We Keep Coding

How to read binary file and convert it to text in Python

I have a time series from 1946-2020 for the discharges of gauge. The file is binary and if I open it in a text editor, or even in a hex-editor, I see values which do not make sense. I have searched a lot and found some code but I don't see any time series and values.
I can imagine that the time series is looking like that:
These values are also correct and are in the data.
t Q
17.11.1972 8,66
04.02.2020 28,2
I copied the beginning part of the file:
##4.00
à?š™™™™™é?ÍÌÌÌÌÌì?ffffffî?¸…ëQ¸î?\Âõ(\ï?®Gáz®ï?×£p=
×ï?V-²ïï?§èH.ÿï?Sš ä ÍÌL= ÿÿÍÌL= _ B €#
## NASIM26760601m³/sffûB°FAˆ¢A ¥¼x?  §=,ðñ=ÿ9jŒA´¯DA;Âò#¿‡Ø½ =|?0¥‡=?1=ÿ]”:A þA ¨ï¿eV4#)¡? i3|?`d‹=ek=ÿ‘_î#5Ý#¼˜DA
©]? cÂ{?Œ%¿=+>ÿÚÍ# %µ#À#•9AN? ýô{?h«=×Í­=ÿð½¢#»MAòöî# ¤¼x?¸~=Xä—=ÿ9jŒA
+BAïÕ#yBѾ ‚Äw?èrÈ=¯k“=ÿ]”:A¼/±#>. #„×9AG€
I copied the last part of the file, because I know there must be the time-discharge of 2020. Maybe it is in the end of the file.
×ï?V-²ïï?+‡ÙÎ÷ï? ÍÌL= ÿÿÍÌL= _ B €#
##
in the following screenshot you see the data , when I open it in Notepad++.
here is my python code and output
with open("time-serie_1946 bis 2020.hqr", "rb") as file:
data = file.read()
with open("out.txt", "w") as f:
f.write(" ".join(map(str,data)))
f.write("\n")
the beginning of output:
6 64 64 52 46 48 48 10 0 0 0 0 0 0 0 224 63 154 153 153 153 153 153 233 63 205 204 204 204 204 204 236 63 102 102 102 102 102 102 238 63 184 30 133 235 81 184 238 63 92 143 194 245 40 92 239 63 174 71 225 122 20 174 239 63 215 163 112 61 10 215 239 63 86 14 45 178 157 239 239 63 30 167 232 72 46 255 239 63 83 78 101 117 98 101 114 101 99 104 110 117 110 103 32 98 105 115 32 50 48 50 48 32 109 105 116 32 117 110 98 101 115 116 228 116 105 103 116 101 110 32 72 81 32 118 111 110 32 49 57 52 54 45 49 57 55 50 32 40 65 110 102 114 97 103 101 32 83 99 104 117 104 109 97 99 104 101 114 44 32 84 82 41 154 7 0 0 228 7 0 0 0 0 0 0
How can I decode it to get the time series?

How to turn a dictionary into a dataframe with all the keys in a column

def weights():
saved = {}
for i in range(len(bread_pairs["key_id"])):
drawing = np.array(bread_pairs['bitmap'][i], dtype=np.uint8)
new_test_cnn = drawing.reshape(1, 28, 28, 1).astype('float32')
new_cnn_predict = model.predict(new_test_cnn, batch_size=32, verbose=0)
w = model.layers[8].get_weights()
w = list(w[0].flatten())
saved[bread_pairs["key_id"][i]] = w
return saved
I have this function that is creating a dictionary of key_ids and mapping them to an associated list of values of length 200. So for example my dictionary looks something like saved = {key_id_1: [1,2,3...200], key_id_2: [1,2,...,200], ....}
I would like to turn this dictionary into a dataframe with a column of key_ids and each element in the associated list of 200 becomes its own column. So there is a total of 201 columns where the first column is the first key_id and then the second column is the first element of the list, the third column is the second element of the list etc. And then the second row first column is the second key_id and then the second row second column is the first element of the key_id's second list and so on. Is there a way to convert this dictionary to a df? I have 10000 key_ids do the dimensions would be 10000x201. Thanks!

