int main(int argc, char *argv[])
{
struct GModule *module;
struct Option *map_opt, *type_opt, *field_opt, *col_opt, *where_opt,
*percentile;
struct Flag *shell_flag, *extended;
struct Map_info Map;
struct field_info *Fi;
dbDriver *Driver;
dbCatValArray Cvarr;
struct line_pnts *Points;
struct line_cats *Cats;
int otype, ofield;
int compatible = 1; /* types are compatible: point+centroid or line+boundary or area */
int nrec, ctype, nlines, line, nareas, area;
int nmissing = 0; /* number of missing atttributes */
int nnull = 0; /* number of null values */
int first = 1;
/* Statistics */
int count = 0; /* number of features with non-null attribute */
double sum = 0.0;
double sumsq = 0.0;
double sumcb = 0.0;
double sumqt = 0.0;
double sum_abs = 0.0;
double min = 0.0 / 0.0; /* init as nan */
double max = 0.0 / 0.0;
double mean, mean_abs, pop_variance, sample_variance, pop_stdev,
sample_stdev, pop_coeff_variation, kurtosis, skewness;
double total_size = 0.0; /* total size: length/area */
/* Extended statistics */
int perc;
module = G_define_module();
G_add_keyword(_("vector"));
G_add_keyword(_("statistics"));
module->label =
_("Calculates univariate statistics for attribute.");
module->description = _("Variance and standard "
"deviation is calculated only for points if specified.");
map_opt = G_define_standard_option(G_OPT_V_MAP);
field_opt = G_define_standard_option(G_OPT_V_FIELD);
type_opt = G_define_standard_option(G_OPT_V_TYPE);
type_opt->options = "point,line,boundary,centroid,area";
col_opt = G_define_standard_option(G_OPT_DB_COLUMN);
col_opt->required = YES;
where_opt = G_define_standard_option(G_OPT_DB_WHERE);
percentile = G_define_option();
percentile->key = "percentile";
percentile->type = TYPE_INTEGER;
percentile->required = NO;
percentile->options = "0-100";
percentile->answer = "90";
percentile->description =
_("Percentile to calculate (requires extended statistics flag)");
shell_flag = G_define_flag();
shell_flag->key = 'g';
shell_flag->description = _("Print the stats in shell script style");
extended = G_define_flag();
extended->key = 'e';
extended->description = _("Calculate extended statistics");
G_gisinit(argv[0]);
if (G_parser(argc, argv))
exit(EXIT_FAILURE);
otype = Vect_option_to_types(type_opt);
perc = atoi(percentile->answer);
Points = Vect_new_line_struct();
Cats = Vect_new_cats_struct();
/* open input vector */
Vect_set_open_level(2);
Vect_open_old2(&Map, map_opt->answer, "", field_opt->answer);
ofield = Vect_get_field_number(&Map, field_opt->answer);
/* Check if types are compatible */
if ((otype & GV_POINTS) && ((otype & GV_LINES) || (otype & GV_AREA)))
compatible = 0;
if ((otype & GV_LINES) && (otype & GV_AREA))
compatible = 0;
if (!compatible) {
G_warning(_("Incompatible vector type(s) specified, only number of features, minimum, maximum and range "
"can be calculated"));
}
if (extended->answer && !(otype & GV_POINTS)) {
G_warning(_("Extended statistics is currently supported only for points/centroids"));
}
/* Read attributes */
db_CatValArray_init(&Cvarr);
Fi = Vect_get_field(&Map, ofield);
if (Fi == NULL) {
G_fatal_error(_(" Database connection not defined for layer <%s>"), field_opt->answer);
}
Driver = db_start_driver_open_database(Fi->driver, Fi->database);
if (Driver == NULL)
G_fatal_error("Unable to open database <%s> by driver <%s>",
Fi->database, Fi->driver);
/* Note do not check if the column exists in the table because it may be an expression */
nrec =
db_select_CatValArray(Driver, Fi->table, Fi->key, col_opt->answer,
where_opt->answer, &Cvarr);
G_debug(2, "nrec = %d", nrec);
ctype = Cvarr.ctype;
if (ctype != DB_C_TYPE_INT && ctype != DB_C_TYPE_DOUBLE)
G_fatal_error(_("Column type not supported"));
if (nrec < 0)
G_fatal_error(_("Unable to select data from table"));
db_close_database_shutdown_driver(Driver);
