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_805A_Power_NC_power.csv')
power.head()

	Unnamed: 0	Test	Die	VDD (V)	Unnamed: 4	Unnamed: 5	Unnamed: 6	Id1 (mA)	Id2 ( mA)	Freq (GHz)	...	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Unnamed: 21	Unnamed: 22	Unnamed: 23	Unnamed: 24	Unnamed: 25	Unnamed: 26
0	694255	POW1	AB10	4	#	#	#	40.87	91.48	6	...	#	#	#	#	#	#	#	#	#	#
1	694255	POW1	AB10	4	#	#	#	40.80	90.39	9	...	#	#	#	#	#	#	#	#	#	#
2	694255	POW1	AB10	4	#	#	#	40.75	90.28	12	...	#	#	#	#	#	#	#	#	#	#
3	694255	POW1	AB10	4	#	#	#	40.68	90.13	15	...	#	#	#	#	#	#	#	#	#	#
4	694255	POW1	AB10	4	#	#	#	40.63	89.54	18	...	#	#	#	#	#	#	#	#	#	#

5 rows × 27 columns

# Drop unnecessary columns
power = power.drop(columns=['Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', '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)	Id1 (mA)	Id2 ( mA)	Freq (GHz)	Pin (dBm)	Pin (dBm).1	Pout (dBm)	Gain (dB)
0	694255	POW1	AB10	4	40.87	91.48	6	-13	-13.02219	10.166	23.166
1	694255	POW1	AB10	4	40.80	90.39	9	-13	-12.97462	9.035	22.035
2	694255	POW1	AB10	4	40.75	90.28	12	-13	-13.02288	9.278	22.278
3	694255	POW1	AB10	4	40.68	90.13	15	-13	-13.03762	9.631	22.631
4	694255	POW1	AB10	4	40.63	89.54	18	-13	-13.01050	7.811	20.811

# Rename columns
power.columns = ['ID', 'Test', 'Die', 'Vdd', 'Id1', 'Id2', 'Freq', 'Pin', 'Pin_measured', 'Pout', 'Gain']
power.head()

	ID	Test	Die	Vdd	Id1	Id2	Freq	Pin	Pin_measured	Pout	Gain
0	694255	POW1	AB10	4	40.87	91.48	6	-13	-13.02219	10.166	23.166
1	694255	POW1	AB10	4	40.80	90.39	9	-13	-12.97462	9.035	22.035
2	694255	POW1	AB10	4	40.75	90.28	12	-13	-13.02288	9.278	22.278
3	694255	POW1	AB10	4	40.68	90.13	15	-13	-13.03762	9.631	22.631
4	694255	POW1	AB10	4	40.63	89.54	18	-13	-13.01050	7.811	20.811

# Check datatypes
print(power.dtypes)

ID                int64
Test             object
Die              object
Vdd               int64
Id1             float64
Id2             float64
Freq              int64
Pin               int64
Pin_measured    float64
Pout            float64
Gain            float64
dtype: object

# Extract only the data for Pin=-13
power = power.loc[power['Pin'] == -13]
power.head()

	ID	Test	Die	Vdd	Id1	Id2	Freq	Pin	Pin_measured	Pout	Gain
0	694255	POW1	AB10	4	40.87	91.48	6	-13	-13.02219	10.166	23.166
1	694255	POW1	AB10	4	40.80	90.39	9	-13	-12.97462	9.035	22.035
2	694255	POW1	AB10	4	40.75	90.28	12	-13	-13.02288	9.278	22.278
3	694255	POW1	AB10	4	40.68	90.13	15	-13	-13.03762	9.631	22.631
4	694255	POW1	AB10	4	40.63	89.54	18	-13	-13.01050	7.811	20.811

Gain Distribution (Pin=-13dBm, 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()

