| 1 |
""" |
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| 2 |
Parse data and assert what data creates and updates monthly NetCDF files. |
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| 3 |
|
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| 4 |
Spongenet mini_andi system parameters sponge data. |
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| 5 |
""" |
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| 6 |
|
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| 7 |
import math |
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| 8 |
import numpy as n |
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| 9 |
import pycdf |
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| 10 |
import datetime |
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| 11 |
import procutil |
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| 12 |
from spongenet.parse import Data |
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| 13 |
|
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| 14 |
nowDt = datetime.datetime.utcnow().replace(microsecond=0) |
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| 15 |
|
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| 16 |
def parser(platform_info, sensor_info, lines): |
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| 17 |
""" |
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| 18 |
Parse and assign sponge data from XML file. |
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| 19 |
""" |
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| 20 |
|
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| 21 |
_data = Data(''.join(lines)) |
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| 22 |
|
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| 23 |
# Each Device tag represents a time sample. |
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| 24 |
num_samples = len(_data.devices) |
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| 25 |
|
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| 26 |
data = { |
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| 27 |
'dt' : n.array(n.ones((num_samples,)) * n.nan, dtype=object), |
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| 28 |
'time' : n.array(n.ones((num_samples,)) * n.nan, dtype=long), |
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| 29 |
'pdt' : n.array(n.ones((num_samples,)) * n.nan, dtype=object), |
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| 30 |
'ptime' : n.array(n.ones((num_samples,)) * n.nan, dtype=long), |
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| 31 |
'ds' : n.array(n.ones((num_samples,)) * n.nan, dtype=object), |
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| 32 |
'session' : n.array(n.ones((num_samples,)) * n.nan, dtype=long), |
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| 33 |
'pds' : n.array(n.ones((num_samples,)) * n.nan, dtype=object), |
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| 34 |
'psession' : n.array(n.ones((num_samples,)) * n.nan, dtype=long), |
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| 35 |
'record' : n.array(n.ones((num_samples,)) * n.nan, dtype=int), |
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| 36 |
'status' : n.array(n.ones((num_samples,)) * n.nan, dtype=int), |
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| 37 |
'pstatus' : n.array(n.ones((num_samples,)) * n.nan, dtype=int), |
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| 38 |
'voltage' : n.array(n.ones((num_samples,)) * n.nan, dtype=float), |
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| 39 |
'memory' : n.array(n.ones((num_samples,)) * n.nan, dtype=int), |
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| 40 |
'interval' : n.array(n.ones((num_samples,)) * n.nan, dtype=int), |
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| 41 |
} |
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| 42 |
|
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| 43 |
for (sample_index, sample) in enumerate(_data.devices): |
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| 44 |
# sample time at the platform |
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| 45 |
dt = {'month' : int(sample['time'][5:7]), |
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'day' : int(sample['time'][8:10]), |
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| 47 |
'year' : int(sample['time'][0:4]), |
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| 48 |
'hour' : int(sample['time'][11:13]), |
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| 49 |
'min' : int(sample['time'][14:16]), |
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| 50 |
'sec' : int(sample['time'][17:19]), |
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| 51 |
} |
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| 52 |
dt = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%(min)02d:%(sec)02d' \ |
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| 53 |
% dt |
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| 54 |
dt = procutil.scanf_datetime(dt, fmt='%m-%d-%Y %H:%M:%S') |
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| 55 |
if sensor_info['utc_offset']: |
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| 56 |
dt = dt + datetime.timedelta(hours=sensor_info['utc_offset']) |
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| 57 |
