def read_logger_file(file, verbose=False):
# init gps container
gps = gps_data()
if isinstance(file, list):
for f in file:
gps = gps + read_logger_file(f)
else:
print('Reads ' + file)
gpsfile = pynmea2.NMEAFile(file)
data = pd.DataFrame()
for d in gpsfile:
if verbose:
print(d)
time = datetime.datetime.combine(d.datestamp, d.timestamp)
data = data.append({'lon': d.longitude, 'lat': d.latitude, 'time': time}, ignore_index=True)
#
gps.d = data.set_index('time')
return gps
def read_gps_alees(file, label='gps', verbose=False):
# init gps container
gp = gps(label=label)
if isinstance(file, list):
for f in file:
gp = gp + read_gps_alees(f, verbose=verbose)
else:
print('Reads ' + file)
gpsfile = pynmea2.NMEAFile(file)
data = pd.DataFrame()
for d in gpsfile:
if verbose:
print(d)
if all([d.datestamp, d.timestamp]):
time = datetime.datetime.combine(d.datestamp, d.timestamp)
data = data.append({'lon': d.longitude,
'lat': d.latitude,
'time': time
},
ignore_index=True)
#
gp.d = data.set_index('time')
return gp
def test_file():
nmeafile = pynmea2.NMEAFile(StringIO(TEST_DATA))
nmea_strings = nmeafile.read()
assert len(nmea_strings) == 10
assert all([isinstance(s, pynmea2.NMEASentence) for s in nmea_strings])
del nmeafile
with pynmea2.NMEAFile(StringIO(TEST_DATA)) as _f:
nmea_strings = [_f.readline() for i in range(10)]
assert len(nmea_strings) == 10
assert all([isinstance(s, pynmea2.NMEASentence) for s in nmea_strings])
with pynmea2.NMEAFile(StringIO(TEST_DATA)) as _f:
nmea_strings = [s for s in _f]
assert len(nmea_strings) == 10
assert all([isinstance(s, pynmea2.NMEASentence) for s in nmea_strings])
with pynmea2.NMEAFile(StringIO(TEST_DATA)) as _f:
nmea_strings = [_f.next() for i in range(10)]
assert len(nmea_strings) == 10
assert all([isinstance(s, pynmea2.NMEASentence) for s in nmea_strings])
def load_nmea(file: Path,
in_cols: Optional[Sequence[str]] = None) -> pd.DataFrame:
"""
NMEA to DataFrame convertor
:param file: NMEA file Path
:param in_cols: attributes to search in pynmea2 NMEASentences (corresponded to needed data in NMEA strings)
:return: pandas DataFrame with in_cols columns (except columns for which no data found) with 'datetime' index
"""
if in_cols is None:
search_fields = (
'latitude', 'longitude', 'spd_over_grnd', 'true_course',
'depth_meters'
) # not includes index. Also Magnetic Variation ('mag_variation') may be useful for ADCP proc
index = 'datetime' # todo: also try 'timestamp' if 'datetime' not found, will requre to add date
else:
index, *search_fields = in_cols
rows = []
d = {}
b_have_time = False
with pynmea2.NMEAFile(file.open(mode='r')) as _f:
while True: # to continue on error