	Id1	Id2	Freq	Pin	Pout	Gain
0	40.87	91.48	6	-13	10.166	23.166
80	40.65	89.51	6	-13	10.164	23.164
160	41.42	89.49	6	-13	10.196	23.196
240	39.96	88.08	6	-13	10.203	23.203
320	40.90	88.66	6	-13	10.190	23.190

# Export data to csv
power_6GHz.to_csv('docs/B18U01_805A_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=-13dBm, Freq=6GHz)

power_6GHz.describe()

	Id1	Id2	Freq	Pin	Pout	Gain
count	235.000000	235.000000	235.0	235.0	235.000000	235.000000
mean	40.023404	87.898936	6.0	-13.0	10.269426	23.269426
std	1.413705	2.947539	0.0	0.0	0.207678	0.207678
min	36.350000	81.940000	6.0	-13.0	8.546000	21.546000
25%	39.085000	85.585000	6.0	-13.0	10.167500	23.167500
50%	40.050000	87.950000	6.0	-13.0	10.290000	23.290000
75%	40.780000	89.505000	6.0	-13.0	10.412500	23.412500
max	46.830000	97.260000	6.0	-13.0	10.622000	23.622000

Gain Distribution (Pin=-13dBm, Freq=9GHz)

# Extract all rows for Freq=9
power_9GHz = power.loc[power['Freq'] == 9].drop(['ID', 'Test', 'Die', 'Vdd', 'Pin_measured'], axis=1)
power_9GHz.head()

	Id1	Id2	Freq	Pin	Pout	Gain
1	40.80	90.39	9	-13	9.035	22.035
81	40.60	88.50	9	-13	9.047	22.047
161	41.40	88.42	9	-13	9.157	22.157
241	39.93	87.12	9	-13	9.108	22.108
321	40.87	87.62	9	-13	9.160	22.160

# Export data to csv
power_9GHz.to_csv('docs/B18U01_805A_Power_NC_power_9GHz.csv')

# Plot gain distribution at 9GHz
power_9GHz.Gain.hist(bins=20)
plt.title('Gain Distribution @9GHz')
plt.xlabel('Gain @9GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (Pin=-13dBm, Freq=9GHz)

power_9GHz.describe()

	Id1	Id2	Freq	Pin	Pout	Gain
count	235.000000	235.000000	235.0	235.0	235.000000	235.000000
mean	39.986596	86.915064	9.0	-13.0	9.216723	22.216723
std	1.404441	2.902639	0.0	0.0	0.186458	0.186458
min	36.380000	80.860000	9.0	-13.0	8.636000	21.636000
25%	39.080000	84.555000	9.0	-13.0	9.107500	22.107500
50%	40.020000	86.960000	9.0	-13.0	9.231000	22.231000
75%	40.770000	88.505000	9.0	-13.0	9.352500	22.352500
max	46.770000	96.090000	9.0	-13.0	9.570000	22.570000

Gain Distribution (Pin=-13dBm, 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()

	Id1	Id2	Freq	Pin	Pout	Gain
2	40.75	90.28	12	-13	9.278	22.278
82	40.52	88.39	12	-13	9.273	22.273
162	41.31	88.37	12	-13	9.415	22.415
242	39.89	87.00	12	-13	9.322	22.322
322	40.79	87.49	12	-13	9.434	22.434

# Export data to csv
power_12GHz.to_csv('docs/B18U01_805A_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=-13dBm, Freq=12GHz)

power_12GHz.describe()

	Id1	Id2	Freq	Pin	Pout	Gain
count	235.000000	235.000000	235.0	235.0	235.000000	235.000000
mean	39.923021	86.849149	12.0	-13.0	9.441706	22.441706
std	1.406057	2.894384	0.0	0.0	0.168541	0.168541
min	36.300000	80.710000	12.0	-13.0	8.895000	21.895000
25%	39.020000	84.520000	12.0	-13.0	9.332000	22.332000
50%	39.950000	86.830000	12.0	-13.0	9.452000	22.452000
75%	40.680000	88.470000	12.0	-13.0	9.570000	22.570000
max	46.740000	95.930000	12.0	-13.0	9.785000	22.785000