data['dt'][sample_index] = dt |
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| 58 |
data['time'][sample_index] = procutil.dt2es(dt) |
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| 59 |
|
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| 60 |
# sample time at the package |
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| 61 |
package_dt = {'month' : int(sample['data_time'][5:7]), |
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| 62 |
'day' : int(sample['data_time'][8:10]), |
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| 63 |
'year' : int(sample['data_time'][0:4]), |
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| 64 |
'hour' : int(sample['data_time'][11:13]), |
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| 65 |
'min' : int(sample['data_time'][14:16]), |
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| 66 |
'sec' : int(sample['data_time'][17:19]), |
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| 67 |
} |
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| 68 |
package_dt = ('%(month)02d-%(day)02d-%(year)04d ' + |
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| 69 |
'%(hour)02d:%(min)02d:%(sec)02d') \ |
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| 70 |
% package_dt |
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| 71 |
package_dt = procutil.scanf_datetime(package_dt, fmt='%m-%d-%Y %H:%M:%S') |
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| 72 |
if sensor_info['utc_offset']: |
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| 73 |
package_dt = package_dt + \ |
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| 74 |
datetime.timedelta(hours=sensor_info['utc_offset']) |
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| 75 |
data['pdt'][sample_index] = package_dt |
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| 76 |
data['ptime'][sample_index] = procutil.dt2es(package_dt) |
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| 77 |
|
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| 78 |
# platform session time |
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| 79 |
ds = {'month' : int(sample['sessionid'][14:16]), |
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'day' : int(sample['sessionid'][17:19]), |
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| 81 |
'year' : int(sample['sessionid'][9:13]), |
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| 82 |
'hour' : int(sample['sessionid'][20:22]), |
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| 83 |
'min' : int(sample['sessionid'][23:25]), |
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| 84 |
'sec' : int(sample['sessionid'][26:28]), |
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| 85 |
} |
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| 86 |
ds = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%(min)02d:%(sec)02d' \ |
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| 87 |
% ds |
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| 88 |
ds = procutil.scanf_datetime(ds, fmt='%m-%d-%Y %H:%M:%S') |
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| 89 |
if sensor_info['utc_offset']: |
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| 90 |
ds = ds + datetime.timedelta(hours=sensor_info['utc_offset']) |
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| 91 |
data['ds'][sample_index] = ds |
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| 92 |
data['session'][sample_index] = procutil.dt2es(ds) |
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| 93 |
|
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| 94 |
# package session time |
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| 95 |
package_ds = {'month' : int(sample['data_sessionid'][5:7]), |
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| 96 |
'day' : int(sample['data_sessionid'][8:10]), |
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'year' : int(sample['data_sessionid'][0:4]), |
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| 98 |
'hour' : int(sample['data_sessionid'][11:13]), |
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'min' : int(sample['data_sessionid'][14:16]), |
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'sec' : int(sample['data_sessionid'][17:19]), |
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| 101 |
} |
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| 102 |
package_ds = ('%(month)02d-%(day)02d-%(year)04d ' + |
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| 103 |
'%(hour)02d:%(min)02d:%(sec)02d') \ |
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% package_ds |
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| 105 |
package_ds = procutil.scanf_datetime(package_ds, fmt='%m-%d-%Y %H:%M:%S') |
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| 106 |
if sensor_info['utc_offset']: |
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| 107 |
package_ds = package_ds + \ |
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| 108 |
datetime.timedelta(hours=sensor_info['utc_offset']) |
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| 109 |
data['pds'][sample_index] = package_ds |
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| 110 |
data['psession'][sample_index] = procutil.dt2es(package_ds) |
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| 111 |
|
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# platform variables |
