I am currently trying to use one NetCDF file (open-source data which can be downloaded from https://doi.pangaea.de/10.1594/PANGAEA.828650) to extract specific latitudes from this dataset (https://www.nodc.noaa.gov/archive/arc0105/0160558/3.3/data/0-data/spco2_1982-2015_MPI_SOM-FFN_v2016.nc, again open source).
The first dataset is defined regions of the global oceans known as biomes, I have successfully extracted the area which covers biomes labelled as 16 and 17 from this region.
This dataset has the following gridtype:
gridtype = generic
gridsize = 64800
xsize = 180
ysize = 360
xname = lat
xunits = "degrees latitude"
yname = lon
yunits = "degrees longitude"
xfirst = -89.5
xinc = 1
yfirst = -179.5
yinc = 1
The second grid type is a global dataset of ocean carbon fluxes (parameter: fgco2_raw) and I wish to extract values from the region defined by biome 16 and 17 in the Time_Varying_Biomes.nc.
This dataset, spco2_1982-2015_MPI_SOM-FFN_v2016.nc, has the following gridtype:
gridtype = lonlat
gridsize = 64800
datatype = float
xsize = 360
ysize = 180
xname = lon
xlongname = "longitude"
xunits = "degrees_east"
yname = lat
ylongname = "latitude"
yunits = "degrees_north"
xfirst = -179.5
xinc = 1
When I have attempted to regrid Time_Varying doing the following:
cdo remapbil,mygridtype Time_Varying_Biomes.nc TVB_rg.nc
I have received this error:
cdo remapbil (Abort): Unsupported generic coordinates (Variable: MeanBiomes)!
Has anyone experienced this issue before, and do you have any idea on how I can fix it? Let me know if you need any further information to solve the problem.
Thanks in advance!
You can solve this using the setgrid operator.
First create a grid file. This is the same as the one you give, but with "generic" replaced by "lonlat":
cdo griddes infile.nc > mygrid
sed -i "s/generic/lonlat/g" mygrid
Then use CDO to set the grid:
cdo setgrid,mygrid infile.nc infile_fixedgrid.nc
You should then be able to regrid the file.
Related
I'm re-posting this question since I didn't make a good example code in last question.
I'm trying to make a nodes to set in specific location.
But I found out that the output drawing is not... fixed. Let me show you the pic.
So this is the one I make with 10 nodes. worked perfectly as I intended.
Also it has plt.text on the bottom left.
And here's the other picture
As you can see, something is wrong. plt.text is gone, and USA's location is weird. Actually that location is where DEU is located in the first pic. Both pics use same code.
Now, let me show you some of my code.
for spec_df, please download from my gdrive:
https://drive.google.com/drive/folders/11X_i5-pRLGBfQ9vIwQ3hfDU5EWIfR3Uo?usp=sharing
auto_flag = 0
spec_df=pd.read_stata("C:\\"Your_file_loc"\\CombinedHS6_example.dta")
#top_10_list = ["USA","CHN","KOR"] (Try for three nodes)
#or
#auto_flag = 1 (Try for 10 nodes)
df_p = spec_df[['partneriso3','tradevalue']]
df_p = df_p.groupby('partneriso3').sum().reset_index()
df_r = spec_df[['reporteriso3','tradevalue']]
df_r = df_r.groupby('reporteriso3').sum().reset_index()
df_r = df_r.rename(columns={'reporteriso3': 'Nation'})
df_r = df_r.rename(columns={'tradevalue': 'tradevalue_r'})
df_p = df_p.rename(columns={'partneriso3': 'Nation'})
df_s = pd.merge(df_r, df_p, on='Nation', how='outer').fillna(0)
df_s["final"] = df_s['tradevalue'] + df_s['tradevalue_r']
if auto_flag == 1:
df_s = df_s.sort_values(by=['final'], ascending = False).reset_index()
cut = df_s[:10]
else:
cut = df_s[(df_s['Nation'].isin(top_10_list))]
cut['final'] = cut['final'].apply(lambda x: normalize(x, cut['final'].max()))
cut['font_size'] = cut['final'] * 13
cut['final'] = cut['final'] * 1500
top_10_list = list(cut["Nation"])
top10 = spec_df[(spec_df['reporteriso3'].isin(top_10_list))&(spec_df['partneriso3'].isin(top_10_list))]
top10['tradevalue'] = top10['tradevalue'].apply(lambda x: normalize(x, top10['tradevalue'].max()))
top10['tradevalue'] = top10['tradevalue']*10
plt.figure(figsize=(10,10), dpi = 100)
G = nx.from_pandas_edgelist(top10, 'reporteriso3', 'partneriso3', 'tradevalue', create_using= nx.DiGraph())
widths = nx.get_edge_attributes(G,'tradevalue')
pos = {}
pos_cord = [(-0.30779309, -0.26419882), (0.26767895, 0.19524759), (-0.38479095, 0.88179998), (0.33785317, 0.96090914), (0.94090464, 0.40707934), (0.9270665, -0.38403114), (0.41246223, -0.85684049), (-0.32083322, -1.0), (-0.99724456, -0.34947554), (-0.87530367, 0.40950993)]
