Using GDAL to read data from GRIB file in Python? Planned maintenance scheduled April 23, 2019...
Problem with display of presentation
Why did Bronn offer to be Tyrion Lannister's champion in trial by combat?
Was the pager message from Nick Fury to Captain Marvel unnecessary?
Table formatting with tabularx?
What are some likely causes to domain member PC losing contact to domain controller?
Baking rewards as operations
Fit odd number of triplets in a measure?
New Order #6: Easter Egg
Centre cell vertically in tabularx
Statistical analysis applied to methods coming out of Machine Learning
Why does BitLocker not use RSA?
Vertical ranges of Column Plots in 12
Besides transaction validation, are there any other uses of the Script language in Bitcoin
Adapting the Chinese Remainder Theorem (CRT) for integers to polynomials
What did Turing mean when saying that "machines cannot give rise to surprises" is due to a fallacy?
Did any compiler fully use 80-bit floating point?
Is this Half-dragon Quaggoth boss monster balanced?
Where did Ptolemy compare the Earth to the distance of fixed stars?
Is this Kuo-toa homebrew race balanced?
Why are current probes so expensive?
Did pre-Columbian Americans know the spherical shape of the Earth?
Did John Wesley plagiarize Matthew Henry...?
Does a random sequence of vectors span a Hilbert space?
As a dual citizen, my US passport will expire one day after traveling to the US. Will this work?
Using GDAL to read data from GRIB file in Python?
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
Announcing the arrival of Valued Associate #679: Cesar Manara
Unicorn Meta Zoo #1: Why another podcast?How to convert a NetCDF file to GeoTIFF using GDALPer-pixel (statistical) calculations on a raster stack using GDALGetting time dimension from GRIB file?Converting GRIB to Geotiff with gdal_translate in PythonReading GRIB band metadata with GDALHow to extract specific information from GRIB files?GRIB to NetCDF with GDAL, specifying dimensionsGet min/max lat and long values from geodataframePython GDAL showing incorrect metadata for GRIB filegdal_calc.py result are false and not the same as “direct” calculation
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ margin-bottom:0;
}
I am attempting to use GDAL to read corresponding latitude, longitude, and temperature values from a GRIB file. I would like to store these data points in a 2-D list. I was able to do this using the NDFD GRIB Decoder. However, this tool required me to first create a CSV of the data. If possible, I would like to avoid creating any files because I will be reading a large number of GRIB files. Thus, I switched to GDAL.
Currently, I am able to use GDAL to iterate through each message/band of the GRIB file and view the metadata of each message/band. However, I don't understand how to extract the data from the message/band. I tried using ReadAsArray() and got a large 2-D list of mostly 9999's (No Data Value). I iterated through this list to do some quick stats on all the non-'No Data Values'.
dataset = gdal.Open('grib_file.grb', gdal.GA_ReadOnly)
message = dataset.GetRasterBand(1)
data_array = message.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value < 9999:
num_list.append(value)
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(statistics.mean(num_list)))
print("Standard Deviation: " + str(statistics.stdev(num_list)))
Comparing the resulting stats to a 'degribbed' CSV of the same message/band, I found that the number of non-9999 GDAL values in my 2-D list is equal the number of data rows in my CSV. So I assume each of these GDAL values correlates to 1 latitude-longitude-temperature data point. However, when looking at the max, min, and mean of the GDAL values, they don't match my CSV's values for latitude, longitude, or temperature. What do these GDAL values actually represent? And what is the best way for me to extract the latitude-longitude-temperature data points?
I am very new to working with GIS data, so I may have a fundamental misunderstanding as to how GDAL or GRIB files work.
Update
After some more research, I found that GDAL automatically converts all temperature values in GRIB files to Celsius by default. Additionally, the NDFD Decoder (aka 'degribber') converts to Fahrenheit and rounds to the nearest integer. My temperature values were correct, just using a different unit than I expected. For future reference, documentation on GDAL GRIB file unit conversion can be found here: https://www.gdal.org/frmt_grib.html.
However, I am still having trouble getting the correct latitude and longitude co-ordinates for each of these temperature values. Here is what I have right now:
import gdal
import numpy as np
import statistics
import osr
import math
# Open file
dataset = gdal.Open('E:/Downloads/YEUZ98_KWBN_201001011259.grb2', gdal.GA_ReadOnly)
message_count = dataset.RasterCount
x_size = dataset.RasterXSize
y_size = dataset.RasterYSize
# Preparing transformation
src_srs = osr.SpatialReference()
src_srs.ImportFromWkt(dataset.GetProjection())
tgt_srs = osr.SpatialReference()
tgt_srs.ImportFromEPSG(4326)
transform = osr.CoordinateTransformation(src_srs, tgt_srs)
# Parsing for valid data points
message = dataset.GetRasterBand(1)
data_array = message.ReadAsArray()
data_points = []
for row in range(y_size):
for col in range(x_size):
temperature = data_array[row][col]
if temperature != message.GetNoDataValue():
lat_long_point = transform.TransformPoint(row, col)
lat = lat_long_point[1]
long = lat_long_point[0]
data_points.append([lat, long, temperature])
# Display statistics for temperature
temperatures = [data_point[2] for data_point in data_points]
print("Count: " + str(len(temperatures)))
print("Max: " + str(np.max(temperatures)))
print("Min: " + str(np.min(temperatures)))
print("Mean: " + str(statistics.mean(temperatures)))
print("Standard Deviation: " + str(statistics.stdev(temperatures)))
# Show 1/20000 of the data points. Each data point holds a temperature and its corresponding lat/long
print("nData Points:")
for i in range(math.floor(len(data_points) / 20000)):
print(data_points[i * 20000])
I get the following output:
Count: 368246
Max: 24.950006103515648
Min: -31.649999999999977
Mean: -4.05918937533062
Standard Deviation: 10.215615846529928
Data Points [Latitude, Longitude, Temperature]:
[25.000890299683032, -94.99952371153155, 5.550012207031273]
[25.00491913062724, -94.99888862379909, -25.549993896484352]
[25.001070152573444, -94.99860090057608, -0.04999389648435226]
[25.00069244683015, -94.99835283836573, 7.249993896484398]
[25.005575607284577, -94.99813446569522, -14.449987792968727]
[25.001942459037867, -94.99790629734547, -2.2500061035156023]
[25.00026077721243, -94.99768802823817, 12.249993896484398]
[25.00249102141579, -94.99747960735287, -6.649999999999977]
[25.00358815549422, -94.99726128117258, -9.449987792968727]
[25.008084618188125, -94.99704286927876, 3.8500000000000227]
[25.000404647896634, -94.99680491081958, 11.150018310546898]
[25.003300367512978, -94.99657660948411, -10.049993896484352]
[25.00069241108848, -94.99633853829565, 12.249993896484398]
[25.00553959660932, -94.99611016365309, -3.3499816894531023]
[25.00364208093872, -94.99586215125959, -3.8499816894531023]
[25.006762621284626, -94.995594121097, 6.150018310546898]
[25.00740110988653, -94.99527655572876, 13.350000000000023]
[25.003453198251332, -94.9948599463964, -0.04999389648435226]
Every latitude-longitude pair seems to be tightly centered around 25 lat, -95 long. However, the GRIB file I am using covers the entire continental U.S. What am I doing wrong?
Downloads for the GRIB file I am working with and the CSV for the 1st message/band of that GRIB can be found here: https://www.dropbox.com/sh/oiaq91jq27isbp8/AADlskNq68sC_dhb7J8GXfBaa?dl=0
python gdal weather grib
asked Jul 12 '18 at 22:03
Jacob HussJacob Huss
64
bumped to the homepage by Community♦ 4 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
I am attempting to use GDAL to read corresponding latitude, longitude, and temperature values from a GRIB file. I would like to store these data points in a 2-D list. I was able to do this using the NDFD GRIB Decoder. However, this tool required me to first create a CSV of the data. If possible, I would like to avoid creating any files because I will be reading a large number of GRIB files. Thus, I switched to GDAL.
Currently, I am able to use GDAL to iterate through each message/band of the GRIB file and view the metadata of each message/band. However, I don't understand how to extract the data from the message/band. I tried using ReadAsArray() and got a large 2-D list of mostly 9999's (No Data Value). I iterated through this list to do some quick stats on all the non-'No Data Values'.
dataset = gdal.Open('grib_file.grb', gdal.GA_ReadOnly)
message = dataset.GetRasterBand(1)
data_array = message.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value < 9999:
num_list.append(value)
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(statistics.mean(num_list)))
print("Standard Deviation: " + str(statistics.stdev(num_list)))
Comparing the resulting stats to a 'degribbed' CSV of the same message/band, I found that the number of non-9999 GDAL values in my 2-D list is equal the number of data rows in my CSV. So I assume each of these GDAL values correlates to 1 latitude-longitude-temperature data point. However, when looking at the max, min, and mean of the GDAL values, they don't match my CSV's values for latitude, longitude, or temperature. What do these GDAL values actually represent? And what is the best way for me to extract the latitude-longitude-temperature data points?