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| 113 |
try: |
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| 114 |
data['record'][sample_index] = int(sample["recordnumber"]) |
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| 115 |
except KeyError: |
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| 116 |
pass |
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| 117 |
|
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| 118 |
try: |
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| 119 |
data['status'][sample_index] = int(sample["status"]. |
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| 120 |
partition(":")[0]) |
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| 121 |
except (KeyError, AttributeError, ): |
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| 122 |
pass |
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| 123 |
|
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| 124 |
# package variables |
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| 125 |
try: |
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| 126 |
data['pstatus'][sample_index] = int(sample.sensors |
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| 127 |
[sensor_info["id_number"]] |
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| 128 |
["status"]. |
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| 129 |
partition(":")[0]) |
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| 130 |
except (KeyError, AttributeError, ): |
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| 131 |
pass |
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| 132 |
|
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| 133 |
try: |
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| 134 |
data['voltage'][sample_index] = float(sample.sensors |
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| 135 |
[sensor_info["id_number"]]. |
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points[sensor_info |
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["voltage_description"]] |
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| 138 |
["value"]) |
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| 139 |
except (KeyError, AttributeError, ): |
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| 140 |
pass |
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| 141 |
|
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| 142 |
try: |
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| 143 |
data['memory'][sample_index] = int(sample.sensors |
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| 144 |
[sensor_info["id_number"]]. |
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| 145 |
points[sensor_info |
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| 146 |
["memory_description"]] |
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| 147 |
["value"]) |
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| 148 |
except (KeyError, AttributeError, ): |
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| 149 |
pass |
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| 150 |
|
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| 151 |
try: |
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data['interval'][sample_index] = int(sample.sensors |
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| 153 |
[sensor_info["id_number"]]. |
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| 154 |
points[sensor_info |
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| 155 |
["interval_description"]] |
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| 156 |
["value"]) |
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| 157 |
except (KeyError, AttributeError, ): |
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| 158 |
pass |
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| 159 |
|
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| 160 |
return data |
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| 161 |
|
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| 162 |
def creator(platform_info, sensor_info, data): |
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| 163 |
# |
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| 164 |
# |
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title_str = sensor_info['description']+' at '+ sensor_info['location'] |
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global_atts = { |
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| 167 |
# Required |
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| 168 |
'title' : title_str, |
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| 169 |
'institution' : platform_info['institution'], |
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| 170 |
'institution_url' : platform_info['institution_url'], |
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| 171 |
'institution_dods_url' : platform_info['institution_dods_url'], |
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| 172 |
'contact' : platform_info['contact'], |
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| 173 |
'Conventions' : platform_info['conventions'], |
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| 174 |
# Required by Scout |
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| 175 |
'format_category_code' : platform_info['format_category_code'], |
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| 176 |