/* Lines */
nlines = Vect_get_num_lines(&Map);
for (line = 1; line <= nlines; line++) {
int i, type;
G_debug(3, "line = %d", line);
type = Vect_read_line(&Map, Points, Cats, line);
if (!(type & otype))
continue;
for (i = 0; i < Cats->n_cats; i++) {
if (Cats->field[i] == ofield) {
double val;
dbCatVal *catval;
G_debug(3, "cat = %d", Cats->cat[i]);
if (db_CatValArray_get_value(&Cvarr, Cats->cat[i], &catval) !=
DB_OK) {
G_debug(3, "No record for cat = %d", Cats->cat[i]);
nmissing++;
continue;
}
if (catval->isNull) {
G_debug(3, "NULL value for cat = %d", Cats->cat[i]);
nnull++;
continue;
}
if (ctype == DB_C_TYPE_INT) {
val = catval->val.i;
}
else if (ctype == DB_C_TYPE_DOUBLE) {
val = catval->val.d;
}
count++;
if (first) {
max = val;
min = val;
first = 0;
}
else {
if (val > max)
max = val;
if (val < min)
min = val;
}
if (compatible) {
if (type & GV_POINTS) {
sum += val;
sumsq += val * val;
sumcb += val * val * val;
sumqt += val * val * val * val;
sum_abs += fabs(val);
}
else { /* GV_LINES */
double l;
l = Vect_line_length(Points);
sum += l * val;
sumsq += l * val * val;
sumcb += l * val * val * val;
sumqt += l * val * val * val * val;
sum_abs += l * fabs(val);
total_size += l;
}
}
G_debug(3, "sum = %f total_size = %f", sum, total_size);
}
}
}
if (otype & GV_AREA) {
nareas = Vect_get_num_areas(&Map);
for (area = 1; area <= nareas; area++) {
int i, centr;
G_debug(3, "area = %d", area);
centr = Vect_get_area_centroid(&Map, area);
if (centr < 1)
continue;
G_debug(3, "centr = %d", centr);
Vect_read_line(&Map, NULL, Cats, centr);
for (i = 0; i < Cats->n_cats; i++) {
if (Cats->field[i] == ofield) {
double val;
dbCatVal *catval;
G_debug(3, "cat = %d", Cats->cat[i]);
if (db_CatValArray_get_value
(&Cvarr, Cats->cat[i], &catval) != DB_OK) {
G_debug(3, "No record for cat = %d", Cats->cat[i]);
nmissing++;
continue;
}
if (catval->isNull) {
G_debug(3, "NULL value for cat = %d", Cats->cat[i]);
nnull++;
continue;
}
if (ctype == DB_C_TYPE_INT) {
val = catval->val.i;
}
else if (ctype == DB_C_TYPE_DOUBLE) {
val = catval->val.d;
}
count++;
if (first) {
max = val;
min = val;
first = 0;
}
else {
if (val > max)
max = val;
if (val < min)
min = val;
}
if (compatible) {
double a;
a = Vect_get_area_area(&Map, area);
sum += a * val;
sumsq += a * val * val;
sumcb += a * val * val * val;
sumqt += a * val * val * val * val;
sum_abs += a * fabs(val);
total_size += a;
}
G_debug(4, "sum = %f total_size = %f", sum, total_size);
}
}
}
}
G_debug(2, "sum = %f total_size = %f", sum, total_size);
if (compatible) {
if ((otype & GV_LINES) || (otype & GV_AREA)) {
mean = sum / total_size;
mean_abs = sum_abs / total_size;
/* Roger Bivand says it is wrong see GRASS devel list 7/2004 */
/*
pop_variance = (sumsq - sum*sum/total_size)/total_size;
pop_stdev = sqrt(pop_variance);
*/
}
else {
double n = count;
mean = sum / count;
mean_abs = sum_abs / count;
pop_variance = (sumsq - sum * sum / count) / count;
pop_stdev = sqrt(pop_variance);
pop_coeff_variation = pop_stdev / (sqrt(sum * sum) / count);
sample_variance = (sumsq - sum * sum / count) / (count - 1);
sample_stdev = sqrt(sample_variance);
kurtosis =
(sumqt / count - 4 * sum * sumcb / (n * n) +
6 * sum * sum * sumsq / (n * n * n) -
3 * sum * sum * sum * sum / (n * n * n * n))
/ (sample_stdev * sample_stdev * sample_stdev *
sample_stdev) - 3;
skewness =
(sumcb / n - 3 * sum * sumsq / (n * n) +
2 * sum * sum * sum / (n * n * n))
/ (sample_stdev * sample_stdev * sample_stdev);
}
}
G_debug(3, "otype %d:", otype);
if (shell_flag->answer) {
fprintf(stdout, "n=%d\n", count);
fprintf(stdout, "nmissing=%d\n", nmissing);
fprintf(stdout, "nnull=%d\n", nnull);
if (count > 0) {
fprintf(stdout, "min=%g\n", min);
fprintf(stdout, "max=%g\n", max);
fprintf(stdout, "range=%g\n", max - min);
if (compatible && (otype & GV_POINTS)) {
