From 038f6223993cfe0c6fa2d1b3c6ddd57c6483a264 Mon Sep 17 00:00:00 2001
From: jay <jlaura@asu.edu>
Date: Wed, 31 Jan 2018 07:31:01 -0700
Subject: [PATCH] Adds prototype socet gpf read from @thareUSGS
---
plio/examples/SocetGXP/InSightE08_XW.gpf | 803 +++++++++++++++++++++++
plio/io/io_gpf.py | 307 +++++++++
plio/io/tests/test_gpf.py | 17 +
3 files changed, 1127 insertions(+)
create mode 100644 plio/examples/SocetGXP/InSightE08_XW.gpf
create mode 100644 plio/io/io_gpf.py
create mode 100644 plio/io/tests/test_gpf.py
diff --git a/plio/examples/SocetGXP/InSightE08_XW.gpf b/plio/examples/SocetGXP/InSightE08_XW.gpf
new file mode 100644
index 0000000..b901ab8
--- /dev/null
+++ b/plio/examples/SocetGXP/InSightE08_XW.gpf
@@ -0,0 +1,803 @@
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diff --git a/plio/io/io_gpf.py b/plio/io/io_gpf.py
new file mode 100644
index 0000000..485dfd9
--- /dev/null
+++ b/plio/io/io_gpf.py
@@ -0,0 +1,307 @@
+import math
+import numpy as np
+import pandas as pd
+
+
+GPF_DATA_DTYPE = np.dtype([('point_id', np.int), ('stat', np.int), ('known', np.int),
+ ('lat_Y_North', np.float), ('long_X_East', np.float), ('ht', np.float),
+ ('sigma0', np.float), ('sigma1', np.float), ('sigma2', np.float),
+ ('res0', np.float), ('res1', np.float), ('res2', np.float)])
+
+
+def read_gpf(input_data):
+ """
+ Read a socet gpf file into a pandas data frame
+
+ Parameters
+ ----------
+ input_file : str
+ path to the input data file
+
+ Returns
+ -------
+ df : pd.DataFrame
+ containing the gfp data with appropriate column names and indices
+ """
+
+ # Mixed types requires read as unicode - let pandas soft convert
+ d = np.genfromtxt(input_data, skip_header=3, dtype='unicode')
+ d = d.reshape(-1, 12)
+
+ #TODO: cols should be used to dynamically generate the column names
+
+ df = pd.DataFrame(d, columns=['point_id', 'stat', 'known',
+ 'lat_y_North', 'long_X_East','ht',
+ 'sigma0', 'sigma1', 'sigma2',
+ 'res0', 'res1', 'res2'])
+ # Soft conversion of numeric types to numerics
+ df = df.apply(pd.to_numeric, errors='ignore')
+ return df
+
+
+
+
+# def GPF_SAV(input_data, ave=True):
+ # # read the IDL .SAV file
+
+ # data = io.readsav(input_data, python_dict=True)
+
+ # # put the spectra into data frames and combine them
+ # df_UV = pd.DataFrame(data['uv'], index=data['defuv'])
+ # df_VIS = pd.DataFrame(data['vis'], index=data['defvis'])
+ # df_VNIR = pd.DataFrame(data['vnir'], index=data['defvnir'])
+ # df_spect = pd.concat([df_UV, df_VIS, df_VNIR])
+ # df_spect.columns = ['shot' + str(i + 1) for i in
+ # df_spect.columns] # add 1 to the columns so they correspond to shot number
+
+ # df_aUV = pd.DataFrame(data['auv'], index=data['defuv'], columns=['average'])
+ # df_aVIS = pd.DataFrame(data['avis'], index=data['defvis'], columns=['average'])
+ # df_aVNIR = pd.DataFrame(data['avnir'], index=data['defvnir'], columns=['average'])
+ # df_ave = pd.concat([df_aUV, df_aVIS, df_aVNIR])
+
+ # df_mUV = pd.DataFrame(data['muv'], index=data['defuv'], columns=['median'])
+ # df_mVIS = pd.DataFrame(data['mvis'], index=data['defvis'], columns=['median'])
+ # df_mVNIR = pd.DataFrame(data['mvnir'], index=data['defvnir'], columns=['median'])
+ # df_med = pd.concat([df_mUV, df_mVIS, df_mVNIR])
+
+ # df = pd.concat([df_spect, df_ave, df_med], axis=1)
+ # # create multiindex to access wavelength values