Load the dict into a DataFrame using pandas.DataFrame.from_dict with the orient parameter, and reset the index with .reset_index()
This will create the DataFrame as requested, however, I recommend leaving the keys as the index, which should make it easier to perform calculations and address specific rows.
If the columns should be named 0...201, then use df.columns = list(range(202)), or use pandas.DataFrame.rename to rename specific columns.
import pandas as pd
# test data
saved = {'key_id_1': list(range(201)), 'key_id_2': list(range(201))}
# create the DataFrame
df = pd.DataFrame.from_dict(saved, orient='index')
# reset the index
df = df.reset_index()
# display(df)
index 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
0 key_id_1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
1 key_id_2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
Alternative Implementation
Create the DataFrame with pandas.DataFrame, transpose the DataFrame with pandas.DataFrame.T, and then reset with .reset_index().
df = pd.DataFrame(saved)
df = df.T.reset_index()

Sliding a subarray along a 2d array

This is something I've been struggling with for a couple of weeks. The algorithm is the following:
Select a subarray as an array of rows and columns from a larger array
Compute the median of the subarray
Replace cells in subarray with median value
Move the subarray to the right by its own length
Repeat to end of array
Move subarray down by its own height
Repeat
I've got steps 1 to 3 as follows:
import numpy as np
w1 = np.arange(100).reshape(10,10)
side = 3
patch = w1[0:side, 0:side]
i, j = patch.shape
for j in range(side):
for i in range(side):
patch[i,j] = np.median(patch)
Eventually, I'll be using a 901x877 array from an image but I'm just trying to get a hold of this simple task first. How can I slide the array along and then down with a loop?

You can use scikit-image's view_as_blocks and NumPy broadcasting to vectorize the operation:
import numpy as np
import skimage
w1 = np.arange(144).reshape(12,12)
print(w1)
# [[ 0 1 2 3 4 5 6 7 8 9 10 11]
# [ 12 13 14 15 16 17 18 19 20 21 22 23]
# [ 24 25 26 27 28 29 30 31 32 33 34 35]
# [ 36 37 38 39 40 41 42 43 44 45 46 47]
# [ 48 49 50 51 52 53 54 55 56 57 58 59]
# [ 60 61 62 63 64 65 66 67 68 69 70 71]
# [ 72 73 74 75 76 77 78 79 80 81 82 83]
# [ 84 85 86 87 88 89 90 91 92 93 94 95]
# [ 96 97 98 99 100 101 102 103 104 105 106 107]
# [108 109 110 111 112 113 114 115 116 117 118 119]
# [120 121 122 123 124 125 126 127 128 129 130 131]
# [132 133 134 135 136 137 138 139 140 141 142 143]]
side = 3
w2 = skimage.util.view_as_blocks(w1, (side, side))
w2[...] = np.median(w2, axis=(-2, -1))[:, :, None, None]
print(w1)
# [[ 13 13 13 16 16 16 19 19 19 22 22 22]
# [ 13 13 13 16 16 16 19 19 19 22 22 22]
# [ 13 13 13 16 16 16 19 19 19 22 22 22]
# [ 49 49 49 52 52 52 55 55 55 58 58 58]
# [ 49 49 49 52 52 52 55 55 55 58 58 58]
# [ 49 49 49 52 52 52 55 55 55 58 58 58]
# [ 85 85 85 88 88 88 91 91 91 94 94 94]
# [ 85 85 85 88 88 88 91 91 91 94 94 94]
# [ 85 85 85 88 88 88 91 91 91 94 94 94]
# [121 121 121 124 124 124 127 127 127 130 130 130]
# [121 121 121 124 124 124 127 127 127 130 130 130]
# [121 121 121 124 124 124 127 127 127 130 130 130]]
Note that I had to change the size of your array to 12x12 so that all of your tiles of 3x3 actually fit in there.