fprintf(stdout, "mean=%g\n", mean);
fprintf(stdout, "mean_abs=%g\n", mean_abs);
fprintf(stdout, "population_stddev=%g\n", pop_stdev);
fprintf(stdout, "population_variance=%g\n", pop_variance);
fprintf(stdout, "population_coeff_variation=%g\n",
pop_coeff_variation);
if (otype & GV_POINTS) {
fprintf(stdout, "sample_stddev=%g\n", sample_stdev);
fprintf(stdout, "sample_variance=%g\n", sample_variance);
fprintf(stdout, "kurtosis=%g\n", kurtosis);
fprintf(stdout, "skewness=%g\n", skewness);
}
}
}
}
else {
fprintf(stdout, "number of features with non NULL attribute: %d\n",
count);
fprintf(stdout, "number of missing attributes: %d\n", nmissing);
fprintf(stdout, "number of NULL attributes: %d\n", nnull);
if (count > 0) {
fprintf(stdout, "minimum: %g\n", min);
fprintf(stdout, "maximum: %g\n", max);
fprintf(stdout, "range: %g\n", max - min);
if (compatible && (otype & GV_POINTS)) {
fprintf(stdout, "mean: %g\n", mean);
fprintf(stdout, "mean of absolute values: %g\n", mean_abs);
fprintf(stdout, "population standard deviation: %g\n",
pop_stdev);
fprintf(stdout, "population variance: %g\n", pop_variance);
fprintf(stdout, "population coefficient of variation: %g\n",
pop_coeff_variation);
if (otype & GV_POINTS) {
fprintf(stdout, "sample standard deviation: %g\n",
sample_stdev);
fprintf(stdout, "sample variance: %g\n", sample_variance);
fprintf(stdout, "kurtosis: %g\n", kurtosis);
fprintf(stdout, "skewness: %g\n", skewness);
}
}
}
}
/* TODO: mode, skewness, kurtosis */
if (extended->answer && compatible && (otype & GV_POINTS) && count > 0) {
double quartile_25 = 0.0, quartile_75 = 0.0, quartile_perc = 0.0;
double median = 0.0;
int qpos_25, qpos_75, qpos_perc;
qpos_25 = (int)(count * 0.25 - 0.5);
qpos_75 = (int)(count * 0.75 - 0.5);
qpos_perc = (int)(count * perc / 100. - 0.5);
if (db_CatValArray_sort_by_value(&Cvarr) != DB_OK)
G_fatal_error(_("Cannot sort the key/value array"));
if (Cvarr.ctype == DB_C_TYPE_INT) {
quartile_25 = (Cvarr.value[qpos_25]).val.i;
if (count % 2) /* odd */
median = (Cvarr.value[(int)(count / 2)]).val.i;
else /* even */
median =
((Cvarr.value[count / 2 - 1]).val.i +
(Cvarr.value[count / 2]).val.i) / 2.0;
quartile_75 = (Cvarr.value[qpos_75]).val.i;
quartile_perc = (Cvarr.value[qpos_perc]).val.i;
}
else { /* must be DB_C_TYPE_DOUBLE */
quartile_25 = (Cvarr.value[qpos_25]).val.d;
if (count % 2) /* odd */
median = (Cvarr.value[(int)(count / 2)]).val.d;
else /* even */
median =
((Cvarr.value[count / 2 - 1]).val.d +
(Cvarr.value[count / 2]).val.d) / 2.0;
quartile_75 = (Cvarr.value[qpos_75]).val.d;
quartile_perc = (Cvarr.value[qpos_perc]).val.d;
}
if (shell_flag->answer) {
fprintf(stdout, "first_quartile=%g\n", quartile_25);
fprintf(stdout, "median=%g\n", median);
fprintf(stdout, "third_quartile=%g\n", quartile_75);
fprintf(stdout, "percentile_%d=%g\n", perc, quartile_perc);
}
else {
fprintf(stdout, "1st quartile: %g\n", quartile_25);
if (count % 2)
fprintf(stdout, "median (odd number of cells): %g\n", median);
else
fprintf(stdout, "median (even number of cells): %g\n",
median);
fprintf(stdout, "3rd quartile: %g\n", quartile_75);
if (perc % 10 == 1 && perc != 11)
fprintf(stdout, "%dst percentile: %g\n", perc, quartile_perc);
else if (perc % 10 == 2 && perc != 12)
fprintf(stdout, "%dnd percentile: %g\n", perc, quartile_perc);
else if (perc % 10 == 3 && perc != 13)
fprintf(stdout, "%drd percentile: %g\n", perc, quartile_perc);
else
fprintf(stdout, "%dth percentile: %g\n", perc, quartile_perc);
}
}
Vect_close(&Map);
exit(EXIT_SUCCESS);
}
void scan_z(struct Map_info *Map, int field,
const char *style, const char *rules,
const struct FPRange *range, struct Colors *colors)
{
int ltype, line, cat, i;
int items_alloc;
double zmin, zmax;
struct line_pnts *Points;
struct line_cats *Cats;
struct Colors vcolors;
dbCatValArray cvarr;
