Commit da1926d1 authored by gitanjali Thakur's avatar gitanjali Thakur
Browse files

specific package to requirements

parent baa9bb4d
Pipeline #32043 failed with stage
in 2 minutes and 13 seconds
%% Cell type:code id: tags:
``` python
```
%% Cell type:markdown id: tags:
## extracting fpar values Howard spring from others_tiff (-12.4943, 131.1523)
%% Cell type:code id: tags:
``` python
!pip install matplotlib
```
%%%% Output: stream
Collecting matplotlib
Downloading matplotlib-3.2.1-cp37-cp37m-manylinux1_x86_64.whl (12.4 MB)
 |████████████████████████████████| 12.4 MB 3.4 MB/s eta 0:00:01 |█▋ | 614 kB 3.4 MB/s eta 0:00:04 |████ | 1.6 MB 3.4 MB/s eta 0:00:04
[?25hCollecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1
Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
 |████████████████████████████████| 67 kB 2.1 MB/s eta 0:00:01
[?25hRequirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib) (2.8.1)
Requirement already satisfied: numpy>=1.11 in /opt/conda/lib/python3.7/site-packages (from matplotlib) (1.18.4)
Collecting kiwisolver>=1.0.1
Downloading kiwisolver-1.2.0-cp37-cp37m-manylinux1_x86_64.whl (88 kB)
 |████████████████████████████████| 88 kB 1.4 MB/s eta 0:00:01
[?25hCollecting cycler>=0.10
Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)
Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib) (1.14.0)
Installing collected packages: pyparsing, kiwisolver, cycler, matplotlib
Successfully installed cycler-0.10.0 kiwisolver-1.2.0 matplotlib-3.2.1 pyparsing-2.4.7
%% Cell type:code id: tags:
``` python
import gdal
import matplotlib.pyplot as plt
import numpy as np
import glob
from PIL import Image
from PIL.TiffTags import TAGS
import pandas as pd
from datetime import datetime, timedelta
import os
from pathlib import Path
```
%%%% Output: error
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-1-b0d3b8197055> in <module>
1 import gdal
----> 2 import matplotlib.pyplot as plt
3 import numpy as np
4 import glob
5 from PIL import Image
ModuleNotFoundError: No module named 'matplotlib'
%% Cell type:code id: tags:
``` python
path = '/home/gitanjalithakur/Documents/Aerodynamic_conductance/lst-retrival-from-fluxnet-data/fpar_data/others_fpar/others_tiff/'
fpar500m=[]
fpar_qc=[]
fpar_lai_qc=[]
fpar_stddev=[]
lai500m=[]
lai_stdev=[]
fparlist = Path(path).glob('**/*_Fpar_500m.tif')
fparqclist = Path(path).glob('**/*_FparExtra_QC.tif')
laiqclist = Path(path).glob('**/*_FparLai_QC.tif')
fparstddevlist = Path(path).glob('**/*_FparStdDev_500m.tif')
lailist = Path(path).glob('**/*_FparStdDev_500m.tif')
laistddevlist = Path(path).glob('**/*_FparStdDev_500m.tif')
for pathfpar in fparlist:
fpar500m.append(str(pathfpar))
for pathfparqc in fparqclist:
fpar_qc.append(str(pathfparqc))
for pathlaiqc in laiqclist:
fpar_lai_qc.append (str(pathlaiqc))
for pathfparstdv in fparstddevlist:
fpar_stddev.append (str(pathfparstdv))
for pathlai in lailist:
lai500m.append (str(pathlai))
for pathlaistdv in laistddevlist:
lai_stdev.append (str(pathlaistdv))
# print(path_in_str)
```
%% Cell type:markdown id: tags:
## metadata
%% Cell type:code id: tags:
``` python
g=gdal.Open(fpar500m[1])
met_data=g.GetMetadata()
#print(met_data)
```
%% Cell type:code id: tags:
``` python
g=gdal.Open(fpar500m[1])
met_data=g.GetMetadata()
g1=gdal.Open(fpar_qc[1])
met_data1=g1.GetMetadata()
