Mercurial > repos > fubar > plotly_tabular_tool
annotate plotly_tabular_tool/plotlytabular.xml @ 3:51a0c2e0fbdf draft
Updated with latest ToolFactory with change_format for outputs so can make a png or html and it has an informative label since on $foo can be used.
| author | fubar |
|---|---|
| date | Fri, 04 Aug 2023 02:00:28 +0000 |
| parents | 08cc7a481af8 |
| children | e2d2b080bae3 |
| rev | line source |
|---|---|
| 0 | 1 <tool name="plotlytabular" id="plotlytabular" version="3.0"> |
| 2 <!--Source in git at: https://github.com/fubar2/galaxy_tf_overlay--> | |
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3 <!--Created by toolfactory@galaxy.org at 04/08/2023 10:38:13 using the Galaxy Tool Factory.--> |
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4 <description>Plotly plot generator for Galaxy tabular data.</description> |
| 0 | 5 <requirements> |
| 6 <requirement version="1.5.3" type="package">pandas</requirement> | |
| 7 <requirement version="5.9.0" type="package">plotly</requirement> | |
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8 <requirement version="0.2.1" type="package">python-kaleido</requirement> |
| 0 | 9 </requirements> |
| 10 <stdio> | |
| 11 <exit_code range="1:" level="fatal"/> | |
| 12 </stdio> | |
| 13 <version_command><![CDATA[echo "3.0"]]></version_command> | |
| 14 <command><![CDATA[python | |
| 15 $runme | |
| 16 --input_tab | |
| 17 $input_tab | |
| 18 --htmlout | |
| 19 $htmlout | |
| 20 --xcol | |
| 21 "$xcol" | |
| 22 --ycol | |
| 23 "$ycol" | |
| 24 --colourcol | |
| 25 "$colourcol" | |
| 26 --hovercol | |
| 27 "$hovercol" | |
| 28 --title | |
| 29 "$title" | |
| 30 --header | |
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31 "$header" |
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32 --image_type |
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33 "$outputimagetype"]]></command> |
| 0 | 34 <configfiles> |
| 35 <configfile name="runme"><![CDATA[#raw | |
| 36 | |
| 37 import argparse | |
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38 import shutil |
| 0 | 39 import sys |
| 40 import math | |
| 41 import plotly.express as px | |
| 42 import pandas as pd | |
| 43 # Ross Lazarus July 2023 | |
| 44 # based on various plotly tutorials | |
| 45 parser = argparse.ArgumentParser() | |
| 46 a = parser.add_argument | |
| 47 a('--input_tab',default='') | |
| 48 a('--header',default='') | |
| 49 a('--htmlout',default="test_run.html") | |
| 50 a('--xcol',default='') | |
| 51 a('--ycol',default='') | |
| 52 a('--colourcol',default='') | |
| 53 a('--hovercol',default='') | |
| 54 a('--title',default='Default plot title') | |
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55 a('--image_type',default='short_html') |
| 0 | 56 args = parser.parse_args() |
| 1 | 57 isColour = False |
| 58 isHover = False | |
| 59 if len(args.colourcol.strip()) > 0: | |
| 60 isColour = True | |
| 61 if len(args.hovercol.strip()) > 0: | |
| 62 isHover = True | |
| 0 | 63 df = pd.read_csv(args.input_tab, sep='\t') |
| 64 MAXLEN=35 | |
| 65 NCOLS = df.columns.size | |
| 66 defaultcols = ['col%d' % (x+1) for x in range(NCOLS)] | |
| 1 | 67 testcols = df.columns |
| 0 | 68 if len(args.header.strip()) > 0: |
| 69 newcols = args.header.split(',') | |
| 70 if len(newcols) == NCOLS: | |
| 71 df.columns = newcols | |
| 72 else: | |
| 73 sys.stderr.write('#### Supplied header %s has %d comma delimited header names - does not match the input tabular file %d columns - using col1,...coln' % (args.header, len(newcols), NCOLS)) | |
| 74 df.columns = defaultcols | |
| 1 | 75 else: # no header supplied - check for a real one that matches the x and y axis column names |
| 76 colsok = (args.xcol in testcols) and (args.ycol in testcols) # if they match, probably ok...should use more code and logic.. | |
| 77 if not colsok: | |
| 2 | 78 sys.stderr.write('replacing first row of data derived header %s with %s' % (testcols, defaultcols)) |
| 1 | 79 df.columns = defaultcols |
| 0 | 80 #df['col11'] = [-math.log(x) for x in df['col11']] # convert so large values reflect statistical surprise |
| 81 if isHover and isColour: | |
| 82 fig = px.scatter(df, x=args.xcol, y=args.ycol, color=args.colourcol, hover_name=args.hovercol) | |
| 83 elif isHover: | |
| 84 fig = px.scatter(df, x=args.xcol, y=args.ycol, hover_name=args.hovercol) | |
| 85 elif isColour: | |
| 86 fig = px.scatter(df, x=args.xcol, y=args.ycol, color=args.colourcol) | |
| 87 else: | |
| 88 fig = px.scatter(df, x=args.xcol, y=args.ycol) | |
| 89 if args.title: | |
| 90 ftitle=dict(text=args.title, font=dict(size=50)) | |
| 91 fig.update_layout(title=ftitle) | |
| 92 for scatter in fig.data: | |
| 93 scatter['x'] = [str(x)[:MAXLEN] + '..' if len(str(x)) > MAXLEN else x for x in scatter['x']] | |