I am very new to working with GIS data, so I may have a fundamental misunderstanding as to how GDAL or GRIB files work.
Update
After some more research, I found that GDAL automatically converts all temperature values in GRIB files to Celsius by default. Additionally, the NDFD Decoder (aka 'degribber') converts to Fahrenheit and rounds to the nearest integer. My temperature values were correct, just using a different unit than I expected. For future reference, documentation on GDAL GRIB file unit conversion can be found here: https://www.gdal.org/frmt_grib.html.
However, I am still having trouble getting the correct latitude and longitude co-ordinates for each of these temperature values. Here is what I have right now:
import gdal
import numpy as np
import statistics
import osr
import math
# Open file
dataset = gdal.Open('E:/Downloads/YEUZ98_KWBN_201001011259.grb2', gdal.GA_ReadOnly)
message_count = dataset.RasterCount
x_size = dataset.RasterXSize
y_size = dataset.RasterYSize
# Preparing transformation
src_srs = osr.SpatialReference()
src_srs.ImportFromWkt(dataset.GetProjection())
tgt_srs = osr.SpatialReference()
tgt_srs.ImportFromEPSG(4326)
transform = osr.CoordinateTransformation(src_srs, tgt_srs)
# Parsing for valid data points
message = dataset.GetRasterBand(1)
data_array = message.ReadAsArray()
data_points = []
for row in range(y_size):
for col in range(x_size):
temperature = data_array[row][col]
if temperature != message.GetNoDataValue():
lat_long_point = transform.TransformPoint(row, col)
lat = lat_long_point[1]
long = lat_long_point[0]
data_points.append([lat, long, temperature])
# Display statistics for temperature
temperatures = [data_point[2] for data_point in data_points]
print("Count: " + str(len(temperatures)))
print("Max: " + str(np.max(temperatures)))
print("Min: " + str(np.min(temperatures)))
print("Mean: " + str(statistics.mean(temperatures)))
print("Standard Deviation: " + str(statistics.stdev(temperatures)))
# Show 1/20000 of the data points. Each data point holds a temperature and its corresponding lat/long
print("nData Points:")
for i in range(math.floor(len(data_points) / 20000)):
print(data_points[i * 20000])
I get the following output:
Count: 368246
Max: 24.950006103515648
Min: -31.649999999999977
Mean: -4.05918937533062
Standard Deviation: 10.215615846529928
Data Points [Latitude, Longitude, Temperature]:
[25.000890299683032, -94.99952371153155, 5.550012207031273]
[25.00491913062724, -94.99888862379909, -25.549993896484352]
[25.001070152573444, -94.99860090057608, -0.04999389648435226]
[25.00069244683015, -94.99835283836573, 7.249993896484398]
[25.005575607284577, -94.99813446569522, -14.449987792968727]
[25.001942459037867, -94.99790629734547, -2.2500061035156023]
[25.00026077721243, -94.99768802823817, 12.249993896484398]
[25.00249102141579, -94.99747960735287, -6.649999999999977]
[25.00358815549422, -94.99726128117258, -9.449987792968727]
[25.008084618188125, -94.99704286927876, 3.8500000000000227]
[25.000404647896634, -94.99680491081958, 11.150018310546898]
[25.003300367512978, -94.99657660948411, -10.049993896484352]
[25.00069241108848, -94.99633853829565, 12.249993896484398]
[25.00553959660932, -94.99611016365309, -3.3499816894531023]
[25.00364208093872, -94.99586215125959, -3.8499816894531023]
[25.006762621284626, -94.995594121097, 6.150018310546898]
[25.00740110988653, -94.99527655572876, 13.350000000000023]
[25.003453198251332, -94.9948599463964, -0.04999389648435226]
Every latitude-longitude pair seems to be tightly centered around 25 lat, -95 long. However, the GRIB file I am using covers the entire continental U.S. What am I doing wrong?
Downloads for the GRIB file I am working with and the CSV for the 1st message/band of that GRIB can be found here: https://www.dropbox.com/sh/oiaq91jq27isbp8/AADlskNq68sC_dhb7J8GXfBaa?dl=0
python gdal weather grib
asked Jul 12 '18 at 22:03
Jacob HussJacob Huss
64
bumped to the homepage by Community♦ 4 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
I am attempting to use GDAL to read corresponding latitude, longitude, and temperature values from a GRIB file. I would like to store these data points in a 2-D list. I was able to do this using the NDFD GRIB Decoder. However, this tool required me to first create a CSV of the data. If possible, I would like to avoid creating any files because I will be reading a large number of GRIB files. Thus, I switched to GDAL.
Currently, I am able to use GDAL to iterate through each message/band of the GRIB file and view the metadata of each message/band. However, I don't understand how to extract the data from the message/band. I tried using ReadAsArray() and got a large 2-D list of mostly 9999's (No Data Value). I iterated through this list to do some quick stats on all the non-'No Data Values'.
dataset = gdal.Open('grib_file.grb', gdal.GA_ReadOnly)
message = dataset.GetRasterBand(1)
data_array = message.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value < 9999:
num_list.append(value)
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(statistics.mean(num_list)))
print("Standard Deviation: " + str(statistics.stdev(num_list)))
Comparing the resulting stats to a 'degribbed' CSV of the same message/band, I found that the number of non-9999 GDAL values in my 2-D list is equal the number of data rows in my CSV. So I assume each of these GDAL values correlates to 1 latitude-longitude-temperature data point. However, when looking at the max, min, and mean of the GDAL values, they don't match my CSV's values for latitude, longitude, or temperature. What do these GDAL values actually represent? And what is the best way for me to extract the latitude-longitude-temperature data points?
I am very new to working with GIS data, so I may have a fundamental misunderstanding as to how GDAL or GRIB files work.
Update
After some more research, I found that GDAL automatically converts all temperature values in GRIB files to Celsius by default. Additionally, the NDFD Decoder (aka 'degribber') converts to Fahrenheit and rounds to the nearest integer. My temperature values were correct, just using a different unit than I expected. For future reference, documentation on GDAL GRIB file unit conversion can be found here: https://www.gdal.org/frmt_grib.html.
However, I am still having trouble getting the correct latitude and longitude co-ordinates for each of these temperature values. Here is what I have right now:
import gdal
import numpy as np
import statistics
import osr
import math
# Open file
dataset = gdal.Open('E:/Downloads/YEUZ98_KWBN_201001011259.grb2', gdal.GA_ReadOnly)
message_count = dataset.RasterCount
x_size = dataset.RasterXSize
y_size = dataset.RasterYSize
# Preparing transformation
src_srs = osr.SpatialReference()
src_srs.ImportFromWkt(dataset.GetProjection())
tgt_srs = osr.SpatialReference()
tgt_srs.ImportFromEPSG(4326)
transform = osr.CoordinateTransformation(src_srs, tgt_srs)
# Parsing for valid data points
message = dataset.GetRasterBand(1)
data_array = message.ReadAsArray()
data_points = []
for row in range(y_size):
for col in range(x_size):
temperature = data_array[row][col]
if temperature != message.GetNoDataValue():
lat_long_point = transform.TransformPoint(row, col)
lat = lat_long_point[1]
long = lat_long_point[0]
data_points.append([lat, long, temperature])
# Display statistics for temperature
temperatures = [data_point[2] for data_point in data_points]
print("Count: " + str(len(temperatures)))
print("Max: " + str(np.max(temperatures)))
print("Min: " + str(np.min(temperatures)))
print("Mean: " + str(statistics.mean(temperatures)))
print("Standard Deviation: " + str(statistics.stdev(temperatures)))
# Show 1/20000 of the data points. Each data point holds a temperature and its corresponding lat/long
print("nData Points:")
for i in range(math.floor(len(data_points) / 20000)):
print(data_points[i * 20000])
I get the following output:
Count: 368246
Max: 24.950006103515648
Min: -31.649999999999977
Mean: -4.05918937533062
Standard Deviation: 10.215615846529928
Data Points [Latitude, Longitude, Temperature]:
[25.000890299683032, -94.99952371153155, 5.550012207031273]
[25.00491913062724, -94.99888862379909, -25.549993896484352]
[25.001070152573444, -94.99860090057608, -0.04999389648435226]
[25.00069244683015, -94.99835283836573, 7.249993896484398]
[25.005575607284577, -94.99813446569522, -14.449987792968727]
[25.001942459037867, -94.99790629734547, -2.2500061035156023]
[25.00026077721243, -94.99768802823817, 12.249993896484398]
[25.00249102141579, -94.99747960735287, -6.649999999999977]
[25.00358815549422, -94.99726128117258, -9.449987792968727]
[25.008084618188125, -94.99704286927876, 3.8500000000000227]
[25.000404647896634, -94.99680491081958, 11.150018310546898]
[25.003300367512978, -94.99657660948411, -10.049993896484352]
[25.00069241108848, -94.99633853829565, 12.249993896484398]
[25.00553959660932, -94.99611016365309, -3.3499816894531023]
[25.00364208093872, -94.99586215125959, -3.8499816894531023]
[25.006762621284626, -94.995594121097, 6.150018310546898]
[25.00740110988653, -94.99527655572876, 13.350000000000023]
[25.003453198251332, -94.9948599463964, -0.04999389648435226]
Every latitude-longitude pair seems to be tightly centered around 25 lat, -95 long. However, the GRIB file I am using covers the entire continental U.S. What am I doing wrong?