try:
for nmea_sentence in _f: # best method because with _f.readline() need to check for EOF, nmea_sentences = _f.read() fails when tries to read all at once
try:
t = getattr(nmea_sentence, index)
if b_have_time:
rows.append(d)
d = {index: t} # new time row begin
else:
d[index] = t
b_have_time = True
except AttributeError:
pass
for f in search_fields: # all except index
try:
d[f] = getattr(nmea_sentence, f)
except AttributeError:
continue
except pynmea2.ParseError:
continue
break
if not rows: # from_records() gets strange output on empty input so we force returning empty dataframe:
return pd.DataFrame()
df = pd.DataFrame.from_records(rows, index=[index], coerce_float=True)
# arg columns=search_fields not works, coerce_float is needed because eliminates objects that we can not write to HDF5
return df
def read(filename):
records = [] #解析列表
gga_sumcount = 0 #gga语句总统计值
gga_validcount = 0 #gga有效语句统计值（固定且差分延迟小于20秒）
hgts = [] #高程值列表
hgt_sum = 0.0 #高程累加值
hgt_min = 0.0 #高程最小值
hgt_max = 0.0 #高程最大值
hgt_ud2cm_count = 0 #±2cm数据条数
hgt_ud1cm_count = 0 #±1cm数据条数
print('Parse file path:',filename)
start_time = time.time()
with pynmea2.NMEAFile(filename) as nmea_file:
for record in nmea_file:
records.append(record)
if(record.sentence_type == 'GGA'):#统计GGA语句条数
gga_sumcount += 1
if((record.gps_qual == 4) and (float(record.age_gps_data) < 20)):#固定解且差分延迟小于20秒，累加高程值
hgts.append(float(record.altitude))#将固定解高程值添加至列表
hgt_sum += float(record.altitude)
gga_validcount += 1
end_time = time.time()
print('Parse used time:%.2f(s)' % (end_time - start_time))
print('1.Total counts of records:', len(records))
print('2.GGA total counts: %d, fixed counts: %d, fixed percent:%.2f(%%)' % (gga_sumcount, gga_validcount, (gga_validcount / gga_sumcount)*100 ))
if gga_validcount != 0:
hgt_avg = hgt_sum / gga_validcount
print('3.Hgt average:%.4f(m),' % hgt_avg, end = ' ')
detla_1cm = 0.01
detla_2cm = 0.02
for i in range(len(hgts)):
if (hgts[i] <= hgt_avg + detla_2cm) and (hgts[i] >= hgt_avg - detla_2cm):
hgt_ud2cm_count += 1
if (hgts[i] <= hgt_avg + detla_1cm) and (hgts[i] >= hgt_avg - detla_1cm):
hgt_ud1cm_count += 1
else:
print('3.Hgt average:Invalid,' , end = ' ')
if len(hgts) != 0:
print('range ±2cm percent:%.2f(%%), range ±1cm percent:%.2f(%%), max-min:%.4f(m)' % ((hgt_ud2cm_count / len(hgts)) * 100, (hgt_ud1cm_count / len(hgts)) * 100, max(hgts) - min(hgts)))
else:
print('range ±2cm percent:Invalid, range ±1cm percent:Invalid, max-min:Invalid')
def read_gps_lops(file, label='gps', verbose=False):
# init gps container
gp = gps(label=label)
if isinstance(file, list):
for f in file:
gp = gp + read_gps_lops(f, verbose=verbose)