Here are a few "code smells" I see.
Start with the range(side) since this number is set to 3 then you are going to have a result of [0,1,2]. Is that what you really want?
you set i,j = patch.size then immediately over write these values, in your for loops.
Finally, you're recalculating median every loop.
Ok, here's what I'd do.
figure out how many patches you'll need in both width and height. and multiply those by the size of the side.
slice your array (matrix) up into those pieces.
assign the patch to the median.
import numpy as np
w1 = np.arange(100).reshape(10,10)
side = 3
w, h = w1.shape
width_index = np.array(range(w//side)) * side
height_index = np.array(range(h//side)) * side
def assign_patch(patch, median, side):
"""Break this loop out to prevent 4 nested 'for' loops"""
for j in range(side):
for i in range(side):
patch[i,j] = median
return patch
for width in width_index:
for height in height_index:
patch = w1[width:width+side, height:height+side]
median = np.median(patch)
assign_patch(patch, median, side)
print w1

Multiplication table format

I've created a simple script that will make a multiplication table and output it. It works and is pretty cool but I would like to know if there's a way I could fix it for when it goes higher then 10. After 10 (on the row) it will be a whitespace off of the rest of the table, how can I fix this little format issue?
if __name__ == '__main__':
for row in range(1, 20+1):
table = ''
for column in range(1, 20+1):
table += '{:4} '.format(row * column)
print(table.strip())
Example:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60
4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120
7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 119 126 133 140
8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136 144 152 160
9 18 27 36 45 54 63 72 81 90 99 108 117 126 135 144 153 162 171 180
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200
11 22 33 44 55 66 77 88 99 110 121 132 143 154 165 176 187 198 209 220
12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240
13 26 39 52 65 78 91 104 117 130 143 156 169 182 195 208 221 234 247 260
14 28 42 56 70 84 98 112 126 140 154 168 182 196 210 224 238 252 266 280
15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300
16 32 48 64 80 96 112 128 144 160 176 192 208 224 240 256 272 288 304 320
17 34 51 68 85 102 119 136 153 170 187 204 221 238 255 272 289 306 323 340
18 36 54 72 90 108 126 144 162 180 198 216 234 252 270 288 306 324 342 360
19 38 57 76 95 114 133 152 171 190 209 228 247 266 285 304 323 342 361 380
20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400

You can left adjust string with spaces with {:<6}
for row in range(1, 20+1):
table = ''
for column in range(1, 20+1):
table += '{:<6} '.format(row * column)
print table.strip()
Output
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60
4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120
7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 119 126 133 140
8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136 144 152 160
9 18 27 36 45 54 63 72 81 90 99 108 117 126 135 144 153 162 171 180
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200
11 22 33 44 55 66 77 88 99 110 121 132 143 154 165 176 187 198 209 220
12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240
13 26 39 52 65 78 91 104 117 130 143 156 169 182 195 208 221 234 247 260
14 28 42 56 70 84 98 112 126 140 154 168 182 196 210 224 238 252 266 280
15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300
16 32 48 64 80 96 112 128 144 160 176 192 208 224 240 256 272 288 304 320
17 34 51 68 85 102 119 136 153 170 187 204 221 238 255 272 289 306 323 340
18 36 54 72 90 108 126 144 162 180 198 216 234 252 270 288 306 324 342 360
19 38 57 76 95 114 133 152 171 190 209 228 247 266 285 304 323 342 361 380
20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400

So a simple way of doing this is using generators (I find it more readable right justified):
>>> n = 10
>>> print('\n'.join(''.join(format(i*j, ' >4') for i in range(1, n+1)) for j in range(1, n+1)))
1 2 3 4 5 6 7 8 9 10
2 4 6 8 10 12 14 16 18 20
3 6 9 12 15 18 21 24 27 30
4 8 12 16 20 24 28 32 36 40
5 10 15 20 25 30 35 40 45 50
6 12 18 24 30 36 42 48 54 60
7 14 21 28 35 42 49 56 63 70
8 16 24 32 40 48 56 64 72 80
9 18 27 36 45 54 63 72 81 90
10 20 30 40 50 60 70 80 90 100
If you need to work out the maximum width dynamically then you can use math.log10():
>>> import math
>>> n = 9
>>> w = int(math.log10(n**2))+1
>>> print('\n'.join(' '.join(format(i*j, ' >'+str(w)) for i in range(1, n+1)) for j in range(1, n+1)))
1 2 3 4 5 6 7 8 9
2 4 6 8 10 12 14 16 18
3 6 9 12 15 18 21 24 27
4 8 12 16 20 24 28 32 36
5 10 15 20 25 30 35 40 45
6 12 18 24 30 36 42 48 54
7 14 21 28 35 42 49 56 63
8 16 24 32 40 48 56 64 72
9 18 27 36 45 54 63 72 81

