0.319539	0.090897	1.679216
min	0.5	3.0	3.680000	17.180000	16.240000	63.522000
25%	0.5	3.0	4.550000	17.970000	16.370000	67.557250
50%	0.5	3.0	4.800000	18.230000	16.430000	69.007500
75%	0.5	3.0	4.930000	18.427500	16.500000	69.852000
max	0.5	3.0	5.580000	18.900000	16.670000	72.617000

P3dB Distribution (Freq=2GHz)

compression_level_3dB_2GHz = compression_level_3dB.loc[compression_level_3dB['Freq'] == 2].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_3dB_2GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
6	2.0	3	3.90	17.86	16.95	70.517
16	2.0	3	3.49	17.54	17.04	68.544
26	2.0	3	3.65	17.65	16.99	69.455
36	2.0	3	3.68	17.68	16.99	69.716
46	2.0	3	3.60	17.58	16.97	68.987

# Export data to csv
compression_level_3dB_2GHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_3dB_2GHz.csv')

# Plot P1dB distribution at 2GHz
compression_level_3dB_2GHz.Pout_comp.hist(bins=20)
plt.title('P3dB Distribution @2GHz')
plt.xlabel('P3dB @2GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P3dB, Freq=2GHz)

compression_level_3dB_2GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	2.0	3.0	3.533535	17.423485	16.885404	68.482773
std	0.0	0.0	0.289194	0.284515	0.079501	1.558927
min	2.0	3.0	2.540000	16.500000	16.660000	63.801000
25%	2.0	3.0	3.330000	17.220000	16.830000	67.309000
50%	2.0	3.0	3.570000	17.450000	16.880000	68.594500
75%	2.0	3.0	3.710000	17.580000	16.947500	69.448250
max	2.0	3.0	4.310000	18.110000	17.070000	72.171000

P3dB Distribution (Freq=6GHz)

compression_level_3dB_6GHz = compression_level_3dB.loc[compression_level_3dB['Freq'] == 6].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_3dB_6GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
7	6.0	3	4.02	17.71	16.69	69.217
17	6.0	3	3.72	17.48	16.75	67.571
27	6.0	3	3.97	17.70	16.73	68.685
37	6.0	3	3.85	17.58	16.72	68.645
47	6.0	3	3.82	17.57	16.75	68.205

# Export data to csv
compression_level_3dB_6GHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_3dB_6GHz.csv')

# Plot P1dB distribution at 6GHz
compression_level_3dB_6GHz.Pout_comp.hist(bins=20)
plt.title('P3dB Distribution @6GHz')
plt.xlabel('P3dB @6GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P3dB, Freq=6GHz)

compression_level_3dB_6GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	6.0	3.0	3.795657	17.469192	16.669040	67.747985
std	0.0	0.0	0.243707	0.240018	0.079775	1.187995
min	6.0	3.0	2.970000	16.680000	16.380000	64.084000
25%	6.0	3.0	3.615000	17.290000	16.612500	66.841750
50%	6.0	3.0	3.835000	17.490000	16.665000	67.885500
75%	6.0	3.0	3.957500	17.637500	16.730000	68.548750
max	6.0	3.0	4.490000	17.990000	16.840000	70.564000

P3dB Distribution (Freq=8GHz)

compression_level_3dB_8GHz = compression_level_3dB.loc[compression_level_3dB['Freq'] == 8].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_3dB_8GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
8	8.0	3	3.80	17.82	17.02	65.642
18	8.0	3	3.21	17.40	17.18	63.935
28	8.0	3	3.47	17.52	17.04	64.799
38	8.0	3	3.57	17.65	17.07	65.293
48	8.0	3	3.47	17.52	17.05	64.497

# Export data to csv
compression_level_3dB_8GHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_3dB_8GHz.csv')

# Plot P1dB distribution at 8GHz
compression_level_3dB_8GHz.Pout_comp.hist(bins=20)
plt.title('P3dB Distribution @8GHz')
plt.xlabel('P3dB @8GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P3dB, Freq=8GHz)

compression_level_3dB_8GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	8.0	3.0	3.483687	17.482374	16.993788	64.283030
std	0.0	0.0	0.246301	0.230673	0.094010	0.962568
min	8.0	3.0	2.560000	16.640000	16.690000	61.184000
25%	8.0	3.0	3.300000	17.320000	16.950000	63.626750
50%	8.0	3.0	3.520000	17.520000	16.980000	64.376000
75%	8.0	3.0	3.620000	17.640000	17.050000	64.925250
max	8.0	3.0	4.140000	18.030000	17.200000	66.522000

P3dB Distribution (Freq=12GHz)

compression_level_3dB_12GHz = compression_level_3dB.loc[compression_level_3dB['Freq'] == 12].drop(['ID', 'Test', 'Die', 'Vdd'], axis=1)
compression_level_3dB_12GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
9	12.0	3	0.06	15.78	18.72	62.861
19	12.0	3	0.37	15.96	18.59	63.196
29	12.0	3	0.24	15.84	18.60	62.452
39	12.0	3	-0.12	15.62	18.75	62.600
49	12.0	3	-0.17	15.54	18.71	61.687

# Export data to csv
compression_level_3dB_12GHz.to_csv('docs/B18U01_804A_Power_NC_compression_level_3dB_12GHz.csv')

# Plot P1dB distribution at 12GHz
compression_level_3dB_12GHz.Pout_comp.hist(bins=20)
plt.title('P3dB Distribution @12GHz')
plt.xlabel('P3dB @12GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P3dB, Freq=12GHz)

compression_level_3dB_12GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain	Id
count	198.0	198.0	198.000000	198.000000	198.000000	198.000000
mean	12.0	3.0	-0.152525	15.539747	18.695404	61.859636
std	0.0	0.0	0.404779	0.321218	0.157910	0.834855
min	12.0	3.0	-1.430000	14.280000	18.050000	59.739000
25%	12.0	3.0	-0.360000	15.372500	18.640000	61.253250
50%	12.0	3.0	-0.160000	15.560000	18.710000	61.821000
75%	12.0	3.0	0.007500	15.737500	18.770000	62.315500
max	12.0	3.0	1.080000	16.560000	19.020000	64.768000