+ # # also, round the wavlength values to a more reasonable level of precision
+ # df.index = [['wvl'] * len(df.index), df.index.values.round(4)]
+ # # transpose so that spectra are rows rather than columns
+ # df = df.T
+
+ # # extract metadata from the file name and add it to the data frame
+ # # use the multiindex label "meta" for all metadata
+
+ # fname = os.path.basename(input_data)
+
+ # # for some reason, some ChemCam files have the 'darkname' key, others call it 'darkspect'
+ # # this try-except pair converts to 'darkname' when needed
+ # try:
+ # data['darkname']
+ # except:
+ # data['darkname'] = data['darkspec']
+
+ # metadata = [fname,
+ # fname[4:13],
+ # fname[25:34].upper(),
+ # fname[34:36],
+ # data['continuumvismin'],
+ # data['continuumvnirmin'],
+ # data['continuumuvmin'],
+ # data['continuumvnirend'],
+ # data['distt'],
+ # data['darkname'],
+ # data['nshots'],
+ # data['dnoiseiter'],
+ # data['dnoisesig'],
+ # data['matchedfilter']]
+ # metadata = np.tile(metadata, (len(df.index), 1))
+ # metadata_cols = list(zip(['meta'] * len(df.index), ['file',
+ # 'sclock',
+ # 'seqid',
+ # 'Pversion',
+ # 'continuumvismin',
+ # 'continuumvnirmin',
+ # 'continuumuvmin',
+ # 'continuumvnirend',
+ # 'distt',
+ # 'dark',
+ # 'nshots',
+ # 'dnoiseiter',
+ # 'dnoisesig',
+ # 'matchedfilter']))
+ # metadata = pd.DataFrame(metadata, columns=pd.MultiIndex.from_tuples(metadata_cols), index=df.index)
+
+ # df = pd.concat([metadata, df], axis=1)
+ # if ave == True:
+ # df = df.loc['average']
+ # df = df.to_frame().T
+ # else:
+ # pass
+
+ # return df
+
+
+# def ccam_batch(directory, searchstring='*.csv', to_csv=None, lookupfile=None, ave=True, progressbar=None):
+ # # Determine if the file is a .csv or .SAV
+ # if '.sav' in searchstring.lower():
+ # is_sav = True
+ # else:
+ # is_sav = False
+ # filelist = file_search(directory, searchstring)
+ # basenames = np.zeros_like(filelist)
+ # sclocks = np.zeros_like(filelist)
+ # P_version = np.zeros_like(filelist, dtype='int')
+
+ # # Extract the sclock and version for each file and ensure that only one
+ # # file per sclock is being read, and that it is the one with the highest version number
+ # for i, name in enumerate(filelist):
+ # basenames[i] = os.path.basename(name)
+ # sclocks[i] = basenames[i][4:13] # extract the sclock
+ # P_version[i] = basenames[i][-5:-4] # extract the version
+
+ # sclocks_unique = np.unique(sclocks) # find unique sclocks
+ # filelist_new = np.array([], dtype='str')
+ # for i in sclocks_unique:
+ # match = (sclocks == i) # find all instances with matching sclocks
+ # maxP = P_version[match] == max(P_version[match]) # find the highest version among these files
+ # filelist_new = np.append(filelist_new, filelist[match][maxP]) # keep only the file with thei highest version
+
+ # filelist = filelist_new
+ # # Should add a progress bar for importing large numbers of files
+ # dt = []
+
+ # for i, file in enumerate(filelist):
+ # print(file)
+ # if is_sav:
+ # tmp = CCAM_SAV(file, ave=ave)
+ # else:
+ # tmp = CCAM_CSV(file, ave=ave)
+ # if i == 0:
+ # combined = tmp
+ # else:
+ # # This ensures that rounding errors are not causing mismatches in columns
+ # cols1 = list(combined['wvl'].columns)
+ # cols2 = list(tmp['wvl'].columns)
+ # if set(cols1) == set(cols2):
+ # combined = pd.concat([combined, tmp])
+ # else:
+ # print("Wavelengths don't match!")