Downloads for the GRIB file I am working with and the CSV for the 1st message/band of that GRIB can be found here: https://www.dropbox.com/sh/oiaq91jq27isbp8/AADlskNq68sC_dhb7J8GXfBaa?dl=0
python gdal weather grib
asked Jul 12 '18 at 22:03
Jacob HussJacob Huss
64
I am attempting to use GDAL to read corresponding latitude, longitude, and temperature values from a GRIB file. I would like to store these data points in a 2-D list. I was able to do this using the NDFD GRIB Decoder. However, this tool required me to first create a CSV of the data. If possible, I would like to avoid creating any files because I will be reading a large number of GRIB files. Thus, I switched to GDAL.
Currently, I am able to use GDAL to iterate through each message/band of the GRIB file and view the metadata of each message/band. However, I don't understand how to extract the data from the message/band. I tried using ReadAsArray() and got a large 2-D list of mostly 9999's (No Data Value). I iterated through this list to do some quick stats on all the non-'No Data Values'.
dataset = gdal.Open('grib_file.grb', gdal.GA_ReadOnly)
message = dataset.GetRasterBand(1)
data_array = message.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value < 9999:
num_list.append(value)
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(statistics.mean(num_list)))
print("Standard Deviation: " + str(statistics.stdev(num_list)))
Comparing the resulting stats to a 'degribbed' CSV of the same message/band, I found that the number of non-9999 GDAL values in my 2-D list is equal the number of data rows in my CSV. So I assume each of these GDAL values correlates to 1 latitude-longitude-temperature data point. However, when looking at the max, min, and mean of the GDAL values, they don't match my CSV's values for latitude, longitude, or temperature. What do these GDAL values actually represent? And what is the best way for me to extract the latitude-longitude-temperature data points?
I am very new to working with GIS data, so I may have a fundamental misunderstanding as to how GDAL or GRIB files work.
Update
After some more research, I found that GDAL automatically converts all temperature values in GRIB files to Celsius by default. Additionally, the NDFD Decoder (aka 'degribber') converts to Fahrenheit and rounds to the nearest integer. My temperature values were correct, just using a different unit than I expected. For future reference, documentation on GDAL GRIB file unit conversion can be found here: https://www.gdal.org/frmt_grib.html.
However, I am still having trouble getting the correct latitude and longitude co-ordinates for each of these temperature values. Here is what I have right now:
import gdal
import numpy as np
import statistics
import osr
import math
# Open file
dataset = gdal.Open('E:/Downloads/YEUZ98_KWBN_201001011259.grb2', gdal.GA_ReadOnly)
message_count = dataset.RasterCount
x_size = dataset.RasterXSize
y_size = dataset.RasterYSize
# Preparing transformation
src_srs = osr.SpatialReference()
src_srs.ImportFromWkt(dataset.GetProjection())
tgt_srs = osr.SpatialReference()
tgt_srs.ImportFromEPSG(4326)
transform = osr.CoordinateTransformation(src_srs, tgt_srs)
# Parsing for valid data points
message = dataset.GetRasterBand(1)
data_array = message.ReadAsArray()
data_points = []
for row in range(y_size):
for col in range(x_size):
temperature = data_array[row][col]
if temperature != message.GetNoDataValue():
lat_long_point = transform.TransformPoint(row, col)
lat = lat_long_point[1]
long = lat_long_point[0]
data_points.append([lat, long, temperature])
# Display statistics for temperature
temperatures = [data_point[2] for data_point in data_points]
print("Count: " + str(len(temperatures)))
print("Max: " + str(np.max(temperatures)))
print("Min: " + str(np.min(temperatures)))
print("Mean: " + str(statistics.mean(temperatures)))
print("Standard Deviation: " + str(statistics.stdev(temperatures)))
# Show 1/20000 of the data points. Each data point holds a temperature and its corresponding lat/long
print("nData Points:")
for i in range(math.floor(len(data_points) / 20000)):
print(data_points[i * 20000])
I get the following output:
Count: 368246
Max: 24.950006103515648
Min: -31.649999999999977
Mean: -4.05918937533062
Standard Deviation: 10.215615846529928
Data Points [Latitude, Longitude, Temperature]:
[25.000890299683032, -94.99952371153155, 5.550012207031273]
[25.00491913062724, -94.99888862379909, -25.549993896484352]
[25.001070152573444, -94.99860090057608, -0.04999389648435226]
[25.00069244683015, -94.99835283836573, 7.249993896484398]
[25.005575607284577, -94.99813446569522, -14.449987792968727]
[25.001942459037867, -94.99790629734547, -2.2500061035156023]
[25.00026077721243, -94.99768802823817, 12.249993896484398]
[25.00249102141579, -94.99747960735287, -6.649999999999977]
[25.00358815549422, -94.99726128117258, -9.449987792968727]
[25.008084618188125, -94.99704286927876, 3.8500000000000227]
[25.000404647896634, -94.99680491081958, 11.150018310546898]
[25.003300367512978, -94.99657660948411, -10.049993896484352]
[25.00069241108848, -94.99633853829565, 12.249993896484398]
[25.00553959660932, -94.99611016365309, -3.3499816894531023]
[25.00364208093872, -94.99586215125959, -3.8499816894531023]
[25.006762621284626, -94.995594121097, 6.150018310546898]
[25.00740110988653, -94.99527655572876, 13.350000000000023]
[25.003453198251332, -94.9948599463964, -0.04999389648435226]
Every latitude-longitude pair seems to be tightly centered around 25 lat, -95 long. However, the GRIB file I am using covers the entire continental U.S. What am I doing wrong?
Downloads for the GRIB file I am working with and the CSV for the 1st message/band of that GRIB can be found here: https://www.dropbox.com/sh/oiaq91jq27isbp8/AADlskNq68sC_dhb7J8GXfBaa?dl=0
python gdal weather grib
python gdal weather grib
asked Jul 12 '18 at 22:03
Jacob HussJacob Huss
64
asked Jul 12 '18 at 22:03
Jacob HussJacob Huss
64
edited Jul 13 '18 at 21:45
Jacob Huss
asked Jul 12 '18 at 22:03
Jacob HussJacob Huss
64
asked Jul 12 '18 at 22:03
Jacob HussJacob Huss
64
asked Jul 12 '18 at 22:03
Jacob HussJacob Huss
64
64
bumped to the homepage by Community♦ 4 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 4 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
I downloaded a free grib file from this link:
gribs2.gmn-usa.com/cgi-bin/weather_fetch.pl?parameter=wind&days=7®ion=Pacific&dataset=nww3
and it was opened with QGIS (it uses gdal and grib raster format is clearly supported); as it can be observed at following image:
In this particular case there are 54 bands and their statistics are different for each one of them.