Gain Distribution (Pin=-13dBm, Freq=15GHz)

# Extract all rows for Freq=15
power_15GHz = power.loc[power['Freq'] == 15].drop(['ID', 'Test', 'Die', 'Vdd', 'Pin_measured'], axis=1)
power_15GHz.head()

	Id1	Id2	Freq	Pin	Pout	Gain
3	40.68	90.13	15	-13	9.631	22.631
83	40.45	88.23	15	-13	9.561	22.561
163	41.25	88.24	15	-13	9.682	22.682
243	39.78	86.87	15	-13	9.544	22.544
323	40.73	87.33	15	-13	9.662	22.662

# Export data to csv
power_15GHz.to_csv('docs/B18U01_805A_Power_NC_power_15GHz.csv')

# Plot gain distribution at 15GHz
power_15GHz.Gain.hist(bins=20)
plt.title('Gain Distribution @15GHz')
plt.xlabel('Gain @15GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (Pin=-13dBm, Freq=15GHz)

power_15GHz.describe()

	Id1	Id2	Freq	Pin	Pout	Gain
count	235.000000	235.000000	235.0	235.0	235.000000	235.000000
mean	39.833149	86.703915	15.0	-13.0	9.652800	22.652800
std	1.402872	2.908010	0.0	0.0	0.129627	0.129627
min	36.210000	80.490000	15.0	-13.0	9.175000	22.175000
25%	38.910000	84.390000	15.0	-13.0	9.563000	22.563000
50%	39.870000	86.690000	15.0	-13.0	9.659000	22.659000
75%	40.610000	88.315000	15.0	-13.0	9.738500	22.738500
max	46.670000	95.820000	15.0	-13.0	10.108000	23.108000

Gain Distribution (Pin=-13dBm, Freq=18GHz)

# Extract all rows for Freq=18
power_18GHz = power.loc[power['Freq'] == 18].drop(['ID', 'Test', 'Die', 'Vdd', 'Pin_measured'], axis=1)
power_18GHz.head()

	Id1	Id2	Freq	Pin	Pout	Gain
4	40.63	89.54	18	-13	7.811	20.811
84	40.39	87.68	18	-13	7.746	20.746
164	41.20	87.62	18	-13	7.836	20.836
244	39.75	86.32	18	-13	7.670	20.670
324	40.66	86.71	18	-13	7.765	20.765

# Export data to csv
power_18GHz.to_csv('docs/B18U01_805A_Power_NC_power_18GHz.csv')

# Plot gain distribution at 18GHz
power_18GHz.Gain.hist(bins=20)
plt.title('Gain Distribution @18GHz')
plt.xlabel('Gain @18GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (Pin=-13dBm, Freq=18GHz)

power_18GHz.describe()

	Id1	Id2	Freq	Pin	Pout	Gain
count	235.000000	235.000000	235.0	235.0	235.000000	235.000000
mean	39.805660	86.129447	18.0	-13.0	7.767506	20.767506
std	1.399622	2.873435	0.0	0.0	0.150884	0.150884
min	36.200000	79.860000	18.0	-13.0	7.272000	20.272000
25%	38.895000	83.865000	18.0	-13.0	7.673000	20.673000
50%	39.830000	86.110000	18.0	-13.0	7.771000	20.771000
75%	40.585000	87.725000	18.0	-13.0	7.848500	20.848500
max	46.630000	95.140000	18.0	-13.0	8.387000	21.387000

ID1 Distribution (Pin=-13dBm, Freq=6GHz)

# Plot Id1 distribution
power_6GHz.Id1.hist(bins=20)
plt.title('Id1 Distribution')
plt.xlabel('Id1')
plt.ylabel('Count')
plt.show()

png

ID2 Distribution (Pin=-13dBm, Freq=6GHz)

