i created a pandas DataFrame for GPS satellite from a text file and i wanted to create a single DataFrame for distinct satellite, all goes as per my expectation but if for a given satellite there is only one observation it created a pandas Series instead of DataFrame. I want DataFrame because i need it to save it in HDF5 file.
Also i get a warning
FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()
For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
Also i get a warning
FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()
For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
import pandas as pd
from textwrap import wrap
from datetime import datetime
def STF(a):
'''STF takes input as a number string containing 'D' or 'E' and return the
value as float and if the number string doesn't contain "D" or "E" it return
the original number in float.
for example: 198701D-02=1987.01
198701E-02=1987.01
198839=198839.00'''
if 'D' in a:
b = a.split("D")
c = (float(b[0]))*(10**(int(b[1])))
return(c)
elif 'E' in a:
b = a.split("E")
c = (float(b[0]))*(10**(int(b[1])))
return(c)
def gpsnav3df(filepath):
'''gpsnav3df takes input as filepath of Rinex3 navigation file of GPS and
give output in the form of dataframe containing all the information about the
satellite like Satellite ID, Epoch, Clock Information etc.'''
with open(filepath) as fp: #open file and returns the corresponding file object
data=fp.read() # read all the data into single string
data1=data.split('END OF HEADER') # split a string into list
#data section
satdata=data1[1]
satdata=satdata.strip() # remove the whitespaces from beginning and end of the string
satdata=wrap(satdata,610) # split string into nth character of each word
#creating Empty list
SatId ,Epoch,SV_clock_Bias,SV_clock_Drift,SV_clock_drift_Rate,IODE,Crs,Delta_n, M0, Cuc, e,Cus, sqrt_A ,Toe, Cic ,OMEGA_0, Cis, i0, Crc ,omega ,OMEGA_dot,IDOT,Codes ,GPS_Week ,L2P_data_Flag,SV_accuracy,SV_health, TGD, TODC ,Transmission_time , Fit_interval = ([] for i in range(31) )
for sat in satdata:
#SV
satId = sat[0:3]
SatId.append(satId)
#EPOCH
time = sat[4:23].replace(' ' , '')
epoch = datetime.strptime(time,'%Y%m%d%H%M%S')
Epoch.append(epoch)
#SV_clock
SV_clock_bias = STF(sat[23:42]) # second
SV_clock_Bias.append(SV_clock_bias)
SV_clock_drift = STF(sat[42:61]) #sec/sec
SV_clock_Drift.append(SV_clock_drift)
SV_clock_drift_rate = STF(sat[61:80]) # sec/sec2
SV_clock_drift_Rate.append(SV_clock_drift_rate)
# Broadcast orbit-1
IOD_E = STF(sat[85:104]) # Issue of Data,Ephemeris
IODE.append(IOD_E)
Cr_s = STF(sat[104:124]) #meters
Crs.append(Cr_s)
Delta_N = STF(sat[124:142])# rad/sec
Delta_n.append(Delta_N)
M_0 = STF(sat[142:161]) # radians
M0.append(M_0)
#broadcast orbit-2
Cu_c = STF(sat[166:185]) #radians
Cuc.append(Cu_c)
e_ = STF(sat[185:204]) #Eccentricity
e.append(e_)
Cu_s = STF(sat[204:223]) #radians
Cus.append(Cu_s)
sqrtA = STF(sat[223:242]) # sqrt(m)
sqrt_A.append(sqrtA)
