Really difficult to find anyone using markdown in a python IDE (I am using pycharm), with both R and python chunks.
Here is my code so far; I am just trying to set up my markdown to use both R and python code; it seems like my python chunk doesn't work; any idea why? Thanks!
R environment
library(readODS) # excel data
library(glmmTMB) # mixed models
library(car) # ANOVA on mixed models
library(DHARMa) # goodness of fit of the model
library(emmeans) # post hoc
library(ggplot2) # plots
library(reticulate) # link between R and python
use_python('C:/Users/saaa/anaconda3/envs/Python_projects/python.exe')
Python environment
import pandas as pd
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
Related
i am following this tutorial
M/EEG analysis with MNE Python
and i have a little errors in this fragment : 01:35:57 Working with BIDS data
i followed all the steps before and also implemented code :
import matplotlib.pyplot as plt
import pathlib
import matplotlib
import mne
import mne_bids
matplotlib.use('Qt5Agg')
directory ='C:/Users/User/mne_data/MNE-sample-data/MEG\sample/sample_audvis_raw.fif'
raw =mne.io.read_raw(directory)
#raw.plot()
#plt.show()
events =mne.find_events(raw)
#print(events)
event_id ={
"Auditory/Left":1,
"Auditory/Right":2,
"Visual/Left":3,
"Visual/Right":4,
"Smiley":5,
"Button":32
}
raw.info['line_freq']=60
raw.load_data()
out_path =pathlib.Path("out_data/sample_bids")
bids_path =mne_bids.BIDSPath(subject='01',session='01',task='audiovisual',run='01',root=out_path)
mne_bids.write_raw_bids(raw,bids_path=bids_path,events_data=events,event_id=event_id,overwrite=True)
but when i am running this code, i am getting issue :
ValueError: The data is already loaded from disk and may be altered. See warning for "allow_preload".
i can't understand reason of this error, how can i fix it?please helo me
I'm following pandas tutorial1 with pycharm.
Tutorial describes how to use method '.plot()' with jupyter-notebook as
enter image description here
But if I write this code in pycharm, nothing is shown.
if I change code as below, it works same with image
import pandas as pd
import matplotlib.pyplot as plt
air_quality = pd.read_csv("air_quality_no2.csv", index_col=0,
parse_dates=True)
plt.plot(air_quality)
plt.show()
Why is there a difference in using method ".plot()" between jupyter notebook and pycharm?
Thanks.
here the exemplary code:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import pickle5 as pickle
#Read
output = pd.read_pickle("Energy_data.pkl")
plt.figure()
#print(output)
output.plot()
I am using Python 3.7 and that is probably the reason for the error message, because this .pkl files were created in Python 3.8 . If my colleague runs it (he created the .pkl-Files), it'll work.
I tried to use this solution (maybe I did not do it correctly) shown here, but it did not work anyway. Can someone show me how to import the pkl files using the example above in Python 3.7?
Thank you very much in advance!
I'm trying to plot some meteorological data in NetCDF format accessed via the Unidata siphon package.
I've imported what the MetPy docs suggest are the relevant libraries
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from netCDF4 import num2date
import numpy as np
import xarray as xr
from siphon.catalog import TDSCatalog
from datetime import datetime
import metpy.calc as mpcalc
from metpy.units import units
and I've constructed a query for data as per the Siphon docs
best_gfs = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p25deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p25deg/Best')
best_ds = best_gfs.datasets[0]
ncss = best_ds.subset()
query = ncss.query()
query.lonlat_box(north=55, south=20, east=-60, west=-90).time(datetime.utcnow())
query.accept('netcdf4')
query.variables('Vertical_velocity_pressure_isobaric','Relative_humidity_isobaric','Temperature_isobaric','u-component_of_wind_isobaric','v-component_of_wind_isobaric','Geopotential_height_isobaric')
data = ncss.get_data(query)
Unfortunately, when I attempt to parse the dataset using the code from the Metpy docs
data = data.metpy.parse_cf()
I get an error: "AttributeError: NetCDF: Attribute not found"
When attempting to fix this problem, I came across another SO post that seems to have the same issue, but the solution suggested there -- to update my metpy to the latest version, -- did not work for me. I updated metpy using Conda but got the same problem as before I updated. Any other ideas on how to get this resolved?
Right now the following code in Siphon
data = ncss.get_data(query)
will return a Dataset object from netcdf4-python. You need one extra step to hand this to xarray, which will make MetPy's parse_cf available:
from xarray.backends import NetCDF4DataStore
ds = xr.open_dataset(NetCDF4DataStore(data))
data = ds.metpy.parse_cf()
Here's a dummy script that makes three plots and saves them to PDF.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.DataFrame({"A":np.random.normal(100),
"B":np.random.chisquare(5, size = 100),
"C":np.random.gamma(5,size = 100)})
for i in df.columns:
plt.hist(df[i])
plt.savefig(i+".pdf", format = "pdf")
plt.close()
I'm using spyder, which uses IPython. When I run this script, three windows pop at me and then go away. It works, but it's a little annoying.
How can I make the figures get saved to pdf without ever being rendered on my screen?
I'm looking for something like R's
pdf("path/to/plot/name.pdf")
commands
dev.off()
inasmuch as nothing gets rendered on the screen, but the pdf gets saved.
Aha. Partially based on the duplicate suggestion (which wasn't exactly a duplicate), this works:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.DataFrame({"A":np.random.normal(100),
"B":np.random.chisquare(5, size = 100),
"C":np.random.gamma(5,size = 100)})
import matplotlib
old_backend = matplotlib.get_backend()
matplotlib.use("pdf")
for i in df.columns:
plt.hist(df[i])
plt.savefig(i+".pdf", format = "pdf")
plt.close()
matplotlib.use(old_backend)
Basically, set the backend to something like a pdf device, and then set it back to whatever you're accustomed to.
I am referring you to this StackOverflow answer which cites this article as an answer. In the SO answer they also suggest plt.ioff() but are concerned that it could disable other functionality should you want it.