Adapting your code for calculating statistics in each particular case (it was corroborated that 999 effectively corresponds to nodata values with band.GetNoDataValue()):
from osgeo import gdal
import numpy as np
path = '/home/zeito/pyqgis_data/Pacific.wind.7days.grb'
dataset = gdal.Open(path)
number_bands = dataset.RasterCount
for i in range(1, number_bands):
band = dataset.GetRasterBand(i)
data_array = band.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value != 9999:
num_list.append(value)
print("band: " + str(i))
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(np.mean(num_list)))
print("Standard Deviation: " + str(np.std(num_list)))
print
dataset = None
I got following results after running above code at Python Console of QGIS:
band: 1
Count: 1768
Max: 12.19
Min: -11.9
Mean: -1.40150452489
Standard Deviation: 5.45187052244
band: 2
Count: 1768
Max: 11.47
Min: -13.47
Mean: -1.01839932127
Standard Deviation: 4.31238540212
band: 3
Count: 1768
Max: 13.26
Min: -13.54
Mean: -1.45571266968
Standard Deviation: 5.75917787612
band: 4
Count: 1768
Max: 11.3700024414
Min: -12.5599975586
Mean: -1.25126452692
Standard Deviation: 4.06757737789
band: 5
Count: 1768
Max: 13.32
Min: -13.57
Mean: -1.20752262443
Standard Deviation: 5.74081361145
band: 6
Count: 1768
Max: 10.37
Min: -11.85
Mean: -0.992839366516
Standard Deviation: 4.10367326254
band: 7
Count: 1768
Max: 13.62
Min: -11.85
Mean: -1.31290158371
Standard Deviation: 5.55638807362
band: 8
Count: 1768
Max: 10.62
Min: -11.57
Mean: -1.05286764706
Standard Deviation: 4.27088900011
band: 9
Count: 1768
Max: 11.9
Min: -14.6
Mean: -1.1304638009
Standard Deviation: 5.70324488985
band: 10
Count: 1768
Max: 11.6700024414
Min: -11.4899975586
Mean: -0.982141223752
Standard Deviation: 4.46024558854
band: 11
Count: 1768
Max: 11.22
Min: -10.84
Mean: -1.27833144796
Standard Deviation: 5.73121182775
band: 12
Count: 1768
Max: 12.0
Min: -11.51
Mean: -1.18134049774
Standard Deviation: 4.26419945773
band: 13
Count: 1768
Max: 11.0
Min: -11.17
Mean: -0.984219457014
Standard Deviation: 5.58978615265
band: 14
Count: 1768
Max: 13.1200024414
Min: -10.9199975586
Mean: -0.93731090701
Standard Deviation: 4.17890524661
band: 15
Count: 1768
Max: 10.83
Min: -11.06
Mean: -1.0338178733
Standard Deviation: 5.6062032107
band: 16
Count: 1768
Max: 11.67
Min: -10.57
Mean: -1.27055429864
Standard Deviation: 3.98614702836
band: 17
Count: 1768
Max: 11.0
Min: -9.92
Mean: -0.751985294118
Standard Deviation: 5.77412558911
band: 18
Count: 1768
Max: 11.1500048828
Min: -10.7299951172
Mean: -1.29815688189
Standard Deviation: 3.84181393968
band: 19
Count: 1768
Max: 10.54
Min: -10.05
Mean: -0.975938914027
Standard Deviation: 5.79508356932
band: 20
Count: 1768
Max: 10.88
Min: -10.13
Mean: -1.65597285068
Standard Deviation: 3.53315735052
band: 21
Count: 1768
Max: 10.06
Min: -10.49
Mean: -0.603054298643
Standard Deviation: 5.66007898045
band: 22
Count: 1768
Max: 10.26
Min: -10.23
Mean: -1.54930995475
Standard Deviation: 3.40112845245
band: 23
Count: 1768
Max: 9.91
Min: -10.53
Mean: -0.779078054299
Standard Deviation: 5.56897826265
band: 24
Count: 1768
Max: 10.63
Min: -9.91
Mean: -1.94167420814
Standard Deviation: 3.23921800486
band: 25
Count: 1768
Max: 10.33
Min: -11.09
Mean: -0.49770361991
Standard Deviation: 5.56991123173
band: 26
Count: 1768
Max: 7.98
Min: -9.98
Mean: -1.92744909502
Standard Deviation: 3.19469235975
band: 27
Count: 1768
Max: 10.57
Min: -11.11
Mean: -0.804773755656
Standard Deviation: 5.38311282455
band: 28
Count: 1768
Max: 6.89
Min: -10.65
Mean: -2.09595588235
Standard Deviation: 3.07309286632
band: 29
Count: 1768
Max: 10.78
Min: -11.12
Mean: -0.540701357466
Standard Deviation: 5.0318391658
band: 30
Count: 1768
Max: 6.76
Min: -11.22
Mean: -1.79594457014
Standard Deviation: 3.33263395947
band: 31
Count: 1768
Max: 9.69
Min: -10.51
Mean: -0.988257918552
Standard Deviation: 4.62198713377
band: 32
Count: 1768
Max: 8.9
Min: -11.47
Mean: -1.96816176471
Standard Deviation: 3.51165366978
band: 33
Count: 1768
Max: 9.31
Min: -10.15
Mean: -0.876538461538
Standard Deviation: 4.51865753946
band: 34
Count: 1768
Max: 9.76000244141
Min: -12.0599975586
Mean: -1.74945457217
Standard Deviation: 3.81762198248
band: 35
Count: 1768
Max: 7.89
Min: -10.86
Mean: -1.28719457014
Standard Deviation: 4.36956017886
band: 36
Count: 1768
Max: 9.42
Min: -12.0
Mean: -1.82090497738
Standard Deviation: 3.74329128053
band: 37
Count: 1768
Max: 7.09
Min: -10.06
Mean: -0.999389140271
Standard Deviation: 4.17636250969
band: 38
Count: 1768
Max: 10.2900024414
Min: -12.3799975586
Mean: -1.54119099751
Standard Deviation: 3.80846816199
band: 39
Count: 1768
Max: 7.67
Min: -9.87
Mean: -1.18821266968
Standard Deviation: 4.19379702144
band: 40
Count: 1768
Max: 10.0300024414
Min: -13.3999975586
Mean: -1.67739574864
Standard Deviation: 3.87766328208
band: 41
Count: 1768
Max: 9.61
Min: -10.14
Mean: -0.94503959276
Standard Deviation: 4.46019183416
band: 42
Count: 1768
Max: 10.9700024414
Min: -13.6999975586
Mean: -1.49990140475
Standard Deviation: 4.12638915891
band: 43
Count: 1768
Max: 9.16
Min: -11.39
Mean: -1.14514705882
Standard Deviation: 4.72513765761
band: 44
Count: 1768
Max: 12.02
Min: -13.47
Mean: -1.55609162896
Standard Deviation: 4.03202472163
band: 45
Count: 1768
Max: 9.87
Min: -10.64
Mean: -0.722222850679
Standard Deviation: 4.70986038237
band: 46
Count: 1768
Max: 13.2000024414
Min: -14.4099975586
Mean: -1.21024642737
Standard Deviation: 4.14966471486
band: 47
Count: 1768
Max: 9.52
Min: -11.27
Mean: -0.741719457014
Standard Deviation: 4.77651013604
band: 48
Count: 1768
Max: 10.6100048828
Min: -15.0999951172
Mean: -1.32743289999
Standard Deviation: 4.13160927345
band: 49
Count: 1768
Max: 9.77
Min: -11.07
Mean: -0.468376696833
Standard Deviation: 4.93777834665
band: 50
Count: 1768
Max: 9.08000732422
Min: -16.6299926758
Mean: -1.3561861147
Standard Deviation: 4.21290280523
band: 51
Count: 1768
Max: 8.86
Min: -11.18
Mean: -0.738229638009
Standard Deviation: 5.03029456774
band: 52
Count: 1768
Max: 8.89000488281
Min: -16.4699951172
Mean: -1.54418629366
Standard Deviation: 4.04909447752
band: 53
Count: 1768
Max: 9.02
Min: -10.28
Mean: -0.458857466063
Standard Deviation: 4.9421665411
band: 54
Count: 1768
Max: 8.55000488281
Min: -16.5399951172
Mean: -1.43630733438
Standard Deviation: 4.08263335492
answered Jul 13 '18 at 1:08
xunilkxunilk
15k31943
Thanks for the example. It helped confirm my direction and realize a few mistakes I made. Unfortunately, I am still struggling to retrieve the correct latitude/longitude for each temperature (see update). If you have any advice there, it would be much appreciated.
– Jacob Huss
Jul 13 '18 at 21:00
add a comment |
Your Answer
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "79"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fgis.stackexchange.com%2fquestions%2f289314%2fusing-gdal-to-read-data-from-grib-file-in-python%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I downloaded a free grib file from this link:
gribs2.gmn-usa.com/cgi-bin/weather_fetch.pl?parameter=wind&days=7®ion=Pacific&dataset=nww3
and it was opened with QGIS (it uses gdal and grib raster format is clearly supported); as it can be observed at following image:
In this particular case there are 54 bands and their statistics are different for each one of them.