| 94 scatter['y'] = [str(x)[:MAXLEN] + '..' if len(str(x)) > MAXLEN else x for x in scatter['y']] | |
| 95 if len(args.colourcol.strip()) == 0: | |
| 96 sl = str(scatter['legendgroup']) | |
| 97 if len(sl) > MAXLEN: | |
| 98 scatter['legendgroup'] = sl[:MAXLEN] | |
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99 if args.image_type == "short_html": |
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100 fig.write_html(args.htmlout, full_html=False, include_plotlyjs='cdn') |
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101 elif args.image_type == "long_html": |
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102 fig.write_html(args.htmlout) |
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103 elif args.image_type == "small_png": |
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104 ht = 768 |
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105 wdth = 1024 |
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106 fig.write_image('plotly.png', height=ht, width=wdth) |
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107 shutil.copyfile('plotly.png', args.htmlout) |
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108 else: |
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109 ht = 1200 |
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110 wdth = 1920 |
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111 fig.write_image('plotly.png', height=ht, width=wdth) |
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112 shutil.copyfile('plotly.png', args.htmlout) |
| 0 | 113 |
| 114 | |
| 115 #end raw]]></configfile> | |
| 116 </configfiles> | |
| 117 <inputs> | |
| 118 <param name="input_tab" type="data" optional="false" label="Tabular input file to plot" help="" format="tabular" multiple="false"/> | |
| 2 | 119 <param name="xcol" type="text" value="sepal_length" label="x axis for plot" help="Use a column name from the header if the file has one, or use one from the list supplied below, or use col1....colN otherwise to select the correct column"/> |
| 120 <param name="ycol" type="text" value="sepal_width" label="y axis for plot" help="Use a column name from the header if the file has one, or use one from the list supplied below, or use col1....colN otherwise to select the correct column"/> | |
| 1 | 121 <param name="colourcol" type="text" value="petal_width" label="column containing a groupable variable for colour. Default none." help="Adds a legend so choose wisely "/> |
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122 <param name="hovercol" type="text" value="species" label="columname for hover string" help="Use a column name from the header if the file has one, or use one from the list supplied below, or use col1....colN otherwise to select the correct column"/> |
| 2 | 123 <param name="title" type="text" value="Iris data" label="Title for the plot" help="Special characters will probably be escaped so do not use them"/> |
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124 <param name="header" type="text" value="" label="Use this comma delimited list of column header names for this tabular file. Default is None when col1...coln will be used" help="The column names supplied for xcol, ycol, hover and colour MUST match either this supplied list, or if none, col1...coln."/> |
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125 <param name="outputimagetype" type="select" label="Select the output format for this plot image" help="Small and large png are not interactive but best for many (__gt__10k) points. Stand-alone HTML includes 3MB of javascript. Short form HTML gets it the usual way so can be cut and paste into documents."> |
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126 <option value="short_html">Short HTML interactive format</option> |
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127 <option value="long_html">Long HTML for stand-alone viewing where network access to libraries is not available.</option> |
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128 <option value="large_png">Large (1920x1200) png image - not interactive so hover column ignored</option> |
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129 <option value="small_png">small (1024x768) png image - not interactive so hover column ignored</option> |
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130 </param> |
| 0 | 131 </inputs> |
| 132 <outputs> | |
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133 <data name="htmlout" format="html" label="Plotlytabular $title on $input_tab.element_identifier" hidden="false"> |
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134 <change_format> |
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135 <when input="outputimagetype" format="png" value="small_png"/> |