g2=gdal.Open(fpar_lai_qc[10])
met_data2=g2.GetMetadata()
g3=gdal.Open(fpar_stddev[1])
met_data3=g3.GetMetadata()
g4=gdal.Open(lai500m[1])
met_data4=g4.GetMetadata()
g5=gdal.Open(lai_stdev[1])
met_data5=g5.GetMetadata()
#print(met_data)
```
%% Cell type:code id: tags:
``` python
met_data['RANGEBEGINNINGDATE']
```
%%%% Output: execute_result
'2017-05-17'
%% Cell type:code id: tags:
``` python
met_data1['RANGEBEGINNINGDATE']
```
%%%% Output: execute_result
'2016-02-10'
%% Cell type:code id: tags:
``` python
met_data2['RANGEBEGINNINGDATE']
```
%%%% Output: execute_result
'2016-08-04'
%% Cell type:code id: tags:
``` python
## Assesing the scale factor for LST tile:
fpar_sf = np.float(met_data['scale_factor']) ## scale factor for photosynthetically avtive radiation
lai_sf= np.float(met_data4['scale_factor']) # scale factor for leaf area index
```
%% Cell type:markdown id: tags:
## reading lat, long
%% Cell type:code id: tags:
``` python
geo_data= g.GetGeoTransform()
print(geo_data)
print(g.RasterYSize)
```
%%%% Output: stream
(121.85119341501115, 0.005682448352416348, 0.0, -9.999999999104968, 0.0, -0.005682448352416348)
1760
%% Cell type:code id: tags:
``` python
x_UL = geo_data[0]
x_UR = geo_data[0] + geo_data[1] * g.RasterXSize
xcoor = np.arange(x_UL, x_UR, geo_data[1]).tolist()
#print(xcoor)
```
%% Cell type:code id: tags:
``` python
y_UL = geo_data[3]
y_UR = geo_data[3] + geo_data[5] * g.RasterYSize
ycoor = np.arange(y_UL, y_UR, geo_data[5]).tolist()
```
%% Cell type:markdown id: tags:
## reading band (FPAR):
%% Cell type:code id: tags:
``` python
## reading raster files :
band = g.GetRasterBand(1)
fpar = band.ReadAsArray().astype(np.float)
fpar
```
%%%% Output: execute_result
array([[254., 254., 254., ..., 255., 255., 255.],
[255., 254., 254., ..., 255., 255., 255.],
[255., 254., 254., ..., 255., 255., 255.],
...,
[255., 255., 255., ..., 32., 255., 255.],
[255., 255., 255., ..., 29., 29., 255.],
[255., 255., 255., ..., 29., 29., 28.]])
%% Cell type:code id: tags:
``` python
### check the arrangement of fpar array
fpar.shape
```
%%%% Output: execute_result
(1760, 2902)
%% Cell type:code id: tags:
``` python
fpar[527][298]
```
%%%% Output: execute_result
254.0
%% Cell type:code id: tags:
``` python
def closest(fpar, k):
fpar= np.asarray(fpar)
idx=(np.abs(fpar - k)).argmin()
return [fpar[idx],idx]
#xcoor.index(-96.84, )
```
%% Cell type:code id: tags:
``` python
closest(xcoor,131.1523) ## x is longitute -12.4943, 131.1523
```
%%%% Output: execute_result
[131.1533613679173, 1637]
%% Cell type:code id: tags:
``` python
closest(ycoor,-12.4943) ## y is latitude
```
%%%% Output: execute_result
[-12.494594825815906, 439]
%% Cell type:markdown id: tags:
### reading values @ (lat, long):
%% Cell type:code id: tags:
``` python
## creating dataframe by extracting variables
fpar_var = pd.DataFrame(columns=["fpar", "fpar_qc","fpar_lai_qc","fpar_stdev", "lai500","lai_stdev", "start_date"]) #"strt_date1"
for i in range(1,len(fpar500m)):
fpar=gdal.Open(fpar500m[i])
met_data=fpar.GetMetadata()
strt_date=met_data['RANGEBEGINNINGDATE'] ### getting date of measurement
fparqc=gdal.Open(fpar_qc[i])
met_data1=fparqc.GetMetadata()
fpar_lai=gdal.Open(fpar_lai_qc[i])
met_data2=fpar_lai.GetMetadata()
fpar_stdv=gdal.Open(fpar_stddev[i])
met_data3=fpar_stdv.GetMetadata()