else:
print('Reads ' + file)
gpsfile = pynmea2.NMEAFile(file)
data = pd.DataFrame()
t = None
for s in gpsfile:
if verbose:
print(s)
if len(s.fields)>0 and hasattr(s, 'is_valid') and s.is_valid:
d = parse_nmea_sentence(s, t=t)
data = data.append(d, ignore_index=True)
t = d['time']
#
gp.d = data.set_index('time')
return gp
def load_nmea(file, cfg):
with pynmea2.NMEAFile(file) as _f:
nmea_sentences = _f.read()
#nmea_strings = [_f.readline() for i in range(10)]
#msg = pynmea2.ptarse('$SDDBT,665.4,f,202.8,M,110.9,F*06')
return nmea_sentences
def read(filename):
records = [] #解析列表
gga_sumcount = 0 #gga语句总统计值
gga_validcount = 0 #gga有效语句统计值（固定且差分延迟小于20秒）
hgts = [] #高程值列表
hgt_sum = 0.0 #高程累加值
hgt_min = 0.0 #高程最小值
hgt_max = 0.0 #高程最大值
hgt_ud2cm_count = 0 #±2cm数据条数
hgt_ud1cm_count = 0 #±1cm数据条数
lat_list = [] #lat列表(有效数据)
lon_list = [] #lon列表(有效数据)
print('Parse file path:', filename)
start_time = time.time()
#使用上下文解析nmea文本数据
with pynmea2.NMEAFile(filename) as nmea_file:
for record in nmea_file:
records.append(record)
if (record.sentence_type == 'GGA'): #统计GGA语句条数
gga_sumcount += 1
if ((record.gps_qual == 4) and
(float(record.age_gps_data) < 20)): #固定解且差分延迟小于20秒，累加高程值
hgts.append(float(record.altitude)) #将固定解高程值添加至列表
lon_list.append(record.longitude) #将固定解经度值添加至列表
lat_list.append(record.latitude) #将固定解纬度值添加至列表
hgt_sum += float(record.altitude)
gga_validcount += 1
end_time = time.time()
print('Parse used time:%.2f(s)' % (end_time - start_time))
print('1.Total counts of records:', len(records))
print('2.GGA total counts: %d, fixed counts: %d, fixed percent:%.2f(%%)' %
(gga_sumcount, gga_validcount,
(gga_validcount / gga_sumcount) * 100))
if gga_validcount != 0:
hgt_avg = hgt_sum / gga_validcount
print('3.Hgt average:%.4f(m),' % hgt_avg, end=' ')
detla_1cm = 0.01
detla_2cm = 0.02
for i in range(len(hgts)):
if (hgts[i] <= hgt_avg + detla_2cm) and (hgts[i] >=
hgt_avg - detla_2cm):
hgt_ud2cm_count += 1
if (hgts[i] <= hgt_avg + detla_1cm) and (hgts[i] >=
hgt_avg - detla_1cm):
hgt_ud1cm_count += 1
else:
print('3.Hgt average:Invalid,', end=' ')
if len(hgts) != 0:
print(
'range ±2cm percent:%.2f(%%), range ±1cm percent:%.2f(%%), max-min:%.4f(m)'
% ((hgt_ud2cm_count / len(hgts)) * 100,
(hgt_ud1cm_count / len(hgts)) * 100, max(hgts) - min(hgts)))
else:
print(
'range ±2cm percent:Invalid, range ±1cm percent:Invalid, max-min:Invalid'
)
'''
绘制高程值趋势图(实际变化值/平均值/平均值+2cm/平均值-2cm四条曲线)
'''
if (gga_validcount != 0) and (len(hgts) != 0):
print('4.Draw plot')
hgt_avg_list = []
hgt_u2cm_list = []
hgt_d2cm_list = []
for i in range(len(hgts)):
hgt_avg_list.append(hgt_avg)
hgt_u2cm_list.append(hgt_avg + detla_2cm)