'institution_code' : platform_info['institution_code'], |
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| 177 |
'platform_code' : platform_info['id'], |
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| 178 |
'package_code' : sensor_info['id'], |
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| 179 |
# Required by Version tracking |
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| 180 |
'format' : platform_info['format'], |
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| 181 |
'seacoos_rt_version' : platform_info['seacoos_rt_version'], |
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| 182 |
# Recommended |
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| 183 |
'_FillValue' : n.nan, |
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| 184 |
'missing_value' : n.nan, |
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| 185 |
'source' : platform_info['source'], |
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| 186 |
'references' : platform_info['references'], |
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| 187 |
'metadata_url' : platform_info['metadata_url'], |
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| 188 |
'history' : 'raw2proc using ' + sensor_info['process_module'], |
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| 189 |
'comment' : 'File created using pycdf ' + \ |
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| 190 |
pycdf.pycdfVersion() + ' and ' + \ |
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| 191 |
pycdf.pycdfArrayPkg() + ' ' + \ |
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| 192 |
n.__version__, |
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| 193 |
'project' : platform_info['project'], |
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| 194 |
'project_url' : platform_info['project_url'], |
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| 195 |
# timeframe of data contained in file yyyy-mm-dd HH:MM:SS |
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| 196 |
# first date in monthly file |
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| 197 |
'start_date' : data['dt'][0].strftime("%Y-%m-%d %H:%M:%S"), |
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| 198 |
# last date in monthly file |
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| 199 |
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"), |
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| 200 |
'release_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
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| 201 |
'creation_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
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| 202 |
'modification_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
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| 203 |
'process_level' : 'level1', |
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| 204 |
# Custom |
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| 205 |
'id_number' : platform_info['id_number'], |
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| 206 |
'description' : platform_info['description'], |
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| 207 |
'serial_number' : platform_info['serial_number'], |
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| 208 |
'product_number' : platform_info['product_number'], |
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| 209 |
'product_name' : platform_info['product_name'], |
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| 210 |
'type' : platform_info['type'], |
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| 211 |
'protocol_version' : platform_info['protocol_version'], |
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| 212 |
'xmlns' : platform_info['xmlns'], |
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| 213 |
'location' : platform_info['location'], |
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| 214 |
'vertical_position': platform_info['vertical_position'], |
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| 215 |
'owner' : platform_info['owner'], |
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| 216 |
'package_id_number' : sensor_info['id_number'], |
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| 217 |
'package_description' : sensor_info['description'], |
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| 218 |
'package_serial_number' : sensor_info['serial_number'], |
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| 219 |
'package_product_number' : sensor_info['product_number'], |
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| 220 |
'package_product_name' : sensor_info['product_name'], |
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| 221 |
'package_adr' : sensor_info['adr'], |
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| 222 |
'package_protocol_version' : sensor_info['protocol_version'], |
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| 223 |
'package_vertical_position' : sensor_info['vertical_position'], |
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| 224 |
} |
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| 225 |
|
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| 226 |
var_atts = { |
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| 227 |
# coordinate variables |
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| 228 |