Adapting your code for calculating statistics in each particular case (it was corroborated that 999 effectively corresponds to nodata values with band.GetNoDataValue()):
from osgeo import gdal
import numpy as np
path = '/home/zeito/pyqgis_data/Pacific.wind.7days.grb'
dataset = gdal.Open(path)
number_bands = dataset.RasterCount
for i in range(1, number_bands):
band = dataset.GetRasterBand(i)
data_array = band.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value != 9999:
num_list.append(value)
print("band: " + str(i))
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(np.mean(num_list)))
print("Standard Deviation: " + str(np.std(num_list)))
print
dataset = None
I got following results after running above code at Python Console of QGIS:
band: 1
Count: 1768
Max: 12.19
Min: -11.9
Mean: -1.40150452489
Standard Deviation: 5.45187052244
band: 2
Count: 1768
Max: 11.47
Min: -13.47
Mean: -1.01839932127
Standard Deviation: 4.31238540212
band: 3
Count: 1768
Max: 13.26
Min: -13.54
Mean: -1.45571266968
Standard Deviation: 5.75917787612
band: 4
Count: 1768
Max: 11.3700024414
Min: -12.5599975586
Mean: -1.25126452692
Standard Deviation: 4.06757737789
band: 5
Count: 1768
Max: 13.32
Min: -13.57
Mean: -1.20752262443
Standard Deviation: 5.74081361145
band: 6
Count: 1768
Max: 10.37
Min: -11.85
Mean: -0.992839366516
Standard Deviation: 4.10367326254
band: 7
Count: 1768
Max: 13.62
Min: -11.85
Mean: -1.31290158371
Standard Deviation: 5.55638807362
band: 8
Count: 1768
Max: 10.62
Min: -11.57
Mean: -1.05286764706
Standard Deviation: 4.27088900011
band: 9
Count: 1768
Max: 11.9
Min: -14.6
Mean: -1.1304638009
Standard Deviation: 5.70324488985
band: 10
Count: 1768
Max: 11.6700024414
Min: -11.4899975586
Mean: -0.982141223752
Standard Deviation: 4.46024558854
band: 11
Count: 1768
Max: 11.22
Min: -10.84
Mean: -1.27833144796
Standard Deviation: 5.73121182775
band: 12
Count: 1768
Max: 12.0
Min: -11.51
Mean: -1.18134049774
Standard Deviation: 4.26419945773
band: 13
Count: 1768
Max: 11.0
Min: -11.17
Mean: -0.984219457014
Standard Deviation: 5.58978615265
band: 14
Count: 1768
Max: 13.1200024414
Min: -10.9199975586
Mean: -0.93731090701
Standard Deviation: 4.17890524661
band: 15
Count: 1768
Max: 10.83
Min: -11.06
Mean: -1.0338178733
Standard Deviation: 5.6062032107
band: 16
Count: 1768
Max: 11.67
Min: -10.57
Mean: -1.27055429864
Standard Deviation: 3.98614702836
band: 17
Count: 1768
Max: 11.0
Min: -9.92
Mean: -0.751985294118
Standard Deviation: 5.77412558911
band: 18
Count: 1768
Max: 11.1500048828
Min: -10.7299951172
Mean: -1.29815688189
Standard Deviation: 3.84181393968
band: 19
Count: 1768
Max: 10.54
Min: -10.05
Mean: -0.975938914027
Standard Deviation: 5.79508356932
band: 20
Count: 1768
Max: 10.88
Min: -10.13
Mean: -1.65597285068
Standard Deviation: 3.53315735052
band: 21
Count: 1768
Max: 10.06
Min: -10.49
Mean: -0.603054298643
Standard Deviation: 5.66007898045
band: 22
Count: 1768
Max: 10.26
Min: -10.23
Mean: -1.54930995475
Standard Deviation: 3.40112845245
band: 23
Count: 1768
Max: 9.91
Min: -10.53
Mean: -0.779078054299
Standard Deviation: 5.56897826265
band: 24
Count: 1768
Max: 10.63
Min: -9.91
Mean: -1.94167420814
Standard Deviation: 3.23921800486
band: 25
Count: 1768
Max: 10.33
Min: -11.09
Mean: -0.49770361991
Standard Deviation: 5.56991123173
band: 26
Count: 1768
Max: 7.98
Min: -9.98
Mean: -1.92744909502
Standard Deviation: 3.19469235975
band: 27
Count: 1768
Max: 10.57
Min: -11.11
Mean: -0.804773755656
Standard Deviation: 5.38311282455
band: 28
Count: 1768
Max: 6.89
Min: -10.65
Mean: -2.09595588235
Standard Deviation: 3.07309286632
band: 29
Count: 1768
Max: 10.78
Min: -11.12
Mean: -0.540701357466
Standard Deviation: 5.0318391658
band: 30
Count: 1768
Max: 6.76
Min: -11.22
Mean: -1.79594457014
Standard Deviation: 3.33263395947
band: 31
Count: 1768
Max: 9.69
Min: -10.51
Mean: -0.988257918552
Standard Deviation: 4.62198713377
band: 32
Count: 1768
Max: 8.9
Min: -11.47
Mean: -1.96816176471
Standard Deviation: 3.51165366978
band: 33
Count: 1768
Max: 9.31
Min: -10.15
Mean: -0.876538461538
Standard Deviation: 4.51865753946
band: 34
Count: 1768
Max: 9.76000244141
Min: -12.0599975586
Mean: -1.74945457217
Standard Deviation: 3.81762198248
band: 35
Count: 1768
Max: 7.89
Min: -10.86
Mean: -1.28719457014
Standard Deviation: 4.36956017886
band: 36
Count: 1768
Max: 9.42
Min: -12.0
Mean: -1.82090497738
Standard Deviation: 3.74329128053
band: 37
Count: 1768
Max: 7.09
Min: -10.06
Mean: -0.999389140271
Standard Deviation: 4.17636250969
band: 38
Count: 1768
Max: 10.2900024414
Min: -12.3799975586
Mean: -1.54119099751
Standard Deviation: 3.80846816199
band: 39
Count: 1768
Max: 7.67
Min: -9.87
Mean: -1.18821266968
Standard Deviation: 4.19379702144
band: 40
Count: 1768
Max: 10.0300024414
Min: -13.3999975586
Mean: -1.67739574864
Standard Deviation: 3.87766328208
band: 41
Count: 1768
Max: 9.61
Min: -10.14
Mean: -0.94503959276
Standard Deviation: 4.46019183416
band: 42
Count: 1768
Max: 10.9700024414
Min: -13.6999975586
Mean: -1.49990140475
Standard Deviation: 4.12638915891
band: 43
Count: 1768
Max: 9.16
Min: -11.39
Mean: -1.14514705882
Standard Deviation: 4.72513765761
band: 44
Count: 1768
Max: 12.02
Min: -13.47
Mean: -1.55609162896
Standard Deviation: 4.03202472163
band: 45
Count: 1768
Max: 9.87
Min: -10.64
Mean: -0.722222850679
Standard Deviation: 4.70986038237
band: 46
Count: 1768
Max: 13.2000024414
Min: -14.4099975586
Mean: -1.21024642737
Standard Deviation: 4.14966471486
band: 47
Count: 1768
Max: 9.52
Min: -11.27
Mean: -0.741719457014
Standard Deviation: 4.77651013604
band: 48
Count: 1768
Max: 10.6100048828
Min: -15.0999951172
Mean: -1.32743289999
Standard Deviation: 4.13160927345
band: 49
Count: 1768
Max: 9.77
Min: -11.07
Mean: -0.468376696833
Standard Deviation: 4.93777834665
band: 50
Count: 1768
Max: 9.08000732422
Min: -16.6299926758
Mean: -1.3561861147
Standard Deviation: 4.21290280523
band: 51
Count: 1768
Max: 8.86
Min: -11.18
Mean: -0.738229638009
Standard Deviation: 5.03029456774
band: 52
Count: 1768
Max: 8.89000488281
Min: -16.4699951172
Mean: -1.54418629366
Standard Deviation: 4.04909447752
band: 53
Count: 1768
Max: 9.02
Min: -10.28
Mean: -0.458857466063
Standard Deviation: 4.9421665411
band: 54
Count: 1768
Max: 8.55000488281
Min: -16.5399951172
Mean: -1.43630733438
Standard Deviation: 4.08263335492
answered Jul 13 '18 at 1:08
xunilkxunilk
15k31943
Thanks for the example. It helped confirm my direction and realize a few mistakes I made. Unfortunately, I am still struggling to retrieve the correct latitude/longitude for each temperature (see update). If you have any advice there, it would be much appreciated.
– Jacob Huss
Jul 13 '18 at 21:00
add a comment |
I downloaded a free grib file from this link:
gribs2.gmn-usa.com/cgi-bin/weather_fetch.pl?parameter=wind&days=7®ion=Pacific&dataset=nww3
and it was opened with QGIS (it uses gdal and grib raster format is clearly supported); as it can be observed at following image:
In this particular case there are 54 bands and their statistics are different for each one of them.