Points = Vect_new_line_struct();
Cats = Vect_new_cats_struct();
items_alloc = 0;
db_CatValArray_init(&cvarr);
cvarr.ctype = DB_C_TYPE_DOUBLE;
Vect_set_constraint_field(Map, field);
Vect_set_constraint_type(Map, GV_POINTS); /* points, centroids or kernels only) */
G_message(_("Reading features..."));
line = i = 0;
while(TRUE) {
ltype = Vect_read_next_line(Map, Points, Cats);
if (ltype == -1)
G_fatal_error(_("Unable to read vector map"));
if (ltype == -2)
break; /* EOF */
G_progress(++line, 1e4);
if (Vect_cat_get(Cats, field, &cat) == -1)
continue; /* skip features without category */
/* add item into cat-value array */
if (i >= items_alloc) {
items_alloc += 1000;
db_CatValArray_realloc(&cvarr, items_alloc);
}
cvarr.n_values++;
cvarr.value[i].cat = cat;
cvarr.value[i++].val.d = Points->z[0];
if (line == 1 || Points->z[0] < zmin)
zmin = Points->z[0];
if (line == 1 || Points->z[0] > zmax)
zmax = Points->z[0];
}
G_progress(1, 1);
/* sort array by z-coordinate */
db_CatValArray_sort_by_value(&cvarr);
if (range) {
if (range->min >= zmin && range->min <= zmax)
zmin = range->min;
else
G_warning(_("Min value (%f) is out of range %f,%f"),
range->min, zmin, zmax);
if (range->max <= zmax && range->max >= zmin)
zmax = range->max;
else
G_warning(_("Max value (%f) is out of range %f,%f"),
range->max, zmin, zmax);
}
if (style)
make_colors(&vcolors, style, (DCELL) zmin, (DCELL) zmax, TRUE);
else if (rules) {
load_colors(&vcolors, rules, (DCELL) zmin, (DCELL) zmax, TRUE);
}
/* color table for categories */
color_rules_to_cats(&cvarr, TRUE, &vcolors, colors);
Vect_destroy_line_struct(Points);
Vect_destroy_cats_struct(Cats);
db_CatValArray_free(&cvarr);
}
int scan_attr(const struct Map_info *Map, int layer, const char *column_name,
const char *style, const char *rules,
const struct FPRange *range, struct Colors *colors)
{
int ctype, is_fp, nrec;
double fmin, fmax;
struct field_info *fi;
struct Colors vcolors;
dbDriver *driver;
dbCatValArray cvarr;
Rast_init_colors(colors);
fi = Vect_get_field(Map, layer);
if (!fi)
G_fatal_error(_("Database connection not defined for layer %d"),
layer);
driver = db_start_driver_open_database(fi->driver, fi->database);
if (!driver)
G_fatal_error(_("Unable to open database <%s> by driver <%s>"),
fi->database, fi->driver);
db_set_error_handler_driver(driver);
ctype = db_column_Ctype(driver, fi->table, column_name);
if (ctype == -1)
G_fatal_error(_("Column <%s> not found in table <%s>"),
column_name, fi->table);
if (ctype != DB_C_TYPE_INT && ctype != DB_C_TYPE_DOUBLE)
G_fatal_error(_("Column <%s> is not numeric"), column_name);
is_fp = ctype == DB_C_TYPE_DOUBLE;
nrec = db_select_CatValArray(driver, fi->table, fi->key, column_name,
NULL, &cvarr);
if (nrec < 1) {
G_important_message(_("No data selected"));
return 0;
}
/* color table for values */
db_CatValArray_sort_by_value(&cvarr);
if (is_fp) {
fmin = cvarr.value[0].val.d;
fmax = cvarr.value[cvarr.n_values-1].val.d;
if (range) {
if (range->min >= fmin && range->min <= fmax)
fmin = range->min;
else
G_warning(_("Min value (%f) is out of range %f,%f"),
range->min, fmin, fmax);
if (range->max <= fmax && range->max >= fmin)
fmax = range->max;
else
G_warning(_("Max value (%f) is out of range %f,%f"),
range->max, fmin, fmax);
}
}
else {
fmin = cvarr.value[0].val.i;
fmax = cvarr.value[cvarr.n_values-1].val.i;
if (range) {
if (range->min >= fmin && range->min <= fmax)
fmin = range->min;
else
G_warning(_("Min value (%d) is out of range %d,%d"),
(int) range->min, (int) fmin, (int) fmax);
if (range->max <= fmax && range->max >= fmin)
fmax = range->max;
else
G_warning(_("Max value (%d) is out of range %d,%d"),
(int) range->max, (int) fmin, (int) fmax);
}
}
if (style)
make_colors(&vcolors, style, (DCELL) fmin, (DCELL) fmax, is_fp);
else if (rules)
load_colors(&vcolors, rules, (DCELL) fmin, (DCELL) fmax, is_fp);