'time' : {'short_name': 'time', |
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| 229 |
'long_name': 'Time', |
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| 230 |
'standard_name': 'time', |
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| 231 |
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC |
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| 232 |
'axis': 'T', |
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| 233 |
}, |
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| 234 |
'lat' : {'short_name': 'lat', |
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| 235 |
'long_name': 'Latitude', |
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| 236 |
'standard_name': 'latitude', |
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| 237 |
'reference':'geographic coordinates', |
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| 238 |
'units': 'degrees_north', |
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| 239 |
'valid_range':(-90.,90.), |
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| 240 |
'axis': 'Y', |
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| 241 |
}, |
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| 242 |
'lon' : {'short_name': 'lon', |
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| 243 |
'long_name': 'Longtitude', |
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| 244 |
'standard_name': 'longtitude', |
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| 245 |
'reference':'geographic coordinates', |
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| 246 |
'units': 'degrees_east', |
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| 247 |
'valid_range':(-180.,180.), |
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| 248 |
'axis': 'Y', |
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| 249 |
}, |
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| 250 |
'z' : {'short_name': 'z', |
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| 251 |
'long_name': 'Height', |
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| 252 |
'standard_name': 'height', |
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| 253 |
'reference':'zero at sea-surface', |
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| 254 |
'positive' : 'up', |
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| 255 |
'units': 'meters', |
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| 256 |
'axis': 'Z', |
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| 257 |
}, |
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| 258 |
# data variables |
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| 259 |
'ptime' : {'short_name': 'ptime', |
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| 260 |
'long_name': 'Package Time', |
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| 261 |
'standard_name': 'none', |
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| 262 |
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC |
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| 263 |
}, |
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| 264 |
'session' : {'short_name': 'session', |
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| 265 |
'long_name': 'Session ID', |
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| 266 |
'standard_name': 'none', |
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| 267 |
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC |
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| 268 |
}, |
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| 269 |
'psession' : {'short_name': 'ptime', |
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| 270 |
'long_name': 'Package Session ID', |
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| 271 |
'standard_name': 'none', |
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| 272 |
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC |
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| 273 |
}, |
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| 274 |
'record' : {'short_name': 'record', |
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| 275 |
'long_name': 'Record Number', |
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| 276 |
'standard_name': 'none', |
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| 277 |
'units' : 'none', |
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| 278 |
}, |
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| 279 |
'status': {'short_name' : 'status', |
|---|
| 280 |
'long_name': 'Platform Status Code', |
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| 281 |
'standard_name': 'none', |
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| 282 |
'units' : 'none', |
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| 283 |
'value_map' : platform_info['status_map'], |
|---|
| 284 |
}, |
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| 285 |
'pstatus': {'short_name' : 'pstatus', |
|---|
| 286 |
'long_name': 'Package Status Code', |
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| 287 |
'standard_name': 'none', |
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| 288 |
'units' : 'none', |
|---|
| 289 |
'value_map' : sensor_info['status_map'], |
|---|
| 290 |
}, |
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| 291 |
'voltage': {'short_name' : 'voltage', |
|---|
| 292 |