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136 <when input="outputimagetype" format="png" value="large_png"/> |
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137 </change_format> |
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138 </data> |
| 0 | 139 </outputs> |
| 140 <tests> | |
| 141 <test> | |
| 142 <output name="htmlout" value="htmlout_sample" compare="sim_size" delta="5000"/> | |
| 143 <param name="input_tab" value="input_tab_sample"/> | |
| 1 | 144 <param name="xcol" value="sepal_length"/> |
| 145 <param name="ycol" value="sepal_width"/> | |
| 146 <param name="colourcol" value="petal_width"/> | |
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147 <param name="hovercol" value="species"/> |
| 2 | 148 <param name="title" value="Iris data"/> |
| 0 | 149 <param name="header" value=""/> |
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150 <param name="outputimagetype" value="short_html"/> |
| 0 | 151 </test> |
| 152 </tests> | |
| 153 <help><![CDATA[ | |
| 154 | |
| 2 | 155 This is a generic version of the plotlyblast specific blastn Galaxy search output file plotter. |
| 0 | 156 |
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157 PNG images are not interactive but best for very large numbers of data points. Hover column will be ignored. |
| 0 | 158 |
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159 HTML interactive plots are best for a few thousand data points at most because |
| 0 | 160 |
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161 the hover information becomes uncontrollable with very dense points. |
| 0 | 162 |
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163 Using the shorter format HTML relies on internet access when viewed, and saves 3MB of javascript being embedded. |
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164 |
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165 The long format is useful if potentially viewed offline. |
| 0 | 166 |
| 167 | |
| 168 | |
| 169 .. class:: warningmark | |
| 170 | |
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171 Long strings in x and y tickmarks WILL BE TRUNCATED if they are too long - ".." is added to indicate truncation - otherwise some plots are squished. |
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172 |
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173 |
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174 |
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175 .. class:: warningmark |
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176 |
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177 Columns with very small scientific notation floats will need to be pre-scaled in a way that doesn't confuse plotly.express with their values. |
| 0 | 178 |
| 179 | |
| 180 | |
| 181 ---- | |
| 182 | |
| 183 | |
| 184 | |
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185 This tool can plot an interactive scatter plot with a hover text column specified, that appears when hovering over each data point, to supply useful additional information. |
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186 |
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187 It is only useful with a relatively small number of points when they can be distinguished. If many thousands, the density makes them relatively useless so use png output and |
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188 |
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189 forget the hover text. |
| 0 | 190 |
| 191 | |
| 192 | |
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193 Column names are auto-generated as col1,...coln *unless* a comma separated list of column names is supplied as the header parameter, *or* pandas can |
| 0 | 194 |
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195 find the values supplied as parameters by the user in the first row of data. This sounds more complex than it is. |
| 0 | 196 |
| 197 | |
| 198 | |
| 199 For example, using a Galaxy blastn output with 25 columns, the following comma delimited string supplied as the "header" parameter will match the names of each column. | |
| 200 | |
| 201 qaccver,saccver,piden,length,mismatch,gapopen,qstart,qend,sstart,send,evalue,bitscore,sallseqid,score,nident,positive,gaps,ppos,qframe,sframe,qseq,sseq,qlen,slen,salltitles | |
| 202 | |
| 203 | |
| 204 | |
| 205 When a header is supplied, the xcol and other column names must match one of those supplied column names. | |
| 206 | |
| 207 So for example, xcol = "qaccver" for the blastn header example rather than xcol = "col1" when no header is supplied. | |
| 208 | |
| 209 | |
| 210 | |
| 211 Relies on Plotly python code released under the MIT licence: https://github.com/plotly/plotly.py/blob/master/LICENSE.txt | |