# Plot Id2 distribution
power_6GHz.Id2.hist(bins=20)
plt.title('Id2 Distribution')
plt.xlabel('Id2')
plt.ylabel('Count')
plt.show()

png

Compression Data Cleaning

compression = pd.read_csv('docs/B18U01_805A_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 (dB)	...	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Unnamed: 21	Unnamed: 22	Unnamed: 23	Unnamed: 24	Unnamed: 25	Unnamed: 26
0	694255	PDB2	AB10	4	#	6	1	-6.61	15.55	23.16	...	#	#	#	#	#	#	PASS	#	#	#
1	694255	PDB2	AB10	4	#	9	1	-5.11	15.92	22.03	...	#	#	#	#	#	#	PASS	#	#	#
2	694255	PDB2	AB10	4	#	12	1	-4.99	16.28	22.27	...	#	#	#	#	#	#	PASS	#	#	#
3	694255	PDB2	AB10	4	#	15	1	-4.98	16.64	22.63	...	#	#	#	#	#	#	PASS	#	#	#
4	694255	PDB2	AB10	4	#	18	1	-2.54	17.26	20.81	...	#	#	#	#	#	#	PASS	#	#	#

5 rows × 27 columns

# Drop unnecessary columns
compression = compression.drop(columns=['Unnamed: 0', 'Unnamed: 4', 'Unnamed: 10', '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()

	Test	Die	VDD (V)	Freq (GHz)	Comp. level (dB)	Pin_comp(dBm)	Pout_comp (dBm)	Linear Gain (dB)
0	PDB2	AB10	4	6	1	-6.61	15.55	23.16
1	PDB2	AB10	4	9	1	-5.11	15.92	22.03
2	PDB2	AB10	4	12	1	-4.99	16.28	22.27
3	PDB2	AB10	4	15	1	-4.98	16.64	22.63
4	PDB2	AB10	4	18	1	-2.54	17.26	20.81

print(compression.dtypes)

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

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

	Test	Die	Vdd	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
0	PDB2	AB10	4	6	1	-6.61	15.55	23.16
1	PDB2	AB10	4	9	1	-5.11	15.92	22.03
2	PDB2	AB10	4	12	1	-4.99	16.28	22.27
3	PDB2	AB10	4	15	1	-4.98	16.64	22.63
4	PDB2	AB10	4	18	1	-2.54	17.26	20.81

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

	Test	Die	Vdd	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
0	PDB2	AB10	4	6	1	-6.61	15.55	23.16
1	PDB2	AB10	4	9	1	-5.11	15.92	22.03
2	PDB2	AB10	4	12	1	-4.99	16.28	22.27
3	PDB2	AB10	4	15	1	-4.98	16.64	22.63
4	PDB2	AB10	4	18	1	-2.54	17.26	20.81

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

	Test	Die	Vdd	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
5	PDB2	AB10	4	6	3	-3.94	16.21	23.16
6	PDB2	AB10	4	9	3	-1.95	17.07	22.03
7	PDB2	AB10	4	12	3	-1.77	17.49	22.27
8	PDB2	AB10	4	15	3	-1.62	18.00	22.63
9	PDB2	AB10	4	18	3	1.01	18.82	20.81

P1dB Distribution (Freq=6GHz)

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

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
0	6	1	-6.61	15.55	23.16
10	6	1	-6.62	15.53	23.16
20	6	1	-6.70	15.49	23.19
30	6	1	-6.70	15.49	23.20
40	6	1	-6.69	15.49	23.19

# Export data to csv
compression_level_1dB_6GHz.to_csv('docs/B18U01_805A_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
count	235.0	235.0	235.000000	235.000000	235.000000
mean	6.0	1.0	-6.942468	15.316511	23.264809
std	0.0	0.0	0.295545	0.187755	0.207964
min	6.0	1.0	-7.680000	14.870000	21.540000
25%	6.0	1.0	-7.100000	15.235000	23.160000
50%	6.0	1.0	-6.940000	15.330000	23.290000
75%	6.0	1.0	-6.755000	15.430000	23.410000
max	6.0	1.0	-5.570000	15.970000	23.620000