#broadcast orbit-3
To_e = STF(sat[247:266]) #Time of Ephemeris(sec of GPS week)
Toe.append(To_e)
Ci_c = STF(sat[266:285]) #radians
Cic.append(Ci_c)
OMEGA0 = STF(sat[285:304]) #radians
OMEGA_0.append(OMEGA0)
Ci_s = STF(sat[304:323]) #radians/sec
Cis.append(Ci_s)
#broadcast orbit-4
i_0 = STF(sat[328:347]) #radian
i0.append(i_0)
Cr_c = STF(sat[347:366]) #meters
Crc.append(Cr_c)
omegA = STF(sat[366:385]) #radians
omega.append(omegA)
OMEGA_Dot = STF(sat[385:404]) #radians/sec
OMEGA_dot.append(OMEGA_Dot)
#broadcast orbit-5
IDO_T = STF(sat[409:428])
IDOT.append(IDO_T)
codes = STF(sat[428:447])
Codes.append(codes)
GPS_week = STF(sat[447:466])
GPS_Week.append(GPS_week)
L2P_data_flag = STF(sat[466:485])
L2P_data_Flag.append(L2P_data_flag)
#broadcast orbit-6
SV_Accuracy = STF(sat[490:509])
SV_accuracy.append(SV_Accuracy)
SV_Health = STF(sat[509:528])
SV_health.append(SV_Health)
TG_D = STF(sat[528:547])
TGD.append(TG_D)
IOD_C = STF(sat[547:566])
TODC.append(IOD_C)
#broadcast orbit-7
Transmission_Time = STF(sat[571:590])
Transmission_time.append(Transmission_Time)
Fit_Interval = STF(sat[590:609])
Fit_interval.append(Fit_Interval)
df = pd.DataFrame(list(zip(SatId ,Epoch,SV_clock_Bias,SV_clock_Drift,SV_clock_drift_Rate,IODE,Crs,Delta_n, M0, Cuc, e,Cus, sqrt_A ,Toe, Cic ,OMEGA_0, Cis, i0, Crc ,omega ,OMEGA_dot,IDOT,Codes ,GPS_Week ,L2P_data_Flag,SV_accuracy,SV_health, TGD, TODC ,Transmission_time , Fit_interval )),
columns =['SatId' ,'Epoch','SV_clock_Bias','SV_clock_Drift','SV_clock_drift_Rate','IODE','Crs','Delta_n', 'M0', 'Cuc', 'e','Cus', 'sqrt_A' ,'Toe', 'Cic' ,'OMEGA_0', 'Cis',' i0',' Crc' ,'omega' ,'OMEGA_dot','IDOT','Codes' ,'GPS_Week' ,'L2P_data_Flag','SV_accuracy','SV_health', 'TGD', 'TODC' ,'Transmission_time' , 'Fit_interval']
,dtype=object).convert_objects()
df=df.set_index('SatId')
return (df)
filepath='E:/Project/RAW_Data/ABMF00GLP_R_20190201400_01H_GN.txt'
GPS=gpsnav3df(filepath)
G01=GPS.loc['G01']
print(G01)
print(type(G01))
G10=GPS.loc['G10']
print(G10)
print(type(G10))Output:Epoch 2019-01-20 06:00:00
SV_clock_Bias -0.000148412
SV_clock_Drift -6.48015e-12
SV_clock_drift_Rate 0
IODE 87
Crs -152.625
Delta_n 4.16696e-09
M0 1.87066
Cuc -7.94232e-06
e 0.00830994
Cus 7.9684e-06
sqrt_A 5153.67
Toe 21600
Cic -2.30968e-07
OMEGA_0 -0.722293
Cis 3.1665e-08
i0 0.974264
Crc 234.531
omega 0.687125
OMEGA_dot -7.9539e-09
IDOT -2.78583e-10
Codes 1
GPS_Week 2037
L2P_data_Flag 0
SV_accuracy 2
SV_health 0
TGD 5.58794e-09
TODC 87
Transmission_time 14400
Fit_interval None
Name: G01, dtype: object
<class 'pandas.core.series.Series'>
Epoch SV_clock_Bias ... Transmission_time Fit_interval
SatId ...
G10 2019-01-20 13:59:44 0.000132 ... 47580.0 None
G10 2019-01-20 16:00:00 0.000132 ... 50400.0 None
[2 rows x 30 columns]
<class 'pandas.core.frame.DataFrame'>
E:/Project/code/n.py:172: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()
For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.Attached Files