/* color table for categories */
color_rules_to_cats(&cvarr, is_fp, &vcolors, colors);
db_close_database(driver);
return is_fp;
}
int main(int argc, char *argv[])
{
struct GModule *module;
struct Option *map_opt, *field_opt, *col_opt, *where_opt;
struct Option *algo_opt, *nbclass_opt;
struct Flag *shell_flag;
struct Map_info Map;
struct field_info *Fi;
dbDriver *Driver;
dbCatValArray Cvarr;
int ofield;
int nrec, ctype, nbclass, nbreaks, *frequencies;
int ret, i;
double finfo;
double *classbreaks, min, max, *data;
struct GASTATS stats;
char *desc;
module = G_define_module();
G_add_keyword(_("vector"));
G_add_keyword(_("classification"));
G_add_keyword(_("attribute table"));
G_add_keyword(_("statistics"));
module->description =
_("Classifies attribute data, e.g. for thematic mapping");
map_opt = G_define_standard_option(G_OPT_V_MAP);
field_opt = G_define_standard_option(G_OPT_V_FIELD);
col_opt = G_define_standard_option(G_OPT_DB_COLUMN);
col_opt->required = YES;
col_opt->description = _("Column name or expression");
where_opt = G_define_standard_option(G_OPT_DB_WHERE);
algo_opt = G_define_option();
algo_opt->key = "algorithm";
algo_opt->type = TYPE_STRING;
algo_opt->required = YES;
algo_opt->multiple = NO;
algo_opt->options = "int,std,qua,equ,dis";
algo_opt->description = _("Algorithm to use for classification");
desc = NULL;
G_asprintf(&desc,
"int;%s;"
"std;%s;"
"qua;%s;"
"equ;%s",
/* "dis;%s" */
_("simple intervals"),
_("standard deviations"),
_("quantiles"),
_("equiprobable (normal distribution)"));
/* _("discontinuities"));currently disabled because of bugs */
algo_opt->descriptions = desc;
nbclass_opt = G_define_option();
nbclass_opt->key = "nbclasses";
nbclass_opt->type = TYPE_INTEGER;
nbclass_opt->required = YES;
nbclass_opt->multiple = NO;
nbclass_opt->description = _("Number of classes to define");
shell_flag = G_define_flag();
shell_flag->key = 'g';
shell_flag->description =
_("Print only class breaks (without min and max)");
G_gisinit(argv[0]);
if (G_parser(argc, argv))
exit(EXIT_FAILURE);
/* open input vector */
Vect_set_open_level(2);
if (Vect_open_old2(&Map, map_opt->answer, "", field_opt->answer) < 0)
G_fatal_error(_("Unable to open vector map <%s>"), map_opt->answer);
ofield = Vect_get_field_number(&Map, field_opt->answer);
/* Read attributes */
db_CatValArray_init(&Cvarr);
Fi = Vect_get_field(&Map, ofield);
if (Fi == NULL) {
G_fatal_error(_("Unable to get layer info for vector map"));
}
Vect_close(&Map);
Driver = db_start_driver_open_database(Fi->driver, Fi->database);
if (Driver == NULL)
G_fatal_error(_("Unable to open database <%s> by driver <%s>"),
Fi->database, Fi->driver);
/* Note: do not check if the column exists in the table because it may be an expression */
nrec =
db_select_CatValArray(Driver, Fi->table, Fi->key, col_opt->answer,
where_opt->answer, &Cvarr);
G_debug(2, "nrec = %d", nrec);
ctype = Cvarr.ctype;
if (ctype != DB_C_TYPE_INT && ctype != DB_C_TYPE_DOUBLE)
G_fatal_error(_("Column type not supported"));
if (nrec < 0)
G_fatal_error(_("Unable to select data from table"));
db_close_database_shutdown_driver(Driver);
ret = db_CatValArray_sort_by_value(&Cvarr);
if (ret == DB_FAILED)
G_fatal_error(_("Unable to sort array of values"));
data = (double *)G_malloc((nrec) * sizeof(double));
for (i = 0; i < nrec; i++)
data[i] = 0.0;
if (ctype == DB_C_TYPE_INT) {
for (i = 0; i < nrec; i++)
data[i] = Cvarr.value[i].val.i;
}
else {
for (i = 0; i < nrec; i++)
data[i] = Cvarr.value[i].val.d;
}
nbclass = atoi(nbclass_opt->answer);
nbreaks = nbclass - 1; /* we need one less classbreaks (min and max exluded) than classes */
classbreaks = (double *)G_malloc((nbreaks) * sizeof(double));
for (i = 0; i < nbreaks; i++)
classbreaks[i] = 0;
/* Get classbreaks for given algorithm and number of classbreaks.