Adapting your code for calculating statistics in each particular case (it was corroborated that 999 effectively corresponds to nodata values with band.GetNoDataValue()):
from osgeo import gdal
import numpy as np
path = '/home/zeito/pyqgis_data/Pacific.wind.7days.grb'
dataset = gdal.Open(path)
number_bands = dataset.RasterCount
for i in range(1, number_bands):
band = dataset.GetRasterBand(i)
data_array = band.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value != 9999:
num_list.append(value)
print("band: " + str(i))
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(np.mean(num_list)))
print("Standard Deviation: " + str(np.std(num_list)))
print
dataset = None
I got following results after running above code at Python Console of QGIS:
band: 1
Count: 1768
Max: 12.19
Min: -11.9
Mean: -1.40150452489
Standard Deviation: 5.45187052244
band: 2
Count: 1768
Max: 11.47
Min: -13.47
Mean: -1.01839932127
Standard Deviation: 4.31238540212
band: 3
Count: 1768
Max: 13.26
Min: -13.54
Mean: -1.45571266968
Standard Deviation: 5.75917787612
band: 4
Count: 1768
Max: 11.3700024414
Min: -12.5599975586
Mean: -1.25126452692
Standard Deviation: 4.06757737789
band: 5
Count: 1768
Max: 13.32
Min: -13.57
Mean: -1.20752262443
Standard Deviation: 5.74081361145
band: 6
Count: 1768
Max: 10.37
Min: -11.85
Mean: -0.992839366516
Standard Deviation: 4.10367326254
band: 7
Count: 1768
Max: 13.62
Min: -11.85
Mean: -1.31290158371
Standard Deviation: 5.55638807362
band: 8
Count: 1768
Max: 10.62
Min: -11.57
Mean: -1.05286764706
Standard Deviation: 4.27088900011
band: 9
Count: 1768
Max: 11.9
Min: -14.6
Mean: -1.1304638009
Standard Deviation: 5.70324488985
band: 10
Count: 1768
Max: 11.6700024414
Min: -11.4899975586
Mean: -0.982141223752
Standard Deviation: 4.46024558854
band: 11
Count: 1768
Max: 11.22
Min: -10.84
Mean: -1.27833144796
Standard Deviation: 5.73121182775
band: 12
Count: 1768
Max: 12.0
Min: -11.51
Mean: -1.18134049774
Standard Deviation: 4.26419945773
band: 13
Count: 1768
Max: 11.0
Min: -11.17
Mean: -0.984219457014
Standard Deviation: 5.58978615265
band: 14
Count: 1768
Max: 13.1200024414
Min: -10.9199975586
Mean: -0.93731090701
Standard Deviation: 4.17890524661
band: 15
Count: 1768
Max: 10.83
Min: -11.06
Mean: -1.0338178733
Standard Deviation: 5.6062032107
band: 16
Count: 1768
Max: 11.67
Min: -10.57
Mean: -1.27055429864
Standard Deviation: 3.98614702836
band: 17
Count: 1768
Max: 11.0
Min: -9.92
Mean: -0.751985294118
Standard Deviation: 5.77412558911
band: 18
Count: 1768
Max: 11.1500048828
Min: -10.7299951172
Mean: -1.29815688189
Standard Deviation: 3.84181393968
band: 19
Count: 1768
Max: 10.54
Min: -10.05
Mean: -0.975938914027
Standard Deviation: 5.79508356932
band: 20
Count: 1768
Max: 10.88
Min: -10.13
Mean: -1.65597285068
Standard Deviation: 3.53315735052
band: 21
Count: 1768
Max: 10.06
Min: -10.49
Mean: -0.603054298643
Standard Deviation: 5.66007898045
band: 22
Count: 1768
Max: 10.26
Min: -10.23
Mean: -1.54930995475
Standard Deviation: 3.40112845245
band: 23
Count: 1768
Max: 9.91
Min: -10.53
Mean: -0.779078054299
Standard Deviation: 5.56897826265
band: 24
Count: 1768
Max: 10.63
Min: -9.91
Mean: -1.94167420814
Standard Deviation: 3.23921800486
band: 25
Count: 1768
Max: 10.33
Min: -11.09
Mean: -0.49770361991
Standard Deviation: 5.56991123173
band: 26
Count: 1768
Max: 7.98
Min: -9.98
Mean: -1.92744909502
Standard Deviation: 3.19469235975
band: 27
Count: 1768
Max: 10.57
Min: -11.11
Mean: -0.804773755656
Standard Deviation: 5.38311282455
band: 28
Count: 1768
Max: 6.89
Min: -10.65
Mean: -2.09595588235
Standard Deviation: 3.07309286632
band: 29
Count: 1768
Max: 10.78
Min: -11.12
Mean: -0.540701357466
Standard Deviation: 5.0318391658
band: 30
Count: 1768
Max: 6.76
Min: -11.22
Mean: -1.79594457014
Standard Deviation: 3.33263395947
band: 31
Count: 1768
Max: 9.69
Min: -10.51
Mean: -0.988257918552
Standard Deviation: 4.62198713377
band: 32
Count: 1768
Max: 8.9
Min: -11.47
Mean: -1.96816176471
Standard Deviation: 3.51165366978
band: 33
Count: 1768
Max: 9.31
Min: -10.15
Mean: -0.876538461538
Standard Deviation: 4.51865753946
band: 34
Count: 1768
Max: 9.76000244141
Min: -12.0599975586
Mean: -1.74945457217
Standard Deviation: 3.81762198248
band: 35
Count: 1768
Max: 7.89
Min: -10.86
Mean: -1.28719457014
Standard Deviation: 4.36956017886
band: 36
Count: 1768
Max: 9.42
Min: -12.0
Mean: -1.82090497738
Standard Deviation: 3.74329128053
band: 37
Count: 1768
Max: 7.09
Min: -10.06
Mean: -0.999389140271
Standard Deviation: 4.17636250969
band: 38
Count: 1768
Max: 10.2900024414
Min: -12.3799975586
Mean: -1.54119099751
Standard Deviation: 3.80846816199
band: 39
Count: 1768
Max: 7.67
Min: -9.87
Mean: -1.18821266968
Standard Deviation: 4.19379702144
band: 40
Count: 1768
Max: 10.0300024414
Min: -13.3999975586
Mean: -1.67739574864
Standard Deviation: 3.87766328208
band: 41
Count: 1768
Max: 9.61
Min: -10.14
Mean: -0.94503959276
Standard Deviation: 4.46019183416
band: 42
Count: 1768
Max: 10.9700024414
Min: -13.6999975586
Mean: -1.49990140475
Standard Deviation: 4.12638915891
band: 43
Count: 1768
Max: 9.16
Min: -11.39
Mean: -1.14514705882
Standard Deviation: 4.72513765761
band: 44
Count: 1768
Max: 12.02
Min: -13.47
Mean: -1.55609162896
Standard Deviation: 4.03202472163
band: 45
Count: 1768
Max: 9.87
Min: -10.64
Mean: -0.722222850679
Standard Deviation: 4.70986038237
band: 46
Count: 1768
Max: 13.2000024414
Min: -14.4099975586
Mean: -1.21024642737
Standard Deviation: 4.14966471486
band: 47
Count: 1768
Max: 9.52
Min: -11.27
Mean: -0.741719457014
Standard Deviation: 4.77651013604
band: 48
Count: 1768
Max: 10.6100048828
Min: -15.0999951172
Mean: -1.32743289999
Standard Deviation: 4.13160927345
band: 49
Count: 1768
Max: 9.77
Min: -11.07
Mean: -0.468376696833
Standard Deviation: 4.93777834665
band: 50
Count: 1768
Max: 9.08000732422
Min: -16.6299926758
Mean: -1.3561861147
Standard Deviation: 4.21290280523
band: 51
Count: 1768
Max: 8.86
Min: -11.18
Mean: -0.738229638009
Standard Deviation: 5.03029456774
band: 52
Count: 1768
Max: 8.89000488281
Min: -16.4699951172
Mean: -1.54418629366
Standard Deviation: 4.04909447752
band: 53
Count: 1768
Max: 9.02
Min: -10.28
Mean: -0.458857466063
Standard Deviation: 4.9421665411
band: 54
Count: 1768
Max: 8.55000488281
Min: -16.5399951172
Mean: -1.43630733438
Standard Deviation: 4.08263335492
answered Jul 13 '18 at 1:08
xunilkxunilk
15k31943
Thanks for the example. It helped confirm my direction and realize a few mistakes I made. Unfortunately, I am still struggling to retrieve the correct latitude/longitude for each temperature (see update). If you have any advice there, it would be much appreciated.
– Jacob Huss
Jul 13 '18 at 21:00
add a comment |
I downloaded a free grib file from this link:
gribs2.gmn-usa.com/cgi-bin/weather_fetch.pl?parameter=wind&days=7®ion=Pacific&dataset=nww3
and it was opened with QGIS (it uses gdal and grib raster format is clearly supported); as it can be observed at following image:
In this particular case there are 54 bands and their statistics are different for each one of them.