Your code example is not working due to the last "strip", which is removing whitespace through the left and shifting your results.
Just removing the strip:
if __name__ == '__main__':
for row in range(1, 20+1):
table = ''
for column in range(1, 20+1):
table += '{:4} '.format(row * column)
print(table)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60
4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120
7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 119 126 133 140
8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136 144 152 160
9 18 27 36 45 54 63 72 81 90 99 108 117 126 135 144 153 162 171 180
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200
11 22 33 44 55 66 77 88 99 110 121 132 143 154 165 176 187 198 209 220
12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240
13 26 39 52 65 78 91 104 117 130 143 156 169 182 195 208 221 234 247 260
14 28 42 56 70 84 98 112 126 140 154 168 182 196 210 224 238 252 266 280
15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300
16 32 48 64 80 96 112 128 144 160 176 192 208 224 240 256 272 288 304 320
17 34 51 68 85 102 119 136 153 170 187 204 221 238 255 272 289 306 323 340
18 36 54 72 90 108 126 144 162 180 198 216 234 252 270 288 306 324 342 360
19 38 57 76 95 114 133 152 171 190 209 228 247 266 285 304 323 342 361 380
20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400

Another possible approach would be to use list comprehensions as follows:
for row in [['{:4}'.format(row * col) for col in range(1, 21)] for row in range(1, 21)]:
print ''.join(row)
This would give you the following output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60
4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120
7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 119 126 133 140
8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136 144 152 160
9 18 27 36 45 54 63 72 81 90 99 108 117 126 135 144 153 162 171 180
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200
11 22 33 44 55 66 77 88 99 110 121 132 143 154 165 176 187 198 209 220
12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240
13 26 39 52 65 78 91 104 117 130 143 156 169 182 195 208 221 234 247 260
14 28 42 56 70 84 98 112 126 140 154 168 182 196 210 224 238 252 266 280
15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300
16 32 48 64 80 96 112 128 144 160 176 192 208 224 240 256 272 288 304 320
17 34 51 68 85 102 119 136 153 170 187 204 221 238 255 272 289 306 323 340
18 36 54 72 90 108 126 144 162 180 198 216 234 252 270 288 306 324 342 360
19 38 57 76 95 114 133 152 171 190 209 228 247 266 285 304 323 342 361 380
20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400

right aligning this column [duplicate]

This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
How do I right-align my text in Python?
4 8 12 16
20 24 28 32
36 40 44 48
52 56 60 64
68 72 76 80
84 88 92 96
100 104 108 112
116 120 124 128
132 136 140 144
148 152 156 160
Right now I have a column that's like this. Can somebody please help me figure out how to right align the columns so that it looks like this:
4 8 12 16
20 24 28 32
36 40 44 48
52 56 60 64
68 72 76 80
84 88 92 96
100 104 108 112
116 120 124 128
132 136 140 144
148 152 156 160

>>> for line in data:
... print ' '.join('{:>3}'.format(i) for i in line.split())
...
4 8 12 16
20 24 28 32
36 40 44 48
52 56 60 64
68 72 76 80
84 88 92 96
100 104 108 112
116 120 124 128
132 136 140 144
148 152 156 160
See the docs on Format String Syntax.

cell_width = 6
for row in data:
#"%-6s"%val will right format 6 spaces
print ("%-"+cell_width+"s ")*len(row) % row
something like that anyway... I should note that % string formatting is considered depreciated ...

As well as string formatting, there's also a built-in function for string str.rjust for this:
for line in data.splitlines():
print ' '.join(el.rjust(5) for el in line.split())