* finfo takes any info coming from the classification algorithms
* equ algorithm can alter number of class breaks */
finfo =
class_apply_algorithm(algo_opt->answer, data, nrec, &nbreaks,
classbreaks);
if (G_strcasecmp(algo_opt->answer, "dis") == 0 && finfo < 3.84148)
G_warning(_("The discontinuities algorithm indicates that some "
"class breaks are not statistically significant at "
"alpha=0.05. You are advised to reduce the number of classes."));
/*output to be piped to other modules ? */
if (shell_flag->answer) {
for (i = 0; i < nbreaks - 1; i++)
fprintf(stdout, "%f,", classbreaks[i]);
fprintf(stdout, "%f", classbreaks[nbreaks - 1]);
fprintf(stdout, "\n");
}
else {
frequencies = (int *)G_malloc((nbreaks + 1) * sizeof(int));
for (i = 0; i < nbreaks + 1; i++)
frequencies[i] = 0;
ret =
class_frequencies(data, nrec, nbreaks, classbreaks, frequencies);
basic_stats(data, nrec, &stats);
min = data[0];
max = data[nrec - 1];
/* as equ algorithm can modify number of breaks we recalculate number of
* classes
*/
fprintf(stdout, _("\nClassification of %s into %i classes\n"),
col_opt->answer, nbreaks + 1);
fprintf(stdout, _("Using algorithm: *** %s ***\n"), algo_opt->answer);
fprintf(stdout, _("Mean: %f\tStandard deviation = %f\n"), stats.mean,
stats.stdev);
if (G_strcasecmp(algo_opt->answer, "dis") == 0) {
fprintf(stdout, _("Lowest chi2 = %f\n"), finfo);
}
if (G_strcasecmp(algo_opt->answer, "std") == 0)
fprintf(stdout, _("Stdev multiplied by %.4f to define step\n"),
finfo);
fprintf(stdout, "\n");
fprintf(stdout, _("%15s%15s%15s\n\n"), "From (excl.)", "To (incl.)",
"Frequency");
fprintf(stdout, "%15.5f%15.5f%15i\n", min, classbreaks[0],
frequencies[0]);
for (i = 1; i < nbreaks; i++) {
fprintf(stdout, "%15.5f%15.5f%15i\n",
classbreaks[i - 1], classbreaks[i], frequencies[i]);
}
fprintf(stdout, "%15.5f%15.5f%15i\n",
classbreaks[nbreaks - 1], max, frequencies[nbreaks]);
fprintf(stdout, _("\nNote: Minimum of first class is including\n\n"));
}
fflush(stdout);
exit(EXIT_SUCCESS);
}
void summary(void)
{
if (compatible) {
if (!geometry->answer && weight_flag->answer) {
mean = sum / total_size;
mean_abs = sum_abs / total_size;
/* Roger Bivand says it is wrong see GRASS devel list 7/2004 */
/*
pop_variance = (sumsq - sum * sum / total_size) / total_size;
pop_stdev = sqrt(pop_variance);
pop_coeff_variation = pop_stdev / (sqrt(sum * sum) / count);
*/
}
else {
double n = count;
mean = sum / count;
mean_abs = sum_abs / count;
pop_variance = (sumsq - sum * sum / count) / count;
pop_stdev = sqrt(pop_variance);
pop_coeff_variation = pop_stdev / (sqrt(sum * sum) / count);
sample_variance = (sumsq - sum * sum / count) / (count - 1);
sample_stdev = sqrt(sample_variance);
kurtosis =
(sumqt / count - 4 * sum * sumcb / (n * n) +
6 * sum * sum * sumsq / (n * n * n) -
3 * sum * sum * sum * sum / (n * n * n * n))
/ (sample_stdev * sample_stdev * sample_stdev *
sample_stdev) - 3;
skewness =
(sumcb / n - 3 * sum * sumsq / (n * n) +
2 * sum * sum * sum / (n * n * n))
/ (sample_stdev * sample_stdev * sample_stdev);
}
}
G_debug(3, "otype %d:", otype);
if (shell_flag->answer) {
fprintf(stdout, "n=%d\n", count);
if (geometry->answer) {
fprintf(stdout, "nzero=%d\n", nzero);
}
else {
fprintf(stdout, "nmissing=%d\n", nmissing);
fprintf(stdout, "nnull=%d\n", nnull);
}
if (count > 0) {
fprintf(stdout, "min=%g\n", min);
fprintf(stdout, "max=%g\n", max);
fprintf(stdout, "range=%g\n", max - min);
fprintf(stdout, "sum=%g\n", sum);
if (compatible) {
fprintf(stdout, "mean=%g\n", mean);
fprintf(stdout, "mean_abs=%g\n", mean_abs);
if (geometry->answer || !weight_flag->answer) {
fprintf(stdout, "population_stddev=%g\n", pop_stdev);