| 212 | |
| 213 | |
| 214 | |
| 215 | |
| 216 | |
| 217 ------ | |
| 218 | |
| 219 | |
| 220 Script:: | |
| 221 | |
| 222 import argparse | |
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223 import shutil |
| 0 | 224 import sys |
| 225 import math | |
| 226 import plotly.express as px | |
| 227 import pandas as pd | |
| 228 # Ross Lazarus July 2023 | |
| 229 # based on various plotly tutorials | |
| 230 parser = argparse.ArgumentParser() | |
| 231 a = parser.add_argument | |
| 232 a('--input_tab',default='') | |
| 233 a('--header',default='') | |
| 234 a('--htmlout',default="test_run.html") | |
| 235 a('--xcol',default='') | |
| 236 a('--ycol',default='') | |
| 237 a('--colourcol',default='') | |
| 238 a('--hovercol',default='') | |
| 239 a('--title',default='Default plot title') | |
|
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240 a('--image_type',default='short_html') |
| 0 | 241 args = parser.parse_args() |
| 1 | 242 isColour = False |
| 243 isHover = False | |
| 244 if len(args.colourcol.strip()) > 0: | |
| 245 isColour = True | |
| 246 if len(args.hovercol.strip()) > 0: | |
| 247 isHover = True | |
| 0 | 248 df = pd.read_csv(args.input_tab, sep='\t') |
| 249 MAXLEN=35 | |
| 250 NCOLS = df.columns.size | |
| 251 defaultcols = ['col%d' % (x+1) for x in range(NCOLS)] | |
| 1 | 252 testcols = df.columns |
| 0 | 253 if len(args.header.strip()) > 0: |
| 254 newcols = args.header.split(',') | |
| 255 if len(newcols) == NCOLS: | |
| 256 df.columns = newcols | |
| 257 else: | |
| 258 sys.stderr.write('#### Supplied header %s has %d comma delimited header names - does not match the input tabular file %d columns - using col1,...coln' % (args.header, len(newcols), NCOLS)) | |
| 259 df.columns = defaultcols | |
| 1 | 260 else: # no header supplied - check for a real one that matches the x and y axis column names |
| 261 colsok = (args.xcol in testcols) and (args.ycol in testcols) # if they match, probably ok...should use more code and logic.. | |
| 262 if not colsok: | |
| 2 | 263 sys.stderr.write('replacing first row of data derived header %s with %s' % (testcols, defaultcols)) |
| 1 | 264 df.columns = defaultcols |
| 0 | 265 #df['col11'] = [-math.log(x) for x in df['col11']] # convert so large values reflect statistical surprise |
| 266 if isHover and isColour: | |
| 267 fig = px.scatter(df, x=args.xcol, y=args.ycol, color=args.colourcol, hover_name=args.hovercol) | |
| 268 elif isHover: | |
| 269 fig = px.scatter(df, x=args.xcol, y=args.ycol, hover_name=args.hovercol) | |
| 270 elif isColour: | |
| 271 fig = px.scatter(df, x=args.xcol, y=args.ycol, color=args.colourcol) | |
| 272 else: | |
| 273 fig = px.scatter(df, x=args.xcol, y=args.ycol) | |
| 274 if args.title: | |
| 275 ftitle=dict(text=args.title, font=dict(size=50)) | |
| 276 fig.update_layout(title=ftitle) | |
| 277 for scatter in fig.data: | |
| 278 scatter['x'] = [str(x)[:MAXLEN] + '..' if len(str(x)) > MAXLEN else x for x in scatter['x']] | |
| 279 scatter['y'] = [str(x)[:MAXLEN] + '..' if len(str(x)) > MAXLEN else x for x in scatter['y']] | |
| 280 if len(args.colourcol.strip()) == 0: | |
| 281 sl = str(scatter['legendgroup']) | |
| 282 if len(sl) > MAXLEN: | |
| 283 scatter['legendgroup'] = sl[:MAXLEN] | |
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284 if args.image_type == "short_html": |
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285 fig.write_html(args.htmlout, full_html=False, include_plotlyjs='cdn') |
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286 elif args.image_type == "long_html": |
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287 fig.write_html(args.htmlout) |
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288 elif args.image_type == "small_png": |
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289 ht = 768 |
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290 wdth = 1024 |
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291 fig.write_image('plotly.png', height=ht, width=wdth) |
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292 shutil.copyfile('plotly.png', args.htmlout) |
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293 else: |
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294 ht = 1200 |
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295 wdth = 1920 |
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296 fig.write_image('plotly.png', height=ht, width=wdth) |
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297 shutil.copyfile('plotly.png', args.htmlout) |
| 0 | 298 |
| 299 ]]></help> | |
| 300 <citations> | |
| 301 <citation type="doi">10.1093/bioinformatics/bts573</citation> | |
| 302 </citations> | |
| 303 </tool> | |
| 304 |