P1dB Distribution (Freq=9GHz)

compression_level_1dB_9GHz = compression_level_1dB.loc[compression_level_1dB['Freq'] == 9].drop(['Test', 'Die', 'Vdd'], axis=1)
compression_level_1dB_9GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
1	9	1	-5.11	15.92	22.03
11	9	1	-5.16	15.87	22.04
21	9	1	-5.31	15.84	22.15
31	9	1	-5.31	15.79	22.10
41	9	1	-5.33	15.82	22.16

# Export data to csv
compression_level_1dB_9GHz.to_csv('docs/B18U01_805A_Power_NC_compression_level_1dB_9GHz.csv')

# Plot P1dB distribution at 9GHz
compression_level_1dB_9GHz.Pout_comp.hist(bins=20)
plt.title('P1dB Distribution @9GHz')
plt.xlabel('P1dB @9GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P1dB, Freq=9GHz)

compression_level_1dB_9GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
count	235.0	235.0	235.000000	235.000000	235.000000
mean	9.0	1.0	-5.603191	15.604043	22.212255
std	0.0	0.0	0.307016	0.173644	0.186583
min	9.0	1.0	-6.390000	15.130000	21.630000
25%	9.0	1.0	-5.800000	15.510000	22.100000
50%	9.0	1.0	-5.630000	15.620000	22.230000
75%	9.0	1.0	-5.405000	15.705000	22.350000
max	9.0	1.0	-4.730000	15.990000	22.570000

P1dB Distribution (Freq=12GHz)

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

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
2	12	1	-4.99	16.28	22.27
12	12	1	-5.10	16.17	22.27
22	12	1	-5.22	16.18	22.41
32	12	1	-5.26	16.05	22.32
42	12	1	-5.29	16.14	22.43

# Export data to csv
compression_level_1dB_12GHz.to_csv('docs/B18U01_805A_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
count	235.0	235.0	235.000000	235.000000	235.000000
mean	12.0	1.0	-5.510723	15.921489	22.437064
std	0.0	0.0	0.284806	0.158192	0.168557
min	12.0	1.0	-6.190000	15.550000	21.890000
25%	12.0	1.0	-5.720000	15.805000	22.330000
50%	12.0	1.0	-5.560000	15.920000	22.450000
75%	12.0	1.0	-5.340000	16.000000	22.570000
max	12.0	1.0	-4.520000	16.370000	22.780000

P1dB Distribution (Freq=15GHz)

compression_level_1dB_15GHz = compression_level_1dB.loc[compression_level_1dB['Freq'] == 15].drop(['Test', 'Die', 'Vdd'], axis=1)
compression_level_1dB_15GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
3	15	1	-4.98	16.64	22.63
13	15	1	-4.97	16.58	22.56
23	15	1	-5.13	16.54	22.68
33	15	1	-5.06	16.47	22.54
43	15	1	-5.15	16.50	22.66

# Export data to csv
compression_level_1dB_15GHz.to_csv('docs/B18U01_805A_Power_NC_compression_level_1dB_15GHz.csv')

# Plot P1dB distribution at 15GHz
compression_level_1dB_15GHz.Pout_comp.hist(bins=20)
plt.title('P1dB Distribution @15GHz')
plt.xlabel('P1dB @15GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P1dB, Freq=15GHz)

compression_level_1dB_15GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
count	235.0	235.0	235.000000	235.000000	235.00000
mean	15.0	1.0	-5.354170	16.288638	22.64834
std	0.0	0.0	0.228975	0.171678	0.12950
min	15.0	1.0	-5.890000	15.910000	22.17000
25%	15.0	1.0	-5.520000	16.165000	22.56000
50%	15.0	1.0	-5.380000	16.290000	22.65000
75%	15.0	1.0	-5.210000	16.390000	22.73500
max	15.0	1.0	-4.720000	16.760000	23.10000