hgt_d2cm_list.append(hgt_avg - detla_2cm)
avg_y = hgt_avg_list
u2cm_y = hgt_u2cm_list
d2cm_y = hgt_d2cm_list
#WGS84经纬度转米勒投影XY坐标系
miller_x = []
miller_y = []
for i in range(len(lon_list)):
millerToXY(lon_list[i], lat_list[i]) #经纬度转换为米勒投影坐标xy
miller_x.append(xy_coordinate[i][0]) #x
miller_y.append(xy_coordinate[i][1]) #y
#WGS84经纬度转CGCS2000坐标系
'''
Beijing 1954 / Gauss-Kruger CM 117E
EPSG:21460 with transformation: 15921
Area of use: China - onshore between 114°E and 120°E
'''
cgcs2000_x = []
cgcs2000_y = []
transformer = Transformer.from_crs(
'epsg:4326', 'epsg:4479'
) #WGS84转CGCS2000坐标系(epsg:4326为WGS84坐标系编号，epsg:4479为CGCS2000坐标系编号)
for i in range(len(lon_list)):
cgc_x, cgc_y, cgc_z = transformer.transform(
lat_list[i], lon_list[i], hgts[i])
cgcs2000_xy.append((cgc_x, cgc_y, cgc_z)) #形成多个元组坐标组成的列表
cgcs2000_x.append(cgc_x)
cgcs2000_y.append(cgc_y)
# print('cgcs2000:',cgcs2000_xy) #打印转换后的cgcs2000坐标点集
x = range(0, len(hgts))
hgt_y = hgts
plt.figure(num=1)
plt.subplot(311) #3行1列 第1行
plt.title('Position Chart')
plt.plot(x, miller_x, label='E-W(m)', linestyle=':') #绘制longitude实际值趋势
plt.xlabel('X-Points')
plt.ylabel('Y')
plt.grid()
plt.legend()
plt.subplot(312) #3行1列 第2行
plt.plot(x, miller_y, label='N-S(m)', linestyle=':') #绘制latitude实际值趋势
plt.xlabel('X-Points')
plt.ylabel('Y')
plt.grid()
plt.legend()
plt.subplot(313) #3行1列 第3行
plt.plot(x, hgt_y, label='rt hgt', linestyle=':') #绘制实际值趋势
plt.plot(x, avg_y, label='avg hgt') #绘制平均值直线
plt.plot(x, u2cm_y, label='avg+2cm hgt') #绘制平均值+2cm直线
plt.plot(x, d2cm_y, label='avg-2cm hgt') #绘制平均值-2cm直线
plt.xlabel('X-Points')
plt.ylabel('Y-Height(m)')
plt.grid()
plt.legend()
plt.figure(num=2)
plt.plot(lon_list, lat_list, linestyle=':') #绘制经纬度分布图
plt.xlabel('X-Longitude')
plt.ylabel('Y-Latitude')
plt.title('Lon-Lat Chart')
plt.grid()
plt.figure(num=3)
plt.plot(miller_x, miller_y, linestyle=':') #绘制米勒投影XY分布图
plt.xlabel('X(m)')
plt.ylabel('Y(m)')
plt.title('Miller_XY Chart')
plt.grid()
plt.figure(num=4)
plt.plot(cgcs2000_x, cgcs2000_y, linestyle=':') #绘制CGCS2000坐标系XY分布图
plt.xlabel('X(m)')
plt.ylabel('Y(m)')
plt.title('CGCS2000_XY Chart')
plt.grid()
plt.show()
else:
print('4.Data invalid,do not draw plot')
def read(filename):
records = [] #解析列表
gga_count = [0, 0, 0, 0, 0, 0] #GGA总数/固定解点数/浮动解点数/差分解点数/单点解点数/未定位点数
hgts = [] #高程值列表
hgt_sum = 0.0 #高程累加值
hgt_min = 0.0 #高程最小值
hgt_max = 0.0 #高程最大值
hgt_ud2cm_count = 0 #±2cm数据条数
hgt_ud1cm_count = 0 #±1cm数据条数
lat_list = [] #lat列表(有效数据)
lon_list = [] #lon列表(有效数据)
sv_list = [] #可用卫星颗数(有效数据)
diff_delay_list = [] #差分延迟列表(有效数据)
print('Parse file path:', filename)
start_time = time.time()
#使用上下文解析nmea文本数据
with pynmea2.NMEAFile(filename) as nmea_file:
for record in nmea_file:
records.append(record)
if (record.sentence_type == 'GGA'): #统计GGA语句条数
gga_count[0] += 1
if (record.gps_qual == 4):
#if(float(record.age_gps_data) < 20)): #差分延迟小于20秒，累加高程值
hgts.append(float(record.altitude)) #将固定解高程值添加至列表
lon_list.append(record.longitude) #将固定解经度值添加至列表
lat_list.append(record.latitude) #将固定解纬度值添加至列表
sv_list.append(int(record.num_sats)) #将固定解可用卫星颗数添加至列表
diff_delay_list.append(float(
record.age_gps_data)) #将固定解差分延迟添加至列表
hgt_sum += float(record.altitude)
gga_count[1] += 1
elif (record.gps_qual == 5): #浮动解
gga_count[2] += 1
elif (record.gps_qual == 2): #差分解
gga_count[3] += 1
elif (record.gps_qual == 1): #单点解
gga_count[4] += 1
else: #未定位
gga_count[5] += 1
end_time = time.time()
print('Parse used time:%.2f(s)' % (end_time - start_time))
print('1.Total counts of records:', len(records))
if gga_count[0] != 0:
print(
'2.GGA total counts:%d, Fixed:%d(%.2f%%), Float:%d(%.2f%%), DGPS:%d(%.2f%%), Single:%d(%.2f%%), NoPos:%d(%.2f%%)'
% (gga_count[0], gga_count[1],
(gga_count[1] / gga_count[0]) * 100, gga_count[2],
(gga_count[2] / gga_count[0]) * 100, gga_count[3],
(gga_count[3] / gga_count[0]) * 100, gga_count[4],
(gga_count[4] / gga_count[0]) * 100, gga_count[5],
(gga_count[5] / gga_count[0]) * 100))
else:
print('2.Not parsed any GGA sentence')
if gga_count[1] != 0:
hgt_avg = hgt_sum / gga_count[1]
print('3.Hgt average:%.4f(m),' % hgt_avg, end=' ')
detla_1cm = 0.01
detla_2cm = 0.02
for i in range(len(hgts)):
if (hgts[i] <= hgt_avg + detla_2cm) and (hgts[i] >=
hgt_avg - detla_2cm):
hgt_ud2cm_count += 1
if (hgts[i] <= hgt_avg + detla_1cm) and (hgts[i] >=
hgt_avg - detla_1cm):
hgt_ud1cm_count += 1
print(
'range ±2cm percent:%.2f(%%), range ±1cm percent:%.2f(%%), max-min:%.4f(m)'
% ((hgt_ud2cm_count / len(hgts)) * 100,
(hgt_ud1cm_count / len(hgts)) * 100, max(hgts) - min(hgts)))
else:
print('3.Hgt average:Invalid,', end=' ')
print(
'range ±2cm percent:Invalid, range ±1cm percent:Invalid, max-min:Invalid'
)
'''
绘制高程值趋势图(实际变化值/平均值/平均值+2cm/平均值-2cm四条曲线)
'''
if gga_count[1] != 0:
print('4.Draw plot')
hgt_avg_list = []
hgt_u2cm_list = []
hgt_d2cm_list = []
for i in range(len(hgts)):
hgt_avg_list.append(hgt_avg)
hgt_u2cm_list.append(hgt_avg + detla_2cm)
hgt_d2cm_list.append(hgt_avg - detla_2cm)
avg_y = hgt_avg_list
u2cm_y = hgt_u2cm_list
d2cm_y = hgt_d2cm_list
#WGS84经纬度转米勒投影XY坐标系
miller_x = []
miller_y = []
for i in range(len(lon_list)):
millerToXY(lon_list[i], lat_list[i]) #经纬度转换为米勒投影坐标xy
miller_x.append(xy_coordinate[i][0]) #x
miller_y.append(xy_coordinate[i][1]) #y
#WGS84经纬度转CGCS2000坐标系
'''
Beijing 1954 / Gauss-Kruger CM 117E
EPSG:21460 with transformation: 15921