for t in range(len(top_10_list)):
if top_10_list == "":
continue
else:
pos[top_10_list[t]] = pos_cord[t]
pos_nodes = nudge(pos, 0, 0.12)
nx.draw_networkx_edges(G,pos, width=list(widths.values()), edge_color = '#9ECAE4')
nx.draw_networkx_nodes(G, pos=pos, nodelist = cut['Nation'], node_size= cut['final'], node_color ='#AB89EF', edgecolors ='#000000')
nx.draw_networkx_labels(G,pos_nodes, font_size=15)
plt.text(-1.15,-1.15,s='hs : ')
plt.savefig(location,dpi=300)
Sorry for the crude code. But I want to ask that I'm using fixed coordinates. So nodes are not supposed to move there location. So I think the plt's size is kinda interacting with the contents...? But I don't know how it does that.
Could anyone enlighten me please? This drives me crazy...
Thanks to #Paul Brodersen's comment, I found a way to fix the location.
I just added these codes in my codes.
fig = plt.figure(figsize=(10,10), dpi = 100)
axes = fig.add_axes([0,0,1,1])
axes.set_xlim([-1.3,1.3])
axes.set_ylim([-1.3,1.3])
Thank you for the help again!
I am trying to learn what the various outputs of predict.coxph() mean. I am currently attempting to fit a cox model on a training set then use the resulting coefficients from the training set to make predictions in a test set (new set of data).
I see from the predict.coxph() help page that I could use type = "survival" to extract and individual's survival probability-- which is equal to exp(-expected).
Here is a code block of what I have attempted so far, using the ISLR2 BrainCancer data.
set.seed(123)
n.training = round(nrow(BrainCancer) * 0.70) # 70:30 split
idx = sample(1:nrow(BrainCancer), size = n.training)
d.training = BrainCancer[idx, ]
d.test = BrainCancer[-idx, ]
# fit a model using the training set
fit = coxph(Surv(time, status) ~ sex + diagnosis + loc + ki + gtv + stereo, data = d.training)
# get predicted survival probabilities for the test set
pred = predict(fit, type = "survival", newdata = d.test)
The predictions generated:
predict(fit, type = "survival", newdata = d.test)
[1] 0.9828659 0.8381164 0.9564982 0.2271862 0.2883800 0.9883625 0.9480138 0.9917512 1.0000000 0.9974775 0.7703657 0.9252100 0.9975044 0.9326234 0.8718161 0.9850815 0.9545622 0.4381646 0.8236644
[20] 0.2455676 0.7289031 0.9063336 0.9126897 0.9988625 0.4399697 0.9360874
Are these survival probabilities associated with a specific time point? From the help page, it sounds like these are survival probabilities at the follow-up times in the newdata argument. Is this correct?
Additional questions:
How is the baseline hazard estimated in predict.coxph? Is it using the Breslow estimator?
If type = "expected" is used, are these values the cumulative hazard? If yes, what are the relevant time points for these?
Thank you!
I am working on face recognition system for my academic project. I want to set the first time an employee was recognized as his first active time and the next time he is being recognized should be recorded as his last active time and then calculate the total active hours based on first active and last active time.
I tried with the following code but I'm getting only the current system time as the start time. can someone help me on what I am doing wrong.
Code:
data = pickle.loads(open(args["encodings"], "rb").read())
vs = VideoStream(src=0).start()
writers = None
time.sleep(2.0)
while True:
frame = vs.read()
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
rgb = imutils.resize(frame, width=750)
r = frame.shape[1] / float(rgb.shape[1])
boxes = face_recognition.face_locations(rgb)
encodings = face_recognition.face_encodings(rgb, boxes)
names = []
face_names = []
for encoding in encodings:
matches = face_recognition.compare_faces(data["encodings"],
encoding)
name = "Unknown"
if True in matches:
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1
name = max(counts, key=counts.get)
names.append(name)
if names != []:
for i in names:
first_active_time = datetime.now().strftime('%H:%M')
last_active_time = datetime.now().strftime('%H:%M')
difference = datetime.strptime(first_active_time, '%H:%M') - datetime.strptime(last_active_time, '%H:%M')
difference = difference.total_seconds()
total_hours = time.strftime("%H:%M", time.gmtime(difference))
face_names.append([i, first_active_time, last_active_time, total_hours])
Given that we could use self-defined metric in LightGBM and use parameter 'feval' to call it during training.