P1dB Distribution (Freq=18GHz)

compression_level_1dB_18GHz = compression_level_1dB.loc[compression_level_1dB['Freq'] == 18].drop(['Test', 'Die', 'Vdd'], axis=1)
compression_level_1dB_18GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
4	18	1	-2.54	17.26	20.81
14	18	1	-2.58	17.16	20.74
24	18	1	-2.66	17.17	20.83
34	18	1	-2.58	17.08	20.67
44	18	1	-2.62	17.14	20.76

# Export data to csv
compression_level_1dB_18GHz.to_csv('docs/B18U01_805A_Power_NC_compression_level_1dB_18GHz.csv')

# Plot P1dB distribution at 18GHz
compression_level_1dB_18GHz.Pout_comp.hist(bins=20)
plt.title('P1dB Distribution @18GHz')
plt.xlabel('P1dB @18GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P1dB, Freq=18GHz)

compression_level_1dB_18GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
count	235.0	235.0	235.000000	235.000000	235.000000
mean	18.0	1.0	-2.820766	16.936894	20.763064
std	0.0	0.0	0.211572	0.169759	0.150790
min	18.0	1.0	-3.400000	16.520000	20.270000
25%	18.0	1.0	-2.960000	16.825000	20.670000
50%	18.0	1.0	-2.830000	16.930000	20.770000
75%	18.0	1.0	-2.670000	17.020000	20.845000
max	18.0	1.0	-2.270000	17.460000	21.380000

P3dB Distribution (Freq=6GHz)

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

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
5	6	3	-3.94	16.21	23.16
15	6	3	-3.93	16.22	23.16
25	6	3	-4.01	16.17	23.19
35	6	3	-3.98	16.21	23.20
45	6	3	-3.96	16.22	23.19

# Export data to csv
compression_level_3dB_6GHz.to_csv('docs/B18U01_805A_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
count	235.0	235.0	235.000000	235.000000	235.000000
mean	6.0	3.0	-4.171830	16.087745	23.264809
std	0.0	0.0	0.280871	0.225693	0.207964
min	6.0	3.0	-4.880000	15.460000	21.540000
25%	6.0	3.0	-4.340000	15.950000	23.160000
50%	6.0	3.0	-4.150000	16.080000	23.290000
75%	6.0	3.0	-3.980000	16.210000	23.410000
max	6.0	3.0	-3.070000	16.990000	23.620000

P3dB Distribution (Freq=9GHz)

compression_level_3dB_9GHz = compression_level_3dB.loc[compression_level_3dB['Freq'] == 9].drop(['Test', 'Die', 'Vdd'], axis=1)
compression_level_3dB_9GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
6	9	3	-1.95	17.07	22.03
16	9	3	-2.02	17.02	22.04
26	9	3	-2.14	17.01	22.15
36	9	3	-2.15	16.95	22.10
46	9	3	-2.16	16.99	22.16

# Export data to csv
compression_level_3dB_9GHz.to_csv('docs/B18U01_805A_Power_NC_compression_level_3dB_9GHz.csv')

# Plot P1dB distribution at 9GHz
compression_level_3dB_9GHz.Pout_comp.hist(bins=20)
plt.title('P3dB Distribution @9GHz')
plt.xlabel('P3dB @9GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P3dB, Freq=9GHz)

compression_level_3dB_9GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
count	235.0	235.0	235.000000	235.000000	235.000000
mean	9.0	3.0	-2.433574	16.773574	22.212255
std	0.0	0.0	0.308600	0.176468	0.186583
min	9.0	3.0	-3.180000	16.330000	21.630000
25%	9.0	3.0	-2.640000	16.655000	22.100000
50%	9.0	3.0	-2.460000	16.790000	22.230000
75%	9.0	3.0	-2.220000	16.890000	22.350000
max	9.0	3.0	-1.550000	17.290000	22.570000

P3dB Distribution (Freq=12GHz)