Area of use: China - onshore between 114°E and 120°E
'''
cgcs2000_x = []
cgcs2000_y = []
transformer = Transformer.from_crs(
'epsg:4326', 'epsg:4479'
) #WGS84转CGCS2000坐标系(epsg:4326为WGS84坐标系编号，epsg:4479为CGCS2000坐标系编号)
for i in range(len(lon_list)):
cgc_x, cgc_y, cgc_z = transformer.transform(
lat_list[i], lon_list[i], hgts[i])
cgcs2000_xy.append((cgc_x, cgc_y, cgc_z)) #形成多个元组坐标组成的列表
cgcs2000_x.append(cgc_x)
cgcs2000_y.append(cgc_y)
# print('cgcs2000:',cgcs2000_xy) #打印转换后的cgcs2000坐标点集
x = range(0, len(hgts))
hgt_y = hgts
print('gga_count', gga_count)
GGAPercentN = 5
percent = [0 for i in range(GGAPercentN)]
percent[0] = gga_count[1] / gga_count[0]
percent[1] = gga_count[2] / gga_count[0]
percent[2] = gga_count[3] / gga_count[0]
percent[3] = gga_count[4] / gga_count[0]
percent[4] = gga_count[5] / gga_count[0]
#print('GGAPercentN',GGAPercentN)
#绘制解状态占比及Avg±2cm Avg±1cm占比条形图
fig = plt.figure(num=0)
#fig.subplots_adjust(bottom=0.025, left=0.025, top = 0.975, right=0.975)
fig.subplots_adjust(bottom=0.1, left=0.025, top=0.975, right=0.975)
plt.title('Summary Chart')
plt.subplot(211) #1行2列 第1列
X1 = np.arange(GGAPercentN)
Y1 = percent * np.ones(GGAPercentN)
#plt.axes([0.025,0.025,0.95,0.95]) #设置坐标轴
plt.bar(X1,
Y1,
width=0.5,
facecolor='#9999ff',
edgecolor='black',
label='Solution Percent') #绘制条形码
plt.legend(loc='upper right')
solution_text = [
'Fix(4)', 'Float(5)', 'DGPS(2)', 'Single(1)', 'NoPos(0)'
]
for x1, y1 in zip(X1, Y1):
plt.text(x1,
y1 + 0.1,
'%.2f(%%)' % float(y1 * 100.00),
ha='center',
va='top') #在对应条形码上方放置条形码数值
for i in range(GGAPercentN):
plt.text(i, -0.15, solution_text[i], ha='center',
va='bottom') #在对应条形码下方放置类型
# 设置横纵坐标上下限及记号
#plt.xlim(-.5,GGAPercentN), plt.xticks([])
plt.ylim(-.2, +1.15), plt.yticks([])
plt.xticks([]), plt.yticks([])
plt.subplot(212) #1行2列 第2列
HgtPercentN = 2
hgtpercent = [0 for i in range(HgtPercentN)]
hgtpercent[0] = hgt_ud2cm_count / len(hgts)
hgtpercent[1] = hgt_ud1cm_count / len(hgts)
X2 = np.arange(HgtPercentN)
Y2 = hgtpercent * np.ones(HgtPercentN)
# plt.axes([0.025,0.025,0.95,0.95]) #设置坐标轴
plt.bar(X2,
Y2,
width=0.3,
facecolor='#ff9999',
edgecolor='black',
label='Avg Percent') #绘制条形码
plt.legend(loc='upper right')
range_text = ['Avg±2cm', 'Avg±1cm']
for x2, y2 in zip(X2, Y2):
plt.text(x2,
y2 + 0.1,
'%.2f(%%)' % float(y2 * 100.00),
ha='center',
va='top') #在对应条形码上方放置条形码数值
for i in range(HgtPercentN):
plt.text(i, -0.15, range_text[i], ha='center',
va='bottom') #在对应条形码下方放置类型
# 设置横纵坐标上下限及记号
#plt.xlim(-.5,HgtPercentN), plt.xticks([])
plt.ylim(-.2, +1.15), plt.yticks([])
plt.xticks([]), plt.yticks([])
plt.figure(num=1)
plt.subplot(211) #2行1列 第1行