Adapting your code for calculating statistics in each particular case (it was corroborated that 999 effectively corresponds to nodata values with band.GetNoDataValue()):
from osgeo import gdal
import numpy as np
path = '/home/zeito/pyqgis_data/Pacific.wind.7days.grb'
dataset = gdal.Open(path)
number_bands = dataset.RasterCount
for i in range(1, number_bands):
band = dataset.GetRasterBand(i)
data_array = band.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value != 9999:
num_list.append(value)
print("band: " + str(i))
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(np.mean(num_list)))
print("Standard Deviation: " + str(np.std(num_list)))
print
dataset = None
I got following results after running above code at Python Console of QGIS:
band: 1
Count: 1768
Max: 12.19
Min: -11.9
Mean: -1.40150452489
Standard Deviation: 5.45187052244
band: 2
Count: 1768
Max: 11.47
Min: -13.47
Mean: -1.01839932127
Standard Deviation: 4.31238540212
band: 3
Count: 1768
Max: 13.26
Min: -13.54
Mean: -1.45571266968
Standard Deviation: 5.75917787612
band: 4
Count: 1768
Max: 11.3700024414
Min: -12.5599975586
Mean: -1.25126452692
Standard Deviation: 4.06757737789
band: 5
Count: 1768
Max: 13.32
Min: -13.57
Mean: -1.20752262443
Standard Deviation: 5.74081361145
band: 6
Count: 1768
Max: 10.37
Min: -11.85
Mean: -0.992839366516
Standard Deviation: 4.10367326254
band: 7
Count: 1768
Max: 13.62
Min: -11.85
Mean: -1.31290158371
Standard Deviation: 5.55638807362
band: 8
Count: 1768
Max: 10.62
Min: -11.57
Mean: -1.05286764706
Standard Deviation: 4.27088900011
band: 9
Count: 1768
Max: 11.9
Min: -14.6
Mean: -1.1304638009
Standard Deviation: 5.70324488985
band: 10
Count: 1768
Max: 11.6700024414
Min: -11.4899975586
Mean: -0.982141223752
Standard Deviation: 4.46024558854
band: 11
Count: 1768
Max: 11.22
Min: -10.84
Mean: -1.27833144796
Standard Deviation: 5.73121182775
band: 12
Count: 1768
Max: 12.0
Min: -11.51
Mean: -1.18134049774
Standard Deviation: 4.26419945773
band: 13
Count: 1768
Max: 11.0
Min: -11.17
Mean: -0.984219457014
Standard Deviation: 5.58978615265
band: 14
Count: 1768
Max: 13.1200024414
Min: -10.9199975586
Mean: -0.93731090701
Standard Deviation: 4.17890524661
band: 15
Count: 1768
Max: 10.83
Min: -11.06
Mean: -1.0338178733
Standard Deviation: 5.6062032107
band: 16
Count: 1768
Max: 11.67
Min: -10.57
Mean: -1.27055429864
Standard Deviation: 3.98614702836
band: 17
Count: 1768
Max: 11.0
Min: -9.92
Mean: -0.751985294118
Standard Deviation: 5.77412558911
band: 18
Count: 1768
Max: 11.1500048828
Min: -10.7299951172
Mean: -1.29815688189
Standard Deviation: 3.84181393968
band: 19
Count: 1768
Max: 10.54
Min: -10.05
Mean: -0.975938914027
Standard Deviation: 5.79508356932
band: 20
Count: 1768
Max: 10.88
Min: -10.13
Mean: -1.65597285068
Standard Deviation: 3.53315735052
band: 21
Count: 1768
Max: 10.06
Min: -10.49
Mean: -0.603054298643
Standard Deviation: 5.66007898045
band: 22
Count: 1768
Max: 10.26
Min: -10.23
Mean: -1.54930995475
Standard Deviation: 3.40112845245
band: 23
Count: 1768
Max: 9.91
Min: -10.53
Mean: -0.779078054299
Standard Deviation: 5.56897826265
band: 24
Count: 1768
Max: 10.63
Min: -9.91
Mean: -1.94167420814
Standard Deviation: 3.23921800486
band: 25
Count: 1768
Max: 10.33
Min: -11.09
Mean: -0.49770361991
Standard Deviation: 5.56991123173
band: 26
Count: 1768
Max: 7.98
Min: -9.98
Mean: -1.92744909502
Standard Deviation: 3.19469235975
band: 27
Count: 1768
Max: 10.57
Min: -11.11
Mean: -0.804773755656
Standard Deviation: 5.38311282455
band: 28
Count: 1768
Max: 6.89
Min: -10.65
Mean: -2.09595588235
Standard Deviation: 3.07309286632
band: 29
Count: 1768
Max: 10.78
Min: -11.12
Mean: -0.540701357466
Standard Deviation: 5.0318391658
band: 30
Count: 1768
Max: 6.76
Min: -11.22
Mean: -1.79594457014
Standard Deviation: 3.33263395947
band: 31
Count: 1768
Max: 9.69
Min: -10.51
Mean: -0.988257918552
Standard Deviation: 4.62198713377
band: 32
Count: 1768
Max: 8.9
Min: -11.47
Mean: -1.96816176471
Standard Deviation: 3.51165366978
band: 33
Count: 1768
Max: 9.31
Min: -10.15
Mean: -0.876538461538
Standard Deviation: 4.51865753946
band: 34
Count: 1768
Max: 9.76000244141
Min: -12.0599975586
Mean: -1.74945457217
Standard Deviation: 3.81762198248
band: 35
Count: 1768
Max: 7.89
Min: -10.86
Mean: -1.28719457014
Standard Deviation: 4.36956017886
band: 36
Count: 1768
Max: 9.42
Min: -12.0
Mean: -1.82090497738
Standard Deviation: 3.74329128053
band: 37
Count: 1768
Max: 7.09
Min: -10.06
Mean: -0.999389140271
Standard Deviation: 4.17636250969
band: 38
Count: 1768
Max: 10.2900024414
Min: -12.3799975586
Mean: -1.54119099751
Standard Deviation: 3.80846816199
band: 39
Count: 1768
Max: 7.67
Min: -9.87
Mean: -1.18821266968
Standard Deviation: 4.19379702144
band: 40
Count: 1768
Max: 10.0300024414
Min: -13.3999975586
Mean: -1.67739574864
Standard Deviation: 3.87766328208
band: 41
Count: 1768
Max: 9.61
Min: -10.14
Mean: -0.94503959276
Standard Deviation: 4.46019183416
band: 42
Count: 1768
Max: 10.9700024414
Min: -13.6999975586
Mean: -1.49990140475
Standard Deviation: 4.12638915891
band: 43
Count: 1768
Max: 9.16
Min: -11.39
Mean: -1.14514705882
Standard Deviation: 4.72513765761
band: 44
Count: 1768
Max: 12.02
Min: -13.47
Mean: -1.55609162896
Standard Deviation: 4.03202472163
band: 45
Count: 1768
Max: 9.87
Min: -10.64
Mean: -0.722222850679
Standard Deviation: 4.70986038237
band: 46
Count: 1768
Max: 13.2000024414
Min: -14.4099975586
Mean: -1.21024642737
Standard Deviation: 4.14966471486
band: 47
Count: 1768
Max: 9.52
Min: -11.27
Mean: -0.741719457014
Standard Deviation: 4.77651013604
band: 48
Count: 1768
Max: 10.6100048828
Min: -15.0999951172
Mean: -1.32743289999
Standard Deviation: 4.13160927345
band: 49
Count: 1768
Max: 9.77
Min: -11.07
Mean: -0.468376696833
Standard Deviation: 4.93777834665
band: 50
Count: 1768
Max: 9.08000732422
Min: -16.6299926758
Mean: -1.3561861147
Standard Deviation: 4.21290280523
band: 51
Count: 1768
Max: 8.86
Min: -11.18
Mean: -0.738229638009
Standard Deviation: 5.03029456774
band: 52
Count: 1768
Max: 8.89000488281
Min: -16.4699951172
Mean: -1.54418629366
Standard Deviation: 4.04909447752
band: 53
Count: 1768
Max: 9.02
Min: -10.28
Mean: -0.458857466063
Standard Deviation: 4.9421665411
band: 54
Count: 1768
Max: 8.55000488281
Min: -16.5399951172
Mean: -1.43630733438
Standard Deviation: 4.08263335492
answered Jul 13 '18 at 1:08
xunilkxunilk
15k31943
I downloaded a free grib file from this link:
gribs2.gmn-usa.com/cgi-bin/weather_fetch.pl?parameter=wind&days=7®ion=Pacific&dataset=nww3
and it was opened with QGIS (it uses gdal and grib raster format is clearly supported); as it can be observed at following image:
In this particular case there are 54 bands and their statistics are different for each one of them.