fprintf(stdout, "population_variance=%g\n", pop_variance);
fprintf(stdout, "population_coeff_variation=%g\n",
pop_coeff_variation);
fprintf(stdout, "sample_stddev=%g\n", sample_stdev);
fprintf(stdout, "sample_variance=%g\n", sample_variance);
fprintf(stdout, "kurtosis=%g\n", kurtosis);
fprintf(stdout, "skewness=%g\n", skewness);
}
}
}
}
else {
if (geometry->answer) {
fprintf(stdout, "number of primitives: %d\n", nlines);
fprintf(stdout, "number of non zero distances: %d\n", count);
fprintf(stdout, "number of zero distances: %d\n", nzero);
}
else {
fprintf(stdout, "number of features with non NULL attribute: %d\n",
count);
fprintf(stdout, "number of missing attributes: %d\n", nmissing);
fprintf(stdout, "number of NULL attributes: %d\n", nnull);
}
if (count > 0) {
fprintf(stdout, "minimum: %g\n", min);
fprintf(stdout, "maximum: %g\n", max);
fprintf(stdout, "range: %g\n", max - min);
fprintf(stdout, "sum: %g\n", sum);
if (compatible) {
fprintf(stdout, "mean: %g\n", mean);
fprintf(stdout, "mean of absolute values: %g\n", mean_abs);
if (geometry->answer || !weight_flag->answer) {
fprintf(stdout, "population standard deviation: %g\n",
pop_stdev);
fprintf(stdout, "population variance: %g\n", pop_variance);
fprintf(stdout, "population coefficient of variation: %g\n",
pop_coeff_variation);
fprintf(stdout, "sample standard deviation: %g\n",
sample_stdev);
fprintf(stdout, "sample variance: %g\n", sample_variance);
fprintf(stdout, "kurtosis: %g\n", kurtosis);
fprintf(stdout, "skewness: %g\n", skewness);
}
}
}
}
/* TODO: mode, skewness, kurtosis */
/* Not possible to calculate for point distance, since we don't collect the population */
if (ext_flag->answer && compatible &&
((otype & GV_POINTS) || !weight_flag->answer) &&
!geometry->answer && count > 0) {
double quartile_25 = 0.0, quartile_75 = 0.0, quartile_perc = 0.0;
double median = 0.0;
int qpos_25, qpos_75, qpos_perc;
qpos_25 = (int)(count * 0.25 - 0.5);
qpos_75 = (int)(count * 0.75 - 0.5);
qpos_perc = (int)(count * perc / 100. - 0.5);
if (db_CatValArray_sort_by_value(&Cvarr) != DB_OK)
G_fatal_error(_("Cannot sort the key/value array"));
if (Cvarr.ctype == DB_C_TYPE_INT) {
quartile_25 = (Cvarr.value[qpos_25]).val.i;
if (count % 2) /* odd */
median = (Cvarr.value[(int)(count / 2)]).val.i;
else /* even */
median =
((Cvarr.value[count / 2 - 1]).val.i +
(Cvarr.value[count / 2]).val.i) / 2.0;
quartile_75 = (Cvarr.value[qpos_75]).val.i;
quartile_perc = (Cvarr.value[qpos_perc]).val.i;
}
else { /* must be DB_C_TYPE_DOUBLE */
quartile_25 = (Cvarr.value[qpos_25]).val.d;
if (count % 2) /* odd */
median = (Cvarr.value[(int)(count / 2)]).val.d;
else /* even */
median =
((Cvarr.value[count / 2 - 1]).val.d +
(Cvarr.value[count / 2]).val.d) / 2.0;
quartile_75 = (Cvarr.value[qpos_75]).val.d;
quartile_perc = (Cvarr.value[qpos_perc]).val.d;
}
if (shell_flag->answer) {
fprintf(stdout, "first_quartile=%g\n", quartile_25);
fprintf(stdout, "median=%g\n", median);
fprintf(stdout, "third_quartile=%g\n", quartile_75);
fprintf(stdout, "percentile_%d=%g\n", perc, quartile_perc);
}
else {
fprintf(stdout, "1st quartile: %g\n", quartile_25);
if (count % 2)
fprintf(stdout, "median (odd number of cells): %g\n", median);
else
fprintf(stdout, "median (even number of cells): %g\n",
median);
fprintf(stdout, "3rd quartile: %g\n", quartile_75);
if (perc % 10 == 1 && perc != 11)
fprintf(stdout, "%dst percentile: %g\n", perc, quartile_perc);
else if (perc % 10 == 2 && perc != 12)
fprintf(stdout, "%dnd percentile: %g\n", perc, quartile_perc);
else if (perc % 10 == 3 && perc != 13)
fprintf(stdout, "%drd percentile: %g\n", perc, quartile_perc);
else
fprintf(stdout, "%dth percentile: %g\n", perc, quartile_perc);
}
}
}
/*!