And for given metric, we could define it in the parameter dict like metric:(l1, l2)
My question is that how call several self-defined metric at the same time? I cannot use feval=(my_metric1, my_metric2) to get the result
params = {}
params['learning_rate'] = 0.003
params['boosting_type'] = 'goss'
params['objective'] = 'multiclassova'
params['metric'] = ['multi_error', 'multi_logloss']
params['sub_feature'] = 0.8
params['num_leaves'] = 15
params['min_data'] = 600
params['tree_learner'] = 'voting'
params['bagging_freq'] = 3
params['num_class'] = 3
params['max_depth'] = -1
params['max_bin'] = 512
params['verbose'] = -1
params['is_unbalance'] = True
evals_result = {}
aa = lgb.train(params,
d_train,
valid_sets=[d_train, d_dev],
evals_result=evals_result,
num_boost_round=4500,
feature_name=f_names,
verbose_eval=10,
categorical_feature = f_names,
learning_rates=lambda iter: (1 / (1 + decay_rate * iter)) * params['learning_rate'])
Lets' discuss on the code I share here. d_train is my training set. d_dev is my validation set (I have a different test set.) evals_result will record our multi_error and multi_logloss per iteration as a list. verbose_eval = 10 will make LightGBM print multi_error and multi_logloss of both training set and validation set at every 10 iterations. If you want to plot multi_error and multi_logloss as a graph:
lgb.plot_metric(evals_result, metric='multi_error')
plt.show()
lgb.plot_metric(evals_result, metric='multi_logloss')
plt.show()
You can find other useful functions from LightGBM documentation. If you can't find what you need, go to XGBoost documentation, a simple trick. If there is something missing, please do not hesitate to ask more.
I want to save pitch,yaw and roll data in excel file for all frames. Eg: if i have 200 frames then i want to save 200 frames information in excel file. I have tried but my code only stores one frame data.exceldata
fitting_model='models/Chehra_f1.0.mat';
load(fitting_model);
mov=VideoReader('7_a.avi'); %Read video file and create an object
c=mov.NumberOfFrames;
for k=1:c
a = read(mov, k);
img=im2double(a);
disp(['Detecting Face in ']);
faceDetector = vision.CascadeObjectDetector(); % detect face in an image
bbox = step(faceDetector, img); %create boundary box around face
test_init_shape = InitShape(bbox,refShape); %initialize facial points in variable
test_init_shape = reshape(test_init_shape,49,2);
if size(img,3) == 3
test_input_image = im2double(rgb2gray(img));
else
test_input_image = im2double((img));
end
disp(['Fitting']);
MaxIter=6;
test_points = Fitting(test_input_image,test_init_shape,RegMat,MaxIter);
load('3D_Shape_Model.mat');
n=49;
test_image=img;
imshow(test_image);hold on;
% % Compute 3D Head Pose
if(n==49)
test_shape=test_points;
[pitch,yaw,roll] = ComputePose(PDM_49,test_shape(:));
filename='framesdata.xlsx';
header = {'Pitch', 'yaw ','roll'};
new_data = num2cell([pitch(:), yaw(:), roll(:)]);
output = [header; new_data];
xlswrite(filename,output);
end
plot(test_shape(:,1),test_shape(:,2),'b*');
title([num2str(i),' : Pitch = ',num2str(pitch),' ; Yaw = ',num2str(yaw),' ; Roll = ',num2str(roll)]);
set(gcf,'units','normalized','outerposition',[0 0 1 1]);
pause(0.5);
close all;
end
As #excaza stated, you will need to move the xlswrite command out of your loop or specify the cells you are writing. Please see the xlswrite Doc for more information. The correct syntax would be :
xlswrite(filename,A,xlRange)
The following is the example they provide:
filename = 'testdata.xlsx';
A = {'Time','Temperature'; 12,98; 13,99; 14,97};
sheet = 2;
xlRange = 'E1';
xlswrite(filename,A,sheet,xlRange)
You will just need to provide xlswrite the address to start writing data.