plt.title('Position Chart')
plt.plot(x,
miller_x,
linewidth=1.0,
color="blue",
label='E-W(m)',
linestyle=':') #绘制longitude实际值趋势
plt.xlabel('Points')
plt.ylabel('Longitude')
plt.grid()
plt.legend()
plt.subplot(212) #2行1列 第2行
plt.plot(x,
miller_y,
linewidth=1.0,
color="red",
label='N-S(m)',
linestyle=':') #绘制latitude实际值趋势
plt.xlabel('Points')
plt.ylabel('Latitude')
plt.grid()
plt.legend()
plt.figure(num=2)
plt.title('Height Chart')
plt.plot(x,
hgt_y,
linewidth=1.0,
color="blue",
label='rt hgt',
linestyle=':') #绘制实际值趋势
plt.plot(x, avg_y, linewidth=1.0, color="green",
label='avg hgt') #绘制平均值直线
plt.plot(x, u2cm_y, linewidth=1.0, color="red",
label='avg+2cm hgt') #绘制平均值+2cm直线
plt.plot(x,
d2cm_y,
linewidth=1.0,
color="magenta",
label='avg-2cm hgt') #绘制平均值-2cm直线
plt.xlabel('Points')
plt.ylabel('Height(m)')
plt.grid()
plt.legend()
plt.figure(num=3)
plt.plot(x, sv_list, linewidth=1.0, color="green",
linestyle='-') #绘制可用卫星颗数分布图
plt.xlabel('Points')
plt.ylabel('SV')
plt.title('Satellite In Used Chart')
plt.grid()
plt.figure(num=4)
plt.plot(x, diff_delay_list, linewidth=1.0, color="red",
linestyle='-') #绘制差分延迟分布图
plt.xlabel('Points')
plt.ylabel('Diff Age(s)')
plt.title('Diff Age Chart')
plt.grid()
plt.figure(num=5)
plt.plot(lon_list, lat_list, linewidth=1.0, linestyle=':') #绘制经纬度分布图
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.xticks(rotation='45') #x轴标签旋转45度
plt.title('Lon-Lat Chart')
plt.grid()
plt.figure(num=6)
plt.plot(miller_x, miller_y, linewidth=1.0,
linestyle=':') #绘制米勒投影XY分布图
plt.xlabel('Miller-X(m)')
plt.ylabel('Miller-Y(m)')
plt.title('Miller_XY Chart')
plt.grid()
plt.figure(num=7)
plt.plot(cgcs2000_x, cgcs2000_y, linewidth=1.0,
linestyle=':') #绘制CGCS2000坐标系XY分布图
plt.xlabel('CGCS2000-X(m)')
plt.ylabel('CGCS2000-Y(m)')
plt.title('CGCS2000_XY Chart')
plt.grid()
plt.show()
else:
print('4.Data invalid,do not draw plot')
#使用pynmea2库对nmea数据文件进行解析,然后打印每一条数据.
'''
参考网址：http://gnss.help/2018/03/01/pynmea2-readme/index.html
1.安装pynmea2模块
pip install pynmea2
2.导入pynmea2
import pynmea2
3.使用NMEAFile()解析文件
修改文件目录('H:/python study/gps_line.txt')即可解析不同NMEA文件
'''
import pynmea2
records = []
nmeafilepath = 'H:/python study/gps_line.txt'
with pynmea2.NMEAFile(nmeafilepath) as nmea_file:
for record in nmea_file:
records.append(record)
print('Parse nmea file path:', nmeafilepath)
print('Count of records:', len(records))
for i in range(len(records)):
print('\n%d nmea sentence:' % i)
print(repr(records[i]))
def read(filename):
records = [] #解析列表
gga_sumcount = 0 #gga语句总统计值
gga_validcount = 0 #gga有效语句统计值（固定且差分延迟小于20秒）
hgts = [] #高程值列表
hgt_sum = 0.0 #高程累加值
hgt_min = 0.0 #高程最小值
hgt_max = 0.0 #高程最大值