+
+ # combined.loc[:, ('meta', 'sclock')] = pd.to_numeric(combined.loc[:, ('meta', 'sclock')])
+
+ # if lookupfile is not None:
+
+ # combined = lookup(combined, lookupfile=lookupfile.replace('[','').replace(']','').replace("'",'').replace(' ','').split(','))
+ # if to_csv is not None:
+ # combined.to_csv(to_csv)
+ # return combined
+
+
+
+# main(int argc, char *argv[])
+# {
+
+ # char gpfFile[FILELEN];
+ # char csvFile[FILELEN];
+ # char pointIDsFile[FILELEN];
+
+ # FILE *gpfFp; // file pointer to input csv file
+ # FILE *csvFp; // file pointer to output csv file
+ # FILE *ptsFp; // file pointer to output point ids list file
+
+ # char gpfLine[LINELENGTH];
+
+ # // check number of command line args and issue help if needed
+ # //-----------------------------------------------------------
+ # if (argc != 2) {
+ # printf ("\nrun %s as follows:\n",argv[0]);
+ # printf (" %s SSgpfFile\n",
+ # argv[0]);
+ # printf ("\nwhere:\n");
+ # printf (" SSgpfFile = Socet Set *.gpf file, from a geographic project\n\n");
+ # printf (" This program will convert a Socet Set ground point file into a CSV\n");
+ # printf (" of lat,lon,height. The output file will have the same core name\n");
+ # printf (" of the input *.gpf file, but with a .csv extension\n\n");
+ # printf (" Also output is the list of point IDs that were converted. This file\n");
+ # printf (" will be used to port the points back to Socet Set later on. The output\n");
+ # printf (" file will have the same core name as the input file, but with a .pointids\n");
+ # printf (" .tiePointIds.txt extension.\n");
+ # exit(1);
+ # }
+
+ # //------------------------------------------------
+ # // get input arguments entered at the command line
+ # //------------------------------------------------
+
+ # strcpy (gpfFile,argv[1]);
+
+ # //-----------------------------
+ # // generate ouput file names
+ # //-----------------------------
+
+ # char corename[FILELEN];
+ # strcpy (corename,gpfFile);
+ # int len = strlen(corename);
+ # corename[len-4] = '\0';
+
+ # strcpy (csvFile,corename);
+ # strcat (csvFile,".csv");
+
+ # strcpy (pointIDsFile,corename);
+ # strcat (pointIDsFile,".tiePointIds.txt");
+
+ # /////////////////////////////////////////////////////////////////////////////
+ # // open files
+ # /////////////////////////////////////////////////////////////////////////////
+
+ # gpfFp = fopen (gpfFile,"r");
+ # if (gpfFp == NULL) {
+ # printf ("unable to open input gpf file: %s\n",gpfFile);
+ # exit (1);
+ # }
+
+ # csvFp = fopen (csvFile,"w");
+ # if (csvFp == NULL) {
+ # printf ("unable to open output csv file: %s\n",csvFile);
+ # fclose(gpfFp);
+ # exit (1);
+ # }
+
+ # ptsFp = fopen (pointIDsFile,"w");
+ # if (csvFp == NULL) {
+ # printf ("unable to open output list file of tie point ids: %s\n",pointIDsFile);
+ # fclose(gpfFp);
+ # fclose(csvFp);
+ # exit (1);
+ # }
+
+ # //------------------------------------------------
+ # //------------------------------------------------
+
+ # // skip the header
+ # fgets(gpfLine,LINELENGTH,gpfFp);
+
+ # // read in number of points in *.gpf file
+ # char value[50];
+ # fgets(gpfLine,LINELENGTH,gpfFp);
+ # sscanf (gpfLine,"%s",value);
+ # int numpts = atoi(value);
+
+ # // skip next header
+ # fgets(gpfLine,LINELENGTH,gpfFp);
+
+ # // Parse gpf, output csv
+ # char pointID[50], valStat[2], valKnown[2];
+ # char valLon[50], valLat[50], Height[50];
+ # double rad2dd = 57.295779513082320876798154814105;
+
+ # for (int i=0; i<numpts; i++) {
+ # fgets(gpfLine,LINELENGTH,gpfFp);
+ # sscanf (gpfLine,"%s %s %s",pointID,valStat,valKnown);
+ # int stat = atoi(valStat);
+ # int known = atoi(valKnown);
+
+ # // get coordinate
+ # fgets(gpfLine,LINELENGTH,gpfFp);
+
+ # //only output tie points that are on
+ # if (stat == 1 && known == 0) {
+ # sscanf (gpfLine,"%s %s %s",valLat,valLon,Height);
+ # double radLat = atof(valLat);
+ # double radLon = atof(valLon);
+ # fprintf(csvFp,"%.14lf,%.14lf,%s\n",rad2dd*radLat,rad2dd*radLon,Height);
+ # fprintf(ptsFp,"%s\n",pointID);
+ # }
+
+ # // skip next three lines in input *.gpf file
+ # for (int j=0; j<3; j++)
+ # fgets(gpfLine,LINELENGTH,gpfFp);
+ # }
+
+ # fclose(gpfFp);
+ # fclose(csvFp);
+
+# } // end of program
diff --git a/plio/io/tests/test_gpf.py b/plio/io/tests/test_gpf.py
new file mode 100644
index 0000000..b92a6ab
--- /dev/null
+++ b/plio/io/tests/test_gpf.py
@@ -0,0 +1,17 @@
+import numpy as np
+import pandas as pd
+
+from plio.io.io_gpf import read_gpf
+from plio.examples import get_path
+
+import pytest
+
+@pytest.fixture
+def insight_gpf():
+ return get_path('InSightE08_XW.gpf')
+
+@pytest.mark.parametrize('gpf, expected', [(insight_gpf(),'foo')])
+def test_read_gfp(gpf, expected):
+ df = read_gpf(gpf)
+ print(df)
+ assert False
--
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