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

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
7	12	3	-1.77	17.49	22.27
17	12	3	-1.84	17.42	22.27
27	12	3	-1.98	17.43	22.41
37	12	3	-1.97	17.34	22.32
47	12	3	-2.03	17.40	22.43

# Export data to csv
compression_level_3dB_12GHz.to_csv('docs/B18U01_805A_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
count	235.0	235.0	235.000000	235.000000	235.000000
mean	12.0	3.0	-2.262723	17.168383	22.437064
std	0.0	0.0	0.303697	0.180232	0.168557
min	12.0	3.0	-2.960000	16.740000	21.890000
25%	12.0	3.0	-2.485000	17.030000	22.330000
50%	12.0	3.0	-2.300000	17.190000	22.450000
75%	12.0	3.0	-2.060000	17.285000	22.570000
max	12.0	3.0	-1.320000	17.610000	22.780000

P3dB Distribution (Freq=15GHz)

compression_level_3dB_15GHz = compression_level_3dB.loc[compression_level_3dB['Freq'] == 15].drop(['Test', 'Die', 'Vdd'], axis=1)
compression_level_3dB_15GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
8	15	3	-1.62	18.00	22.63
18	15	3	-1.63	17.93	22.56
28	15	3	-1.77	17.90	22.68
38	15	3	-1.69	17.85	22.54
48	15	3	-1.77	17.88	22.66

# Export data to csv
compression_level_3dB_15GHz.to_csv('docs/B18U01_805A_Power_NC_compression_level_3dB_15GHz.csv')

# Plot P1dB distribution at 15GHz
compression_level_3dB_15GHz.Pout_comp.hist(bins=20)
plt.title('P3dB Distribution @15GHz')
plt.xlabel('P3dB @15GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P3dB, Freq=15GHz)

compression_level_3dB_15GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
count	235.0	235.0	235.000000	235.000000	235.00000
mean	15.0	3.0	-1.965532	17.678170	22.64834
std	0.0	0.0	0.228694	0.174215	0.12950
min	15.0	3.0	-2.540000	17.250000	22.17000
25%	15.0	3.0	-2.125000	17.550000	22.56000
50%	15.0	3.0	-1.970000	17.690000	22.65000
75%	15.0	3.0	-1.810000	17.790000	22.73500
max	15.0	3.0	-1.360000	18.140000	23.10000

P3dB Distribution (Freq=18GHz)

compression_level_3dB_18GHz = compression_level_3dB.loc[compression_level_3dB['Freq'] == 18].drop(['Test', 'Die', 'Vdd'], axis=1)
compression_level_3dB_18GHz.head()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
9	18	3	1.01	18.82	20.81
19	18	3	1.00	18.75	20.74
29	18	3	0.88	18.72	20.83
39	18	3	1.01	18.68	20.67
49	18	3	0.93	18.70	20.76

# Export data to csv
compression_level_3dB_18GHz.to_csv('docs/B18U01_805A_Power_NC_compression_level_3dB_18GHz.csv')

# Plot P1dB distribution at 18GHz
compression_level_3dB_18GHz.Pout_comp.hist(bins=20)
plt.title('P3dB Distribution @18GHz')
plt.xlabel('P3dB @18GHz')
plt.ylabel('Count')
plt.show()

png

Key Statistics (P3dB, Freq=18GHz)

compression_level_3dB_18GHz.describe()

	Freq	Compression Level	Pin_comp	Pout_comp	Linear Gain
count	235.0	235.0	235.000000	235.000000	235.000000
mean	18.0	3.0	0.741404	18.508809	20.763064
std	0.0	0.0	0.224315	0.172328	0.150790
min	18.0	3.0	0.130000	18.080000	20.270000
25%	18.0	3.0	0.575000	18.385000	20.670000
50%	18.0	3.0	0.750000	18.530000	20.770000
75%	18.0	3.0	0.910000	18.620000	20.845000
max	18.0	3.0	1.300000	18.960000	21.380000