lai=gdal.Open(lai500m[i])
met_data4=lai.GetMetadata()
lai_stdv=gdal.Open(lai_stdev[i])
met_data5=lai_stdv.GetMetadata()
#####
geo_fpar= fpar.GetGeoTransform()
band = fpar.GetRasterBand(1)
fpar_val = band.ReadAsArray().astype(np.float)
fpar_data=fpar_val[439][1637]
####
geo_fparqc= fparqc.GetGeoTransform()
band = fparqc.GetRasterBand(1)
qc_val = band.ReadAsArray().astype(np.float)
qc_data=qc_val[439][1637]
###
geo_laiqc= fpar_lai.GetGeoTransform()
band =fpar_lai.GetRasterBand(1)
laiqc_val = band.ReadAsArray().astype(np.float)
laiqc_data=laiqc_val[439][1637]
###
geo_fparstdvt=fpar_stdv .GetGeoTransform()
band = fpar_stdv.GetRasterBand(1)
fparstdv_val = band.ReadAsArray().astype(np.float)
fparstdv_data=fparstdv_val[439][1637]
geo_lai=lai.GetGeoTransform()
band = lai.GetRasterBand(1)
lai_val = band.ReadAsArray().astype(np.float)
lai_data=lai_val[439][1637]
geo_laistdv=lai_stdv.GetGeoTransform()
band = lai_stdv.GetRasterBand(1)
laistdv_val = band.ReadAsArray().astype(np.float)
laistdv_data=laistdv_val[439][1637]
### Appending the data in pandas series:
fpar_var.loc[i] = [fpar_data * fpar_sf,qc_data,laiqc_data, fparstdv_data,lai_data*lai_sf,laistdv_data,strt_date]
```
%% Cell type:code id: tags:
``` python
fpar_var
```
%%%% Output: execute_result
fpar fpar_qc fpar_lai_qc fpar_stdev lai500 lai_stdev start_date
1 0.60 128.0 16.0 4.0 0.04 4.0 2017-05-17
2 0.58 136.0 0.0 3.0 0.03 3.0 2016-06-17
3 0.52 136.0 8.0 4.0 0.04 4.0 2018-01-25
4 0.60 128.0 8.0 4.0 0.04 4.0 2018-03-30
5 0.67 136.0 0.0 7.0 0.07 7.0 2016-02-10
.. ... ... ... ... ... ... ...
131 0.55 160.0 0.0 4.0 0.04 4.0 2018-01-09
132 0.49 128.0 0.0 6.0 0.06 6.0 2017-07-28
133 0.46 136.0 0.0 4.0 0.04 4.0 2017-09-14
134 0.65 136.0 0.0 5.0 0.05 5.0 2016-05-16
135 0.70 128.0 0.0 4.0 0.04 4.0 2017-03-14
[135 rows x 7 columns]
%% Cell type:markdown id: tags:
### creating index fpar dataframe:
%% Cell type:code id: tags:
``` python
Df=fpar_var.copy()
Df.index=Df.start_date
```
%% Cell type:markdown id: tags:
### timestamp:
%% Cell type:code id: tags:
``` python
Df['Year'] = pd.DatetimeIndex(Df['start_date']).year
Df['Month'] = pd. DatetimeIndex(Df['start_date']).month
Df['Day'] = pd.DatetimeIndex(Df['start_date']).day
```
%% Cell type:code id: tags:
``` python
Df['timestamp']= pd.to_datetime(Df[['Year' ,'Month','Day']])
Df_sort=Df.sort_values(by='timestamp')
```
%% Cell type:code id: tags:
``` python
Df_sort.index=Df_sort.timestamp
```
%% Cell type:code id: tags:
``` python
df=Df_sort.copy()
#[Df_sort.index.year==2018]
plt.figure(figsize=(20,6))
plt.plot(df.index,df.fpar, color='green')
plt.xticks(rotation=45)
plt.xlabel('Timestamp', ha='center',fontsize=20)
plt.ylabel('FPAR', labelpad=10, va='center',fontsize=20)
#plt.legend(['Upwelling','Downwelling'])
plottitle = 'Howard Spring FPAR derived from MOD15A2H'
fname = 'Howar_spr longw'
plt.title(plottitle)
plt.grid()
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id: tags:
``` python
### converting the dATA TO TXT
Df_sort.to_csv('/home/gitanjalithakur/Documents/Aerodynamic_conductance/lst-retrival-from-fluxnet-data/fpar_csv/how_spr_fpar.csv')
```
%% Cell type:code id: tags:
``` python
```
......
numpy
pandas
GDAL==2.2.3
pyModis
\ No newline at end of file
pyModis
matplotlib
datetime
Path
\ No newline at end of file
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