\brief Lines z-bulk labeling
Automated labeling (z coordinate assignment) of vector lines (iso-lines).
\param Map pointer to Map_info
\param List list of selected lines
\param point_start_end staring and ending point
\param start starting value
\param step step value
\return number of modified features
\return -1 on error
*/
int Vedit_bulk_labeling(struct Map_info *Map, struct ilist *List,
double x1, double y1, double x2, double y2,
double start, double step)
{
int i, cv_i, p_i;
int line, type, temp_line;
int nlines_modified;
double value, dist;
struct line_cats *Cats;
struct line_pnts *Points, *Points_se; /* start - end */
struct bound_box box, box_se;
/* for intersection */
struct line_pnts **Points_a, **Points_b;
int nlines_a, nlines_b;
dbCatValArray cv; /* line_id / dist */
nlines_modified = 0;
value = start;
Points = Vect_new_line_struct();
Points_se = Vect_new_line_struct();
Cats = Vect_new_cats_struct();
db_CatValArray_alloc(&cv, List->n_values);
cv.ctype = DB_C_TYPE_DOUBLE;
cv.n_values = 0;
Vect_append_point(Points_se, x1, y1, -PORT_DOUBLE_MAX);
Vect_append_point(Points_se, x2, y2, PORT_DOUBLE_MAX);
/* write temporaly line */
temp_line = Vect_write_line(Map, GV_LINE, Points_se, Cats);
if (temp_line < 0) {
return -1;
}
Vect_line_box(Points_se, &box_se);
/* determine order of lines */
cv_i = 0;
for (i = 0; i < List->n_values; i++) {
line = List->value[i];
if (!Vect_line_alive(Map, line))
continue;
type = Vect_read_line(Map, Points, NULL, line);
if (!(type & GV_LINE))
continue;
Vect_line_box(Points, &box);
if (Vect_line_check_intersection(Points_se, Points, WITH_Z)) {
Vect_line_intersection(Points_se, Points, &box_se, &box,
&Points_a, &Points_b, &nlines_a, &nlines_b,
WITHOUT_Z);
if (nlines_a < 2 || nlines_b < 1) /* should not happen */
continue;
/* calculate distance start point -> point of intersection */
for (p_i = 0; p_i < Points_a[0]->n_points; p_i++) {
Points_a[0]->z[p_i] = 0;
}
dist = Vect_line_length(Points_a[0]); /* always first line in array? */
cv.value[cv_i].cat = line;
cv.value[cv_i++].val.d = dist;
cv.n_values++;
}
}
/* sort array by distance */
db_CatValArray_sort_by_value(&cv);
/* z bulk-labeling */
for (cv_i = 0; cv_i < cv.n_values; cv_i++) {
line = cv.value[cv_i].cat;
type = Vect_read_line(Map, Points, Cats, line);
for (p_i = 0; p_i < Points->n_points; p_i++) {
Points->z[p_i] = value;
}
if (Vect_rewrite_line(Map, line, type, Points, Cats) < 0) {
return -1;
}
nlines_modified++;
value += step;
}
if (Vect_delete_line(Map, temp_line) < 0) {
return -1;
}
db_CatValArray_free(&cv);
Vect_destroy_line_struct(Points);
Vect_destroy_line_struct(Points_se);
Vect_destroy_cats_struct(Cats);
return nlines_modified;
}