Adapting your code for calculating statistics in each particular case (it was corroborated that 999 effectively corresponds to nodata values with band.GetNoDataValue()):
from osgeo import gdal
import numpy as np
path = '/home/zeito/pyqgis_data/Pacific.wind.7days.grb'
dataset = gdal.Open(path)
number_bands = dataset.RasterCount
for i in range(1, number_bands):
band = dataset.GetRasterBand(i)
data_array = band.ReadAsArray()
num_list = []
for row in data_array:
for value in row:
if value != 9999:
num_list.append(value)
print("band: " + str(i))
print("Count: " + str(len(num_list)))
print("Max: " + str(np.max(num_list)))
print("Min: " + str(np.min(num_list)))
print("Mean: " + str(np.mean(num_list)))
print("Standard Deviation: " + str(np.std(num_list)))
print
dataset = None
I got following results after running above code at Python Console of QGIS:
band: 1
Count: 1768
Max: 12.19
Min: -11.9
Mean: -1.40150452489
Standard Deviation: 5.45187052244
band: 2
Count: 1768
Max: 11.47
Min: -13.47
Mean: -1.01839932127
Standard Deviation: 4.31238540212
band: 3
Count: 1768
Max: 13.26
Min: -13.54
Mean: -1.45571266968
Standard Deviation: 5.75917787612
band: 4
Count: 1768
Max: 11.3700024414
Min: -12.5599975586
Mean: -1.25126452692
Standard Deviation: 4.06757737789
band: 5
Count: 1768
Max: 13.32
Min: -13.57
Mean: -1.20752262443
Standard Deviation: 5.74081361145
band: 6
Count: 1768
Max: 10.37
Min: -11.85
Mean: -0.992839366516
Standard Deviation: 4.10367326254
band: 7
Count: 1768
Max: 13.62
Min: -11.85
Mean: -1.31290158371
Standard Deviation: 5.55638807362
band: 8
Count: 1768
Max: 10.62
Min: -11.57
Mean: -1.05286764706
Standard Deviation: 4.27088900011
band: 9
Count: 1768
Max: 11.9
Min: -14.6
Mean: -1.1304638009
Standard Deviation: 5.70324488985
band: 10
Count: 1768
Max: 11.6700024414
Min: -11.4899975586
Mean: -0.982141223752
Standard Deviation: 4.46024558854
band: 11
Count: 1768
Max: 11.22
Min: -10.84
Mean: -1.27833144796
Standard Deviation: 5.73121182775
band: 12
Count: 1768
Max: 12.0
Min: -11.51
Mean: -1.18134049774
Standard Deviation: 4.26419945773
band: 13
Count: 1768
Max: 11.0
Min: -11.17
Mean: -0.984219457014
Standard Deviation: 5.58978615265
band: 14
Count: 1768
Max: 13.1200024414
Min: -10.9199975586
Mean: -0.93731090701
Standard Deviation: 4.17890524661
band: 15
Count: 1768
Max: 10.83
Min: -11.06
Mean: -1.0338178733
Standard Deviation: 5.6062032107
band: 16
Count: 1768
Max: 11.67
Min: -10.57
Mean: -1.27055429864
Standard Deviation: 3.98614702836
band: 17
Count: 1768
Max: 11.0
Min: -9.92
Mean: -0.751985294118
Standard Deviation: 5.77412558911
band: 18
Count: 1768
Max: 11.1500048828
Min: -10.7299951172
Mean: -1.29815688189
Standard Deviation: 3.84181393968
band: 19
Count: 1768
Max: 10.54
Min: -10.05
Mean: -0.975938914027
Standard Deviation: 5.79508356932
band: 20
Count: 1768
Max: 10.88
Min: -10.13
Mean: -1.65597285068
Standard Deviation: 3.53315735052
band: 21
Count: 1768
Max: 10.06
Min: -10.49
Mean: -0.603054298643
Standard Deviation: 5.66007898045
band: 22
Count: 1768
Max: 10.26
Min: -10.23
Mean: -1.54930995475
Standard Deviation: 3.40112845245
band: 23
Count: 1768
Max: 9.91
Min: -10.53
Mean: -0.779078054299
Standard Deviation: 5.56897826265
band: 24
Count: 1768
Max: 10.63
Min: -9.91
Mean: -1.94167420814
Standard Deviation: 3.23921800486
band: 25
Count: 1768
Max: 10.33
Min: -11.09
Mean: -0.49770361991
Standard Deviation: 5.56991123173
band: 26
Count: 1768
Max: 7.98
Min: -9.98
Mean: -1.92744909502
Standard Deviation: 3.19469235975
band: 27
Count: 1768
Max: 10.57
Min: -11.11
Mean: -0.804773755656
Standard Deviation: 5.38311282455
band: 28
Count: 1768
Max: 6.89
Min: -10.65
Mean: -2.09595588235
Standard Deviation: 3.07309286632
band: 29
Count: 1768
Max: 10.78
Min: -11.12
Mean: -0.540701357466
Standard Deviation: 5.0318391658
band: 30
Count: 1768
Max: 6.76
Min: -11.22
Mean: -1.79594457014
Standard Deviation: 3.33263395947
band: 31
Count: 1768
Max: 9.69
Min: -10.51
Mean: -0.988257918552
Standard Deviation: 4.62198713377
band: 32
Count: 1768
Max: 8.9
Min: -11.47
Mean: -1.96816176471
Standard Deviation: 3.51165366978
band: 33
Count: 1768
Max: 9.31
Min: -10.15
Mean: -0.876538461538
Standard Deviation: 4.51865753946
band: 34
Count: 1768
Max: 9.76000244141
Min: -12.0599975586
Mean: -1.74945457217
Standard Deviation: 3.81762198248
band: 35
Count: 1768
Max: 7.89
Min: -10.86
Mean: -1.28719457014
Standard Deviation: 4.36956017886
band: 36
Count: 1768
Max: 9.42
Min: -12.0
Mean: -1.82090497738
Standard Deviation: 3.74329128053
band: 37
Count: 1768
Max: 7.09
Min: -10.06
Mean: -0.999389140271
Standard Deviation: 4.17636250969
band: 38
Count: 1768
Max: 10.2900024414
Min: -12.3799975586
Mean: -1.54119099751
Standard Deviation: 3.80846816199
band: 39
Count: 1768
Max: 7.67
Min: -9.87
Mean: -1.18821266968
Standard Deviation: 4.19379702144
band: 40
Count: 1768
Max: 10.0300024414
Min: -13.3999975586
Mean: -1.67739574864
Standard Deviation: 3.87766328208
band: 41
Count: 1768
Max: 9.61
Min: -10.14
Mean: -0.94503959276
Standard Deviation: 4.46019183416
band: 42
Count: 1768
Max: 10.9700024414
Min: -13.6999975586
Mean: -1.49990140475
Standard Deviation: 4.12638915891
band: 43
Count: 1768
Max: 9.16
Min: -11.39
Mean: -1.14514705882
Standard Deviation: 4.72513765761
band: 44
Count: 1768
Max: 12.02
Min: -13.47
Mean: -1.55609162896
Standard Deviation: 4.03202472163
band: 45
Count: 1768
Max: 9.87
Min: -10.64
Mean: -0.722222850679
Standard Deviation: 4.70986038237
band: 46
Count: 1768
Max: 13.2000024414
Min: -14.4099975586
Mean: -1.21024642737
Standard Deviation: 4.14966471486
band: 47
Count: 1768
Max: 9.52
Min: -11.27
Mean: -0.741719457014
Standard Deviation: 4.77651013604
band: 48
Count: 1768
Max: 10.6100048828
Min: -15.0999951172
Mean: -1.32743289999
Standard Deviation: 4.13160927345
band: 49
Count: 1768
Max: 9.77
Min: -11.07
Mean: -0.468376696833
Standard Deviation: 4.93777834665
band: 50
Count: 1768
Max: 9.08000732422
Min: -16.6299926758
Mean: -1.3561861147
Standard Deviation: 4.21290280523
band: 51
Count: 1768
Max: 8.86
Min: -11.18
Mean: -0.738229638009
Standard Deviation: 5.03029456774
band: 52
Count: 1768
Max: 8.89000488281
Min: -16.4699951172
Mean: -1.54418629366
Standard Deviation: 4.04909447752
band: 53
Count: 1768
Max: 9.02
Min: -10.28
Mean: -0.458857466063
Standard Deviation: 4.9421665411
band: 54
Count: 1768
Max: 8.55000488281
Min: -16.5399951172
Mean: -1.43630733438
Standard Deviation: 4.08263335492
answered Jul 13 '18 at 1:08
xunilkxunilk
15k31943
edited Jul 13 '18 at 1:59
answered Jul 13 '18 at 1:08
xunilkxunilk
15k31943
answered Jul 13 '18 at 1:08
xunilkxunilk
15k31943
answered Jul 13 '18 at 1:08
xunilkxunilk
15k31943
15k31943
Thanks for the example. It helped confirm my direction and realize a few mistakes I made. Unfortunately, I am still struggling to retrieve the correct latitude/longitude for each temperature (see update). If you have any advice there, it would be much appreciated.
– Jacob Huss
Jul 13 '18 at 21:00
add a comment |
Thanks for the example. It helped confirm my direction and realize a few mistakes I made. Unfortunately, I am still struggling to retrieve the correct latitude/longitude for each temperature (see update). If you have any advice there, it would be much appreciated.
– Jacob Huss
Jul 13 '18 at 21:00
Thanks for the example. It helped confirm my direction and realize a few mistakes I made. Unfortunately, I am still struggling to retrieve the correct latitude/longitude for each temperature (see update). If you have any advice there, it would be much appreciated.
– Jacob Huss
Jul 13 '18 at 21:00
Thanks for the example. It helped confirm my direction and realize a few mistakes I made. Unfortunately, I am still struggling to retrieve the correct latitude/longitude for each temperature (see update). If you have any advice there, it would be much appreciated.
– Jacob Huss
Jul 13 '18 at 21:00
add a comment |
Thanks for contributing an answer to Geographic Information Systems Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fgis.stackexchange.com%2fquestions%2f289314%2fusing-gdal-to-read-data-from-grib-file-in-python%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