'long_name': sensor_info['voltage_description'], |
|---|
| 293 |
'standard_name': 'none', |
|---|
| 294 |
'units': 'volts', |
|---|
| 295 |
'id_number' : sensor_info['voltage_id'], |
|---|
| 296 |
'type' : sensor_info['voltage_type'], |
|---|
| 297 |
'format' : sensor_info['voltage_format'], |
|---|
| 298 |
'non_standard_units' : sensor_info['voltage_units'], |
|---|
| 299 |
'range_min' : sensor_info['voltage_range_min'], |
|---|
| 300 |
'range_max' : sensor_info['voltage_range_max'], |
|---|
| 301 |
}, |
|---|
| 302 |
'memory': {'short_name' : 'memory', |
|---|
| 303 |
'long_name': sensor_info['memory_description'], |
|---|
| 304 |
'standard_name': 'none', |
|---|
| 305 |
'units' : 'none', |
|---|
| 306 |
'id_number' : sensor_info['memory_id'], |
|---|
| 307 |
'type' : sensor_info['memory_type'], |
|---|
| 308 |
'format' : sensor_info['memory_format'], |
|---|
| 309 |
'non_standard_units' : sensor_info['memory_units'], |
|---|
| 310 |
'range_min' : sensor_info['memory_range_min'], |
|---|
| 311 |
'range_max' : sensor_info['memory_range_max'], |
|---|
| 312 |
}, |
|---|
| 313 |
'interval': {'short_name' : 'interval', |
|---|
| 314 |
'long_name': 'Averaging Interval', |
|---|
| 315 |
'standard_name': 'averaging_interval', |
|---|
| 316 |
'units': 'seconds/1000', |
|---|
| 317 |
'id_number' : sensor_info['interval_id'], |
|---|
| 318 |
'type' : sensor_info['interval_type'], |
|---|
| 319 |
'format' : sensor_info['interval_format'], |
|---|
| 320 |
'non_standard_units' : sensor_info['interval_units'], |
|---|
| 321 |
'range_min' : sensor_info['interval_range_min'], |
|---|
| 322 |
'range_max' : sensor_info['interval_range_max'], |
|---|
| 323 |
}, |
|---|
| 324 |
} |
|---|
| 325 |
|
|---|
| 326 |
# dimension names use tuple so order of initialization is maintained |
|---|
| 327 |
dim_inits = ( |
|---|
| 328 |
('ntime', pycdf.NC.UNLIMITED), |
|---|
| 329 |
('nlat', 1), |
|---|
| 330 |
('nlon', 1), |
|---|
| 331 |
('nz', 1), |
|---|
| 332 |
) |
|---|
| 333 |
|
|---|
| 334 |
# using tuple of tuples so order of initialization is maintained |
|---|
| 335 |
# using dict for attributes order of init not important |
|---|
| 336 |
# use dimension names not values |
|---|
| 337 |
# (varName, varType, (dimName1, [dimName2], ...)) |
|---|
| 338 |
var_inits = ( |
|---|
| 339 |
# coordinate variables |
|---|
| 340 |
('time', pycdf.NC.INT, ('ntime',)), |
|---|
| 341 |
('lat', pycdf.NC.FLOAT, ('nlat',)), |
|---|
| 342 |
('lon', pycdf.NC.FLOAT, ('nlon',)), |
|---|
| 343 |
('z', pycdf.NC.FLOAT, ('nz',)), |
|---|
| 344 |
# data variables |
|---|
| 345 |
('ptime', pycdf.NC.INT, ('ntime',)), |
|---|
| 346 |
('session', pycdf.NC.INT, ('ntime',)), |
|---|
| 347 |
('psession', pycdf.NC.INT, ('ntime',)), |
|---|
| 348 |
('record', pycdf.NC.INT, ('ntime',)), |
|---|
| 349 |
('status', pycdf.NC.INT, ('ntime',)), |
|---|
| 350 |
('pstatus', pycdf.NC.INT, ('ntime',)), |
|---|
| 351 |
('voltage', pycdf.NC.FLOAT, ('ntime',)), |
|---|
| 352 |
('memory', pycdf.NC.INT, ('ntime',)), |
|---|
| 353 |
('interval', pycdf.NC.INT, ('ntime',)), |
|---|
| 354 |
) |
|---|
| 355 |
|
|---|
| 356 |
# subset data only to month being processed (see raw2proc.process()) |
|---|
| 357 |
i = data['in'] |
|---|
| 358 |
|
|---|
| 359 |
# var data |
|---|
| 360 |
var_data = ( |
|---|
| 361 |
('time', data['time'][i]), |
|---|
| 362 |
('lat', sensor_info['lat']), |
|---|
| 363 |
('lon', sensor_info['lat']), |
|---|
| 364 |
('z', sensor_info['elevation']), |
|---|
| 365 |
('ptime', data['ptime'][i]), |
|---|
| 366 |
('session', data['session'][i]), |
|---|
| 367 |
('psession', data['psession'][i]), |
|---|
| 368 |
('record', data['record'][i]), |
|---|
| 369 |
('status', data['status'][i]), |
|---|
| 370 |
('pstatus', data['pstatus'][i]), |
|---|
| 371 |
('voltage', data['voltage'][i]), |
|---|
| 372 |
('memory', data['memory'][i]), |
|---|
| 373 |
('interval', data['interval'][i]), |
|---|
| 374 |
) |
|---|
| 375 |
|
|---|
| 376 |
return (global_atts, var_atts, dim_inits, var_inits, var_data) |
|---|
| 377 |
|
|---|
| 378 |
def updater(platform_info, sensor_info, data): |
|---|
| 379 |
# |
|---|
| 380 |
global_atts = { |
|---|
| 381 |
# update times of data contained in file (yyyy-mm-dd HH:MM:SS) |
|---|
| 382 |
# last date in monthly file |
|---|
| 383 |
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 384 |
'release_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 385 |
# |
|---|
| 386 |
'modification_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 387 |
} |
|---|
| 388 |
|
|---|
| 389 |
# data variables |
|---|
| 390 |
# update any variable attributes like range, min, max |
|---|
| 391 |
var_atts = {} |
|---|
| 392 |
|
|---|
| 393 |
# subset data only to month being processed (see raw2proc.process()) |
|---|
| 394 |
i = data['in'] |
|---|
| 395 |
|
|---|
| 396 |
# data |
|---|
| 397 |
var_data = ( |
|---|
| 398 |
('time', data['time'][i]), |
|---|
| 399 |
('ptime', data['ptime'][i]), |
|---|
| 400 |
('session', data['session'][i]), |
|---|
| 401 |
('psession', data['psession'][i]), |
|---|
| 402 |
('record', data['record'][i]), |
|---|
| 403 |
('status', data['status'][i]), |
|---|
| 404 |
('pstatus', data['pstatus'][i]), |
|---|
| 405 |
('voltage', data['voltage'][i]), |
|---|
| 406 |
('memory', data['memory'][i]), |
|---|
| 407 |
('interval', data['interval'][i]), |
|---|
| 408 |
) |
|---|
| 409 |
|
|---|
| 410 |
return (global_atts, var_atts, var_data) |
|---|
| 411 |
|
|---|