hgt_ud2cm_count = 0 #±2cm数据条数
hgt_ud1cm_count = 0 #±1cm数据条数
lat_list = [] #lat列表(有效数据)
lon_list = [] #lon列表(有效数据)
print('Parse file path:', filename)
start_time = time.time()
#使用上下文解析nmea文本数据
with pynmea2.NMEAFile(filename) as nmea_file:
for record in nmea_file:
records.append(record)
if (record.sentence_type == 'GGA'): #统计GGA语句条数
gga_sumcount += 1
if ((record.gps_qual == 4) and
(float(record.age_gps_data) < 20)): #固定解且差分延迟小于20秒，累加高程值
hgts.append(float(record.altitude)) #将固定解高程值添加至列表
lon_list.append(record.longitude) #将固定解经度值添加至列表
lat_list.append(record.latitude) #将固定解纬度值添加至列表
hgt_sum += float(record.altitude)
gga_validcount += 1
end_time = time.time()
print('Parse used time:%.2f(s)' % (end_time - start_time))
print('1.Total counts of records:', len(records))
print('2.GGA total counts: %d, fixed counts: %d, fixed percent:%.2f(%%)' %
(gga_sumcount, gga_validcount,
(gga_validcount / gga_sumcount) * 100))
if gga_validcount != 0:
hgt_avg = hgt_sum / gga_validcount
print('3.Hgt average:%.4f(m),' % hgt_avg, end=' ')
detla_1cm = 0.01
detla_2cm = 0.02
for i in range(len(hgts)):
if (hgts[i] <= hgt_avg + detla_2cm) and (hgts[i] >=
hgt_avg - detla_2cm):
hgt_ud2cm_count += 1
if (hgts[i] <= hgt_avg + detla_1cm) and (hgts[i] >=
hgt_avg - detla_1cm):
hgt_ud1cm_count += 1
else:
print('3.Hgt average:Invalid,', end=' ')
if len(hgts) != 0:
print(
'range ±2cm percent:%.2f(%%), range ±1cm percent:%.2f(%%), max-min:%.4f(m)'
% ((hgt_ud2cm_count / len(hgts)) * 100,
(hgt_ud1cm_count / len(hgts)) * 100, max(hgts) - min(hgts)))
else:
print(
'range ±2cm percent:Invalid, range ±1cm percent:Invalid, max-min:Invalid'
)
'''
绘制高程值趋势图(实际变化值/平均值/平均值+2cm/平均值-2cm四条曲线)
'''
if (gga_validcount != 0) and (len(hgts) != 0):
print('4.Draw plot')
hgt_avg_list = []
hgt_u2cm_list = []
hgt_d2cm_list = []
for i in range(len(hgts)):
hgt_avg_list.append(hgt_avg)
hgt_u2cm_list.append(hgt_avg + detla_2cm)
hgt_d2cm_list.append(hgt_avg - detla_2cm)
avg_y = hgt_avg_list
u2cm_y = hgt_u2cm_list
d2cm_y = hgt_d2cm_list
miller_x = []
miller_y = []
for i in range(len(lon_list)):
millerToXY(lon_list[i], lat_list[i]) #经纬度转换为米勒投影坐标xy
miller_x.append(xy_coordinate[i][0]) #x
miller_y.append(xy_coordinate[i][1]) #y
x = range(0, len(hgts))
hgt_y = hgts
plt.figure(num=1)
plt.plot(x, hgt_y) #绘制实际值趋势
plt.plot(x, avg_y) #绘制平均值直线
plt.plot(x, u2cm_y) #绘制平均值+2cm直线
plt.plot(x, d2cm_y) #绘制平均值-2cm直线
plt.figure(num=2)
plt.plot(lon_list, lat_list) #绘制经纬度分布图
plt.figure(num=3)
plt.plot(miller_x, miller_y) #绘制XY分布图
plt.show()
else:
print('4.Data invalid,do not draw plot')
def dump_file(filename):
with pynmea2.NMEAFile(filename) as file:
for msg in file:
print("%r\n" % msg)
