These files are associated with this blog post.
Note
These filesa are named *.tcl
to get syntax highlighting in the gists, should be *.folk
when copied to your Folk system.
set fd [file tempfile py script.py] | |
puts $fd { | |
import pandas as pd | |
file_path = '~/image-label/points.parquet' | |
df = pd.read_parquet(file_path) | |
json_data = df.to_json(orient='records') | |
print(json_data) | |
}; close $fd | |
Retract $this claims $this has python output /anything/ | |
On process [list apply {{this py} { | |
set output [exec python3 $py] | |
Assert $this claims $this has python output $output | |
Assert $this claims $this has data $output | |
Step | |
}} $this $py] | |
When $this has python output /o/ { | |
Wish $this is labelled $o | |
} | |
Wish $this is outlined white | |
package require json | |
proc cleanJSON {inputStr} { | |
# Use the json::json2dict function from the Tcllib JSON package | |
set dict [json::json2dict $inputStr] | |
return $dict | |
} | |
When $this points down with length 0.4 at /target/ { | |
When $target has data /data/ { | |
set cleanedData [cleanJSON $data] | |
Wish $this is labelled $cleanedData | |
Claim $this has cleaned data $cleanedData | |
} | |
} | |
Wish $this is outlined red | |
When $this points down with length 0.4 at /target/ & /target/ has cleaned data /data/ & $this has region /r/ { | |
lassign [region top $r] x y | |
foreach point $data { | |
set pointX [expr {$x + [dict get $point x] * 5}] | |
set pointY [expr {$y + [dict get $point y] * 5}] | |
Display::circle $pointX $pointY 10 10 white | |
Display::text [expr {$pointX + 100}] $pointY 2 [dict get $point label] 0 | |
} | |
} |
These files are associated with this blog post.
Note
These filesa are named *.tcl
to get syntax highlighting in the gists, should be *.folk
when copied to your Folk system.
# Step 1: Import necessary libraries | |
import pandas as pd | |
import pyarrow as pa | |
import pyarrow.parquet as pq | |
# Step 2: Create a DataFrame | |
df = pd.DataFrame({ | |
'x': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], | |
'y': [15, 2, 8, 10, 28, 56, 35, 88, 99, 82], | |
'label': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'] | |
}) | |
# Step 3: Convert the DataFrame to PyArrow Table | |
table = pa.Table.from_pandas(df) | |
# Step 4: Write the PyArrow Table to Parquet | |
pq.write_table(table, 'points.parquet') |